CN110865329B - Electric energy metering method and system based on big data self-diagnosis - Google Patents
Electric energy metering method and system based on big data self-diagnosis Download PDFInfo
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Abstract
The invention provides an electric energy metering method and system based on big data self-diagnosis, wherein the method comprises the following steps: s1, calling and testing an intelligent ammeter through a power consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data; s2, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction; and S3, calculating abnormal data according to the statistical analysis and the extracted characteristic quantity of the big data platform, and searching the corresponding intelligent electric meter according to the abnormal data to perform early warning. According to the electric energy metering method and system based on big data self-diagnosis, the electric energy metering data collected by the intelligent electric meter and the information data of the intelligent electric meter are quickly processed through the distributed storage system, the data processing efficiency is high, and the intelligent electric meter is timely found out of faults through processing the data, so that the problem of labor waste and labor waste caused by manual troubleshooting is avoided.
Description
Technical Field
The invention belongs to the technical field of electric energy metering, and particularly relates to an electric energy metering method and system based on big data self-diagnosis.
Background
Along with the development of electric power technology, smart electric meter has begun to popularize, smart electric meter brings very big facility for measurement data acquisition, but receive the environment, it is artificial, the influence of design factor, smart electric meter also can break down, if through artifical investigation, the work load is too big, and can not in time discover the trouble, bring inconvenience for the user, bring the loss for the electric power department, if can in time discover the trouble through the analytic processing to the measurement data, will bring great facility, but the collection and the processing of a large amount of measurement data face the requirement of big data bulk and high processing speed, very high requirement has been put forward for current electric energy measurement.
Therefore, it is very necessary to provide an electric energy metering method and system based on big data self-diagnosis for the above-mentioned defects in the prior art.
Disclosure of Invention
Aiming at the defects that the workload of manual troubleshooting of the intelligent electric meter in the prior art is large and the workload is large through data analysis, the invention provides an electric energy metering method and system based on big data self-diagnosis, so as to solve the technical problems.
In a first aspect, the invention provides an electric energy metering method based on big data self-diagnosis, which comprises the following steps:
s1, calling and testing an intelligent ammeter through a power consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
s2, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction;
and S3, calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching a corresponding fault intelligent electric meter according to the abnormal data to perform early warning.
Further, the step S2 specifically includes the following steps:
s21, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform;
and S22, the big data platform divides the electric energy metering data and the intelligent electric meter information data, and performs statistical analysis and characteristic quantity extraction through a distributed storage system. The big data platform cuts the electric energy metering data and the information data of the intelligent electric meter through the distributed storage system and then stores and statistically analyzes the electric energy metering data and the information data of the intelligent electric meter, and therefore data processing efficiency is improved.
Further, the step S22 specifically includes the following steps:
s221, the big data platform divides the electric energy metering data and the intelligent electric meter information data;
s222, the big data platform conducts statistical analysis on the segmented electric energy metering data and the intelligent electric meter information data through each sub-node of the distributed storage system, extracts characteristic quantities, and divides the data into a training data set and a verification data set;
s223, generating an electric energy metering self-diagnosis model by each sub-node according to the characteristic quantity, and training the electric energy metering self-diagnosis model through a training data set;
and S224, each sub-node corrects the trained electric energy metering self-diagnosis model through the verification data set. The big data platform divides the electric energy metering data and the information data of the intelligent electric meter into two parts, wherein one part is used for training the electric energy metering self-diagnosis model, and the other part is used for verifying the electric energy metering self-diagnosis model and correcting the electric energy metering self-diagnosis model.
Further, the step S3 specifically includes the following steps:
s31, calculating abnormal data by each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity;
and S32, summarizing the abnormal data of each node by the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
Further, the step S31 specifically includes the following steps:
s311, each child node of the big data platform obtains data to be verified and historical data from the statistical analysis result;
s312, generating prediction data by each sub-node according to the historical data and the electric energy metering self-diagnosis model;
s313, each child node judges whether the predicted data is consistent with the data to be verified;
if the data to be verified are consistent, judging that the data to be verified are abnormal data;
and if the data to be verified are not consistent, judging that the data to be verified are normal data. According to
In a second aspect, the invention provides an electric energy metering system based on big data self-diagnosis, which comprises
The data acquisition module is used for calling and testing the intelligent ammeter through the electricity consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
the statistical analysis module is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction;
and the abnormal data calculation module is used for calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching the corresponding fault intelligent electric meter according to the abnormal data to perform early warning.
Further, the statistical analysis module comprises:
the data uploading unit is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity utilization information acquisition system to the big data platform;
and the segmentation processing unit is used for segmenting the electric energy metering data and the intelligent electric meter information data through the big data platform, and performing statistical analysis and characteristic quantity extraction through the distributed storage system.
Further, the segmentation processing unit includes:
the data dividing subunit is used for dividing the electric energy metering data and the intelligent electric meter information data through the big data platform;
the data set dividing subunit is used for carrying out statistical analysis on the divided electric energy metering data and the information data of the intelligent electric meter through the big data platform through each sub-node of the distributed storage system, extracting characteristic quantities and dividing the data into a training data set and a verification data set;
the model generation and training subunit is used for generating an electric energy metering self-diagnosis model according to the characteristic quantity through each sub-node and training the electric energy metering self-diagnosis model through a training data set;
and the model correction subunit is used for correcting the trained electric energy metering self-diagnosis model through the verification data set by each sub-node.
Further, the abnormal data calculation module includes:
the abnormal data calculation unit is used for calculating abnormal data through each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity;
and the abnormal data summarizing unit is used for summarizing the abnormal data of each node through the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
Further, the abnormal data calculating unit includes:
the data to be verified acquisition subunit is used for acquiring data to be verified and historical data from the statistical analysis result through each child node of the big data platform;
the prediction data generation subunit is used for generating prediction data according to the historical data and the electric energy metering self-diagnosis model through each sub-node;
the abnormal data judgment subunit is used for judging whether the predicted data is consistent with the data to be verified through each child node; when the data to be verified are consistent, judging that the data to be verified are abnormal data; and when the data are inconsistent, judging that the data to be verified are normal data.
The beneficial effect of the invention is that,
according to the electric energy metering method and system based on big data self-diagnosis, the electric energy metering data collected by the intelligent electric meter and the information data of the intelligent electric meter are quickly processed through the distributed storage system, the data processing efficiency is high, and the intelligent electric meter is timely found out of faults through processing the data, so that the problem of labor waste and labor waste caused by manual troubleshooting is avoided.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
Therefore, compared with the prior art, the invention has prominent substantive features and remarkable progress, and the beneficial effects of the implementation are also obvious.
Drawings
In order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a first flowchart of a method of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a third process flow diagram of the present invention;
FIG. 4 is a schematic diagram of the system of the present invention;
in the figure, 1-data acquisition module; 2-a statistical analysis module; 2.1-a data uploading unit; 2.2-a segmentation processing unit; 2.2.1-data partitioning subunit; 2.2.2-data set partitioning sub-units; 2.2.3-model generation and training subunit; 2.2.4-model revision subunit; 3-an abnormal data calculation module; 3.1-an abnormal data calculation unit; 3.1.1-data to be verified acquisition subunit; 3.1.2 — a prediction data generation subunit; 3.1.3-abnormal data judgment subunit; 3.2-abnormal data summarization unit.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
as shown in fig. 1, the present invention provides an electric energy metering method based on big data self-diagnosis, comprising the following steps:
s1, calling and testing an intelligent ammeter through a power consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
s2, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction;
and S3, calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching a corresponding fault intelligent electric meter according to the abnormal data to perform early warning.
Example 2:
as shown in fig. 1, the present invention provides an electric energy metering method based on big data self-diagnosis, comprising the following steps:
s1, calling and testing an intelligent ammeter through a power consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
s2, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction; as shown in fig. 2, the specific steps are as follows:
s21, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform;
s22, the big data platform divides the electric energy metering data and the intelligent electric meter information data, and performs statistical analysis and characteristic quantity extraction through a distributed storage system; the method comprises the following specific steps:
s221, the big data platform divides the electric energy metering data and the intelligent electric meter information data;
s222, the big data platform conducts statistical analysis on the segmented electric energy metering data and the intelligent electric meter information data through each sub-node of the distributed storage system, extracts characteristic quantities, and divides the data into a training data set and a verification data set;
s223, generating an electric energy metering self-diagnosis model by each sub-node according to the characteristic quantity, and training the electric energy metering self-diagnosis model through a training data set;
s224, each sub-node corrects the trained electric energy metering self-diagnosis model through a verification data set;
s3, calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching a corresponding fault intelligent electric meter according to the abnormal data to perform early warning; as shown in fig. 3, the specific steps are as follows:
s31, calculating abnormal data by each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity; the method comprises the following specific steps:
s311, each child node of the big data platform obtains data to be verified and historical data from the statistical analysis result;
s312, generating prediction data by each sub-node according to the historical data and the electric energy metering self-diagnosis model;
s313, each child node judges whether the predicted data is consistent with the data to be verified;
if the data to be verified are consistent, judging that the data to be verified are abnormal data;
if the data to be verified are not consistent with the normal data, judging that the data to be verified are normal data;
and S32, summarizing the abnormal data of each node by the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
Example 3:
as shown in fig. 4, the present invention provides an electric energy metering system based on big data self-diagnosis, comprising:
the data acquisition module 1 is used for calling and testing the intelligent ammeter through the electricity consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
the statistical analysis module 2 is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction; the statistical analysis module 2 includes:
the data uploading unit 2.1 is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity information acquisition system to the big data platform;
the segmentation processing unit 2.2 is used for segmenting the electric energy metering data and the intelligent electric meter information data through the big data platform, and performing statistical analysis and characteristic quantity extraction through the distributed storage system; the segmentation processing unit 2.2 comprises:
the data dividing subunit 2.2.1 is used for dividing the electric energy metering data and the intelligent electric meter information data through a big data platform;
the data set dividing subunit 2.2.2 is used for performing statistical analysis on the divided electric energy metering data and the information data of the intelligent electric meter through each subunit of the distributed storage system through the large data platform, extracting characteristic quantities, and dividing the data into a training data set and a verification data set;
the model generation and training subunit 2.2.3 is used for generating an electric energy metering self-diagnosis model according to the characteristic quantity through each sub-node and training the electric energy metering self-diagnosis model through a training data set;
the model correction subunit 2.2.4 is used for correcting the trained electric energy metering self-diagnosis model through the verification data set by each sub node;
the abnormal data calculation module 3 is used for calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching a corresponding fault intelligent electric meter according to the abnormal data to perform early warning; the abnormal data calculation module 3 includes:
the abnormal data calculating unit 3.1 is used for calculating abnormal data through each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity; the abnormal data calculation unit 3.1 comprises:
a data to be verified acquisition subunit 3.1.1, configured to acquire data to be verified and historical data from the statistical analysis result through each child node of the big data platform;
the prediction data generation subunit 3.1.2 is used for generating prediction data according to the historical data and the electric energy metering self-diagnosis model by each sub node;
an abnormal data judgment subunit 3.1.3, configured to judge, by each child node, whether the predicted data is consistent with the data to be verified; when the data to be verified are consistent, judging that the data to be verified are abnormal data; if the data are inconsistent, judging that the data to be verified are normal data;
and the abnormal data summarizing unit 3.2 is used for summarizing the abnormal data of each node through the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (2)
1. An electric energy metering method based on big data self-diagnosis is characterized by comprising the following steps:
s1, calling and testing an intelligent ammeter through a power consumption information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
s2, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction; the method comprises the following specific steps:
s21, uploading electric energy metering data and intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform;
s22, the big data platform divides the electric energy metering data and the intelligent electric meter information data, and performs statistical analysis and characteristic quantity extraction through a distributed storage system; the method comprises the following specific steps:
s221, the big data platform divides the electric energy metering data and the intelligent electric meter information data;
s222, the big data platform conducts statistical analysis on the segmented electric energy metering data and the intelligent electric meter information data through each sub-node of the distributed storage system, extracts characteristic quantities, and divides the data into a training data set and a verification data set;
s223, generating an electric energy metering self-diagnosis model by each sub-node according to the characteristic quantity, and training the electric energy metering self-diagnosis model through a training data set;
s224, each sub-node corrects the trained electric energy metering self-diagnosis model through a verification data set;
s3, calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching a corresponding fault intelligent electric meter according to the abnormal data to perform early warning; the method comprises the following specific steps:
s31, calculating abnormal data by each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity; the method comprises the following specific steps:
s311, each child node of the big data platform obtains data to be verified and historical data from the statistical analysis result;
s312, generating prediction data by each sub-node according to the historical data and the electric energy metering self-diagnosis model;
s313, each child node judges whether the predicted data is consistent with the data to be verified;
if the data to be verified are consistent, judging that the data to be verified are abnormal data;
if the data to be verified are not consistent with the normal data, judging that the data to be verified are normal data;
and S32, summarizing the abnormal data of each node by the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
2. An electric energy metering system based on big data self-diagnosis is characterized by comprising
The data acquisition module (1) is used for calling and testing the intelligent ammeter through the electricity utilization information acquisition system to acquire electric energy metering data and intelligent ammeter information data;
the statistical analysis module (2) is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity utilization information acquisition system to a big data platform for statistical analysis and characteristic quantity extraction; the statistical analysis module (2) comprises:
the data uploading unit (2.1) is used for uploading the electric energy metering data and the intelligent electric meter information data acquired by the electricity utilization information acquisition system to the big data platform;
the segmentation processing unit (2.2) is used for segmenting the electric energy metering data and the intelligent electric meter information data through the big data platform, and performing statistical analysis and characteristic quantity extraction through the distributed storage system; the segmentation processing unit (2.2) comprises:
the data segmentation subunit (2.2.1) is used for segmenting the electric energy metering data and the intelligent electric meter information data through a big data platform;
the data set dividing subunit (2.2.2) is used for carrying out statistical analysis on the divided electric energy metering data and the information data of the intelligent electric meter through each subunit of the distributed storage system through the big data platform, extracting characteristic quantities and dividing the data into a training data set and a verification data set;
the model generation and training subunit (2.2.3) is used for generating an electric energy metering self-diagnosis model according to the characteristic quantity through each sub-node and training the electric energy metering self-diagnosis model through a training data set;
the model correction subunit (2.2.4) is used for correcting the trained electric energy metering self-diagnosis model through the verification data set by each sub node;
the abnormal data calculation module (3) is used for calculating abnormal data according to the statistical analysis result of the big data platform and the extracted characteristic quantity, and searching the corresponding fault intelligent electric meter according to the abnormal data to perform early warning; the abnormal data calculation module (3) includes:
the abnormal data calculation unit (3.1) is used for calculating abnormal data through each child node of the big data platform according to the statistical analysis result and the extracted characteristic quantity; the abnormal data calculation unit (3.1) comprises:
the data to be verified acquisition subunit (3.1.1) is used for acquiring data to be verified and historical data from the statistical analysis result through each child node of the big data platform;
a prediction data generation subunit (3.1.2) for generating prediction data from the history data and the electric energy metering self-diagnosis model by each of the child nodes;
an abnormal data judgment subunit (3.1.3) for judging whether the predicted data is consistent with the data to be verified through each child node; when the data to be verified are consistent, judging that the data to be verified are abnormal data; if the data are inconsistent, judging that the data to be verified are normal data;
and the abnormal data summarizing unit (3.2) is used for summarizing the abnormal data of each node through the big data platform, and searching the corresponding fault intelligent electric meter according to the summarized abnormal data to perform early warning.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107895017A (en) * | 2017-11-14 | 2018-04-10 | 国网江苏省电力公司电力科学研究院 | A kind of electric energy quality monitoring system construction method based on big data technology |
| CN108694175A (en) * | 2017-04-05 | 2018-10-23 | 郑州博信知识产权代理有限公司 | A method of electric energy detection big data is analyzed based on data mining algorithm |
| CN110019098A (en) * | 2017-12-30 | 2019-07-16 | 广州明领基因科技有限公司 | Electrical energy measurement big data analysis system based on Hadoop frame |
| CN110046145A (en) * | 2019-04-02 | 2019-07-23 | 国网安徽省电力有限公司 | Expert intelligence Analysis Service platform based on electric flux big data research |
-
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- 2019-12-21 CN CN201911331514.4A patent/CN110865329B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108694175A (en) * | 2017-04-05 | 2018-10-23 | 郑州博信知识产权代理有限公司 | A method of electric energy detection big data is analyzed based on data mining algorithm |
| CN107895017A (en) * | 2017-11-14 | 2018-04-10 | 国网江苏省电力公司电力科学研究院 | A kind of electric energy quality monitoring system construction method based on big data technology |
| CN110019098A (en) * | 2017-12-30 | 2019-07-16 | 广州明领基因科技有限公司 | Electrical energy measurement big data analysis system based on Hadoop frame |
| CN110046145A (en) * | 2019-04-02 | 2019-07-23 | 国网安徽省电力有限公司 | Expert intelligence Analysis Service platform based on electric flux big data research |
Non-Patent Citations (1)
| Title |
|---|
| "基于大数据的智能电能表精准更换技术";李鑫等;《大众用电》;20190228(第2期);第39-40页 * |
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