Energy storage equipment data storage and display method based on relational database

Abstract

The invention relates to an energy storage equipment data storage and display method based on a relational database, and relates to energy storage data storage and visualization, and the method comprises the following steps: S1, carrying out data collection by taking energy storage equipment as a unit, defining data collection granularity, and collecting parameters of the energy storage equipment; s2, according to different collection granularities of the parameters, multiple storage modes are divided; in each re-storage mode, creating a database table according to the acquisition density of the parameters; s3, arranging SQL statements, and performing downsampling processing on read data through query statements; and S4, executing the query statement, extracting the time sequence meeting the condition from the database table, and improving the data display granularity by adjusting the range of the navigator until the original data is displayed. According to the method, the persistence speed of data acquisition of the energy storage equipment is increased, and segmentation, downsampling and segment-by-segment rendering of mass data to a front-end chart are realized. The waiting time of data reading and visualization is directly shortened, and the user experience is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and relates to energy storage data storage and visualization, and specifically to an energy storage device data storage and display method based on a relational database. Background Art

[0002] In recent years, with the continuous increase in the capacity of domestic wind power and photovoltaic power generation equipment, the rapid development of the new energy storage industry dominated by "wind and light", and the rapid establishment and commissioning of various energy storage stations, the monitoring and operation of energy storage power stations have become an issue that users and builders focus on. Effectively recording historical data and analyzing equipment failures and grid-connected and off-grid operation rules are indispensable digital infrastructure.

[0003] The current characteristics of energy storage business data are high collection frequency, large data volume, and time-consuming access, so more effective data storage and display methods are needed to meet the needs. Summary of the invention

[0004] The purpose of the present invention is to provide a method for storing and displaying energy storage device data based on a relational database in view of the deficiencies in the prior art.

[0005] The present invention is achieved through the following technical solutions:

[0006] A method for storing and displaying energy storage device data based on a relational database comprises the following steps:

[0007] S1: Collect data based on energy storage devices, define data collection granularity, and collect parameters of energy storage devices;

[0008] S2: The data collected is a plurality of time series, which are divided into multiple storage modes according to the different collection granularities of the parameters. In each storage mode, a database table is created according to the collection granularity of the parameters, and indexes are added to the key fields of the database table to persist the collected time series in the database table.

[0009] S3: Arrange SQL statements, determine the sampling interval and calculate the parameter model according to the data storage rules of the database table, the selected parameters, and the amount of data displayed, organize query statements, and perform downsampling processing on the read data through query statements;

[0010] S4: Execute query statements to extract time series that meet the conditions from the database table, organize the extracted data, push the data to the front end, and finally render it to the browser segment by segment; when the number of reading points is fixed, the data display granularity is increased by adjusting the navigator range until the original data is displayed.

[0011] Moreover, the S1 specifically includes:

[0012] S11 sets the sampling frequency according to the purpose of the data before data collection;

[0013] The parameters of S12 data collection include: parameter name, data type, check point, sampling index, byte order, coefficient, and check value.

[0014] Moreover, the sampling frequency is set to 200 Hz for the data before and after the equipment fails and needs to be collected at a high frequency, while the collection frequency of the normal operating parameters of the equipment is set to 1 Hz.

[0015] Moreover, the S2 specifically includes:

[0016] S21: The data collected is a number of time series, and the collection granularity of each time series is a different interval. According to the definition of data collection granularity, the data storage method is determined. The hour level is used to store key parameters of equipment operation or some key data before and after a fault, while the day level storage is used for some statistical data or data with larger time granularity requirements.

[0017] S22: Create a table and index, name the database table according to the data collection granularity, and save the data that is a set of collected data defined in S12, which consists of time and multiple columns of numerical parameter values. The time here is saved as a numerical type to facilitate later query of the table and value where the positioning parameter is located;

[0018] S23: Save the collected data as is into the database table.

[0019] Moreover, the S3 specifically includes:

[0020] S31: Calculate the downsampling model and extract multiple groups of data with different sampling intervals for analysis;

[0021] S32: Merge multiple database tables and fields and organize SQL statements.

[0022] Furthermore, in S31, multiple groups of data with different sampling intervals are extracted for analysis, specifically:

[0023] S311: If the amount of data to be displayed is fixed, the downsampling module is calculated according to the amount of data displayed, the collection granularity, and the time period; if the number of points is not fixed, the downsampling module is directly obtained by the data collection granularity and the time interval required for data display;

[0024] S312: When the sampling interval conflicts with the amount of displayed data, in order to speed up the page rendering speed, the amount of data is given priority; when multiple groups of data are merged for query, the collection granularity big data will be directly considered as the sampling interval to participate in the calculation to obtain the downsampling model.

[0025] Moreover, the S4 specifically includes:

[0026] S41: Execute SQL statements to query database table data, obtain original data, and reorganize the data format at the business logic layer;

[0027] S42: Push the S41 data to the front end, and the front end renders the data curve in sections;

[0028] S43: Adjust the navigator, repeat the steps of S3-S4, and extract the time series that meets the conditions.

[0029] An electronic device comprises a memory, wherein the processors are communicatively connected to each other, the memory stores computer instructions, and the processor executes any of the above-mentioned methods for storing and displaying energy storage device data based on a relational database by executing the computer instructions.

[0030] A computer storage medium, wherein the computer readable storage medium stores computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above-mentioned methods for storing and displaying energy storage device data based on a relational database.

[0031] The advantages and beneficial effects of the present invention are:

[0032] The method of the present invention not only improves the persistence speed of energy storage device data collection, but also realizes the segmentation, downsampling and segment-by-segment rendering of massive data to the front-end chart, directly shortens the waiting time for data reading and visualization, and improves the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 The present invention is a flow chart of a method for storing and displaying energy storage device data based on a relational database. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention is further described below in conjunction with specific embodiments.

[0035] A method for storing and displaying energy storage device data based on a relational database, such as Figure 1 As shown, the method comprises the following steps:

[0036] S1: Collect the operating parameters of energy storage devices at high frequency. The maximum collection frequency can be set to 200HZ according to actual needs.

[0037] S11: Define data acquisition energy storage equipment, configure communication protocol, acquisition granularity, etc. Different sampling frequencies can be set according to the purpose of the data. For example: when the equipment fails, the data before and after the failure need to be collected at a high frequency, the acquisition frequency can be set to 200Hz, while the acquisition frequency of the normal operating parameters of the equipment can be set to 1HZ.

[0038] S12: Define the parameters for data collection. Parameter attributes include: parameter name, data type, check point, sampling index, byte order, coefficient, check value, etc.

[0039] S2: Create a database table based on the density of data collection. Because the data collection granularity is different, it can be divided into multiple storage methods such as "hour level, day level, month level, grade level, independent storage, etc." Add indexes to the key fields of the database table and persist the collected time series into the relational database table.

[0040] S21: The data collected is multiple time series, and the collection granularity of each time series is different. According to the definition of S11 data collection granularity, the data storage method is determined. The hour level generally stores the key parameters of the device operation or some key data before and after the failure, while the day level storage is suitable for some statistical data or data with larger time granularity requirements.

[0041] S22: Create tables and indexes, name the database table according to the data collection granularity, and save the data that is a set of collected data defined in S12, which consists of time and multi-column numerical parameter values. The time here is saved as a numerical type to facilitate later query and locate the table and value of the parameter.

[0042] S23: Save the collected data as is into the database table.

[0043] S3: Arrange SQL statements, determine the sampling interval and calculate the parameter model according to the data storage rules, selected parameters, and the amount of data displayed, organize database query statements, and downsample the read data through query statements.

[0044] S31: Calculate the downsampling model and extract multiple groups of data with different sampling intervals for analysis.

[0045] S311: If the amount of data to be displayed is fixed, we will calculate the downsampling model based on the amount of data displayed, the collection granularity, and the time period. If the number of points is not fixed, the downsampling model can be directly obtained by the data collection granularity and the time interval required for data display.

[0046] S312: When the sampling interval conflicts with the amount of displayed data, in order to speed up the page rendering speed, the amount of data is given priority; when multiple groups of data are merged for query, the collection granularity big data will be directly considered as the sampling interval to participate in the calculation to obtain the downsampling model.

[0047] S32: Merge multiple database tables and fields and organize SQL statements.

[0048] S4: Execute query statements to organize database tables, extract time series that meet the conditions, organize the extracted data, push the data to the front end, and finally render it to the browser segment by segment. When the number of reading points is fixed, the granularity of data display can be increased by adjusting the navigator range until the original data is displayed.

[0049] S41: Execute SQL statements to query database table data, obtain original data, and reorganize the data format at the business logic layer.

[0050] S42: Push the S41 data to the front end, and the front end renders the data curve in sections.

[0051] S43: Adjust the navigator, repeat the steps of S3-S4, and extract the time series that meets the conditions.

[0052] SQL is a language for operating databases. Structured Query Language (SQL) is a database query and programming language used to access data and query, update and manage relational database systems.

[0053] An electronic device includes a memory, processors that are communicatively connected to each other, the memory stores computer instructions, and the processor executes the above-mentioned energy storage device data storage and display method based on a relational database by executing the computer instructions.

[0054] A computer storage medium, wherein the computer readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above-mentioned energy storage device data storage and display method based on a relational database.

[0055] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] The above-described embodiment has described the technical solution of the present invention in detail. It should be noted that this embodiment is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications and improvements made within the scope of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for storing and displaying energy storage device data based on a relational database, characterized in that: The following steps are involved: S1: Collect data based on energy storage devices, define data collection granularity, and collect parameters of energy storage devices; S2: The data collected is a plurality of time series, which are divided into multiple storage modes according to the different collection granularities of the parameters. In each storage mode, a database table is created according to the collection granularity of the parameters, and indexes are added to the key fields of the database table to persist the collected time series in the database table. S3: Arrange SQL statements, determine the sampling interval and calculate the parameter model according to the data storage rules of the database table, the selected parameters, and the amount of data displayed, organize query statements, and perform downsampling processing on the read data through query statements; S4: Execute query statements to extract time series that meet the conditions from the database table, organize the extracted data, push the data to the front end, and finally render it to the browser segment by segment; when the number of reading points is fixed, the data display granularity is increased by adjusting the navigator range until the original data is displayed.

2. The method for storing and displaying energy storage device data based on a relational database according to claim 1, characterized in that: The S1 specifically includes: S11 sets the sampling frequency according to the purpose of the data before data collection; The parameters of S12 data collection include: parameter name, data type, check point, sampling index, byte order, coefficient, and check value.

3. The method for storing and displaying energy storage device data based on a relational database according to claim 2, characterized in that: The sampling frequency is set to 200 Hz for the data before and after the equipment fails and needs to be collected at a high frequency, while the collection frequency of the equipment's normal operating parameters is set to 1 Hz.

4. The method for storing and displaying energy storage device data based on a relational database according to claim 1, characterized in that: The S2 specifically includes: S21: The data collected is a number of time series, and the collection granularity of each time series is a different interval. According to the definition of data collection granularity, the data storage method is determined. The hour level is used to store key parameters of equipment operation or some key data before and after a fault, while the day level storage is used for some statistical data or data with larger time granularity requirements. S22: Create a table and index, name the database table according to the data collection granularity, and save the data that is a set of collected data defined in S12, which consists of time and multiple columns of numerical parameter values. The time here is saved as a numerical type to facilitate later query of the table and value where the positioning parameter is located; S23: Save the collected data as is into the database table.

5. The method for storing and displaying energy storage device data based on a relational database according to claim 1, characterized in that: The S3 specifically includes: S31: Calculate the downsampling model and extract multiple groups of data with different sampling intervals for analysis; S32: Merge multiple database tables and fields and organize SQL statements.

6. The method for storing and displaying energy storage device data based on a relational database according to claim 5, characterized in that: In S31, multiple groups of data with different sampling intervals are extracted for analysis, specifically: S311: If the amount of data to be displayed is fixed, the downsampling module is calculated according to the amount of data displayed, the collection granularity, and the time period; if the number of points is not fixed, the downsampling module is directly obtained by the data collection granularity and the time interval required for data display; S312: When the sampling interval conflicts with the amount of displayed data, in order to speed up the page rendering speed, the amount of data is given priority; when multiple groups of data are merged for query, the collection granularity big data will be directly considered as the sampling interval to participate in the calculation to obtain the downsampling model.

7. The method for storing and displaying energy storage device data based on a relational database according to claim 1, characterized in that: The S4 specifically includes: S41: Execute SQL statements to query database table data, obtain original data, and reorganize the data format at the business logic layer; S42: Push the S41 data to the front end, and the front end renders the data curve in sections; S43: Adjust the navigator, repeat the steps of S3-S4, and extract the time series that meets the conditions.

8. An electronic device, comprising a memory, wherein the processors are communicatively connected to each other, wherein the memory stores computer instructions, and wherein the processor executes the energy storage device data storage and display method based on a relational database as described in any one of claims 1 to 7 by executing the computer instructions.

9. A computer storage medium, wherein the computer readable storage medium stores computer instructions, wherein the computer instructions are used to enable a computer to execute the energy storage device data storage and display method based on a relational database as described in any one of claims 1 to 7.

Images