A data model construction method and device, electronic equipment and storage medium

Abstract

The application discloses a data model construction method and device, electronic equipment and a storage medium. The method comprises the following steps: obtaining a model generation request, wherein the model generation request carries model information, and the model information comprises a model type; obtaining a corresponding target data source and a target data standard matched with the target data source according to the model type; assembling standard codes of the target data source according to the target data standard to obtain a modeling program script; and constructing the data model according to the modeling program script. The application determines the target data source and the target data standard matched with the target data source according to the model type, and solves the technical problem that an enterprise data standardization function cannot be supported by a data modeling tool and intelligent data standard definition cannot be implemented.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of data models, and in particular to a data model construction method and device, electronic equipment and storage medium. BACKGROUND

[0002] Currently, the two mature data model design tools, Erwin and Power Designer, are commonly used in the industry to implement data model design.

[0003] The inventors have found that the above-mentioned Erwin and Power Designer are commercial data model design tools launched by commercial companies, and have very high cost, especially in the case of many R&D designers in medium and large enterprises needing to use model design tools, the cost will become very large, which seriously affects normal IT construction and causes certain impact on enterprise operation decision analysis. Moreover, the above-mentioned commercial data modeling tools do not support enterprise data standardization functions, and cannot realize intelligent data standard definition, data standard loading, and global data model standardization. SUMMARY

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides a data model construction method, device, electronic equipment and storage medium.

[0005] According to an aspect of an embodiment of the present application, a data model construction method is provided, comprising:

[0006] obtaining a model generation request, the model generation request carrying model information, the model information including a model type;

[0007] obtaining a corresponding target data source according to the model type, and a target data standard matched with the target data source;

[0008] assembling a standard code of the target data source according to the target data standard to obtain a modeling program script;

[0009] constructing the data model according to the modeling program script.

[0010] Further, the method further comprises:

[0011] receiving a pre-configured data source and data standard;

[0012] determining a mapping relationship between the data source and the data standard according to a preset rule.

[0013] Further, the obtaining of the model generation request comprises:

[0014] acquire a model type label list, the model type label list recording at least two model type labels for indicating a business;

[0015] acquire a selection operation acting on the model type label list;

[0016] generate the model generation request according to the selection operation, the model generation request including a model type corresponding to the selection operation, the model type including a relational type and a dimensional type.

[0017] Further, when the model type is the relational type, the acquiring of the corresponding business data and the data standard matched with the business data according to the model type includes:

[0018] determine a data table for constructing a relational data model;

[0019] acquire first key information;

[0020] acquire field information for constructing the data table from a pre-configured data source according to the first key information, and determine a data standard corresponding to the field information according to the mapping relationship.

[0021] Further, when the model type is the dimensional type, the acquiring of the corresponding business data and the data standard matched with the business data according to the model type further includes:

[0022] determine a fact table and a dimension table for constructing a dimensional data model;

[0023] acquire second key information;

[0024] acquire attribute information for constructing the fact table and the dimension table from a pre-configured data source according to the second key information, and determine a data standard corresponding to the attribute information according to the mapping relationship.

[0025] Further, the assembling of the standard code information of the target data source according to the target data standard to obtain a modeling program script includes:

[0026] determine standard code information of the target data source according to the target data standard;

[0027] assemble the standard code information to obtain a table creation statement;

[0028] convert the table creation statement and a preset model map into a modeling program script.

[0029] Further, the method further includes:

[0030] receiving a data processing request, the data processing request being used to acquire modeling information of at least one data model;

[0031] analyzing the modeling information to determine abnormal modeling information;

[0032] performing an abnormal processing operation according to the abnormal modeling information.

[0033] According to still another aspect of the embodiments of the present application, a data model construction apparatus is further provided, comprising:

[0034] an obtaining module, configured to obtain a model generation request, the model generation request carrying model information, the model information including a model type;

[0035] a processing module, configured to obtain a target data source corresponding to the model type and a target data standard matched with the target data source;

[0036] an assembling module, configured to assemble standard codes of the target data source according to the target data standard to obtain a modeling program script;

[0037] a construction module, configured to construct the data model according to the modeling program script.

[0038] According to another aspect of the embodiments of the present application, a storage medium is further provided, which includes a stored program, and the program performs the above steps when running.

[0039] According to another aspect of the embodiments of the present application, an electronic device is further provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete mutual communication through the communication bus; the memory is used to store a computer program; and the processor is used to execute the steps in the above method by running the program stored in the memory.

[0040] The embodiments of the present application further provide a computer program product including instructions, which, when running on a computer, cause the computer to execute the steps in the above method.

[0041] The above technical solution provided by the embodiments of the present application has the following advantages compared with the prior art: in the embodiments of the present application, the target data source and the target data standard matched with the target data source are determined according to the model type; and the technical problem that the data modeling tool does not support enterprise data standardization function and cannot realize intelligent data standard definition is solved. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application together with the specification.

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the accompanying drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced as follows. Obviously, the drawings described below are only some embodiments of the present application, and thus, for those of ordinary skill in the art, other drawings can be obtained on the basis of these drawings without any creative effort.

[0044] Figure 1 A schematic diagram of a data model construction platform provided by an embodiment of the present application;

[0045] Figure 2 A design interface schematic diagram of a business term provided by an embodiment of the present application;

[0046] Figure 3 A design interface schematic diagram of a naming standard provided by an embodiment of the present application;

[0047] Figure 4 A design interface schematic diagram of a standard code provided by an embodiment of the present application;

[0048] Figure 5 A flowchart of a data model construction method provided by an embodiment of the present application;

[0049] Figure 6 A flowchart of a data model construction provided by another embodiment of the present application;

[0050] Figure 7 A block diagram of a data model construction apparatus provided by an embodiment of the present application;

[0051] Figure 8 A structural schematic diagram of an electronic device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. The illustrative embodiments of the present application and the descriptions thereof are used to explain the present application, and do not constitute an improper limitation on the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without any creative effort belong to the scope of protection of the present application.

[0053] It should be noted that, in this document, the terms such as "first" and "second" and the like are used only to distinguish one entity or operation from another, and do not necessarily require or imply any actual such relationship or order between such entities or operations. Moreover, the terms "include", "contain" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or device. Without more limitations, the element defined by the statement "includes a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0054] In the process of implementing the present application, the inventors found that the existing data model construction tools have the following disadvantages: (1) high purchase cost. The cost of deploying an instance on a single machine may reach hundreds of thousands of yuan, and in the case of thousands or tens of thousands of R&D teams, the deployment cost will reach hundreds of millions of yuan. (2) Commercial data model tools are difficult to interface with enterprise data standards, and it is difficult to achieve unified standardized modeling in data model construction. For example: enterprise business terminology, enterprise data dictionary, naming standard, data architecture standard, enterprise business theme standard, etc. (3) Commercial data modeling does not have data standard support, resulting in multiple definitions of the same business entity in different data environments, which causes great resource cost consumption in the integration, cleaning and analysis of such data.

[0055] Therefore, the embodiment of the present application provides a data model construction platform, which comprises a data standard module, a model engine module, an operation module and a model adaptation module.

[0056] The data standard module in the embodiment is used to provide naming standards, data dictionaries, standard codes and business terminologies.

[0057] Specifically, Figure 2 The design interface diagram of the business terminology provided by the embodiment of the present application is shown in Figure 2 The business terminology is used to realize the consensus of the enterprise internal business entity information description, such as order, commodity, etc.

[0058] Figure 3 The design interface diagram of the business terminology provided by the embodiment of the present application is shown in Figure 3As shown in the figure, the naming standard implements naming rules and constraints of objects such as projects, tables, indexes, processes, and packages. A naming rule example is: object type--business domain abbreviation--business abbreviation--other additional feature keywords. The object type corresponds to the object range as above, the business domain is consistent with the business classification of the enterprise and provides a business classification code, the business abbreviation refers to the specific business implemented by the object, and the other additional feature keywords are optional items. The naming standard information item implements the mapping relationship between the model naming standard and the field type, length, and precision of various databases.

[0059] The data model construction platform provided by the embodiment of the application also provides a data dictionary standard, which is used to implement dictionary table management of member values, meanings of self-defined enumerations and code values in various information systems of an enterprise.

[0060] Figure 4 The design interface of the business terminology provided by the embodiment of the application is shown in the figure Figure 4 As shown in the figure, the standard code implements enumeration type information commonly used in an enterprise, such as gender information represented by 0 and 1, which is usually consistent with international and industry standard codes.

[0061] The model engine module in the embodiment is used for model visualization design, data source management, model change, and model construction.

[0062] The model running module in the embodiment is used for engineering management, version management, log management, and the like.

[0063] The model adaptation module in the embodiment is used for constructing a data model according to a model program script. The model adaptation module includes an RDB adapter, an NOSQL adapter, and a SOL generator.

[0064] The embodiment of the application provides a data model construction method based on the data model construction platform described above. The method provided by the embodiment of the application can be applied to any required electronic device, for example, a server, a terminal, or the like, without specific limitation here. For the convenience of description, the electronic device is referred to as an electronic device hereinafter.

[0065] Figure 5 The flowchart of the data model construction method provided by the embodiment of the application is shown in the figure Figure 5 The method includes the following steps.

[0066] In step S11, a model generation request is acquired, the model generation request carries model information, and the model information includes a model type;

[0067] In the embodiment, the data source and the data standard also need to be configured before the model generation request is acquired. Specifically, the pre-configured data source and the data standard are received, and the mapping relationship between the data source and the data standard is determined according to a preset rule. It can be understood that the data source can include business data of multiple businesses, and the data standard includes naming standards, standard codes, business terms, and the like corresponding to each business data.

[0068] In addition, the data warehouse also needs to be configured. Specifically, the original data is acquired, and then the original data is processed according to the content definition and the logical generation method in the template, so as to obtain an initial data warehouse. In the initial data warehouse, the generation of fact tables, dimension tables, and the association relationship between the tables can be directly defined. For example, in the data warehouse corresponding to the transaction type business, the transaction subject data can be used to construct a transaction fact table; meanwhile, the relevant data of the order dimension, the commodity dimension, the buyer dimension, and the seller dimension can be used to construct a first-level dimension table directly associated with the transaction fact table; and a second-level dimension table associated with the first-level dimension table can also be constructed under the first-level dimension table, such as a city dimension table created according to the different regions where the buyers are located under the branch of the buyer dimension table.

[0069] After the initial data warehouse is generated, if there is no special modeling requirement, the initial data warehouse can be directly provided to the demand user; if the user has a modeling requirement, the iterative update operation can be performed on the basis of the initial data warehouse, such as adding and / or deleting part of the data tables, adding and / or deleting the data table fields in the data tables, adjusting the association relationship between the data tables, and the like, and then the updated data warehouse is provided to the demand user.

[0070] In addition, after the data warehouse is generated, the data in the data warehouse can be summarized and processed to generate a derived indicator summary table corresponding to the data warehouse. In the data warehouse generated based on the transaction type business, the derived indicators for the buyer information are generated by taking the buyer as the statistical granularity, the payment amount as the atomic indicator, the wireless type online transaction as the business limitation, and the last 7 days as the time period, and then the derived indicators are summarized to generate a buyer summary table. The derived indicator summary table can still be structured and laid out in the form of the data warehouse, so that the enterprise user can quickly understand various indicators of his / her own business, and thus make reasonable decisions based on the derived indicators.

[0071] In this step, the model generation request can be a login operation acting on a construction platform of a data model, a model type selection interface is acquired according to the login operation, the model type selection interface includes a model type label list, the model type label list records at least two model type labels for indicating a business, a selected operation acting on the model type label list is acquired, the model type of the data model is determined according to the selected operation, and the model type includes a relational type and a dimension type.

[0072] In step S12, the corresponding target data source is acquired according to the model type, and the target data standard matched with the target data source is acquired.

[0073] It should be noted that different model types require different data sources. The data source required by the relational data model mainly includes the table structure of the data table, the view, and the index constraint, etc. The data source required by the dimensional data model mainly includes the dimension table and the fact table.

[0074] In the embodiment, when the model type is relational, the corresponding business data is acquired according to the model type, and the data standard matched with the business data is acquired, including: determining the data table used for constructing the relational data model, acquiring the first key information, acquiring the field information used for constructing the data table from the pre-configured data source according to the first key information, and determining the data standard corresponding to the field information according to the mapping relationship.

[0075] It should be noted that the first key information can be obtained by the input information received by the client. The first key information includes: the table name of the data table, the database type, the virtual group, the character set encoding, and the business system. In this step, the field information used for constructing the data table is obtained by querying the virtual group and the character set encoding. The field information can include the primary key, the user number, the user ID, etc. The data standard corresponding to the primary key includes: named id, field type is bight, length is 10, etc. The data standard corresponding to the user number includes: named user_no, field type is varchar, length is 64, etc.

[0076] In the embodiment, when the model type is dimensional, the corresponding business data is acquired according to the model type, and the data standard matched with the business data is acquired, further including: determining the fact table and the dimension table used for constructing the dimensional data model, acquiring the second key information, acquiring the attribute information used for constructing the fact table and the dimension table from the pre-configured data source according to the second key information, and determining the data standard corresponding to the attribute information according to the mapping relationship.

[0077] It should be noted that the second key information can be obtained from the input information received by the client. This second key information includes: the table name of the fact table or dimension table, the business system, the mart to which it belongs, the data hierarchy, etc. In this step, the attribute information is mainly determined through the business system, the mart to which it belongs, and the data hierarchy. Attribute information can be primary keys, document type codes, gender, etc. Specifically, the attribute type corresponding to the primary key is fact, the attribute name is id, and the data type is bold; the attribute type corresponding to the document type code is dimension, the attribute name is cert_type, and the data type is string; the attribute type corresponding to gender is dimension, the attribute name is sex_type, and the data type is string, etc.

[0078] Dimensional modeling is a data modeling method used in data warehouse construction. It's a logical design method that structures data, dividing the objective world into metrics and contexts. Kimball first proposed this concept. Simply put, it involves building data warehouses and data marts based on fact tables and dimension tables. A data mart, also called a data marketplace, meets the needs of specific departments or users by storing data in a multi-dimensional way. This includes defining dimensions, metrics to be calculated, and the hierarchy of dimensions, generating data cubes oriented towards decision analysis needs. It is a subset of a data warehouse.

[0079] Step S13: Assemble the standard code of the target data source according to the target data standard to obtain the modeling program script;

[0080] In this embodiment, the standard code of the target data source is assembled according to the target data standard to obtain a modeling program script, including: determining the standard code information of the target data source according to the target data standard, assembling the standard code information to obtain a table creation statement, and converting the table creation statement and the preset model map into a modeling program script.

[0081] For example, the amount is a floating-point data field. In Oracle database, its data type is FLOAT; in Hive, it is double.

[0082] RDB Adapter: Enables support for relational databases such as MySQL, Oracle, and SQL Server during the data model design process (model to table creation scripts). NoSQL Adapter: Enables support for NoSQL databases such as Hive during the data model design process (model to table creation scripts, mapping to model program scripts, database reverse engineering, etc.). SQL Generator: Generates DDL table creation statements for the designed data model, facilitating designers to materialize the model into the database.

[0083] It should be noted that the mapping of the data model refers to the mapping relationship between the data model and the data model to which the target data source belongs, for example: the target data source included in the commodity data model has a daily inventory table and a commodity-inventory summary table, wherein the daily inventory table corresponds to the inventory statistics model, and the commodity-inventory summary table corresponds to the commodity summary model.

[0084] Step S14, constructing a data model according to the modeling program script.

[0085] In this step, when the data model is established in the Power Designer, the modeling program script (SQL language) can be directly executed in the Power Designer to establish the data model.

[0086] Data modeling refers to the abstract organization of various data in the real world, determining the scope of the database to be governed, the organization form of the data, and the like until it is converted into a real database. The process of converting the conceptual model abstracted after system analysis into a physical model and establishing database entities and relationships between entities in visio or erwin and the like tools.

[0087] In the embodiment of the present application, the target data source and the target data standard matched with the target data source are determined through the model type, and the technical problem that the data modeling tool does not support enterprise data standardization function and cannot realize intelligent data standard definition is solved.

[0088] Figure 6 A flowchart of a data model construction method provided in the embodiment of the present application is shown in FIG. 1, which can include the following steps: Figure 6

[0089] Step S21, receiving a data processing request, the data processing request being used to acquire modeling information of at least one data model;

[0090] Step S22, analyzing the modeling information to determine abnormal modeling information;

[0091] Step S23, performing an abnormal processing operation according to the abnormal modeling information.

[0092] The embodiment can analyze and verify the operation data of the user in the modeling process by analyzing the modeling information to determine abnormal modeling information, for example: data type error in the modeling information or abnormal table creation statement and the like, and the problem occurring in the modeling can be traced by analyzing and verifying.

[0093] Figure 7 ​A block diagram of a data model construction device provided by an embodiment of the present application. The device can be implemented by software, hardware or a combination of both as part of or all of an electronic device. As shown in the figure, the device includes: Figure 7

[0094] An acquisition module 31 is configured to acquire a model generation request, the model generation request carrying model information, the model information including a model type;

[0095] A processing module 32 is configured to acquire a corresponding target data source according to the model type, and a target data standard matched with the target data source;

[0096] An assembly module 33 is configured to assemble standard code of the target data source according to the target data standard to obtain a modeling program script;

[0097] A construction module 34 is configured to construct a data model according to the modeling program script.

[0098] The further data model construction device further includes a configuration module configured to receive a pre-configured data source and a data standard, and determine a mapping relationship between the data source and the data standard according to a preset rule.

[0099] The further acquisition module 31 is specifically configured to acquire a model type tag list, the model type tag list recording at least two model type tags for indicating a business; acquire a selection operation acting on the model type tag list; generate a model generation request according to the selection operation, the model generation request including a model type corresponding to the selection operation, the model type including a relational type and a dimension type.

[0100] Further, when the model type is the relational type, the processing module 33 is specifically configured to determine a data table for constructing a relational data model, acquire first key information, and acquire field information for constructing the data table from the pre-configured data source according to the first key information, and determine a data standard corresponding to the field information according to the mapping relationship.

[0101] Further, when the model type is the dimension type, the processing module 33 is specifically configured to determine a fact table and a dimension table for constructing a dimension data model, acquire second key information, and acquire attribute information for constructing the fact table and the dimension table from the pre-configured data source according to the second key information, and determine a data standard corresponding to the attribute information according to the mapping relationship.

[0102] The further assembly module 33 is specifically configured to determine standard code information of the target data source according to the target data standard, assemble the standard code information to obtain a table creation statement, and convert the table creation statement and a preset model map into the modeling program script.

[0103] ​The embodiments of the present application further provide an electronic device, such as Figure 8 As shown in the figure, the electronic device can include a processor 1501, a communication interface 1502, a memory 1503 and a communication bus 1504, wherein the processor 1501, the communication interface 1502 and the memory 1503 complete mutual communication through the communication bus 1504.

[0104] The memory 1503 is used for storing computer programs.

[0105] The processor 1501 is used for executing the computer programs stored in the memory 1503, so as to realize the steps of the above embodiments.

[0106] The communication bus mentioned in the above terminal can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0107] The communication interface is used for communication between the above terminal and other devices.

[0108] The memory can include a random access memory (RAM) and can also include a non-volatile memory, for example, at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0109] The processor mentioned above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0110] In a further embodiment provided by the present application, a computer readable storage medium is provided, which stores instructions, when executed on a computer, cause the computer to perform the method for constructing a data model according to any one of the above embodiments.

[0111] In a further embodiment provided by the present application, a computer program product is provided, which contains instructions, when executed on a computer, cause the computer to perform the method for constructing a data model according to any one of the above embodiments.

[0112] In the above embodiments, the implementation can be wholly or partially in software, hardware, firmware or any combination thereof. When implemented in software, the implementation can be in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, the computer instructions cause the computer to perform the processes or functions described in the embodiments of the present application. The computer can be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions can be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line) or wireless (such as infrared, wireless, microwave, etc.) manner. The computer readable storage medium can be any available medium accessible by a computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be magnetic media (such as floppy disk, hard disk, magnetic tape), optical media (such as DVD), or semiconductor media (such as Solid State Disk) etc.

[0113] The above only describes the preferred embodiments of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0114] The above only describes the specific embodiments of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features applied herein.

Claims

1. A method of constructing a data model, characterized by, The method comprises the following steps: acquiring a model generation request, wherein the model generation request carries model information, and the model information comprises a model type; acquiring a corresponding target data source and a target data standard matched with the target data source according to the model type; assembling standard codes of the target data source according to the target data standard to obtain a modeling program script; constructing the data model according to the modeling program script; The method further comprises the following steps: receiving a pre-configured data source and a data standard; determining a mapping relationship between the data source and the data standard according to a preset rule; when the model type is a relational type, acquiring corresponding business data and a data standard matched with the business data, comprising: determining a data table used for constructing a relational data model, acquiring first key information, acquiring field information used for constructing the data table from the pre-configured data source according to the first key information, and determining a data standard corresponding to the field information according to the mapping relationship.

2. The method of claim 1, wherein, The step of acquiring the model generation request comprises the following steps: acquiring a model type tag list, wherein the model type tag list records at least two model type tags used for indicating a business; acquiring a selection operation acting on the model type tag list; generating the model generation request according to the selection operation, wherein the model generation request comprises a model type corresponding to the selection operation, and the model type comprises a relational type and a dimension type.

3. The method of claim 2, wherein, When the model type is a dimension type, the step of acquiring corresponding business data and a data standard matched with the business data according to the model type further comprises the following steps: determining a fact table and a dimension table used for constructing a dimension data model; acquiring second key information; acquiring attribute information used for constructing the fact table and the dimension table from the pre-configured data source according to the second key information, and determining a data standard corresponding to the attribute information according to the mapping relationship.

4. The method of claim 1, wherein, The step of assembling the standard code information of the target data source according to the target data standard to obtain a modeling program script comprises the following steps: determining the standard code information of the target data source according to the target data standard; assembling the standard code information to obtain a table creation statement; converting the table creation statement and a preset model map into the modeling program script.

5. The method of claim 1, wherein, The method further comprises the following steps: receiving a data processing request, wherein the data processing request is used for acquiring modeling information of at least one data model; analyzing the modeling information to determine abnormal modeling information; performing an abnormal processing operation according to the abnormal modeling information.

6. An apparatus for constructing a data model, characterized by The method comprises the following steps: an acquiring module, configured to acquire a model generation request, wherein the model generation request carries model information, and the model information comprises a model type; a processing module, configured to acquire a corresponding target data source and a target data standard matched with the target data source according to the model type; an assembling module, configured to assemble standard codes of the target data source according to the target data standard to obtain a modeling program script; a constructing module, configured to construct the data model according to the modeling program script; The configuration module is configured to receive a pre-configured data source and a data standard, and determine a mapping relationship between the data source and the data standard according to a preset rule; When the model type is relational, the processing module is specifically configured to determine a data table for constructing a relational data model, acquire first key information, acquire field information for constructing the data table from the pre-configured data source according to the first key information, and determine a data standard corresponding to the field information according to the mapping relationship.

7. A storage medium, characterized by The storage medium includes a stored program, wherein the program executes the method steps of any one of claims 1 to 5 when running.

8. An electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein, The processor, the communication interface, and the memory complete mutual communication through a communication bus; wherein: The memory is configured to store a computer program. The processor is configured to execute the method steps of any one of claims 1 to 5 by running the program stored in the memory.

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