CN205302385U - Electric wire netting equipment state monitoring data analysis platform - Google Patents
Electric wire netting equipment state monitoring data analysis platform Download PDFInfo
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- CN205302385U CN205302385U CN201521002867.7U CN201521002867U CN205302385U CN 205302385 U CN205302385 U CN 205302385U CN 201521002867 U CN201521002867 U CN 201521002867U CN 205302385 U CN205302385 U CN 205302385U
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Abstract
The utility model discloses an electric wire netting equipment state monitoring data analysis platform, including the monitor that is used for gathering electric wire netting equipment state monitoring data, be used for carrying out forwardding ware, data storage server and excavating the data analysis server of algorithm work based on related rule of classification to input data, the output and the input of forwardding the ware of monitor are connected and are communicated based on the TCP agreement, it connects to forward the output of ware the data input end of data storage server, data analysis server's input is connected the data output end of data storage server, the electric wire netting equipment state monitoring data that the monitor was gathered are passed through it is in to forward the ware storage in the data storage server, data analysis server reads the data of storage in the data storage server are used simultaneously related rule and are excavated the algorithm and handle the data that read and obtain analysis result.
Description
Technical field
This utility model relates to a kind of grid equipment Condition Monitoring Data analysis platform.
Background technology
All kinds of status monitoring means of current grid equipment become increasingly abundant, and Monitoring Data scale is increasing, and so the status monitoring for grid equipment brings problems with: 1 data separate limited efficacy, in the mass of redundancy data that valid data often flood; 2 failure predications and the current profile data of the many utilizations of equipment evaluation, lack the comprehensive analysis of panoramic view data in equipment full lifetime, it is impossible to the generation of fault of equipment, development are predicted, and risk predictability is relatively low.
Utility model content
Technical problem to be solved in the utility model is: provide a kind of grid equipment Condition Monitoring Data analysis platform.
Solving above-mentioned technical problem, the technical scheme that this utility model adopts is as follows:
A kind of grid equipment Condition Monitoring Data analysis platform, it is characterised in that: described grid equipment Condition Monitoring Data analysis platform includes the monitor for gathering grid equipment Condition Monitoring Data, for input data are carried out the transponder of classification process, data storage server and the data analytics server based on association rules mining algorithm work, the outfan of described monitor is connected with the input of transponder and communicates based on Transmission Control Protocol, the outfan of described transponder connects the data input pin of described data storage server, the input of described data analytics server connects the data output end of described data storage server, the grid equipment Condition Monitoring Data that described monitor collects is stored in described data storage server by described transponder, described data analytics server reads the data of storage in described data storage server and carries out process by the association rules mining algorithm data to reading and obtain analyzing result.
As a kind of embodiment of the present utility model, described monitor includes data acquisition unit, infrared camera and visible image capturing head.
As a kind of improvement of the present utility model, described data storage server is provided with Hadoop distributed file system, and the grid equipment Condition Monitoring Data that described monitor collects is stored in described data storage server by the form of Hadoop distributed file system by described transponder.
As a kind of embodiment of the present utility model, described association rules mining algorithm is any one in Apriori algorithm, FP growth algorithm, multi-level association rules mining and multidimensional association rule algorithm.
As a kind of improvement of the present utility model, described grid equipment Condition Monitoring Data analysis platform also includes client; Described client connects described data analytics server, described client is able to receive that data analytics server processes the analysis result drawn and displays, and can read, by described data analytics server, the data being stored in described data storage server.
As a kind of improvement of the present utility model, described grid equipment Condition Monitoring Data analysis platform also includes alarm; Described alarm connects described transponder, and described alarm can send warning when the grid equipment Condition Monitoring Data that described transponder receives described monitor collection occurs and interrupts.
Compared with prior art, this utility model has the advantages that
The first, the grid equipment Condition Monitoring Data that monitor is collected by this utility model by transponder is stored in data storage server, make data analytics server can read grid equipment Condition Monitoring Data and carry out process with association rules mining algorithm and draw analysis result, thus excavating the incidence relation of the grid equipment state implied in grid equipment Condition Monitoring Data so that staff can utilize the analysis effective pre-control grid equipment risk of result.
Second, data analytics server of the present utility model can adopt any one in Apriori algorithm, FP growth algorithm, multi-level association rules mining and multidimensional association rule algorithm as association rules mining algorithm, has the advantage that magnanimity grid equipment Condition Monitoring Data process performance is high.
3rd, data storage server of the present utility model adopts Hadoop distributed file system, improves the storage efficiency of magnanimity grid equipment Condition Monitoring Data.
4th, this utility model is provided with client so that staff can carry out monitor in real time and the inquiry of historical data by the grid equipment Condition Monitoring Data that monitor is collected by client, and is easy to understand analysis result.
5th, this utility model is provided with alarm such that it is able to the loss of data of grid equipment Condition Monitoring Data is carried out alert process, improves the reliability of magnanimity grid equipment Condition Monitoring Data collection transmission.
Accompanying drawing explanation
Below in conjunction with the drawings and specific embodiments, the utility model is described in further detail:
Fig. 1 is the schematic block circuit diagram of grid equipment Condition Monitoring Data analysis platform of the present utility model;
In figure: 1. monitor, 2. transponder, 3. alarm, 4. data storage server, 5. data analytics server, 6. client.
Detailed description of the invention
As shown in Figure 1, grid equipment Condition Monitoring Data analysis platform of the present utility model, including the monitor 1 for gathering grid equipment Condition Monitoring Data, for carrying out the transponder 2 of classification process, the data storage server 4 being provided with Hadoop distributed file system and the data analytics server 5 worked based on association rules mining algorithm to input data;The outfan of monitor 1 is connected with the input of transponder 2 and communicates based on Transmission Control Protocol, the outfan of transponder 2 connects the data input pin of data storage server 4, the input of data analytics server 5 connects the data output end of data storage server 4, the grid equipment Condition Monitoring Data that monitor 1 collects is stored in data storage server 4 by transponder 2 by the form of Hadoop distributed file system, data analytics server 5 reads in data storage server 4 data of storage and carries out process by the association rules mining algorithm data to reading and obtain analyzing result. wherein, monitor 1 includes data acquisition unit, infrared camera and visible image capturing head, and grid equipment Condition Monitoring Data includes the grid equipment video data that the operating state datas such as the grid equipment electric current of data acquisition unit collection, voltage, infrared camera and visible image capturing head gather, the classifying rules of input data can be set by transponder 2 according to actual needs, for instance can carry out classification according to data type and merge forwarding, association rules mining algorithm is any one in Apriori algorithm, FP growth algorithm, multi-level association rules mining and multidimensional association rule algorithm.
Grid equipment Condition Monitoring Data analysis platform of the present utility model also can arrange client 6; Client 6 connects data analytics server 5, and client 6 is able to receive that data analytics server 5 processes the analysis result drawn and displays, and can pass through the data that data analytics server 5 reading is stored in data storage server 4.
Grid equipment Condition Monitoring Data analysis platform of the present utility model also can arrange alarm 3; Alarm 3 connects transponder 2, and alarm 3 can send warning when the grid equipment Condition Monitoring Data that transponder 2 receives monitor 1 collection occurs and interrupts.
During grid equipment Condition Monitoring Data analysis platform of the present utility model work, monitor 1 comprises the first-class collection grid equipment data of data acquisition unit, infrared camera and visible image capturing, picture, video information are sent to transponder 2, with data are carried out classification merge forward after upload in data storage server 4 and store; Event of data loss is reported to the police by judging by alarm 3; The structure of magnanimity, non-structural, partly-structured data are quickly analyzed and processed based on association rules mining algorithm and draw analysis result by data analytics server 5; Client 6 obtains effective data results.
This utility model is not limited to above-mentioned detailed description of the invention; according to foregoing; ordinary technical knowledge and customary means according to this area; without departing under the above-mentioned basic fundamental thought premise of this utility model; this utility model can also make the equivalent modifications of other various ways, replacement or change, all falls among protection domain of the present utility model.
Claims (6)
1. a grid equipment Condition Monitoring Data analysis platform, it is characterised in that: described grid equipment Condition Monitoring Data analysis platform includes the monitor (1) for gathering grid equipment Condition Monitoring Data, is used for input data are carried out the transponder (2) of classification process, data storage server (4) and the data analytics server (5) based on association rules mining algorithm work, the outfan of described monitor (1) is connected with the input of transponder (2) and communicates based on Transmission Control Protocol, the outfan of described transponder (2) connects the data input pin of described data storage server (4), the input of described data analytics server (5) connects the data output end of described data storage server (4), the grid equipment Condition Monitoring Data that described monitor (1) collects is stored in described data storage server (4) by described transponder (2), described data analytics server (5) reads the data of storage in described data storage server (4) and carries out process by the association rules mining algorithm data to reading and obtain analyzing result.
2. grid equipment Condition Monitoring Data analysis platform according to claim 1, it is characterised in that: described monitor (1) includes data acquisition unit, infrared camera and visible image capturing head.
3. grid equipment Condition Monitoring Data analysis platform according to claim 1, it is characterized in that: described data storage server (4) is provided with Hadoop distributed file system, and the grid equipment Condition Monitoring Data that described monitor (1) collects is stored in described data storage server (4) by the form of Hadoop distributed file system by described transponder (2).
4. grid equipment Condition Monitoring Data analysis platform according to claim 1, it is characterised in that: described association rules mining algorithm is any one in Apriori algorithm, FP growth algorithm, multi-level association rules mining and multidimensional association rule algorithm.
5. the grid equipment Condition Monitoring Data analysis platform according to Claims 1-4 any one, it is characterised in that: described grid equipment Condition Monitoring Data analysis platform also includes client (6); Described client (6) connects described data analytics server (5), described client (6) is able to receive that data analytics server (5) processes the analysis result drawn and displays, and can read, by described data analytics server (5), the data being stored in described data storage server (4).
6. the grid equipment Condition Monitoring Data analysis platform according to Claims 1-4 any one, it is characterised in that: described grid equipment Condition Monitoring Data analysis platform also includes alarm (3); Described alarm (3) connects described transponder (2), and described alarm (3) can receive when the grid equipment Condition Monitoring Data that described monitor (1) gathers occurs and interrupts in described transponder (2) and send warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201521002867.7U CN205302385U (en) | 2015-12-04 | 2015-12-04 | Electric wire netting equipment state monitoring data analysis platform |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201521002867.7U CN205302385U (en) | 2015-12-04 | 2015-12-04 | Electric wire netting equipment state monitoring data analysis platform |
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| CN205302385U true CN205302385U (en) | 2016-06-08 |
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| CN201521002867.7U Active CN205302385U (en) | 2015-12-04 | 2015-12-04 | Electric wire netting equipment state monitoring data analysis platform |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106443272A (en) * | 2016-11-03 | 2017-02-22 | 衢州学院 | Power grid equipment state monitoring data analysis platform |
| CN107688126A (en) * | 2017-06-30 | 2018-02-13 | 国网浙江省电力公司 | Grid equipment data multidimensional association analysis method and system |
| CN107843811A (en) * | 2017-11-02 | 2018-03-27 | 广东电网有限责任公司中山供电局 | A kind of analysis method and system of grid equipment online monitoring data |
| CN110442640A (en) * | 2019-08-05 | 2019-11-12 | 西南交通大学 | Subway fault correlation recommended method based on priori weight and multilayer TFP algorithm |
| CN115208903A (en) * | 2022-06-02 | 2022-10-18 | 广州番禺电缆集团有限公司 | Intelligent cable based on distributed service |
-
2015
- 2015-12-04 CN CN201521002867.7U patent/CN205302385U/en active Active
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106443272A (en) * | 2016-11-03 | 2017-02-22 | 衢州学院 | Power grid equipment state monitoring data analysis platform |
| CN107688126A (en) * | 2017-06-30 | 2018-02-13 | 国网浙江省电力公司 | Grid equipment data multidimensional association analysis method and system |
| CN107688126B (en) * | 2017-06-30 | 2023-08-15 | 国网浙江省电力公司 | Multi-dimensional correlation analysis method and system for power grid equipment data |
| CN107843811A (en) * | 2017-11-02 | 2018-03-27 | 广东电网有限责任公司中山供电局 | A kind of analysis method and system of grid equipment online monitoring data |
| CN110442640A (en) * | 2019-08-05 | 2019-11-12 | 西南交通大学 | Subway fault correlation recommended method based on priori weight and multilayer TFP algorithm |
| CN110442640B (en) * | 2019-08-05 | 2021-08-31 | 西南交通大学 | Subway fault association recommendation method based on prior weight and multilayer TFP algorithm |
| CN115208903A (en) * | 2022-06-02 | 2022-10-18 | 广州番禺电缆集团有限公司 | Intelligent cable based on distributed service |
| CN115208903B (en) * | 2022-06-02 | 2023-10-24 | 广州番禺电缆集团有限公司 | Intelligent cable based on distributed service |
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