CN118656357A - Data migration method, device, electronic device, storage medium and product - Google Patents

Abstract

The present disclosure provides a data migration method, apparatus, electronic device, storage medium and product, the method comprising: obtaining a first index of a task to be migrated and a mapping relation between a first data model and a second data model; acquiring first data of the first index in the first data model and second data of the first index in the second data model, and determining a first difference verification result between the first data and the second data according to an index verification rule; determining a first risk level of the task to be migrated according to the first difference verification result; and according to the first risk level and the mapping relation, migrating the data to be migrated of the task to be migrated in the first data model to the second data model, so that risk assessment is carried out before migration, migration risk is reduced, and efficiency and accuracy are improved.

Description

Data migration method, device, electronic device, storage medium and product

Technical Field

The present disclosure relates to the field of data processing technology, and in particular to a data migration method, device, electronic device, storage medium and product.

Background Art

The data model can represent a logical information structure model that is directly oriented to data, and can provide an important basis for analysis and decision-making of system operations. However, as the business develops and changes, there is usually a need to iterate and upgrade the data model. After the data model is iterated and upgraded, data migration is required. However, the information in the system is usually complex, and errors may occur during the migration process, resulting in migration failure, which reduces migration efficiency and stability.

Summary of the invention

The present disclosure provides a data migration method, device, electronic device, storage medium and product.

In a first aspect, the present disclosure provides a data migration method, the data migration method comprising:

Obtaining a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model;

Acquire first data of the first indicator in the first data model, and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to an indicator verification rule;

Determining a first risk level of the task to be migrated according to the first difference verification result;

According to the first risk level and the mapping relationship, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model.

In a second aspect, the present disclosure provides a data migration device, the data migration device comprising:

An acquisition module, used to obtain a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model;

a risk assessment module, configured to obtain first data of the first indicator in the first data model and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to an indicator verification rule, and determine a first risk level of the task to be migrated according to the first difference verification result;

A migration module is used to migrate the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship.

In a third aspect, the present disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores one or more computer programs executable by the at least one processor, and one or more of the computer programs are executed by the at least one processor so that the at least one processor can perform the above-mentioned data migration method.

In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the above-mentioned data migration method when executed by a processor.

In a fifth aspect, the present disclosure provides a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned data migration method.

The data migration method provided by the embodiment of the present disclosure automatically collects information, obtains the first indicator of the task to be migrated, and the mapping relationship between the first data model and the second data model, and then determines the first difference verification result corresponding to the first indicator according to the indicator verification rule, and determines the first risk level of the task to be migrated according to the first difference verification result, and migrates the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship. In this way, by collecting information and verifying the differences between the first data model and the second data model, risk assessment is performed before migration, and migration can be guided according to the risk assessment results, thereby reducing migration risks, making the migration process smoother and more accurate, and improving migration efficiency and migration performance.

It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings are used to provide a further understanding of the present disclosure and constitute a part of the specification. Together with the embodiments of the present disclosure, they are used to explain the present disclosure and do not constitute a limitation of the present disclosure. The above and other features and advantages will become more apparent to those skilled in the art by describing detailed example embodiments with reference to the accompanying drawings. In the accompanying drawings:

FIG1 is an application scenario diagram of a data migration method and device provided by an embodiment of the present disclosure;

FIG2 is a flow chart of a data migration method provided by an embodiment of the present disclosure;

FIG3 is a block diagram of the principle of task execution information collection in an embodiment of the present disclosure;

FIG4 is an example diagram of information analysis arrangement in an embodiment of the present disclosure;

FIG5 is an example diagram of indicator classification in an embodiment of the present disclosure;

FIG6 is a schematic diagram of the principle of risk assessment in an embodiment of the present disclosure;

FIG7 is a schematic diagram showing the principle of task migration in an embodiment of the present disclosure;

FIG8 is a schematic diagram of the overall architecture of the data migration method according to an embodiment of the present disclosure;

FIG9 is a schematic diagram of a flow chart of a data migration method in an embodiment of the present disclosure;

FIG10 is a block diagram of a data migration device provided by an embodiment of the present disclosure;

FIG. 11 is a block diagram of an electronic device provided in an embodiment of the present disclosure.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.

As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "including" and/or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and/or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and/or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.

The data model can represent a logical information structure model that is directly oriented to data, and can provide an important basis for analysis and decision-making of system operations. However, as the business develops and changes, the original data model may not meet current needs, resulting in problems such as increasing and confusing data tables. Therefore, there is usually a need for iterative upgrades of the data model. After the data model is iteratively upgraded, data migration is required. The data migration plan is very important for the correct operation of the system. However, the information in the system is usually complex, and related technologies cannot assess the migration risks. Uncertain risks and errors may occur during the migration process, resulting in problems or failures in the migration, reducing the efficiency and stability of the migration.

Therefore, a data migration method is provided in an embodiment of the present disclosure, which obtains a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model, and performs a difference check on the first data model and the second data model according to the indicator verification rule, so as to determine the first risk level of the task to be migrated, and performs data migration on the task to be migrated according to the first risk level and the mapping relationship. In this way, a risk assessment of migration is performed through information collection and difference verification. Risk assessment can be performed first during migration, and migration can be guided according to the first risk level, thereby reducing the migration risk, making the migration process smoother and more accurate, improving migration efficiency and accuracy, and enhancing migration performance.

FIG1 schematically shows an application scenario diagram of the data migration method and device provided in an embodiment of the present disclosure.

As shown in Fig. 1, the application scenario of the embodiment of the present disclosure may include a terminal device 101, a network 103 and a server 102. The network 103 is used to provide a medium for a communication link between the terminal device 101 and the server 102. The network 103 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

The user can use the terminal device 101 to interact with the server 102 through the network 103 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

The terminal device 101 may be any electronic device having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, a laptop computer, a desktop computer, and the like.

The server 102 may be a server that provides various services, such as a background management server (only as an example) that provides support for websites browsed by users using the terminal device 101. The background management server may analyze and process received data such as user requests, and feed back processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

It should be noted that the data migration method and apparatus provided in the embodiments of the present disclosure may be executed by the server 102. Accordingly, the data migration method and apparatus provided in the embodiments of the present disclosure may be arranged in the server 102. The data migration method and apparatus provided in the embodiments of the present disclosure may also be executed by a server or server cluster that is different from the server 102 and that can communicate with the terminal device 101 and/or the server 102. Accordingly, the data migration method and apparatus provided in the embodiments of the present disclosure may also be arranged in a server or server cluster that is different from the server 102 and that can communicate with the terminal device 101 and/or the server 102.

It should be understood that the number of terminal devices, networks and servers in Figure 1 is only illustrative. Any number of terminal devices, networks and servers may be provided according to implementation requirements.

FIG2 is a flow chart of a data migration method provided by an embodiment of the present disclosure. Referring to FIG2 , the method includes:

Step S201: obtaining a first indicator of a task to be migrated, and a mapping relationship between a first data model and a second data model.

In the disclosed embodiments, the data model iteration upgrade scenario of the business system is mainly targeted. The data model is an abstraction of data features, and is usually used in the database system to provide a formal framework for information representation and operation means. The data model includes the structural part of the database data, the operation part of the database data, and the constraint conditions of the database data. The database system includes multiple data tables. The data table is an object used to store data in the database system. The data table includes multiple fields. Usually, there are upstream and downstream dependencies between data tables or data, and data is usually obtained by task execution. Therefore, the dependency between data can be understood as the dependency between tasks. When the upstream task is upgraded or updated, it may have an impact on the downstream tasks with dependencies.

Therefore, when the upstream task is upgraded or updated, a corresponding new data model is constructed based on the previous old first data model, and the mapping relationship between the new data model and the first data model is configured to complete the construction of the new data model. If the previous old first data model is upgraded, the tasks that depend on the first data model need to be migrated to the new data model, wherein the task can be understood as the execution unit of the business process, and the task to be migrated is the task that needs to be migrated to the new data model. In a possible embodiment, the tasks to be migrated include downstream tasks, and the first upstream task that the downstream task depends on in the first data model is updated. In this way, the tasks to be migrated can be determined first to improve the migration efficiency.

S202: Obtain first data of the first indicator in the first data model and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to the indicator verification rule.

In the embodiment of the present disclosure, the second data model can represent a new data model after the first data model is updated. After the first data model is iteratively upgraded, the difference between the second data model and the first data model will affect the migration accuracy. Corresponding indicator verification rules can be configured in advance for different first indicators. The indicator verification rules are used to indicate the comparison method or comparison conditions of the first indicators. The specific embodiments of the present disclosure are not limited and can be configured according to actual conditions and needs. For example, a certain indicator verification rule is: the difference in field types of the field indicator in the first data model and the second data model.

S203: Determine a first risk level of the task to be migrated according to the first difference verification result.

S204: Migrate the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship.

In the disclosed embodiment, a task to be migrated and a first indicator of the task to be migrated are determined, and then according to the indicator verification rule, a first difference verification result between the first data of the first indicator in the first data model and the second data in the second data model is determined, and according to the first difference verification result, a first risk level of the task to be migrated is determined, and then according to the first risk level and the mapping relationship between the first data model and the second data model, the task to be migrated is migrated. In this way, before the migration, the risk assessment of the task to be migrated can be performed, the migration risk can be reduced, the migration can be made smoother, continuous and without impact fluctuations, and the migration efficiency can be improved.

The data migration method of the embodiment of the present disclosure is described in detail below.

As described above, in the embodiments of the present disclosure, after the upgraded or updated second data model is constructed for the first data model, information automatic collection is first performed to solve the migration problem from the first data model to the second data model.

In a specific possible implementation manner, before obtaining the first indicator of the task to be migrated in the above step S201, the task to be migrated can also be determined first. Specifically, a possible implementation manner is provided, which obtains first task scheduling information of multiple tasks in the first data model and second task scheduling information of multiple tasks in the second data model; based on the first migration identifier in the first task scheduling information and the second migration identifier in the second task scheduling information, the task to be migrated is screened out from the multiple tasks.

For ease of description, the first task scheduling information and the second task scheduling information are collectively referred to as task scheduling information. The task scheduling information represents the rules, policies or conditions of task scheduling, etc., and can automatically trigger and execute tasks based on the task scheduling information. For example, the task scheduling information may include: migration identification, the dependency link relationship of the task, the relationship between the task and the data table, the task resource consumption, the start time of the task, the completion time of the first upstream task in the first data model, the completion time of the second upstream task in the second data model, the service level agreement (SLA) time and importance level of the task, etc., which are not limited in the specific embodiments of the present disclosure, and can be used for information collection and screening according to actual needs, wherein the migration identification is used to indicate whether to migrate. When the second data model is constructed based on the first data model, it is possible to know which tasks have been updated, and then it is possible to configure whether the corresponding downstream tasks of the task need to be migrated to the second data model. For example, refer to Table 1, which is an example of task scheduling information in the embodiments of the present disclosure.

Table 1.

As can be seen from Table 1, the first upstream task that Task A depends on in the first data model is Task B. Task B has been upgraded or updated, and the corresponding updated task in the second data model is Task B'. Task B has been updated, so the migration flag corresponding to the downstream task A that depends on Task B is yes, that is, Task A is a task to be migrated. In addition, the SLA time in Table 1 indicates the latest allowable startup time, which is to ensure that the provided services meet the needs.

It should be noted that Table 1 only lists several possible task scheduling information, which should not limit the embodiments of the present disclosure. In practice, different or more other task scheduling information may be collected.

In a possible implementation manner, the first indicator of the task to be migrated obtained in the above step S201 may specifically include:

1) Obtain first task execution information of the task to be migrated for the first data model, and second task execution information of the task to be migrated for the second data model; determine the first data table and the first field for executing the task to be migrated according to the first task execution information; determine the second data table and the second field for executing the task to be migrated according to the second task execution information.

In the disclosed embodiment, the Structured Query Language (SQL) log generated by the execution of the acquisition task can be parsed to obtain the data tables and fields for executing the task to be migrated, that is, it can be known which data tables and fields in the data tables are used when the task to be migrated is executed. In this way, when performing risk assessment later, it is only necessary to perform difference check on the used data tables and fields, without performing difference check on all data tables or fields, which can improve efficiency, and it is also possible to parse and obtain the restriction conditions or data of the used data. Other information such as scope can also be used for subsequent risk difference verification or test verification during migration. For example, refer to Figure 3, which is a principle block diagram of task execution information collection in the embodiment of the present disclosure. The SQL log generated by the task execution is obtained from the resource management system (such as the resource management cluster), and the SQL log is parsed, such as SQL keyword separation, identifier segmentation, metadata mapping, syntax analysis processing, etc., where the keyword is, for example, select (select), etc., and the identifier is, for example, a custom name, variable, etc., and the paragraphs, subqueries, etc. in the SQL log can be split, so that the data table, field, restriction conditions and other information involved in the SQL log can be parsed.

2) Acquire first metadata of the first data table and the first field, and second metadata of the second data table and the second field.

Among them, metadata refers to data that records and describes data-related information, including data attributes (such as name, size, data type), structure (such as length, field, index), feature information, etc. In the embodiment of the present disclosure, the first metadata corresponding to the first data model and the second metadata corresponding to the second data model are obtained respectively, which can also be used for subsequent risk difference verification or test verification during migration.

For example, see Table 2, which is an example of metadata in an embodiment of the present disclosure.

Table 2.

In the embodiment of the present disclosure, data table information, field information, field type, whether the storage is partitioned, storage period and other information recorded in the metadata can be obtained. Table 2 is only a possible metadata example, which can correspond to the first data model or the second data model. Here, for the sake of convenience of explanation, the first metadata and the second metadata are collectively referred to as metadata.

3) Generate association information of the task to be migrated according to the first task scheduling information and the second task scheduling information, the first data table and the first field, the second data table and the second field, the first metadata and the second metadata.

Among them, in the embodiment of the present disclosure, multiple information dimensions can also be pre-divided, and the associated information represents the information classified under the corresponding information dimension, and the information dimension represents the preset content category for describing the information.

In the embodiments of the present disclosure, task scheduling information, task execution information, metadata, etc. corresponding to the aforementioned first data model and second data model may be collected periodically or based on certain trigger conditions, and then the collected information may be analyzed and arranged, wherein the analysis and arrangement may represent the process of sorting, organizing, and managing data during data processing, such as including operations such as cleaning, converting, aggregating, filtering, and merging data, analyzing, classifying, modeling, and visualizing the data, and then, in the embodiments of the present disclosure, there may be multiple tasks to be migrated in the collected information, and each task to be migrated may be analyzed and arranged separately to obtain the associated information of each task to be migrated, which facilitates subsequent analysis and processing of the tasks to be migrated.

For example, refer to Figure 4, which is an example diagram of information analysis and arrangement in the embodiment of the present disclosure. As shown in Figure 4, task scheduling information, task execution information and metadata are obtained, such as information of tasks to be migrated, task lineage information, task execution information, the relationship between tasks and data tables, the mapping relationship between the first data model and the second data model, storage information of the first data model and the second data model, data table information, data table storage information, data table lineage, the relationship between tasks and fields, importance levels and other information. Then, taking the task as the dimension, for each task to be migrated, the associated information of each task to be migrated in multiple information dimensions is generated, as shown in Figure 4. For example, the information dimensions include task information, data table information, the first data table and the second data table that execute the task to be migrated, the associated first field and the second field and other dimensions. The specific embodiment of the present disclosure is not limited thereto. In this way, various information is classified, arranged and sorted in multiple information dimensions to improve the efficiency of information management and processing.

4) According to the pre-configured indicator set to be verified, the first indicator of the task to be migrated is selected from the associated information.

In the embodiments of the present disclosure, since the information obtained is usually complicated, not all information may be available or valid for risk assessment. Therefore, in the embodiments of the present disclosure, a set of indicators to be verified can be pre-configured based on experience or needs, where an indicator represents a certain information, for example, a certain field. The number of indicators in the indicator set to be verified is not limited. For example, the indicator set to be verified is configured to have 10 indicators to be verified, and then these 10 indicators to be verified (that is, the first indicator of the task to be migrated that is filtered out) can be screened out based on the associated information of the task to be migrated. The indicator set to be verified is only a reference and can be unified for multiple tasks to be migrated. It is not necessarily required to be able to filter out all the indicators in the indicator set. As long as all or part of the indicators of the task to be migrated are filtered out, it can be used for subsequent risk assessment.

Furthermore, in an embodiment of the present disclosure, an orchestration analysis can be performed based on the automatically collected information to conduct a risk assessment on the migration task. In a possible implementation, specifically with respect to the above-mentioned step S202, the present disclosure provides a possible implementation, including: classifying the first indicator into the corresponding section type according to the preset section type; obtaining the first data of the first indicator in the first data model within a preset time period, and the second data of the first indicator in the second data model; comparing the first data and the second data corresponding to the first indicator according to the indicator verification rule corresponding to the section type, and determining a first difference verification result between the first data and the second data.

In the disclosed embodiments, the section type division and the configuration of the indicator verification rules can be performed in advance, wherein the section types can be divided based on needs, for example, the section types include task sections, data sections, field sections and other sections, the task sections represent the indicator information for the users, the data sections mainly represent the indicator information for the developers, and the field sections represent the indicator information of the field types. In this way, the indicators are uniformly classified into different section types to improve the efficiency of information processing, and the corresponding section weights can be set based on the different section types, which is also convenient for subsequent risk assessment and risk tracing.

For example, refer to Figure 5, which is an example diagram of indicator classification in the embodiment of the present disclosure. As shown in Figure 5, based on the obtained associated information of the task to be migrated, the first indicator is screened out and the first indicator is classified into different section types. For different section types, corresponding indicator verification rules are configured, and dynamic adjustment of indicator verification rules can also be supported. An indicator verification rule can involve multiple indicators, and an indicator can also correspond to multiple indicator verification rules, which is not limited in the embodiment of the present disclosure. For example, the indicator verification rules corresponding to the task section include: Rule 1, the start time of the downstream task is compared with the completion time of the upstream task in the second data model, Rule 2, the completion time of the upstream task in the second data model is compared with the completion time of the upstream task in the first data model. For another example, the indicator verification rules corresponding to the field section include: Rule 1, the difference in field type of the field in the first data model and the second data model, Rule 2, the data difference of the field in the first data model and the second data model, etc. Furthermore, in the embodiment of the present disclosure, based on the indicator verification rule, the first data of the first data model and the second data of the second data model within a preset time period can be selected, and for each task to be migrated, the first difference verification results of multiple first indicators under the task to be migrated can be calculated in parallel. Furthermore, the first difference verification results can also be uniformly stored based on the task to be migrated, the sector type and the first indicator. For example, as shown in Table 3, this is an example of the first difference verification result in the embodiment of the present disclosure.

Table 3.

As shown in Table 3, for example, for the task A to be migrated, the section type is the field section, the first indicator is the customer identifier, the indicator verification rule used is rule 1, the first difference verification result is 1, the type is 01, and the weight is 1, wherein the higher the difference score in the first difference verification result, the higher the difference, and the type indicates the risk level, such as 01 for medium risk, 02 for high risk, 03 for low risk, etc. The weight indicates the importance of the indicator in the risk assessment of the task to be migrated.

For the above-mentioned step S203, the present disclosure provides a possible implementation method, including: obtaining risk parameter information corresponding to the first indicator; determining the risk score of the first indicator based on the first difference verification result and the risk parameter information, and determining the first risk level of the task to be migrated based on the risk score of the first indicator.

The risk parameter information represents preset parameter information for evaluating the risk level. For example, the risk parameter information may include at least one of the following: indicator weight, importance level of the task to be migrated, and difference correction information.

For example, refer to Figure 6, which is a schematic diagram of the principle of risk assessment in the embodiment of the present disclosure. As shown in Figure 6, the calculated first difference verification result and multiple risk parameter information can be input into a trained risk assessment model, wherein the risk parameter information, for example, includes the importance level of the task to be migrated, the indicator weight, and the difference correction information. The difference correction information can be determined based on the sample data difference or difference trend between the second data model and the first data model in the historical time period, and/or based on the difference offset score of the indicator. It can also include other preset information, such as sector type, sector weight, special field judgment criteria, etc., and then based on the risk assessment model, the risk assessment of the task to be migrated is performed to obtain the first risk level of the task to be migrated.

In the embodiment of the present disclosure, for each task to be migrated, the first risk level is determined respectively, and then a risk assessment report including multiple tasks to be migrated can be generated. Based on the risk assessment report, migration can be guided. For the above step S204, several possible embodiments are also provided:

In a possible embodiment, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the first risk level and the mapping relationship, including: when the first risk level is less than the preset level, determining the migration rule according to the mapping relationship; and migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the migration rule.

In the disclosed embodiment, for tasks to be migrated with relatively low risks, a migration script can be generated to directly perform the migration, wherein the migration rules in the migration script include, for example, data table mapping relationships, field mapping relationships, etc., and then the data to be migrated of the tasks to be migrated in the first data model can be processed by the migration rules and then passed to the second data model.

In a possible embodiment, according to the first risk level and the mapping relationship, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model, including: 1) when the first risk level is greater than or equal to the preset level, according to the first difference verification result, the second indicator that meets the difference condition is screened out from the first indicator; 2) according to the preconfigured downgrade processing strategy, the indicator verification rule of the second indicator is adjusted to obtain the adjusted indicator verification rule; 3) the third data of the second indicator in the first data model and the fourth data of the second indicator in the second data model are obtained, and according to the adjusted indicator verification rule, the second difference verification result between the third data and the fourth data is determined; 4) according to the second difference verification result, the second risk level of the task to be migrated is determined; 5) when the second risk level is less than the preset level, according to the mapping relationship, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model.

In the disclosed embodiment, for tasks to be migrated with higher risks, it is possible to determine which first indicators are more different or risky based on the first difference check result, and then screen out the second indicators that meet the difference conditions, and then downgrade these second indicators. For example, the downgrade strategy includes converting the field type of the second indicator in the first data model and the second data model so that the corresponding field types of the second indicator in the first data model and the second data model are the same; for example, the data that is invalid and garbled for the second indicator in the first data model or the second data model is cleaned up, new data is re-screened, and the data comparison range is narrowed down for statistics; for example, the difference of the indicators set in the task section is downgraded, such as setting the start time of the task to be delayed according to the task execution time. The latest startup time is not limited in the specific embodiments of the present disclosure and can also be adjusted dynamically. The purpose is to reduce the difference verification requirements or risk judgment requirements for some indicators with little or no risk impact, and then after the indicator is adjusted, the difference verification calculation of the indicator and the risk assessment of the migration task are performed again. If the risk is smaller after adjustment, data migration can be performed. However, if the risk is still high after adjustment, and the second indicator can no longer be adjusted based on the downgrade processing strategy, it means that the risk of the task to be migrated is indeed very high, and direct migration will cause data errors, migration failures and other problems. Therefore, the task to be migrated will not be migrated at this time, and it can be pushed to relevant personnel for manual judgment and analysis to determine a more appropriate and accurate processing strategy.

In this way, in the embodiment of the present disclosure, risk assessment can be performed in advance during the migration process, and for tasks to be migrated with higher risks, difference problems can be located and tracked in a timely and accurate manner, and then adaptive processing can be performed to improve migration efficiency and accuracy.

In a possible embodiment, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the first risk level and the mapping relationship, including: when the first risk level is greater than or equal to the preset level, sending a prompt message, the prompt message is used to prompt to adjust the risk level; receiving adjustment information input based on the prompt message, wherein the adjustment information at least includes a modified third risk level corresponding to the first risk level; when the third risk level is less than the preset level, migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the mapping relationship.

In the disclosed embodiment, when the risk is high, relevant personnel may be directly prompted to conduct analysis and determination. If the relevant personnel determine that the risk is small, the first risk level may be modified, and migration may be performed based on the mapping relationship.

Furthermore, in the disclosed embodiment, when generating a migration script, it can also be generated based on the mapping relationship and task execution information. The generated migration script includes not only migration rules but also verification rules, which can be used to verify the correctness of the migrated data.

That is to say, in the embodiment of the present disclosure, through the risk assessment report, corresponding processing can be performed on the tasks to be migrated with different risk levels to ensure the correctness of the migration. For example, refer to Figure 7, which is a schematic diagram of the principle of task migration in the embodiment of the present disclosure. As shown in Figure 7, when the first risk level is less than the preset level, a migration script is generated, and a test migration is performed based on the migration rules in the migration script. The correctness of the migrated data in the test migration is verified based on the verification rules in the migration script. After the verification is passed, the migration of the task to be migrated is completed; when the first risk level is greater than or equal to the preset level, it can be processed through manual analysis and/or based on the downgrade processing strategy, and then the risk assessment can be re-performed, or if the manual analysis determines that the risk is small, the first risk level can be modified to force the generation of a migration script and perform migration, thereby completing the migration of the tasks to be migrated in the entire first data model to the second data model.

In this way, in the embodiment of the present disclosure, a migration risk assessment is performed before migration, so that the migration risk can be understood in advance and unknown risks that may occur during the migration process can be reduced. In addition, the risk assessment is based on the verification results of various indicators, and the indicators are derived from the collection and observation of the usage of the task to be migrated itself. The risk assessment is more comprehensive and detailed, and more in line with the needs of the task to be migrated itself, with higher accuracy. Therefore, the migration risk of the task to be migrated and the risk positioning of the indicators in the task to be migrated can be accurately known, and corresponding responses and adjustments can be made quickly. The second data model can be corrected, and the tasks to be migrated with high risks can be tracked and processed accordingly, so as to reduce the migration risk and improve the efficiency, completeness and accuracy of the entire migration process.

The data migration method in the embodiment of the present disclosure is described below using a specific application scenario. Referring to FIG8 , it is a schematic diagram of the overall architecture of the data migration method in the embodiment of the present disclosure.

In the embodiments of the present disclosure, as the business in the system develops, the data model describing the data structure in the database of the system also needs to be continuously iterated and updated, as shown in Figure 8. In the embodiments of the present disclosure, it can be applied to any business system, such as a reporting system scenario, a variable management scenario, etc. According to business needs, a second data model is designed and constructed based on the first data model. In order to ensure the stability and availability of the business system, the tasks to be migrated in the first data model need to be migrated to the second data model, which may specifically include the following parts.

1) The first part: information collection and analysis and arrangement. This part is mainly used for collecting various information. The collected objects include the second data model, the first data model, metadata, scheduling system, resource management system and downstream business system.

Specifically, in the embodiments of the present disclosure, it is possible to obtain a mapping relationship between a first data model and a second data model, obtain first metadata of the first data model and second metadata of the second data model, obtain task scheduling information from a scheduling system, obtain task execution information from a resource management system, and in addition, obtain usage of the data tables or fields involved from downstream business systems, etc., in order to determine the importance of the data tables or fields, or to perform migration testing and verification, etc., and then perform orchestration processing based on the various information collected.

In this way, in the embodiments of the present disclosure, information collection can be performed automatically online without relying on manual labor, thereby improving the efficiency and accuracy of information collection.

2) The second part: Generate a risk assessment report. This part is mainly based on the arranged information, screens out the first indicator, and classifies the first indicator into the corresponding section type, and then determines the first difference verification result between the first data model and the second data model according to the indicator verification rule, and then determines the first risk level of the task to be migrated to generate a risk assessment report.

3) The third part: generating a migration script. This part mainly processes the task to be migrated according to different first risk levels to generate a migration script, and then migrates the task to be migrated according to the migration script, wherein the migration script at least includes a migration rule.

In the disclosed embodiments, information collection is automated and risk assessment is performed before migration, which can reduce high risks or uncertain risks caused by migration. High-risk tasks to be migrated can be quickly located and adjusted, and migration scripts can be directly generated and migrated for low-risk tasks to be migrated, thereby improving migration accuracy and reducing maintenance investment costs for downstream tasks to be migrated after upstream tasks are updated, reducing resource consumption and migration risks. The entire migration process can be automated, reducing the participation process and costs of multiple relevant personnel, and further improving migration efficiency and accuracy.

Based on the above embodiment, refer to FIG. 9 , which is a flow chart of a data migration method in an embodiment of the present disclosure. As shown in FIG. 9 , the data migration method includes:

S901: Information collection.

As shown in FIG. 9 , in the embodiment of the present disclosure, information collection may include a mapping relationship between a first data model and a second data model, task scheduling information, task execution information, metadata, and the like.

For example, by collecting metadata such as data tables, fields, storage cycles, etc. of the first data model and the second data model, the mapping relationship between data tables and fields can be analyzed; by collecting task scheduling information, the task lineage relationship associated with the task to be migrated can be obtained based on the collected task scheduling information, and the later expansion and use of downstream tasks that depend on the task to be migrated can be analyzed; by collecting task execution information, the data tables and fields associated with the execution can be obtained based on the execution command of the task to be migrated, and the field lineage relationship can be analyzed.

S902: Information compilation and summary.

In the disclosed embodiment, the collected information may be split, aggregated, classified, and arranged to obtain associated information of each task to be migrated.

S903: Configure indicator verification rules.

In the embodiment of the present disclosure, the first indicator to be verified can be screened out based on the associated information of the task to be migrated, and the first indicator can be classified into the corresponding block type according to the preset block type. In addition, in the embodiment of the present disclosure, corresponding indicator verification rules can be configured for different block types and first indicators.

S904: Calculate the indicator difference verification result.

Specifically, first data of the first indicator in the first data model and second data in the second data model are obtained, and a first difference verification result between the first data and the second data is determined according to the indicator verification rule.

S905: Generate a risk assessment report.

The risk assessment report at least includes the first risk level corresponding to each task to be migrated.

Specifically, the risk score of the first indicator may be determined according to the first difference verification result and risk parameter information corresponding to the first indicator, and the first risk level of the task to be migrated may be determined according to the risk score of the first indicator.

In addition, as shown in FIG9 , risk assessment can also be performed by inputting risk assessment models based on information such as special field comparison standards set manually, thereby improving the accuracy of risk assessment.

S906: Generate a migration script.

In the embodiment of the present disclosure, when it is determined that the first risk level of the task to be migrated is less than the preset level, a migration script can be directly generated, so that migration is performed based on the migration script.

Furthermore, when it is determined that the first risk level of the task to be migrated is greater than or equal to the preset level, the risk indicator can be located and tracked, and adaptive adjustments can be made accordingly to reduce the migration risk.

S907: Perform migration based on the migration script.

In the disclosed embodiments, information collection, arrangement and summary, indicator verification rule configuration, indicator difference verification result calculation, risk assessment and migration can be performed for the tasks to be migrated, and the sources of the indicators to be verified are the actual online execution of the tasks to be migrated themselves, the transmission and use of downstream fields and data, and automated collection improves efficiency, the comprehensiveness and accuracy of risk assessment, and risk assessment is performed before migration to reduce migration risks.

It should be noted that the data migration method in the disclosed embodiment does not limit the application scenarios, for example, data warehouse upgrade and transformation projects, financial account system upgrade and transformation projects, etc. Taking the data warehouse upgrade and transformation project as an example, after the data warehouse completes the transformation process of the new data model (i.e., the second data model) and configures the mapping relationship between the second data model and the first data model, the task scheduling information, task execution information, metadata and other information of the task to be migrated are collected based on the mapping relationship, and the information is compiled and summarized. The collected information is classified by indicators, and the difference verification results are calculated according to the indicator verification rules. Therefore, according to the difference verification results, the risk level of the task to be migrated is determined, and finally a risk assessment report is generated, and then the migration of downstream tasks to be migrated is guided based on the risk assessment report.

In this way, in the disclosed embodiment, the entire data migration process is automatically executed, which reduces the migration time and cost. In addition, risk assessment is performed first during migration, and migration risks are accurately controlled, so that the entire migration project can be completed efficiently, quickly and accurately.

It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation method, the specific execution order of each step should be determined according to its function and possible internal logic.

In addition, the present disclosure also provides a data migration device, an electronic device, and a computer-readable storage medium, all of which can be used to implement any data migration method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.

FIG10 is a block diagram of a data migration device provided by an embodiment of the present disclosure. Referring to FIG10 , an embodiment of the present disclosure provides a data migration device, the data migration device comprising:

An obtaining module 1010 is used to obtain a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model;

The risk assessment module 1020 is used to obtain first data of the first indicator in the first data model and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to an indicator verification rule, and determine a first risk level of the task to be migrated according to the first difference verification result;

The migration module 1030 is used to migrate the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship.

In an optional embodiment, before obtaining the first indicator of the task to be migrated, the obtaining module 1010 is further used to:

Acquire first task scheduling information of a plurality of tasks in the first data model, and second task scheduling information of a plurality of tasks in the second data model;

Tasks to be migrated are screened out from the multiple tasks according to the first migration identifier in the first task scheduling information and the second migration identifier in the second task scheduling information.

In an optional embodiment, when obtaining the first indicator of the task to be migrated, the obtaining module 1010 is used to:

Acquire first task execution information of the task to be migrated for the first data model, and second task execution information of the task to be migrated for the second data model;

Determine, according to the first task execution information, a first data table and a first field for executing the task to be migrated;

Determine, according to the second task execution information, a second data table and a second field for executing the task to be migrated;

Acquire first metadata of the first data table and the first field, and second metadata of the second data table and the second field;

generating association information of the task to be migrated according to the first task scheduling information and the second task scheduling information, the first data table and the first field, the second data table and the second field, the first metadata and the second metadata;

According to a preconfigured indicator set to be verified, the first indicator of the task to be migrated is filtered out from the associated information.

In an optional embodiment, when determining the first risk level of the task to be migrated according to the first difference verification result, the risk assessment module 1020 is used to:

Obtaining risk parameter information of the first indicator;

Determining a risk score of the first indicator according to the first difference verification result and the risk parameter information;

A first risk level of the task to be migrated is determined according to the risk score of the first indicator.

In an optional embodiment, when migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship, the migration module 1030 is used to:

When the first risk level is lower than a preset level, determining a migration rule according to the mapping relationship;

According to the migration rule, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model.

In an optional embodiment, when migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship, the migration module 1030 is used to:

When the first risk level is greater than or equal to a preset level, selecting a second indicator that meets a difference condition from the first indicator according to the first difference verification result;

According to a preconfigured degradation processing strategy, adjusting the indicator verification rule of the second indicator to obtain an adjusted indicator verification rule;

Acquire third data of the second indicator in the first data model, and fourth data of the second indicator in the second data model, and determine a second difference verification result between the third data and the fourth data according to the adjusted indicator verification rule;

Determining a second risk level of the task to be migrated according to the second difference verification result;

When the second risk level is lower than a preset level, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the mapping relationship.

In an optional embodiment, when migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship, the migration module 1030 is used to:

When the first risk level is greater than or equal to a preset level, sending a prompt message, wherein the prompt message is used to prompt to adjust the risk level;

receiving adjustment information input based on the prompt information, wherein the adjustment information at least includes a third risk level modified corresponding to the first risk level;

When the third risk level is lower than a preset level, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the mapping relationship.

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

FIG. 11 is a block diagram of an electronic device provided in an embodiment of the present disclosure.

11 , an embodiment of the present disclosure provides an electronic device, which includes: at least one processor 1101; at least one memory 1102, and one or more I/O interfaces 1103, which are connected between the processor 1101 and the memory 1102; wherein the memory 1102 stores one or more computer programs that can be executed by at least one processor 1101, and the one or more computer programs are executed by at least one processor 1101 so that the at least one processor 1101 can execute the above-mentioned data migration method.

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

The present disclosure also provides a computer-readable storage medium on which a computer program is stored, wherein the computer program implements the above-mentioned data migration method when executed by a processor. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

The embodiment of the present disclosure also provides a computer program product, including a computer-readable code, or a non-volatile computer-readable storage medium carrying the computer-readable code. When the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the above-mentioned data migration method.

It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules/units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).

As is known to those of ordinary skill in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.

The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing/processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and/or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and/or edge servers. The network adapter card or network interface in each computing/processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing/processing device.

The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.

Various aspects of the present disclosure are described herein with reference to the flowcharts and/or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and/or block diagram and the combination of each box in the flowchart and/or block diagram can be implemented by computer-readable program instructions.

These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions/actions specified in one or more boxes in the flowchart and/or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and/or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions/actions specified in one or more boxes in the flowchart and/or block diagram.

The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and the part of the module, program segment or instruction contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and/or flow chart, and the combination of the square boxes in the block diagram and/or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.

Example embodiments have been disclosed herein, and although specific terms are employed, they are used and should be interpreted only in a general illustrative sense and not for limiting purposes. In some instances, it will be apparent to those skilled in the art that, unless otherwise expressly noted, features, characteristics, and/or elements described in conjunction with a particular embodiment may be used alone or in combination with features, characteristics, and/or elements described in conjunction with other embodiments. Therefore, those skilled in the art will appreciate that various changes in form and detail may be made without departing from the scope of the present disclosure as set forth in the appended claims.

Claims (11)

1. A data migration method, comprising: Obtaining a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model; Acquire first data of the first indicator in the first data model, and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to an indicator verification rule; Determining a first risk level of the task to be migrated according to the first difference verification result; According to the first risk level and the mapping relationship, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model. 2. The data migration method according to claim 1, characterized in that before obtaining the first indicator of the task to be migrated, the method further comprises: Acquire first task scheduling information of a plurality of tasks in the first data model, and second task scheduling information of a plurality of tasks in the second data model; Tasks to be migrated are screened out from the multiple tasks according to the first migration identifier in the first task scheduling information and the second migration identifier in the second task scheduling information. 3. The data migration method according to claim 2, wherein obtaining the first indicator of the task to be migrated comprises: Acquire first task execution information of the task to be migrated for the first data model, and second task execution information of the task to be migrated for the second data model; Determine, according to the first task execution information, a first data table and a first field for executing the task to be migrated; Determine, according to the second task execution information, a second data table and a second field for executing the task to be migrated; Acquire first metadata of the first data table and the first field, and second metadata of the second data table and the second field; generating association information of the task to be migrated according to the first task scheduling information and the second task scheduling information, the first data table and the first field, the second data table and the second field, the first metadata and the second metadata; According to a preconfigured indicator set to be verified, the first indicator of the task to be migrated is filtered out from the associated information. 4. The data migration method according to claim 1, wherein determining the first risk level of the task to be migrated according to the first difference verification result comprises: Obtaining risk parameter information of the first indicator; Determining a risk score of the first indicator according to the first difference verification result and the risk parameter information; A first risk level of the task to be migrated is determined according to the risk score of the first indicator. 5. The data migration method according to any one of claims 1 to 4, characterized in that the step of migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship comprises: When the first risk level is lower than a preset level, determining a migration rule according to the mapping relationship; According to the migration rule, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model. 6. The data migration method according to any one of claims 1 to 4, characterized in that the step of migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship comprises: When the first risk level is greater than or equal to a preset level, selecting a second indicator that meets a difference condition from the first indicator according to the first difference verification result; According to a preconfigured degradation processing strategy, adjusting the indicator verification rule of the second indicator to obtain an adjusted indicator verification rule; Acquire third data of the second indicator in the first data model, and fourth data of the second indicator in the second data model, and determine a second difference verification result between the third data and the fourth data according to the adjusted indicator verification rule; Determining a second risk level of the task to be migrated according to the second difference verification result; When the second risk level is lower than a preset level, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the mapping relationship. 7. The data migration method according to any one of claims 1 to 4, characterized in that the step of migrating the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship comprises: When the first risk level is greater than or equal to a preset level, sending a prompt message, wherein the prompt message is used to prompt to adjust the risk level; receiving adjustment information input based on the prompt information, wherein the adjustment information at least includes a third risk level modified corresponding to the first risk level; When the third risk level is lower than a preset level, the data to be migrated of the task to be migrated in the first data model is migrated to the second data model according to the mapping relationship. 8. A data migration device, comprising: An acquisition module, used to obtain a first indicator of the task to be migrated, and a mapping relationship between the first data model and the second data model; a risk assessment module, configured to obtain first data of the first indicator in the first data model and second data of the first indicator in the second data model, and determine a first difference verification result between the first data and the second data according to an indicator verification rule, and determine a first risk level of the task to be migrated according to the first difference verification result; A migration module is used to migrate the data to be migrated of the task to be migrated in the first data model to the second data model according to the first risk level and the mapping relationship. 9. An electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores one or more computer programs executable by the at least one processor, and the one or more computer programs are executed by the at least one processor so that the at least one processor can perform the data migration method according to any one of claims 1 to 7. 10. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the data migration method according to any one of claims 1 to 7. 11. A computer program product, characterized in that it includes a computer-readable code, or a non-volatile computer-readable storage medium carrying a computer-readable code, and when the computer-readable code runs in a processor of an electronic device, the processor in the electronic device executes the data migration method as described in any one of claims 1-7.