CN118170752A - Cloud computing-based power big data analysis method and system - Google Patents
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Abstract
The invention discloses a cloud computing-based power big data analysis method and a cloud computing-based power big data analysis system, which belong to the technical field of power data analysis and prediction, and in the system, the collected related power grid data information is stored and backed up in a cloud collecting space by collecting the related power grid data information; processing the acquired power grid data by using a cloud computing technology; and finally, outputting the analysis result and optimizing, wherein the invention can shorten the power grid data query time, thereby realizing accurate monitoring of the power grid data. Through testing, the monitoring analysis effect of the invention is proved to be good, and the invention has great popularization value.
Description
Technical Field
The invention relates to the field of power data analysis and prediction, in particular to a cloud computing-based power big data analysis method and a cloud computing-based power big data analysis system.
Background
With the continuous improvement of the technology level and the improvement of the intelligent level of various electric power equipment, the service that needs to be provided by the electric power system in the aspect of business is also increasing. To guarantee better power requirements, extensive computational analysis and data mining analysis are required. Traditional centralized computing analysis and data mining have failed to meet the needs of large-scale computing analysis and data mining analysis, and as devices in power systems increase and mobile terminal devices are used for real-time, remote and wide-range control of the power systems, power grid loads are continuously increasing.
Therefore, the research and the application of the big data of the power grid have great significance for the healthy development of the power grid, and the analysis and the utilization of the big data of the power grid are promoted to play an important role in the development of power engineering.
Disclosure of Invention
The invention overcomes the defects of the prior art and makes the following improvements and optimizations aiming at the defects of the prior art.
The aim of the invention is achieved by the following technical scheme:
In one aspect, the invention provides a cloud computing-based power big data analysis method, which comprises the following steps:
s1, acquiring relevant power grid data information, and storing and backing up the acquired relevant power grid data information in an acquisition cloud space;
s2, processing the acquired power grid data by using a cloud computing technology;
and S3, outputting an analysis result and optimizing.
Preferably, in the step S1, the related power grid data information includes power transformation data, power transmission data, power distribution data, monitoring data, early warning data and daily power generation amount.
Preferably, the grid manager performs a series of operations on the relevant grid data, including receiving views, modifications, deletions and additions.
Preferably, in the step S2, interaction with the collected power grid data is performed by accessing a configuration network, so as to implement parallel processing of network storage and data; the screening range of the abnormal data of the power grid monitoring is as follows:
wherein, data is the initial monitoring range, data v and V are both the quantity parameters of the monitoring Data, and d is the monitoring range.
More preferably, the processing by cloud computing technology specifically comprises:
Wherein, T ima is the power grid monitoring telemetry information, C n is the number of cloud computing iterations, G N (Data) is represented as a fitting distribution coefficient of the power grid monitoring Data, when G N (Data) =1, the monitoring Data is converged on the cloud space, at this time, the power grid monitoring Data is affected by the fitting coefficient, and the power grid monitoring Data is directly output to the monitoring database, so that the effectiveness of the monitoring Data can be ensured; when G N (Data) > 1 or G N (Data) < 1, the monitor Data does not converge on the cloud space.
Preferably, the processing by cloud computing technology specifically includes:
Wherein, T ima is the power grid monitoring telemetry information, C n is the number of cloud computing iterations, G N (Data) is represented as a fitting distribution coefficient of the power grid monitoring Data, when G N (Data) =1, the monitoring Data is converged on the cloud space, at this time, the power grid monitoring Data is affected by the fitting coefficient, and the power grid monitoring Data is directly output to the monitoring database, so that the effectiveness of the monitoring Data can be ensured; when G N (Data) > 1 or G N (Data) < 1, the monitor Data does not converge on the cloud space.
More preferably, the hierarchical processing technology and the SQL system decompose the whole related grid data stored in the cloud space into a plurality of layers of data, and then compare and analyze the data with the stored historical grid data.
In another aspect, the present invention further provides a cloud computing-based power big data analysis system, which specifically includes:
The acquisition module is used for acquiring related power grid data information, and storing and backing up the acquired related power grid data information in an acquisition cloud space;
The cloud computing processing module is used for processing the acquired power grid data by utilizing a cloud computing technology;
the data analysis module is used for analyzing the related power grid data information stored in the cloud space;
and the cloud space is used for storing related power grid data information.
The invention provides a cloud computing-based power big data analysis method and a cloud computing-based power big data analysis system, which can reduce missing acquisition conditions of power grid data through a power grid data intelligent acquisition module; the hierarchical processing technique is combined with the SQL system to remove duplicate and invalid data; and the power grid data query time is shortened, and accurate monitoring analysis of the power grid data is further realized. Through testing, the monitoring analysis effect of the invention is proved to be good, and the invention has great popularization value.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of a method for analyzing big electric power data based on cloud computing;
FIG. 2 is a frame diagram of a power big data analysis system based on cloud computing;
FIG. 3 is a system login interface according to a preferred embodiment of the present invention;
FIG. 4 is a system test result of a preferred embodiment of the present invention.
Detailed Description
The following describes in further detail a method and a system for analyzing big data of electric power based on cloud computing with reference to specific embodiments, which are only used for comparison and explanation purposes, and the present invention is not limited to these embodiments.
In the description of the present invention, it should be understood that the directions or positional relationships indicated by the terms "upper", "lower", "left", "right", "top", "bottom", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention.
As shown in fig. 1, in one aspect, the present invention provides a method for analyzing big electric power data based on cloud computing, including:
s1, acquiring relevant power grid data information, and storing and backing up the acquired relevant power grid data information in an acquisition cloud space;
s2, processing the acquired power grid data by using a cloud computing technology;
and S3, outputting an analysis result and optimizing.
Preferably, in the step S1, the related power grid data information includes power transformation data, power transmission data, power distribution data, monitoring data, early warning data and daily power generation amount.
Preferably, the grid manager performs a series of operations on the relevant grid data, including receiving views, modifications, deletions and additions.
Preferably, in the step S2, interaction with the collected power grid data is performed by accessing a configuration network, so as to implement parallel processing of network storage and data; the screening range of the abnormal data of the power grid monitoring is as follows:
wherein, data is the initial monitoring range, data v and V are both the quantity parameters of the monitoring Data, and d is the monitoring range.
More preferably, the processing by cloud computing technology specifically comprises:
Wherein, T ima is the power grid monitoring telemetry information, C n is the number of cloud computing iterations, G N (Data) is represented as a fitting distribution coefficient of the power grid monitoring Data, when G N (Data) =1, the monitoring Data is converged on the cloud space, at this time, the power grid monitoring Data is affected by the fitting coefficient, and the power grid monitoring Data is directly output to the monitoring database, so that the effectiveness of the monitoring Data can be ensured; when G N (Data) > 1 or G N (Data) < 1, the monitor Data does not converge on the cloud space.
Preferably, the processing by cloud computing technology specifically includes:
Wherein, T ima is the power grid monitoring telemetry information, C n is the number of cloud computing iterations, G N (Data) is represented as a fitting distribution coefficient of the power grid monitoring Data, when G N (Data) =1, the monitoring Data is converged on the cloud space, at this time, the power grid monitoring Data is affected by the fitting coefficient, and the power grid monitoring Data is directly output to the monitoring database, so that the effectiveness of the monitoring Data can be ensured; when G N (Data) > 1 or G N (Data) < 1, the monitor Data does not converge on the cloud space.
And storing the monitoring data after the cloud computing processing in a cloud space, so that the monitoring data query is simpler and more convenient. According to the invention, the monitoring data are stored in the cloud space, so that the integrity of the power grid data is ensured, and the specific content of the monitoring database comprises the steps of respectively storing a user name, a login address, power transformation data, power transmission data, power distribution data, monitoring data, early warning data, power generation data and the like in the monitoring database, and taking the user name as a main key. The power grid data is stored and backed up in the monitoring database in the cloud space, so that the data searching time is shortened, and the data storage effect is ensured.
More preferably, the hierarchical processing technology and the SQL system decompose the whole related grid data stored in the cloud space into a plurality of layers of data, and then compare and analyze the data with the stored historical grid data.
The method is mainly characterized in that the data of the power system is efficiently processed by an automatic processing system in a computer and related technical means. Then further forming a complete SQL sentence detection technology and optimizing the data processing space of the computer according to the complete SQL sentence detection technology. By the mode, a data processing system can be technically guaranteed, the overall working efficiency and the working quality are continuously improved, and a solid theoretical and technical foundation is better laid for the development of the power industry.
As shown in fig. 2, in another aspect, the present invention further provides a cloud computing-based power big data analysis system, which specifically includes:
The acquisition module is used for acquiring related power grid data information, and storing and backing up the acquired related power grid data information in an acquisition cloud space;
The cloud computing processing module is used for processing the acquired power grid data by utilizing a cloud computing technology;
the data analysis module is used for analyzing the related power grid data information stored in the cloud space;
and the cloud space is used for storing related power grid data information.
In an embodiment, a login verification module is set up in a cloud computing-based power big data analysis system, user names and passwords with different identities are input in a system login interface, as shown in fig. 3, and the system can be accessed by clicking login. After logging in, clicking a corresponding acquisition module, a cloud computing processing module, a data analysis module, a cloud space and the like, and generating corresponding power grid data to indicate that the whole system operates normally.
The administrator can check, modify, delete, add and the like the user information, is convenient to operate, can monitor real-time data, inquire event data, generating capacity data, abnormal data early warning, display data and the like. The test results are consistent with the expectations, and the test results are shown in fig. 4, which show that the system performance is good.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (8)
1. The power big data analysis method based on cloud computing is characterized by comprising the following steps of:
s1, acquiring relevant power grid data information, and storing and backing up the acquired relevant power grid data information in an acquisition cloud space;
s2, processing the acquired power grid data by using a cloud computing technology;
and S3, outputting an analysis result and optimizing.
2. The cloud computing-based power big data analysis method according to claim 1, wherein in S1, the related power grid data information includes power transformation data, power transmission data, power distribution data, monitoring data, early warning data and daily power generation amount.
3. The cloud computing-based power big data analysis method of claim 2, wherein a grid manager performs a series of operations on the relevant grid data, including receiving views, modifications, deletions, and additions.
4. The cloud computing-based power big data analysis method according to claim 1, wherein in the step S2, network storage and parallel processing of data are achieved through interaction between access configuration network and collected power grid data; the screening range of the abnormal data of the power grid monitoring is as follows:
wherein, data is the initial monitoring range, data v and V are both the quantity parameters of the monitoring Data, and d is the monitoring range.
5. The cloud computing-based power big data analysis method according to claim 4, wherein the processing by the cloud computing technology specifically comprises:
Wherein, T ima is the power grid monitoring telemetry information, C n is the number of cloud computing iterations, G N (Data) is represented as a fitting distribution coefficient of the power grid monitoring Data, when G N (Data) =1, the monitoring Data is converged on the cloud space, at this time, the power grid monitoring Data is affected by the fitting coefficient, and the power grid monitoring Data is directly output to the monitoring database, so that the effectiveness of the monitoring Data can be ensured; when G N (Data) > 1 or G N (Data) < 1, the monitor Data does not converge on the cloud space.
6. The cloud computing-based power big data analysis method according to claim 1, wherein in S3, monitoring data after cloud computing processing is stored in a monitoring database, and related grid data information is computed and analyzed in cloud space by combining hierarchical processing technology with an SQL system.
7. The cloud computing-based power big data analysis method according to claim 6, wherein the hierarchical processing technology and the SQL system decompose the whole related grid data stored in the cloud space into a plurality of layers of data, and then compare and analyze the data with the stored historical grid data.
8. The utility model provides a big data analysis system of electric power based on cloud calculates which characterized in that specifically includes:
The acquisition module is used for acquiring related power grid data information, and storing and backing up the acquired related power grid data information in an acquisition cloud space;
The cloud computing processing module is used for processing the acquired power grid data by utilizing a cloud computing technology;
the data analysis module is used for analyzing the related power grid data information stored in the cloud space;
and the cloud space is used for storing related power grid data information.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130007265A1 (en) * | 2011-06-30 | 2013-01-03 | International Business Machines Corporation | Monitoring resources in a cloud-computing environment |
| CN111144687A (en) * | 2019-11-20 | 2020-05-12 | 北京中电飞华通信股份有限公司 | Novel electric power big data analysis system |
| CN114911842A (en) * | 2022-04-14 | 2022-08-16 | 山东青年政治学院 | Data analysis device based on cloud computing |
| CN117353450A (en) * | 2023-09-28 | 2024-01-05 | 国网河南省电力公司濮阳供电公司 | Cloud computing-based power grid data maintenance system and method |
| CN117611388A (en) * | 2023-12-05 | 2024-02-27 | 北京鼎磐科技有限公司 | Electric power informatization management system based on cloud computing |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130007265A1 (en) * | 2011-06-30 | 2013-01-03 | International Business Machines Corporation | Monitoring resources in a cloud-computing environment |
| CN111144687A (en) * | 2019-11-20 | 2020-05-12 | 北京中电飞华通信股份有限公司 | Novel electric power big data analysis system |
| CN114911842A (en) * | 2022-04-14 | 2022-08-16 | 山东青年政治学院 | Data analysis device based on cloud computing |
| CN117353450A (en) * | 2023-09-28 | 2024-01-05 | 国网河南省电力公司濮阳供电公司 | Cloud computing-based power grid data maintenance system and method |
| CN117611388A (en) * | 2023-12-05 | 2024-02-27 | 北京鼎磐科技有限公司 | Electric power informatization management system based on cloud computing |
Non-Patent Citations (1)
| Title |
|---|
| 陈欣 等: "基于云计算的电网智能监控系统设计", 光源与照明, no. 6, 30 June 2022 (2022-06-30), pages 166 - 168 * |
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