Time sequence database tool chain development method based on graphical programming

Abstract

A time sequence database tool chain development method based on graphical programming is characterized by comprising the following steps: 1, constructing a time sequence database tool chain development system based on graphical programming; 2, acquiring electric signal data of the sensor equipment as original data a in real time by the acquisition equipment; 3, acquiring original data a by a data acquisition function primitive in the block diagram workbench; 4, the data processing primitive performs data processing operation on the data a to obtain time sequence data b; 5, the database function primitive stores the time sequence data b into a corresponding time sequence database according to database information provided by the database resource primitive; 6, the user side sends a data demand instruction to the block diagram workbench through the big data application, and the database function primitive queries corresponding target time sequence data c from the time sequence database; and 7, the data asset processing function primitive processes the target time sequence data c into a corresponding data resource primitive d, and transmits the data resource primitive d to a big data application for a user to use.

Description

Technical Field

[0001] The present invention relates to the technical field of graphical programming, and in particular to a method for developing a time series database tool chain based on graphical programming. Background Art

[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 designed primarily to process and store data that changes over time. This type of data usually has the characteristics of many measurement points, high sampling frequency, and high storage cost. With the development of the Internet of Things, the demand for time series databases is increasing, and they are used in many fields such as hydrological monitoring, factory equipment monitoring, national security data monitoring, communication monitoring, financial industry indicator data, sensor data, etc. In the Internet industry, time series databases are also used to store the behavior trajectories of users visiting websites, log data generated by applications, etc.

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

[0005] Time series databases are showing a rapid growth trend in the current development, especially in the Internet of Things and other fields, making time series databases an important pillar in the field of data processing. At present, the development and application of time series databases are mostly carried out by text code programming. In the field of graphical programming, there are few studies on the methods of developing and applying time series databases using graphical programming. Summary of the invention

[0006] The present invention provides a graphical programming-based time series database tool chain development method, which can realize the development and application of time series databases through graphical programming.

[0007] To achieve the above object, the present invention provides a method for developing a time series database tool chain based on graphical programming, the key of which is to include the following steps:

[0008] Step 1: Build a time series database tool chain development system based on graphical programming, wherein the time series database tool chain development system is provided with a cloud server, and the cloud server is connected to a technical development end, a user end, and an acquisition device through a network; the cloud server is provided with a graphical programming platform and a time series database service, and the graphical programming platform and the time series database service realize two-way data interaction through a standardized interface;

[0009] The graphical programming platform is provided with a block diagram workbench and a big data application, wherein the block diagram workbench includes a data acquisition function primitive, a data processing function primitive, a database function primitive, a database resource primitive, and a data asset processing function primitive;

[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 primitive in the block diagram workbench acquires the original data a and passes it to the data processing primitive;

[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 passes the data 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 according to the database information provided by the database resource primitive;

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

[0015] Step 7: The data asset processing function element processes the target time series data c into corresponding data resource element d according to the data demand instruction, and 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 a time series database more intuitive and efficient. Developers can quickly implement complex functions through modular drag and drop, logical connections, and graphical operations, thereby saving a lot of development time and effort and speeding up the progress of the project. At the same time, the graphical interface allows for a more intuitive understanding and operation of the database without the need to have an in-depth understanding of the database's internal structure and query language, effectively improving the user's ease of use and experience, and reducing the learning cost of using a time series database.

[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 timing database service, the storage unit provides storage space for the graphical programming platform and the timing database service, and the business database is used to store function graph information data.

[0018] The computing unit and the storage unit provide enhanced assurance for the rapid and stable operation of the time series database tool chain development system.

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

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

[0021] As a preferred embodiment, the method further includes the step of creating a function graph element, which is as follows:

[0022] Step A1: the technical development end creates a function graph element according to the use requirements, and obtains text code data, function icon and function graph element information of the corresponding function graph element;

[0023] Step A2: the technical development end imports the database function graphic element information into the business database through the technical interface, and imports the text code data and function icon into the hard disk in the computing unit;

[0024] Step A3: According to the use requirements, the text code data, function icon and function graphic element information are imported into the block diagram workbench to obtain the corresponding function graphic element.

[0025] Preferably: in step 1, the graphical programming platform is also 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, and the holographic twin is also connected to the debugger.

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

[0027] As a preferred embodiment, the time series database tool chain development and debugging steps are also included, as follows:

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

[0029] Step B2: the translator translates the graphical code into text code and passes it to the compiler, and the translator sends the mapping relationship between the graphic element and the text to the holographic twin;

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

[0031] Step B4: the block diagram workbench debugs the graphical code according to the running status of the graphical code, and sends the debugging behavior to the holographic twin through the debugger, and the holographic twin sends the line number information of the debugging behavior instruction mapping text code to the sandbox;

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

[0033] Step B6: The block diagram workbench further debugs the graphical code according to the correctness of the execution result data until the execution result data is completely correct, that is, the debugging is completed.

[0034] Through the above design, the underlying logic debugging of the time series database tool chain is realized to ensure the accurate and efficient operation of the time series database tool chain.

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

[0036] Through the above design, the data processing primitive converts the original data points collected by the data acquisition function primitive into waveform time series data with different frequencies to meet the user's demand for using time series data with different frequencies.

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

[0038] For example: when the target time series data c is power data, the related time series data is the corresponding current data and voltage data;

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

[0040] Preferably, in step 7, the data asset processing function element processes the target time series data c into blockchain asset data with an asset identifier, namely, data resource element d.

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

[0042] Beneficial effects of the present invention:

[0043] 1. The ability to use graphical programming to develop and apply time series databases has greatly lowered the threshold for developing time series databases. Developers do not need to deeply understand the underlying database principles and programming language details, but can complete database design and development through intuitive interface operations, thereby attracting more non-professional developers to participate in the development of time series databases.

[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, connection and other operations, thus saving a lot of development time and energy and speeding up the progress of the project.

[0045] 3. For users of time series databases, a graphical interface allows them to more intuitively understand and operate the database without having to deeply understand the internal structure and query language of the database. This improves the user's convenience and experience and reduces the learning cost of using a time series database.

[0046] 4. The successful application of this invention has promoted the application of visual programming tools in the database field and provided new ideas and methods for database development in other fields. This will also help promote the wider application and development of visual programming tools in the field of software development. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the structure flow of the present invention;

[0048] Figure 2 It is a schematic diagram of the development and debugging process of the present invention. DETAILED DESCRIPTION

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

[0050] like Figure 1 As shown: A method for developing a time series database tool chain based on graphical programming, the key of which is to include the following steps:

[0051] Step 1: Build a time series database tool chain development system based on graphical programming, wherein the time series database tool chain development system is provided with a cloud server, and the cloud server is connected to a technical development end, a user end, and an acquisition device through a network; the cloud server is provided with a graphical programming platform and a time series database service, and the graphical programming platform and the time series database service realize two-way data interaction through a standardized interface;

[0052] The graphical programming platform is provided with a block diagram workbench and a big data application, wherein the block diagram workbench includes a data acquisition function primitive, a data processing function primitive, a database function primitive, a database resource primitive, and a data asset processing function primitive;

[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 primitive in the block diagram workbench acquires the original data a and passes it to the data processing primitive;

[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 passes the data 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 according to the database information provided by the database resource primitive;

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

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

[0059] In step 1, the cloud server is also 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 timing database service, the storage unit provides storage space for the graphical programming platform and the timing database service, and the business database is used to store function graph information data.

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

[0061] In step 1, the graphical programming platform is also 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, and the holographic twin is also connected to the debugger.

[0062] In step 3, the data processing operation is to downsample the original data a into time series data b of the corresponding sampling frequency by using different downsampling rates.

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

[0064] In step 7, the data asset processing function element processes the target time series data c into blockchain asset data with an asset identifier, namely, data resource element d.

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

[0066] like Figure 2 As shown: It also includes the steps of creating function graph elements, as follows:

[0067] Step A1: the technical development end creates a function graph element according to the use requirements, and obtains text code data, function icon and function graph element information of the corresponding function graph element;

[0068] Step A2: the technical development end imports the database function graphic element information into the business database through the technical interface, and imports the text code data and function icon into the hard disk in the computing unit;

[0069] Step A3: According to the use requirements, the text code data, function icon and function graphic element information are imported into the block diagram workbench to obtain the corresponding function graphic element.

[0070] It also includes the time series database tool chain development and debugging steps, as follows:

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

[0072] Step B2: the translator translates the graphical code into text code and passes it to the compiler, and the translator sends the mapping relationship between the graphic element and the text to the holographic twin;

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

[0074] Step B4: the block diagram workbench debugs the graphical code according to the running status of the graphical code, and sends the debugging behavior to the holographic twin through the debugger, and the holographic twin sends the line number information of the debugging behavior instruction mapping text code to the sandbox;

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

[0076] Step B6: The block diagram workbench further debugs the graphical code according to the correctness of the execution result data until the execution result data is completely correct, that is, the debugging is completed.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for developing a time series database tool chain based on graphical programming, characterized in that: The following steps are involved: Step 1: Build a time series database tool chain development system based on graphical programming, wherein the time series database tool chain development system is provided with a cloud server, and the cloud server is connected to a technical development end, a user end, and an acquisition device through a network; the cloud server is provided with a graphical programming platform and a time series database service, and the graphical programming platform and the time series database service realize two-way data interaction through a standardized interface; The graphical programming platform is provided with a block diagram workbench and a big data application, wherein the block diagram workbench includes a data acquisition function primitive, a data processing function primitive, a database function primitive, a database resource primitive, and a data asset processing function primitive; 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 primitive in the block diagram workbench acquires the original data a and passes it to the data processing primitive; 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 passes the data 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 according to the database information provided by the database resource primitive; Step 6: The user terminal sends a data demand instruction to the block diagram workbench through the big data application. The database function graph element queries the corresponding target time series data c from the time series database according to the data demand instruction, and passes it to the data asset processing function graph element; Step 7: The data asset processing function element processes the target time series data c into corresponding data resource element d according to the data demand instruction, and passes it to the big data application for user use.

2. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In step 1, the cloud server is also 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 timing database service, the storage unit provides storage space for the graphical programming platform and the timing database service, and the business database is used to store function graph information data.

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

4. The method for developing a time series database tool chain based on graphical programming according to claim 3, characterized in that: It also includes the steps for creating function graph elements, as follows: Step A1: the technical development end creates a function graph element according to the use requirements, and obtains text code data, function icon and function graph element information of the corresponding function graph element; Step A2: the technical development end imports the database function graphic element information into the business database through the technical interface, and imports the text code data and function icon into the hard disk in the computing unit; Step A3: According to the use requirements, the text code data, function icon and function graphic element information are imported into the block diagram workbench to obtain the corresponding function graphic element.

5. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In step 1, the graphical programming platform is also 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, and the holographic twin is also connected to the debugger.

6. The method for developing a time series database tool chain based on graphical programming according to claim 5, characterized in that: It also includes the time series database tool chain development and debugging steps, as follows: Step B1: The technical development end imports the data acquisition function primitive, the data processing function primitive, the database function primitive, the database resource primitive and the data asset processing function primitive into the block diagram workbench, and logically connects the primitives according to the usage requirements, generates corresponding graphical codes, and then passes them to the translator; Step B2: the translator translates the graphical code into text code and passes it to the compiler, and the translator sends the mapping relationship between the graphic element and the text to the holographic twin; Step B3: the compiler compiles the text code into machine code data and passes it to the sandbox, and the sandbox maps the machine code data into the holographic twin; Step B4: the block diagram workbench debugs the graphical code according to the running status of the graphical code, and sends the debugging behavior to the holographic twin through the debugger, and the holographic twin sends the line number information of the debugging behavior instruction mapping text code to the sandbox; Step B5: the sandbox executes the machine code data according to the debugging behavior to obtain execution result data, and maps the execution result data into graphic element information through the holographic twin and sends it to the block diagram workbench; Step B6: The block diagram workbench further debugs the graphical code according to the correctness of the execution result data until the execution result data is completely correct, that is, the debugging is completed.

7. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In step 3, the data processing operation is to downsample the original data a into time series data b of a corresponding sampling frequency by using different downsampling rates.

8. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In step 6, when the target time series data c involved in the data requirement instruction does not directly exist in the time series database, the database function graph element queries the relevant time series data from the time series database according to the properties of the target time series data c, and converts the relevant time series data into the target time series data c that meets the requirements through the corresponding function graph element.

9. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In step 7, the data asset processing function element processes the target time series data c into blockchain asset data with an asset identifier, namely, data resource element d.

10. The method for developing a time series database tool chain based on graphical programming according to claim 1, characterized in that: In the big data application, the data resource element d is used for AI model training, data analysis, or data visualization.

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