Data Processing Method, Data Processing Device and Storage Medium

Abstract

The present invention discloses a data processing method, a data processing device, and a storage medium, belonging to the field of Internet technology. Among them, the method includes: obtaining a plurality of service data; extracting target information corresponding to each of the service data according to a plurality of preset fields in a model configuration table; establishing a correspondence between each of the target information and storage information of information fields in a plurality of preset data models according to the priorities of the preset fields; and updating the plurality of preset data models according to the correspondence, where different preset data models are used to store different types of service information. This application realizes avoiding the loss of the relationship between data during the merging process, reducing manual verification, and thus improving the efficiency of data merging.

Description

Technical Field

[0001] The present invention relates to the field of the Internet, and in particular, to a data processing method, a data processing device, and a storage medium. Background Art

[0002] With the informatization of more and more small and medium-sized enterprises in different scenarios, a large number of requirements based on business modeling have emerged. At present, classical data modeling methods are often used to construct data models for business. The data models constructed by this modeling method can be used to store a large amount of data. However, when merging data and adding new fields to this data model, due to the lack of flexibility of the model, problems such as loss of data relationships are likely to occur, resulting in low data merging efficiency.

[0003] The above content is only used to assist in understanding the technical solution of the present invention, and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of the present invention is to provide a data processing method, a data processing device, and a storage medium, aiming to improve the data model merging efficiency.

[0005] To achieve the above purpose, the present invention provides a data processing method, which includes the following steps:

[0006] Obtain a plurality of business data;

[0007] Extract the target information corresponding to each of the business data according to a plurality of preset fields in a model configuration table;

[0008] Establish a correspondence between each of the target information and the storage information of information fields in a plurality of preset data models according to the priorities of the preset fields;

[0009] Update the plurality of preset data models according to the correspondence, and different preset data models are used to store different types of business information.

[0010] Optionally, the priority is the priority between characteristic fields with identification functions in the preset fields, and the step of establishing a correspondence between each of the target information and the storage information of information fields in a plurality of preset data models according to the priorities of the preset fields includes:

[0011] Sequentially determine target fields according to the priorities of the characteristic fields, and the characteristic fields are fields for storing business entity information;

[0012] Determine the first field information corresponding to the target field in the target information, and determine the storage information corresponding to the target field as the second field information;

[0013] When the first field information matches the second field information, associate the first identifier corresponding to the target information and the second identifier corresponding to the stored information to obtain the corresponding relationship;

[0014] Wherein, the first identifier is used to identify the business entity to which the corresponding business data belongs, and the second identifier is used to identify the business entity to which different stored information in the multiple preset data models belongs.

[0015] Optionally, the data type of the business data includes the business entity, the business event executed by the business entity, and the entity relationship between business entities in the corresponding business scenario. The multiple preset data models include an entity model, an event model, and a relationship model. Before the step of establishing the correspondence between each target information and the stored information of the information fields in the multiple preset data models according to the priorities of the preset fields, it further includes:

[0016] Determine the entity model according to the business entity;

[0017] Determine the event model according to the business event;

[0018] Determine the relationship model according to the entity relationship.

[0019] Optionally, the step of extracting each target information corresponding to the business data according to the multiple preset fields in the model configuration table includes:

[0020] Match the information in each business data with the multiple preset fields respectively;

[0021] Determine the information matched by each preset field in the business data as the target sub-information, and obtain multiple target sub-informations corresponding to each business data, where the target information includes the multiple target sub-informations.

[0022] Optionally, the step of updating the multiple preset data models according to the corresponding relationship includes:

[0023] When the current time reaches the preset update time, determine the target information and the stored information corresponding to the corresponding relationship as the first information and the second information respectively;

[0024] Update the multiple preset data models according to the first information and the second information.

[0025] Optionally, the step of updating the multiple preset data models according to the first information and the second information includes:

[0026] According to the preset field corresponding to the first information, the model to which it belongs in the multiple preset data models is the target model;

[0027] Determine an information processing rule according to the target model;

[0028] Update the target model according to the first information, the second information, and the information processing rule.

[0029] Optionally, the step of determining an information processing rule according to the target model includes:

[0030] Determine the information processing rule from among a coverage merge rule, an append merge rule, and a binding merge rule according to the target model.

[0031] Optionally, the multiple preset data models include an entity model, an event model, and a relationship model. The step of determining the information processing rule from among a coverage merge rule, an append merge rule, and a binding merge rule according to the target model includes:

[0032] When the target model is the entity model, determine the coverage merge rule as the information processing rule, where the entity model is used to store information about business entities;

[0033] When the target model is the event model, determine the append merge rule as the information processing rule, where the event model is used to store event information executed by business entities;

[0034] When the target model is the relationship model, determine the binding merge rule as the information processing rule, where the relationship model is used to store information about the association relationships between different business entities.

[0035] In addition, to achieve the above object, the present invention further provides a data processing device, where the data processing device includes: a memory, a processor, and a data processing program stored on the memory and executable on the processor, and the data processing program is configured to implement the steps of the data processing method described in any one of the above.

[0036] In addition, to achieve the above object, the present invention further provides a storage medium, where a data processing program is stored on the storage medium, and when the data processing program is executed by a processor, it implements the steps of the data management method described in any one of the above.

[0037] The present invention provides a data processing method, which includes: obtaining a plurality of service data; extracting target information corresponding to each of the service data according to a plurality of preset fields in a model configuration table; establishing a correspondence between each of the target information and storage information of information fields in a plurality of preset data models according to the priorities of the preset fields; and updating the plurality of preset data models according to the correspondence, where different preset data models are used to store different types of service information. Compared with the data models constructed by classical data modeling methods, the data processing method of the present application can clarify the correspondence of data to be merged according to the priorities of the preset fields, and update the data of the data model according to the correspondence, which can avoid the loss of the relationships between data during the merging process, reduce manual verification, and improve the efficiency of data merging. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic structural diagram of a data processing device in a hardware operating environment related to the solution of an embodiment of the present invention;

[0039] Figure 2 is a schematic flowchart of the first embodiment of the present invention;

[0040] Figure 3 is a schematic flowchart of the second embodiment of the present invention;

[0041] Figure 4 is a schematic flowchart of the third embodiment of the present invention;

[0042] Figure 5 is a schematic flowchart of the fourth embodiment of the present invention;

[0043] Figure 6 is a schematic flowchart of the fifth embodiment of the present invention.

[0044] The implementation, functional features, and advantages of the objectives of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0046] Referring to Figure 1 , Figure 1 is a schematic structural diagram of a data processing device in a hardware operating environment related to the solution of an embodiment of the present invention.

[0047] As Figure 1As shown in the figure, the data processing device may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Among them, the communication bus 1002 is used to implement connection communication between these components. The user interface 1003 may include a display screen and an input unit such as a keyboard. Optionally, the user interface 1003 may further include a standard wired interface and a wireless interface. The network interface 1004 may optionally include a standard wired interface and a wireless interface (such as a Wireless-Fidelity (WI-FI) interface). The memory 1005 may be a high-speed Random Access Memory (RAM) or a stable non-volatile memory (Non-Volatile Memory, NVM), such as a disk memory. Optionally, the memory 1005 may also be a storage device independent of the aforementioned processor 1001.

[0048] Those skilled in the art can understand that Figure 1 the structure shown in the figure does not constitute a limitation on the data processing device, and it may include more or fewer components than shown in the figure, or combine some components, or have different component arrangements.

[0049] As Figure 1 shown, the memory 1005, as a storage medium, may include an operating system, a data storage module, a network communication module, a user interface module, and a data processing program.

[0050] In Figure 1 the data processing device shown in the figure, the network interface 1004 is mainly used for data communication with other devices; the user interface 1003 is mainly used for data interaction with users; the processor 1001 and the memory 1005 in the data processing device of the present invention may be disposed in the data processing device. The data processing device calls the data processing program stored in the memory 1005 through the processor 1001 and executes the data processing method provided by the embodiments of the present invention.

[0051] The embodiments of the present invention provide a data processing method. Referring to Figure 2 , Figure 2 it is a schematic flowchart of the first embodiment of a data processing method of the present invention.

[0052] In this embodiment, the data processing method includes:

[0053] Step S10, obtaining a plurality of service data;

[0054] The business data here is data of the same entity. Specifically, in this embodiment, the business data may be: personal information of customer A, the car purchase contract of customer A, the relationship between customer A and vehicle B, the logistics information of vehicle B, and the vehicle information of vehicle B, etc. The multiple business information is business data of multiple entities.

[0055] Step S20, extract the target information corresponding to each said business data according to multiple preset fields in the model configuration table;

[0056] The model configuration table here records multiple preset fields, the merging rules of the preset fields, the identifier of the data model, and the preset fields corresponding to the data model. When a new field needs to be added, it is necessary to add a preset field in the model configuration table to set the merging rules of the new field, and add the new field to the corresponding data model. When a new preset data model needs to be added, it is necessary to add a new preset data model in the model configuration table. The target information here refers to the data in the business data that is the same as the preset field.

[0057] Specifically, in this embodiment, the preset fields may be the customer's ID number, mobile phone number, name, gender, the customer's car license plate, etc. The business data is the customer's ID number, mobile phone number, work unit, car license plate, etc. The target information here can be determined as: the customer's ID number, mobile phone number, and the customer's car license plate.

[0058] Step S30, establish a correspondence relationship between each said target information and the storage information of the information fields in multiple preset data models according to the priorities of the preset fields;

[0059] The multiple preset data models are frameworks for storing target information. The framework can be a data table, and the data table includes the information fields. The priority here is the sorting of the information fields in multiple preset data models, indicating the order of determining the same entity by the information fields. The information of the field with a higher priority in the target information is preferentially used to match the storage information. When the match is successful, a correspondence relationship is established between the target information and the storage information of the information fields in multiple preset data models. The correspondence relationship established here means determining that the target information and the storage information are of the same business entity. Among them, the same entity can be determined according to the data of one field, or can be determined as the same entity according to the data of multiple fields.

[0060] Specifically, in this embodiment, the priority of the customer ID number is higher than that of the customer's mobile phone number. First, use the ID number in the target information to match the information stored in the model. When the information stored in the model is the same as the ID number in the target information, a correspondence relationship is established, that is, it is determined that the information stored in the model and the target information are of the same business entity.

[0061] Step S40: Update the multiple preset data models according to the corresponding relationship, where different preset data models are used to store different types of business information.

[0062] The update here refers to updating the target information to the multiple preset data models. The specific update method can be to overwrite the target information to the multiple preset data models, or to combine the target information with the information of the corresponding fields in the multiple preset data models and then overwrite the multiple preset data models. It can also be to directly store the target information into the multiple preset data models, or to add fields to the multiple preset data models or add a new preset data model and store the target data into the corresponding fields or the newly added preset data model. Specifically, the fields of the target information can adopt the same update mode, or different update methods.

[0063] Compared with the data model constructed by the classical data modeling method in this embodiment, the data processing method of the present application can clarify the corresponding relationship of the data to be merged according to the priority of the preset fields, and update the data of the data model according to the corresponding relationship, which can avoid the loss of the relationship between data during the merging process, reduce manual verification, and improve the efficiency of data merging.

[0064] Further, based on the first embodiment, a second embodiment of the data processing method of the present invention is proposed. In this embodiment, refer to Figure 3 , where the priority is the priority between the characteristic fields with identification functions among the preset fields. The establishment of the corresponding relationship between each target information and the storage information of the information fields in the multiple preset data models according to the priority of each preset field includes:

[0065] Step S31: Sequentially determine the target fields according to the priority of each characteristic field, where the characteristic field is a field for storing business entity information;

[0066] The characteristic field here refers to a field that can identify the entity, which can be the customer's ID number, the customer's mobile phone number, the vehicle number, and the logistics order number. The priority refers to the priority of the characteristic field to identify the entity, and the target field refers to the field with identification function in the target information. Since the target information is determined in step S20, the priority of the target field in the target information can be determined according to the priority.

[0067] Step S32: Determine the first field information corresponding to the target field in the target information, and determine the storage information corresponding to the target field as the second field information;

[0068] The first field information here is determined by the target field. Specifically, according to the priority of the target field, the target field with the highest priority is used as the first field information. The second field information here refers to the field information in the storage information corresponding to the target field. Specifically, the type of the second field information is the same as that of the first field information.

[0069] Step S33, when the first field information matches the second field information, associate the first identifier of the corresponding target information and the second identifier of the corresponding storage information to obtain the corresponding corresponding relationship, where the first identifier is used to identify the business entity to which the corresponding business data belongs, and the second identifier is used to identify the business entities to which different storage information in the multiple preset data models belongs.

[0070] The matching of the first field information and the second field information means that the first field information and the second field information are numerically equal or are determined to be the same according to the matching logic. The first identifier and the second identifier are identifiers of the same type and represent identifiers of different entities. Here, the first identifier and the second identifier are a type of field information that can identify entities in multiple preset data models, and the first identifier and the second identifier can determine a unique entity. The association here refers to establishing an association between the first identifier and the second identifier. The association here can be confirmed by establishing an association table or determined by establishing an association function. The corresponding relationship means determining that the first field information and the second field information are the same entity. The entity here can be a customer or a vehicle. Specifically, in this embodiment, the information of the first field is the user's ID number 123123. When the second field is the user's ID number 123123, the two match, and the ID number, mobile phone number, and corresponding license plate number in the target information are associated with the corresponding fields in the storage information. In other embodiments, the stored data is respectively matched with the ID number and the mobile phone number in the target information. After obtaining the results, then according to the priority relationship between the ID number and the mobile phone number, a connection is established with the data matched with the ID number.

[0071] In this embodiment, the first field information of the target information is determined according to the priority, and the second field information of the stored data is determined. When the first field information matches the second field information, the first identifier of the corresponding target information and the second identifier of the corresponding storage information are associated. It clarifies whether the entity of the target information is the same as the entity in the stored data, avoids identifying the same customer as two customers, and improves the accuracy of data merging.

[0072] Further, based on the first embodiment, the third embodiment of the data processing method of the present invention is proposed. In this embodiment, refer to Figure 4, the data types of the service data include business entities, business events executed by the business entities, and the entity relationships between business entities in the corresponding business scenarios. The multiple preset data models include entity models, event models, and relationship models. Before the step of establishing the correspondence between each of the target information and the storage information of the information fields in the multiple preset data models according to the priorities of the preset fields, it further includes:

[0073] Step S301, determining the entity model according to the business entity;

[0074] The business entity here refers to the acting entity in the business. Specifically, in this embodiment, the business entity can be a customer, a salesperson, a vehicle, etc. The entity model includes fields that can be used to store information of the business entity, such as user gender, user age, and user address, etc.

[0075] Step S302, determining the event model according to the business event;

[0076] The business event here refers to the event executed by the business entity. Specifically, in this embodiment, the business event can be a customer determining a car purchase and vehicle logistics information, etc. The event model refers to including fields that can be used to store business events, such as time and location fields in logistics information.

[0077] Step S303, determining the relationship model according to the entity relationship.

[0078] The entity relationship here refers to the relationship between business entities. Specifically, in this embodiment, the entity relationship can be the entity relationship between customer A and vehicle B, the entity relationship between car purchase contract C and vehicle D, etc. The relationship model includes storing the entity relationship of the entity.

[0079] In this embodiment, the multiple preset data models include models for storing different types. When the target information only includes a single data preset data model, the use of other preset data models can be reduced, which is convenient for improving the efficiency of data merging.

[0080] Further, based on the first embodiment, the fourth embodiment of the data processing method of the present invention is proposed. In this embodiment, referring to Figure 5 , the extracting each piece of service data corresponding target information according to multiple preset fields in the model configuration table includes:

[0081] Step S21, matching the information in each piece of service data with the multiple preset fields respectively;

[0082] The information in the business here is the information generated by the actual business, which includes parts that are the same as the multiple preset fields and also includes parts that do not have the multiple preset fields. Separately matching means splitting the business data into multiple parts and separately matching each split part with the multiple preset fields.

[0083] Step S22, determine the information matched by each of the preset fields in the business data as the target sub-information, obtain multiple target sub-informations corresponding to each business data, and the target information includes the multiple target sub-informations.

[0084] The target sub-information here refers to the data in which each split part of the business data matches the multiple preset fields successfully. The target information is composed of multiple target sub-informations. Specifically, in this embodiment, the business data includes: ID card number, mobile phone number, and customer gender, the multiple preset fields include ID card number and mobile phone number, and the target sub-informations are ID card number and mobile phone number, and they jointly form the target information. In other embodiments, when the name of the business data is the same as the preset field, determine the business data as the target information.

[0085] By separately matching the information in the business data with the multiple preset fields, the data that needs to be merged can be screened out, avoiding unnecessary data from participating in the merger, thereby improving the efficiency of data merger.

[0086] Further, based on the first embodiment, the fifth embodiment of the data processing method of the present invention is proposed. In this embodiment, refer to Figure 6 , the updating of the multiple preset data models according to the corresponding relationship includes:

[0087] Step S41, when the current time reaches the preset update time, determine the target information and the stored information corresponding to the corresponding relationship as the first information and the second information respectively;

[0088] The current time refers to the current moment time of the system during the data processing process. The preset update time can be manually input or determined by the data processing system. The first information and the second information are the target information and the stored information in the corresponding relationship respectively.

[0089] Step S42, update the multiple preset data models according to the first information and the second information.

[0090] Specifically, in this embodiment, the first information is the user's last maintenance time, and the second information is the user's last maintenance information. Overwrite the user's last maintenance time in the first information to the user's last maintenance information. In other embodiments, the user's last maintenance time in the first information is merged with the user's last maintenance information in the second information, and the merged information is updated to the preset data model.

[0091] By setting a preset update time, it is possible to avoid updating data during peak model usage, and avoid affecting the use of multiple data models due to the impact on operating efficiency caused by the update.

[0092] Specifically, step S42 further includes:

[0093] Step S421, determining the target model as the model to which the preset field corresponding to the first information belongs among the multiple preset data models;

[0094] The target model here refers to a model that stores different types of business data, such as a user entity model, a car entity model, a user car purchase event model, a user-car relationship model, etc. The first information includes multiple fields, and the multiple fields can belong to different models, so they have different merging regulations.

[0095] Step S422, determining an information processing rule according to the target model;

[0096] The information processing regulation here refers to the rule used for data merging.

[0097] Step S423, updating the target model according to the first information, the second information, and the information processing rule.

[0098] Specifically, in this embodiment, the first information includes preset fields of a user entity model and a car entity model, a user mobile phone number, a car color, and a car license plate, etc. The user mobile phone number in the target model is updated respectively according to the user entity model and the corresponding information processing rule. The car color and the car license plate in the target model are updated according to the car entity model and the corresponding information processing rule.

[0099] In this embodiment, for different preset fields of the first information, according to different information processing rules, it can be ensured that the data types stored in the merged preset data model do not change, improving the accuracy of data merging.

[0100] Further, based on the fifth embodiment, a sixth embodiment of the data processing method of the present invention is proposed. In this embodiment, the determining the information processing rule according to the target model includes:

[0101] Determining the information processing rule from among a coverage merging rule, an append merging rule, and a binding merging rule according to the target model.

[0102] The overwrite merge rule refers to the merge rule that overwrites the original data. The append merge rule means adding new data after the original data. The binding merge rule refers to adding or deleting the main relationship in the relational model. In this embodiment, specifically, the target model may be a vehicle logistics model, and the first information is the information of the fields of the current time and the current location of the vehicle. Using the append merge rule, the information of the fields of the current time and the current location of the vehicle is added to the original data to form a vehicle logistics model.

[0103] The multiple information processing rules of this embodiment can be used for different preset data models, and it can be clarified that the data type stored in the preset data model after merging does not change, improving the accuracy of data merging.

[0104] In this embodiment, the multiple preset data models include an entity model, an event model, and a relational model. Determining the information processing rule from the overwrite merge rule, the append merge rule, and the binding merge rule according to the target model includes:

[0105] When the target model is the entity model, determine the overwrite merge rule as the information processing rule, and the entity model is used to store the information of the business entity;

[0106] The overwrite merge rule here means overwriting the original data. The condition for judging whether to overwrite can be based on the chronological order of the recording times of the first information and the second information. When the first information is later than the second information, the storage location of the second information is overwritten. The condition for judging whether to overwrite can also be whether it has been manually verified. Specifically, the color of car B in the second information is black, and the first information is a color painting contract, and the color of car B is purple. If it has been manually verified, the color of car B is overwritten with purple.

[0107] When the target model is the event model, determine the append merge rule as the information processing rule, and the event model is used to store the event information executed by the business entity;

[0108] The append merge rule here means adding new data after the original data. The condition for judging whether to append is whether the first information is the same as the second information. Specifically, the first information is the time and the vehicle logistics location. When the vehicle logistics location in the first information is the same as that in the second information, the time and vehicle logistics location of the first information are not appended to the storage location of the second information. When the vehicle logistics location in the first information is different from that in the second information, the time and vehicle logistics location of the first information are appended to the storage location of the second information.

[0109] When the target model is the relationship model, determine that the binding type merging rule is the information processing rule, where the relationship model is used to store information about the association relationships between different business entities.

[0110] The binding type merging rule here refers to adding or deleting entity relationships in the relationship model, where the condition for determining whether binding or unbinding is required is to determine whether the second information includes the binding or unbinding information in the target information. Specifically, when the first information includes the binding information of customer A and vehicle B, determine whether the second information in the preset data model is the same. When the second information does not include the binding information of customer A and vehicle B, bind customer A and vehicle B.

[0111] In this embodiment, different merging rules are determined through different target models, providing a standardized merging logic, enabling the preset model after merging data to meet the requirements of the preset data model, and reducing data merging errors or losses that may occur after merging.

[0112] The present invention also provides an implementation manner of a storage medium for data processing. A data processing program is stored on the storage medium, and when the data processing program is executed by a processor, it implements the embodiments of the data processing method described in any one of the above first to sixth items.

[0113] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, article or system. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of another identical element in the process, method, article or system including that element.

[0114] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0115] Through the description of the above embodiments, those skilled in the art can clearly understand that the above embodiment methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium as described above (such as ROM / RAM, magnetic disk, optical disc), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0116] The above are only the preferred embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A data processing method, characterized in that, The described data processing method includes the following steps: Obtain multiple business data; Extract the target information corresponding to each of the business data according to multiple preset fields in the model configuration table; Determine the target fields in sequence according to the priorities of each feature field, where the feature field is a field for storing business entity information, and the priority is the priority among the feature fields with identification functions in the preset fields; Determine the first field information corresponding to the target field in the target information, and determine the stored information corresponding to the target field as the second field information; When the first field information matches the second field information, associate the first identifier of the corresponding target information and the second identifier of the corresponding stored information to obtain the corresponding correspondence; Wherein, the first identifier is used to identify the business entity to which the corresponding business data belongs, and the second identifier is used to identify the business entities to which different stored information in multiple preset data models belongs; When the current time reaches the preset update time, determine the target information and the stored information corresponding to the correspondence as the first information and the second information respectively; Determine the target model as the model to which the preset field corresponding to the first information belongs in the multiple preset data models; Determine the information processing rule according to the target model; Update the target model according to the first information, the second information and the information processing rule, and different preset data models are used to store different types of business information.

2. The data processing method according to claim 1, characterized in that, The data type of the business data includes the business entity in the corresponding business scenario, the business events executed by the business entity, and the entity relationship between different business entities. The multiple preset data models include an entity model, an event model, and a relationship model. Before the step of determining the target fields in sequence according to the priorities of each feature field, it further includes: Determine the entity model according to the business entity; Determine the event model according to the business event; Determine the relationship model according to the entity relationship.

3. The data processing method according to claim 1, characterized in that, The step of extracting the target information corresponding to each of the business data according to multiple preset fields in the model configuration table includes: Match the information in each of the business data with the multiple preset fields respectively; Determine the information matched by each of the preset fields in the business data as the target sub-information, and obtain multiple target sub-informations corresponding to each of the business data, and the target information includes the multiple target sub-informations.

4. The data processing method according to claim 1, characterized in that, The step of determining the information processing rule according to the target model includes: Determine the information processing rule from the overwrite merge rule, the append merge rule, and the binding merge rule according to the target model.

5. The data processing method according to claim 4, characterized in that, The multiple preset data models include an entity model, an event model, and a relationship model. The step of determining the information processing rule from the overwrite merge rule, the append merge rule, and the binding merge rule according to the target model includes: When the target model is the entity model, determine the overwrite merge rule as the information processing rule, and the entity model is used to store the information of the business entity; When the target model is the event model, determine that the append-type merging rule is the information processing rule, where the event model is used to store event information executed by a business entity; When the target model is the relationship model, determine that the binding-type merging rule is the information processing rule, where the relationship model is used to store information about the association relationships between different business entities.

6. A data processing device, characterized in that, The data processing device includes: a memory, a processor, and a data processing program stored on the memory and running on the processor, where the data processing program is configured to implement the steps of the data processing method according to any one of claims 1 to 5.

7. A storage medium, characterized in that, A data processing program is stored on the storage medium, and when the data processing program is executed by a processor, it implements the steps of the data processing method according to any one of claims 1 to 5.

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