Graphical programming-based time series database tool chain development method

Abstract

A time series database tool chain development method based on graphical programming, characterized by comprising the following steps: 1: constructing a time series database tool chain development system based on graphical programming; 2: collecting the electric signal data of a sensor device as raw data a by a real-time collection device; 3: collecting the raw data a by a data collection function graph element in a block diagram workstation; 4: performing data processing operations on the data a by a data processing graph element to obtain time series data b; 5: storing the time series data b into a corresponding time series database according to the database information provided by a database resource graph element; 6: sending a data demand instruction to the block diagram workstation by a big data application on a user side, and querying corresponding target time series data c from the time series database by the database function graph element; and 7: processing the target time series data c into a corresponding data resource graph element d by a data asset processing function graph element, and delivering the data resource graph element d to the big data application for use by a user.

Description

Technical Field

[0001] This invention relates to the field of graphical programming technology, and in particular to a method for developing a time-series database toolchain based on graphical programming. Background Technology

[0002] Time-series databases are one of the fastest-growing types in the current database field, especially in non-relational databases.

[0003] Time-series databases are primarily designed to process and store data that changes over time. This type of data typically features numerous measurement points, high sampling frequencies, and high storage costs. With the development of the Internet of Things (IoT), the demand for time-series databases is increasing, finding applications in various fields such as hydrological monitoring, factory equipment monitoring, national security data monitoring, communications monitoring, financial industry indicator data, and sensor data. In the internet industry, time-series databases are also used to store user website browsing behavior trajectories and application-generated log data.

[0004] In recent years, time-series databases have developed rapidly and are widely used not only globally, but also in China, where the number of time-series databases has reached 53, accounting for 15.4% of non-relational databases. This indicates that time-series databases have become an indispensable part of the data processing field.

[0005] Time-series databases are experiencing rapid growth, especially with their widespread application in fields such as the Internet of Things (IoT), making them a crucial pillar in data processing. Currently, most time-series database development and applications utilize text-based code programming, with limited research on graphical programming methods for developing and applying time-series databases. Summary of the Invention

[0006] This invention provides a time-series database toolchain development method based on graphical programming, which enables the development and application of time-series databases through graphical programming.

[0007] To achieve the above objectives, this invention provides a method for developing a time-series database toolchain based on graphical programming, the key of which includes the following steps:

[0008] Step 1: Construct a time-series database toolchain development system based on graphical programming. The time-series database toolchain development system is equipped with a cloud server, which is connected to the technical development end, user end, and data acquisition device via a network. The cloud server is equipped with a graphical programming platform and a time-series database service. The graphical programming platform and the time-series database service achieve bidirectional data interaction through a standardized interface.

[0009] The graphical programming platform includes a diagram workbench and big data applications. The diagram workbench contains data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives.

[0010] Step 2: The acquisition device acquires the electrical signal data of the sensor device in real time as raw data a, and transmits it to the cloud server;

[0011] Step 3: The data acquisition function element in the block diagram workbench acquires the raw data 'a' and passes it to the data processing element;

[0012] Step 4: The data processing primitive performs data processing operations on the original data a to obtain time-series data b with a unique identifier, and then passes it to the database function primitive;

[0013] Step 5: The database function primitive stores the time series data b into the corresponding time series database in the time series database service based on the database information provided by the database resource primitive;

[0014] Step 6: The user sends a data request instruction to the block diagram workbench through the big data application. The database function element queries the corresponding target time series data c from the time series database according to the data request instruction and passes it to the data asset processing function element.

[0015] Step 7: The data asset processing function primitive processes the target time-series data c into the corresponding data resource primitive d according to the data demand instruction, and then passes it to the big data application for user use.

[0016] Through the above design, the use of a graphical programming platform makes the development process of time-series databases more intuitive and efficient. Developers can quickly implement complex functions through modular drag-and-drop, logical connections, and graphical operations, thereby saving significant development time and effort and accelerating project progress. Simultaneously, the graphical interface allows for a more intuitive understanding and manipulation of the database without requiring in-depth knowledge of its internal structure and query language, effectively improving user convenience and experience, and reducing the learning curve for using time-series databases.

[0017] Preferably, in step 1, the cloud server is further provided with a computing power unit, a storage unit, and a business database. The computing power unit provides computing power for the graphical programming platform and the time-series database service, the storage unit provides storage space for the graphical programming platform and the time-series database service, and the business database is used to store function primitive information data.

[0018] The computing and storage units enhance the rapid and stable operation of the time-series database toolchain development system.

[0019] Preferably, the computing unit is equipped with a GPU and a CPU, and the storage unit is equipped with memory and a hard disk.

[0020] Through the above design, the development of database function primitives was realized. These database function primitives include operations such as initialization, writing, reading, deletion, and closing of the corresponding time-series database.

[0021] As a preferred option, the steps for creating function primitives are also included, as follows:

[0022] Step A1: The technology development end creates function primitives according to usage requirements, and obtains the text code data, function icon and function primitive information of the corresponding function primitive;

[0023] Step A2: The technology development end imports the database function primitive information into the business database through the technology interface, and imports the text code data and function icons into the hard disk in the computing unit;

[0024] Step A3: According to the usage requirements, import the text code data, function icons and function primitive information into the block diagram workbench to obtain the corresponding function primitives.

[0025] Preferably, in step 1, the graphical programming platform is further provided with a translator, a compiler, a sandbox, a holographic twin, and a debugger. The block diagram workbench is connected to the translator and the debugger. The translator is connected to the compiler. The compiler is connected to the sandbox. The sandbox is bidirectionally connected to the holographic twin. The holographic twin is also connected to the debugger.

[0026] The sandbox is the runtime environment for the compiled executable file, i.e., machine code data.

[0027] As a preferred option, it also includes the development and debugging steps for time-series database toolchains, as detailed below:

[0028] Step B1: The technology development end imports the data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives into the block diagram workbench, and connects the primitives logically according to the usage requirements to generate the corresponding graphical code, and then passes it to the translator;

[0029] Step B2: The translator translates the graphical code into text code and passes it to the compiler. The translator sends the mapping relationship between primitives and text to the holographic twin.

[0030] Step B3: The compiler compiles the text code into machine code data and passes it to the sandbox, which maps the machine code data into the holographic twin.

[0031] Step B4: The block diagram workbench debugs the graphical code based on its execution status, and sends the debugging behavior to the holographic twin through the debugger. The holographic twin sends the line number information of the debugging behavior instruction mapped text code to the sandbox.

[0032] Step B5: The sandbox executes the machine code data according to the debugging behavior, obtains execution result data, and maps the execution result data into primitive information through the holographic twin and sends it to the block diagram workbench;

[0033] Step B6: The flowchart workbench further debugs the graphical code based on the correctness of the execution result data until the execution result data is completely correct, thus completing the debugging.

[0034] The above design enables the debugging of the underlying logic of the time-series database toolchain, ensuring its accurate and efficient operation.

[0035] Preferably, in step 3, the data processing operation is to downsample the original data a to time-series data b with a corresponding sampling frequency using different downsampling rates.

[0036] Through the above design, the data processing primitive transforms the raw data points acquired by the data acquisition function primitive into waveform time series data with different frequencies, so as to meet the user's needs for using time series data of different frequencies.

[0037] Preferably, in step 6, when the target time series data c involved in the data demand instruction does not directly exist in the time series database, the database function primitive queries relevant time series data from the time series database according to the properties of the target time series data c, and transforms the relevant time series data into the target time series data c that meets the requirements through the corresponding function primitive.

[0038] For example, when the target timing data c is power data, the relevant timing data are the corresponding current data and voltage data;

[0039] When the target time series data c is distance data, the relevant time series data are the corresponding speed data and time data.

[0040] Preferably, in step 7, the data asset processing function primitive processes the target time-series data c into blockchain asset data with asset identifiers, i.e., data resource primitive d.

[0041] Preferably, in the big data application, the data resource primitive d is used for AI model training, data analysis, or data visualization.

[0042] The beneficial effects of this invention are:

[0043] 1. The ability to develop and apply time-series databases using graphical programming significantly lowers the barrier to entry. Developers do not need to deeply understand the underlying database principles and programming language details; instead, they can complete database design and development through an intuitive interface, thereby attracting more non-professional developers to participate in time-series database development.

[0044] 2. Graphical programming tools make the development process of time-series databases more intuitive and efficient. Developers can complete complex functional designs through simple drag-and-drop and connection operations, thereby saving a lot of development time and effort and accelerating the project progress.

[0045] 3. For users of time-series databases, the graphical interface allows for a more intuitive understanding and operation of the database without requiring in-depth knowledge of its internal structure and query language. This improves user convenience and experience, and reduces the learning curve for using time-series databases.

[0046] 4. The successful application of this invention has promoted the use of visual programming tools in the database field, providing new ideas and methods for database development in other fields. This also helps to promote the wider application and development of visual programming tools in the software development field. Attached Figure Description

[0047] Figure 1 This is a schematic diagram of the structural process of the present invention;

[0048] Figure 2 This is a schematic diagram of the development and debugging process of the present invention. Detailed Implementation

[0049] The present invention will be further described in detail below with reference to the accompanying drawings and specific examples. The following embodiments or drawings are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0050] like Figure 1 The diagram illustrates a method for developing a time-series database toolchain based on graphical programming. The key steps include:

[0051] Step 1: Construct a time-series database toolchain development system based on graphical programming. The time-series database toolchain development system is equipped with a cloud server, which is connected to the technical development end, user end, and data acquisition device via a network. The cloud server is equipped with a graphical programming platform and a time-series database service. The graphical programming platform and the time-series database service achieve bidirectional data interaction through a standardized interface.

[0052] The graphical programming platform includes a diagram workbench and big data applications. The diagram workbench contains data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives.

[0053] Step 2: The acquisition device acquires the electrical signal data of the sensor device in real time as raw data a, and transmits it to the cloud server;

[0054] Step 3: The data acquisition function element in the block diagram workbench acquires the raw data 'a' and passes it to the data processing element;

[0055] Step 4: The data processing primitive performs data processing operations on the original data a to obtain time-series data b with a unique identifier, and then passes it to the database function primitive;

[0056] Step 5: The database function primitive stores the time series data b into the corresponding time series database in the time series database service based on the database information provided by the database resource primitive;

[0057] Step 6: The user sends a data request instruction to the block diagram workbench through the big data application. The database function element queries the corresponding target time series data c from the time series database according to the data request instruction and passes it to the data asset processing function element.

[0058] Step 7: The data asset processing function primitive processes the target time-series data c into the corresponding data resource primitive d according to the data demand instruction, and then passes it to the big data application for user use.

[0059] In step 1, the cloud server is further equipped with a computing power unit, a storage unit, and a business database. The computing power unit provides computing power for the graphical programming platform and the time-series database service, the storage unit provides storage space for the graphical programming platform and the time-series database service, and the business database is used to store function primitive information data.

[0060] The computing unit is equipped with a GPU and a CPU, and the storage unit is equipped with memory and a hard disk.

[0061] In step 1, the graphical programming platform is further equipped with a translator, a compiler, a sandbox, a holographic twin, and a debugger. The block diagram workbench is connected to the translator and the debugger. The translator is connected to the compiler. The compiler is connected to the sandbox. The sandbox is bidirectionally connected to the holographic twin. The holographic twin is also connected to the debugger.

[0062] In step 3, the data processing operation involves downsampling the original data a to time-series data b with a corresponding sampling frequency using different downsampling rates.

[0063] In step 6, when the target time series data c involved in the data requirement instruction does not exist directly in the time series database, the database function primitive queries relevant time series data from the time series database according to the properties of the target time series data c, and transforms the relevant time series data into the target time series data c that meets the requirements through the corresponding function primitive.

[0064] In step 7, the data asset processing function primitive processes the target time-series data c into blockchain asset data with asset identifiers, i.e., data resource primitive d.

[0065] In the big data application, the data resource primitive d is used for AI model training, data analysis, or data visualization.

[0066] like Figure 2 As shown: It also includes the steps for creating function primitives, as detailed below:

[0067] Step A1: The technology development end creates function primitives according to usage requirements, and obtains the text code data, function icon and function primitive information of the corresponding function primitive;

[0068] Step A2: The technology development end imports the database function primitive information into the business database through the technology interface, and imports the text code data and function icons into the hard disk in the computing unit;

[0069] Step A3: According to the usage requirements, import the text code data, function icons and function primitive information into the block diagram workbench to obtain the corresponding function primitives.

[0070] It also includes the development and debugging steps for time-series database toolchains, as detailed below:

[0071] Step B1: The technology development end imports the data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives into the block diagram workbench, and connects the primitives logically according to the usage requirements to generate the corresponding graphical code, and then passes it to the translator;

[0072] Step B2: The translator translates the graphical code into text code and passes it to the compiler. The translator sends the mapping relationship between primitives and text to the holographic twin.

[0073] Step B3: The compiler compiles the text code into machine code data and passes it to the sandbox, which maps the machine code data into the holographic twin.

[0074] Step B4: The block diagram workbench debugs the graphical code based on its execution status, and sends the debugging behavior to the holographic twin through the debugger. The holographic twin sends the line number information of the debugging behavior instruction mapped text code to the sandbox.

[0075] Step B5: The sandbox executes the machine code data according to the debugging behavior, obtains execution result data, and maps the execution result data into primitive information through the holographic twin and sends it to the block diagram workbench;

[0076] Step B6: The flowchart workbench further debugs the graphical code based on the correctness of the execution result data until the execution result data is completely correct, thus completing the debugging.

[0077] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for developing a time-series database toolchain based on graphical programming, characterized in that, Includes the following steps: Step 1: Construct a time-series database toolchain development system based on graphical programming. The time-series database toolchain development system is equipped with a cloud server, which is connected to the technical development end, user end, and data acquisition device via a network. The cloud server is equipped with a graphical programming platform and a time-series database service. The graphical programming platform and the time-series database service achieve bidirectional data interaction through a standardized interface. The graphical programming platform includes a diagram workbench and big data applications. The diagram workbench contains data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives. Step 2: The acquisition device acquires the electrical signal data of the sensor device in real time as raw data a, and transmits it to the cloud server; Step 3: The data acquisition function element in the block diagram workbench acquires the raw data 'a' and passes it to the data processing element; Step 4: The data processing primitive performs data processing operations on the original data a to obtain time-series data b with a unique identifier, and then passes it to the database function primitive; Step 5: The database function primitive stores the time series data b into the corresponding time series database in the time series database service based on the database information provided by the database resource primitive; Step 6: The user sends a data request instruction to the block diagram workbench through the big data application. The database function element queries the corresponding target time series data c from the time series database according to the data request instruction and passes it to the data asset processing function element. Step 7: The data asset processing function primitive processes the target time-series data c into the corresponding data resource primitive d according to the data demand instruction, and then transmits it to the big data application for user use; In step 3, the data processing operation is to downsample the original data a to time-series data b with a corresponding sampling frequency using different downsampling rates; The data processing primitives transform the raw data points acquired by the data acquisition function primitives into waveform time series data with different frequencies to meet the user's needs for time series data with different frequencies. In step 6, when the target time series data c involved in the data demand instruction does not exist directly in the time series database, the database function primitive queries the relevant time series data from the time series database according to the properties of the target time series data c, and transforms the relevant time series data into the target time series data c that meets the requirements through the corresponding function primitive. In the big data application, the data resource primitive d is used for AI model training, data analysis, or data visualization.

2. The method for developing a time-series database toolchain based on graphical programming according to claim 1, characterized in that: In step 1, the cloud server is further equipped with a computing power unit, a storage unit, and a business database. The computing power unit provides computing power for the graphical programming platform and the time-series database service, the storage unit provides storage space for the graphical programming platform and the time-series database service, and the business database is used to store function primitive information data.

3. The method for developing a time-series database toolchain based on graphical programming according to claim 2, characterized in that: The computing unit is equipped with a GPU and a CPU, and the storage unit is equipped with memory and a hard disk.

4. The method for developing a time-series database toolchain based on graphical programming according to claim 3, characterized in that: It also includes the steps for creating function primitives, as detailed below: Step A1: The technology development end creates function primitives according to usage requirements, and obtains the text code data, function icon and function primitive information of the corresponding function primitive; Step A2: The technology development end imports the database function primitive information into the business database through the technology interface, and imports the text code data and function icons into the hard disk in the computing unit; Step A3: According to the usage requirements, import the text code data, function icons and function primitive information into the block diagram workbench to obtain the corresponding function primitives.

5. The method for developing a time-series database toolchain based on graphical programming according to claim 1, characterized in that: In step 1, the graphical programming platform is further equipped with a translator, a compiler, a sandbox, a holographic twin, and a debugger. The block diagram workbench is connected to the translator and the debugger. The translator is connected to the compiler. The compiler is connected to the sandbox. The sandbox is bidirectionally connected to the holographic twin. The holographic twin is also connected to the debugger.

6. The method for developing a time-series database toolchain based on graphical programming according to claim 5, characterized in that: It also includes the development and debugging steps for time-series database toolchains, as detailed below: Step B1: The technology development end imports the data acquisition function primitives, data processing function primitives, database function primitives, database resource primitives, and data asset processing function primitives into the block diagram workbench, and connects the primitives logically according to the usage requirements to generate the corresponding graphical code, and then passes it to the translator; Step B2: The translator translates the graphical code into text code and passes it to the compiler. The translator sends the mapping relationship between primitives and text to the holographic twin. Step B3: The compiler compiles the text code into machine code data and passes it to the sandbox, which maps the machine code data into the holographic twin. Step B4: The block diagram workbench debugs the graphical code based on its execution status, and sends the debugging behavior to the holographic twin through the debugger. The holographic twin sends the line number information of the debugging behavior instruction mapped text code to the sandbox. Step B5: The sandbox executes the machine code data according to the debugging behavior, obtains execution result data, and maps the execution result data into primitive information through the holographic twin and sends it to the block diagram workbench; Step B6: The flowchart workbench further debugs the graphical code based on the correctness of the execution result data until the execution result data is completely correct, thus completing the debugging.

7. The method for developing a time-series database toolchain based on graphical programming according to claim 1, characterized in that: In step 7, the data asset processing function primitive processes the target time-series data c into blockchain asset data with asset identifiers, i.e., data resource primitive d.

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