Data model construction method and device, computer device and storage medium

Abstract

The embodiment of the application belongs to the field of big data, is applied to the field of data model construction under the big data business scene, and relates to a data model construction method and device, computer equipment and a storage medium, which comprises the following steps: obtaining business data based on a data access layer and a first configuration item; unfolding and extracting the business data through a data construction layer, and performing initial construction of an instance model through a second configuration item; screening m initial constructed instance models according to a business scene, combining the m initial constructed instance models according to a data combination layer, obtaining a third configuration item, initializing a target data model based on the third configuration item, and judging whether the target data model needs to be extended. The method can complete data rendering in one interaction, improve data security, and avoid the problem of excessive data pressure caused by blindly constructing a data cache area under the big data business scene.

Description

Technical Field

[0001] This application relates to the field of data model construction technology in big data and big data business scenarios, and in particular to a data model construction method, apparatus, computer equipment and storage medium. Background Technology

[0002] Existing large-scale data model construction schemes mostly obtain different objects through a single configuration file, initialize them, and then render them one by one into the business scenario. This model leads to too many interactions between the database and the display interface, which can easily cause data leakage, especially in the case of big data processing.

[0003] Currently, another approach is to obtain the attributes of different individual objects and integrate them into a unified cache for data rendering. While this approach solves the problem of multiple rendering interactions, it significantly increases the amount of code that backend developers need to write. Moreover, building a cache in a big data scenario will undoubtedly increase the pressure on the local repository. Summary of the Invention

[0004] The purpose of this application is to propose a data model construction method and related equipment, so as to complete data rendering in one interaction, improve data security, and avoid the problem of excessive data pressure caused by blindly building a data cache in big data business scenarios.

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

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

[0007] Business data is obtained based on the data access layer and the first configuration item;

[0008] The data construction layer performs format conversion and target data extraction on the business data, and uses a preset constructor and a second configuration item to initially construct an instance model of the extracted target data.

[0009] Based on the business scenario, m initially constructed instance models are selected and applied to the business scenario. The m initially constructed instance models are combined according to the data combination layer to obtain the third configuration item. The target data model is initialized based on the constructor and the third configuration item, where m is a positive integer greater than 1.

[0010] Determine whether the data contained in the target data model can represent the target data mapping requirements of the business scenario;

[0011] Based on the judgment result, the target data model is differentiated.

[0012] Furthermore, the first configuration item includes a local configuration file that establishes a connection with the local database, and / or a remote configuration file that establishes a connection with the corresponding remote access interface. The step of obtaining business data through the data access layer and the first configuration item specifically includes:

[0013] The data access layer obtains business data that meets the requirements of the business scenario by using a proxy value retrieval method through a preset database accessor and the local configuration file;

[0014] The data access layer uses a preset external system accessor and the remote configuration file to obtain business data that meets the requirements of the business scenario but is deployed in an external system via remote calls.

[0015] Furthermore, the steps of format conversion and target data extraction of the business data through the data construction layer specifically include:

[0016] The data construction layer uses a preset pre-converter to convert the large String type data field corresponding to the business data into JSON format to obtain JSON type business data. The large String type data field represents a String type data field that exceeds a preset character length.

[0017] The JSON-type business data is categorized and retrieved using a preset value retriever in the data construction layer. This retrieves business scenario configuration data, business scenario assignment data, and business scenario basic data. Specifically, the data used to configure the display format of the business scenario is designated as business scenario configuration data, the data used for display in the business scenario and which changes continuously is designated as business scenario assignment data, and the data used for display in the business scenario and which remains constant is designated as business scenario basic data.

[0018] Furthermore, the second configuration item represents a configuration file for initializing the instance object. The step of initially constructing the instance model from the extracted target data using a preset constructor and the second configuration item specifically includes:

[0019] Obtain the configuration attributes of the second configuration item and the mapping relationship between the configuration attributes and the target data;

[0020] Based on the constructor, the configuration attributes of the second configuration item, and the mapping relationship, the instance model is initialized to complete the initial construction of the instance model.

[0021] Furthermore, the third configuration item represents a configuration file for reconstructing the instance object. The step of combining the m initially constructed instance models according to the data combination layer to obtain the third configuration item, and initializing the target data model based on the constructor and the third configuration item, specifically includes:

[0022] Obtain the m initially constructed instance models;

[0023] The target data model is used as the target configuration object of the third configuration item;

[0024] The m initially constructed instance models are used as configuration attributes of the third configuration item. Based on the combined model in the data combination layer, the mapping relationship between the configuration attributes and the m initially constructed instance models is configured, and the configuration of the third configuration item is completed.

[0025] Based on the constructor, the configuration attributes of the third configuration item, and the mapping relationship, the target configuration object is initialized to obtain the target data model.

[0026] Furthermore, before the step of configuring the third configuration item by using the m initially constructed instance models as configuration attributes of the third configuration item, and configuring the mapping relationship between the configuration attributes and the m initially constructed instance models based on the combined model in the data combination layer, the method further includes:

[0027] Reserve a configuration redundancy area within the third configuration item that will be configured;

[0028] Each of the m initially constructed instance models is treated as a configuration attribute and added line by line to the configuration redundancy area.

[0029] Furthermore, before the step of adding each of the m initially constructed instance models as a configuration attribute to the configuration redundancy area line by line, the method further includes:

[0030] Based on a preset importance filtering model, the m initially constructed instance models are sorted by importance to obtain the importance ranking result. The filtering criteria of the importance filtering model can be the estimated number of data interactions that the m initially constructed instance models will have in the business scenario within a preset time period.

[0031] The step of adding each of the m initially constructed instance models as a configuration attribute, row by row, to the configuration redundancy area specifically includes:

[0032] Using the combined model, the m initially constructed instance models are added to the configuration redundancy area according to the importance ranking result.

[0033] Furthermore, the step of distinguishing the target data model based on the judgment result specifically includes:

[0034] If the data contained in the target data model can fully characterize the target data mapping requirements of the business scenario, then the target data model is constructed.

[0035] If the data contained in the target data model cannot fully represent the target data mapping requirements of the business scenario, then the target data model is supplemented and expanded based on the data expansion layer to obtain the data that the business scenario still needs to be expanded, until the expansion is completed, and the target data model is constructed.

[0036] To address the aforementioned technical problems, this application also provides a data model construction apparatus, which employs the following technical solution:

[0037] A data model construction apparatus, comprising:

[0038] The data acquisition module is used to acquire business data based on the data access layer and the first configuration item;

[0039] The single-instance construction module is used to perform format conversion and target data extraction on the business data through the data construction layer, and to perform initial instance model construction on the extracted target data through a preset constructor and a second configuration item.

[0040] The single instance combination module is used to select m initially constructed instance models applicable to the business scenario based on the business scenario, combine the m initially constructed instance models according to the data combination layer, obtain the third configuration item, and initialize the target data model based on the constructor and the third configuration item, where m is a positive integer greater than 1;

[0041] The business mapping judgment module is used to determine whether the data contained in the target data model can fully represent the target data mapping requirements of the business scenario.

[0042] The differentiation processing module is used to differentiate the target data model based on the judgment result.

[0043] To address the aforementioned technical problems, this application also provides a computer device that employs the following technical solution:

[0044] 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 construction method described above.

[0045] To address the aforementioned technical problems, this application also provides a computer-readable storage medium, employing the technical solution described below:

[0046] A computer-readable storage medium storing computer-readable instructions that, when executed by a processor, implement the steps of the data model construction method described above.

[0047] Compared with the prior art, the embodiments of this application have the following main advantages:

[0048] The data model construction method described in this application involves: acquiring business data based on a data access layer and a first configuration item; expanding and extracting the business data through a data construction layer; initially constructing an instance model using a second configuration item; selecting m initially constructed instance models based on the business scenario; combining the m initially constructed instance models using a data combination layer to obtain a third configuration item; initializing the target data model based on the third configuration item; and determining whether the target data model needs to be extended. This method can complete data rendering in a single interaction, improving data security, and avoids the problem of excessive data pressure caused by blindly building data caches in big data business scenarios. Attached Figure Description

[0049] 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.

[0050] Figure 1 This is an exemplary system architecture diagram to which this application can be applied;

[0051] Figure 2 A flowchart of an embodiment of the data model construction method according to this application;

[0052] Figure 3 yes Figure 2 A flowchart of a specific implementation of step 201 shown;

[0053] Figure 4 yes Figure 2 A flowchart of a specific implementation of step 202 shown;

[0054] Figure 5 yes Figure 2 A flowchart of a specific implementation of step 203 shown;

[0055] Figure 6 It shows Figure 2 A flowchart of a specific implementation of step 205 shown;

[0056] Figure 7 A schematic diagram of a structural embodiment of the data model construction device according to this application;

[0057] Figure 8 yes Figure 7 A structural diagram of one specific embodiment of 703 is shown;

[0058] Figure 9 A schematic diagram of the structure of an embodiment of the computer device according to this application. Detailed Implementation

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] 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.

[0066] It should be noted that the data model construction method provided in the embodiments of this application is generally executed by a server / terminal device, and correspondingly, the data model construction device is generally set in the server / terminal device.

[0067] It should be understood that Figure 1 The 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.

[0068] Continue to refer to Figure 2 A flowchart of an embodiment of the data model construction method according to this application is shown. The data model construction method includes the following steps:

[0069] Step 201: Obtain business data based on the data access layer and the first configuration item.

[0070] In this embodiment, the first configuration item includes a local configuration file that connects to a local database, and / or a remote configuration file that connects to a corresponding remote access interface. The step of obtaining business data through the data access layer and the first configuration item specifically includes: the data access layer obtaining business data cached in the database that meets the requirements of the business scenario by using a proxy value retrieval method through a preset database accessor and the local configuration file; the data access layer obtaining business data that meets the requirements of the business scenario but is deployed in an external system by using a remote call method through a preset external system accessor and the remote configuration file.

[0071] Taking employee management as an example, for companies with a large number of employees, to avoid overloading a single storage device, employee information is deployed separately in various distributed storage devices or caching platforms. Suppose the company's human resources department needs to compile statistics on employees' recent work information. In this case, it is necessary to integrate information from both the human resources department's basic employee information storage or caching platform and the employee work information storage or caching platforms of each employee's department. In this scenario, the human resources department's basic employee information storage or caching platform is equivalent to a local database, while the employee work information storage or caching platforms of each employee's department are equivalent to remote databases. By using a preset database accessor and a configuration file that connects to the local database, the basic employee information cached in the database is retrieved using a proxy value retrieval method. Similarly, by using a preset external system accessor and a configuration file that establishes a connection with the corresponding remote access interface when performing remote data retrieval, the employee work information of each department is retrieved using a remote call method. Only by jointly retrieving data from both can the integrity of the business data corresponding to the entire business scenario be ensured, avoiding the data dimension limitations caused by the local database.

[0072] Continue to refer to Figure 3 , Figure 3 yes Figure 2 A flowchart of a specific implementation of step 201 shown includes the following steps:

[0073] Step 301: The data access layer obtains business data that meets the requirements of the business scenario by using a proxy value retrieval method through a preset database accessor and the local configuration file.

[0074] In this embodiment, the method of obtaining business data cached in the database that meets the requirements of the business scenario by means of a preset database accessor and the configuration file connected to the local database and using proxy value retrieval is specifically implemented as follows: when the service starts, the XML configuration file is read, the basic access information of the corresponding data access instance is obtained according to the attribute name of the object to be accessed in the XML configuration file, the tag attributes in the XML configuration file are accessed according to the proxy accessor configured in the XML configuration file, and the data image of the corresponding access instance is obtained. The data image is assigned to the access attributes corresponding to the basic access information through proxy assignment.

[0075] Step 302: The data access layer obtains business data that meets the requirements of the business scenario but is deployed in an external system by using a remote call through a preset external system accessor and the remote configuration file.

[0076] In this embodiment, the configuration file that establishes a connection with the corresponding remote access interface when performing remote data acquisition is obtained by using a preset external system accessor and a remote call method to obtain business data that is not cached in the database but is required by the business scenario and is deployed in the external system. The specific implementation method is as follows: when the service starts, it reads the XML configuration file, and according to the external interface corresponding to the attribute name of the object to be accessed in the XML configuration file, it obtains the basic access information of the corresponding data access instance through the interface call.

[0077] In this embodiment, the configuration file that connects to the local database or the configuration file that establishes a connection with the corresponding remote access interface when performing remote data acquisition in the first configuration item can be the same configuration file or different configuration files.

[0078] Step 202: The business data is format-converted and target data is extracted through the data construction layer. The extracted target data is initially constructed using a preset constructor and a second configuration item.

[0079] In this embodiment, the steps of expanding the business data and extracting target data through the data construction layer specifically include: converting the large String type data field corresponding to the business data into JSON format using a preset pre-converter in the data construction layer to obtain JSON type business data, wherein the large String type data field represents a String type data field exceeding a preset character length; classifying and extracting values ​​from the JSON type business data using a preset value extractor in the data construction layer to obtain business scenario configuration data, business scenario assignment data, and business scenario basic data, wherein the data used to configure the display format of the business scenario is used as business scenario configuration data, the data used for display in the business scenario and continuously changing is used as business scenario assignment data, and the data used for display in the business scenario and remaining unchanged is used as business scenario basic data.

[0080] Taking an e-commerce platform business scenario as an example, after obtaining the business data through a local database and a remote data platform, the obtained business data is transmitted to the e-commerce platform in the form of a byte / character stream. The String type string corresponding to the byte / character stream needs to be pre-converted and converted into a standardized JSON type data to facilitate value retrieval operations and page rendering.

[0081] Depending on the specific business scenarios of an e-commerce platform, data such as in-store signage (e.g., shopping cart icons, and "Internet Violations Will Be Prosecuted" at the bottom of the page) is generally fixed and immutable, and is considered basic data for the business scenario. Data such as product page prices and images configure the display format for the business scenario and are not frequently changed except in cases of business changes; their purpose is to ensure proper display within the business scenario, and is considered configuration data for the business scenario. Data such as purchase quantity, shipping address, and recipient information are subjective and, unlike product images and in-store signage, require shopper input and are considered valued data for the business scenario.

[0082] In this embodiment, the second configuration item represents a configuration file for initializing and constructing an instance object. The step of initially constructing an instance model from the extracted target data using a preset constructor and the second configuration item specifically includes: obtaining the configuration attributes of the second configuration item and the mapping relationship between the configuration attributes and the target data; and initializing the instance model based on the constructor, the configuration attributes of the second configuration item, and the mapping relationship to complete the initial construction of the instance model.

[0083] Taking an existing project as an example, after extracting target data (i.e., business scenario configuration data, business scenario assignment data, and business scenario basic data) from the data used to assist the investigation department in its investigation, the configuration attributes of the second configuration item and the mapping relationship between the configuration attributes and the target data are obtained to ensure that the target data can be transmitted to the investigation department. Based on the constructor, the configuration attributes of the second configuration item, and the mapping relationship, the instance model is initialized to complete the initial construction of the instance model. The detected image objects, person objects, and time objects are initialized. Often, the target instance model corresponding to a business scenario consists of multiple instance models corresponding to individual objects.

[0084] Continue to refer to Figure 4 , Figure 4 yes Figure 2 A flowchart of a specific implementation of step 202 shown includes the following steps:

[0085] Step 401: Using the preset pre-converter in the data construction layer, the large String type data field corresponding to the business data is converted to JSON format to obtain JSON type business data;

[0086] Step 402: Using the preset value retriever in the data construction layer, classify and retrieve the business data of the JSON type to obtain business scenario configuration data, business scenario assignment data and business scenario basic data respectively;

[0087] Step 403: Use the business scenario configuration data, business scenario assignment data, and business scenario basic data as target data;

[0088] Step 404: Obtain the configuration attributes of the second configuration item and the mapping relationship between the configuration attributes and the target data;

[0089] Step 405: Based on the constructor, the configuration attributes of the second configuration item, and the mapping relationship, the instance model is initialized to complete the initial construction of the instance model.

[0090] In this embodiment, the second configuration item and the first configuration item can be different configuration items within the same configuration file, or they can refer to different configuration files respectively.

[0091] Step 203: Select m initially constructed instance models applicable to the business scenario based on the business scenario, combine the m initially constructed instance models according to the data combination layer to obtain the third configuration item, and initialize the target data model based on the constructor and the third configuration item, where m is a positive integer greater than 1.

[0092] In this embodiment, the third configuration item represents a configuration file for reconstructing the instance object. The step of combining the m initially constructed instance models according to the data combination layer to obtain the third configuration item, and initializing the target data model based on the constructor and the third configuration item specifically includes: obtaining the m initially constructed instance models; using the target data model as the target configuration object of the third configuration item; using the m initially constructed instance models as configuration attributes of the third configuration item; configuring the mapping relationship between the configuration attributes and the m initially constructed instance models based on the combined model in the data combination layer to complete the configuration of the third configuration item; and initializing the target configuration object based on the constructor, the configuration attributes of the third configuration item, and the mapping relationship to obtain the target data model.

[0093] By using multiple initially constructed instance models as configuration attributes and the target instance model of the entire business scenario as the target configuration object, configuration is performed within the third configuration item. The initialization of the business scenario is then completed through the constructor and the third configuration item. The m initially constructed instance models are combined according to a preset combination model to obtain the third configuration item. The target data model is initialized based on the constructor and the third configuration item. Using this configuration item approach, there is no need to establish a cache. The required single instance model is directly queried through the third configuration item, and the target data is then queried through the second configuration item corresponding to the single instance model. A single rendering is then performed to complete the data interaction of the business scenario. This reduces the number of interactions, ensures data security, and avoids the problem of excessive data pressure caused by blindly building a cache in big data business scenarios.

[0094] In this embodiment, before the step of configuring the third configuration item by using the m initially constructed instance models as configuration attributes of the third configuration item and configuring the mapping relationship between the configuration attributes and the m initially constructed instance models based on the combined model in the data combination layer, the method further includes: reserving a configuration redundancy area in the third configuration item to be configured; and adding each instance model in the m initially constructed instance models as a configuration attribute, line by line, to the configuration redundancy area.

[0095] In this embodiment, before the step of adding each of the m initially constructed instance models as a configuration attribute to the configuration redundancy area row by row, the method further includes: sorting the m initially constructed instance models by importance based on a preset importance filtering model, and obtaining the importance ranking result. The filtering criterion of the importance filtering model can be the estimated number of data interactions that the m initially constructed instance models will experience in the business scenario within a preset time period. The step of adding each of the m initially constructed instance models as a configuration attribute to the configuration redundancy area row by row specifically includes: using the combined model to add each of the m initially constructed instance models to the configuration redundancy area according to the importance ranking result.

[0096] By using redundant regions, it is easy to reuse the configured single instance model, and it is also easy to accumulate configuration when a new single instance model needs to be added in the business scenario.

[0097] Continue to refer to Figure 5 , Figure 5 yes Figure 2 A flowchart of a specific implementation of step 203 shown includes the following steps:

[0098] Step 501: Obtain the m initially constructed instance models;

[0099] Step 502: Use the target data model as the target configuration object of the third configuration item;

[0100] Step 503: Reserve a configuration redundancy area within the third configuration item;

[0101] Step 504: Based on the preset importance screening model, sort the m initially constructed instance models by importance and obtain the importance sorting results;

[0102] Step 505: Using the combination model in the data combination layer, add the m initially constructed instance models to the configuration redundancy area according to the importance sorting result.

[0103] Step 506: Use the m initially constructed instance models as configuration attributes of the third configuration item, and complete the mapping relationship configuration between the configuration attributes and the m initially constructed instance models based on the combined model, thereby completing the configuration of the third configuration item;

[0104] By combining multiple initially constructed instance models into a single target data model, the excessive number of instance models, resulting in too many interactive entities and excessive service pressure, can be avoided when accessing business scenarios.

[0105] Step 507: Based on the constructor, the configuration attributes of the third configuration item, and the mapping relationship, initialize the target configuration object and obtain the target data model.

[0106] In this embodiment, the third configuration item, the second configuration item, and the first configuration item can be different configuration items within the same configuration file, or they can be different configuration files.

[0107] Step 204: Determine whether the data contained in the target data model can represent the target data mapping requirements of the business scenario.

[0108] In this embodiment, whether the data contained in the target data model can fully represent the target data mapping requirements of the business scenario should be determined in conjunction with the business scenario. It should be judged whether the data contained in the target data model can fully cover the total amount of data that the business scenario needs to use and access. If it can fully cover the data, then the target data mapping requirements of the business scenario are met. If it cannot fully cover the data, then the target data mapping requirements of the business scenario are not met.

[0109] Step 205: Based on the judgment result, the target data model is differentiated.

[0110] In this embodiment, the step of distinguishing the target data model based on the judgment result specifically includes: if the data contained in the target data model can fully represent the target data mapping requirements of the business scenario, then the target data model is constructed; if the data contained in the target data model cannot fully represent the target data mapping requirements of the business scenario, then the target data model is supplemented and expanded based on the data expansion layer to obtain the data that the business scenario still needs to be expanded, until the expansion is completed, and the target data model is constructed.

[0111] Continue to refer to Figure 6 , Figure 6 It shows Figure 2 A flowchart of a specific implementation of step 205 shown includes the following steps:

[0112] Step 601: If the data contained in the target data model can fully characterize the target data mapping requirements of the business scenario, then the construction of the target data model is complete.

[0113] Step 602: If the data contained in the target data model cannot fully represent the target data mapping requirements of the business scenario, then the target data model is supplemented and expanded based on the data expansion layer to obtain the data that the business scenario still needs to be expanded, until the expansion is completed, and the target data model is constructed.

[0114] By judging the business scenario requirements of the target data model generated in step 203, it is determined whether the target data model needs to be expanded. This ensures that when a single instance model or the target data model has personalized data, the expansion process can be carried out without the need to construct an instance model, which is more in line with business needs.

[0115] This application acquires business data based on a data access layer and a first configuration item; expands and extracts the business data through a data construction layer; initially constructs an instance model through a second configuration item; selects m initially constructed instance models according to the business scenario; combines the m initially constructed instance models according to a data combination layer to obtain a third configuration item; initializes the target data model based on the third configuration item; and determines whether the target data model needs to be extended. This approach can complete data rendering in a single interaction, improving data security, and avoids the problem of excessive data pressure caused by blindly building data caches in big data business scenarios.

[0116] 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.

[0117] 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.

[0118] In this embodiment of the application, when accessing the business data, an artificial intelligence model can be used to access data in big data business scenarios, or an automated model can be used to add the third configuration item when constructing the target data model.

[0119] Further reference Figure 7 As a response to the above Figure 2 To implement the method shown, this application provides an embodiment of a data model construction apparatus, which is similar to... Figure 2 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0120] like Figure 7As shown, the data model construction device 700 described in this embodiment includes: a data acquisition module 701, a single instance construction module 702, a single instance combination module 703, a business mapping judgment module 704, and a differentiation processing module 705. Wherein:

[0121] Data acquisition module 701 is used to acquire business data based on the data access layer and the first configuration item;

[0122] The single instance construction module 702 is used to expand the business data and extract the target data through the data construction layer, and to perform initial instance model construction on the extracted target data through a preset constructor and a second configuration item.

[0123] The single instance combination module 703 is used to select m initially constructed instance models applied to the business scenario according to the business scenario, combine the m initially constructed instance models according to the data combination layer, obtain a third configuration item, and initialize the target data model based on the constructor and the third configuration item, where m is a positive integer greater than 1.

[0124] The business mapping judgment module 704 is used to determine whether the data contained in the target data model can fully represent the target data mapping requirements of the business scenario.

[0125] The differentiation processing module 705 is used to differentiate the target data model based on the judgment result.

[0126] This application acquires business data based on a data access layer and a first configuration item; expands and extracts the business data through a data construction layer; initially constructs an instance model through a second configuration item; selects m initially constructed instance models according to the business scenario; combines the m initially constructed instance models according to a data combination layer to obtain a third configuration item; initializes the target data model based on the third configuration item; and determines whether the target data model needs to be extended. This approach can complete data rendering in a single interaction, improving data security, and avoids the problem of excessive data pressure caused by blindly building data caches in big data business scenarios.

[0127] Continue to refer to Figure 8 , Figure 8 yes Figure 7 The diagram shows a structural representation of a specific embodiment of module 703. The single-instance combination module 703 includes an initial instance model acquisition submodule 7031, a configuration addition and sorting submodule 7032, and a target data model initialization submodule 7033.

[0128] The initial construction instance model acquisition submodule 7031 is used to acquire the m initially constructed instance models and use the m initially constructed instance models as the configuration attributes of the third configuration item.

[0129] The configuration addition and sorting submodule 7032 is used to reserve a configuration redundancy area in the third configuration item, sort the importance of the m initially constructed instance models based on a preset importance filtering model, obtain the importance sorting result, and use the combination model in the data combination layer to add the m initially constructed instance models one by one to the configuration redundancy area according to the importance sorting result.

[0130] The target data model initialization submodule 7033 is used to configure the mapping relationship between the configuration attributes and the m initially constructed instance models based on the combined model, configure the third configuration item, initialize the target configuration object based on the constructor, the configuration attributes of the third configuration item and the mapping relationship, and obtain the target data model.

[0131] By using the initial construction instance model acquisition submodule, configuration addition sorting submodule, and target data model initialization submodule, the process of adding a single instance model as a configuration attribute to the configuration file and then initializing the configuration file is completed, thereby initializing the target data model and facilitating interactive use of the data.

[0132] 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).

[0133] 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.

[0134] To address the aforementioned technical problems, embodiments of this application also provide a computer device. Please refer to [link / reference needed]. Figure 9 , Figure 9This is a basic structural block diagram of the computer device in this embodiment.

[0135] The computer device 9 includes a memory 9a, a processor 9b, and a network interface 9c that are interconnected via a system bus. It should be noted that only the computer device 9 with components 9a-9c 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.

[0136] 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.

[0137] The memory 9a 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 9a may be an internal storage unit of the computer device 9, such as the hard disk or memory of the computer device 9. In other embodiments, the memory 9a may also be an external storage device of the computer device 9, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the computer device 9. Of course, the memory 9a may also include both the internal storage unit and its external storage device of the computer device 9. In this embodiment, the memory 9a is typically used to store the operating system and various application software installed on the computer device 9, such as computer-readable instructions for data model construction methods. In addition, the memory 9a can also be used to temporarily store various types of data that have been output or will be output.

[0138] In some embodiments, the processor 9b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 9b is typically used to control the overall operation of the computer device 9. In this embodiment, the processor 9b is used to execute computer-readable instructions stored in the memory 9a or to process data, for example, to execute computer-readable instructions of the data model construction method.

[0139] The network interface 9c may include a wireless network interface or a wired network interface, which is typically used to establish communication connections between the computer device 9 and other electronic devices.

[0140] The computer device proposed in this embodiment is applied to the field of data model construction technology in big data business scenarios. This application acquires business data based on a data access layer and a first configuration item; expands and extracts the business data through a data construction layer; initially constructs an instance model using a second configuration item; selects m initially constructed instance models according to the business scenario; combines the m initially constructed instance models according to a data combination layer to obtain a third configuration item; initializes a target data model based on the third configuration item; and determines whether the target data model needs to be extended. This approach can complete data rendering in a single interaction, improving data security, and avoids the problem of excessive data pressure caused by blindly building data caches in big data business scenarios.

[0141] 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 construction method described above.

[0142] The computer-readable storage medium proposed in this embodiment is applied to the field of data model construction technology in big data business scenarios. This application acquires business data based on a data access layer and a first configuration item; expands and extracts the business data through a data construction layer; initially constructs an instance model through a second configuration item; selects m initially constructed instance models according to the business scenario; combines the m initially constructed instance models according to a data combination layer to obtain a third configuration item; initializes a target data model based on the third configuration item; and determines whether the target data model needs to be extended. This approach can complete data rendering in a single interaction, improving data security, and avoids the problem of excessive data pressure caused by blindly building data caches in big data business scenarios.

[0143] 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.

[0144] 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 construction method, characterized in that, Includes the following steps: Business data is obtained based on the data access layer and the first configuration item; The data construction layer performs format conversion and target data extraction on the business data, and uses a preset constructor and a second configuration item to initially construct an instance model of the extracted target data. Based on the business scenario, m initially constructed instance models are selected and applied to the business scenario. The m initially constructed instance models are combined according to the data combination layer to obtain the third configuration item. The target data model is initialized based on the constructor and the third configuration item, where m is a positive integer greater than 1. Determine whether the data contained in the target data model can represent the target data mapping requirements of the business scenario; Based on the judgment result, the target data model is differentiated and processed, specifically including: If the data contained in the target data model can fully characterize the target data mapping requirements of the business scenario, then the target data model is constructed. If the data contained in the target data model cannot fully represent the target data mapping requirements of the business scenario, then the target data model is supplemented and expanded based on the data expansion layer to obtain the data that the business scenario still needs to be expanded, until the expansion is completed, and the target data model is constructed.

2. The data model construction method according to claim 1, characterized in that, The first configuration item includes a local configuration file that establishes a connection with the local database, and / or a remote configuration file that establishes a connection with the corresponding remote access interface. The step of obtaining business data based on the data access layer and the first configuration item specifically includes: The data access layer obtains business data that meets the business scenario requirements by using a proxy value retrieval method through a preset database accessor and the local configuration file; The data access layer uses a preset external system accessor and the remote configuration file to obtain business data that meets the requirements of the business scenario but is deployed in an external system via remote calls.

3. The data model construction method according to claim 1, characterized in that, The steps of format conversion and target data extraction of the business data through the data construction layer specifically include: The data construction layer uses a preset pre-converter to convert the large String type data field corresponding to the business data into JSON format to obtain JSON type business data. The large String type data field represents a String type data field that exceeds a preset character length. The JSON-type business data is categorized and retrieved using a preset value retriever in the data construction layer. This retrieves business scenario configuration data, business scenario assignment data, and business scenario basic data. Specifically, the data used to configure the display format of the business scenario is designated as business scenario configuration data, the data used for display in the business scenario and which changes continuously is designated as business scenario assignment data, and the data used for display in the business scenario and which remains constant is designated as business scenario basic data.

4. The data model construction method according to claim 1, characterized in that, The second configuration item represents the configuration file for initializing the instance object. The step of initially constructing the instance model from the extracted target data using the preset constructor and the second configuration item specifically includes: Obtain the configuration attributes of the second configuration item and the mapping relationship between the configuration attributes and the target data; Based on the constructor, the configuration attributes of the second configuration item, and the mapping relationship, the instance model is initialized to complete the initial construction of the instance model.

5. The data model construction method according to claim 4, characterized in that, The third configuration item represents a configuration file for reconstructing the instance object. The step of combining the m initially constructed instance models according to the data combination layer to obtain the third configuration item, and initializing the target data model based on the constructor and the third configuration item, specifically includes: Obtain the m initially constructed instance models; The target data model is used as the target configuration object of the third configuration item; The m initially constructed instance models are used as configuration attributes of the third configuration item. Based on the combined model in the data combination layer, the mapping relationship between the configuration attributes and the m initially constructed instance models is configured, and the configuration of the third configuration item is completed. Based on the constructor, the configuration attributes of the third configuration item, and the mapping relationship, the target configuration object is initialized to obtain the target data model.

6. The data model construction method according to claim 5, characterized in that, Before the step of configuring the m initially constructed instance models as configuration attributes of the third configuration item, and configuring the mapping relationship between the configuration attributes and the m initially constructed instance models based on the combined model in the data combination layer, and completing the configuration of the third configuration item, the method further includes: Reserve a configuration redundancy area within the third configuration item that will be configured; Each of the m initially constructed instance models is treated as a configuration attribute and added line by line to the configuration redundancy area.

7. The data model construction method according to claim 6, characterized in that, Before the step of adding each of the m initially constructed instance models as a configuration attribute to the configuration redundancy area line by line, the method further includes: Based on a preset importance filtering model, the m initially constructed instance models are sorted by importance to obtain the importance ranking result. The filtering criteria of the importance filtering model can be the estimated number of data interactions that the m initially constructed instance models will have in the business scenario within a preset time period. The step of adding each of the m initially constructed instance models as a configuration attribute, row by row, to the configuration redundancy area specifically includes: Using the combined model, the m initially constructed instance models are added to the configuration redundancy area according to the importance ranking result.

8. A data model construction apparatus, characterized in that, include: The data acquisition module is used to acquire business data based on the data access layer and the first configuration item; The single-instance construction module is used to perform format conversion and target data extraction on the business data through the data construction layer, and to perform initial instance model construction on the extracted target data through a preset constructor and a second configuration item. The single instance combination module is used to select m initially constructed instance models applicable to the business scenario based on the business scenario, combine the m initially constructed instance models according to the data combination layer, obtain the third configuration item, and initialize the target data model based on the constructor and the third configuration item, where m is a positive integer greater than 1; The business mapping judgment module is used to determine whether the data contained in the target data model can fully represent the target data mapping requirements of the business scenario. The differentiation processing module is used to differentiate the target data model based on the judgment result, specifically including: If the data contained in the target data model can fully characterize the target data mapping requirements of the business scenario, then the target data model is constructed. If the data contained in the target data model cannot fully represent the target data mapping requirements of the business scenario, then the target data model is supplemented and expanded based on the data expansion layer to obtain the data that the business scenario still needs to be expanded, until the expansion is completed, and the target data model is constructed.

9. 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 construction method as described in any one of claims 1 to 7.

10. 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 construction method as described in any one of claims 1 to 7.

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