Data updating method and device, computer equipment, storage medium and program product

Abstract

The invention discloses a data updating method and device, computer equipment, a storage medium and a program product. According to the data updating method and device, service data of an application can be obtained from an application database; performing data structure conversion on the business data to obtain control group data; obtaining target data from a third-party database or a to-be-updated database according to the data attribute of the control group data, and constructing experimental group data according to the target data; and performing data comparison on the experimental group data and the control group data, and updating the database to be updated according to a comparison result, so that the accuracy of data updating is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and specifically to a data updating method, apparatus, computer equipment, storage medium and program product, wherein the storage medium is a computer-readable storage medium, and the program product is a computer program product. Background Art

[0002] In the process of database maintenance, it is often necessary to obtain data within a certain period of time to update the database. However, simply updating the data content in the database based on local data within a period of time will inevitably lead to data errors, omissions or redundancies, reducing the accuracy of data updates. Summary of the invention

[0003] The embodiments of the present application provide a data updating method, apparatus, computer equipment, storage medium and program product, which can improve the accuracy of data updating.

[0004] To solve the above technical problems, the present application provides the following technical solutions:

[0005] The present application provides a data updating method, including:

[0006] Get the application's business data from the application database;

[0007] Performing data structure transformation on the business data to obtain control group data;

[0008] According to the data attributes of the control group data, target data is obtained from a third-party database or a database to be updated, and experimental group data is constructed according to the target data;

[0009] The experimental group data is compared with the control group data, and the database to be updated is updated according to the comparison result.

[0010] According to one aspect of the present application, there is also provided a data updating device, including:

[0011] The acquisition module is used to obtain the business data of the application from the application database;

[0012] A conversion module, used for performing data structure conversion on the business data to obtain control group data;

[0013] A construction module, used to obtain target data from a third-party database or a database to be updated according to data attributes of the control group data, and construct experimental group data according to the target data;

[0014] The updating module is used to compare the experimental group data with the control group data, and update the database to be updated according to the comparison result.

[0015] According to one aspect of the present application, a computer device is also provided, including a processor and a memory, wherein a computer program is stored in the memory, and when the processor calls the computer program in the memory, any data updating method provided in the embodiments of the present application is executed.

[0016] According to one aspect of the present application, a storage medium is also provided, wherein the storage medium is used to store a computer program, and the computer program is loaded by a processor to execute any one of the data updating methods provided in the embodiments of the present application.

[0017] According to one aspect of the present application, a computer program product is also provided, including a computer program, wherein the computer program is loaded by a processor to execute any one of the data updating methods provided in the embodiments of the present application.

[0018] The present application can obtain the business data of the application from the application database, perform data structure transformation on the business data, and obtain control group data. Also, according to the data attributes of the control group data, the target data can be obtained from a third-party database or a database to be updated, and the required experimental group data can be accurately constructed according to the target data. At this time, the experimental group data can be compared with the control group data, and the database to be updated can be accurately updated according to the comparison results. This solution obtains the control group data by performing data structure transformation on the business data of the application, and accurately constructs the experimental group data according to the target data obtained according to the data attributes of the control group data, so that the control group data can be accurately compared with the test group data, and then the database is accurately updated, thereby improving the accuracy of data update. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a schematic diagram of a scenario in which the data updating method provided in an embodiment of the present application is applied;

[0021] Figure 2 It is a flowchart of a data updating method provided in an embodiment of the present application;

[0022] Figure 3 is another flow chart of the data updating method provided in the embodiment of the present application;

[0023] Figure 4 is another flow chart of the data updating method provided in the embodiment of the present application;

[0024] Figure 5 It is a scenario diagram of a data update method under a microservice architecture provided in an embodiment of the present application;

[0025] Figure 6 It is a schematic diagram of the interactive process of the data updating method provided in the embodiment of the present application;

[0026] Figure 7 is a schematic diagram of a data updating device provided in an embodiment of the present application;

[0027] Figure 8 It is a schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

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

[0029] In the following description of the present application, reference is made to “some embodiments”, which describe a subset of all possible embodiments, but it can be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0030] In the following description of the present application, the terms "first\second" and the like are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It can be understood that "first\second" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0032] In order to better understand the embodiments of the present application, a data updating method, apparatus, computer equipment, storage medium and program product are provided. The application environment applicable to the embodiments of the present application is described below.

[0033] See also Figure 1 , Figure 1A schematic diagram of an application environment of the data update method provided in an embodiment of the present application is shown. As an implementation mode, the methods of acquiring business data, converting the data structure of business data, acquiring experimental group data, updating the database, etc. provided in the embodiment of the present application can be applied to the same computer device. The computer device can be as follows: Figure 1 The server 10 shown in the figure can be connected to the terminal 20 via a network. The network is used to provide a medium for a communication link between the server 10 and the terminal 20. The network can include various connection types, such as a wired communication link, a wireless communication link, etc., which are not limited in the embodiment of the present application. Optionally, in other embodiments, the computer device can also be a smart phone or a computer, etc.

[0034] It should be understood that Figure 1 The server 10, network and terminal 20 are merely schematic. Depending on the implementation needs, there can be any number of servers, networks and terminals. Exemplarily, the server 10 can be a physical server, or a server cluster or distributed system composed of multiple servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms, but is not limited to this. The terminal 20 can be a mobile phone, a computer, an intelligent voice interaction device, a smart home appliance, and a vehicle-mounted terminal. It is understandable that the embodiments of the present application can also allow multiple terminals 20 to access the server 10 at the same time.

[0035] In some embodiments, the server 10 can receive business data of an application sent by the terminal 20. After the server 10 receives the business data, it can use the data updating method described in the embodiment of the present application to adaptively transform the business data into a data structure to obtain control group data, and then compare the experimental group data obtained based on the data attributes of the control group data, so as to accurately update the database to be updated based on the comparison results.

[0036] As another embodiment, the methods for obtaining business data, data structure conversion of business data, obtaining experimental group data, updating of database, etc. provided in the embodiments of the present application can be applied to different computer devices. For example, the method for obtaining business data is applied to computer device A, the method for converting the data structure of business data is applied to computer device B, the method for obtaining experimental group data is applied to computer device C, and the method for updating the database is applied to computer device D, and so on. In addition, any combination of the methods for obtaining business data, data structure conversion of business data, obtaining experimental group data, updating of database, etc. provided in the embodiments of the present application can be applied to the same computer device. For example, the method for obtaining business data is applied to computer device A, and the methods for converting the data structure of business data, obtaining experimental group data, updating of database, etc. are applied to computer device B, and so on. The embodiments of the present application do not limit the computer devices to which the above methods are applied.

[0037] It should be noted that Figure 1 The scenario diagram of the data update method application shown is only an example. The data update method application and scenario described in the embodiment of the present application are intended to more clearly illustrate the technical solution of the embodiment of the present application, and do not constitute a limitation on the technical solution provided by the embodiment of the present application. Ordinary technicians in this field can know that with the evolution of the application of the data update method and the emergence of new business scenarios, the technical solution provided by the embodiment of the present application is also applicable to similar technical problems.

[0038] In the existing data update method, a simple update is directly performed based on local data within a period of time, which leads to low accuracy of data update. In order to solve this technical problem, the present application obtains the business data of the application from the application database, performs data structure transformation on the business data, and obtains the control group data. And according to the data attributes of the control group data, the target data is obtained from a third-party database or a database to be updated, and the required experimental group data is accurately constructed according to the target data. At this time, the experimental group data can be compared with the control group data, and the database to be updated can be accurately updated according to the comparison results. This solution obtains the control group data by performing data structure transformation on the business data of the application, and accurately constructs the experimental group data according to the target data obtained according to the data attributes of the control group data, so that the control group data and the experimental group data can be accurately compared, and then the database is accurately updated, which improves the accuracy of data update.

[0039] It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments.

[0040] In this embodiment, the description will be made from the perspective of a data updating device, which can be integrated into a computer device such as a server. The server can be a cluster server or an independent server, which is not limited here.

[0041] See also Figure 2 , Figure 2 : is a flow chart of a data updating method provided by an embodiment of the present application. The data updating method may include:

[0042] S101. Acquire application business data from an application database.

[0043] Among them, the application may include instant messaging, short video applications, financial applications, shopping applications and other different types of applications. The business data of the application may include business data corresponding to the knowledge classification, and the business data may be full business data or non-full business data, which is not limited here. For the same application, the knowledge classification corresponding to different scenarios may be different. For example, for the customer service scenario, the knowledge classification may include the classification of various different knowledge types such as opening words recommendation and multi-round dialogue. For another example, for the data access scenario, the knowledge classification may include the classification of metadata or data dictionary for describing descriptive information corresponding to different data. Business data may be different according to the different knowledge classifications of the application. For example, for the customer service business of the application, the business data may include global data of various knowledge types such as opening words recommendation and multi-round dialogue. The specific type of the application, the specific content of the knowledge classification and the specific content of the business data can be flexibly set according to actual needs, which is not limited here.

[0044] The business data acquisition method may include: querying the business data of one or more applications stored in the application database, and extracting the business data of the required application from the application database, for example, extracting the business data related to knowledge classification from the business configuration table of the application in the application database. Alternatively, the business data of one or more applications for knowledge classification sent by the terminal may be received; and so on. Of course, business data may also be acquired in other ways, which are not limited here.

[0045] In order to improve the accuracy of business data acquisition, business data is acquired through matching data acquisition strategies for different types of applications, such as Figure 3 As shown, in some implementations, obtaining the business data of an application from an application database may include:

[0046] S1011. Obtain an application type of the application, and determine a data acquisition strategy according to the application type;

[0047] S1012. Acquire the business data of the application from the application database according to the data acquisition strategy.

[0048] Specifically, the business data acquisition methods for different types of applications can be different. First, when data update is required, the application type of the application can be obtained, and the data acquisition strategy can be determined according to the application type. For example, under conditions such as timing or detection of changes in business data, a data update instruction can be generated in time, and the application type of the application can be obtained in response to the data update instruction. The data acquisition strategy is determined according to the application type. For example, the corresponding data acquisition strategy a1 for applications of application type A, the corresponding data acquisition strategy b1 for applications of application type B, and the corresponding data acquisition strategy c1 for applications of application type C, etc. Then, the business data corresponding to the knowledge classification of the application can be obtained according to the data acquisition strategy corresponding to the application. For example, for application A, the application database of application A can be queried based on the data identifier of application A to obtain the business data of application A. For example, for application B, the business data of application B related to knowledge classification can be obtained by querying the business configuration table in the application database of application B. Of course, the data server of application C can also be accessed to obtain the business data of application C, or the business data of application D can be obtained from the file system of application D; etc., which are not limited here. For different types of applications, the corresponding business data can be obtained by different data acquisition methods, which can improve the flexibility and accuracy of business data acquisition.

[0049] In order to trigger the timeliness of data update, data update can be initiated by responding to the data update instruction. In some embodiments, the application type of the application can be obtained in response to the data update instruction, and the data acquisition strategy can be determined according to the application type. Further, in order to improve the timeliness, convenience and flexibility of data update, data update can be initiated when a preset time arrives, changes in business data are detected, or the user actively initiates data update. In some embodiments, before obtaining the application type of the application in response to the data update instruction, the data update method can also include:

[0050] When it is detected that the preset time has arrived, a data update instruction is generated; or,

[0051] When a change is detected in the application's business data, a data update instruction is generated; or,

[0052] When an update trigger operation is received from the user, a data update instruction is generated.

[0053] For example, a data update instruction may be generated regularly or at intervals of a preset time. Specifically, the preset time may be detected. When it is detected that the preset time has not been reached, the system continues to wait. When it is detected that the preset time has been reached, a data update instruction may be generated to execute a data update operation in response to the data update instruction, thereby improving the timeliness and flexibility of the data update trigger. The preset time may be flexibly set according to actual needs and is not limited here.

[0054] For another example, it is possible to detect whether there are changes in the business data of an application. When it is detected that there are no changes in the business data of an application, the data update operation may not be performed. When it is detected that there are changes in the business data of an application, a data update operation is performed based on generating a data update instruction in response to the data update instruction, thereby improving the timeliness and convenience of the data update trigger.

[0055] For another example, it is possible to detect whether a user input update trigger operation is received. When the user input update trigger operation is not received, the data update operation may not be performed; when the user input update trigger operation is received, a data update instruction may be generated to perform the data update operation in response to the data update instruction, thereby improving the flexibility and convenience of the data update trigger. Among them, the update trigger operation may be input by the user through the update control in the pre-set data display interface. The data display interface may display the business data of the application and other information. When the user finds that the data needs to be updated through the data displayed in the data display interface, the data update may be initiated actively. Of course, the business data of the application may also be analyzed through a neural network model or a data analysis model, and a data update instruction may be generated based on the analysis result when the preset conditions are met, so as to perform the data update operation in response to the data update instruction. The preset conditions may be flexibly set according to actual needs and are not limited here.

[0056] It should be noted that in order to automatically deduplicate the acquired business data, the acquired business data can be stored in the Redis database and stored according to the Set data structure to achieve automatic deduplication of the business data. When there are multiple applications, the business data of multiple applications can be obtained, and the business data corresponding to each application can be stored in the Redis database based on the data identifier (such as msgID) and stored according to the Set data structure to achieve automatic deduplication of the business data of each application. When the business data needs to be further processed, the corresponding business data can be obtained from the Redis database based on the data identifier.

[0057] S102: Perform data structure conversion on the business data to obtain control group data.

[0058] After obtaining the business data of the application, the business data may be transformed into a data structure to obtain control group data. In order to improve the reliability of data transformation, the business data may be verified first, and then the business data may be transformed into a data structure after the verification is passed. In some implementations, the business data may be transformed into a data structure, which may include:

[0059] Determine the verification strategy corresponding to the business data according to the application type of the application, and verify the business data according to the verification strategy;

[0060] If the verification is successful, the business data will be transformed into a data structure.

[0061] After obtaining the business data, the business data can be verified. In order to improve the accuracy and reliability of data verification, different verification strategies can be used for different types of applications. Specifically, the application type of the application can be obtained. For example, the application identifier of the application can be obtained, and the application type of the application can be determined based on the application identifier, or the application data of the application can be obtained, and the application type of the application can be determined based on the type identifier carried in the application data, and so on. Then, the verification strategy corresponding to the business data can be determined based on the application type of the application, so that the business data can be verified based on the verification strategy. The verification strategy may include verifying the integrity of the data, verifying whether the data format is correct, verifying whether the data format conversion is normal, verifying whether the data needs to be filtered, etc. The specific content of the verification strategy is not limited here. For example, the application of application type A corresponds to verification strategy a2, the application of application type B corresponds to verification strategy b2, the application of application type C corresponds to verification strategy c2, and so on.

[0062] In order to reduce the data processing pressure on business data and reduce data processing delay, some data can be extracted for preliminary data comparison. If the data comparison passes, there is no need to perform subsequent full data comparison. If the data comparison fails, subsequent data comparison is performed. In some implementations, the verification strategy corresponding to the business data is determined according to the application type of the application, which may include:

[0063] Extract some data from the business data as preliminary comparative data;

[0064] Obtain preliminary verification data from the database to be updated;

[0065] Perform data comparison on the preliminary comparison data and the preliminary verification data;

[0066] If the data comparison fails, the verification strategy corresponding to the business data is determined according to the application type of the application.

[0067] Specifically, first, the application type of the application can be obtained, and the data extraction strategy of the application can be determined according to the application type. The data extraction strategy can include extracting data of a preset time period from the business data, randomly extracting part of the data, extracting data of different knowledge types according to a preset ratio, etc. Of course, other strategies can also be included, which are not limited here. For example, the application corresponding to application type A corresponds to data extraction strategy a3, the application corresponding to application type B corresponds to data extraction strategy b3, the application corresponding to application type C corresponds to data extraction strategy c3, and so on. The specific content corresponding to different data extraction strategies is not limited here. Then, according to the data extraction strategy of the application, some data can be extracted from the business data of the application as preliminary comparison data. By setting different data extraction strategies for different types of applications, it is possible to effectively extract data based on the data extraction strategy matching the application type, thereby improving the effectiveness and reliability of data extraction.

[0068] And, preliminary verification data is obtained from the database to be updated. The database to be updated may store business data corresponding to multiple applications, for example, business data for knowledge classification, and maintenance tables of knowledge classification reference relationships corresponding to multiple applications. The database to be updated may be a relational database or a non-relational database, etc. The specific storage data content and database type of the database to be updated are not limited here. For example, data matching the data identifier of the preliminary comparison data can be obtained from the database to be updated to obtain preliminary verification data. For another example, data matching the knowledge type of the preliminary comparison data can be obtained from the database to be updated to obtain preliminary verification data. For another example, the data in the database to be updated can be directly used as preliminary verification data, etc. At this time, the extracted preliminary comparison data can be compared with the preliminary verification data obtained in the database to be updated. When the preliminary comparison data is consistent with the preliminary verification data, or the preliminary comparison data is included in the preliminary verification data, it is determined that the data comparison is passed, otherwise the data comparison fails. If the data comparison passes, there is no need to perform subsequent data comparison. If the data comparison fails, the verification strategy corresponding to the business data is determined according to the application type of the application, so that the business data is verified based on the verification strategy to perform subsequent data comparison.

[0069] In order to improve the flexibility and convenience of data extraction, some data may be randomly extracted, data of a preset time period may be extracted, or data of different knowledge types may be extracted according to a preset ratio. In some implementations, extracting some data from the business data as preliminary comparison data may include:

[0070] Randomly extract some data from the business data as preliminary comparison data; or,

[0071] Extract data for a preset time period from the business data as preliminary comparative data; or,

[0072] From the business data, data of different knowledge types corresponding to the application are extracted according to preset proportions as preliminary comparison data.

[0073] For example, when the data extraction strategy is random extraction, some data can be randomly extracted from the business data as preliminary comparison data, so that the randomly extracted preliminary comparison data can be compared with the preliminary verification data obtained from the database to be updated, thereby improving the flexibility of data extraction.

[0074] For another example, when the data extraction strategy is to extract data in a preset time period, data in the preset time period can be extracted from the business data as preliminary comparison data, so that the randomly extracted preliminary comparison data can be compared with the preliminary verification data obtained from the database to be updated, wherein the preset time period can be flexibly set according to actual needs and is not limited here. For example, data in different time periods can be extracted every day as preliminary comparison data, or corresponding data can be extracted at every preset time interval as preliminary comparison data, etc. By cost-effectively extracting data by time period, the convenience and reliability of data extraction can be improved.

[0075] For another example, when the data extraction strategy is to extract data of different knowledge types according to a preset ratio, data of different knowledge types can be extracted from the business data according to a preset ratio based on the knowledge type of the knowledge classification of the application as preliminary comparison data. For example, the extraction ratio corresponding to knowledge type 1, knowledge type 2, and knowledge type 3 of application A is 3:3:4. At this time, data of knowledge type 1, data of knowledge type 2, and data of knowledge type 3 can be extracted from the business data according to a ratio of 3:3:4 as preliminary comparison data. Among them, the knowledge type can be different for different application scenarios. For example, for the application scenario of customer service, the knowledge type can include a variety of different knowledge types such as opening words recommendation and multi-round dialogue. By extracting data of different knowledge types as preliminary comparison data according to a preset ratio, the flexibility and accuracy of data extraction can be improved.

[0076] In order to improve the accuracy of the preliminary data comparison, the reverse data comparison can be achieved by extracting the data in the database to be updated and comparing it with the business data of the application. In some implementations, the verification strategy corresponding to the business data is determined according to the application type of the application, which may include:

[0077] Extract some data from the database to be updated as preliminary verification data;

[0078] Compare the preliminary verification data with the business data;

[0079] If the data comparison fails, the verification strategy corresponding to the business data is determined according to the application type of the application.

[0080] Specifically, some data can be randomly extracted from the database to be updated as preliminary verification data, or data of a preset time period can be extracted from the database to be updated as preliminary verification data, or data of different knowledge types can be extracted from the database to be updated according to a preset ratio as preliminary verification data, and so on. Then, the preliminary verification data can be compared with the business data, or the preliminary verification data can be compared with some data extracted from the business data, which is not limited here. When the preliminary verification data is included in the business data, or the preliminary verification data matches the business data, it is determined that the data comparison is passed, otherwise the data comparison is passed. If the data comparison fails, there is no need to perform subsequent data comparison. If the data comparison fails, the verification strategy corresponding to the business data is determined according to the application type of the application, so as to verify the business data based on the verification strategy to perform subsequent data comparison. Through reverse data comparison, the correctness of the data (such as the reference relationship of knowledge classification) can be reversely checked, and the accuracy of the preliminary data comparison can be improved.

[0081] It should be noted that in addition to independently executing the reverse data comparison by extracting part of the data in the database to be updated and comparing it with the business data of the application, or independently executing the forward data comparison by extracting part of the data from the business data and comparing it with the data obtained from the database to be updated, it is also possible to simultaneously execute the reverse data comparison and the forward data comparison, and determine that the data comparison has passed when both data comparisons pass. The accuracy of the data comparison can be improved through double data comparison.

[0082] After determining the verification strategy corresponding to the business data, the business data can be verified according to the verification strategy. For example, the business data can be converted into a data format, verified for data format correctness, filtered, and verified for data integrity. Figure 4 As shown, in some implementations, verifying the business data according to the verification strategy includes:

[0083] S1021. Convert the data format of the business data according to the verification strategy to obtain converted data;

[0084] S1022. Filter the converted data according to the business requirements of the application to obtain filtered data.

[0085] S1023. Verify the integrity of the filtered data.

[0086] Specifically, since the data formats of different data of different applications may differ, and the data format of the acquired business data may be inconsistent with the data format required for verification, in order to facilitate unified processing of data, when the verification strategy includes data format conversion, data filtering processing, and data integrity verification, the data format of the application's business data can be converted first to obtain the converted data, wherein the data format of the converted data can be flexibly set according to actual needs, and is not limited here. For example, it can include data formats such as JSON and XML.

[0087] And, the business requirements of the application can be obtained, and the filtering strategy can be determined according to the business requirements of the application. Different business requirements can correspond to different filtering strategies. The business requirements may include customer service management business, data access business, order management business, and transaction management business, etc., which are not limited here. For example, the business requirements for application A correspond to filtering strategy a4, the business requirements for application B correspond to filtering strategy b4, the business requirements for application C correspond to filtering strategy c4, and so on. The specific content corresponding to different filtering strategies is not limited here. At this time, the converted data can be filtered according to the filtering strategy to obtain filtered data. The filtered data may include key information such as knowledge type typeEnum, batch number batchNo, and total configuration quantity totalConfigNum, and may also include other information, which is not limited here. Among them, the knowledge type can be used to distinguish different types of data classified by knowledge, the batch number can be used to distinguish different batches of business data, which is convenient for tracking data sources, data management and comparative analysis, etc., to ensure the accuracy and integrity of data processing, and the total configuration quantity can be used to verify the integrity of data. For the business data of different applications, different filtering strategies can be set according to different business requirements for data filtering, which can improve the accuracy and reliability of data filtering.

[0088] After obtaining the filtered data, the integrity of the filtered data may be verified. In some implementations, the integrity verification of the filtered data may include:

[0089] If the amount of filtered data is consistent with the total configured amount, it is determined that the integrity verification of the filtered data has passed; or,

[0090] If the filtered data carries a data frame completion identifier, it is determined that the integrity verification of the filtered data has passed.

[0091] For example, the data volume of the filtered data can be counted, and the total configuration quantity can be extracted from the filtered data carrying the total configuration quantity, and the data volume of the filtered data can be compared with the total configuration quantity carried. If the data volume of the filtered data is inconsistent with the total configuration quantity carried, it is determined that the integrity verification of the filtered data has failed; if the data volume of the filtered data is consistent with the total configuration quantity carried, it is determined that the integrity verification of the filtered data has passed. Verifying data integrity by comparing the total configuration quantity can improve the convenience of data integrity verification.

[0092] For another example, during the data filtering process, after the data filtering is completed, a data frame completion mark (also referred to as a frame end mark) may be set at the end of the data frame of the filtered data. After obtaining the filtered data, it may be detected whether the filtered data carries the data frame completion mark. If the filtered data does not carry the data frame completion mark, it is determined that the integrity verification of the filtered data has failed. If the filtered data carries the data frame completion mark, it is determined that the integrity verification of the filtered data has passed. By verifying the integrity of the data through marks, the efficiency and flexibility of data verification can be improved.

[0093] It should be noted that the data filtering process and the data integrity check can be completed by different data processing modules on the server. At this time, when the data filtering process module performs data format conversion and data filtering process on the business data to obtain the filtered data, the filtered data can be sent to the data integrity check module by the data filtering process module. At this time, the received filtered data can be checked for data integrity by the data integrity check module. Optionally, in the process of sending the filtered data to the data integrity check module by the data filtering process module, when the data sending is completed, the data sending completion mark can be sent to the data integrity check module by the data filtering process module. In the process of receiving the filtered data by the data integrity check module, it can be detected whether the data sending completion mark is received. If the data sending completion mark is received, it is determined that the filtered data is received and the integrity verification of the filtered data is passed. Optionally, in order to avoid repeated reception of filtered data, the received filtered data can be stored in the Redis database based on the data identifier (such as msgID) and stored according to the Set data structure to realize the automatic deduplication operation of the filtered data. When the filtered data needs to be verified, the corresponding filtered data can be obtained from the Redis database based on the data identifier for data integrity verification.

[0094] When the business data is verified, the knowledge type of the business data can be obtained, and according to the knowledge type of the business data, the data structure of the business data can be transformed to obtain the control group data. The control group data may include fields such as knowledge classification ID (classificationID), knowledge classification reference relationship (referenceRelation), operation status (such as in use / offline status), version ID (such as multi-round dialogue version ID), and may also include other information, which is not limited here.

[0095] The data structure conversion method corresponding to different knowledge types of the application can be different, and the fields contained in the control group data obtained by conversion can be different, thereby improving the flexibility and accuracy of data structure conversion. For example, when the knowledge type is multi-round dialogue, the control group data obtained by data structure conversion of business data can include the field of operation status. For another example, when the knowledge type is opening words recommendation, the control group data obtained by data structure conversion of business data can include relevant fields such as logistics and commodities.

[0096] It should be noted that the control group data can be obtained by directly performing data structure transformation on the business data, or the business data can be firstly converted into data format to obtain converted data, and then the converted data can be filtered to obtain filtered data, and then the filtered data can be converted into data structure to obtain the control group data. There is no limitation on this.

[0097] S103. According to the data attributes of the control group data, target data is obtained from a third-party database or a database to be updated, and experimental group data is constructed according to the target data.

[0098] After obtaining the control group data, the data attributes of the control group data can be obtained, and the data attributes may include data format, data type, and data field, etc., which are not limited here. Then, the experimental group data can be obtained according to the data attributes of the control group data, and the data structure and format of the control group data can be consistent with the data structure and format of the experimental group data. Depending on the difference in the control group data, the method of obtaining the experimental group data can be different. For example, all or part of the control group data can be converted into preset format data to obtain the experimental group data. For another example, when the fields of the control group data and the experimental group data are inconsistent, the third-party data can be called according to the fields of the control group data to obtain data matching the experimental group data for comparison. For another example, the knowledge classification reference data matching the knowledge type typeEnum of the control group data can be obtained from the database to be updated to construct the experimental group data. Of course, the experimental group data can also be obtained in other ways, which are not limited here.

[0099] In some embodiments, obtaining experimental group data from a third-party database or a database to be updated according to data attributes of the control group data may include:

[0100] Determine the knowledge type of the control group data according to the data attributes of the control group data, and obtain target data matching the knowledge type from the database to be updated; or

[0101] The data identifier is determined according to the data attributes of the control group data, and the target data in the third-party database is called according to the data identifier.

[0102] For example, when it is determined based on the data attributes of the control group data that data needs to be obtained from the database to be updated, the knowledge type of the control group data can be determined based on the data attributes of the control group data, and target data matching the knowledge type can be obtained from the database to be updated. For example, knowledge classification reference data matching the knowledge type of the control group data can be obtained from the knowledge classification reference relationship maintenance table of the database to be updated to obtain the target data, thereby improving the convenience and flexibility of acquiring the target data.

[0103] For another example, when it is determined that the fields of the control group data and the experimental group data are inconsistent according to the data attributes of the control group data, in order to make the control group data and the experimental group data comparable, the data identifier can be obtained, and the third-party data pre-stored in the third-party database can be called according to the data identifier, and the third-party data can be used as the target data. For example, when the control group data includes a field of data ID, and the target data includes a field of user name, but does not include a field of data ID, in order to make the control group data and the experimental group data comparable, the data ID pre-stored in the third-party database can be called according to the user name of the target data, and the experimental group data can be constructed according to the data ID, so that the experimental group data contains the data ID, which has a comparison basis with the data ID contained in the control group data. By calling the third-party data to make the control group data and the experimental group data comparable, the reliability and accuracy of the experimental group data acquisition can be improved. Of course, the data identifier of the control group data can also be obtained according to the data attributes of the control group data, and the third-party data can be called according to the data identifier to update the control group data, so that the control group data can be compared with the experimental group data, which is not limited here.

[0104] After obtaining the target data, experimental group data may be constructed according to the target data. In some embodiments, constructing the experimental group data according to the target data includes:

[0105] When the data structure of the target data is consistent with that of the control group data, the target data is used as the experimental group data; or,

[0106] When the data structure of the target data is inconsistent with the data structure of the control group data, the data structure of the target data will be transformed to obtain the experimental group data that is consistent with the data structure of the control group data.

[0107] Specifically, after obtaining the target data, the data structure of the target data can be compared with the control group data to determine whether the data structure of the target data is consistent with the data structure of the control group data. When the data structure of the target data is consistent with the data structure of the control group data, the target data can be used as the experimental group data. When the data structure of the target data is inconsistent with the data structure of the control group data, the data structure of the target data can be transformed to obtain the experimental group data consistent with the data structure of the control group data. The experimental group data can include knowledge classification ID, knowledge classification reference relationship, and other information. By extracting data from the database to be updated to construct the experimental group data, the flexibility and convenience of experimental group data acquisition can be improved. And, by calling third-party data to construct the experimental group data, the reliability and accuracy of experimental group data acquisition can be improved. It should be noted that when the control group data is full business data, the corresponding experimental group data obtained by obtaining data from the database to be updated can also be full business data, so that the experimental group data and the control group data can be compared with each other in full, avoiding the lack, redundancy, and inaccuracy of knowledge classification, and improving the accuracy of data comparison.

[0108] S104: Compare the experimental group data with the control group data, and update the database to be updated according to the comparison result.

[0109] After obtaining the experimental group data, the experimental group data and the control group data can be compared to obtain a comparison result. The comparison result may include consistency between the experimental group data and the control group data, inconsistency between the experimental group data and the control group data, etc. Of course, other information may also be included. Among them, the inconsistency between the experimental group data and the control group data may include inconsistency in knowledge classification reference relationship (referenceRelation), inconsistency in knowledge classification ID (classificationId), and other information inconsistency, which is not limited here.

[0110] After obtaining the comparison result between the experimental group data and the control group data, the database to be updated can be updated according to the comparison result. In one embodiment, updating the database to be updated according to the comparison result can include: comparing the control group data with the experimental group data, and obtaining data that is different from the experimental group data from the control group data, and adding the different data to the database to be updated; or obtaining data that is different from the control group data from the experimental group data, and deleting the different data from the database to be updated.

[0111] For example, when the data structures of the experimental group data and the control group data are consistent, the set difference operation can be used to find the inconsistency between the experimental group data and the control group data, which can be specifically expressed as: 需要新增数据 = control group data - experimental group data, U 需要删除数据 = experimental group data - control group data. At this time, the database to be updated can be updated according to the comparison results. For example, if U 需要新增数据 is a non-empty set, then U 需要新增数据 Add to the database to be updated, so as to update the database to be updated in a timely and accurate manner, avoiding the loss of data in the database to be updated. 需要删除数据 is a non-empty set, then we can 需要删除数据 The corresponding data in the database to be updated is deleted, and the data in the database to be updated is updated timely and accurately, avoiding the redundancy of the data in the database to be updated. 需要新增数据 and U 需要删除数据 are all non-empty sets. To prevent the newly added data from being accidentally deleted in some cases, the deletion operation can be performed first, and then the addition operation. 需要新增数据 is an empty set, or U 需要删除数据 If it is an empty set, it means that the data of the experimental group is consistent with the data of the control group, and the current state of the database to be updated is maintained.

[0112] It should be noted that after obtaining the comparison results, in addition to updating the database to be updated according to the comparison results, the comparison results can also be traced back to the software development program itself, thereby effectively revealing and locating potential vulnerabilities in the code, so that developers can improve development quality and efficiency. In addition, the knowledge classification reference relationship verification and update of each knowledge type corresponding to the knowledge classification in the database to be updated can be independent and independent of each other, so that in the data update process, the modular update of one or several knowledge types can be triggered in a personalized manner, improving the flexibility and scalability of data updates.

[0113] In the process of data updating, batch updating of business data corresponding to knowledge classification based on multiple applications may be included. At this time, different priorities may be set for different applications, and data may be processed according to the priorities to improve the efficiency and accuracy of data updating. In some implementations, when there are business data of multiple applications, the control group data includes control group data corresponding to each application, and the experimental group data includes experimental group data corresponding to each application. Comparing the experimental group data with the control group data may include:

[0114] In order of priority of each application from high to low, the experimental group data corresponding to each application is compared with the control group data.

[0115] Specifically, priorities can be set in advance for multiple applications, and the priorities can include multiple different priority levels such as high priority, medium priority, and low priority, or the priorities can include multiple different priority levels from high to low, such as first priority, second priority, third priority, fourth priority, fifth priority, and sixth priority, which are not limited here. For example, the application type of the application can be obtained, and the priority of the application can be set according to the application type, such as application type A is set to high priority, application type B is set to low priority, application type C is set to medium priority, and so on. For another example, the data type of the data to be processed by the application can be obtained, and the priority of the application can be set according to the data type, such as data type a is set to first priority, data type b is set to second priority, data type c is set to third priority, and so on.

[0116] After presetting the priorities of different applications, obtaining the business data of multiple applications in the above manner, verifying the business data according to the verification strategy corresponding to each application, and obtaining the control group data and experimental group data of each application, the priorities corresponding to the multiple applications can be obtained, and the application with the highest priority can be selected as the current processing application. Since the service may have multiple machines to perform data comparison, in order to effectively prevent the problem of data confusion caused by multiple machines operating a batch of data at the same time, a distributed locking operation can be performed at this time. The distributed locking can be used to select one of the applications from multiple applications as the current processing application for processing according to the batch number batchNo. After the data processing of the current processing application is completed, the next application is selected as the current processing application for processing, so that multiple applications can be processed in order. Then, the experimental group data of the current processing application can be compared with the control group data according to the above data comparison method to obtain the comparison result, and only the database to be updated can be updated according to the comparison result, such as the knowledge classification reference relationship of the maintenance table in the database to be updated can be updated. After the data comparison and data update processing of the currently processed application are completed, the application with the second highest priority among the multiple applications can be used as the current processing application, and the data comparison operation is returned to execute until all the applications are processed, and the distributed unlocking operation is executed, and the data processing flow of the batch of multiple applications ends here.

[0117] It should be noted that in addition to data processing based on priority and distributed locking operations during data comparison, it can also be executed during data structure conversion or data verification. For example, the business data corresponding to the application's knowledge classification can be obtained, as well as the priorities of multiple applications, and the application with the highest priority can be screened out as the current processing application, and a distributed locking operation can be performed; the verification strategy corresponding to the business data is determined according to the application type of the current processing application, and the business data is verified according to the verification strategy; when the verification passes, the business data is transformed into a data structure according to the knowledge type of the business data to obtain the control group data; the experimental group data is obtained according to the data attributes of the control group data; the experimental group data of the current processing application is compared with the control group data to obtain a comparison result, and the database to be updated is updated according to the comparison result; the application with the second highest priority among multiple applications is used as the current processing application, and returns to perform data verification, data structure conversion, data comparison, and data update operations until multiple applications are processed and a distributed unlocking operation is performed.

[0118] For another example, the business data of an application can be obtained, and the verification strategy corresponding to the business data can be determined according to the application type of the application, and the business data can be verified according to the verification strategy; when the verification passes, the priorities of multiple applications can be obtained, the application with the highest priority can be screened out as the current processing application, and a distributed locking operation can be performed; according to the knowledge type of the business data of the current processing application, the business data can be transformed into a data structure to obtain the control group data; according to the data attributes of the control group data, the experimental group data can be obtained; the experimental group data of the current processing application can be compared with the control group data to obtain a comparison result, and the database to be updated can be updated according to the comparison result; the application with the second highest priority among multiple applications can be used as the current processing application, and operations such as data structure transformation, data comparison, and data update can be returned until multiple applications have been processed and a distributed unlocking operation is performed.

[0119] The present application can obtain the business data of the application from the application database, perform data structure transformation on the business data, and obtain control group data. Also, according to the data attributes of the control group data, the target data can be obtained from a third-party database or a database to be updated, and the required experimental group data can be accurately constructed according to the target data. At this time, the experimental group data can be compared with the control group data, and the database to be updated can be accurately updated according to the comparison results. This solution obtains the control group data by performing data structure transformation on the business data of the application, and accurately constructs the experimental group data according to the target data obtained according to the data attributes of the control group data, so that the control group data can be accurately compared with the test group data, and then the database is accurately updated, thereby improving the accuracy of data update.

[0120] The method described in the above embodiment is further described in detail below with examples.

[0121] This embodiment takes the data updating device integrated in the server as an example, takes the business data as the full business data, the database to be updated as the knowledge classification database, and the server updates the knowledge classification reference relationship in the knowledge classification database for detailed description of the specific application scenario. Figure 5 , Figure 5 A schematic diagram of a scenario of a method for updating data on a server under a microservice architecture provided in an embodiment of the present application. The microservice architecture may include application services, message middleware, and maintenance services, etc. The application services and maintenance services may be integrated on the same server or on different servers, which is not limited here. The microservice may be a software architecture style that splits large, complex applications into a series of small, independent services, each of which focuses on completing a specific business function and can be run and deployed in an autonomous manner.

[0122] Among them, the server can perform operations such as data collection, data conversion, data filtering and data sending through the application service, wherein data collection can be the application service collecting the full amount of business data corresponding to multiple applications such as application A, application B and application C, data conversion can be the data format conversion of the full amount of business data of each application to obtain the converted data, data filtering can be the data filtering processing of the converted data to obtain the filtered data, and data sending can be the application service sending the filtered data to the maintenance service through the message middleware. The server can perform operations such as data reception, data structure conversion, data comparison, and data update through the maintenance service. Specifically, the maintenance service can receive the filtered data sent by the application service and determine whether the data reception is completed. If the data reception is not completed, it will continue to wait until the data reception is completed. If the data reception is completed, the maintenance service can perform data structure conversion on the received filtered data to obtain the control group data, and obtain the experimental group data by querying the knowledge classification database, and then compare the control group data with the experimental group data (i.e., data comparison) to determine whether there is a difference between the control group data and the experimental group data. If there is no difference between the control group data and the experimental group data, there is no need to perform a data update operation. If there is a difference between the control group data and the experimental group data, and it is determined that new data needs to be added, the knowledge classification reference relationship in the knowledge classification database is updated based on the new data. If there is a difference between the control group data and the experimental group data, and it is determined that the data needs to be deleted, the knowledge classification reference relationship in the knowledge classification database is updated based on the deleted data. The following will be explained in detail.

[0123] See also Figure 6 , Figure 6A schematic diagram of the interactive process of the data updating method provided in the embodiment of the present application. The method process may include:

[0124] S201 . The application service generates a data update instruction, and in response to the data update instruction, determines a data acquisition strategy for each application according to application types corresponding to a plurality of applications.

[0125] In the customer service scenario, there is a large amount of question-and-answer knowledge base content, which is widely used in various customer service modules, including but not limited to carefully designed opening words recommendation strategies, fixed recommendation position settings for displaying important information, trigger nodes in multi-round dialogue processes and related question chain construction, and even special rules such as algorithm disabling. Although the inherent logical relationship of knowledge references is systematically maintained when configuring these modules, errors, omissions or redundancies are inevitable in the program, which may cause the knowledge classification reference relationship to be missing, redundant or inaccurate, thereby affecting the comprehensive evaluation of customer service quality and efficiency. In order to solve these problems, the data update method of the present application can automatically detect and verify the validity and integrity of these knowledge classification reference relationships on a regular basis to ensure that the knowledge classification reference relationships of various customer service modules in the customer service system can be accurately managed and updated.

[0126] Among them, the opening words recommended can refer to professional and friendly greetings designed for customer service staff when they first contact customers. These recommended opening words are intended to establish a good customer service relationship, convey a positive service attitude, and encourage customers to share their needs or problems. For example: Hello, welcome to our store! I am customer service specialist XX, and I am very happy to serve you. Is there anything I can help you with?

[0127] Fixed recommendation positions can be pre-set locations for displaying specific information or services. For example, in the customer service dialogue window, there may be some fixed areas for recommending hot-selling products, FAQ links, promotion information, or other important notifications. These positions can be automatically filled by the system and always remain in a prominent position so that customer service staff can quickly quote or guide customers to obtain the required information when communicating with them.

[0128] Multi-round dialogue refers to the interaction between the customer service system (such as an intelligent customer service robot) and the user, which is not a one-time simple question and answer, but a series of continuous and logically related dialogue rounds. This form of dialogue can dynamically adjust the response content according to the context, and gradually collect and understand user needs through multiple exchanges, ultimately helping users solve problems or complete specific tasks.

[0129] The following will take the updating of knowledge classification reference relationships in a knowledge classification database for customer service services of multiple applications as an example for detailed explanation. Specifically, during the data update process, in order to trigger timely and accurate updating of the knowledge classification database, the application service can generate a data update instruction when the preset time arrives, changes in the application's business data are detected, or the user actively initiates a data update operation. In response to the data update instruction, the data acquisition strategy for each application is determined according to the application types corresponding to the multiple applications providing customer service services, so as to acquire full business data for different types of applications through matching data acquisition strategies.

[0130] S202. The application service obtains the full amount of business data corresponding to the knowledge classification of each application according to the data acquisition strategy of each application.

[0131] After determining the data acquisition strategy for each application, the application service can obtain the full business data corresponding to the knowledge classifications such as opening word recommendation, fixed recommendation position, and multi-round dialogue for each application according to the data acquisition strategy of each application providing customer service. For example, for application A, the full business data of application A can be obtained by querying the database of application A. For application B, the full business data of application B can be obtained from the file system of application B. For application C, the data server of application C can be accessed to obtain the full business data of application C, etc. For different types of applications, the full business data of each application can be accurately obtained through different data acquisition strategies, which realizes different full business data acquisition methods for different types of applications, and can improve the flexibility and accuracy of full business data acquisition.

[0132] S203: The application service performs data format conversion on the full amount of business data of each application to obtain converted data corresponding to each application.

[0133] After obtaining the full business data of each application, the application service can convert the data format of the full business data of each application to obtain the converted data corresponding to each application, thereby avoiding the differences in data formats of different data of different applications and facilitating subsequent unified processing of the data. The data format of the converted data may include data formats such as JSON and XML, which are not limited here.

[0134] It should be noted that before performing a full data comparison, you can first extract some data for preliminary data comparison in the above manner. If the data comparison passes, there is no need to perform a subsequent full data comparison. If the data comparison does not pass, then perform a subsequent full data comparison, which will not be elaborated here.

[0135] S204: The application service determines a filtering strategy according to the business requirements of each application, and performs data filtering processing on the converted data according to the filtering strategy to obtain filtered data corresponding to each application.

[0136] The application service can obtain the business needs of the application and determine the filtering strategy based on the business needs of the application. Different business needs may correspond to different filtering strategies. For example, the business needs of application A correspond to filtering strategy 1, the business needs of application B correspond to filtering strategy 2, the business needs of application C correspond to filtering strategy 3, and so on. The specific content corresponding to different filtering strategies is not limited here. At this time, the converted data can be filtered according to the filtering strategy to obtain filtered data. The filtered data may include key information such as knowledge type typeEnum, batch number batchNo, and total configuration quantity totalConfigNum, and may also include other information, which is not limited here. For the full amount of business data of different applications, different filtering strategies can be set according to different business needs for data filtering, which can improve the accuracy and reliability of data filtering.

[0137] S205: The application service sends the filtered data corresponding to each application to the maintenance service through the message middleware.

[0138] After obtaining the filtered data, the application service can send the filtered data corresponding to each application to the maintenance service through the message middleware. Of course, the application service can also send the filtered data corresponding to each application to the maintenance service through Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), Transmission Control Protocol (TCP), User Datagram Protocol (UDP) and other communication methods, which are not limited here.

[0139] S206: The maintenance service stores the received filtered data corresponding to each application in the Redis database for automatic deduplication, and verifies the integrity of the filtered data corresponding to each application. If the verification fails, execute step S207; if the verification passes, execute step S208.

[0140] In order to avoid repeated reception of filtered data, the maintenance service can store the received filtered data of each application in the Redis database based on the data identifier (such as msgID) and store it in the Set data structure to achieve automatic deduplication of the filtered data. When the filtered data needs to be verified, the corresponding filtered data can be obtained from the Redis database based on the data identifier for data integrity verification.

[0141] In the process of integrity verification of the filtered data corresponding to each application, the maintenance service can count the data volume of the filtered data, extract the total configuration quantity from the filtered data carrying the total configuration quantity, and compare the data volume of the filtered data with the total configuration quantity carried. If the data volume of the filtered data is inconsistent with the total configuration quantity carried, it is determined that the integrity verification of the filtered data has failed; if the data volume of the filtered data is consistent with the total configuration quantity carried, it is determined that the integrity verification of the filtered data has passed. Verifying data integrity by comparing the total configuration quantity can improve the convenience of data integrity verification. Alternatively, the maintenance service can detect whether the data sending completion mark is received. If the data sending completion mark is not received, it is determined that the filtered data has not been received and the integrity verification of the filtered data has failed; if the data sending completion mark is received, it is determined that the filtered data has been received and the integrity verification of the filtered data has passed.

[0142] S207. When the verification fails, the maintenance service sends relevant prompt information of the verification failure to the application service, so that the application service can check the cause of the data transmission abnormality based on the relevant prompt information and resend the filtered data.

[0143] When the verification fails, the maintenance service can send the relevant prompt information of the verification failure to the application service, so that the application service can check the cause of the abnormal data transmission based on the relevant prompt information, and then directly resend the filtered data according to the cause of the abnormal data transmission, or regenerate the filtered data according to the cause of the abnormal data transmission, and resend the filtered data, etc. The application service can also output the relevant prompt information of the verification failure to the user for viewing, so that the user can take corresponding measures in a targeted manner.

[0144] S208. When the verification is passed, the maintenance service selects the application with the highest priority as the current processing application according to the priority of each application, and performs a distributed locking operation.

[0145] When the verification is passed, the maintenance service can perform batch updates of the full amount of business data corresponding to the knowledge classification based on multiple applications. At this time, different priorities can be set for different applications, and data can be processed according to the priorities, and batch data processing can be performed through distributed locking operations or unlocking operations to improve the efficiency and accuracy of data updates. For example, the maintenance service can set priorities for multiple applications in advance, and the priorities can include multiple different priority levels such as high priority, medium priority, and low priority, or the priorities can be from high to low, including first priority, second priority, third priority, fourth priority, fifth priority, and sixth priority. Multiple different priority levels are not limited here. At this time, the maintenance service can obtain the priorities corresponding to multiple applications, and filter out the application with the highest priority as the current processing application. Since the service may have multiple machines to perform data comparison, in order to effectively prevent the problem of data confusion caused by multiple machines operating a batch of data at the same time, a distributed locking operation can be performed at this time. The distributed locking can be used to select one of the applications from multiple applications according to the batch number batchNo as the current processing application for processing. After the data processing of the current processing application is completed, the next application is selected as the current processing application for processing, so that multiple applications can be processed in order.

[0146] S209: The maintenance service performs data structure conversion on the filtered data according to the knowledge type of the filtered data of the current processing application to obtain the control group data of the current processing application.

[0147] The maintenance service can obtain the knowledge type of the filtered data of the current processing application, and perform data structure transformation on the filtered data according to the knowledge type of the filtered data to obtain control group data. The data structure transformation methods corresponding to different knowledge types of the application can be different, and the fields contained in the control group data obtained by the transformation can be different, thereby improving the flexibility and accuracy of the data structure transformation. For example, when the knowledge type is a multi-round dialogue, the control group data obtained by performing data structure transformation on the filtered data can include a field of operating status. For another example, when the knowledge type is an opening word recommendation, the control group data obtained by performing data structure transformation on the filtered data can include relevant fields such as logistics and commodities.

[0148] S210. The maintenance service obtains knowledge classification reference data that matches the knowledge type of the control group data from the knowledge classification database, and constructs the experimental group data according to the knowledge classification reference data.

[0149] The maintenance service can obtain the knowledge classification reference data that matches the knowledge type of the control group data from the knowledge classification reference relationship maintenance table of the knowledge classification database, and construct the experimental group data based on the knowledge classification reference data. The experimental group data may include the knowledge classification ID, the knowledge classification reference relationship, and other information. The knowledge classification database may contain data corresponding to multiple knowledge classifications such as opening word recommendations, fixed recommendation positions, and multiple rounds of dialogue. By extracting data from the knowledge classification database to construct the experimental group data, the flexibility and convenience of obtaining the experimental group data can be improved. It should be noted that when the control group data is the full amount of business data, the corresponding experimental group data obtained from the knowledge classification database can also be the full amount of data, so that the full amount of data can be compared between the experimental group data and the control group data, avoiding the missing, redundancy, and inaccuracy of knowledge classification, and improving the accuracy of data comparison.

[0150] S211. The maintenance service compares the control group data of the current processing application with the experimental group data, and updates the knowledge classification reference relationship in the knowledge classification database according to the comparison result.

[0151] After obtaining the experimental group data, the maintenance service can compare the experimental group data with the control group data to obtain a comparison result, which may include consistency between the experimental group data and the control group data, and inconsistency between the experimental group data and the control group data, etc., wherein the inconsistency between the experimental group data and the control group data may include inconsistency in the knowledge classification reference relationship, inconsistency in the knowledge classification ID, and inconsistency in other information, etc., which are not limited here. At this time, the knowledge classification reference relationship in the knowledge classification database can be updated according to the comparison result. For example, if it is determined according to the comparison result that new data needs to be added, the knowledge classification reference relationship in the knowledge classification database is updated based on the data that needs to be added. If it is determined according to the comparison result that data needs to be deleted, the knowledge classification reference relationship in the knowledge classification database is updated based on the data that needs to be deleted.

[0152] Among them, U 需要新增数据 = control group data - experimental group data, U 需要删除数据 = experimental group data - control group data, if U 需要新增数据 is a non-empty set, then U 需要新增数据 Add to the knowledge classification database, and timely and accurately update the knowledge classification reference relationship in the knowledge classification database, avoiding the loss of knowledge classification in the knowledge classification database. 需要删除数据 is a non-empty set, then we can 需要删除数据 The corresponding data in the knowledge classification database is deleted, and the knowledge classification reference relationship in the knowledge classification database is updated timely and accurately, avoiding the redundancy of knowledge classification in the knowledge classification database. 需要新增数据 and U 需要删除数据are all non-empty sets. To prevent the newly added data from being accidentally deleted in some cases, the deletion operation can be performed first, and then the addition operation. 需要新增数据 is an empty set, or U 需要删除数据 If it is an empty set, it means that the data of the experimental group is consistent with the data of the control group, and the current state of the knowledge classification database is maintained.

[0153] S212: The maintenance service uses the application with the second highest priority among the multiple applications as the current processing application, returns to perform the data structure conversion operation, and performs the distributed unlocking operation until the multiple applications are processed.

[0154] After completing the data structure transformation, data comparison, and data update of the current processing application, the maintenance service uses the application with the second highest priority among multiple applications as the current processing application, returns to perform the data structure transformation operation, and performs the distributed unlocking operation until all applications are processed.

[0155] It should be noted that when there are multiple applications with the same priority level, one of the applications can be randomly selected (or selected by a user selection instruction) from the multiple applications corresponding to the priority level as the current processing application for processing. After the current processing application is processed, another application corresponding to the priority level is randomly selected as the current processing application for processing. This process continues until all the applications corresponding to the priority level are processed. Then, the application with the next priority level is processed as the current processing application. There is no limitation here.

[0156] S213. When the data update is completed, the maintenance service sends a message of the data update completion to the application service to inform the application service that the data update is completed.

[0157] After completing data structure conversion, data comparison, and data update for multiple applications, the maintenance service can send a data update completion message to the application service to inform the application service that the data update is complete. The application service can output the data update completion message to the user for viewing.

[0158] After completing the data update of the knowledge classification database, customer service can be provided for multiple applications based on the updated knowledge classification database. This achieves accurate updating of the knowledge classification database containing multiple knowledge classifications such as opening word recommendations, fixed recommendation positions, and multiple rounds of conversations, thereby improving the service quality of customer service based on the knowledge classification reference relationship in the knowledge classification database.

[0159] In the above embodiments, the description of each embodiment has its own emphasis. For the part that is not described in detail in a certain embodiment, please refer to the detailed description of the system testing method above, and will not be repeated here.

[0160] The application service of the embodiment of the present application can determine the data acquisition strategy of each application according to the application type corresponding to multiple applications, and according to the data acquisition strategy of each application, respectively obtain the full business data corresponding to the knowledge classification of each application, convert the data format of the full business data of each application to obtain the converted data corresponding to each application, and determine the filtering strategy according to the business needs of each application, filter the converted data according to the filtering strategy to obtain the filtered data corresponding to each application, and send the filtered data corresponding to each application to the maintenance service through the message middleware. The maintenance service can perform integrity verification on the filtered data corresponding to each application received. When the verification is passed, the application with the highest priority is selected as the current processing application according to the priority of each application, and a distributed locking operation is performed. Then, according to the knowledge type of the filtered data of the current processing application, the filtered data is accurately transformed into a data structure to obtain the control group data of the current processing application, and the knowledge classification reference data matching the knowledge type of the control group data is obtained from the knowledge classification database to construct the experimental group data. Then, the control group data of the current processing application is compared with the experimental group data, and the knowledge classification reference relationship in the knowledge classification database is accurately updated according to the comparison result. At this time, the application with the second highest priority among multiple applications is used as the current processing application, and the operation of executing the data structure transformation is returned until multiple applications are processed and the distributed unlocking operation is performed. This solution not only improves the accuracy of data update, but also ensures that the knowledge classification reference relationship in the knowledge classification database can be accurately managed and updated, and improves the quality of services provided based on the knowledge classification reference relationship in the knowledge classification database.

[0161] In order to better implement the data updating method provided in the embodiment of the present application, the embodiment of the present application also provides a device based on the above data updating method. The meanings of the terms are the same as those in the above data updating method, and the specific implementation details can refer to the description in the method embodiment.

[0162] See also Figure 7 , Figure 7 This is a structural diagram of a data updating device provided in an embodiment of the present application, wherein the data updating device 300 may include an acquisition module 301, a conversion module 302, a construction module 303, and an update module 304, etc.

[0163] The acquisition module 301 is used to acquire the business data of the application from the application database.

[0164] The conversion module 302 is used to perform data structure conversion on the business data to obtain control group data.

[0165] The construction module 303 is used to obtain target data from a third-party database or a database to be updated according to data attributes of the control group data, and construct the experimental group data according to the target data.

[0166] The updating module 304 is used to compare the experimental group data with the control group data, and update the database to be updated according to the comparison result.

[0167] In some embodiments, the construction module 303 is specifically used to: determine the knowledge type of the control group data according to the data attributes of the control group data, and obtain target data matching the knowledge type from the database to be updated; or determine the data identifier according to the data attributes of the control group data, and call the target data in a third-party database according to the data identifier.

[0168] In some embodiments, the construction module 303 is specifically used to: when the data structure of the target data is consistent with the data structure of the control group data, use the target data as the experimental group data; or, when the data structure of the target data is inconsistent with the data structure of the control group data, perform data structure transformation on the target data to obtain experimental group data that is consistent with the data structure of the control group data.

[0169] In some embodiments, when there is business data of multiple applications, the control group data includes control group data corresponding to each application, and the experimental group data includes experimental group data corresponding to each application. The update module 304 is specifically used to compare the experimental group data corresponding to each application with the control group data in order from high to low priority of each application, and update the database to be updated according to the comparison results.

[0170] In some embodiments, the update module 304 is specifically used to compare the control group data with the experimental group data, and obtain data that is different from the experimental group data from the control group data, and add the different data to the database to be updated; or, obtain data that is different from the control group data from the experimental group data, and delete the different data from the database to be updated.

[0171] In some embodiments, the conversion module 302 includes:

[0172] A verification submodule, configured to determine a verification strategy corresponding to the business data according to an application type of the application, and verify the business data according to the verification strategy;

[0173] The conversion submodule is used to convert the business data into a data structure if the verification is passed.

[0174] In some embodiments, the verification submodule is specifically used to extract part of the data from the business data as preliminary comparison data; obtain preliminary verification data from the database to be updated; compare the preliminary comparison data with the preliminary verification data; if the data comparison fails, determine the verification strategy corresponding to the business data according to the application type of the application.

[0175] In some embodiments, the verification submodule is specifically used to randomly extract part of the data from the business data as preliminary comparison data; or, to extract data of a preset time period from the business data as preliminary comparison data; or, to extract data of different knowledge types corresponding to the application from the business data according to a preset ratio as preliminary comparison data.

[0176] In some embodiments, the verification submodule is specifically used to extract part of the data from the database to be updated as preliminary verification data; compare the preliminary verification data with the business data; if the data comparison fails, determine the verification strategy corresponding to the business data according to the application type of the application.

[0177] In some embodiments, the verification submodule is specifically used to convert the data format of the business data according to the verification strategy to obtain converted data; perform data filtering processing on the converted data according to the business requirements of the application to obtain filtered data; and perform integrity verification on the filtered data.

[0178] In some embodiments, the verification submodule is specifically used to determine that the integrity verification of the filtered data has passed if the amount of data of the filtered data is consistent with the total configured amount; or, if the filtered data carries a data frame completion identifier, determine that the integrity verification of the filtered data has passed.

[0179] In some embodiments, the acquisition module 301 is specifically used to acquire the application type of the application, determine a data acquisition strategy according to the application type; and acquire the business data of the application from an application database according to the data acquisition strategy.

[0180] In the embodiment of the present application, the acquisition module 301 can acquire the business data of the application from the application database, and the conversion module 302 can perform data structure conversion on the business data to obtain the control group data. Also, the construction module 303 can acquire the target data from the third-party database or the database to be updated according to the data attributes of the control group data, and accurately construct the required experimental group data according to the target data. At this time, the update module 304 can compare the experimental group data with the control group data, and accurately update the database to be updated according to the comparison results. This solution obtains the control group data by performing data structure conversion on the business data of the application, and accurately constructs the experimental group data according to the target data obtained according to the data attributes of the control group data, so that the control group data can be accurately compared with the test group data, and then the database is accurately updated, thereby improving the accuracy of data update.

[0181] In the embodiments of the present application, the term "module" or "unit" refers to a computer program or a part of a computer program with a predetermined function, and works together with other related parts to achieve a predetermined goal, and can be implemented in whole or in part by using software, hardware (such as processing circuits or memories) or a combination thereof. Similarly, a processor (or multiple processors or memories) can be used to implement one or more modules or units. In addition, each module or unit can be part of an overall module or unit that includes the function of the module or unit.

[0182] The present application also provides a computer device, which may be a server, etc. Figure 8 As shown, it shows a schematic diagram of the structure of the computer device involved in the embodiment of the present application, specifically:

[0183] The computer device may include components such as a processor 401 with one or more processing cores, a memory 402 with one or more computer-readable storage media, a power supply 403, and an input unit 404. Those skilled in the art will appreciate that Figure 8 The computer device structure shown in the figure does not constitute a limitation on the computer device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently. Among them:

[0184] The processor 401 is the control center of the computer device, and uses various interfaces and lines to connect various parts of the entire computer device. By running or executing software programs and / or modules stored in the memory 402, and calling data stored in the memory 402, the processor 401 performs various functions of the computer device and processes data. Optionally, the processor 401 may include one or more processing cores; preferably, the processor 401 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It is understandable that the above-mentioned modem processor may not be integrated into the processor 401.

[0185] The memory 402 can be used to store software programs and modules. The processor 401 executes various functional applications and data processing by running the software programs and modules stored in the memory 402. The memory 402 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 402 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory 402 may also include a memory controller to provide the processor 401 with access to the memory 402.

[0186] The computer device also includes a power supply 403 for supplying power to various components. Preferably, the power supply 403 can be logically connected to the processor 401 through a power management system, so as to manage charging, discharging, and power consumption through the power management system. The power supply 403 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0187] The computer device may further include an input unit 404, which may be used to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control.

[0188] Although not shown, the computer device may further include a display unit, etc., which will not be described in detail herein. Specifically in this embodiment, the processor 401 in the computer device will load the executable files corresponding to the processes of one or more application programs into the memory 402 according to the following instructions, and the processor 401 will run the application programs stored in the memory 402, thereby realizing various functions, as follows:

[0189] Acquire the business data of the application from the application database; perform data structure conversion on the business data to obtain control group data; acquire target data from a third-party database or a database to be updated according to data attributes of the control group data, and construct experimental group data according to the target data; perform data comparison between the experimental group data and the control group data, and update the database to be updated according to the comparison result.

[0190] In some implementations, when acquiring target data from a third-party database or a database to be updated according to the data attributes of the control group, the processor 401 further executes:

[0191] Determine the knowledge type of the control group data according to the data attribute of the control group data, and obtain target data matching the knowledge type from the database to be updated; or

[0192] The data identifier is determined according to the data attribute of the control group data, and the target data in the third-party database is called according to the data identifier.

[0193] In some implementations, when constructing the experimental group data according to the target data, the processor 401 further executes:

[0194] When the data structure of the target data is consistent with the data structure of the control group data, the target data is used as the experimental group data; or,

[0195] When the data structure of the target data is inconsistent with the data structure of the control group data, the data structure of the target data is transformed to obtain experimental group data that is consistent with the data structure of the control group data.

[0196] In some implementations, when there are business data of multiple applications, the control group data includes control group data corresponding to each application, and the experimental group data includes experimental group data corresponding to each application. When comparing the experimental group data with the control group data, the processor 401 further executes:

[0197] In order of priority of each application from high to low, the experimental group data corresponding to each application is compared with the control group data.

[0198] In some implementations, when updating the to-be-updated database according to the comparison result, the processor 401 further executes:

[0199] Compare the control group data with the experimental group data, and obtain data from the control group data that are different from the experimental group data, and add the different data to the database to be updated; or obtain data from the experimental group data that are different from the control group data, and delete the different data from the database to be updated.

[0200] In some implementations, when performing data structure conversion on the business data, the processor 401 further executes:

[0201] Determine a verification strategy corresponding to the business data according to the application type of the application, and verify the business data according to the verification strategy;

[0202] If the verification is successful, the business data is converted into a data structure.

[0203] In some implementations, when determining the verification strategy corresponding to the business data according to the application type of the application, the processor 401 further executes:

[0204] Extracting part of the business data as preliminary comparative data;

[0205] Acquire preliminary verification data from the database to be updated;

[0206] Performing data comparison between the preliminary comparison data and the preliminary verification data;

[0207] If the data comparison fails, a verification strategy corresponding to the business data is determined according to the application type of the application.

[0208] In some implementations, when extracting part of the data from the business data as preliminary comparison data, the processor 401 further executes:

[0209] Randomly extract some data from the business data as preliminary comparison data; or,

[0210] Extracting data of a preset time period from the business data as preliminary comparison data; or,

[0211] From the business data, data of different knowledge types corresponding to the application are extracted according to a preset ratio as preliminary comparison data.

[0212] In some implementations, when determining the verification strategy corresponding to the business data according to the application type of the application, the processor 401 further executes:

[0213] Extracting part of the data from the database to be updated as preliminary verification data;

[0214] Performing data comparison between the preliminary verification data and the business data;

[0215] If the data comparison fails, a verification strategy corresponding to the business data is determined according to the application type of the application.

[0216] In some implementations, when verifying the service data according to the verification strategy, the processor 401 further executes:

[0217] Convert the business data into a data format according to the verification strategy to obtain converted data;

[0218] According to the business requirements of the application, the converted data is subjected to data filtering processing to obtain filtered data;

[0219] The filtered data is verified for integrity.

[0220] In some implementations, when performing integrity verification on the filtered data, the processor 401 further executes:

[0221] If the amount of filtered data is consistent with the total configured amount, it is determined that the integrity verification of the filtered data has passed; or,

[0222] If the filtered data carries a data frame completion identifier, it is determined that the integrity verification of the filtered data has passed.

[0223] In some implementations, when acquiring the service data of the application from the application database, the processor 401 further executes:

[0224] Acquire the application type of the application, and determine a data acquisition strategy according to the application type;

[0225] According to the data acquisition strategy, the business data of the application is acquired from the application database.

[0226] In the above embodiments, the description of each embodiment has its own focus. For the part that is not described in detail in a certain embodiment, please refer to the detailed description of the data updating method above, which will not be repeated here.

[0227] In an embodiment of the present application, the computer device can obtain the business data of the application from the application database, perform data structure transformation on the business data, and obtain control group data. Also, according to the data attributes of the control group data, the target data can be obtained from a third-party database or a database to be updated, and the required experimental group data can be accurately constructed according to the target data. At this time, the experimental group data can be compared with the control group data, and the database to be updated can be accurately updated according to the comparison results. This solution obtains the control group data by performing data structure transformation on the business data of the application, and accurately constructs the experimental group data according to the target data obtained according to the data attributes of the control group data, so that the control group data can be accurately compared with the test group data, and then the database is accurately updated, thereby improving the accuracy of data update.

[0228] According to one aspect of the present application, a computer program product or a computer program is provided, the computer program product or the computer program including computer instructions, the computer instructions being stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the methods provided in various optional implementations in the above-mentioned embodiments.

[0229] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by computer instructions, or by controlling related hardware through computer instructions, and the computer instructions can be stored in a computer-readable storage medium (i.e., storage medium) and loaded and executed by a processor. To this end, an embodiment of the present application provides a storage medium in which a computer program is stored, and the computer program may include computer instructions, and the computer program can be loaded by a processor to execute any of the data update methods provided in the embodiments of the present application.

[0230] The specific implementation of the above operations can be found in the previous embodiments, which will not be described in detail here.

[0231] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0232] Since the instructions stored in the storage medium can execute the steps in any data updating method provided in the embodiments of the present application, the beneficial effects that can be achieved by any data updating method provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0233] The above is a detailed introduction to a data updating method, device, computer equipment, storage medium and program product provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A data updating method, characterized in that: include: Get the application's business data from the application database; Performing data structure transformation on the business data to obtain control group data; According to the data attributes of the control group data, target data is obtained from a third-party database or a database to be updated, and experimental group data is constructed according to the target data; The experimental group data is compared with the control group data, and the database to be updated is updated according to the comparison result.

2. The data updating method according to claim 1, characterized in that: The step of acquiring target data from a third-party database or a database to be updated according to the data attributes of the control group includes: Determine the knowledge type of the control group data according to the data attribute of the control group data, and obtain target data matching the knowledge type from the database to be updated; or The data identifier is determined according to the data attribute of the control group data, and the target data in the third-party database is called according to the data identifier.

3. The data updating method according to claim 1, characterized in that: The step of constructing the experimental group data according to the target data includes: When the data structure of the target data is consistent with the data structure of the control group data, the target data is used as the experimental group data; or, When the data structure of the target data is inconsistent with the data structure of the control group data, the data structure of the target data is transformed to obtain experimental group data that is consistent with the data structure of the control group data.

4. The data updating method according to claim 1, characterized in that: When there are business data of multiple applications, the control group data includes control group data corresponding to each application, the experimental group data includes experimental group data corresponding to each application, and the data comparison of the experimental group data with the control group data includes: In order of priority of each application from high to low, the experimental group data corresponding to each application is compared with the control group data.

5. The data updating method according to claim 1, characterized in that: The updating of the database to be updated according to the comparison result includes: The control group data is compared with the experimental group data, and data different from the experimental group data is obtained from the control group data, and the different data is added to the database to be updated; or, data different from the control group data is obtained from the experimental group data, and the different data is deleted from the database to be updated.

6. The data updating method according to claim 1, characterized in that: The data structure conversion of the business data includes: Determine a verification strategy corresponding to the business data according to the application type of the application, and verify the business data according to the verification strategy; If the verification is successful, the business data is converted into a data structure.

7. The data updating method according to claim 6, characterized in that: The determining, according to the application type of the application, a verification strategy corresponding to the business data includes: Extracting part of the business data as preliminary comparison data; Acquire preliminary verification data from the database to be updated; Performing data comparison between the preliminary comparison data and the preliminary verification data; If the data comparison fails, a verification strategy corresponding to the business data is determined according to the application type of the application.

8. The data updating method according to claim 7, characterized in that: The extracting part of the data from the business data as preliminary comparison data includes: Randomly extract some data from the business data as preliminary comparison data; or, Extracting data of a preset time period from the business data as preliminary comparison data; or, From the business data, data of different knowledge types corresponding to the application are extracted according to a preset ratio as preliminary comparison data.

9. The data updating method according to claim 6, characterized in that: The determining, according to the application type of the application, a verification strategy corresponding to the business data includes: Extracting part of the data from the database to be updated as preliminary verification data; Performing data comparison between the preliminary verification data and the business data; If the data comparison fails, a verification strategy corresponding to the business data is determined according to the application type of the application.

10. The data updating method according to claim 6, characterized in that: The verifying the business data according to the verification strategy includes: Convert the business data into a data format according to the verification strategy to obtain converted data; According to the business requirements of the application, the converted data is subjected to data filtering processing to obtain filtered data; The filtered data is verified for integrity.

11. The data updating method according to claim 10, characterized in that: The integrity verification of the filtered data includes: If the amount of filtered data is consistent with the total configured amount, it is determined that the integrity verification of the filtered data has passed; or, If the filtered data carries a data frame completion identifier, it is determined that the integrity verification of the filtered data has passed.

12. The data updating method according to any one of claims 1 to 11, characterized in that: The obtaining of the business data of the application from the application database includes: Acquire the application type of the application, and determine a data acquisition strategy according to the application type; According to the data acquisition strategy, the business data of the application is acquired from the application database.

13. A data updating device, characterized in that: include: The acquisition module is used to obtain the business data of the application from the application database; A conversion module, used for performing data structure conversion on the business data to obtain control group data; A construction module, used to obtain target data from a third-party database or a database to be updated according to data attributes of the control group data, and construct experimental group data according to the target data; The updating module is used to compare the experimental group data with the control group data, and update the database to be updated according to the comparison result.

14. A computer device, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and the processor executes the data updating method according to any one of claims 1 to 12 when calling the computer program in the memory.

15. A storage medium, characterized in that: The storage medium is used to store a computer program, and the computer program is loaded by a processor to execute the data updating method according to any one of claims 1 to 12.

16. A computer program product, comprising a computer program, wherein the computer program is loaded by a processor to execute the data updating method according to any one of claims 1 to 12.

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