Data processing method and device, electronic equipment and computer readable storage medium

Abstract

The application relates to the field of big data, and discloses a data processing method, a data processing device, electronic equipment and a computer readable storage medium. The data processing method comprises the following steps: acquiring original data, wherein the original data comprises a plurality of business fields and business data corresponding to each business field; for any preset paradigm attribute, determining a target business field related to the semantic of the preset paradigm attribute based on a semantic big model, and establishing a corresponding relationship between the business data corresponding to the target business field and the preset paradigm attribute. The data processing method, the data processing device, the electronic equipment and the computer readable storage medium provided by the application can reduce the labor cost in the data processing process and improve the data processing efficiency.

Description

Technical Field

[0001] The present application relates to the field of big data, and in particular to a data processing method, a data processing device, an electronic device, and a computer-readable storage medium. Background Art

[0002] In traditional smart city development, data governance generally adopts a dimensional modeling + ETL cleansing approach. The underlying data warehouse is business-process-oriented, constructing a star schema by combining fact tables (such as corporate tax records) with dimension tables (such as basic corporate information). While this model simplifies query logic, it leads to increased data redundancy and governance complexity: basic information for the same entity (e.g., a company) must be stored repeatedly across multiple business tables, such as tax, market regulation, and intellectual property, and each system has different definitions of corporate attributes (e.g., whether registered capital is paid-in or paid-in).

[0003] To eliminate redundancy, manual field mapping and cleansing must be performed using ETL tools, a lengthy process involving "data research → data collection → conversion → auditing." For example, in a city with a population of 3 million, completing data governance for 17 commissions and bureaus took 3-4 months, and the system was unable to adapt to dynamic business changes. Consequently, existing big data processing technologies were labor-intensive and inefficient. Summary of the Invention

[0004] In view of this, it is necessary to provide a data processing method, a data processing device, an electronic device and a computer-readable storage medium to achieve the purpose of reducing the labor cost in the data processing process while improving the data processing efficiency.

[0005] In order to achieve the above-mentioned purpose, the present application provides a data processing method, which is applied to a data processing device including several preset paradigm attributes, including: obtaining original data, the original data including several business fields and business data corresponding to each of the business fields; for any of the preset paradigm attributes, determining the target business field semantically related to the preset paradigm attribute based on the semantic big model, and establishing a correspondence between the business data corresponding to the target business field and the preset paradigm attribute.

[0006] In a possible embodiment, the preset paradigm attributes include multiple vector elements, and establishing the correspondence between the business data corresponding to the target business field and the preset paradigm attributes includes: determining whether there is a target vector element semantically related to the target business field based on the semantic big model; in response to the existence of the target vector element, establishing the correspondence between the business data corresponding to the target business field and the target vector element; in response to the absence of the target vector element, storing the target business field in the element space to be added.

[0007] In a possible embodiment, the data processing method further includes: for any of the preset paradigm attributes, in response to the existence of at least two target business fields, merging business data corresponding to the at least two target business fields.

[0008] In a possible embodiment, the merging of the business data corresponding to the at least two target business fields includes: responding to the presence of at least two conflicting fields with semantic conflicts in the at least two target business fields; selecting a recommended conflict field from the at least two conflicting fields based on other preset paradigm attributes; determining a target conflict field based on the recommended conflict field, and merging the business data corresponding to the target conflict field.

[0009] In a possible embodiment, the data processing method further includes: storing source data corresponding to each of the conflict fields; determining the target conflict field based on the recommended conflict field includes: sending the recommended conflict field and all the conflict fields to a preset terminal for confirmation, and receiving the confirmation result of the preset terminal, wherein the confirmation result includes the target conflict field.

[0010] In a possible embodiment, the data processing method further includes: in response to receiving a business change instruction, the business change instruction includes a changed business field and a change operation, and determining a mapping relationship between the changed business field and the preset paradigm attribute according to the change operation.

[0011] In a possible embodiment, the data processing method further includes: in response to receiving a data output instruction, the data output instruction includes an output paradigm attribute, an output path, and a receiving terminal; and sending business data corresponding to the output paradigm attribute to the receiving terminal based on the output path.

[0012] The present application also provides a data processing device, including: a data access unit, the data access unit is used to obtain original data, the original data includes several business fields and business data corresponding to each of the business fields; a paradigm engine unit, the paradigm engine unit is used to construct several preset paradigm attributes; a mapping unit, for any of the business fields, the mapping unit is used to determine the target paradigm attribute related to the semantics of the business field based on the semantic big model, and establish a corresponding relationship between the business data corresponding to the business field and the target paradigm attribute.

[0013] The present application also provides an electronic device, including a memory and a processor, wherein the memory is used to store programs; the processor is coupled to the memory and is used to execute the programs stored in the memory to implement the data processing method as described above.

[0014] The present application also provides a computer-readable storage medium, which is characterized in that it is used to store computer-readable programs or instructions, and when the programs or instructions are executed by a processor, the data processing method as described above can be implemented.

[0015] The beneficial effects of this application are:

[0016] Compared with the related art, the data processing method provided in the present application pre-sets several preset paradigm attributes. After obtaining the original data, it is determined based on the semantic big model whether each preset paradigm attribute is semantically related to each business field in the original data, and the semantically related preset paradigm attributes and target business fields are mapped, and a correspondence between the business data corresponding to the target business field and the preset paradigm attributes is established, thereby automatically classifying the business data in the original data and reducing manual intervention in the data processing process, thereby achieving the purpose of reducing the labor cost in the data processing process while improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] FIG1 is a flow chart of a data processing method provided by an embodiment of the present application;

[0018] Figure 2 for Figure 1 A method flow chart of an embodiment of step S102;

[0019] Figure 3 A flow chart of a data processing method provided in another embodiment of the present application;

[0020] Figure 4 A flow chart of a data processing method provided in yet another embodiment of the present application;

[0021] Figure 5 A schematic diagram of the structure of a data processing device provided in one embodiment of the present application;

[0022] Figure 6 A schematic structural diagram of a data processing device provided in another embodiment of the present application;

[0023] Figure 7 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0024] The preferred embodiments of the present application are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of the present application and are used together with the embodiments of the present application to illustrate the principles of the present application, and are not used to limit the scope of the present application.

[0025] Please refer to Figure 1 A specific embodiment of the present application discloses a data processing method, which is applied to a data processing device including a plurality of preset paradigm attributes, comprising the following steps:

[0026] Step S101: obtaining original data, where the original data includes several business fields and business data corresponding to each business field.

[0027] Step S102: For any preset paradigm attribute, determine a target business field semantically related to the preset paradigm attribute based on the semantic big model.

[0028] Step S103: establishing a correspondence between the business data corresponding to the target business field and the preset paradigm attributes.

[0029] Compared with the related art, in the data processing method provided in the embodiment of the present application, several preset paradigm attributes are pre-set. After obtaining the original data, it is determined based on the semantic big model whether each preset paradigm attribute is semantically related to each business field in the original data, and the semantically related preset paradigm attributes and target business fields are mapped, and a correspondence between the business data corresponding to the target business field and the preset paradigm attributes is established, so as to automatically classify the business data in the original data and reduce manual intervention in the data processing process, thereby achieving the purpose of reducing the labor cost in the data processing process while improving data processing efficiency.

[0030] Specifically, a data processing device pre-sets a paradigm template, which includes a number of preset paradigm attributes. These preset paradigm attributes can be selected based on the application scenario of the original data. For example, if the original data is urban management data, the preset paradigm attributes can specifically include five major categories: people, places, things, objects, and organizations. Furthermore, multiple preset paradigm attributes can be further set within the five major categories of people, places, things, objects, and organizations. For example, the organizational attributes can also include "name," "address," and "funds."

[0031] In step S101, the raw data can be obtained from multiple different data sources. The different data sources can be, for example, different external business systems such as the tax system, the market supervision system, and the credit reporting system. The business field is the data classification field in the raw data, and the business data is the data corresponding to the business field under each specific business entity. For example, in the tax system, the business field can specifically include "company name", "company address", "company registered capital", etc. The business data can specifically be a specific "company name", "company address", "company registered capital", such as xxxx Co., Ltd., xxx Office, No. xxx, xxx Street, Room xxx, xxx Building, xxx Yuan, xxx USD, etc.

[0032] Furthermore, in step S102, the semantic big model is a natural language processing model based on deep learning, which can learn the grammar and semantics of natural language. By learning a large amount of text data, it maps words, phrases or sentences into continuous vector representations, and determines the semantic relevance between two text units through the distance between different text units in the vector space.

[0033] In an embodiment of the present application, each business field and each pre-set paradigm attribute is input into a semantic macro model. Based on the semantic similarity between each business field and each pre-set paradigm attribute, the semantic macro model outputs semantically related business fields and pre-set paradigm attributes. For example, the business field "Company Name" and the pre-set paradigm attribute "Name" are output as semantically related business fields and pre-set paradigm attributes. Based on this, the business field "Company Name" is the target business field semantically related to the pre-set paradigm attribute "Name."

[0034] Furthermore, in some embodiments of the present application, each preset paradigm attribute can be encoded as a high-dimensional vector, which can include multiple vector elements. For example, the preset paradigm attribute "registered capital" can be encoded as a high-dimensional vector, which can include multiple vector elements such as "subscribed amount", "paid amount", "currency", and "timestamp".

[0035] On this basis, please refer to Figure 2 The corresponding relationship between the business data corresponding to the target business field and the preset paradigm attributes established in step S102 may specifically include:

[0036] Step S201: Determine whether there is a target vector element semantically related to the target business field based on the semantic big model.

[0037] Step S202: In response to the existence of the target vector element, a corresponding relationship between the business data corresponding to the target business field and the target vector element is established.

[0038] Step S203: In response to the absence of the target vector element, the target service field is stored in the space of elements to be added.

[0039] The preset paradigm attributes are encoded as high-dimensional vectors. When there are new business fields in the original data, the target business fields are stored in the element space to be added. The element space to be added is a reserved space for storing new business fields. The new business fields can be added to the preset paradigm attributes as new vector elements. Compared with directly adding preset paradigm attributes in related technologies, adding them in the form of vector elements has less impact on the structure of the basic paradigm template and can make it easier to add new business fields. Furthermore, the semantic big model is used to determine whether the business fields and vector elements are semantically related, and then the new business fields are automatically added to the preset paradigm attributes as new vector elements, thereby realizing automatic addition and dynamic expansion of new business fields, reducing manual intervention, further reducing labor costs in the data processing process, and improving data processing efficiency.

[0040] It can be understood that in the aforementioned step S203, in response to the absence of the target vector element, storing the target business field into the element space to be added is only an example of a specific implementation method in one embodiment of the present application. In some other embodiments of the present application, the target business field can also be directly stored in the element space to be added in response to the absence of the target vector element.

[0041] Please refer to Figure 3 In some embodiments of the present application, the data processing method may further include:

[0042] Step S104: For any preset paradigm attribute, in response to the existence of at least two target business fields, merge the business data corresponding to the at least two target business fields.

[0043] Specifically, in this step, merging the business data corresponding to at least two target business fields can be performed by selecting one of the at least two target business fields as the merged business data, or by mixing the at least two target business fields to form new business data as the merged business data. For example, for the business data "Technology Park" and "Building A, No. 101", they can be mixed to form new business data "Technology Park, Building A, No. 101" as the merged business data; or for the business data "Technology Park, Building A, No. 101" and "Technology Park, No. 101", the business data "Technology Park, Building A, No. 101" can be selected as the merged business data.

[0044] For at least two target business fields that are semantically related to the same preset paradigm attribute, there is usually a certain amount of information overlap. By merging the business data corresponding to the at least two target business fields, data redundancy can be reduced.

[0045] Furthermore, the above is only a specific description of merging the business data corresponding to at least two target business fields when at least two target business fields are related business fields. In some embodiments of the present application, at least two target business fields that are semantically related to the preset paradigm attribute may also be conflicting fields with mutual semantic conflicts. For example, for the preset paradigm attribute "enterprise capital", there may be two semantically related target business fields, "enterprise subscribed capital" and "enterprise paid-in capital". Although "enterprise subscribed capital" and "enterprise paid-in capital" can both be mapped to "enterprise capital", they are not the same and there is a semantic conflict. On this basis, the business data corresponding to at least two target business fields merged in step S104 can specifically be to select a recommended conflict field from at least two conflicting fields based on other preset paradigm attributes; determine the target conflict field based on the recommended conflict field, and merge the business data corresponding to the target conflict field.

[0046] The method of selecting a recommended conflicting field from at least two conflicting fields based on other preset paradigm attributes specifically involves determining a current application scenario based on the other preset paradigm attributes, and then selecting a recommended conflicting field from at least two conflicting fields based on the current application scenario. For example, for the conflicting fields "Enterprise Subscribed Capital" and "Enterprise Paid-in Capital," if the other preset paradigm attributes are loan-related attributes such as "Loan Amount" and "Loan Term," the current application scenario may be determined to be a loan scenario, and since loans are typically based on paid-in capital, "Enterprise Paid-in Capital" may be selected as the recommended conflicting field.

[0047] Specifically, the above derivation process can be derived using a causal inference model. A causal inference model is a mathematical model used to analyze causal relationships between variables. Its core goal is to infer the causal effect between an "intervention" (e.g., a change in a variable) and an "outcome" from observational or experimental data. In the embodiments of this application, for example, any of a variety of causal inference models can be selected, such as a randomized controlled trial model, an observational study model, or a Bayesian network model.

[0048] In an embodiment of the present application, determining the target conflict field based on the recommended conflict field can specifically include directly using the recommended conflict field as the target conflict field for data merging. Directly using the recommended conflict field as the target conflict field can further reduce manual intervention in the data processing process.

[0049] In an embodiment of the present application, in addition to being directly deduced through a causal reasoning model, the above-mentioned derivation process can also be based on the current application scenario, obtaining a preset arbitration policy corresponding to the current application scenario from a preset arbitration policy library. In response to obtaining the preset arbitration policy from the preset arbitration policy library, a recommended conflict field is directly selected from at least two conflict fields based on the obtained preset arbitration policy; conversely, in response to not obtaining the preset arbitration policy from the preset arbitration policy library, deduction can be performed through a causal reasoning model, and the derivation result can be added to the preset arbitration policy library as a new arbitration policy.

[0050] Alternatively, in some embodiments of the present application, the recommended conflict field and all conflict fields may be sent together to a preset terminal, and confirmed by a staff member at the preset terminal. After the staff member determines the target conflict field at the preset terminal, the staff member returns the target conflict field as a confirmation result to the data processing device. The data processing device re-receives the confirmation result of the preset terminal and directly determines the target conflict field from the confirmation result.

[0051] In an embodiment of the present application, before sending the recommended conflict field and all conflict fields to the preset terminal, the source data corresponding to each conflict field can also be stored. Pre-storing the source data of the conflict field can facilitate tracing the source of each conflict field and obtaining the original data after the target conflict field is determined, and then facilitate data merging of the target conflict field, thereby improving the integrity of the business data corresponding to the conflict field during the merge; in addition, storing the source data corresponding to each conflict field can also facilitate the rollback of the processing results. Among them, the source data corresponding to the conflict field can specifically include timestamp, source, original value and other data.

[0052] Please refer to Figure 4 In some embodiments of the present application, the data processing method may further include:

[0053] Step S105: In response to receiving a service change instruction, the service change instruction includes a changed service field and a change operation, and determining a mapping relationship between the changed service field and a preset paradigm attribute according to the change operation.

[0054] Specifically, the data processing device can monitor the data source providing the raw data in real time. When a business field in the data source changes, a corresponding business change instruction is generated to adjust the business field in the data processing device accordingly. For example, if a business field Q is added to the data source, a corresponding business change instruction can be generated, which includes changing business field Q and the corresponding change operation "add business field". Then, according to the change operation "add business field", a new vector element "business field Q" can be added to the preset paradigm attributes semantically related to business field Q. If a business field W is deleted from the data source, a corresponding business change instruction can be generated, which includes changing business field W and the corresponding change operation "delete business field". Then, according to the change operation "delete business field", the vector element "business field W" can be deleted from the preset paradigm attributes semantically related to business field W.

[0055] It can be understood that the above is only a specific operation plan when the business fields in the data source change in some embodiments of the present application. In some other embodiments of the present application, when the business fields in the data source change, the changed business fields can be stored in the processing space to facilitate the staff to understand the business changes of the data source and formulate corresponding strategies based on the business changes of the data source.

[0056] In an embodiment of the present application, after completing data processing, the data processing device may also output the processed data. Specifically, upon receiving a data output instruction, the device may select, based on the output paradigm attributes, output path, and receiving terminal included in the data output instruction, business data corresponding to the output paradigm attributes and transmitted to the receiving terminal via the specified output path.

[0057] In some embodiments of the present application, the data processing device can also perform anomaly analysis based on the processed data. Specifically, anomaly analysis can be performed based on the causal reasoning model according to the change pattern of the business data corresponding to the preset paradigm attributes and the correlation between the preset paradigm attributes. For example, for the preset paradigm attributes "enterprise cost" and "enterprise revenue", if the business data corresponding to the preset paradigm attribute "enterprise cost" shows that the enterprise cost is rising, and the business data corresponding to the preset paradigm attribute "enterprise revenue" shows that the enterprise revenue is declining, it is determined that the enterprise-related data may be abnormal, triggering an early warning and pushing it to the monitoring center.

[0058] Please refer to Figure 5 , an embodiment of the present application provides a data processing device, including:

[0059] The data access unit 501 is used to obtain original data, which includes several business fields and business data corresponding to each business field;

[0060] A paradigm engine unit 502, which is used to construct a number of preset paradigm attributes;

[0061] Mapping unit 503, for any business field, is used to determine target paradigm attributes related to the semantics of the business field based on the semantic big model, and establish a correspondence between business data corresponding to the business field and the target paradigm attributes.

[0062] For further information, please refer to Figure 6 The data processing device provided in some other embodiments of the present application may also include a conflict resolution unit 504, which is responsive to the presence of at least two conflicting fields with semantic conflicts in at least two target business fields; the conflict resolution unit 504 is used to select a recommended conflict field from the at least two conflicting fields based on other preset paradigm attributes; determine the target conflict field based on the recommended conflict field, and merge the business data corresponding to the target conflict field.

[0063] Please refer to Figure 7 The embodiment of the present application also provides an electronic device 700. The electronic device 700 includes a processor 701, a memory 702, and a display 703. Figure 7 Only some of the components of the electronic device 700 are shown, but it should be understood that it is not required to implement all of the shown components, and more or fewer components may be implemented instead.

[0064] In some embodiments, the processor 701 may be a central processing unit (CPU), a microprocessor, or other data processing chip, configured to execute program codes stored in the memory 702 or process data, such as the data processing method of the present invention.

[0065] In some embodiments, processor 701 may be a single server or a server group. The server group may be centralized or distributed. In some embodiments, processor 701 may be local or remote. In some embodiments, processor 701 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an on-premises cloud, a multi-cloud, or any combination thereof.

[0066] In some embodiments, the memory 702 may be an internal storage unit of the electronic device 700, such as a hard disk or memory of the electronic device 700. In other embodiments, the memory 702 may also be an external storage device of the electronic device 700, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 700.

[0067] Furthermore, the memory 702 may include both an internal storage unit of the electronic device 700 and an external storage device. The memory 702 is used to store application software installed in the electronic device 700 and various data.

[0068] In some embodiments, display 703 can be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 703 is used to display information on electronic device 700 and to display a visual user interface. Components 701-703 of electronic device 700 communicate with each other via a system bus.

[0069] In one embodiment, when the processor 701 executes the data processing program in the memory 702, the following steps may be implemented:

[0070] Acquire original data, where the original data includes a plurality of business fields and business data corresponding to each of the business fields;

[0071] For any of the preset paradigm attributes, a target business field semantically related to the preset paradigm attribute is determined based on the semantic big model, and a corresponding relationship between the business data corresponding to the target business field and the preset paradigm attribute is established.

[0072] It should be understood that, when the processor 701 executes the data processing program in the memory 702 , in addition to the above functions, it can also implement other functions. For details, please refer to the description of the corresponding method embodiment above.

[0073] Accordingly, an embodiment of the present application also provides a computer-readable storage medium, which is used to store computer-readable programs or instructions. When the program or instructions are executed by a processor, it can implement the steps or functions in the data processing method provided in the above-mentioned method embodiments.

[0074] Those skilled in the art will appreciate that all or part of the process flow of the above-described method embodiment can be implemented by instructing related hardware (such as a processor, controller, etc.) through a computer program, and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0075] The above is only a preferred specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any changes or replacements that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed in this application should be covered by the scope of protection of the present application.

Claims

1. A data processing method, applied to a data processing device including several preset paradigm attributes, characterized in that: include: Acquire original data, where the original data includes a plurality of business fields and business data corresponding to each of the business fields; For any of the preset paradigm attributes, determining a target business field semantically related to the preset paradigm attribute based on the semantic big model, and establishing a corresponding relationship between the business data corresponding to the target business field and the preset paradigm attribute; For any of the preset paradigm attributes, in response to the existence of at least two target business fields, merging the business data corresponding to the at least two target business fields; The merging of the business data corresponding to the at least two target business fields includes: In response to at least two conflicting fields having semantic conflicts existing in the at least two target service fields; Selecting a recommended conflicting field from at least two conflicting fields based on other preset paradigm attributes; Determine a target conflict field according to the recommended conflict field, and merge the business data corresponding to the target conflict field; The selecting a recommended conflicting field from at least two conflicting fields based on other preset paradigm attributes includes: A current application scenario is determined based on other preset paradigm attributes, and the recommended conflicting field is selected from at least two conflicting fields based on the current application scenario.

2. The data processing method according to claim 1, wherein: The preset paradigm attribute includes a plurality of vector elements, and establishing a correspondence between the business data corresponding to the target business field and the preset paradigm attribute includes: Determining whether there is a target vector element semantically related to the target business field based on the semantic big model; In response to the existence of the target vector element, establishing a corresponding relationship between the service data corresponding to the target service field and the target vector element; In response to the target vector element not existing, the target service field is stored in the to-be-added element space.

3. The data processing method according to claim 1, wherein: The data processing method further includes: Storing source data corresponding to each of the conflicting fields; The determining the target conflict field according to the recommended conflict field includes: The recommended conflict field and all the conflict fields are sent to a preset terminal for confirmation, and a confirmation result of the preset terminal is received, where the confirmation result includes a target conflict field.

4. The data processing method according to claim 1, wherein: The data processing method further includes: In response to receiving a service change instruction, the service change instruction includes a changed service field and a change operation, and determining a mapping relationship between the changed service field and the preset paradigm attribute according to the change operation.

5. The data processing method according to any one of claims 1 to 4, characterized in that: The data processing method further includes: In response to receiving a data output instruction, the data output instruction includes an output paradigm attribute, an output path, and a receiving terminal; The service data corresponding to the output paradigm attribute is sent to the receiving terminal based on the output path.

6. A data processing device, characterized in that: include: A data access unit, the data access unit is used to obtain original data, the original data including a plurality of business fields and business data corresponding to each of the business fields; A paradigm engine unit, wherein the paradigm engine unit is used to construct a plurality of preset paradigm attributes; A mapping unit, for any of the business fields, configured to determine, based on a semantic large model, a target paradigm attribute semantically related to the business field, and establish a correspondence between the business data corresponding to the business field and the target paradigm attribute; a conflict resolution unit configured to, for any of the preset paradigm attributes, merge the service data corresponding to the at least two target service fields in response to the existence of the at least two target service fields; The merging of the business data corresponding to the at least two target business fields includes: In response to at least two conflicting fields having semantic conflicts existing in the at least two target service fields; Selecting a recommended conflicting field from at least two conflicting fields based on other preset paradigm attributes; Determine a target conflict field according to the recommended conflict field, and merge the business data corresponding to the target conflict field; The selecting a recommended conflicting field from at least two conflicting fields based on other preset paradigm attributes includes: A current application scenario is determined based on other preset paradigm attributes, and the recommended conflicting field is selected from at least two conflicting fields based on the current application scenario.

7. An electronic device, characterized in that: comprising a memory and a processor, wherein, Memory, used to store programs; A processor, coupled to the memory, is configured to execute a program stored in the memory to implement the data processing method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that Used to store computer-readable programs or instructions, which can implement the data processing method according to any one of claims 1 to 5 when executed by a processor.

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