Data storage method and device of power system, medium and program product

Abstract

The invention discloses a data storage method and device of a power system, a medium and a program product, and relates to the technical field of data processing, and the key points of the technical scheme are as follows: creating an index folder and a hash table; receiving and analyzing data uploaded by at least one acquisition node to obtain an ID, a sampling timestamp and sampling data of the acquisition node; querying whether the ID of the acquisition node has a corresponding primary key index in the hash table through the hash table, and if not, configuring the primary key index corresponding to the ID of the acquisition node in the hash table; generating a storage file and an index file according to the sampling timestamp; whether the index file exists in an index folder or not is judged, and if yes, an index node value corresponding to the index file in the index folder is read according to the primary key index; and judging whether the index node value is 0 or not, if the index node value is 0, writing the currently received sampling data at the tail of the storage file, and updating the value of the main key index corresponding to the hash table into the offset of the sampling data in the storage file.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a data storage method, device, medium, and program product for a power system. Background Technology

[0002] Power systems contain a large amount of measurement data such as voltage, current, and power, which changes constantly every second. When storing historical data, it is usually stored every 1 minute or 5 minutes. In order to retain as much detail as possible, the maximum, minimum, average, and the time when the maximum / minimum value occurred within this period are also stored.

[0003] Most power automation systems use traditional relational databases for historical data storage, such as MySQL, SQL Server, and Oracle. When dealing with millions or even more data acquisition nodes, this is achieved by adding more database servers and instances. This approach places extremely high demands on hardware resources and disk space, and data query performance is low when dealing with large amounts of data. A small number of power automation systems use real-time database technology for historical data storage. While this offers high performance, it is too expensive and is typically only suitable for small projects with tens of thousands of data acquisition nodes. Summary of the Invention

[0004] The purpose of this invention is to provide a data storage method, device, medium, and program product for power systems, which solves the problems of insufficient data carrying capacity and data query response efficiency in the prior art.

[0005] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0006] A first aspect of the present invention provides a data storage method for a power system, the method comprising:

[0007] An index folder is created based on the storage time of the power system acquisition nodes; each index file in the index folder is used to store the index node values ​​of the data.

[0008] Create a hash table with the ID of the collection node as the primary key and the primary key index as the content;

[0009] Receive and parse data uploaded by at least one data collection node to obtain the ID of the data collection node, the sampling timestamp, and the sampling data;

[0010] The system queries the hash table to see if there is a corresponding primary key index for the ID of the collection node. If not, it configures a primary key index corresponding to the ID of the collection node in the hash table.

[0011] Generate storage files and index files based on the sampling timestamps;

[0012] Determine if the index file exists in the index folder. If it exists, read the inode value of the corresponding index file in the index folder based on the primary key index.

[0013] Check if the inode value is 0. If it is 0, write the currently received sampled data to the end of the storage file and update the value of the primary key index of the hash table to the offset of the sampled data in the storage file.

[0014] In one implementation, if the index file does not exist in the index folder, a new index file is created in the index folder, and the index node value of all collection nodes is initialized to the first identifier; where the first identifier indicates that there is no sampled data.

[0015] In one implementation, if the inode value is not 0, the target data location in the storage file is located based on the inode value, and the target data location is overwritten to write the sampled data.

[0016] In one implementation, the storage file is named in the order of year, month, day, and hour / minute.

[0017] In one implementation, the method further includes:

[0018] Obtain user-generated data query information; wherein, the data query information includes the query time point and the query ID;

[0019] Find the primary key index corresponding to the query ID using a hash table;

[0020] The query index file is determined by the query time point;

[0021] Based on the primary key index, retrieve the query index node values ​​of historical data from the query index file;

[0022] The corresponding data block is located in the target storage file according to the offset of the query inode value, and the data block is returned to the user to complete the data query.

[0023] In one implementation, the method further includes:

[0024] Obtain user-generated data deletion information; wherein, the data deletion information includes the deletion time and deletion ID;

[0025] Find the primary key index corresponding to the deletion ID using the hash table;

[0026] The deleted index file is determined by the deletion time point;

[0027] Based on the deletion of the primary key index, the deleted index node values ​​of historical data are read from the deleted index file;

[0028] Locate the corresponding data block in the target storage file according to the offset of the deleted inode value, write 0 into the data block, and complete the data deletion.

[0029] In one implementation, the method further includes:

[0030] Obtain user-generated data modification information; wherein, the data deletion information includes the modification time and modification ID;

[0031] Find the primary key index corresponding to the modified ID using the hash table;

[0032] The modified index file is determined by modifying the time point;

[0033] Based on the modification of the primary key index, the modified index node values ​​of historical data are read from the modified index file;

[0034] In the target storage file, locate the corresponding data block according to the offset of the modified inode value, modify it, and complete the data modification.

[0035] A second aspect of the present invention provides an electronic device, including a memory and a processor;

[0036] A memory for storing computer programs, the computer programs including program instructions;

[0037] A processor is configured to execute the program instructions to cause the electronic device to perform the steps of a data storage method for a power system as provided in the first aspect of the present invention.

[0038] A third aspect of the present invention provides a computer program product comprising program instructions that, when executed by an electronic device, cause the electronic device to perform the steps of a data storage method for a power system as provided in the first aspect of the present invention.

[0039] A fourth aspect of the present invention provides a computer-readable storage medium comprising a computer program that, when executed by one or more processors, implements a data storage method for a power system as provided in the first aspect of the present invention.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] In the technical solution provided by this invention, firstly, an index folder is created based on the storage time of the power system acquisition nodes; a hash table is created with the acquisition node ID as the primary key and the primary key index as the content; data uploaded by at least one acquisition node is received and parsed to obtain the acquisition node ID, sampling timestamp, and sampling data; the acquisition node ID is queried through the hash table to see if there is a corresponding primary key index in the hash table; if not, a primary key index corresponding to the acquisition node ID is configured in the hash table; a storage file and an index file are generated based on the sampling timestamp; it is determined whether the index file exists in the index folder; if it exists, the index node value of the corresponding index file in the index folder is read based on the primary key index; it is determined whether the index node value is 0; if it is 0, the currently received sampling data is written to the end of the storage file, and the value of the corresponding primary key index in the hash table is updated to the offset of the sampling data in the storage file. By assigning a unique identifier (ID / primary key index) to each acquisition node and adopting a brand-new structured storage algorithm, it balances the real-time nature of high-frequency data writing with the ability to compress and store historical data, thereby achieving accurate storage and efficient management of time-series data generated by each acquisition node. Compared with traditional relational databases, it significantly reduces storage space usage and improves the data carrying capacity and query response efficiency of the data storage process, making it particularly suitable for big data scenarios such as smart grids and energy monitoring. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0043] Figure 1 This is a flowchart illustrating a data storage method for a power system provided in an embodiment of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are only for explaining this invention and are not intended to limit this invention.

[0045] It should be noted that the terms "comprising" or "may include" used in the various embodiments of this application indicate the presence of the claimed function, operation, or element, and do not limit the addition of one or more functions, operations, or elements. Furthermore, as used in the various embodiments of this application, the terms "comprising," "having," and their cognates are intended only to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing, and should not be construed as primarily excluding the presence of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing, or adding one or more combinations of the foregoing.

[0046] It should be understood that terms such as "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0047] Figure 1 This is a flowchart illustrating a data storage method for a power system provided in an embodiment of the present invention, as shown below. Figure 1 As shown, the method includes:

[0048] S101, Based on the storage time of the power system acquisition nodes, create an index folder; wherein, each index file in the index folder is used to store the index node value of the data.

[0049] In this embodiment, a data acquisition node refers to a physical acquisition point or logical acquisition unit in a power system used to collect data on specific electrical parameters (such as voltage, current, power, frequency, etc.). Each data acquisition node corresponds to a unique monitoring object and monitoring parameter, such as the voltage acquisition point of a certain line in a substation or the current acquisition point of a certain transformer.

[0050] In power systems, full data is typically stored at each storage time point. Therefore, this embodiment generates a data index folder for each storage time point. The index files in this folder store the index node values ​​of the data. Each node in this index is 4 bytes, and 1 million acquisition nodes occupy approximately 4 * 1 million ≈ 4MB. Initially, the index data for each acquisition node is 0, representing no data. When data is added to the database, the index number is immediately updated, thus accurately identifying the measurement point corresponding to historical data.

[0051] An important point here is that historical data is stored in a structured format, and the size of each type of data is fixed. This method is suitable for storing electrical parameter data in power systems, but not for storing events or orders.

[0052] S102, create a hash table with the ID of the collection node as the primary key and the primary key index as the content.

[0053] Specifically, firstly, all data collection nodes are indexed as a whole. Each data point is assigned an integer identifier starting from 1, in the form of key-value pairs. Assuming there are 1 million data collection nodes, regardless of their original IDs, they are re-indexed from 1 to 1 million according to their order of addition. Here, the original ID of the data is PtID, and the primary key index is PtKey. PtKey is a number from 1 to 1 million, while PtID is assumed to be a number distributed between 20 million and 30 million. In memory, an array is created with PtKey as the index and PtID as the content. The efficiency of finding PtID by PtKey is O(1). At the same time, a hash table is created with PtID as the primary key and PtKey as the content. Using binary search, the efficiency of finding PtKey by PtID is O(log n). In the worst case of 1 million points, the number of comparisons is 20, thus improving efficiency. The main purpose of the overall indexing of data collection nodes is to assign each data collection node an ID starting from 1.

[0054] S103: Receive and parse data uploaded by at least one acquisition node to obtain the acquisition node's ID, sampling timestamp, and sampling data.

[0055] Specifically, receiving and parsing data is common knowledge in this technical field, and this embodiment will not describe it in detail. Secondly, the sampled data includes measurement point values, maximum / minimum / average values ​​within the period, such as maximum voltage value, average voltage value, etc.

[0056] The sampling timestamp includes year, month, day, hour, and minute, with a minimum storage interval of 1 minute.

[0057] The ID uses a 4-byte integer, which can represent up to 4.2 billion, enough to assign a unique ID to each collection node in the system.

[0058] The measurement point values ​​are 8-byte double-precision floating-point numbers.

[0059] The maximum, minimum, and average values ​​within the measurement point storage period are represented by three 8-byte double-precision floating-point numbers.

[0060] The maximum and minimum occurrence times within the measurement point storage period include year, month, day, hour, minute, second, and millisecond, which can be represented by two 8-byte integers.

[0061] The status bit uses a 1-byte integer, with each bit representing a status, such as whether the measured value is valid, whether the maximum value is valid, whether the minimum value is valid, etc.

[0062] Using the indexing algorithm in this invention, a single historical record only needs to store 49 bytes, including measurement point value, statistical value, timestamp, and status bit; while in existing relational databases, additional storage of ID, timestamp, primary key, foreign key, index, etc. is required, which is approximately 120 bytes in actual calculation. This invention can save about 60% of storage space.

[0063] S104. Check if the ID of the collection node has a corresponding primary key index in the hash table. If not, configure the primary key index corresponding to the ID of the collection node in the hash table.

[0064] Specifically, in addition to measurement point values, statistical values, timestamps, and PtID, if the received data does not contain a corresponding PtKey, the PtKey is found through the PtID and merged into the data.

[0065] S105, Generate storage file and index file based on sampling timestamp.

[0066] Specifically, the storage files are named in the order of year, month, day, and hour / minute. A folder named after the year and month is created to manage the data files by month. Within each month, a subfolder named after the day is created, further managing the data files by day. The data files are named by the hour and minute, and each timestamp contains an index file and a data file. Assuming the sampling timestamp is 1 minute, then 1440 storage files for storing data can be generated per day.

[0067] For example, parse the sampling timestamp to determine the corresponding year-month folder, day folder, and target file name (e.g., if the timestamp is 2025-01-02 10:00:00, then the index file is "202501 / 02 / 1000.index" and the storage file is "202501 / 02 / 1000.dat").

[0068] S106, determine whether the index file exists in the index folder. If it exists, read the inode value of the corresponding index file in the index folder based on the primary key index.

[0069] Specifically, during data storage, first, determine if the corresponding index file exists in the index folder. If it does, read the inode value corresponding to the current PtKey in that index file (i.e., the offset of the data in the data storage file .dat). Assuming there is data from 5 measurement points (2011-2015) that needs to be stored at 10:00:00 on 2025-1-2, first find the PtKeys corresponding to 2011-2015, i.e., 11-15. Locate the 02 folder (2nd) under the 202501 folder (January 2025). If the index file 1000.index, which indicates the data at 10:00, does not exist, create a new index file 1000.index in the index folder and initialize the inode value of all acquisition nodes to the first identifier. For example, a first identifier of 0 indicates no sampled data. Write 100 four-byte zeros into it to initialize the historical data offset of the 100 measurement points.

[0070] S107, determine whether the index node value is 0. If it is 0, write the currently received sampled data to the end of the storage file and update the value of the primary key index corresponding to the hash table to the offset of the sampled data in the storage file.

[0071] Specifically, an index node value of 0 means that the currently received data is newly added. Therefore, the 5 records submitted by the user are written into the storage file 1000.dat, and positions 11 to 15 of the index file 1000.index are written with 1 to 5, representing the storage of the first 5 historical data records, to inform the user that the storage was successful.

[0072] Specifically, if the inode value is not 0, it means that the currently received data is not newly added. In this case, the target data position in the storage file is located based on the inode value, and the target data position is overwritten to write the sampled data.

[0073] Specifically, this embodiment of the invention also provides a data query operation. The data query process is as follows: obtaining user-generated data query information; wherein, the data query information includes a query time point and a query ID; finding the query primary key index corresponding to the query ID through a hash table; determining the query index file through the query time point; reading the query index node values ​​of historical data from the query index file based on the query primary key index; finding the corresponding data block in the target storage file according to the offset of the query index node value, and returning the data block to the user to complete the data query.

[0074] Specifically, the data query is mainly used for statistical analysis, curve viewing, and report generation, implementing several query services according to their purpose. For example, querying daily historical data from multiple data collection nodes: This query requires retrieving historical data from data packets at multiple historical data points. Assuming the current storage interval is on the minute level, we use file services to find all historical index files for the day, read the historical data offsets from each historical index, and then retrieve the corresponding data from the historical data packets. This query can be executed in parallel based on the number of CPUs on the server, directly locating historical data through the index, which is far more efficient than relational databases. Querying large amounts of measurement point data from a single or few time points: This query focuses on reading large amounts of data from a single file. Therefore, when querying, the historical file can be opened and released only after all data has been queried. Similarly, the historical data offset for each measurement point is found through the measurement point index, and then the target data is retrieved.

[0075] For example, suppose a user needs to query historical data for the time 10:00:00 on January 2, 2025, with node ID 2015. First, find the primary key index PtKey corresponding to 2015, which is 15. Then, find the corresponding index file 1000.index based on the year, month, day, and hour. Read the index node value of the historical data from the 15th position in index file 1000.index, which is 5. Finally, find the 5th data block in the storage file 1000.dat at the offset, read it, and return the data to the user.

[0076] Specifically, this embodiment of the invention also provides a data deletion operation. The data deletion process is as follows: obtaining user-generated data deletion information; wherein, the data deletion information includes a deletion time point and a deletion ID; finding the deletion primary key index corresponding to the deletion ID through a hash table; determining the deletion index file through the deletion time point; reading the deletion index node value of historical data from the deletion index file based on the deletion primary key index; finding the corresponding data block in the target storage file according to the offset of the deletion index node value, writing 0 into the data block, and completing the data deletion.

[0077] Specifically, the index file corresponding to a given time is located directly using the timestamp; if it does not exist, it is skipped. The inode values ​​of historical data are read from the index file; if they are not 0, they are updated to 0. It should be noted that the data deletion service provided in this embodiment is actually a pseudo-deletion function; it does not release the occupied space. In actual storage scenarios, data deletion is rarely performed in power systems, which aligns with real-world usage.

[0078] For example, suppose a user deletes historical data from January 2, 2025 at 10:00:00, collecting data from node ID 2015. First, the primary key index PtKey corresponding to 2015 is found, which is 15. Then, the corresponding index file 1000.index is found based on the year, month, day, and hour. The historical data index is read from the 15th position of index file 1000.index, which is 5. Finally, 0 is written to the 15th position of storage file 1000.dat. In reality, the historical data is not deleted; it is merely marked, and the user is informed that the deletion was successful.

[0079] Specifically, this embodiment of the invention also provides a data modification operation. The data deletion process specifically includes: obtaining user-generated data modification information; wherein, the data deletion information includes a modification time point and a modification ID; finding the modification primary key index corresponding to the modification ID through a hash table; determining the modification index file through the modification time point; reading the modification index node values ​​of historical data from the modification index file based on the modification primary key index; finding the corresponding data block in the target storage file according to the offset of the modification index node value and modifying it to complete the data modification.

[0080] Specifically, to ensure consistency during data insertion, data for each timestamp must be inserted sequentially, while data for multiple timestamps can be inserted in parallel. In addition to measurement points, statistical values, timestamps, and PtIDs, if the user-submitted data does not contain a corresponding PtKey in the database, the first step is to find the PtKey using the PtID and merge it into the data. Then, the historical data index file corresponding to that time is directly located using the timestamp; if it does not exist, a new one is generated. The historical data index is read from the index file. If it is found to be 0 (i.e., not existing), data is written to the end of the historical data, and the historical data index node value is updated simultaneously. If the historical data index node value already exists, the latest data is updated to the corresponding historical data.

[0081] For example, suppose a user wants to modify historical data collected at 10:00:00 on January 2, 2025, with node ID 2015. First, find the primary key index PtKey corresponding to 2015, which is 15. Then, find the corresponding index file 1000.index based on the year, month, day, and hour. Read the index node value of the historical data at the 15th position in 1000.index, which is 5. Find the 5th data block in the storage file 1000.dat at the offset position, modify the content, and inform the user that the modification was successful.

[0082] This invention also provides an electronic device. The electronic device includes a processor, a memory, a communication interface, and at least one communication bus for connecting the processor, the memory, and the communication interface. The memory includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (PROM), or portable read-only memory (CD-ROM), and is used for related instructions and data.

[0083] The communication interface is used to receive and send data. The processor can be one or more CPUs; if the processor is a single CPU, it can be a single-core CPU or a multi-core CPU. The processor in the electronic device reads one or more programs stored in the memory and performs the following operations: creating an index folder based on the storage time of the power system acquisition nodes; wherein each index file in the index folder is used to store the index node value of the data; creating a hash table with the acquisition node ID as the primary key and the primary key index as the content; receiving and parsing data uploaded by at least one acquisition node to obtain the acquisition node ID, sampling timestamp, and sampling data; querying the hash table to see if the acquisition node ID has a corresponding primary key index; if not, configuring the primary key index corresponding to the acquisition node ID in the hash table; generating a storage file and an index file based on the sampling timestamp; determining if the index file exists in the index folder; if it exists, reading the index node value of the corresponding index file in the index folder based on the primary key index; determining if the index node value is 0; if it is 0, writing the currently received sampling data to the end of the storage file and updating the value of the corresponding primary key index in the hash table to the offset of the sampling data in the storage file.

[0084] It should be noted that the specific implementation of each operation can be described above. Figure 1 The corresponding description of the method embodiments shown indicates that the electronic device can be used to execute a power system data storage method according to the above method embodiments of this application, which will not be described in detail here.

[0085] This invention also provides a computer-readable storage medium, which is a memory device in a computer device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of a terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the data storage method for a power system in the above embodiments. Those skilled in the art should understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can 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 code.

[0086] This invention also provides a computer program product containing program instructions. The computer program product may be software or program products containing program instructions, capable of running on a computing device or stored on any usable medium. When the computer program product is run on at least one electronic device, it causes the at least one electronic device to perform a data storage method for a power system.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. 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 data storage method for a power system, characterized in that, The methods include: An index folder is created based on the storage time of the power system acquisition nodes; each index file in the index folder is used to store the index node values ​​of the data. Create a hash table with the ID of the collection node as the primary key and the primary key index as the content; Receive and parse data uploaded by at least one data collection node to obtain the ID of the data collection node, the sampling timestamp, and the sampling data; The system queries the hash table to see if there is a corresponding primary key index for the ID of the collection node. If not, it configures a primary key index corresponding to the ID of the collection node in the hash table. Generate storage files and index files based on the sampling timestamps; Determine if the index file exists in the index folder. If it exists, read the inode value of the corresponding index file in the index folder based on the primary key index. Check if the inode value is 0. If it is 0, write the currently received sampled data to the end of the storage file and update the value of the primary key index of the hash table to the offset of the sampled data in the storage file.

2. The method according to claim 1, characterized in that, If the index file does not exist in the index folder, a new index file is created in the index folder, and the index node value of all collection nodes is initialized to the first identifier; where the first identifier indicates that there is no sampled data.

3. The method according to claim 1, characterized in that, If the inode value is not 0, the target data location in the storage file is located based on the inode value, and the target data location is overwritten to write the sampled data.

4. The method according to claim 1, characterized in that, The stored files are named in the order of year, month, day, and hour / minute.

5. The method according to claim 1, characterized in that, The method further includes: Obtain user-generated data query information; wherein, the data query information includes the query time point and the query ID; Find the primary key index corresponding to the query ID using a hash table; The query index file is determined by the query time point; Based on the primary key index, retrieve the query index node values ​​of historical data from the query index file; The corresponding data block is located in the target storage file according to the offset of the query inode value, and the data block is returned to the user to complete the data query.

6. The method according to claim 1, characterized in that, The method further includes: Obtain user-generated data deletion information; wherein, the data deletion information includes the deletion time and deletion ID; Find the primary key index corresponding to the deletion ID using the hash table; The deleted index file is determined by the deletion time point; Based on the deletion of the primary key index, the deleted index node values ​​of historical data are read from the deleted index file; Locate the corresponding data block in the target storage file according to the offset of the deleted inode value, write 0 into the data block, and complete the data deletion.

7. The method according to claim 1, characterized in that, The method further includes: Obtain user-generated data modification information; wherein, the data deletion information includes the modification time and modification ID; Find the primary key index corresponding to the modified ID using the hash table; The modified index file is determined by modifying the time point; Based on the modification of the primary key index, the modified index node values ​​of historical data are read from the modified index file; In the target storage file, locate the corresponding data block according to the offset of the modified inode value, modify it, and complete the data modification.

8. An electronic device, characterized in that, Including memory and processor; A memory for storing computer programs, the computer programs including program instructions; A processor is configured to execute the program instructions to cause the electronic device to perform the steps of a data storage method for a power system as described in any one of claims 1 to 7.

9. A computer program product containing program instructions, characterized in that, When the program instructions are executed by the electronic device, the electronic device performs the steps of a power system data storage method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a computer program that, when executed by one or more processors, implements a data storage method for a power system as described in any one of claims 1 to 7.

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