Dynamic signal data loading method based on Web graphical programming

Abstract

The invention discloses a signal data dynamic loading method based on Web graphical programming. The method comprises the steps that a server collects waveform data; the waveform data caching module caches waveform data and sends the waveform data to the adapter module and the downsampling module; the down-sampling module carries out down-sampling on the waveform data, and the down-sampled data is sent to the adapter module through the down-sampled data cache module; the database adapter writes basic information into a service database, and the time sequence database adapter writes time sequence data into a time sequence database; the browser obtains a data loading request instruction; the query module determines a query condition; the query module queries corresponding waveform target data; the query module preprocesses the waveform target data and sends the waveform target data to the browser; and the waveform display control performs graphical format conversion on the graphical waveform data and displays the graphical waveform data in a browser. The method has the advantages that dynamic loading and visual display of the real-time data can be achieved in a graphical mode, and a new solution is provided for monitoring, analyzing and processing the real-time data.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic data loading, and in particular to a signal data dynamic loading method based on Web graphical programming. Background Art

[0002] With the development of the Internet and Web technology, more and more applications need to dynamically load and graphically display real-time data in the Web environment. Especially in application fields that need to monitor, analyze or process real-time signal data, such as industrial control, Internet of Things, medical health, etc., higher requirements are put forward for the efficient acquisition, storage and display of real-time data.

[0003] At present, in the process of loading data, text programming is usually used to load data, while in the field of graphical programming, it is less involved; and the operability and interactivity of the data are low.

[0004] Disadvantages of existing technology:

[0005] 1. Time series databases are rarely involved in the field of graphical programming;

[0006] 2. In the process of dynamic signal data loading, there is a lack of complete technical solutions for real-time data acquisition and processing;

[0007] 3. Lack of effective data storage and management technology for real-time collected data;

[0008] 4. Implementing dynamic loading and visualization of signal data in a Web environment is a challenging technical problem;

[0009] 5. The operability and interactivity of the data are low, and the user experience is poor. Summary of the invention

[0010] The present invention provides a signal data dynamic loading method based on Web graphical programming, which can realize the dynamic loading and visual display of real-time data in a graphical manner.

[0011] To achieve the above-mentioned purpose, the present invention provides a signal data dynamic loading method based on Web graphical programming, the key of which is to include step A of real-time data collection and storage and step B of dynamic loading of graphical data:

[0012] The step A of real-time data collection and storage includes the following steps:

[0013] Step A1: The server collects waveform data with time series attributes from the device in real time and caches it in a waveform data cache module;

[0014] Step A2: the waveform data cache module sends the waveform data to the adapter module and the downsampling module; the downsampling module downsamples the waveform data to obtain downsampled data, and then sends it to the downsampled data cache module; the downsampled data cache module caches the downsampled data and sends it to the adapter module;

[0015] Step A3: the database adapter in the adapter module writes the basic information in the waveform data or the downsampled data into the business database, and the time series database adapter in the adapter module writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database;

[0016] The business database is at least one of MongoDB, MySq1, Postgresq, and Sqlite, and the time series database is at least one of InfluxDB, lotDB, TimeScale, and TDengine.

[0017] The business database and time series database are either database modules arranged in the server or external database modules connected to the server.

[0018] Through the above design, the waveform data in the sensor is collected in real time through the server and cached. Then, the basic information in the waveform data and the original time series data are stored in the business database and the time series database through the adapter module, which is convenient for subsequent users to dynamically load and display the waveform data, so that users can easily monitor and analyze real-time data and discover data changes and abnormal situations in time.

[0019] The server downsamples the waveform data through the waveform data downsampling module, and stores the basic information of the downsampled data of different frequencies and the downsampled timing data in the business database and the timing database through the adapter module, so as to facilitate the dynamic loading of the data of the corresponding frequency according to the user's needs and improve the signal data loading efficiency.

[0020] The step B of dynamically loading graphical data comprises the following steps:

[0021] Step B1: The browser obtains a data loading request instruction and transmits the data loading request instruction to a data loading component; at the same time, the data loading component senses the resolution information of the browser device and sends the resolution information and the data loading request instruction to a server;

[0022] Step B2: the query module in the server determines the query condition according to the request parameter of the data loading request instruction;

[0023] Step B3: the query module queries the waveform data cache module, the down-sampling data cache module, or the timing database through the adapter module according to the query condition;

[0024] Step B4: The query module performs preprocessing operations on the waveform target data to obtain graphic waveform data and sends it to the browser; the data retrieved from the server needs to be preprocessed so that it can be transmitted on the network and processed in the browser. This mainly involves data encoding and decoding.

[0025] Step B5: The waveform display control in the browser converts the graphic waveform data into a graphic format, and then displays it in the browser. Each point on the graphic waveform data corresponds to a time series data.

[0026] During the dynamic data loading process, the query module selects whether to read the corresponding waveform data or the downsampled data of the corresponding frequency from the data cache module or read the corresponding waveform data or the downsampled data of the corresponding frequency from the time series database according to the query conditions;

[0027] If you choose to read from a time series database, the database adapter needs to search for the corresponding business information in the business database through the data channel information in the query condition, and then the time series database adapter searches for the corresponding time series data in the time series database based on the business information.

[0028] Through the above design, the corresponding time series data is obtained from the business database and the time series database in a graphical way and converted into graphic waveform data, which realizes dynamic data loading and display. This enables the browser to dynamically load the required graphic waveform data instead of loading all the data at once, which helps to improve data loading efficiency and reduce network bandwidth consumption.

[0029] Users can dynamically load and display the real-time data signals of the device on the browser according to their own needs, so that users can easily monitor and analyze real-time data and promptly discover data changes and abnormal situations; at the same time, users can select graphic waveform data through a simple and intuitive graphical operation interface by selecting, clicking, and double-clicking, which improves the user's data usage efficiency and operation experience.

[0030] Preferably, in step A2, the downsampling module uses an LTTB algorithm to downsample the waveform data. When the downsampling module downsamples the waveform data, the downsampling multiple is dynamically determined according to a downsampling strategy.

[0031] LTTB (Largest-Triangle-Three-Buckets) is an algorithm for data downsampling, which aims to downsample the original data into a data set with a smaller data volume but still retains sufficient trends and features. The LTTB algorithm is a simple and effective downsampling method based on the area of ​​a triangle.

[0032] The basic idea of ​​the LTTB algorithm is to divide the original data into several buckets, and then select a triangle with the largest area in each bucket as a representative point to achieve data downsampling. The key steps of the algorithm include:

[0033] The original data is divided into several buckets according to certain rules, and each bucket contains a fixed number of data points.

[0034] In each bucket, a representative point is selected by calculating the area of ​​the triangle. Specifically, for each bucket, the algorithm calculates the area of ​​the triangle formed by all the data points in the bucket and selects the data point corresponding to the triangle with the largest area as the representative point.

[0035] All selected representative points are combined to form a downsampled dataset.

[0036] The LTTB algorithm is simple and efficient, and can retain the main trends and features of the original data while reducing the amount of data. It is suitable for downsampling of large-scale data. Due to its simplicity and performance advantages, the LTTB algorithm has been widely used in the field of data visualization, especially in graphics drawing and time series data processing.

[0037] When the downsampling module performs downsampling processing on the waveform data, the downsampling multiple is dynamically determined according to the downsampling strategy.

[0038] It can be equidistant downsampling, or capturing the maximum and minimum values ​​of the waveform within the resolution time period s, and then dividing the time period m between the adjacent maximum and minimum values ​​in the waveform into a corresponding number of unit time periods t according to the set unit frequency time, and randomly determining a downsampling point in each unit time period t; where s>m>t>0.

[0039] The waveform data and down-sampling data in the server are the basis for dynamic data loading on the browser. For any data loading request instruction obtained by the browser, the server can query the corresponding time series data from either the waveform data cache module, the down-sampling data cache module, or the time series database according to its request parameters.

[0040] The downsampling algorithm may be an LTTB algorithm, an interval extreme value downsampling algorithm, a fixed-point sampling algorithm, or any combination of the above algorithms.

[0041] Preferably, in step A3, the basic information includes but is not limited to data type, data characteristics and data channel.

[0042] Different data channels correspond to different devices. According to the selection of data channel, you can choose which device to display real-time data.

[0043] Preferably, in step A3, the database adapter writes the basic information in the waveform data or downsampled data into a business database, including the following steps:

[0044] Step A31: Information parsing: the database adapter reads the basic information, parses the basic information format, and then extracts the parsed basic information;

[0045] Step A32: Information conversion: performing a data format conversion operation or a data cleaning operation on the parsed basic information to obtain converted basic information to meet the data format requirements and structure requirements of the business database;

[0046] Step A33: Information writing: writing the converted basic information into the business database; the data writing process involves operations such as database connection, data insertion or update, and the database adapter will correctly store the data into the corresponding data table according to the structure and requirements of the business database.

[0047] Step A34: Error handling and log recording: When an error occurs during the basic information writing process, the database adapter will perform error handling and record the relevant error information in the log for subsequent investigation and processing. Errors include data format errors, database connection failures, etc.

[0048] If the business database needs to be synchronized with other systems or data sources, the database adapter needs to perform data synchronization operations, which involve incremental synchronization or full synchronization of data to ensure data consistency and integrity.

[0049] Preferably, in step A3, the time series database adapter writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database, including the following steps:

[0050] Step A3-1: data parsing: the time series database adapter reads the time series data, parses the time series data format, and then extracts the parsed time series data;

[0051] Step A3-2: data conversion: converting or processing the parsed time series data to obtain converted time series data to meet the data format and structure requirements of the time series database;

[0052] Step A3-3: Data writing: writing the converted time series data into the time series database; this involves operations such as connecting to the time series database, inserting or updating data, etc. The time series database adapter will correctly write the data into the corresponding time series according to the structure and requirements of the time series database.

[0053] Step A3-4: Data indexing and optimization: The time series database adapter creates indexes and performs partition optimization processing on the written time series data to accelerate subsequent data queries and improve the efficiency of data retrieval and query;

[0054] Step A3-5: Error handling and log recording: When an error occurs during the time series data writing process, the time series database adapter will perform error handling and record the relevant error information in the log for subsequent investigation and processing.

[0055] If the time series database needs to be synchronized with other systems or data sources, the time series database adapter may need to perform data synchronization operations, which involve operations such as incremental synchronization or full synchronization of data to ensure data consistency and integrity.

[0056] Preferably, in step B1, the resolution information includes graphics card resolution information and display resolution information.

[0057] The data loading component automatically senses the resolution information of the browser device and optimizes data loading according to the resolution information, ensuring that the loaded graphic waveform data meets the display requirements of the hardware device while meeting the data information selection requirements, thereby reducing the amount of loaded data and improving the loading speed.

[0058] Preferably, in step B3, the request parameters include but are not limited to data channel parameters, time series data start time parameters, time series data end time parameters and browser resolution parameters.

[0059] Filter the data to be loaded through request parameters to obtain time series data that meets the requirements.

[0060] As a preference: the dynamic loading response speed of signal data is in milliseconds, which is invisible to the human eye, and the entire dynamic data loading process is fast and smooth.

[0061] Preferably, the downsampling module is used to downsample the waveform data into downsampled data of corresponding frequency according to a downsampling rate including but not limited to once a quarter, once a month, once a day, once an hour, once a minute, and once a second.

[0062] Ensure that during the data loading process, the corresponding time series data that meets the requirements can be quickly queried.

[0063] Preferably, the waveform data cache module and the downsampling data cache module complete the caching of corresponding data through a cache strategy, the downsampling module completes the downsampling of waveform data through a downsampling strategy and a downsampling algorithm, and the query module completes the query of corresponding waveform target data through a query strategy.

[0064] The cache strategy is an important means for optimizing data reading and storage performance. When formulating the cache strategy, it is necessary to balance the length of the cached data and the memory usage to ensure that the cache can improve the data reading speed while not occupying too much memory resources.

[0065] The downsampling strategy is a method for reducing the amount of data to improve data processing and display efficiency. In the downsampling process, it is necessary to consider the selection of the target frequency and the cutoff frequency to ensure that the downsampled data can retain enough information while reducing unnecessary data redundancy. The downsampling algorithm is the LTTB algorithm.

[0066] The query strategy is the strategy adopted when performing data query. When formulating the query strategy, it is necessary to select the appropriate sampling frequency according to the specific query conditions and data characteristics, and decide whether to read data from the memory or from the database. Through a reasonable query strategy, the efficiency and accuracy of data query can be improved, thereby optimizing the performance of the system.

[0067] Beneficial effects of the present invention:

[0068] 1. The graphical waveform of real-time signal data can be loaded and displayed in real time in the browser, so that users can easily monitor and analyze real-time data and discover data changes and abnormal situations in time.

[0069] 2. Graphic waveform data can be selected by selecting, clicking, and double-clicking through a simple and intuitive graphical interface, which enables dynamic interaction with data and improves the user's data usage efficiency and operating experience.

[0070] 3. The real-time loading and display of waveform data collected by the device is realized, ensuring the real-time nature of the data. At the same time, the accuracy of the data can be ensured through the processing of the database adapter and the time series database adapter.

[0071] 4. By storing waveform data in the business database and time series database, effective storage and management of real-time data is achieved, allowing users to retrieve and access historical data at any time as needed, facilitating data management and analysis.

[0072] 5. The present invention can be widely used in various fields, including industrial control, Internet of Things, medical health, etc., providing a new solution for real-time data monitoring, analysis and processing, which helps to promote technological progress and application innovation in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a structural block diagram of the present invention;

[0074] Figure 2 A flowchart of the steps for real-time data collection and storage;

[0075] Figure 3 A flowchart of the steps for dynamically loading graphical data;

[0076] Figure 4 Flowchart for writing basic information to the business database for the database adapter;

[0077] Figure 5 Flowchart for writing time series data to a time series database for a time series database adapter. DETAILED DESCRIPTION

[0078] 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.

[0079] like Figure 1 As shown: A signal data dynamic loading method based on Web graphical programming is implemented through a signal data dynamic loading system, wherein the signal data dynamic loading system is provided with a device, a server and a browser, wherein the device is connected to the server, and the server is also connected to the browser.

[0080] The server is provided with a waveform data cache module, a downsampling module, a downsampling data cache module and an adapter module, the waveform data cache module is connected to the adapter module and the downsampling module respectively, and the downsampling module is also connected to the adapter module via the downsampling data cache module;

[0081] The adapter module is provided with a database adapter and a time series database adapter. The database adapter is bidirectionally connected to the business database, and the time series database adapter is bidirectionally connected to the time series database.

[0082] The server is also provided with a query module, which is connected to the waveform data buffer module, the down-sampling data buffer module and the adapter module.

[0083] The browser is provided with a data loading component and a waveform display control.

[0084] A signal data dynamic loading method based on Web graphical programming, the key of which is to include step A of real-time data collection and storage and step B of dynamic loading of graphical data:

[0085] like Figure 2 As shown: the step A of real-time data collection and storage includes the following steps:

[0086] Step A1: The server collects waveform data with time series attributes from the device in real time and caches it in a waveform data cache module;

[0087] Step A2: the waveform data cache module sends the waveform data to the adapter module and the downsampling module; the downsampling module downsamples the waveform data to obtain downsampled data, and then sends it to the downsampled data cache module; the downsampled data cache module caches the downsampled data and sends it to the adapter module;

[0088] Step A3: the database adapter in the adapter module writes the basic information in the waveform data or the downsampled data into the business database, and the time series database adapter in the adapter module writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database;

[0089] In the step A2, the downsampling module uses the LTTB algorithm to downsample the waveform data. When the downsampling module downsamples the waveform data, the downsampling multiple is dynamically determined according to the downsampling strategy.

[0090] LTTB (Largest-Triangle-Three-Buckets) is an algorithm for data downsampling, which aims to downsample the original data into a data set with a smaller data volume but still retains sufficient trends and features. The LTTB algorithm is a simple and effective downsampling method based on the area of ​​a triangle.

[0091] The basic idea of ​​the LTTB algorithm is to divide the original data into several buckets, and then select a triangle with the largest area in each bucket as a representative point to achieve data downsampling. The key steps of the algorithm include:

[0092] The original data is divided into several buckets according to certain rules, and each bucket contains a fixed number of data points.

[0093] In each bucket, a representative point is selected by calculating the area of ​​the triangle. Specifically, for each bucket, the algorithm calculates the area of ​​the triangle formed by all the data points in the bucket and selects the data point corresponding to the triangle with the largest area as the representative point.

[0094] All selected representative points are combined to form a downsampled dataset.

[0095] The LTTB algorithm is simple and efficient, and can retain the main trends and features of the original data while reducing the amount of data. It is suitable for downsampling of large-scale data. Due to its simplicity and performance advantages, the LTTB algorithm has been widely used in the field of data visualization, especially in graphics drawing and time series data processing.

[0096] In this embodiment, the business database adopts MongoDB, and the time series database adopts lotDB.

[0097] In step A3, the basic information includes but is not limited to data type, data characteristics and data channel.

[0098] like Figure 4 As shown: In the step A3, the database adapter writes the basic information in the waveform data or down-sampled data into the business database, including the following steps:

[0099] Step A31: Information parsing: the database adapter reads the basic information, parses the basic information format, and then extracts the parsed basic information;

[0100] Step A32: Information conversion: performing a data format conversion operation or a data cleaning operation on the parsed basic information to obtain converted basic information;

[0101] Step A33: Information writing: writing the converted basic information into the business database;

[0102] Step A34: Error handling and log recording: When an error occurs during the basic information writing process, the database adapter will perform error handling and record the relevant error information in the log.

[0103] like Figure 5 As shown: In the step A3, the time series database adapter writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database, including the following steps:

[0104] Step A3-1: data parsing: the time series database adapter reads the time series data, parses the time series data format, and then extracts the parsed time series data;

[0105] Step A3-2: data conversion: converting or processing the parsed time series data to obtain converted time series data;

[0106] Step A3-3: Data writing: writing the converted time series data into a time series database;

[0107] Step A3-4: Data indexing and optimization: the time series database adapter creates indexes and performs partition optimization processing on the written time series data;

[0108] Step A3-5: Error handling and log recording: When an error occurs during the time series data writing process, the time series database adapter will perform error handling and record the relevant error information in the log.

[0109] like Figure 3 As shown: Step B of dynamically loading graphical data includes the following steps:

[0110] Step B1: The browser obtains a data loading request instruction and transmits the data loading request instruction to a data loading component; at the same time, the data loading component senses the resolution information of the browser device and sends the resolution information and the data loading request instruction to a server;

[0111] Step B2: the query module in the server determines the query condition according to the request parameter of the data loading request instruction;

[0112] Step B3: the query module queries the waveform data cache module, the down-sampling data cache module, or the timing database through the adapter module according to the query condition;

[0113] Step B4: the query module performs a preprocessing operation on the waveform target data to obtain graphic waveform data, and sends the data to the browser;

[0114] Step B5: The waveform display control in the browser converts the graphic waveform data into a graphic format, and then displays it in the browser.

[0115] In the step B1, the resolution information includes graphics card resolution information and display resolution information.

[0116] In step B3, the request parameters include data channel parameters, time series data start time parameters, time series data end time parameters and browser resolution parameters.

[0117] The dynamic loading response speed of signal data is in milliseconds.

[0118] The waveform data cache module and the downsampling data cache module complete the caching work of the corresponding data through the cache strategy, the downsampling module completes the downsampling work of the waveform data through the downsampling strategy and downsampling algorithm, and the query module completes the query work of the corresponding waveform target data through the query strategy.

[0119] The cache strategy is an important means for optimizing data reading and storage performance. When formulating the cache strategy, it is necessary to balance the length of the cached data and the memory usage to ensure that the cache can improve the data reading speed while not occupying too much memory resources.

[0120] The downsampling strategy is a method for reducing the amount of data to improve data processing and display efficiency. In the downsampling process, it is necessary to consider the selection of the target frequency and the cutoff frequency to ensure that the downsampled data can retain enough information while reducing unnecessary data redundancy. The downsampling algorithm is the LTTB algorithm.

[0121] The query strategy is the strategy adopted when performing data query. When formulating the query strategy, it is necessary to select the appropriate sampling frequency according to the specific query conditions and data characteristics, and decide whether to read data from the memory or from the database. Through a reasonable query strategy, the efficiency and accuracy of data query can be improved, thereby optimizing the performance of the system.

[0122] 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 signal data dynamic loading method based on Web graphical programming, characterized in that: It includes step A of real-time data collection and storage and step B of dynamic loading of graphical data: The step A of real-time data collection and storage includes the following steps: Step A1: The server collects waveform data with time series attributes from the device in real time and caches it in a waveform data cache module; Step A2: the waveform data cache module sends the waveform data to the adapter module and the downsampling module; the downsampling module downsamples the waveform data to obtain downsampled data, and then sends it to the downsampled data cache module; the downsampled data cache module caches the downsampled data and sends it to the adapter module; Step A3: the database adapter in the adapter module writes the basic information in the waveform data or the downsampled data into the business database, and the time series database adapter in the adapter module writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database; The step B of dynamically loading graphical data comprises the following steps: Step B1: The browser obtains a data loading request instruction and transmits the data loading request instruction to a data loading component; at the same time, the data loading component senses the resolution information of the browser device and sends the resolution information and the data loading request instruction to a server; Step B2: the query module in the server determines the query condition according to the request parameter of the data loading request instruction; Step B3: the query module queries the waveform data cache module, the down-sampling data cache module, or the timing database through the adapter module according to the query condition; Step B4: the query module performs a preprocessing operation on the waveform target data to obtain graphic waveform data, and sends the data to the browser; Step B5: The waveform display control in the browser converts the graphic waveform data into a graphic format, and then displays it in the browser.

2. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized in that: In the step A2, the downsampling module uses the LTTB algorithm to downsample the waveform data. When the downsampling module downsamples the waveform data, the downsampling multiple is dynamically determined according to the downsampling strategy.

3. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized in that: In step A3, the basic information includes but is not limited to data type, data characteristics and data channel.

4. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized in that: In the step A3, the database adapter writes the basic information in the waveform data or down-sampled data into the business database, including the following steps: Step A31: Information parsing: the database adapter reads the basic information, parses the basic information format, and then extracts the parsed basic information; Step A32: Information conversion: performing a data format conversion operation or a data cleaning operation on the parsed basic information to obtain converted basic information; Step A33: Information writing: writing the converted basic information into the business database; Step A34: Error handling and log recording: When an error occurs during the basic information writing process, the database adapter will perform error handling and record the relevant error information in the log.

5. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized in that: In the step A3, the time series database adapter writes the original time series data in the waveform data and the downsampled time series data in the downsampled data into the time series database, including the following steps: Step A3-1: data parsing: the time series database adapter reads the time series data, parses the time series data format, and then extracts the parsed time series data; Step A3-2: data conversion: converting or processing the parsed time series data to obtain converted time series data; Step A3-3: Data writing: writing the converted time series data into a time series database; Step A3-4: Data indexing and optimization: the time series database adapter creates indexes and performs partition optimization processing on the written time series data; Step A3-5: Error handling and log recording: When an error occurs during the time series data writing process, the time series database adapter will perform error handling and record the relevant error information in the log.

6. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized by: In the step B1, the resolution information includes graphics card resolution information and display resolution information.

7. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized by: In step B3, the request parameters include but are not limited to data channel parameters, time series data start time parameters, time series data end time parameters and browser resolution parameters.

8. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized by: The dynamic loading response speed of signal data is in milliseconds.

9. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized by: The downsampling module is used to downsample the waveform data into downsampled data of corresponding frequency according to a downsampling rate including but not limited to once a quarter, once a month, once a day, once an hour, once a minute, and once a second.

10. The signal data dynamic loading method based on Web graphical programming according to claim 1 is characterized by: The waveform data cache module and the downsampling data cache module complete the caching work of the corresponding data through the cache strategy, the downsampling module completes the downsampling work of the waveform data through the downsampling strategy and downsampling algorithm, and the query module completes the query work of the corresponding waveform target data through the query strategy.

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