Data model reconstruction method and device, equipment and storage medium thereof

Abstract

The embodiment of the application belongs to the technical field of data processing and is applied to the field of data model reconstruction. The embodiment relates to a data model reconstruction method, device and equipment and a storage medium thereof. The method comprises the following steps: receiving a data model reconstruction instruction; obtaining a theme table that can be selected for reconstruction from a source layer of a preset data warehouse; performing numbering processing on the theme table that can be selected according to a preset numbering rule to obtain a numbering processing result; obtaining an optimal combination mode between the theme tables that can be selected during reconstruction according to a preset constraint condition and a preset configuration file; generating a combination table corresponding to a target theme domain according to the optimal combination mode and the distinguished identification information; replacing the target theme domain, and cyclically executing the above steps until combination tables corresponding to all theme domains are generated, and the cycle is stopped, and data model reconstruction is completed. The data model is automatically reconstructed in a cyclic generation mode, and the coupling degree of the reconstruction result is reduced by directly obtaining the source layer data.

Description

Technical Field

[0001] This application relates to the field of data processing technology, and in particular to a data model reconstruction method, apparatus, device and storage medium thereof. Background Technology

[0002] Data application reports are crucial tools for company operational decision-makers to conduct statistical analysis, identify market opportunities, and adjust strategies. With the explosive growth of data volume, business needs have led to diverse combinations of dimensions for market analysis, resulting in more complex underlying data models and continuously increasing computing resources. The ability to quickly support the dimensions and metrics used by the business and generate reasonable data models has become a challenge for every data developer.

[0003] In existing technologies, during the initial stages of data application, data teams often develop siloed data models to quickly meet business needs, continuously iterating and updating them. Later updates become costly, primarily due to: the high skill level required for later data model reconstruction, which is time-consuming, labor-intensive, and time-consuming; and the complexity of business analysis combinations, leading to highly coupled wide tables in the data model and difficulties in maintenance. Therefore, existing technologies for data model reconstruction also suffer from high coupling of reconstruction results and time-consuming, labor-intensive reconstruction processes. Summary of the Invention

[0004] The purpose of this application is to propose a data model reconstruction method, apparatus, device and storage medium to solve the problems of high coupling of reconstruction results and time-consuming and labor-intensive reconstruction process in the prior art when reconstructing data models.

[0005] To address the aforementioned technical problems, this application provides a data model reconstruction method, employing the following technical solution:

[0006] A data model reconstruction method includes the following steps:

[0007] Step 201: Receive a data model reconstruction instruction issued with the target subject domain as the reconstruction target, wherein the data model reconstruction instruction includes the distinguishing identifier information of the target subject domain;

[0008] Step 202: Parse the data model reconstruction instruction to obtain the distinguishing identifier information of the target topic domain;

[0009] Step 203: Obtain the subject table that can be used to reconstruct the target subject domain from the source layer of the preset data warehouse, wherein the subject table is the data base table in the source layer, and the subject domain represents a wide table composed of several subject tables that can cover at least one actual business process.

[0010] Step 204: According to the preset numbering rules, the selectable topic table is numbered, and the numbering result is obtained;

[0011] Step 205: Based on preset constraints and preset configuration files, obtain the optimal combination of the available topic tables when reconstructing the target topic domain;

[0012] Step 206: Generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information;

[0013] Step 207: Change the target subject domain and repeat steps 201 to 207 until all subject domains have generated corresponding composite tables. Stop the loop, and the data model reconstruction is complete.

[0014] Furthermore, the step of numbering the selectable topic table according to a preset numbering rule and obtaining the numbering result specifically includes:

[0015] Obtain the unique primary key information corresponding to each of the optional topic tables, wherein the primary key information includes the table name;

[0016] The available topic tables are numbered using positive integers from 1 to n, where n represents the number of available topic tables;

[0017] The number and its corresponding unique primary key information are cached as key-value pairs, and the cached key-value pairs are used as the processing result of the number.

[0018] Furthermore, the constraints specifically include minimizing the number of target topic tables participating in the combination and minimizing the number of excess topic tables in the target topic tables participating in the combination. The step of obtaining the optimal combination of the selectable topic tables when reconstructing the target topic domain based on preset constraints and a preset configuration file specifically includes:

[0019] Based on the configuration file, all data application fields required for reconstructing the target subject domain are determined, and all data application fields required for reconstructing the target subject domain are pre-written in the configuration file;

[0020] Based on all the data application fields, a selection of target topic tables is made from the available topic tables, which contains at least one of the data application fields. A set of target topic tables is then constructed.

[0021] A topic table with more than a preset threshold of data rows is designated as an overloaded topic table.

[0022] Based on the constraints, the optimal combination of all data application fields is selected from the target subject table set;

[0023] The optimal combination method is used as the optimal combination method among the available subject tables when reconstructing the target subject domain.

[0024] Furthermore, the step of selecting a target topic table from the available topic tables that contains at least one of the data application fields from all the data application fields, and constructing a target topic table set, specifically includes:

[0025] Based on the key-value pairs corresponding to the target topic tables, obtain the number of each target topic table;

[0026] Obtain the data application fields contained in the target topic table respectively;

[0027] Based on the number of each target topic table and the data application fields contained therein, a representation field is constructed for each target topic table;

[0028] Each of the target topic tables is added to a preset set to complete the construction of the target topic table set.

[0029] Furthermore, after performing the step of constructing representation fields for each target topic table based on the number of each target topic table and the data application fields contained therein, the method further includes:

[0030] Based on the representation fields corresponding to each target topic table, a comparison and identification process is performed;

[0031] Based on the comparison and identification results, all target topic tables are divided into two categories: necessary topic tables and unnecessary topic tables. The necessary topic tables are target topic tables in which any one or more data application fields exist only.

[0032] Based on the different categories, set distinguishing fields for each target topic table after categorization;

[0033] The distinguishing fields are inserted into the corresponding representation fields of each target topic table to update the representation fields.

[0034] Furthermore, the step of filtering out the optimal combination of all data application fields from the target topic table set specifically includes:

[0035] Based on the updated representation fields, obtain the number of the necessary topic table, as well as the data application fields not included in all necessary topic tables;

[0036] Using the data application fields not included as the combination target, the optimal combination that meets the constraints is selected from the non-essential topic table based on the updated representation fields;

[0037] Obtain the numbers of each non-essential topic table in the optimal combination that meets the constraints;

[0038] The numbers of the non-essential topic tables and the numbers of the essential topic tables are used as the numbers of the optional topic tables when reconstructing the target topic domain.

[0039] Furthermore, the step of generating the combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information specifically includes:

[0040] Obtain the number of the available topic table when reconstructing the target topic domain, as the optimal combination method;

[0041] Based on the available topic table numbers, the available topic tables are obtained together to construct an integrated wide table, and the distinguishing identifier information is used as the table name of the integrated wide table to complete the generation of the combined table.

[0042] To address the aforementioned technical problems, this application also provides a data model reconstruction apparatus, which employs the following technical solution:

[0043] A data model reconstruction apparatus, comprising:

[0044] A reconstruction instruction receiving module is used to receive a data model reconstruction instruction issued with a target subject domain as the reconstruction target, wherein the data model reconstruction instruction includes the distinguishing identification information of the target subject domain;

[0045] The parsing and acquisition module is used to parse the data model reconstruction instructions and obtain the distinguishing identifier information of the target topic domain;

[0046] The topic table acquisition module is used to acquire the topic tables that can be used to reconstruct the target topic domain from the posting source layer of the preset data warehouse. The topic table is the data base table in the posting source layer, and the topic domain represents a wide table composed of several topic tables that can cover at least one actual business process.

[0047] The topic table numbering module is used to number the selectable topic tables according to preset numbering rules and obtain the numbering results;

[0048] The optimal combination module is used to obtain the optimal combination of the available topic tables when reconstructing the target topic domain, based on preset constraints and preset configuration files.

[0049] The combination table generation module is used to generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information.

[0050] The loop control module is used to change the target subject domain and repeatedly execute the above steps until all subject domains have generated corresponding composite tables, at which point the loop stops and the data model reconstruction is complete.

[0051] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0052] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor executes the computer-readable instructions to implement the steps of the data model reconstruction method described above.

[0053] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0054] A computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of the data model reconstruction method described above.

[0055] Compared with the prior art, the embodiments of this application have the following main advantages:

[0056] The data model reconstruction method described in this application involves receiving a data model reconstruction instruction targeting a specific subject domain; parsing the instruction to obtain the distinguishing identifier information of the target subject domain; obtaining a selectable subject table from the source layer of a preset data warehouse; numbering the selectable subject table according to a preset numbering rule and obtaining the numbering result; determining the optimal combination of the selectable subject tables when reconstructing the target subject domain based on preset constraints and a preset configuration file; generating a combination table corresponding to the target subject domain based on the optimal combination method and the distinguishing identifier information; changing the target subject domain and repeating the above steps until all subject domains have generated corresponding combination tables, at which point the loop stops, and the data model reconstruction is complete. This cyclic generation method automates the reconstruction of the data model and directly obtains source layer data, reducing the coupling of the reconstruction results. Attached Figure Description

[0057] To more clearly illustrate the solutions in this application, the accompanying drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0059] Figure 2 A flowchart of an embodiment of the data model reconstruction method according to this application;

[0060] Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 205 shown;

[0061] Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 302 shown;

[0062] Figure 5 yes Figure 3 A flowchart of a specific embodiment of step 304 shown;

[0063] Figure 6 A schematic diagram of a structural embodiment of the data model reconstruction apparatus according to this application;

[0064] Figure 7 A schematic diagram of the structure of an embodiment of the computer device according to this application. Detailed Implementation

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application, are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0066] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0067] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

[0068] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0069] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social media platform software, etc.

[0070] Terminal devices 101, 102, and 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 players (Moving Picture Experts Group Audio Layer IV), laptops, and desktop computers, etc.

[0071] Server 105 can be a server that provides various services, such as a backend server that supports the pages displayed on terminal devices 101, 102, and 103.

[0072] It should be noted that the data model reconstruction method provided in this application embodiment is generally executed by a server / terminal device, and correspondingly, the data model reconstruction device is generally set in the server / terminal device.

[0073] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0074] Continue to refer to Figure 2 A flowchart of an embodiment of the data model reconstruction method according to this application is shown. The data model reconstruction method includes the following steps:

[0075] Step 201: Receive the data model reconstruction instruction issued with the target subject domain as the reconstruction target.

[0076] In this embodiment, the data model reconstruction instruction includes the distinguishing identifier information of the target subject domain.

[0077] In practical data warehouse construction scenarios, subject domains are often defined as a whole based on the business system that executes the actual business process. Each subject domain consists of several subject tables, each corresponding to a specific entity class. Each subject table contains several data application fields, which often include attribute fields of the entity class and processing fields set in the actual business process.

[0078] Taking the insurance industry as an example, when building a data warehouse, it is often divided into production systems, sales systems, underwriting systems, claims systems, etc. Each system has a complete set of actual business processes. Subject domains are set for each system as a whole, and different identification information is set for each subject domain according to the different systems.

[0079] By receiving refactoring instructions, the subject area of ​​the data model being refactored can be identified, making it easier to determine the subject area that needs to be refactored based on the instructions, thus avoiding blindly operating on the existing data in the data warehouse.

[0080] Step 202: Parse the data model reconstruction instruction to obtain the distinguishing identifier information of the target subject domain.

[0081] Step 203: Obtain the available topic table for reconstructing the target topic domain from the source layer of the preset data warehouse.

[0082] In this embodiment, the topic table is the data base table in the post source layer, and the topic domain represents a wide table composed of several topic tables that can cover at least one actual business process.

[0083] In this embodiment, the source layer, i.e. the ODS layer in the data warehouse, contains source data that has been cleaned and corrected, and is stored in the lowest-dimensional data base table.

[0084] By directly obtaining the data base tables from the ODS layer, the problem of obtaining already processed data tables is avoided. Data model reconstruction is then performed based on the data base tables of the ODS layer, greatly reducing the impact of dirty data generated by siloed development of the data warehouse on model reconstruction.

[0085] Step 204: According to the preset numbering rules, the selectable topic table is numbered, and the numbering result is obtained.

[0086] In this embodiment, the step of numbering the selectable topic tables according to a preset numbering rule and obtaining the numbering result specifically includes: obtaining the unique primary key information corresponding to each of the selectable topic tables, wherein the primary key information includes the table name; numbering the selectable topic tables using positive integers from 1 to n, wherein n represents the number of selectable topic tables; caching the number and its corresponding unique primary key information as key-value pairs, and using the cached key-value pairs as the numbering result.

[0087] Step 205: Based on preset constraints and preset configuration files, obtain the optimal combination of the available topic tables when reconstructing the target topic domain.

[0088] In this embodiment, the constraint condition is specifically the combination method when the number of target topic tables participating in the combination is the minimum and the number of excess topic tables in the target topic tables participating in the combination is also the minimum.

[0089] Based on the constraints, the optimal combination in an ideal state is that the data application fields in a single target topic table can satisfy the business process requirements of the target topic domain. Furthermore, the target topic table shown is not an overloaded topic table. Therefore, the target topic table shown can be directly set as a composite table.

[0090] However, in most practical business processes, multiple, even dozens or hundreds, of subject tables are often needed to construct the combined table required for the subject domain. In this case, assuming the number of subject tables is n, there are two extreme scenarios: first, a single table can satisfy the combined table requirement for the subject domain; second, all subject tables, i.e., the wide table formed by combining n subject tables. Clearly, other combinations are: 2 n -n-1, meaning the combination methods in non-extreme cases belong to 2. n -n-1 combination methods. n topic tables are combined to form a combination table corresponding to the topic domain. This can be achieved by combining any two of the n tables, or by combining n-1 of the n tables.

[0091] Therefore, when combining, the fewer the number of subject tables, the more server processing resources are saved. At the same time, it also minimizes the possibility of including useless fields when combining more subject tables.

[0092] In addition, consideration was given to selecting the combination method with the fewest excess subject tables when combining the combined tables. This avoids the situation where the more excess subject tables are combined, the more data fields will be in the combined table, effectively preventing the final reconstructed data model from affecting data processing efficiency in actual business processes.

[0093] Continue to refer to Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific embodiment of step 205 shown includes:

[0094] Step 301: Based on the configuration file, determine all data application fields required for reconstructing the target subject domain. The configuration file pre-writes all data application fields required for reconstructing the target subject domain.

[0095] Step 302: Based on all the data application fields, filter the data and select the target topic table from the available topic tables that contains at least one of the data application fields. Construct a target topic table set.

[0096] Continue to refer to Figure 4 , Figure 4 yes Figure 3 A flowchart of a specific embodiment of step 302 shown includes:

[0097] Step 401: Obtain the number of each target topic table based on the key-value pairs corresponding to each target topic table;

[0098] Step 402: Obtain the data application fields contained in the target topic table respectively;

[0099] Step 403: Construct representation fields for each target topic table based on the number of each target topic table and the data application fields it contains;

[0100] In this embodiment, after performing the step of constructing representation fields for each target topic table based on the number of each target topic table and the data application fields it contains, the method further includes: performing comparison and identification based on the representation fields corresponding to each target topic table; dividing all target topic tables into two categories, necessary topic tables and unnecessary topic tables, based on the comparison and identification results, wherein a necessary topic table is a target topic table in which any one or more data application fields exist only; setting a distinguishing field for each target topic table after the category division, based on the different categories; and inserting the distinguishing field into the representation fields corresponding to each target topic table to update the representation fields;

[0101] By dividing the subject tables into necessary and unnecessary ones, the combination tables corresponding to the target subject domains can be determined first, including the necessary subject tables and the optional subject tables. This avoids blindly combining all target subject tables and saves combination adjustment time.

[0102] Step 404: Add the representation fields corresponding to each of the target topic tables to a preset set to complete the construction of the target topic table set;

[0103] Step 303: Pre-define topic tables with more data rows than a preset row count threshold as excessive topic tables;

[0104] Step 304: Based on the constraints, select the optimal combination of all data application fields from the target subject table set;

[0105] Continue to refer to Figure 5 , Figure 5 yes Figure 3 A flowchart of a specific embodiment of step 304 shown includes:

[0106] Step 501: Based on the updated representation fields, obtain the number of the necessary topic table and all data application fields not included in the necessary topic tables;

[0107] Step 502: Using the data application fields not included as the combination target, select the optimal combination method that meets the constraints from the non-essential topic table according to the updated representation fields;

[0108] Step 503: Obtain the numbers of each non-essential topic table in the optimal combination that meets the constraints;

[0109] Step 504: Use the numbers of the non-essential topic tables and the numbers of the essential topic tables as the numbers of the optional topic tables when reconstructing the target topic domain;

[0110] Step 305: The optimal combination method is used as the optimal combination method among the selectable subject tables when reconstructing the target subject domain.

[0111] Step 206: Generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information;

[0112] In this embodiment, the step of generating a combined table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information specifically includes: obtaining the number of the optional topic table when reconstructing the target topic domain, as the optimal combination method; obtaining the optional topic tables based on the number of the optional topic table to jointly construct an integrated wide table, using the distinguishing identifier information as the table name of the integrated wide table, and completing the generation of the combined table.

[0113] Step 207: Change the target subject domain and repeat steps 201 to 207 until all subject domains have generated corresponding composite tables. Stop the loop, and the data model reconstruction is complete.

[0114] This application involves receiving a data model reconstruction instruction targeting a specific subject domain; parsing the instruction to obtain the distinguishing identifier information of the target subject domain; retrieving a selectable subject table from the source layer of a preset data warehouse; numbering the selectable subject table according to a preset numbering rule and obtaining the numbering result; determining the optimal combination of the selectable subject tables for reconstructing the target subject domain based on preset constraints and a preset configuration file; generating a combination table corresponding to the target subject domain based on the optimal combination and the distinguishing identifier information; changing the target subject domain and repeating the above steps until all subject domains have generated corresponding combination tables, at which point the loop stops, and the data model reconstruction is complete. This cyclic generation method automates the data model reconstruction and directly obtains source layer data, reducing the coupling of the reconstruction results.

[0115] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0116] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0117] In this embodiment, a cyclic generation method is used to automatically reconstruct the data model by manipulating the data in the data warehouse, and the data from the source layer is directly obtained to reduce the coupling of the reconstruction results.

[0118] Further reference Figure 6 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data model reconstruction apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0119] like Figure 6 As shown, the data model reconstruction device 600 described in this embodiment includes: a reconstruction instruction receiving module 601, a parsing and acquisition module 602, a topic table acquisition module 603, a topic table numbering module 604, an optimal combination module 605, a combination table generation module 606, and a loop control module 607. Wherein:

[0120] The reconstruction instruction receiving module 601 is used to receive a data model reconstruction instruction issued with the target subject domain as the reconstruction target, wherein the data model reconstruction instruction includes the distinguishing identification information of the target subject domain;

[0121] The parsing and acquisition module 602 is used to parse the data model reconstruction instruction and obtain the distinguishing identifier information of the target topic domain;

[0122] The topic table acquisition module 603 is used to acquire the topic tables that can be used to reconstruct the target topic domain from the posting source layer of the preset data warehouse, wherein the topic table is the data base table in the posting source layer, and the topic domain represents a wide table composed of several topic tables that can cover at least one actual business process.

[0123] The topic table numbering module 604 is used to number the selectable topic tables according to preset numbering rules and obtain the numbering processing results;

[0124] The optimal combination module 605 is used to obtain the optimal combination method among the selectable topic tables when reconstructing the target topic domain, based on preset constraints and preset configuration files.

[0125] The combination table generation module 606 is used to generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information.

[0126] The loop control module 607 is used to change the target subject domain and repeatedly execute the above steps until all subject domains have generated corresponding combination tables, at which point the loop stops and the data model reconstruction is complete.

[0127] This application involves receiving a data model reconstruction instruction targeting a specific subject domain; parsing the instruction to obtain the distinguishing identifier information of the target subject domain; retrieving a selectable subject table from the source layer of a preset data warehouse; numbering the selectable subject table according to a preset numbering rule and obtaining the numbering result; determining the optimal combination of the selectable subject tables for reconstructing the target subject domain based on preset constraints and a preset configuration file; generating a combination table corresponding to the target subject domain based on the optimal combination and the distinguishing identifier information; changing the target subject domain and repeating the above steps until all subject domains have generated corresponding combination tables, at which point the loop stops, and the data model reconstruction is complete. This cyclic generation method automates the data model reconstruction and directly obtains source layer data, reducing the coupling of the reconstruction results.

[0128] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by instructing related hardware through computer-readable instructions. These computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, optical disk, or read-only memory (ROM), or random access memory (RAM).

[0129] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0130] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 7 , Figure 7 This is a basic structural block diagram of the computer device in this embodiment.

[0131] The computer device 7 includes a memory 7a, a processor 7b, and a network interface 7c that are interconnected via a system bus. It should be noted that only the computer device 7 with components 7a-7c is shown in the figure; however, it should be understood that it is not required to implement all the shown components, and more or fewer components can be implemented alternatively. Those skilled in the art will understand that the computer device described here is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0132] The computer device can be a desktop computer, laptop, handheld computer, or cloud server, etc. The computer device can interact with the user via a keyboard, mouse, remote control, touchpad, or voice control.

[0133] The memory 7a includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 7a may be an internal storage unit of the computer device 7, such as the hard disk or memory of the computer device 7. In other embodiments, the memory 7a may also be an external storage device of the computer device 7, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 7. Of course, the memory 7a may also include both the internal storage unit and its external storage device of the computer device 7. In this embodiment, the memory 7a is typically used to store the operating system and various application software installed on the computer device 7, such as computer-readable instructions for data model reconstruction methods. In addition, the memory 7a can also be used to temporarily store various types of data that have been output or will be output.

[0134] In some embodiments, the processor 7b may be a central processing unit (CPU), controller, microcontroller, microprocessor, or other data processing chip. The processor 7b is typically used to control the overall operation of the computer device 7. In this embodiment, the processor 7b is used to execute computer-readable instructions stored in the memory 7a or to process data, for example, to execute computer-readable instructions for the data model reconstruction method.

[0135] The network interface 7c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 7 and other electronic devices.

[0136] The computer device proposed in this embodiment belongs to the field of data model reconstruction technology. This application receives a data model reconstruction instruction issued with a target subject domain as the reconstruction target; parses the data model reconstruction instruction to obtain the distinguishing identifier information of the target subject domain; obtains a selectable subject table for reconstructing the target subject domain from the source layer of a preset data warehouse; performs numbering processing on the selectable subject table according to preset numbering rules, and obtains the numbering processing result; obtains the optimal combination method among the selectable subject tables when reconstructing the target subject domain according to preset constraints and preset configuration files; generates a combination table corresponding to the target subject domain according to the optimal combination method and the distinguishing identifier information; changes the target subject domain, and repeats the above steps until all subject domains have generated corresponding combination tables, then stops the loop, and the data model reconstruction is completed. This cyclic generation method automates the reconstruction of the data model, and directly obtaining source layer data reduces the coupling of the reconstruction results.

[0137] This application also provides another embodiment, namely, providing a computer-readable storage medium storing computer-readable instructions that can be executed by a processor to cause the processor to perform the steps of the data model reconstruction method described above.

[0138] The computer-readable storage medium proposed in this embodiment belongs to the field of data model reconstruction technology. This application involves receiving a data model reconstruction instruction issued with a target subject domain as the reconstruction target; parsing the data model reconstruction instruction to obtain the distinguishing identifier information of the target subject domain; obtaining a selectable subject table for reconstructing the target subject domain from the source layer of a preset data warehouse; numbering the selectable subject table according to a preset numbering rule and obtaining the numbering result; obtaining the optimal combination method among the selectable subject tables when reconstructing the target subject domain according to preset constraints and a preset configuration file; generating a combination table corresponding to the target subject domain according to the optimal combination method and the distinguishing identifier information; changing the target subject domain and repeating the above steps until all subject domains have generated corresponding combination tables, stopping the loop, and completing the data model reconstruction. This cyclic generation method automates the reconstruction of the data model, and directly obtaining source layer data reduces the coupling of the reconstruction results.

[0139] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, 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 (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0140] Obviously, the embodiments described above are only some embodiments of this application, not all embodiments. The accompanying drawings show preferred embodiments of this application, but do not limit the patent scope of this application. This application can be implemented in many different forms; rather, the purpose of providing these embodiments is to provide a more thorough and comprehensive understanding of the disclosure of this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent substitutions for some of the technical features. Any equivalent structures made using the content of this application's specification and drawings, directly or indirectly applied to other related technical fields, are similarly within the scope of patent protection of this application.

Claims

1. A data model reconstruction method, characterized in that, Includes the following steps: Step 201: Receive a data model reconstruction instruction issued with the target subject domain as the reconstruction target, wherein the data model reconstruction instruction includes the distinguishing identifier information of the target subject domain; Step 202: Parse the data model reconstruction instruction to obtain the distinguishing identifier information of the target topic domain; Step 203: Obtain the subject table that can be used to reconstruct the target subject domain from the source layer of the preset data warehouse, wherein the subject table is the data base table in the source layer, and the subject domain represents a wide table composed of several subject tables that can cover at least one actual business process. Step 204: According to the preset numbering rules, the selectable topic table is numbered, and the numbering result is obtained; Step 205: Based on preset constraints and a preset configuration file, obtain the optimal combination of the selectable topic tables when reconstructing the target topic domain. Specifically, the constraints are that the number of target topic tables participating in the combination is minimized, and the number of excess topic tables in the target topic tables participating in the combination is also minimized. The step of obtaining the optimal combination of the selectable topic tables when reconstructing the target topic domain based on preset constraints and a preset configuration file specifically includes: Based on the configuration file, all data application fields required for reconstructing the target subject domain are determined, and all data application fields required for reconstructing the target subject domain are pre-written in the configuration file; Based on all the data application fields, a selection of target topic tables is made from the available topic tables, which contains at least one of the data application fields. A set of target topic tables is then constructed. A topic table with more than a preset threshold of data rows is designated as an overloaded topic table. Based on the constraints, the optimal combination of all data application fields is selected from the target subject table set; The optimal combination method is used as the optimal combination method among the available topic tables when reconstructing the target topic domain; Step 206: Generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information; Step 207: Change the target subject domain and repeat steps 201 to 207 until all subject domains have generated corresponding composite tables. Stop the loop, and the data model reconstruction is complete.

2. The data model reconstruction method according to claim 1, characterized in that, The step of numbering the selectable topic table according to a preset numbering rule and obtaining the numbering result specifically includes: Obtain the unique primary key information corresponding to each of the optional topic tables, wherein the primary key information includes the table name; The available topic tables are numbered using positive integers from 1 to n, where n represents the number of available topic tables; The number and its corresponding unique primary key information are cached as key-value pairs, and the cached key-value pairs are used as the processing result of the number.

3. The data model reconstruction method according to claim 2, characterized in that, The step of selecting a target topic table set by filtering the available topic tables to include at least one of the data application fields from all the available topic tables specifically includes: Based on the key-value pairs corresponding to the target topic tables, obtain the number of each target topic table; Obtain the data application fields contained in the target topic table respectively; Based on the number of each target topic table and the data application fields contained therein, a representation field is constructed for each target topic table; Each of the target topic tables is added to a preset set to complete the construction of the target topic table set.

4. The data model reconstruction method according to claim 3, characterized in that, After performing the step of constructing representation fields for each target topic table based on the number of each target topic table and the data application fields contained therein, the method further includes: Based on the representation fields corresponding to each target topic table, a comparison and identification process is performed; Based on the comparison and identification results, all target topic tables are divided into two categories: necessary topic tables and unnecessary topic tables. The necessary topic tables are target topic tables in which any one or more data application fields exist only. Based on the different categories, set distinguishing fields for each target topic table after categorization; The distinguishing fields are inserted into the corresponding representation fields of each target topic table to update the representation fields.

5. The data model reconstruction method according to claim 4, characterized in that, The step of selecting the optimal combination of all data application fields from the target topic table set specifically includes: Based on the updated representation fields, obtain the number of the necessary topic table, as well as the data application fields not included in all necessary topic tables; Using the data application fields not included as the combination target, the optimal combination that meets the constraints is selected from the non-essential topic table based on the updated representation fields; Obtain the numbers of each non-essential topic table in the optimal combination that meets the constraints; The numbers of the non-essential topic tables and the numbers of the essential topic tables are used as the numbers of the optional topic tables when reconstructing the target topic domain.

6. The data model reconstruction method according to any one of claims 1 to 5, characterized in that, The step of generating the combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information specifically includes: Obtain the number of the available topic table when reconstructing the target topic domain, as the optimal combination method; Based on the available topic table numbers, the available topic tables are obtained together to construct an integrated wide table, and the distinguishing identifier information is used as the table name of the integrated wide table to complete the generation of the combined table.

7. A data model reconstruction apparatus, characterized in that, include: A reconstruction instruction receiving module is used to receive a data model reconstruction instruction issued with a target subject domain as the reconstruction target, wherein the data model reconstruction instruction includes the distinguishing identification information of the target subject domain; The parsing and acquisition module is used to parse the data model reconstruction instructions and obtain the distinguishing identifier information of the target topic domain; The topic table acquisition module is used to acquire the topic tables that can be used to reconstruct the target topic domain from the posting source layer of the preset data warehouse. The topic table is the data base table in the posting source layer, and the topic domain represents a wide table composed of several topic tables that can cover at least one actual business process. The topic table numbering module is used to number the selectable topic tables according to preset numbering rules and obtain the numbering results; The optimal combination module is used to obtain the optimal combination method among the selectable topic tables when reconstructing the target topic domain, based on preset constraints and a preset configuration file. Specifically, the constraints are that the number of target topic tables participating in the combination is minimized, and the number of excess topic tables among the target topic tables participating in the combination is also minimized. The process of obtaining the optimal combination method among the selectable topic tables when reconstructing the target topic domain, based on the preset constraints and the preset configuration file, specifically includes: Based on the configuration file, all data application fields required for reconstructing the target subject domain are determined, and all data application fields required for reconstructing the target subject domain are pre-written in the configuration file; Based on all the data application fields, a selection of target topic tables is made from the available topic tables, which contains at least one of the data application fields. A set of target topic tables is then constructed. A topic table with more than a preset threshold of data rows is designated as an overloaded topic table. Based on the constraints, the optimal combination of all data application fields is selected from the target subject table set; The optimal combination method is used as the optimal combination method among the available topic tables when reconstructing the target topic domain; The combination table generation module is used to generate a combination table corresponding to the target topic domain based on the optimal combination method and the distinguishing identifier information. The loop control module is used to change the target subject domain and repeatedly execute the above steps until all subject domains have generated corresponding composite tables, at which point the loop stops and the data model reconstruction is complete.

8. A computer device comprising a memory and a processor, the memory storing computer-readable instructions, wherein the processor, when executing the computer-readable instructions, implements the steps of the data model reconstruction method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the data model reconstruction method as described in any one of claims 1 to 6.

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