CN118113710A

CN118113710A - Database falling method, device, computer equipment and storage medium

Info

Publication number: CN118113710A
Application number: CN202410008681.XA
Authority: CN
Inventors: 尹如鹏
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2024-01-03
Filing date: 2024-01-03
Publication date: 2024-05-31

Abstract

The embodiment of the application belongs to the field of artificial intelligence and big data, is applied to the field of smart cities, and relates to a database falling method, which comprises the steps of acquiring initialization data corresponding to a target service scene when monitoring that the service scene has data change; determining a limit data processing strategy corresponding to the initialization data according to the data type of the initialization data, wherein each data type corresponds to one data processing strategy; performing data processing on the initialization data through a data processing strategy corresponding to the initialization data to obtain database waiting data corresponding to the service scene; and carrying out database falling on the database to be cooled through a preset search engine to obtain an updated database. The application also provides a database device, computer equipment and a storage medium. The method solves the problems that data in a database system is not real-time, so that a large amount of bandwidth cost is required for data export, and the exported data cannot be tracked in real time and then is solved.

Description

Database falling method, device, computer equipment and storage medium

Technical Field

The present application relates to the field of artificial intelligence and big data technologies, and in particular, to a database falling method, apparatus, computer device, and storage medium.

Background

The statistics of the data report form is always an indispensable tracking index for a large company, especially for a huge amount of data scene management level. The business index achievement condition and the change trend can be mastered from each scene dimension through the data report, and the manager operation and personnel can be helped to analyze and follow up in time. The existing large data bottom processing logic is complex, a synchronization process is needed from a data source table to a large data end, the bottom wide table processing data is calculated off-line, the data storage operation cost is high, the larger the data volume is, the larger the operation is, the more than T+1 of the data statistics result is delayed, the data is non-real-time, a large amount of bandwidth cost is needed for data export, and the exported data cannot be tracked in real time to solve the problem.

Disclosure of Invention

The embodiment of the application aims to provide a database falling method, a database falling device, computer equipment and a storage medium, which are used for solving the problems that data is not real-time in a database falling system, so that a large amount of bandwidth cost is required for data export, and the exported data cannot be tracked in real time and then is solved.

In order to solve the above technical problems, the embodiment of the present application provides a database-dropping method, which adopts the following technical scheme:

When the data change of the service scene is monitored, initializing data corresponding to the target service scene is acquired;

Determining a limit data processing strategy corresponding to the initialization data according to the data type of the initialization data, wherein each data type corresponds to one data processing strategy;

performing data processing on the initialization data through a data processing strategy corresponding to the initialization data to obtain database waiting data corresponding to the service scene;

And carrying out database falling on the database to be cooled through a preset search engine to obtain an updated database.

Further, the step of obtaining the initialization data corresponding to the target service scene includes:

acquiring data change information of a target service scene;

Carrying out log record on the data change information to obtain log data of the target service scene;

and obtaining initialization data corresponding to the target service scene based on the log data.

Further, the step of determining the data processing policy corresponding to the initialization data according to the data type of the initialization data includes:

when the type of the broadside data is monitored, determining broadside data processing corresponding to the initialization data;

and when the original data type is monitored, determining the data cleaning processing corresponding to the initialization data.

Further, the step of performing data processing on the initialization data through a data processing policy corresponding to the initialization data to obtain to-be-dropped database data corresponding to the service scene includes:

Processing the broadside data of the initialization data to obtain first database data to be dropped corresponding to the service scene;

And performing data cleaning processing on the initialization data to obtain second database waiting data corresponding to the service scene.

Further, the step of obtaining the updated database includes the steps of:

The first database to be dropped is dropped through a preset search engine, and an updated first database is obtained;

And carrying out database falling on the second database to be subjected to database falling through a preset search engine to obtain an updated second database.

Further, the method further comprises:

And when service data is lost in the data processing process, automatically comparing the consistency of the service scene data through the preset search engine, and carrying out data retrieval on the service data.

Further, the method further comprises:

and when the business data has repeated information or abnormal consumption, carrying out the same ID index through the preset search engine to obtain the latest data, and covering the business data by using the criminal data.

In order to solve the above technical problems, the embodiment of the present application further provides a database dropping device, which adopts the following technical scheme:

the acquisition module is used for acquiring initialization data corresponding to the target service scene when the data change of the service scene is monitored;

The determining module is used for determining a data processing strategy corresponding to the initialization data according to the data type of the initialization data, wherein each data type corresponds to one data processing strategy;

the processing module is used for carrying out data processing on the initialization data through a data processing strategy corresponding to the initialization data to obtain database waiting data corresponding to the service scene;

And the database falling module is used for falling the database to be cooled through a preset search engine to obtain an updated database.

In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical schemes:

the computer device comprises a memory and a processor, wherein the memory stores computer readable instructions, and the processor executes the computer readable instructions to realize the steps of the database dropping method according to any one of the embodiments of the application.

In order to solve the above technical problems, an embodiment of the present application further provides a computer readable storage medium, which adopts the following technical schemes:

the computer readable storage medium has stored thereon computer readable instructions which when executed by a processor implement the steps of the database method of any of the embodiments of the present application

Compared with the prior art, the embodiment of the application has the following main beneficial effects: when the data change of the service scene is monitored, initializing data corresponding to the target service scene is obtained, a data processing strategy corresponding to the initializing data is determined according to the data type of the initializing data, the initializing data is subjected to data processing through the data processing strategy corresponding to the initializing data, database to-be-dropped data corresponding to the service scene is obtained, database to-be-dropped data is dropped through a preset search engine, and therefore an updated database is obtained, the problem that data in a database dropping system are non-real-time, a large amount of bandwidth cost is required for data derivation, and the derived data cannot be tracked in real time and can not be timely tracked and solved is solved, so that the real-time data propelling capability is improved, and the management efficiency is improved.

Drawings

In order to more clearly illustrate the solution of the present application, a brief description will be given below of the drawings required for the description of the embodiments of the present application, it being apparent that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without the exercise of inventive effort for a person of ordinary skill in the art.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow chart of one embodiment of a database method according to the present application;

FIG. 3 is a flow chart of one embodiment of step S201 in FIG. 2;

FIG. 4 is a flow chart of one embodiment of step S202 of FIG. 2;

FIG. 5 is a flow chart of one embodiment of step S203 of FIG. 2;

FIG. 6 is a flow chart of one embodiment of step S204 of FIG. 2;

FIG. 7 is a flow chart of another embodiment of FIG. 2;

FIG. 8 is a flow chart of another embodiment of FIG. 2;

FIG. 9 is a schematic diagram of an embodiment of a database apparatus according to the present application;

FIG. 10 is a schematic diagram illustrating a configuration of an embodiment of the acquisition module 901 of FIG. 9;

FIG. 11 is a schematic diagram of one embodiment of the determination module 902 of FIG. 9;

FIG. 12 is a schematic diagram of one embodiment of the processing module 903 of FIG. 11;

FIG. 13 is a schematic diagram illustrating the structure of one embodiment of the database module 904 of FIG. 12;

FIG. 14 is a schematic diagram illustrating the structure of one embodiment of the database apparatus 900 of FIG. 9;

FIG. 15 is a schematic diagram illustrating the structure of one embodiment of the database apparatus 900 in FIG. 9;

FIG. 16 is a schematic structural view of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description of the application and the claims and the description of the drawings above are intended to cover a non-exclusive inclusion. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

In order to make the person skilled in the art better understand the solution of the present application, the technical solution of the embodiment of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture ExpertsGroup Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving PictureExperts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.

It should be noted that, the database dropping method provided by the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the database dropping device is generally disposed in the server/terminal device.

It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow chart of one embodiment of a database method according to the present application is shown. The database falling method comprises the following steps:

Step S201, when the data change of the service scene is monitored, initializing data corresponding to the target service scene is obtained.

In this embodiment, the electronic device (for example, the server shown in fig. 1) on which the database dropping method operates may receive the query request of the terminal device through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection.

The terminal equipment can be terminal equipment with remote interaction functions, such as personal assistants, intelligent customer service and the like, and the database dropping method can be applied to services such as online database dropping management, online database dropping consultation and the like.

Specifically, the electronic device can be used for different industry types such as insurance class database, financial class database, health class database, store class database and the like.

The service scenarios may include different service scenarios, and may be service flows, service rules, and the like.

The above data change refers to a process of modifying data, such as data addition, data modification, data deletion, etc.

The initialization data refers to the original data stored in the database.

When the target service scene can monitor data change through the terminal equipment and finish data change information, the server receives the data change information and acquires initialization data corresponding to the target service scene.

Step S202, determining a data processing strategy corresponding to the initialization data according to the data type of the initialization data.

In this embodiment, the data processing policy corresponding to the initialization data may be determined by the data type of the initialization data. Each data type corresponds to a data processing policy.

The definition of the data type in the data structure is a set of values and a set of operations defined on the set of values; the data types may be primitive types, algebraic data types, abstract data types, reference types, and function types.

The data processing strategy refers to a series of data processing methods and rules formulated according to service requirements and data characteristics in the data processing process, and can be of the types of original data, broadside data, algebraic data, abstract data and the like.

Step S203, data processing is carried out on the initialized data through a data processing strategy corresponding to the initialized data, and database waiting data corresponding to the service scene is obtained.

In this embodiment, after obtaining a data processing policy corresponding to the initialization data, the electronic device may perform data processing on the initialization data through the data processing policy corresponding to the initialization data, to obtain to-be-dropped database data corresponding to the service scenario.

The data processing refers to the extraction, cleaning, analysis and processing of information from data to produce valuable results for decision making and decision making, which may be data cleaning, data processing, etc.

The above-mentioned data to be dropped refers to data that has not been stored in the database.

It should be noted that each data type corresponds to one data processing policy.

Step S204, database falling is carried out on database falling data through a preset search engine, and an updated database is obtained.

In this embodiment, after obtaining the database to be dropped, the electronic device may drop the database to be dropped through a preset search engine to obtain an updated database.

Further, after the updated database is obtained, the updated database may be stored in the corresponding database.

The preset search engine may be an ES-based search engine.

ES is an open source distributed search engine based on a RESTful web interface and built on top of Apache Lucene. At the same time, the ES is a distributed document database, wherein each field can be indexed, and the data of each field can be searched, and the ES can be laterally expanded to hundreds of servers for storing and processing PB-level data. ESs can store, search and analyze a large amount of data in an extremely short time.

Specifically, the ES supports various data types, including structured and unstructured data, geographic position and time series data, and the like, and can process various types of data and extract painful information therefrom, and the ES is internally provided with a plurality of data analysis and aggregation functions, so that the data can be conveniently analyzed and mined.

It should be noted that, the ES may monitor the database data changes, and all the database data changes may send the data change information to the service system through the ES. And the processing monitoring of the pure data layer is realized, the data is cleaned in the data processing layer, and the original system is zero in invasion.

When the application monitors that the data change occurs in the service scene, the application obtains the initialization data corresponding to the target service scene, determines the data processing strategy corresponding to the initialization data according to the data type of the initialization data, carries out data processing on the initialization data through the data processing strategy corresponding to the initialization data to obtain the data to be dropped corresponding to the service scene, and carries out the database dropping of the data to be dropped through the preset search engine, thereby obtaining the updated database, solving the problems that the data is not real-time in the database dropping system, a large amount of bandwidth cost is required for data export, and the exported data cannot be tracked in real time and can not be timely followed up, thereby improving the real-time propelling capability of the data and improving the management efficiency.

With continued reference to FIG. 3, a flow chart of one embodiment of step S201 in FIG. 2 is shown. In step S201, the method specifically includes the steps of:

s2011, acquiring data change information of a target service scene.

In this embodiment, the above-mentioned data change refers to a process of modifying the database, such as data addition, data modification, data deletion, and the like.

The data change information refers to information generated during data modification.

And S2012, carrying out log record on the data change information to obtain log data of the target service scene.

In this embodiment, the above log record refers to recording and tracking the data change operation in the database management system.

The log data may be binlog of log data, and the information of SQL statements updated by the user to the database is recorded, for example, both the SQL statements for changing the database table and changing the content are recorded in binlog, but the query for the content such as the library table is not recorded.

It should be noted that, when the data change occurs in the service scenario, log data may be generated during the modification process of the database, and the log data may be used as a source for subsequent data processing.

S2013, obtaining initialization data corresponding to the target business scene based on the log data.

In this embodiment, after obtaining log data of a target service scenario, the electronic device may obtain initialization data corresponding to the target service scenario through the log data.

The initialization data refers to the original data stored in the database.

In one embodiment, through log data, data change information can be monitored in an mq mode to obtain initialization data corresponding to a target service scene.

The mq is a message queue, and is a communication method from application to application.

In another embodiment, through log data, an OGG change message may be monitored in an ogg+kfk manner, so as to obtain initialization data corresponding to a target service scenario.

The OGG, collectively referred to as Oracle GoldenGate, is a business tool provided by Oracle authorities for addressing data replication in heterogeneous data environments. The OGG enables data to be migrated and backed up among different databases through a data replication technology, so that the availability and reliability of the data are improved.

The above KFK (KafKa) is a message queue based on a publish-subscribe model. The KFK is used for realizing the functions of asynchronous message transmission, load balancing, fault detection and the like. The KFK publishes the message to the queue through a publish-subscribe mode, thereby realizing the transmission and processing of the message. KFK may support multiple message sources and multiple message targets, thus implementing complex messaging and processing scenarios.

According to the method and the device, the data change information of the target service scene is obtained, the log record is carried out on the data change information, so that the log data of the target service scene is obtained, the initial data corresponding to the target service scene is obtained by utilizing the log data, and further the data change is processed and analyzed, so that the accuracy is improved.

With continued reference to FIG. 4, a flowchart of one embodiment of step S202 of FIG. 2 is shown. In step S202, the method specifically includes the following steps:

In step S2021, when the broadside data type is monitored, broadside data processing corresponding to the initialization data is determined.

In this embodiment, the monitoring may be performed by the mq method or the ogg+kfk method.

The above broadside data type is a data type in which data is stored and organized in one dimension (width).

The processing of the broadside data refers to the process of processing and operating the broadside data types. The broadside data manipulation process may be data format conversion, data calculation, or the like.

In step S2022, when the original data type is monitored, a data cleansing process corresponding to the initialization data is determined.

In this embodiment, the original data type refers to the data type of the data itself before the data processing.

The data cleansing process refers to a process of rechecking and checking data, and aims to delete duplicate information, correct errors, and provide data consistency.

According to the application, the broadside data type of the initialization data is monitored, the corresponding broadside data processing is carried out on the initialization data is determined, the original data type of the initialization data is monitored, so that the data cleaning processing corresponding to the initialization data is determined, the efficiency and the precision of the data processing can be improved, and the data requirements and specifications are further met.

With continued reference to fig. 5, a flow chart of one embodiment of step S203 of fig. 2 is shown. In step S203, the method specifically includes the following steps:

Step S2031, performing broadside data processing on the initialization data to obtain first database data to be dropped corresponding to the service scene.

In this embodiment, the processing of the broadside data refers to a process of processing and operating the broadside data type. The broadside data manipulation process may be data format conversion, data calculation, or the like.

Step S2032, performing data cleaning processing on the initialization data to obtain second database waiting data corresponding to the service scene.

In this embodiment, the data cleansing process refers to a process of rechecking and checking data, and aims to delete duplicate information, correct errors, and provide data consistency.

According to the application, the first database to be dropped corresponding to the service scene is obtained by processing the initialization data, the second database to be dropped corresponding to the service scene is obtained by cleaning the initialization data, and one data type corresponds to one data processing strategy, so that the accuracy of data processing is ensured.

With continued reference to FIG. 6, a flow chart of one embodiment of step S204 of FIG. 2 is shown. In step S204, the method specifically includes the following steps:

In step S2041, the first database to be dropped is dropped by a preset search engine, so as to obtain an updated first database.

In this embodiment, the preset search engine may be an ES-based search engine.

In one embodiment, the ES supports multiple data types, including structured and unstructured data, geographic location and time series data, etc., and can process various types of data and extract painful information therefrom, and the ES incorporates many data analysis and aggregation functions to facilitate analysis and mining of the data.

The first database data to be dropped is obtained by processing the broadside data of the initialization data.

Step S2042, the second database to be dropped is dropped through a preset search engine, and an updated second database is obtained.

In this embodiment, the second to-be-dropped database data is obtained by performing a data cleaning process on the initialization data.

According to the method and the device, the first database to be dropped is dropped through the preset search engine, so that the updated first database is obtained, and the second database to be dropped is dropped through the preset search engine, so that the updated second database is obtained, and the management efficiency is further improved.

With continued reference to fig. 7, a flow chart of another embodiment of fig. 2 is shown. The method specifically comprises the following steps:

In step S701, the service data is lost during the data processing process, and the service data is automatically compared with the consistency of the service scene data by the preset search engine, so as to perform data retrieval.

In this embodiment, the preset search engine may be an ES-based search engine, and may automatically compare the data consistency of the source end and the target end at regular time, and perform data return. .

The service data loss refers to the phenomenon that data is accidentally and permanently lost or cannot be retrieved during the process of data storage, transmission or processing.

The above data consistency refers to the integrity and accuracy of the data.

It should be noted that if the service data is lost or inconsistent, errors and unreliability of the data may be caused, thereby affecting the normal operation of the service.

In one embodiment, the service is down during the data processing process, and the consistency of the service scene is automatically compared through a preset search engine, and the data is complemented.

In the application, in the abnormal situations of service data loss, service downtime and the like in the data processing process, the consistency of service scenes is compared through a preset search engine, and the data retrieval is carried out on the service data, so that the accuracy of the data is ensured.

With continued reference to fig. 8, a flow chart of another embodiment of fig. 2 is shown. The method specifically comprises the following steps:

In step S801, when the service data has repeated message or abnormal consumption, the same ID index is performed by the preset search engine to obtain the latest data, and the latest data is used to cover the service data.

In this embodiment, the above message repetition refers to the same information.

The abnormal consumption refers to the consumption behavior which is found to have a significant difference from the normal consumption behavior by performing abnormality detection and analysis on the consumption behavior in the big data analysis.

The same ID index refers to the index of the same ID; the latest data related to the ID can be quickly found by the same ID index.

The above data overlay is a process of rewriting data to remove original data and replace it with new data.

The application carries out the same ID index through the preset search engine when the information of the data is repeated or abnormally consumed, obtains the latest data, and uses the latest data to cover the business data so as to avoid the occurrence of waste data.

The embodiment of the application can acquire and process the related data required by the knowledge base configuration based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.

Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions. The method can be applied to online database management and online database consultation, thereby promoting the construction of smart cities.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.

With further reference to fig. 9, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a database dropping device, where the embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be applied to various electronic devices.

As shown in fig. 9, the database dropping device 900 according to the present embodiment includes: an acquisition module 901, a determination module 902, a processing module 903, and a database module 904. Wherein:

The acquiring module 901 is configured to acquire initialization data corresponding to the target service scene when it is monitored that the service scene has data change;

A determining module 902, configured to determine, according to the data type of the initialization data, a data processing policy corresponding to the initialization data, where each data type corresponds to one data processing policy;

The processing module 903 is configured to perform data processing on the initialization data according to a data processing policy corresponding to the initialization data, so as to obtain database waiting data corresponding to the service scenario;

and the database falling module 904 is configured to perform database falling on the database to be cooled through a preset search engine, so as to obtain an updated database.

According to the method and the device, after the questionnaire data of the user to be evaluated are obtained, the corresponding evaluation model is determined according to the questionnaire data, the questionnaire data are processed by the evaluation model, so that a database falling result of the user to be evaluated can be obtained, and the questionnaire data are dynamically adjusted according to the above semantics filled by the user because the questionnaire data are generated after the questionnaire to be evaluated is filled by the user to be evaluated, so that the obtained questionnaire data are related to the semantics of the user to be evaluated, and the questionnaire data which are more concerned by the user to be evaluated can be further mined, so that the accuracy of the database falling is improved.

Referring to fig. 10, which is a schematic structural diagram of one embodiment of the obtaining module 901 in fig. 9, in some alternative implementations of this embodiment, the obtaining module 901 includes an item obtaining sub-module 9011, a recording sub-module 9012, and a first processing sub-module 9013. Wherein:

an acquisition submodule 9011, configured to acquire data change information of a target service scenario;

A recording submodule 9012, configured to log the data change information to obtain log data of the target service scenario;

the first processing submodule 9013 is configured to obtain initialization data corresponding to the target service scenario based on the log data.

Referring to fig. 11, which is a schematic structural diagram of an embodiment of the item determining module 902 in fig. 9, the determining module 902 includes a first determining submodule 9021 and a second determining submodule 9022. Wherein:

A first determining submodule 9021, configured to determine, when it is monitored that the type of the broadside data is broadside data, processing the broadside data corresponding to the initialization data;

And the second determining submodule 9022 is used for determining the data cleaning process corresponding to the initialization data when the original data type is monitored.

Referring to fig. 12, which is a schematic structural diagram of an embodiment of the processing module 903 in fig. 9, the processing module 903 includes a second processing sub-module 9031 and a third processing sub-module 9032. Wherein:

The second processing sub-module 9031 is configured to perform broadside data processing on the initialization data to obtain first database waiting data corresponding to the service scene;

and the third processing sub-module 9032 is configured to perform data cleaning processing on the initialization data to obtain second database waiting data corresponding to the service scene.

Referring to fig. 13, which is a schematic structural diagram of an embodiment of the database module 904 in fig. 9, the database module 904 includes a first database-dropping sub-model 9041 and a second database-dropping sub-module 9042. Wherein:

the first database-falling sub-model 9041 is configured to perform database-falling on the first database to be dropped through a preset search engine to obtain an updated first database;

And the second database-falling sub-module 9042 is configured to perform database-falling on the second database to be cooled through a preset search engine, so as to obtain an updated second database.

Referring to fig. 14, which is a schematic structural diagram of an embodiment of the database apparatus 900 in fig. 9, the database apparatus 900 further includes a data retrieval module 905. Wherein:

And a data retrieval module 905, configured to automatically compare the consistency of the service scene data by the preset search engine when the service data is lost in the data processing process, and retrieve the data from the service data.

Referring to fig. 15, which is a schematic structural diagram of another embodiment of the database apparatus 900 in fig. 9, the database apparatus 900 includes a data overlay module 906. Wherein:

and a data coverage module 906, configured to, when the service data has repeated message or abnormal consumption, perform the same ID index through the preset search engine to obtain the latest data, and use the latest data to cover the service data.

The application carries out the same ID index through the preset search engine when the information of the data appears repeatedly or abnormally, obtains the latest data, and uses the latest data to cover the business data, thereby avoiding the occurrence of dirty data and ensuring the consistency and the integrity of the data.

In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 16, fig. 16 is a basic structural block diagram of a computer device according to the present embodiment.

The computer device 16 includes a memory 161, a processor 162, and a network interface 163 communicatively coupled to each other via a system bus. It should be noted that only computer device 16 having components 161-163 is shown in the figures, but it should be understood that not all of the illustrated components need be implemented, and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and its hardware includes, but is not limited to, a microprocessor, an Application SPECIFIC INTEGRATED Circuit (ASIC), a Programmable gate array (Field-Programmable GATE ARRAY, FPGA), a digital Processor (DIGITAL SIGNAL Processor, DSP), an embedded device, and the like.

The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.

The memory 161 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 161 may be an internal storage unit of the computer device 16, such as a hard disk or a memory of the computer device 16. In other embodiments, the memory 161 may also be an external storage device of the computer device 16, such as a plug-in hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD) or the like, which are provided on the computer device 16. Of course, the memory 161 may also include both internal storage units of the computer device 16 and external storage devices. In this embodiment, the memory 161 is typically used to store an operating system and various application software installed on the computer device 16, such as computer readable instructions of a database method, and the like. Further, the memory 161 may be used to temporarily store various types of data that have been output or are to be output.

The processor 162 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 162 is generally used to control the overall operation of the computer device 16. In this embodiment, the processor 162 is configured to execute computer readable instructions stored in the memory 161 or process data, such as computer readable instructions for executing the database method.

The network interface 163 may include a wireless network interface or a wired network interface, and the network interface 163 is typically used to establish communication connections between the computer device 16 and other electronic devices.

In this embodiment, the influence data of the system in the knowledge base may be used to generate a database influenced by the target system to be tested for the user, so that the user may intuitively obtain the influence range of the target system to be tested, evaluate the influence of modification more accurately and more quickly according to the influence range of the target system to be tested, eliminate irrelevant interference points, simplify regression use cases, avoid that partial correlations are found to be not processed when early evaluation influence points are missed, and save development time and supplement logic when joint debugging is performed, so that serious possible scheme design needs to override the remarked questions, thereby improving the efficiency of project development.

The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of a database-dropping method as described above.

From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present application.

It is apparent that the above-described embodiments are only some embodiments of the present application, but not all embodiments, and the preferred embodiments of the present application are shown in the drawings, which do not limit the scope of the patent claims. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a thorough and complete understanding of the present disclosure. Although the application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing description, or equivalents may be substituted for elements thereof. All equivalent structures made by the content of the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the scope of the application.

Claims

1. A method for database dropping, comprising the steps of:

determining a data processing strategy corresponding to the initialization data according to the data type of the initialization data, wherein each data type corresponds to one data processing strategy;

2. The database method according to claim 1, wherein the step of obtaining initialization data corresponding to the target service scenario includes:

acquiring data change information of a target service scene;

carrying out log record on the log data information to obtain log data of the target service scene;

3. The database dropping method as recited in claim 2, wherein the step of determining the data processing policy corresponding to the initialization data according to the data type of the initialization data comprises:

and when the data type is monitored to be the original data type, determining the data cleaning processing corresponding to the initialization data.

4. The database dropping method according to claim 3, wherein the step of performing data processing on the initialization data by using a data processing policy corresponding to the initialization data to obtain the database to be dropped corresponding to the service scenario includes:

Performing data processing on the initialization data to obtain first database data to be dropped corresponding to the service scene;

5. The method for database dropping according to claim 4, wherein the step of dropping the database to be dropped by a preset search engine to obtain an updated database comprises:

6. The database method of claim 5, further comprising:

7. The database dropping method as recited in claim 6, further comprising:

And when the service data is repeatedly or abnormally consumed, searching the same ID through the preset search engine to obtain the latest data, and covering the service data by using the latest data.

8. A database dropping device, comprising:

9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the database method of any of claims 1 to 7.

10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the database method of any of claims 1 to 7.