Data processing method, device and server of distributed cloud platform

Abstract

The application provides a data processing method, device and server of a distributed cloud platform, and relates to the technical field of distributed cloud computing. The method comprises the following steps: obtaining a power station operation data viewing request; determining a corresponding user time zone and target operation data of a corresponding target power station based on the power station operation data viewing request; the user time zone is used to represent the time zone where the user is located; the target operation data comprises first timestamp information of a server time zone; the power station time zone is the local time zone of the target power station; generating corresponding operation display data according to the first timestamp information, the user time zone and the target operation data; the operation display data at least comprises third timestamp information corresponding to the user time zone; and sending the operation display data to a user equipment to enable the user equipment to display the operation display data. The method improves the data statistical accuracy of the distributed cloud platform.

Description

Technical Field

[0001] This application belongs to the field of distributed cloud computing technology, specifically relating to a data processing method, apparatus and server for a distributed cloud platform. Background Technology

[0002] With the development of the Global Energy Internet, distributed cloud platforms have been widely used for remote monitoring and management of new energy power plants. Among these applications, the power plant operation data services provided by distributed cloud platforms can be offered to users worldwide.

[0003] Currently, nodes of distributed cloud platforms may be deployed in various regions globally. Therefore, the collection, statistics, and display of power plant operation data typically involve multiple time zones. Time zone differences can easily lead to inaccuracies in power plant operation data, such as power generation and revenue statistics. For example, an international station deployed in Singapore (UTC+8) may connect to a power plant located in Brazil (UTC+3 to UTC+9), while users accessing that power plant's data may be located in China (UTC+8). In this case, when the power plant equipment collects power generation data according to its local time zone and uploads it to the Singapore server, it may cause timestamp misalignment, resulting in deviations in power generation calculations for daily, weekly, and other statistical periods, thus affecting the accuracy of subsequent data processing.

[0004] Therefore, the current distributed cloud platform suffers from inaccurate data statistics in cross-time zone deployment scenarios, which requires further optimization. Summary of the Invention

[0005] The technical problem to be solved by this application is to address the above-mentioned shortcomings of the existing technology by providing a data processing method, apparatus and server for a distributed cloud platform. Using the data processing method of the distributed cloud platform can improve the accuracy of data statistics in the scenario of cross-time zone deployment of the distributed cloud platform.

[0006] In a first aspect, embodiments of this application provide a data processing method for a distributed cloud platform, applied to a server, the method comprising: Request to view power plant operation data; Based on the power plant operation data viewing request, the corresponding user time zone and the target operation data of the target power plant are determined; the user time zone is used to represent the user's time zone; the target operation data includes the first timestamp information of the server time zone; the first timestamp information is generated by converting the second timestamp information of the power plant time zone; the power plant time zone is the local time zone of the target power plant. The corresponding operational display data is generated based on the first timestamp information, the user's time zone, and the target operational data; the operational display data shall at least include the third timestamp information corresponding to the user's time zone. Send operational display data to user devices so that user devices can display operational display data.

[0007] In some implementations of the first aspect, determining the target operating data of the corresponding user's time zone and the corresponding power station based on the power station operation data viewing request includes: Determine the corresponding browser's time zone information based on the power plant operation data viewing request; Determine the user's time zone based on the browser's time zone information; Based on the power plant operation data viewing request, the target operation data of the target power plant is determined from the first preset database.

[0008] In some implementations of the first aspect, corresponding operational display data is generated based on the first timestamp, the user's time zone, and the target operational data, including: Based on Coordinated Universal Time (UTC), the first timestamp information is converted into a third timestamp information corresponding to the user's time zone; The corresponding operational display data is generated based on the target operational data and third-party timestamp information.

[0009] In some implementations of the first aspect, before determining the target operating data of the corresponding user's time zone and the corresponding target power station based on the power station operation data viewing request, the following are included: Obtain the target power plant's operation data and unique identifier; the power plant operation data includes a second timestamp. The power station's time zone is determined from a second preset database based on the power station's unique identifier; Based on the power plant's time zone and Coordinated Universal Time (UTC), the second timestamp information is converted into the first timestamp information corresponding to the server's time zone; Based on the first timestamp information and the local preset statistical period, the power plant operation data is statistically calculated to generate target operation data.

[0010] In some implementations of the first aspect, the second timestamp information is converted into first timestamp information corresponding to the server's time zone based on the power plant's time zone and Coordinated Universal Time, including: The second timestamp information is converted into the fourth timestamp information corresponding to Coordinated Universal Time based on the power station's time zone. The fourth timestamp information is converted into the first timestamp information corresponding to the server's time zone.

[0011] In some embodiments of the first aspect, the power plant operating data is power generation; Based on the first timestamp information and the locally preset statistical period, statistical calculations are performed on the power plant operation data to generate target operation data, including: Based on the first timestamp information, the predetermined electricity price calculation rules, and the local preset statistical period, the power generation is statistically calculated to generate the corresponding power plant revenue; Target operating data is generated based on power generation and power plant revenue.

[0012] In some embodiments of the first aspect, before determining the power station time zone from the second preset database based on the power station's unique identifier, the method further includes: Construct a second pre-defined database; Obtain the time zone data, unique identifier of each power station, and server time zone data for all power stations; Establish a mapping relationship between time zone data and unique identifiers of each power station.

[0013] In some implementations of the first aspect, before generating the corresponding operational display data based on the target operational data and the third timestamp information, the method further includes: The first and third timestamp information are converted and verified to generate corresponding verification results. If the verification result shows that the timestamp deviation is greater than the preset deviation threshold, then the first timestamp information and the third timestamp information are re-converted.

[0014] Based on the same inventive concept, in a second aspect, embodiments of this application also provide a data processing apparatus for a distributed cloud platform, located on a server, the apparatus comprising: The acquisition module is used to retrieve requests for viewing power plant operation data; The determination module is used to determine the corresponding user's time zone and the target operation data of the corresponding target power station based on the power station operation data viewing request; the user's time zone is used to represent the user's time zone; the target operation data includes the first timestamp information of the server time zone; the first timestamp information is generated by converting the second timestamp information of the power station time zone; the power station time zone is the local time zone of the target power station. The generation module is used to generate corresponding operational display data based on the first timestamp information, the user's time zone, and the target operational data; the operational display data shall at least include the third timestamp information corresponding to the user's time zone. The sending module is used to send operational display data to user devices so that user devices can display the operational display data.

[0015] In some implementations of the second aspect, the determining module is specifically used for: Determine the corresponding browser's time zone information based on the power plant operation data viewing request; The user's time zone is determined based on the browser's time zone information; the target operating data of the target power station is determined from the first preset database based on the power station operation data viewing request.

[0016] In some embodiments of the second aspect, the generation module is specifically used for: Based on Coordinated Universal Time (UTC), the first timestamp information is converted into a third timestamp information corresponding to the user's time zone; corresponding operational display data is generated based on the target operational data and the third timestamp information.

[0017] In some embodiments of the second aspect, the apparatus further includes: The statistics module is used to obtain the power plant operation data and unique identifier of the target power plant; the power plant operation data includes a second timestamp information; the power plant time zone is determined from a second preset database based on the unique identifier of the power plant; the second timestamp information is converted into the first timestamp information corresponding to the server time zone according to the power plant time zone and Coordinated Universal Time; the power plant operation data is statistically calculated based on the first timestamp information and the local preset statistical period to generate the target operation data.

[0018] In some implementations of the second aspect, when the statistics module converts the second timestamp information into the first timestamp information corresponding to the server's time zone based on the power plant's time zone and UTC, it is specifically used for: The second timestamp information is converted into the fourth timestamp information corresponding to Coordinated Universal Time (UTC) based on the power station's time zone; the fourth timestamp information is then converted into the first timestamp information corresponding to the server's time zone.

[0019] In some embodiments of the second aspect, the power plant operating data is power generation; When the statistics module performs statistical calculations on power plant operation data based on the first timestamp information and the locally preset statistical period to generate target operation data, it is specifically used for: Based on the first timestamp information, the predetermined electricity price calculation rules, and the local preset statistical period, the power generation is statistically calculated to generate the corresponding power plant revenue; target operation data is generated based on the power generation and power plant revenue.

[0020] In some implementations of the second aspect, the statistics module is also used for: Construct a second preset database; obtain the time zone data, unique identifier of each power station, and server time zone data of all power stations; construct the mapping relationship between the time zone data and unique identifier of each power station.

[0021] In some embodiments of the second aspect, the generation module is further configured to: The first and third timestamp information are converted and verified to generate corresponding verification results. If the verification result is that the timestamp deviation is greater than the preset deviation threshold, the first and third timestamp information are converted again.

[0022] Based on the same inventive concept, in a third aspect, embodiments of this application also provide a server, including: a memory and a processor; The memory stores the instructions that the computer executes; The processor executes computer-executable instructions stored in memory to implement a data processing method for a distributed cloud platform as described in any of the first aspects.

[0023] Based on the same inventive concept, in a fourth aspect, embodiments of this application also provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the data processing method of a distributed cloud platform as described in any of the first aspects.

[0024] According to the data processing method, apparatus, and server of the distributed cloud platform provided in this application embodiment, by performing time zone conversion on the power plant operation data of the target power plant, the second timestamp information of the power plant's time zone is converted into the first timestamp information of the server's time zone. When a request to view power plant operation data is received, corresponding operation display data is generated based on the first timestamp information, the user's time zone, and the target operation data, and the operation display data contains at least a third timestamp information corresponding to the user's time zone. Thus, through the above time zone conversion, the problem of data timestamp misalignment caused by time zone confusion is solved, thereby improving the data statistical accuracy of the distributed cloud platform. Furthermore, the operation display data can more intuitively and clearly display data in the user's time zone, preventing users from experiencing confusion regarding the time of the data. Attached Figure Description

[0025] Figure 1 This illustration shows a flowchart of a data processing method for a distributed cloud platform provided in an embodiment of this application. Figure 2 This illustration shows another flowchart of the data processing method for the distributed cloud platform provided in an embodiment of this application; Figure 3 This diagram illustrates the overall flow of the data processing method for the distributed cloud platform provided in an embodiment of this application. Figure 4 This diagram illustrates the architecture of the distributed cloud platform provided in an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solutions of this application, the application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0027] The features and exemplary embodiments of various aspects of this application will now be described in detail. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only configured to explain this application and are not configured to limit this application. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples of this application.

[0028] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0029] As described in the background section, distributed cloud platforms are currently deployed in various regions around the world, and the collection, statistics, and display of power plant operation data generally involve multiple time zones. However, time zone differences can easily lead to inaccurate statistics on power generation and revenue, thus requiring further optimization.

[0030] Example 1

[0031] The data processing method for a distributed cloud platform provided in this application is applied to a server. The server can be a computer or a device within a computer used to implement the data processing method for the distributed cloud platform. The following description uses the execution of the data processing method for the distributed cloud platform by the server as an example.

[0032] like Figure 1 As shown, the data processing method of the distributed cloud platform provided in this application embodiment may include steps S101 to S103.

[0033] S101, Request to view power plant operation data.

[0034] For example, the power plant operation data viewing request can be obtained from the user equipment, such as directly receiving the power plant operation data viewing request sent by the user equipment, or it can be obtained from the intermediate node, which forwards the power plant operation data viewing request generated by the user equipment to the execution subject of this embodiment.

[0035] The request to view power plant operation data is based on the user's viewing action, such as viewing operation data through a browser. The user equipment generates the request to view power plant operation data based on this action.

[0036] A request to view power plant operation data may include information such as user session identifier, user time zone information, browser information, network address, and user identifier.

[0037] S102. Based on the power plant operation data viewing request, determine the corresponding user's time zone and the target operation data of the target power plant. The user's time zone is used to represent the user's current time zone. The target operation data includes the first timestamp information of the server's time zone. The first timestamp information is generated by converting the second timestamp information of the power plant's time zone. The power plant's time zone is the local time zone of the target power plant.

[0038] For example, the user time zone is used to represent the user's current time zone. It can be the time zone of the user's device, such as when the user uses a mobile phone to view data, the time zone of the mobile phone is the user's time zone. It can also be the time zone of the user's usual residence area. This time zone can be pre-set by the user, for example, by binding the user's time zone to the user's identifier, so that the corresponding user's time zone can be directly determined after receiving a request to view power plant operation data.

[0039] For example, a user's time zone can be determined by obtaining the browser's time zone information or by obtaining the user's device's system time zone. Obtaining the browser's time zone information is the preferred method for automatic acquisition. A manual time zone selection option can also be designed to allow users to choose their own time zone. This only requires binding the corresponding time zone information with the user identifier (or user session identifier) ​​and entering it into the database to adapt to subsequent time zone conversion logic, thus ensuring compatibility with different acquisition methods.

[0040] The user time zone is mainly used to make the display dimensions of the subsequently generated operational data easier for users to understand, and to prevent users from being confused about the time of the data.

[0041] For example, the target operational data includes a first timestamp information of the server's time zone and the power plant's operational data. It may also include a second timestamp information of the power plant's time zone. Alternatively, it may include the first timestamp information of the server's time zone and the power plant's operational data, but not the second timestamp information; this embodiment does not limit this.

[0042] The power plant's operational data includes data on power generation, operating status, and revenue.

[0043] For example, the target power plant can be one or more power plants.

[0044] S103. Generate corresponding operational display data based on the first timestamp information, the user's time zone, and the target operational data. The operational display data must include at least the third timestamp information corresponding to the user's time zone.

[0045] The operational data displayed includes at least a third timestamp corresponding to the user's time zone, in addition to the power plant operation data. This allows for a more intuitive and clear display of the user's time zone data, preventing confusion for users regarding the time of the data.

[0046] For example, the operational display data may also include the first timestamp information of the server time zone and the second timestamp information of the power plant time zone, so that users can view the display data from different dimensions.

[0047] S104. Send operational display data to the user equipment so that the user equipment can display the operational display data.

[0048] For example, operational data can be displayed in the form of graphs, tables, etc. Additionally, users can manually switch the display time zone, including various display modes such as power plant time zone and user time zone.

[0049] According to the data processing method of the distributed cloud platform provided in this application embodiment, by performing time zone conversion on the power plant operation data of the target power plant, the second timestamp information of the power plant time zone is converted into the first timestamp information of the server time zone. When a request to view power plant operation data is received, corresponding operation display data is generated based on the first timestamp information, the user's time zone, and the target operation data. This operation display data at least includes a third timestamp information corresponding to the user's time zone. Thus, the above time zone conversion solves the problem of data timestamp misalignment caused by time zone confusion, thereby improving the data statistical accuracy of the distributed cloud platform. Furthermore, this operation display data can more intuitively and clearly display data in the user's time zone, preventing users from experiencing confusion regarding the time of the data.

[0050] Example 2

[0051] like Figure 2 As shown, the data processing method for a distributed cloud platform provided in this application embodiment is based on the data processing method for a distributed cloud platform provided in embodiment 1 of this application, and adds a process description for data collection and statistics, which may include steps S201 to S208.

[0052] S201. Obtain the target power plant's operation data and unique identifier. The power plant operation data includes a second timestamp.

[0053] For example, power plant operation data may include a second timestamp information, and may also include power plant time zone information, such as daylight saving time zone change rules, power plant time zone, etc.

[0054] S202. Determine the time zone of the power station from the second preset database based on the unique identifier of the power station.

[0055] For example, the second preset database can store the mapping relationship between the power station's unique identifier and the power station's time zone. At the same time, it can also store data such as the server time zone, user time zone, user identifier, user session identifier, and server node identifier.

[0056] In some implementations, prior to S202, a database construction process and a data acquisition and storage process are also included, as detailed below: Construct a second preset database.

[0057] Obtain the time zone data, unique identifier of each power station, and server time zone data for all power stations.

[0058] Establish a mapping relationship between time zone data and unique identifiers of each power station.

[0059] For example, during data collection and statistics, the power station operation data and unique identifiers of other power stations besides the target power station can also be obtained simultaneously, and subsequent data processing procedures can be carried out.

[0060] For example, the second preset database can be built by a server node. The time zone data for all power plants includes the power plant time zone, daylight saving time change rules, etc.

[0061] S203. Based on the power station's time zone and Coordinated Universal Time (UTC), convert the second timestamp information into the first timestamp information corresponding to the server's time zone.

[0062] For example, based on Coordinated Universal Time (UTC), the second timestamp information can be first converted into the fourth timestamp information of UTC, and then the fourth timestamp information of UTC can be converted into the first timestamp information corresponding to the server's time zone.

[0063] S203 can be specifically defined as follows: The second timestamp information is converted into the fourth timestamp information corresponding to Coordinated Universal Time (UTC) based on the power station's time zone.

[0064] The fourth timestamp information is converted into the first timestamp information corresponding to the server's time zone.

[0065] By using Coordinated Universal Time (UTC) as the benchmark, more accurate timestamp information conversion can be achieved, thereby solving the problem of timestamp misalignment in cross-time zone scenarios and ensuring the accuracy of power plant operation data statistics.

[0066] S204. Based on the first timestamp information and the local preset statistical period, perform statistical calculations on the power plant operation data to generate target operation data.

[0067] For example, the local preset statistical period can be one day, one week, etc.

[0068] In some implementations, power plant operating data is power generation.

[0069] S204 can be specifically described as follows: Based on the first timestamp information, the predetermined electricity price calculation rules, and the local preset statistical period, the power generation is statistically calculated to generate the corresponding power plant revenue.

[0070] Target operating data is generated based on power generation and power plant revenue.

[0071] For example, electricity price calculation rules information includes electricity price calculation rules, exchange rate information, etc.

[0072] S205, Request to view power plant operation data.

[0073] S206. Based on the power station operation data viewing request, determine the corresponding user's time zone and the target operation data of the corresponding target power station.

[0074] In some implementations, S206 may be specifically as follows: The browser's time zone information is determined based on the power plant operation data viewing request.

[0075] The user's time zone is determined based on the browser's time zone information.

[0076] Based on the power plant operation data viewing request, the target operation data of the target power plant is determined from the first preset database.

[0077] For example, browser timezone information can be determined from the "Time-Zone" field in the HTTP (Hypertext Transfer Protocol) request header. The timezone data in the browser's timezone information can be used as the user's timezone, representing the user's current timezone.

[0078] The first preset database stores target operation data, unique power plant identifiers, and other data. The first preset database and the second preset database can be the same database or different databases; this embodiment does not limit this.

[0079] S207. Generate corresponding operational display data based on the first timestamp information, user time zone, and target operational data.

[0080] In some implementations, S207 may be specifically as follows: Based on Coordinated Universal Time (UTC), the first timestamp information is converted into a third timestamp information corresponding to the user's time zone.

[0081] The corresponding operational display data is generated based on the target operational data and third-party timestamp information.

[0082] For example, the first timestamp information can be converted into the fourth timestamp information of UTC, and the fourth timestamp information of UTC can be converted into the third timestamp information corresponding to the user's time zone.

[0083] In some implementations, before generating the corresponding operational display data based on the target operational data and the third timestamp information, the following steps are also included: The first and third timestamp information are converted and verified to generate corresponding verification results.

[0084] If the verification result shows that the timestamp deviation is greater than the preset deviation threshold, then the first timestamp information and the third timestamp information are re-converted.

[0085] Conversion verification can improve the accuracy of timestamp conversion.

[0086] For example, the preset deviation threshold can be set to 1 minute, 2 minutes, etc.

[0087] For example, the conversion of the first, second, and third timestamp information can be periodically verified, and the power plant time zone, server time zone, and other data recorded in the database can be periodically updated to improve the accuracy of timestamp conversion.

[0088] For example, in areas where daylight saving time changes, the following two steps are taken to ensure data continuity and accuracy during the daylight saving time switch: 1. Automatic time zone information synchronization and update: Regularly verify and synchronize the database, and automatically update the time zone code and offset of the corresponding power station to the time zone corresponding to the daylight saving time rule (such as switching Brazil from West 3 to West 2), to ensure the accuracy of the time zone reference for subsequent data collection and conversion.

[0089] 2. Historical and Real-Time Data Re-verification / Conversion: All data at the daylight saving time switching point (e.g., the previous day in Western Time Zone 3, the next day in Western Time Zone 2) undergoes timestamp deviation verification. If the deviation exceeds a preset threshold (e.g., 1 minute), a re-conversion process is automatically triggered, calibrating the timestamps based on the new time zone benchmark and performing subsequent data calculations. Simultaneously, all data conversion anomalies before and after the switch can be recorded, ensuring seamless data continuity.

[0090] S208. Send operational display data to the user equipment so that the user equipment can display the operational display data.

[0091] In this embodiment, the operational data is displayed according to the statistical period of the user's time zone. The core is to reorganize and display the power generation and revenue data of the power station's time zone by converting it into the time dimension of the user's time zone based on the intermediate UTC benchmark. Taking the statistical period from 0:00 to 24:00 on June 15th in the East 8th time zone as an example, the UTC timestamp interval corresponding to this time range is extracted, and then all power generation data of the power station (West 3rd time zone) falling within this UTC interval are matched (which may include some time period data of June 14th and 16th in the West 3rd time zone). The corresponding revenue is calculated simultaneously, and finally displayed uniformly according to the daily dimension of June 15th in the East 8th time zone, so as to achieve accurate adaptation between the user's time zone time perception and data display.

[0092] Furthermore, the data processing method of the distributed cloud platform in this embodiment has the following effects: 1. Using Coordinated Universal Time (UTC) as an intermediate benchmark, a three-level conversion mechanism of "power plant time zone - server time zone - user time zone" was constructed, which completely solved the problem of timestamp misalignment in cross-time zone scenarios and ensured the accuracy of power plant operation data such as power generation statistics. In actual tests, in the scenario of connecting a Singapore server to a Brazilian power plant and accessing it from Chinese users, the deviation of power generation statistics was reduced to less than 0.5%, which is far better than the 5% deviation rate of existing technologies.

[0093] 2. By linking electricity pricing rules to the power station's time zone, revenue calculations are accurately matched with the billing standards of the power station's location, avoiding revenue calculation errors caused by time zone differences.

[0094] 3. Automatically adapts to the user's browser time zone, displaying data according to the user's current time zone, improving the efficiency of data comprehension and reducing the time cost of operational decision-making. It also provides an option to manually select the time zone, catering to user needs in special scenarios.

[0095] 4. It has the function of dynamic time zone data update and verification, and can automatically adapt to scenarios such as daylight saving time changes and power plant and server deployment location adjustments, which improves the stability and adaptability of the system and is suitable for power plant management scenarios on a global distributed cloud platform.

[0096] To better understand the data processing method of the distributed cloud platform provided in this application embodiment, the data processing method of the distributed cloud platform in this embodiment will be described in more detail below.

[0097] The method in this embodiment is based on a distributed cloud platform, which includes power plant nodes, server nodes, user equipment nodes, etc. Figure 3 As shown, the data processing method flow of the distributed cloud platform in this embodiment is as follows: Time zone information collection and storage: Collect global standard time zone data - collect specific time zone data where the power station is located - collect time zone data from the user's browser - store the above three types of time zone data in the time zone information database.

[0098] The central node of the distributed cloud platform constructs a time zone information database (i.e., the aforementioned first preset database and / or second preset database), collecting and storing three types of time zone data: ① Power plant time zone data, obtained by locating the time zone (including daylight saving time rules) of the power plant deployment location and binding it with the unique identifier of the power plant. ② Server time zone data, collected from the local time zone of servers at each deployment site (Frankfurt, Singapore, China, etc.) and bound to the server node identifier. ③ User browser time zone data, obtained by obtaining the user's time zone information through the HTTP request header of the user's browser, or by providing a manual time zone selection entry for user confirmation, and bound to the user session identifier.

[0099] Power plant data acquisition and time zone marking: The power plant acquires power generation data according to the local time zone, adds a local time zone mark to the acquired power generation data, and uploads the power generation data with the time zone mark to the server.

[0100] Each power station's data acquisition terminal collects raw power generation data (including power generation, duration, and timestamp) according to its local time zone. When uploading the data, it automatically carries the unique identifier of the power station and the corresponding time zone information, forming a raw data message with a time zone mark, and uploads it to the server node in the corresponding region.

[0101] Server time zone to power plant time zone conversion and calibration: The server receives power generation data with time zone markings - converts the power plant time zone data to server time zone data based on UTC - calibrates the converted data - calculates the power generation based on the calibrated data.

[0102] After receiving the raw data packets, the server node extracts the unique identifier of the power station and matches the corresponding power station time zone from the time zone information database. Using UTC as an intermediate reference, the server node converts the power station time zone timestamp of the raw data to a UTC timestamp. Then, based on the offset between the server's local time zone and UTC, it completes the conversion from UTC timestamp to server time zone timestamp information, achieving time zone calibration of the raw data on the server side. Simultaneously, based on the calibrated timestamp information, the power station's power generation data is calculated according to a preset statistical period (such as hour, day, month).

[0103] Power generation and revenue calculation: Obtain the electricity price rules bound to the power station's time zone - calculate revenue based on the electricity price rules and power generation data.

[0104] Based on the calibrated power generation data and the preset electricity pricing rules (including price differences in different time zones and exchange rate settlement rules), the power plant revenue for the corresponding statistical period is calculated. The electricity pricing rules are linked to the power plant's time zone to ensure that revenue calculations are consistent with the billing standards of the power plant's location.

[0105] User time zone to power plant time zone conversion display: The server uses UTC as the reference, obtains the user's browser time zone information, converts the server time zone data to the user's browser time zone data, and displays the converted user browser time zone data.

[0106] When a user accesses power plant data through a browser, the system extracts the browser's timezone information corresponding to the user's session identifier and retrieves the calculated power generation and revenue data (including UTC timestamps) from the server. Using UTC as an intermediate reference, the system converts the data's UTC timestamps to the timestamps of the user's browser's timezone. Simultaneously, the power generation and revenue data are reorganized according to the user's timezone (e.g., displayed according to the "day" statistical period of the user's timezone) and fed back to the user's browser.

[0107] Dynamic update and verification of time zone data: Regularly update the data in the time zone information database - verify the updated time zone information - if the time zone information deviation exceeds the preset threshold, trigger the data to be reconverted.

[0108] Regularly synchronize the time zone information (including daylight saving time changes) of each power station and server, and update the time zone information database. At the same time, verify the converted timestamps and statistical data. If the conversion deviation is found to exceed the preset threshold (such as 1 minute), automatically trigger the re-conversion process and record the exception log.

[0109] The following detailed description uses a specific embodiment as an example: a distributed cloud platform (Singapore server node, Brazilian power plant, and Chinese user). Time zone information collection and storage: The central node constructs a time zone information database, recording the unique identifier "ST-001" and corresponding time zone information of the Brazilian power plant (assuming it is located in Rio de Janeiro, UTC+3, including daylight saving time rules: daylight saving time is observed from October to February of the following year, changing the time zone to UTC+2). It also records the identifier "SV-002" and corresponding time zone information of the Singapore server node (UTC+8). When Chinese users access the system through a browser, the distributed cloud platform system obtains the user's time zone as UTC+8 from the "Time-Zone" field of the HTTP request header and binds it to the user session identifier "US-003".

[0110] Power plant data acquisition and time zone marking: The acquisition terminal of the Brazilian power plant collected the raw power generation data (power generation capacity 50MW, power generation duration 1h, acquisition timestamp "2025-06-1510:00:00-03:00") from 10:00 to 11:00 on June 15, 2025, according to the Western Time Zone 3, and carried the power plant identifier "ST-001" and the Western Time Zone 3 information to form a raw data message, which was then uploaded to the Singapore server node.

[0111] Server time zone to power plant time zone conversion and calibration: After receiving the message, the Singapore server extracts "ST-001" and matches it to the UTC West 3 time zone from the time zone information database. The original UTC West 3 timestamp "2025-06-15 10:00:00-03:00" is converted to a UTC timestamp "2025-06-15 13:00:00Z". Then, based on the offset (+8h) between Singapore East 8 time zone and UTC, the UTC timestamp is converted to a Singapore time zone timestamp "2025-06-16 00:00:00+08:00". Based on the daily statistical cycle, the power generation of the power plant on June 15 (UTC West 3) is calculated to be 50MW × 1h = 50MWh.

[0112] Electricity generation and revenue calculation: The system extracts the corresponding electricity price in the Western Brazil Region 3 (0.15 USD / MWh) from the electricity price rule library, and combines it with the exchange rate of the day (1 USD = 6.9 RMB) to calculate the revenue on June 15 as 50MWh × 0.15 USD / MWh × 6.9 RMB / USD = 51.75 RMB.

[0113] The system displays the conversion between user timezone and power plant timezone: When a Chinese user accesses the site, the system extracts the East 8 timezone corresponding to "US-003". The UTC timestamp "2025-06-15 13:00:00Z" of the power generation data is converted to the East 8 timezone timestamp "2025-06-15 21:00:00+08:00". Based on the daily statistical cycle of the user's timezone, the system displays "2025-06-15 (East 8 timezone) corresponds to a Brazilian power plant generating 50MWh on June 15th (West 3 timezone), with a revenue of 51.75 RMB".

[0114] Dynamic Time Zone Data Update and Verification: After Brazil enters Daylight Saving Time in October of that year, the system automatically synchronizes the time zone information database, updating the time zone of "ST-001" to UTC+2. Subsequent data collection undergoes conversion and verification. If a data entry is found to have a timestamp deviation of 2 minutes after conversion, a re-conversion is automatically triggered, and an exception log is recorded on the server.

[0115] The system architecture of this embodiment is as follows: Figure 4As shown, it includes: a central node (with a built-in time zone information management module), a data display module, a user browser, and cloud platform nodes (e.g., Frankfurt, Singapore, China, others). Each cloud platform node has a built-in time zone conversion module, data calculation module, verification and logging module, and power plant data acquisition module.

[0116] The time zone information management module uses a MySQL database to build a time zone information repository, including a power plant time zone table (power plant ID, time zone code, daylight saving time rule, update time), a server time zone table (server ID, deployment location, time zone code, update time), and a user time zone table (session ID, user ID, time zone code, retrieval method). It provides a RESTful (REpresentational State Transfer) interface to implement CRUD operations on time zone data.

[0117] Power plant data acquisition module: Deployed based on edge computing gateway, it uses Modbus (Modbus bus protocol) to acquire power generation data of power plant PLC (Programmable Logic Controller), and uploads data through MQTT (Message Queuing Telemetry Transport) protocol. Before uploading, time zone mark (time zone code, daylight saving time mark) is added by Python script.

[0118] Time zone conversion module: Developed in Java, integrating the Joda-Time time processing library to implement UTC intermediate reference conversion logic. It provides two core interfaces: stationToServer(String stationTime, String stationTimeZone, String serverTimeZone) (station time zone to server time zone) and serverToUser(String serverTime, String serverTimeZone, String userTimeZone) (server time zone to user time zone).

[0119] Data computation module: Utilizes the Spark Streaming engine for streaming computation, aggregating and calculating data based on calibrated power generation data according to statistical periods (hours / days / months). It also connects to a Redis (Remote Dictionary Server) cached electricity price rule base (caching electricity prices and exchange rates by time zone) to calculate revenue in real time.

[0120] Data display module: The front-end interface is developed based on Vue.js (a progressive framework) and integrates the Echarts (Enterprise Charts) chart library to display power generation trend charts and revenue statistics tables according to the user's time zone. A "Time Zone Switch" button is provided to allow users to manually select the display time zone (such as UTC, power plant time zone, or local time zone).

[0121] The verification and logging module uses a scheduled task (every 5 minutes) to verify the transformed timestamps and calculate the deviation from the original timestamps. It uses the ELK (Elasticsearch, Logstash, Kibana, ELK log management platform) logging system to record exception information, including exception type, data ID, timestamps before and after transformation, and processing status.

[0122] Distributed cloud platform nodes: Tomcat application servers and Spark compute nodes are deployed on the Singapore server node. Time zone information and statistical data are synchronized with the central node via VPN (Virtual Private Network). Load balancing is supported to handle concurrent data uploads from multiple power stations.

[0123] This embodiment has the following characteristics at the system level: (1) Construct a multi-dimensional time zone information database with unique associated identifiers to achieve unified management of three types of time zone data.

[0124] (2) A dedicated time zone conversion module based on Joda-Time provides customized conversion interfaces for power plant-server and server-user.

[0125] (3) The design of Spark Streaming streaming computing and Redis caching electricity price library is used to realize real-time calculation of the calibrated data.

[0126] (4) A display module that supports three modes: UTC, power plant time zone, and local time zone, taking into account both automatic adaptation and manual selection requirements.

[0127] (5) The time zone information synchronization mechanism between distributed cloud platform nodes and central nodes is adapted to global multi-region deployment scenarios.

[0128] It is understood that the various method embodiments mentioned above in this application can be combined with each other to form combined embodiments without violating the principle and logic. Due to space limitations, this application will not elaborate further. Those skilled in the art will understand that in the above methods of specific implementation, the specific execution order of each step should be determined by its function and possible internal logic.

[0129] Example 3

[0130] The data processing device for the distributed cloud platform provided in this application embodiment is located on a server, and the data processing device for the distributed cloud platform may include: The acquisition module is used to retrieve requests for viewing power plant operation data.

[0131] The determination module is used to determine the corresponding user's time zone and the target operation data of the target power station based on the power station operation data viewing request. The user's time zone represents the user's current time zone. The target operation data includes the first timestamp information of the server's time zone. The first timestamp information is generated by converting the second timestamp information of the power station's time zone. The power station's time zone is the local time zone of the target power station.

[0132] The generation module is used to generate corresponding operational display data based on the first timestamp information, the user's time zone, and the target operational data. The operational display data must include at least the third timestamp information corresponding to the user's time zone.

[0133] The sending module is used to send operational display data to user devices so that user devices can display the operational display data.

[0134] In some implementations, the determining module is specifically used for: The browser's time zone information is determined based on the power plant operation data viewing request.

[0135] The user's time zone is determined based on the browser's time zone information. The target operating data for the target power station is determined from a first preset database based on the power station operation data viewing request.

[0136] In some implementations, the generation module is specifically used for: Based on Coordinated Universal Time (UTC), the first timestamp information is converted into a third timestamp information corresponding to the user's time zone. Corresponding operational display data is then generated based on the target operational data and the third timestamp information.

[0137] In some implementations, the data processing apparatus of the distributed cloud platform further includes: The statistics module is used to acquire the target power plant's operational data and unique identifier. The power plant's operational data includes a second timestamp. Based on the unique identifier, the power plant's time zone is determined from a second preset database. According to the power plant's time zone and Coordinated Universal Time (UTC), the second timestamp is converted into a first timestamp corresponding to the server's time zone. Based on the first timestamp and a locally preset statistical period, statistical calculations are performed on the power plant's operational data to generate the target operational data.

[0138] In some implementations, when the statistics module converts the second timestamp information into the first timestamp information corresponding to the server's time zone based on the power plant's time zone and Coordinated Universal Time (UTC), it is specifically used for: The second timestamp information is converted into the fourth timestamp information corresponding to UTC based on the power station's time zone. The fourth timestamp information is then converted into the first timestamp information corresponding to the server's time zone.

[0139] In some implementations, power plant operating data is power generation.

[0140] When the statistics module performs statistical calculations on power plant operation data based on the first timestamp information and the locally preset statistical period to generate target operation data, it is specifically used for: Based on the first-timestamp information, the predetermined electricity price calculation rules, and the local preset statistical period, the power generation is statistically calculated to generate the corresponding power plant revenue. Target operating data is then generated based on the power generation and power plant revenue.

[0141] In some implementations, the statistics module is also used for: Construct a second pre-defined database. Obtain the time zone data, unique identifiers of all power plants, and server time zone data. Establish a mapping relationship between the time zone data and unique identifiers of each power plant.

[0142] In some implementations, the generation module is also used for: The first and third timestamp information are converted and verified, and corresponding verification results are generated. If the verification result shows that the timestamp deviation is greater than a preset deviation threshold, the first and third timestamp information are converted again.

[0143] The data processing apparatus for a distributed cloud platform provided in this application has the beneficial effects and implementation methods of the data processing methods for a distributed cloud platform provided in Embodiments 1 and 2 of this application. For details, please refer to the specific descriptions of the data processing methods for a distributed cloud platform in Embodiments 1 and 2 above. This embodiment will not repeat them here.

[0144] Example 4

[0145] This application also provides a server, which is intended for various forms of devices with data processing capabilities, such as servers and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0146] The server includes a processor and memory. The various components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processor processes the instructions executed within the server.

[0147] The memory is the non-transitory computer-readable storage medium provided in this application. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the data processing method of the distributed cloud platform provided in this application. The non-transitory computer-readable storage medium of this application stores computer instructions for causing a computer to perform the data processing method of the distributed cloud platform provided in this application.

[0148] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the data processing method of the distributed cloud platform in the embodiments of this application. The processor executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory, thereby implementing the data processing method of the distributed cloud platform in the above method embodiments.

[0149] The server provided in this application embodiment has the beneficial effects and implementation methods of the data processing method of the distributed cloud platform provided in Embodiments 1 and 2 of this application. For details, please refer to the specific description of the data processing method of the distributed cloud platform in Embodiments 1 and 2 above. This embodiment will not repeat the description here.

[0150] Example 5

[0151] This embodiment also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the data processing method of the distributed cloud platform in Embodiment 1 or Embodiment 2 above.

[0152] The computer-readable storage medium provided in this application embodiment has the beneficial effects and implementation methods of the data processing method of the distributed cloud platform in Embodiments 1 and 2 of this application. For details, please refer to the specific description of the data processing method of the distributed cloud platform in Embodiments 1 and 2 above. This embodiment will not repeat the description here.

[0153] As is known to those skilled in the art, computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, as is known to those skilled in the art, communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0154] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0155] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of this application, and this application is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and substance of this application, and these modifications and improvements are also considered to be within the scope of protection of this application.

Claims

1. A data processing method for a distributed cloud platform, characterized in that, Applied to a server, the method includes: Request to view power plant operation data; Based on the power plant operation data viewing request, the corresponding user time zone and the target operation data of the corresponding target power plant are determined; the user time zone is used to represent the user's time zone; the target operation data includes the first timestamp information of the server time zone; the first timestamp information is generated after time zone conversion of the second timestamp information of the power plant time zone; the power plant time zone is the local time zone of the target power plant. Based on the first timestamp information, the user's time zone, and the target operational data, corresponding operational display data is generated; the operational display data at least includes a third timestamp information corresponding to the user's time zone. The operation display data is sent to the user equipment so that the user equipment displays the operation display data.

2. The method according to claim 1, characterized in that, The process of determining the target operating data of the corresponding power station and the corresponding user's time zone based on the power station operation data viewing request includes: The corresponding browser time zone information is determined based on the power plant operation data viewing request; The user's time zone is determined based on the browser's time zone information; Based on the power plant operation data viewing request, the target operation data of the target power plant is determined from the first preset database.

3. The method according to claim 1, characterized in that, The step of generating corresponding operational display data based on the first timestamp, the user's time zone, and the target operational data includes: Based on Coordinated Universal Time (UTC), the first timestamp information is converted into a third timestamp information corresponding to the user's time zone; Based on the target operational data and the third timestamp information, corresponding operational display data is generated.

4. The method according to any one of claims 1 to 3, characterized in that, Before determining the corresponding user's time zone and the target operating data of the corresponding target power station based on the power station operation data viewing request, the process includes: Obtain the power plant operation data and unique identifier of the target power plant; the power plant operation data includes the second timestamp information; The time zone of the power station is determined from the second preset database based on the unique identifier of the power station. Based on the power station's time zone and Coordinated Universal Time (UTC), the second timestamp information is converted into the first timestamp information corresponding to the server's time zone; Based on the first timestamp information and the local preset statistical period, the power plant operation data is statistically calculated to generate the target operation data.

5. The method according to claim 4, characterized in that, The step of converting the second timestamp information into the first timestamp information corresponding to the server's time zone based on the power station's time zone and Coordinated Universal Time includes: The second timestamp information is converted into the fourth timestamp information corresponding to the Coordinated Universal Time (UTC) based on the time zone of the power station. The fourth timestamp information is converted into the first timestamp information corresponding to the server's time zone.

6. The method according to claim 4, characterized in that, The power plant's operating data is the amount of electricity generated. The step of statistically calculating the power plant operation data based on the first timestamp information and a locally preset statistical period to generate the target operation data includes: Based on the first timestamp information, the predetermined electricity price calculation rules, and the local preset statistical period, the power generation is statistically calculated to generate the corresponding power plant revenue; The target operating data is generated based on the power generation and the power plant revenue.

7. The method according to claim 4, characterized in that, Before determining the time zone of the power station from the second preset database based on the power station's unique identifier, the process also includes: Construct the second preset database; Obtain the time zone data, unique identifier of each power station, and time zone data of the server for all power stations; Construct a mapping relationship between the time zone data of each power station and the unique identifier of each power station.

8. The method according to claim 3, characterized in that, Before generating the corresponding operational display data based on the target operational data and the third timestamp information, the method further includes: The first timestamp information and the third timestamp information are converted and verified to generate corresponding verification results; If the verification result shows that the timestamp deviation is greater than the preset deviation threshold, then the first timestamp information and the third timestamp information are re-converted.

9. A data processing device for a distributed cloud platform, characterized in that, Located on a server, the device includes: The acquisition module is used to retrieve requests for viewing power plant operation data; The determination module is used to determine the corresponding user's time zone and the target operation data of the corresponding target power station based on the power station operation data viewing request; the user's time zone is used to represent the user's current time zone; the target operation data includes the first timestamp information of the server's time zone; the first timestamp information is generated by converting the second timestamp information of the power station's time zone; the power station's time zone is the local time zone of the target power station. The generation module is used to generate corresponding operation display data based on the first timestamp information, the user's time zone, and the target operation data; the operation display data includes at least a third timestamp information corresponding to the user's time zone; The sending module is used to send the operation display data to the user equipment so that the user equipment can display the operation display data.

10. A server, characterized in that, include: Memory and processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the data processing method of the distributed cloud platform as described in any one of claims 1 to 8.

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