CN116842052A

CN116842052A - Data processing method, device, equipment and medium based on integration model

Info

Publication number: CN116842052A
Application number: CN202310760846.4A
Authority: CN
Inventors: 林素芬
Original assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Current assignee: China Construction Bank Corp; CCB Finetech Co Ltd
Priority date: 2023-06-26
Filing date: 2023-06-26
Publication date: 2023-10-03

Abstract

The embodiment of the disclosure provides a data processing method, device, equipment and medium based on an integration model, so as to solve the problem that the data processing in the related art is low in reusability and expansibility, and therefore the efficiency and practicality of data management cannot meet requirements, and the data processing method based on the integration model comprises the following steps: acquiring patch source data for data processing, wherein the patch source data comprises a plurality of data items; determining at least one entity in the patch source data, and hooking data items with the same granularity as the entity on the entity; defining attributes of each entity based on a plurality of data items hung on the entity; and analyzing by using the defined data items of the entity to obtain an analysis result of the patch source data.

Description

Data processing method, device, equipment and medium based on integration model

Technical Field

The disclosure relates to the field of computer technology and data processing, and in particular relates to a data processing method, device, equipment and medium based on an integrated model.

Background

In the related art, there are various drawbacks to the calculation of the asset management scale (Asset under management, AUM). Specifically, the time-efficiency of the AUM supply is affected by the processing of data at multiple levels. And the efficiency is low when multi-table association and a large number of queries are involved, and the reusability and expansibility of the model are low, so that an effective index management, maintenance and notification mechanism is difficult to form from the point of data management.

Disclosure of Invention

The disclosure provides a data processing method, device, equipment and medium based on an integration model, so as to solve the technical problem that the data processing in the related art is low in reusability and expansibility, and therefore the efficiency and practicality of data management cannot meet requirements. The technical scheme of the present disclosure is as follows:

in a first aspect, an embodiment of the present disclosure provides a data processing method based on an integration model, including:

acquiring paste source data for data processing, wherein the paste source data comprises a plurality of data items;

determining at least one entity in the source data, and hanging data items with the same granularity as the entity on the entity;

defining attributes of each entity based on a plurality of data items hooked on each entity;

and analyzing by using the defined data items of the entity to obtain an analysis result of the patch source data.

In a possible implementation manner, in the method provided by the embodiment of the present application, after determining at least one entity in the source data and hooking a data item with the same granularity as the entity on the entity, the method further includes:

based on the attribute of each data item, the data items hung on each entity are subjected to the processes of deduplication, merging and splitting.

In a possible implementation manner, in the method provided by the embodiment of the present application, based on the attribute of each data item, deduplication, merging and splitting are performed on the data item hooked on each entity, including:

sequentially analyzing the naming and definition of the data items hung on each entity;

carrying out de-duplication treatment on the data items which are named the same and are defined the same;

splitting data items named the same but defined differently;

data items with the same definition but different names are subjected to merging processing.

In a possible implementation manner, in a method provided by an embodiment of the present application, defining an attribute of each entity based on a plurality of data items hooked on each entity includes:

for each entity's attribute, the naming and source of the attribute is clarified;

for each entity's attributes, the business rules and business apertures of the attributes are defined.

In a possible implementation manner, in the method provided by the embodiment of the present application, after defining the attribute of each entity based on the plurality of data items hooked on each entity, the method further includes:

binding each entity with a corresponding data table and column based on the attributes of the entity.

In a second aspect, an embodiment of the present disclosure further provides a data processing apparatus based on an integration model, including:

the data processing device comprises an acquisition unit, a data processing unit and a data processing unit, wherein the acquisition unit is used for acquiring patch source data for data processing, and the patch source data comprises a plurality of data items;

the processing unit is used for determining at least one entity in the source data and hooking the data items with the same granularity as the entity on the entity;

a definition unit, configured to define an attribute of each entity based on a plurality of data items hooked on each entity;

and the analysis unit is used for analyzing the defined data items of the entities to obtain an analysis result of the patch source data.

In a possible implementation manner, in the apparatus provided by the embodiment of the present application, the processing unit is further configured to:

In a possible implementation manner, in the device provided by the embodiment of the present application, the processing unit is specifically configured to:

splitting data items named the same but defined differently;

In one possible implementation manner, in the device provided by the embodiment of the present application, the defining unit is specifically configured to:

In one possible implementation manner, in the device provided by the embodiment of the present application, the defining unit is further configured to:

In a third aspect, embodiments of the present disclosure further provide an electronic device, including:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the method of the first aspect.

In a fourth aspect, embodiments of the present disclosure further provide a computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the method according to the first aspect.

In a fifth aspect, embodiments of the present disclosure further provide a computer program product comprising a computer program/instruction, characterized in that the computer program/instruction, when executed by a processor, implements the method according to the first aspect.

The technical scheme provided by the embodiment of the disclosure at least brings the following beneficial effects:

in the embodiment of the disclosure, firstly, the patch source data for data processing is acquired, then at least one entity in the patch source data is determined, the data items with the same granularity as the entity are hung on the entity, then the attribute of each entity is defined based on a plurality of data items hung on each entity, and finally the analysis result of the patch source data is obtained by utilizing the data items of the defined entity. The data processing scheme based on the integrated model can optimize the data processing path by hooking and defining the source data when the model is built, reduce the complex relevance during data processing, improve the data processing efficiency and improve the practicability.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure and do not constitute an undue limitation on the disclosure.

Fig. 1 is a schematic flow chart of a data processing method based on an integration model according to an embodiment of the disclosure;

FIG. 2 is a schematic flowchart of a data processing method based on an integration model according to an embodiment of the disclosure;

FIG. 3 is a schematic structural diagram of a data processing apparatus based on an integrated model according to an embodiment of the disclosure;

fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the disclosure.

Detailed Description

In order to enable those skilled in the art to better understand the technical solutions of the present disclosure, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the disclosure described herein may be capable of operation in sequences other than those illustrated or described herein. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

Before describing in detail the data processing method based on the integration model provided in the present disclosure, the following description will preferentially introduce key terms involved, where:

model (pamadigm model): mainly refers to an overall framework of widely accepted theory, methods and assumptions in a field or discipline.

Source data (source data): refers to raw data collected from the real world prior to data analysis, processing and application.

Entity (Entity): refers to a particular concept or thing, such as "person, organization, merchandise," etc.

Particle size: refers to the level of detail of the data, or the way in which the data is organized and processed.

Data item: is the smallest unit of data that is indivisible in a database and is typically used to represent a particular piece of information about a particular thing or entity. For example, a person's name is a data item and the date of an order is a data item.

Attributes: is a feature or property that can be described by a data item, and is a fundamental descriptive component of the data item. For example, a person's name may have two attributes, namely "last name" and "first name", and an order's date may have three attributes, namely "year", "month" and "day". Thus, an attribute can be said to be a description of a data item, which is a specific implementation of the attribute.

AUM (Asset under management, AUM): i.e., asset management scale, refers to the total asset value managed by an asset management company or investor. These assets may include various asset categories of stocks, bonds, cash, real estate, and the like.

Particle size model: the method is a logic model in a large-width table form formed by serially connecting original and derivative data with the same data granularity according to the data granularity.

In the related art, there are various drawbacks to the calculation of AUM. Specifically, the time-efficiency of the AUM supply is affected by the processing of data at multiple levels. And the efficiency is low when multi-table association and a large number of queries are involved, and the reusability and expansibility of the model are low, so that an effective index management, maintenance and notification mechanism is difficult to form from the point of data management.

The following describes in detail the technical solutions provided by the embodiments of the present disclosure with reference to the accompanying drawings.

Fig. 1 is a flowchart of a data processing method based on an integrated model according to a first embodiment of the present disclosure, as shown in fig. 1, the data processing method based on the integrated model may include the following steps:

s101, acquiring paste source data for data processing, wherein the paste source data comprises a plurality of data items.

In specific implementation, patch source data for data processing is acquired, wherein the patch source data is original data and comprises a plurality of unprocessed data items.

S102, determining at least one entity in the paste source data, and hanging data items with the same granularity as the entity on the entity.

In specific implementation, the entity and the data granularity are determined from the data items of the source data, and the data items with the same granularity are hung on the same granularity, namely, one entity.

In this step, the data items may be subjected to deduplication, merging and splitting, specifically, the naming and definition of the data items hung on each entity are analyzed sequentially, then the data items with the same naming and the same definition are subjected to deduplication, the data items with the same naming and different definitions are subjected to splitting, and the data items with the same definition and different naming are subjected to merging.

S103, defining the attribute of each entity based on a plurality of data items hung on each entity.

In specific implementation, for each entity attribute, the naming and source of the attribute are defined, for each entity attribute, the service rule and service caliber of the attribute are defined, and after the definition, each entity can be bound with the corresponding data table and column based on the entity attribute, namely, the data landing is realized.

S104, analyzing by using the defined data items of the entity to obtain an analysis result of the patch source data.

When the data is analyzed or the data is called for processing, the required data can be quickly called through entity attributes and the like, and an analysis result or a processing result can be obtained.

Next, taking an example of application to a banking system, a detailed description will be given of a data processing method based on an integrated model provided in the present disclosure with reference to fig. 2.

The current data warehouse is different from the AUM value caliber long-term and product service assembly balance report form and management analysis assembly assessment report form, the business department identifies the mechanism attribution of AUM products and gives trouble to the total branch users in the aspects of layering and grading of customer management and performance judgment of branch assessment. The old model of the AUM is designed based on a three-model, a large number of intermediate tables with derivative granularity are designed for meeting the requirement of summarizing AUM values to different granularities, and the intermediate tables are summarized to clients and institutions with granularity, so that the multi-level processing influences the AUM supply timeliness; the original product account granularity physically separates attributes from balances, and large table associations when used also reduce efficiency. Downstream items using AUM related indexes are more, the application scene is complex, the downstream of the AUM is not subjected to carding and investigation for a long time, and an effective index management, maintenance and notification mechanism cannot be formed from the perspective of data management.

Fig. 2 is a specific flow chart of a data processing method based on an integration model according to an embodiment of the disclosure, which specifically includes the following steps as shown in fig. 2:

s201, acquiring paste source data for data processing, wherein the paste source data comprises a plurality of data items.

In one example, the source data of the product service component at the upstream of the bank is accessed as a data item source, including the data of a deposit system, a financial system and a history integration model, and relevant history data is imported through a data model management platform.

S202, determining at least one entity in the paste source data, and hanging data items with the same granularity as the entity on the entity.

In the implementation, the entity and the data granularity are determined from the data items of the patch source data, then the entity is defined and described in detail, and then the data items are hung.

In one example, the primary work of analysis includes: whether the naming of the data item is standard, what the source field of the data item is, what the processing rule of the data item is, what the definition of the data item is, etc., for the newly added data item, the definition needs to be performed from the service level, and the hooking of the data item is to hook the data item with the same granularity into the same granularity.

And S203, performing de-duplication, merging and splitting processing on the data items hung on each entity based on the attribute of each data item.

In the specific implementation, all data items are analyzed firstly, then the data items are subjected to de-duplication, merging and splitting treatment according to the analysis result, specifically, the naming and definition of the data items hung on each entity are analyzed sequentially, then the data items with the same naming and the same definition are subjected to de-duplication treatment, the data items with the same naming and different definitions are subjected to splitting treatment, and the data items with the same definition and the different naming are subjected to merging treatment.

S204, defining the attribute of each entity based on a plurality of data items hung on each entity.

When the method is implemented, after the attributes with the same granularity are hung on the entities with the same granularity, the attributes in the entities need to be defined in detail one by one, and detailed information contained in the attributes is explicitly described.

In one example, the specific steps for defining the attributes are as follows:

firstly, defining attribute naming according to naming specifications, then describing definition, purpose and range of the attribute in detail, then determining range of attribute values, then defining attribute source components, source tables, source field information and the like, and finally describing business rules, and clearly defining business rules and business apertures of the attribute.

S205, binding each entity with the corresponding data table and column based on the attributes of the entities.

In specific implementation, each entity is bound with the corresponding data table and column based on the attribute of the entity, i.e. each entity and attribute are bound with the corresponding data table and column. This ensures proper storage, querying, manipulation and management of data in the database. This process is called "word-paragraph", i.e., mapping fields in the conceptual model (logical model) to columns in the data table (physical model).

S206, analyzing by using the defined data items of the entity to obtain an analysis result of the patch source data.

By applying the data processing method based on the integrated model, which is provided by the embodiment of the disclosure, the expandability and reusability of the model for processing the AUM are improved, the optimization of the AUM data implementation path is realized, compared with a paradigm model used in the prior art, an S layer and a basic theme are canceled, basic data is directly obtained from a paste source, the data processing path is shortened, and the account granularity redundancy client number, the institution number and the saleable product number are convenient to summarize from the account granularity to the client granularity and the institution granularity. Thus, AUM processing of any granularity only requires one more layer to be processed on an account basis. Account granularity important entities such as funds, national bonds, financial, trust, living deposit, credit cards and the like are built in the cloud digital bin, and important indexes such as month average/year average balance and the like are calculated in the account granularity preferentially, so that individual customer marketing analysis and mechanism performance assessment except AUM can be effectively supported, and the processing speed and practicality of data processing are improved.

Based on the same inventive concept, the embodiment of the disclosure also provides a data processing device based on the integration model. As shown in fig. 3, the data processing apparatus 300 based on the integrated model includes:

an acquiring unit 301, configured to acquire patch source data for data processing, where the patch source data includes a plurality of data items;

a processing unit 302, configured to determine at least one entity in the patch source data, and attach a data item with the same granularity as the entity to the entity;

a defining unit 303, configured to define an attribute of each entity based on a plurality of data items hooked on each entity;

and the analysis unit 304 is configured to perform analysis by using the defined data item of the entity, so as to obtain an analysis result of the patch source data.

In a possible implementation manner, in the apparatus provided by the embodiment of the present application, the processing unit 302 is further configured to:

In a possible implementation manner, in the apparatus provided by the embodiment of the present application, the processing unit 302 is specifically configured to:

splitting data items named the same but defined differently;

In one possible implementation manner, in the apparatus provided by the embodiment of the present application, the defining unit 303 is specifically configured to:

In one possible implementation manner, in the apparatus provided by the embodiment of the present application, the defining unit 303 is further configured to:

The specific implementation manner and technical effects of the device provided in the embodiment of the present disclosure are similar to those of the embodiment of the method described above, and are not repeated here.

In addition, the data processing method and apparatus based on the integrated model according to the embodiments of the present application described in connection with fig. 1 to 3 may be implemented by an electronic device. Fig. 4 shows a schematic hardware structure of an electronic device according to an embodiment of the present application.

As shown in fig. 4, the electronic device 400 may include a processing means (e.g., a central processor, a graphics processor, etc.) 401, which may perform various suitable actions and processes according to a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage means 408 into a Random Access Memory (RAM) 403 to implement the data processing method based on the integrated model of the embodiments as described in the present disclosure. In the RAM 403, various programs and data necessary for the operation of the electronic device 400 are also stored. The processing device 401, the ROM 402, and the RAM 403 are connected to each other by a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.

In general, the following devices may be connected to the I/O interface 405: input devices 406 including, for example, a touch screen, touchpad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 407 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 408 including, for example, magnetic tape, hard disk, etc.; and a communication device 409. The communication means 409 may allow the electronic device 400 to communicate with other devices wirelessly or by wire to exchange data. While fig. 4 shows an electronic device 400 having various means, it is to be understood that not all of the illustrated means are required to be implemented or provided. More or fewer devices may be implemented or provided instead.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a non-transitory computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts, thereby implementing the speech control method as described above. In such an embodiment, the computer program may be downloaded and installed from a network via communications device 409, or from storage 408, or from ROM 402. The above-described functions defined in the methods of the embodiments of the present disclosure are performed when the computer program is executed by the processing device 401.

The embodiment of the disclosure also provides a vehicle, which comprises the data processing device and the electronic equipment based on the integrated model.

It should be noted that the computer readable medium described in the present disclosure may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this disclosure, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

In some implementations, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText Transfer Protocol ), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed networks.

The computer readable medium may be contained in the electronic device; or may exist alone without being incorporated into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:

Alternatively, the electronic device may perform other steps described in the above embodiments when the above one or more programs are executed by the electronic device.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including, but not limited to, an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units involved in the embodiments of the present disclosure may be implemented by means of software, or may be implemented by means of hardware. Wherein the names of the units do not constitute a limitation of the units themselves in some cases.

The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The data processing method based on the integrated model, which is provided by the embodiment of the application, comprises the following steps:

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A data processing method based on an integration model, comprising:

acquiring patch source data for data processing, wherein the patch source data comprises a plurality of data items;

determining at least one entity in the patch source data, and hooking data items with the same granularity as the entity on the entity;

defining attributes of each entity based on a plurality of data items hung on the entity;

2. The method of claim 1, wherein after said determining at least one entity in said source data and hooking data items of the same granularity as an entity onto said entity, said method further comprises:

and carrying out de-duplication, merging and splitting processing on the data items hung on each entity based on the attribute of each data item.

3. The method according to claim 2, wherein the performing deduplication, merging, and splitting processing on the data items hooked on each entity based on the attribute of each data item comprises:

analyzing the naming and definition of the data items hung on each entity in turn;

splitting data items named the same but defined differently;

4. The method of claim 1, wherein defining the attribute of each entity based on the plurality of data items hooked on each entity comprises:

for each attribute of the entity, defining the name and source of the attribute;

for each attribute of the entity, defining a business rule and a business caliber of the attribute.

5. The method of claim 4, wherein after defining the attributes of each entity based on the plurality of data items hooked to each entity, the method further comprises:

binding each entity with a corresponding data table and column based on the attributes of the entities.

6. A data processing apparatus based on an integrated model, comprising:

the device comprises an acquisition unit, a data processing unit and a data processing unit, wherein the acquisition unit is used for acquiring patch source data for data processing, and the patch source data comprises a plurality of data items;

the processing unit is used for determining at least one entity in the paste source data and hooking data items with the same granularity as the entity on the entity;

7. The apparatus of claim 6, wherein the processing unit is further configured to:

8. The apparatus according to claim 7, wherein the processing unit is specifically configured to:

splitting data items named the same but defined differently;

9. The apparatus according to claim 6, wherein the definition unit is specifically configured to:

10. The apparatus of claim 9, wherein the definition unit is further configured to:

11. An electronic device, comprising:

a processor;

a memory for storing the processor-executable instructions;

wherein the processor is configured to execute the instructions to implement the integrated model based data processing method of any of claims 1-5.

12. A computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the data processing method based on an integrated model according to any one of claims 1-5.

13. A computer program product comprising computer programs/instructions which, when executed by a processor, implement the data processing method based on an integrated model according to any of claims 1-5.