Data acquisition method and device, electronic equipment and storage medium

Abstract

The invention relates to the technical field of computers, and provides a data acquisition method and device, electronic equipment and a storage medium. The method comprises the following steps: receiving a query request for a target data set through a standardized query interface, and determining a connection mode according to a data source corresponding to the target data set; establishing connection with a data source corresponding to the target data set based on the connection mode, and executing the query request through the data source to obtain target data from the target data set; and performing format conversion on the target data to generate a standardized data output result. Wherein the query request is received through the standardized query interface, and the connection with the data source is established based on the determined connection mode, so that unified access and access to multiple types of data sources are realized, the system can complete data query and result output under the same architecture, the data acquisition process is simplified, and the access efficiency is improved. And by performing format conversion on the query result, a standardized data output result with a consistent structure is generated, so that the unification of data formats is realized.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a data acquisition method, apparatus, electronic device and storage medium. Background Technology

[0002] As enterprises continue to improve their level of informatization, the sources of data generated by internal and external systems are becoming increasingly diverse. Common data sources include relational databases (such as MySQL and PostgreSQL), web service interfaces (such as RESTful and GraphQL services), and structured files (such as CSV and Excel).

[0003] In existing technologies, different types of data sources typically require separate configuration and access. For example, accessing the database via JDBC driver, accessing the network interface via HTTP client, and reading file data via file parsing module. Furthermore, the data formats returned by different query methods are inconsistent, increasing the difficulty of data processing at the application layer. Summary of the Invention

[0004] In view of this, embodiments of this application provide a data acquisition method, apparatus, electronic device, and storage medium to solve the problems of complex data acquisition methods and inconsistent data formats in the prior art.

[0005] A first aspect of this application provides a data acquisition method, which includes: receiving a query request for a target dataset and determining a connection method based on the data source corresponding to the target dataset; establishing a connection with the data source corresponding to the target dataset based on the connection method and executing the query request through the data source to acquire target data from the target dataset; and converting the target data into a format to generate standardized data output results. A second aspect of this application provides a data acquisition apparatus, comprising: a determining module, configured to receive a query request for a target dataset and determine a connection method based on the data source corresponding to the target dataset; a query module, configured to establish a connection with the data source corresponding to the target dataset based on the connection method and execute the query request through the data source to acquire target data from the target dataset; and an output module, configured to perform format conversion on the target data to generate standardized data output results.

[0006] A third aspect of this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method.

[0007] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described method.

[0008] The beneficial effects of this application's embodiments compared to existing technologies are as follows: The method in this application receives query requests for a target dataset through a standardized query interface and determines the connection method based on the data source corresponding to the target dataset; a connection is established with the data source corresponding to the target dataset based on the connection method, and the query request is executed through the data source to obtain target data from the target dataset; the target data is then format-converted to generate standardized data output results. Specifically, receiving query requests through a standardized query interface and establishing a connection with the data source based on the determined connection method enables unified access to various types of data sources such as databases, network services, and files, allowing the system to complete data querying and result output under the same architecture, thereby simplifying the data acquisition process and improving access efficiency. Furthermore, by format-converting the query results to generate standardized data output results with consistent structure, data format uniformity is achieved, avoiding the problems of complex data acquisition methods and inconsistent data formats in existing technologies. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart illustrating a data acquisition method provided in an embodiment of this application; Figure 2 This is a flowchart illustrating another data acquisition method provided in an embodiment of this application; Figure 3 This is a schematic diagram illustrating the creation of a data source according to an embodiment of this application; Figure 4 This is a schematic diagram illustrating the creation of an SQL dataset according to an embodiment of this application; Figure 5 This is a schematic diagram illustrating the creation of a REST dataset according to an embodiment of this application; Figure 6 This is a schematic diagram illustrating the creation of a GraphQL dataset according to an embodiment of this application; Figure 7 This is a schematic diagram illustrating the creation of a CSV dataset according to an embodiment of this application; Figure 8This is a schematic diagram of a data acquisition device provided in an embodiment of this application; Figure 9 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0011] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, data acquisition devices, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0012] A data acquisition method and a data acquisition apparatus according to embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0013] Figure 1 This application provides a data acquisition method, such as... Figure 1 As shown, the method includes: S101. Receive query requests for the target dataset through a standardized query interface, and determine the connection method based on the data source corresponding to the target dataset. S102. Establish a connection with the data source corresponding to the target dataset based on the connection method, and execute a query request through the data source to obtain the target data from the target dataset; S103. Convert the target data to generate standardized data output results.

[0014] It is understandable that the aforementioned data sources include, but are not limited to, databases, network services, and files. In existing solutions, different types of data sources typically require independent configuration and access. For example, in existing solutions, database data needs to be accessed through a JDBC driver, network interface data needs to be accessed through an HTTP client, and file data needs to be read through a file parsing module. This distributed configuration and access method results in a complex system architecture, high maintenance costs, and low data access efficiency.

[0015] To address this, this application provides a standardized data acquisition mechanism that enables unified access and data acquisition from different types of data sources by setting up a standardized query interface. In this embodiment, the standardized query interface is used to receive query requests for a target dataset and determine the connection method based on the data source corresponding to the target dataset. Each data source corresponds to a dataset, and the target dataset is the dataset pointed to by the query request.

[0016] In some examples, the connection method is determined based on the data source corresponding to the target dataset. Specifically, when the data source corresponding to the target dataset is a database, the connection method is determined to be to establish a database connection using a database connection driver; when the data source corresponding to the target dataset is a network service, the connection method is determined to be to establish a network connection using a network connection protocol; and when the data source corresponding to the target dataset is a file, the connection method is determined to establish a file access connection using a file read / write interface.

[0017] By receiving query requests for the target dataset through the standardized query interface, this application can achieve unified connection and access to multiple types of data sources under the same standardized framework, thereby simplifying the system configuration process, reducing development and maintenance costs, and improving data access efficiency.

[0018] In some examples, a connection is established with the data source corresponding to the target dataset based on the connection method, and a query request is executed through the data source to retrieve the target data from the target dataset. Understandably, after obtaining the connection method, the system will select the appropriate connection mechanism and query strategy based on the data source type corresponding to the connection method to ensure the compatibility and efficiency of the data access process.

[0019] After obtaining the target data from the target dataset, this application will perform format conversion on the target data to generate standardized data output results, thereby realizing unified reduction and standardization processing of data returned from different types of data sources.

[0020] According to the solution provided in this application, a standardized query interface is used to receive query requests for a target dataset, and the connection method is determined based on the data source corresponding to the target dataset. A connection is established with the data source corresponding to the target dataset based on the connection method, and the query request is executed through the data source to obtain the target data from the target dataset. The target data is then format-converted to generate standardized data output results. Specifically, receiving query requests through the standardized query interface and establishing a connection with the data source based on the determined connection method enables unified access to various types of data sources such as databases, network services, and files. This allows the system to complete data querying and result output under the same architecture, thereby simplifying the data acquisition process and improving access efficiency. Furthermore, by format-converting the query results to generate standardized data output results with a consistent structure, data format uniformity is achieved, avoiding the problems of complex data acquisition methods and inconsistent data formats in existing technologies.

[0021] In some examples, a query request is executed through a data source to retrieve target data from a target dataset. This includes: when the data source is a database, generating a database query instruction based on the query request and executing the database query instruction through the database to retrieve the target data corresponding to the database query instruction from the target dataset corresponding to the database; when the data source is a network service, generating a network request message based on the query request and sending the network request message through the network service to retrieve the target data from the target dataset corresponding to the network service; and when the data source is a file, determining the file path based on the query request and retrieving the target data from the target dataset corresponding to the file based on the file path.

[0022] In some examples, this application also validates the received query request to ensure the legality and completeness of the data source identifier, query parameters, and access permissions contained in the query request. Once the query request parameters have been validated and passed, a corresponding connection is established based on the data source, thereby enabling access to and retrieval of the target data.

[0023] For example, when the data source is a database, the query parameters are parsed to obtain information such as the database address, port number, username, password, and database name; when SQL query statements are included, the query statement format is also validated to ensure that the SQL statement conforms to the SELECT syntax specification.

[0024] For example, when the data source is a network service, for network data queries, the query statement is parsed to obtain information including the service address, port number, communication protocol, and request method (such as GET or POST); the header and body parameters in the query request message are parsed, and both the header and body parameters are optional. For example, when the data source is a file, for file data queries, the query statement is parsed to obtain information including the file access path, file type, and read permissions, ensuring that the file can be loaded correctly.

[0025] To better understand this method, the following examples illustrate its application: When the data source is a database, a database connection is established through a database connection pool; when the data source is a web service, an HTTP connection is established through an HTTP service interface; when the data source is a file, the target file content is loaded and parsed. After a successful connection is established, the corresponding data query operation is executed based on the different types of data sources. For example: when the data source is a database, an SQL query instruction is generated based on the query request and executed through the database to obtain the target data; when the data source is a web service, a network request message is generated based on the query request and sent through the web service interface to execute a RESTful or GraphQL query to obtain the target data; when the data source is a file, the system determines the target file path based on the query request and performs a CSV file parsing operation to obtain the target data from the file.

[0026] In some examples, to further improve the configuration efficiency of data sources and datasets, this application can adopt a single-file integration mechanism. Specifically, the system can define the configuration information of both the data source and dataset simultaneously through a single JSON file, thereby avoiding repeated configuration in different interfaces or multiple steps. With this approach, users only need to upload a single file containing complete configuration information to complete the creation and binding of all data sources and datasets at once, achieving atomic operations and avoiding inconsistencies in intermediate states caused by step-by-step operations.

[0027] In other examples, to enhance the system's scalability and flexibility, this application adopts a type-driven plug-in architecture design. The system defines data source types through the `type` field. Different data source types are registered and loaded in the system as plug-ins. Therefore, when new data source types need to be supported, only the corresponding type enumeration and adaptation module need to be added to achieve expansion, without modifying existing logic. Furthermore, each type of data source has independent connection and query structure configurations, achieving configuration isolation between different data source types and ensuring that system modules do not interfere with each other.

[0028] Furthermore, to improve the versatility and maintainability of queries, this application also adopts a parameterized design mechanism. The system supports a unified parameter syntax in query statements or request messages, such as using the {param} placeholder to express parameter positions, applicable to all data source types. At the same time, each parameter can be configured with a default value to maintain the stability and continuity of query requests even under default parameters or abnormal conditions.

[0029] Through the above design, this application realizes the unified definition of data sources and datasets, modular management of type extensions, and flexible parameterized configuration, which significantly improves the configurability, scalability, and operational stability of the system.

[0030] In some examples, such as Figure 2 As shown, before executing a query request through a data source to retrieve the target data from the target dataset, the method further includes: S201, Receive configuration instructions from at least two data sources; S202. Connect at least two data sources to the connection pool according to the configuration instructions in order to manage and reuse at least two data sources.

[0031] Specifically, taking at least two data sources, including databases, files, and network services, as an example, the system first receives user-inputted data source configuration information through a configuration interface or API. During the data source creation process, such as... Figure 3 As shown, users can select the data source types supported by the system and enter the corresponding connection parameters. After receiving the configuration information, the system verifies and tests the input connection parameters (test link), and saves the data source information after verifying the connection availability.

[0032] In some examples, the configuration information for different types of data sources includes, but is not limited to, the following: Database type data source: This includes information such as data source name, database type (e.g., MySQL, PostgreSQL, SQL Server, etc.), hostname or IP address, port number, database name, username, password, and SSL connection options; Network service type data source: includes data source name, web service address (e.g., http: / / domain name or IP address:port / project name), header parameter name, security parameters, security authentication certificate and authorization information (Bearer Token can be added to the header); File type data source: including data source name, file path or upload interface, file format and parsing rules, etc.

[0033] After configuration, the system registers different types of data sources into a unified connection pool, thereby enabling centralized management and reuse of database connections, HTTP connections, and file access connections. This significantly reduces the repeated establishment and destruction of connections, improving system performance and resource utilization.

[0034] After the data source is successfully configured, the system can create corresponding datasets based on different types of data sources to achieve unified management and access to structured, semi-structured, and file-based data.

[0035] For example, SQL datasets are created based on predefined database connections and are used for direct access to the database table structure. The creation process includes: Figure 4 As shown, select the created database data source; generate a query statement by dragging and dropping table fields in the visual designer or manually entering SQL statements; execute the SQL query and extract a structured description of the query results through the SQL parser to generate a dataset. This dataset can be used directly for report design or bound to component elements.

[0036] RESTful dataset creation: RESTful datasets use data returned from a network interface as their data source. The creation process includes: (e.g., ...) Figure 5 As shown, select the created HTTP service; enter the dataset name, remarks, request method (POST / GET), target API, and parameters; execute the interface request, the system parses the response body structure, and saves the data structure description. After creation, this dataset can be used for report or component binding.

[0037] GraphQL dataset creation: GraphQL datasets are based on GraphQL service definitions and retrieve data using the GraphQL query language. For example... Figure 6 As shown, the creation process includes: selecting an existing HTTP service; entering the dataset name, remarks, and request method (POST / GET); manually entering or visually generating GraphQL query statements through the designer; executing the request and saving the dataset after parsing the structured description of the response body.

[0038] CSV file dataset creation: CSV datasets are created based on uploaded file data. The creation process includes: Figure 7 As shown, enter the dataset name and upload the target file; parse the file content and display the file data structure; select the target fields and save it as a dataset. After creation, the dataset can be used directly by the report module.

[0039] Through the above configuration and creation process, this application achieves unified access and centralized management of multiple data sources, including databases, network services, and files. By reusing and maintaining connections to multiple data sources using connection pooling technology, not only is the configuration and testing process for data sources simplified, but the system's connection efficiency and stability are also significantly improved. Furthermore, by automatically creating SQL, RESTful, GraphQL, and CSV datasets based on different data source types, unified management and visual configuration of multi-source heterogeneous data are achieved, effectively reducing development barriers and system maintenance costs.

[0040] In some examples, the target data is formatted to generate standardized data output, including: parsing the target data to obtain the target fields; and converting the target fields according to preset field mapping rules to generate standardized data output.

[0041] Specifically, the system can perform structural reduction and unified encapsulation on raw data from different data sources, transforming heterogeneous data into a unified key-value data structure. Through this field mapping rule method, compatibility and integration of different data sources under the same output standard can be achieved, thereby facilitating subsequent report generation, data display, or interface output.

[0042] In some examples, the field mapping rules follow these design principles: A unified entry point principle: The system adopts a single-file integration mechanism, defining both the data source and dataset information in a single JSON file, achieving integrated configuration and data definition. Users only need to upload this file to complete the full configuration of the data source and dataset, thus achieving atomic operations and avoiding inconsistencies in intermediate states caused by step-by-step configuration.

[0043] The type-driven extensibility system adopts a plug-in architecture design, enabling dynamic support for multiple data source types through the `type` field. When a new data source type needs to be added, only the corresponding type enumeration and plug-in module need to be added, without modifying the core logic. Furthermore, each data source type has independent connection and query structure configurations, achieving configuration isolation and independence between different data source types.

[0044] The parameterized design system employs a unified parameter syntax in query statements and request messages, using the {param} placeholder to achieve parameter position substitution, applicable to all data source types. Furthermore, each parameter can be configured with a default value to ensure the stability of queries and data retrieval in the event of missing or abnormal parameters.

[0045] Through the above design, this application achieves full-process automation of data parsing, field mapping, structure reduction and format unification, effectively solving the problems of inconsistent formats and incompatible structures of multi-source data in the prior art, and significantly improving data processing efficiency and system scalability.

[0046] In some examples, after converting the target data to generate standardized data output, the method further includes storing the standardized data output to a data caching module. Specifically, after completing the format conversion, the system can store the standardized data results in a preset data caching area, such as a memory cache or a distributed caching system (e.g., Redis, Memcached). Through the caching mechanism, temporary storage and rapid reuse of query results can be achieved. When the same or similar query requests are received subsequently, the system can directly read the corresponding data results from the cache module, thereby avoiding repeated connection establishment and query execution, significantly improving data access efficiency and system response speed.

[0047] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0048] Based on the same concept, this application also provides a data acquisition device, such as... Figure 8 As shown, the data acquisition device includes: The determination module 801 is used to receive query requests for the target dataset and determine the connection method based on the data source corresponding to the target dataset; The query module 802 is used to establish a connection with the data source corresponding to the target dataset based on the connection method, and execute a query request through the data source to obtain the target data from the target dataset; Output module 803 is used to convert the target data into a format to generate standardized data output results.

[0049] In some examples, the determination module 801 is also used to determine the connection method as follows: when the data source corresponding to the target dataset is a database, the connection method is to establish a database connection using a database connection driver; when the data source corresponding to the target dataset is a network service, the connection method is to establish a network connection using a network connection protocol; and when the data source corresponding to the target dataset is a file, the connection method is to establish a file access connection using a file read / write interface.

[0050] In some examples, the query module 802 is also used to generate a database query instruction based on the query request when the data source is a database, and execute the database query instruction through the database to obtain the target data corresponding to the database query instruction from the target dataset corresponding to the database; when the data source is a network service, generate a network request message based on the query request, and send the network request message through the network service to obtain the target data from the target dataset corresponding to the network service; when the data source is a file, determine the file path based on the query request, and obtain the target data from the target dataset corresponding to the file based on the file path.

[0051] In some examples, the device is also used to connect at least two data sources to a connection pool according to configuration instructions, so as to manage and reuse at least two data sources.

[0052] In some examples, the output module 803 is also used to parse the target data to obtain the target fields; and to transform the target fields according to preset field mapping rules to generate standardized data output results.

[0053] In some examples, the output module 803 is also used to store the standardized data output results to the data cache module.

[0054] According to the solution provided in this application, a query request for a target dataset is received through a standardized query interface, and a connection method is determined based on the data source corresponding to the target dataset. A connection is established with the data source corresponding to the target dataset based on the connection method, and the query request is executed through the data source to obtain target data from the target dataset. The target data is then format-converted to generate standardized data output results. Specifically, receiving query requests through a standardized query interface and establishing a connection with the data source based on the determined connection method enables unified access to various types of data sources such as databases, network services, and files. This allows the system to complete data querying and result output under the same architecture, thereby simplifying the data acquisition process and improving access efficiency. Furthermore, by format-converting the query results to generate standardized data output results with a consistent structure, data format uniformity is achieved, avoiding the problems of complex data acquisition methods and inconsistent data formats in existing technologies.

[0055] Figure 9 This is a schematic diagram of the electronic device 9 provided in an embodiment of this application. Figure 9 As shown, the electronic device 9 of this embodiment includes a processor 901, a memory 902, and a computer program 903 stored in the memory 902 and executable on the processor 901. When the processor 901 executes the computer program 903, it implements the steps in the various method embodiments described above. Alternatively, when the processor 901 executes the computer program 903, it implements the functions of each module / unit in the various data acquisition device embodiments described above.

[0056] Electronic device 9 can be a desktop computer, laptop, handheld computer, cloud server, or other electronic device. Electronic device 9 may include, but is not limited to, processor 901 and memory 902. Those skilled in the art will understand that... Figure 9 This is merely an example of electronic device 9 and does not constitute a limitation on electronic device 9. It may include more or fewer components than shown, or different components.

[0057] The processor 901 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0058] The memory 902 can be an internal storage unit of the electronic device 9, such as a hard disk or RAM of the electronic device 9. The memory 902 can also be an external storage device of the electronic device 9, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, Flash Card, etc., equipped on the electronic device 9. The memory 902 can also include both internal and external storage units of the electronic device 9. The memory 902 is used to store computer programs and other programs and data required by the electronic device.

[0059] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the data acquisition device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0060] If an integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program may include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. A computer-readable medium may include: any entity or data acquisition device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in a computer-readable medium can be appropriately added or removed according to regional requirements and patent practice requirements. For example, in some regions, according to regional requirements and patent practice, a computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0061] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A data acquisition method, characterized in that, The data acquisition method includes: The system receives query requests for the target dataset through a standardized query interface and determines the connection method based on the data source corresponding to the target dataset. A connection is established with the data source corresponding to the target dataset based on the connection method, and the query request is executed through the data source to obtain the target data from the target dataset; The target data is format-converted to generate standardized data output results.

2. The method according to claim 1, characterized in that, The connection method is determined based on the data source corresponding to the target dataset, including: When the data source corresponding to the target dataset is a database, the connection method is determined to be establishing a database connection using a database connection driver; When the data source corresponding to the target dataset is a network service, the connection method is determined to be establishing a network connection using a network connection protocol; When the data source corresponding to the target dataset is a file, the connection method is determined to be to establish a file access connection using a file read / write interface.

3. The method according to claim 2, characterized in that, Executing the query request through the data source to obtain target data from the target dataset includes: When the data source is a database, a database query instruction is generated based on the query request, and the database query instruction is executed through the database to obtain the target data corresponding to the database query instruction from the target dataset corresponding to the database. When the data source is a network service, a network request message is generated based on the query request, and the network request message is sent through the network service to obtain the target data from the target dataset corresponding to the network service; When the data source is a file, the file path is determined based on the query request, and the target data is obtained from the target dataset corresponding to the file based on the file path.

4. The method according to claim 1, characterized in that, Before executing the query request through the data source to obtain target data from the target dataset, the method further includes: Receive configuration instructions from at least two of the data sources; At least two of the data sources are connected to the connection pool according to the configuration instructions in order to manage and reuse the at least two data sources.

5. The method according to claim 1, characterized in that, The target data is format-converted to generate standardized data output, including: The target data is parsed to obtain the target fields; The target field is transformed according to the preset field mapping rules to generate the standardized data output result.

6. The method according to claim 1, characterized in that, After converting the target data to generate standardized data output, the method further includes: The standardized data output results are stored in the data cache module.

7. A data acquisition device, characterized in that, The data acquisition device includes: The determination module is used to receive query requests for the target dataset and determine the connection method based on the data source corresponding to the target dataset; The query module is used to establish a connection with the data source corresponding to the target dataset based on the connection method, and execute the query request through the data source to obtain target data from the target dataset; The output module is used to convert the target data into a format to generate standardized data output results.

8. The apparatus according to claim 7, characterized in that, The determining module is further configured to determine the connection method as establishing a database connection using a database connection driver when the data source corresponding to the target dataset is a database; determine the connection method as establishing a network connection using a network connection protocol when the data source corresponding to the target dataset is a network service; and determine the connection method as establishing a file access connection using a file read / write interface when the data source corresponding to the target dataset is a file.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 6.

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