Database access method and device, equipment and medium

Abstract

The invention relates to the technical field of databases, and discloses a database access method and device, equipment and a medium, comprising: in response to a first query request, determining a target database corresponding to a target measurement point in the first query request, and calling a target connector for the target database; converting the first query request into a calling instruction adaptive to a target database based on the target connector; initiating a second query request to the target database based on the target connector and the calling instruction, and receiving an original query result returned by the target database; and processing the original query result, and generating and displaying a target chart. Different types of time sequence databases are adapted through the connector, so that unified processing of query requests and data formats is realized, and the efficiency of data query and report generation is improved.

Description

Technical Field

[0001] This invention relates to the field of database technology, and more specifically to database access methods, apparatus, devices, and media. Background Technology

[0002] Current reporting systems generally follow a fixed process for supporting time-series databases: establishing a data source, defining a dataset, and configuring reports. The data source establishment phase involves configuring the time-series database's server address, port, access username, and password, and establishing communication with the database by loading a specific driver. Based on the user's query and selected report type, the fields in the dataset are configured as report dimensions, values, or filter conditions, and the data is ultimately displayed in the report. However, existing data models simplify time-series data into two-dimensional tables, severing the hierarchical relationship between data and business entities, thus impacting query efficiency. Furthermore, some mainstream time-series databases do not support access via standard interfaces, only providing vendor-defined software development kits as data access points. Existing systems' dataset models require data sources to return two-dimensional data with row and column structures to construct datasets. This data format is incompatible with the two-dimensional structure requirements of dataset models, preventing existing systems from effectively accessing such time-series databases for data extraction and report generation. This limits the system's adaptability and fails to meet enterprises' needs for integrated analysis of diverse time-series databases, also impacting the efficiency of data report generation. Summary of the Invention

[0003] In view of this, the present invention provides a database access method, apparatus, device and medium to solve the problem of low efficiency in database query and report generation.

[0004] In a first aspect, the present invention provides a database access method, the method comprising: In response to the first query request, the target database corresponding to the target measurement point in the first query request is determined, and the target connector for the target database is invoked; Based on the target connector, the first query request is converted into a call instruction adapted to the target database; Based on the target connector and the invocation instruction, a second query request is initiated to the target database, and the original query results returned by the target database are received; The original query results are processed to generate and display the target chart.

[0005] The database access method provided in this embodiment includes, in response to a first query request, determining a target database corresponding to the target measurement point in the first query request, and invoking a target connector for the target database; converting the first query request into a call instruction adapted to the target database based on the target connector; initiating a second query request to the target database based on the target connector and the call instruction, and receiving the original query results returned by the target database; processing the original query results to generate and display a target chart. This invention adapts different types of time-series databases through connectors, thereby achieving unified processing of query requests and data formats, thus improving the efficiency of data querying and report generation.

[0006] In one optional implementation, the step of converting the first query request into a call instruction adapted to the target database based on the target connector includes: Based on the adaptation layer of the target connector, the parameters in the first query request are mapped and encapsulated into a call instruction that conforms to the native query protocol of the target database.

[0007] In one optional implementation, processing the original query results to generate and display the target chart includes: The original query results are normalized to generate structured data that conforms to a preset standard format; The target chart is generated and displayed based on the structured data.

[0008] In an optional implementation, before receiving the first query request, the method further includes: A query model is constructed, which includes at least one device node, which includes at least one component node, and the component node includes at least one measurement point node; the measurement point node is configured to correspond to at least one time-series data source in a preset database.

[0009] In one optional implementation, constructing the query model includes: In response to a new operation on a device node, the first configuration interface of the device node is displayed; In response to the configuration operation on the first configuration interface, the attributes of the device node are determined.

[0010] In one optional implementation, the construction of the query model further includes: In response to a new component node added to the device node, a second configuration interface for the component node is displayed; In response to a configuration operation on the second configuration interface, the attributes of the component node are determined.

[0011] In one optional implementation, the construction of the query model further includes: In response to the addition of a measurement point node to the component node, the third configuration interface of the measurement point node is displayed; In response to the configuration operation on the third configuration interface, the attributes of the measurement point node are determined.

[0012] In a second aspect, the present invention provides a database access device, the device comprising: The calling module is used to respond to the first query request, determine the target database corresponding to the target measurement point in the first query request, and call the target connector for the target database; The request conversion module is used to convert the first query request into a call instruction adapted to the target database based on the target connector; The result receiving module is used to initiate a second query request to the target database based on the target connector and the calling instruction, and to receive the original query results returned by the target database; The chart generation module is used to process the original query results, generate and display the target chart.

[0013] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the database access method of the first aspect or any corresponding embodiment described above.

[0014] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the database access method of the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0015] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating a database access method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a query model according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the first configuration interface according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the second configuration interface according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the third configuration interface according to an embodiment of the present invention; Figure 6 This is a structural block diagram of a database access device according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] With the rapid development of fields such as the Industrial Internet and intelligent manufacturing, the scale of time-series data continues to expand, and its analytical value is becoming increasingly prominent. Enterprises typically rely on business intelligence / reporting systems to integrate and visualize time-series data to support business decisions. Existing reporting systems generally follow a fixed process for supporting time-series databases: establishing a data source, defining a dataset, and configuring the report. In establishing the data source, the system needs to configure the server address, port, access username and password, and upload the corresponding JDBC (Java Database Connectivity) driver. The system then loads the driver, establishes communication with the time-series database, executes user-input SQL queries or selected target data tables, extracts field names, data types, and related metadata from the query results, maps them to a system-compatible dataset, and finally, the user selects a preset report type (such as a line chart or bar chart) and configures the fields in the dataset as report dimensions, values, or filter conditions. The system automatically converts these configurations into corresponding SQL statements, submits them to the time-series database again via JDBC for query execution, retrieves the final data, and displays it in the report. However, existing technical solutions struggle to meet the demands of real-world business scenarios. Firstly, existing systems' dataset models are designed based on relational database table structures, making them more suitable for processing structured relational data. In practical applications, time-series data often resides at specific business entity levels. Taking the power industry as an example, time-series data originates from various equipment. A wind turbine, as equipment, comprises multiple components such as blades and generators, each corresponding to multiple nodes such as rotational speed, angular velocity, and power generation. Existing dataset models simplify time-series data into two-dimensional tables, severing the hierarchical relationship between data and business entities. This prevents users from efficiently locating and analyzing target data based on the business logic from equipment to components to nodes, significantly reducing the efficiency and accuracy of data analysis. Current mainstream time-series databases do not support access via standard interfaces, only providing vendor-defined software development kits (SDKs) as data access entry points. Existing systems' dataset models have inherent limitations, requiring data sources to return two-dimensional data with row and column structures to construct the dataset. However, time-series data obtained through these custom SDKs is essentially one-dimensional time-series stream data that changes continuously over time. This data format is incompatible with the two-dimensional structure requirements of the dataset model, preventing existing systems from accessing such time-series databases and effectively extracting data and generating reports. This limits the system's adaptability and fails to meet enterprises' integrated analysis needs for diverse time-series databases. Therefore, this invention provides a database access method applied to a reporting system.

[0019] According to an embodiment of the present invention, a database access method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0020] This embodiment provides a database access method. Figure 1 This is a flowchart of a database access method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: In response to the first query request, determine the target database corresponding to the target measurement point in the first query request, and call the target connector for the target database.

[0021] The first query request is initiated by the user based on a pre-set query model. The structure of the query model is device-component-measurement point. The goal of the first query request is to obtain time-series data of the target measurement point and perform visualization analysis. The first query request includes at least the relevant information of the target measurement point, specifically including the unique identifier of the target measurement point, and query conditions such as time range and data query type. The unique identifier of the target measurement point can be the measurement point code. The target measurement point in the first query request originates from the selection of a specific measurement point node in the query model, which can be initiated by inputting or selecting the target measurement point and related query conditions.

[0022] The system has a pre-defined mapping relationship between measurement point nodes and the database. When a first query request is received, the system parses the unique identifier of the target measurement point in the first query request and matches it with the target database to which the target measurement point belongs. The target database stores the time series data of the target measurement point. The target database can be a time series database that supports access via a custom SDK, a time series database that supports access via JDBC, etc.

[0023] The target connector is a pluggable functional module specifically designed to adapt to target databases. Each target database has a corresponding dedicated connector, and all connectors adhere to a unified interface specification and data processing standard. The system uses the target database type identifier to call the corresponding target connector from a pre-defined connector resource pool (containing dedicated connectors for various databases), achieving precise adaptation between the connector and the target database. If a new target database type is added subsequently, only the corresponding dedicated connector needs to be added and connected to the resource pool; no modification to the core system architecture is required, demonstrating excellent scalability.

[0024] Step S102: Based on the target connector, the first query request is converted into a call instruction adapted to the target database.

[0025] The target connector has built-in interface adaptation rules and parameter mapping relationships for the target database. The interface adaptation rules include the correspondence between the parameters in the first query request and the native interface parameters of the target database. The parameters in the first query request may include the identifier of the target measurement point, the time range, etc., and the native interface parameters of the target database may include the measurement point identifier, the timestamp range, etc. in the SDK.

[0026] The target connector transforms and encapsulates the parameters in the first query request to obtain the calling instructions for the target database.

[0027] Step S103: Based on the target connector and the calling instruction, initiate a second query request to the target database and receive the original query results returned by the target database.

[0028] The second query request is a native query request that the target database can directly execute. It is generated and initiated by the target connector based on the transformed calling instructions. After executing the second query request, the target database returns the original query results, which are in the target database's native data format.

[0029] Step S104: Process the original query results to generate and display the target chart.

[0030] To address the issue of inconsistent raw data formats across different databases, the original query results are normalized and converted into a pre-defined unified data format.

[0031] The first query request may also include the chart type selected by the user. The chart type may include line chart, bar chart, scatter plot, etc. The normalized data is converted into the target chart and the target chart is displayed.

[0032] The database access method provided in this embodiment includes, in response to a first query request, determining a target database corresponding to the target measurement point in the first query request, and invoking a target connector for the target database; converting the first query request into a call instruction adapted to the target database based on the target connector; initiating a second query request to the target database based on the target connector and the call instruction, and receiving the original query results returned by the target database; processing the original query results to generate and display a target chart. This invention adapts different types of time-series databases through connectors, thereby achieving unified processing of query requests and data formats, thus improving the efficiency of data querying and report generation.

[0033] In some optional implementations, step S102 above includes: mapping and encapsulating the parameters in the first query request into a call instruction that conforms to the native query protocol of the target database based on the adapter layer of the target connector.

[0034] The target connector features a built-in layered architecture, with the adaptation layer specifically responsible for query request transformation. This layer uses pre-defined adaptation rules to interface with the target database's native interface. The adaptation layer is the core functional module of the connector. Different target databases have different built-in adaptation logic in their adaptation layers. For the target connector corresponding to the Gengton time series library, its adaptation layer stores the interface parameter specifications and protocol format requirements of the Gengton SDK; for the target connector corresponding to the SmartReal time series library, its adaptation layer stores the SmartReal SDK's proprietary adaptation rules. This allows for the development of only the mapping rules and protocol encapsulation logic for the corresponding adaptation layer when adding a new target database, without requiring changes to other layers of the connector or the core system architecture, further enhancing the connector's pluggable scalability.

[0035] The target measurement point in the first query request is mapped to the measurement point identifier inside the target database. The query conditions in the first query request are converted into a format supported by the native query protocol of the target database. After the parameter mapping is completed, the adaptation layer encapsulates the mapped parameters into a call instruction that can be executed directly based on the native query protocol of the target database.

[0036] In some optional implementations, step S104 includes: normalizing the original query results to generate structured data that conforms to a preset standard format; and generating and displaying a target chart based on the structured data.

[0037] The original query results returned by the target database are normalized, thereby converting the original query data into structured data containing fixed fields such as timestamp, measurement point name, measurement point value, the component to which the measurement point belongs, and the equipment to which the component belongs. Based on the normalized structured data, the appropriate chart type is automatically matched according to user needs or according to the chart type set in the first query request, and the structured data is rendered to generate and display the target chart.

[0038] In some optional implementations, prior to step S101, the method further includes: constructing a query model. The query model includes at least one device node, each device node includes at least one component node, and each component node includes at least one measurement point node; the measurement point node is configured to correspond to at least one time-series data source in a preset database.

[0039] The query model is a hierarchical model of equipment-component-measurement point, linking business entities with time-series data sources through a tree structure. Equipment nodes correspond to physical equipment in actual applications (such as wind turbines and motors). Each equipment node can be associated with at least one component node (such as the blades of a wind turbine or the core components of a generator). Each component node is further associated with at least one measurement point node (such as the speed of the blades or the voltage of the generator, representing data acquisition points). Each measurement point node has a pre-defined mapping relationship with a time-series data source in a specific database (including data source type, storage address, and access identifier). As an example, taking a wind turbine as an example, its query model is as follows: Figure 2 As shown, the equipment node is the wind turbine, the pitch system, generator and frequency converter system, transmission system and yaw system are component nodes, and the subordinate nodes of each component node are measurement point nodes.

[0040] The query model provided in this embodiment can quickly locate target data from the business logic level without having to worry about the storage details of the underlying data source, laying the foundation for subsequent database queries and chart generation.

[0041] Furthermore, constructing the query model includes: in response to a new operation on a device node, displaying the first configuration interface of the device node; and in response to a configuration operation on the first configuration interface, determining the attributes of the device node.

[0042] When a user initiates a new device node addition operation, including clicking the "Add Device" button, the first configuration interface is displayed. As an example, the first configuration interface looks like this: Figure 3 As shown, a device node includes basic attributes such as device type, device name, and device code, and can be expanded in extended attributes.

[0043] Furthermore, the query model construction also includes: displaying a second configuration interface for the component node in response to a new operation on the device node; and determining the attributes of the component node in response to a configuration operation on the second configuration interface.

[0044] When a user initiates a new operation on a component node of a device node, the second configuration interface is displayed. As an example, the second configuration interface looks like this: Figure 4 As shown, the component node includes basic attributes such as component type, component name, and component code, and can be expanded in extended attributes.

[0045] Furthermore, the query model also includes: displaying a third configuration interface for the measurement point node in response to the addition of a measurement point node to the component node; and determining the attributes of the measurement point node in response to the configuration operation on the third configuration interface.

[0046] When a user initiates a new operation on a component node, the third configuration interface is displayed. As an example, the third configuration interface looks like this: Figure 5 As shown, this includes information such as measurement point codes, measurement point descriptions, and data sources. The data source includes information such as the IP address, port, username, and password of the time series database. Figure 2 Taking the medium-sized wind turbine-transmission system-gearbox as an example, the measurement points include vibration, tooth root stress, temperature, and oil condition. Figure 5 Displays temperature measurement point information on the gearbox. The measurement point is a time series formed by multiple data points arranged in ascending order of timestamps. Each measurement point represents a collection point location, which can periodically collect physical quantities of the surrounding environment.

[0047] Optionally, users can select any device node, component node, or measurement point node to modify or delete. For batch modifications, users can select any device and click the "Export" button to export the device's table file. After modifying the table file, the modified table file can be imported using the batch editing option, thus achieving batch modification.

[0048] This embodiment also provides a database access device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0049] This embodiment provides a database access device, such as... Figure 6 As shown, it includes: The calling module is used to respond to the first query request, determine the target database corresponding to the target measurement point in the first query request, and call the target connector for the target database; The request conversion module is used to convert the first query request into a call instruction adapted to the target database based on the target connector; The result receiving module is used to initiate a second query request to the target database based on the target connector and the calling instruction, and to receive the original query results returned by the target database; The chart generation module is used to process the original query results, generate and display the target chart.

[0050] In some alternative implementations, the request conversion module includes: The instruction generation unit is used to map and encapsulate the parameters in the first query request into a call instruction that conforms to the native query protocol of the target database, based on the adaptation layer of the target connector.

[0051] In some alternative implementations, the chart generation module includes: The result processing unit is used to normalize the original query results and generate structured data that conforms to a preset standard format. The chart generation unit is used to generate and display the target chart based on the structured data.

[0052] In some alternative embodiments, the apparatus further includes: The model building module is used to build a query model, which includes at least one device node, at least one component node, and at least one measurement point node; the measurement point node is configured to correspond to at least one time-series data source in a preset database.

[0053] In some alternative implementations, the model building module includes: The first newly added unit is used to display the first configuration interface of the device node in response to the operation of adding a device node; The first configuration unit is configured to determine the attributes of the device node in response to a configuration operation on the first configuration interface.

[0054] In some optional implementations, the model building module further includes: The second addition unit is used to display the second configuration interface of the component node in response to the addition operation of the component node of the device node; The second configuration unit is used to determine the attributes of the component node in response to a configuration operation on the second configuration interface.

[0055] In some optional implementations, the model building module further includes: The third newly added unit is used to display the third configuration interface of the measuring point node in response to the operation of adding a measuring point node to the component node. The third configuration unit is used to determine the attributes of the measurement point node in response to the configuration operation on the third configuration interface.

[0056] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0057] In this embodiment, the database access device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0058] This invention also provides a computer device having the above-described features. Figure 6 The database access device shown.

[0059] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0060] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0061] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0062] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0063] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0064] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0065] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0066] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0067] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the invention.

Claims

1. A database access method, characterized in that, The method includes: In response to the first query request, the target database corresponding to the target measurement point in the first query request is determined, and the target connector for the target database is invoked; Based on the target connector, the first query request is converted into a call instruction adapted to the target database; Based on the target connector and the invocation instruction, a second query request is initiated to the target database, and the original query results returned by the target database are received; The original query results are processed to generate and display the target chart.

2. The database access method according to claim 1, characterized in that, The step of converting the first query request into a call instruction adapted to the target database based on the target connector includes: Based on the adaptation layer of the target connector, the parameters in the first query request are mapped and encapsulated into a call instruction that conforms to the native query protocol of the target database.

3. The database access method according to claim 1, characterized in that, The process of processing the original query results to generate and display the target chart includes: The original query results are normalized to generate structured data that conforms to a preset standard format; The target chart is generated and displayed based on the structured data.

4. The database access method according to claim 1, characterized in that, Before receiving the first query request, the method further includes: A query model is constructed, which includes at least one device node, which includes at least one component node, and the component node includes at least one measurement point node; the measurement point node is configured to correspond to at least one time-series data source in a preset database.

5. The database access method according to claim 4, characterized in that, The construction of the query model includes: In response to a new operation on a device node, the first configuration interface of the device node is displayed; In response to the configuration operation on the first configuration interface, the attributes of the device node are determined.

6. The database access method according to claim 5, characterized in that, The construction of the query model also includes: In response to a new component node added to the device node, a second configuration interface for the component node is displayed; In response to a configuration operation on the second configuration interface, the attributes of the component node are determined.

7. The database access method according to claim 6, characterized in that, The construction of the query model also includes: In response to the addition of a measurement point node to the component node, the third configuration interface of the measurement point node is displayed; In response to the configuration operation on the third configuration interface, the attributes of the measurement point node are determined.

8. A database access device, characterized in that, The device includes: The calling module is used to respond to the first query request, determine the target database corresponding to the target measurement point in the first query request, and call the target connector for the target database; The request conversion module is used to convert the first query request into a call instruction adapted to the target database based on the target connector; The result receiving module is used to initiate a second query request to the target database based on the target connector and the calling instruction, and to receive the original query results returned by the target database; The chart generation module is used to process the original query results, generate and display the target chart.

9. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory storing computer instructions, and the processor executing the computer instructions to perform the database access method of any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the database access method according to any one of claims 1 to 7.

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