CN117253129A - Deep learning substation equipment monitoring and analyzing system based on AR technology - Google Patents
Deep learning substation equipment monitoring and analyzing system based on AR technology Download PDFInfo
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Abstract
The invention discloses an AR technology-based deep learning substation equipment monitoring and analyzing system, and particularly relates to the technical field of substation equipment monitoring. According to the invention, after multispectral images are acquired and processed in the transformer substation equipment, the neural network is adopted to carry out advanced training analysis and AR visual processing, AR visual monitoring and analysis in the transformer substation equipment can be achieved, the monitoring equipment and the early warning equipment are combined to display early warning information on the monitoring equipment in real time through an AR interface, corresponding maintenance or early warning processing operation can be triggered, historical monitoring data of the transformer substation equipment is mined and fault predicted through the data mining and fault prediction module, and a basis is provided for subsequent maintenance of the equipment by staff.
Description
Technical Field
The invention relates to the technical field of substation monitoring, in particular to an AR technology-based deep learning substation equipment monitoring and analyzing system.
Background
At present, the power system needs to realize large-scale interconnection of power transmission and distribution lines through a transformer substation so as to meet user demands, however, once transformer substation equipment fails, abnormal conditions are easy to occur under overload operation of a power grid, and further stability and reliability of the system are affected, so that monitoring equipment needs to be configured in the transformer substation so as to monitor equipment states in the transformer substation in real time, report staff when equipment abnormality is found, and avoid unnecessary loss.
The invention patent of patent application number CN202010801794.7 discloses a method and a system for detecting the state of intelligent substation equipment based on deep learning, wherein the method comprises the steps that a thermal imager collects the equipment state data of the intelligent substation; according to the intelligent substation equipment state monitoring method, the intelligent substation equipment state can be monitored in real time, measures can be processed in time once fault equipment is found, economic losses caused by faults of the substation are reduced, the intelligent substation equipment state monitoring method is higher in detection accuracy and applicability, and interference resistance is higher.
However, the monitoring method of the transformer substation only monitors transformer substation equipment in a thermal imaging mode and performs training analysis by adopting a neural network model, a large number of power equipment is installed in the transformer substation equipment, the installation position of the power equipment and each line are complicated, comprehensive monitoring of the power equipment is difficult to achieve, visual processing of AR technology is difficult to achieve for the power equipment in the transformer substation equipment, workers are difficult to know what equipment in the transformer substation equipment and lines are faulty, and burden is caused to workers for maintenance of the power equipment in a subsequent transformer substation.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, the present invention provides an AR technology-based deep learning substation equipment monitoring and analysis system, so as to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present invention provides the following technical solutions: the monitoring and analyzing system for the deep learning substation equipment based on the AR technology comprises a plurality of spectrum image collecting equipment which is arranged inside the substation equipment, wherein the spectrum image collecting equipment is connected with a monitoring and analyzing subsystem, the monitoring and analyzing subsystem is connected with a cloud server, the cloud server is connected with a data center library, the data center library is connected with a display module, the display module is connected with a mobile terminal device, the mobile terminal device is connected with a monitoring device, the monitoring device is connected with an early warning device, and the early warning device is connected with a data mining and fault predicting module.
Preferably, four spectrum image acquisition devices are installed in each substation device, and the four spectrum image acquisition devices are distributed at four corners of the substation device respectively and used for carrying out multispectral image acquisition on the substation device.
Preferably, the number of the monitoring and analyzing subsystems is the same as that of the spectral image acquisition equipment and the substation equipment, the monitoring and analyzing subsystems are concentrated in a cloud server, and each monitoring and analyzing subsystem is used for processing and storing part of monitoring data of the substation equipment and uploading the monitoring data to a data center library through the internet.
Preferably, the data center library is used for receiving and processing the monitoring data uploaded from the cloud server and performing deep learning analysis and result visualization AR processing, and comprises a data processing and receiving module and a deep learning module, and the data processing and receiving module is connected with the deep learning module.
Preferably, the display module is used for displaying the visualized analysis result through the display device and transmitting the visualized analysis result to the mobile terminal device through the internet, and the mobile terminal device realizes AR processing of the visualized result based on AR application.
Preferably, the monitoring device and the early warning device display early warning information on the monitoring device in real time through an AR interface based on an AR application running on the mobile terminal device, and can trigger corresponding maintenance or early warning operation processing.
Preferably, the data mining and fault prediction module performs mining and fault prediction on historical monitoring data of the substation equipment, and provides basis for equipment maintenance.
The invention also provides a deep learning substation equipment monitoring and analyzing method based on the AR technology, which comprises the following steps of:
s1, respectively installing spectrum image acquisition equipment at four corners of each transformer substation equipment, acquiring spectrum images of the transformer substation equipment, uploading the acquired images to a cloud server, and carrying out monitoring analysis processing and storage on monitoring data of part of transformer substation equipment by the cloud server based on an internal monitoring and analysis subsystem and uploading the monitoring data to a data center library;
s2, a neural network is established based on a deep learning module in a data center library, and the acquired spectrum image acquisition picture is subjected to technical processing through a data processing and receiving module;
s3, extracting features of the spectrum images processed by the technology, importing the spectrum images into a neural network model for training and deep learning analysis, and performing visual AR processing on the results of the deep learning analysis;
s4, an AR application is installed on the mobile terminal equipment, the analysis result of the visual AR processing is displayed and analyzed based on the AR application on the mobile terminal equipment, the monitoring equipment and the early warning equipment are utilized to display early warning information on the monitoring equipment in real time through an AR interface based on the AR application running on the mobile terminal equipment, and corresponding maintenance or processing early warning operation can be triggered;
s5, mining and fault prediction are carried out by adopting a data mining and fault prediction module according to historical monitoring data of substation equipment stored in the system, so that a basis is provided for equipment maintenance.
Preferably, when the acquired picture is processed technically, the spectral characteristics of the target are left by adopting a radiation correction technology, the optimal image resolution and spectral information are acquired by adopting geometric correction, and the trained neural network model comprises an input layer, a convolution layer, an excitation layer, a pooling layer, a full connection layer and an output layer.
Preferably, the mobile terminal device includes, but is not limited to, tablet, notebook, computer and cell phone, and the AR application is based on running use on the mobile terminal device and displaying the trained and visualized AR application with the AR processing picture on the mobile terminal device.
The invention has the technical effects and advantages that:
1. the multi-spectrum picture is acquired in the transformer substation equipment, the acquired picture is subjected to technical processing, the neural network is adopted for advanced training analysis, AR visual processing is carried out, AR application on the mobile terminal equipment is adopted for displaying the visual AR picture, AR visual monitoring and analysis in the transformer substation equipment can be achieved, a worker can realize fault investigation on electric facilities in the transformer substation equipment without opening the transformer substation equipment, the visualization of the electric facilities in the transformer substation equipment is guaranteed to be in front of the worker, and convenience is provided for maintenance of the transformer substation equipment by the worker;
2. the early warning information is displayed on the monitoring equipment in real time through an AR interface by combining the monitoring equipment and the early warning equipment based on the AR application running on the mobile terminal equipment, corresponding maintenance or early warning operation processing can be triggered, historical monitoring data of the substation equipment are mined and subjected to fault prediction through the data mining and fault prediction module, basis is provided for subsequent maintenance of the equipment by staff, and maintenance efficiency of the substation equipment is improved.
Drawings
Fig. 1 is an overall system diagram of the present invention.
FIG. 2 is a diagram of a data center library system of the present invention.
FIG. 3 is a flow chart of the steps of the present invention.
The reference numerals are: 1. a spectral image acquisition device; 2. a monitoring and analysis subsystem; 3. a cloud server; 4. a data center library; 5. a display module; 6. a mobile terminal device; 7. monitoring equipment; 8. early warning equipment; 9. the data mining and fault prediction module; 10. a data processing and receiving module; 11. and a deep learning module.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1,
The embodiment provides a deep learning substation equipment monitoring and analysis system based on AR technology, the system includes a plurality of spectral image acquisition equipment 1 of installing inside substation equipment, spectral image acquisition equipment 1 is connected with monitoring and analysis subsystem 2, monitoring and analysis subsystem 2 is connected with cloud server 3, cloud server 3 is connected with data center storehouse 4, data center storehouse 4 is connected with display module 5, display module 5 is connected with mobile terminal equipment 6, mobile terminal equipment 6 is connected with monitoring equipment 7, monitoring equipment 7 is connected with early warning equipment 8, early warning equipment 8 is connected with data mining and fault prediction module 9.
Further, four spectrum image acquisition devices 1 are installed in each transformer substation device, and the four spectrum image acquisition devices 1 are distributed at four corners of the transformer substation device respectively and used for carrying out multispectral image acquisition on the transformer substation device.
Specifically, in this structure, the spectral image capturing device 1 may be a plurality of spectral capturing cameras, which are respectively installed at four corners of the substation device, and perform multi-angle multi-spectral image capturing on the inside of the substation device, and provide image basis for detection and analysis of the subsequent substation device.
Further, the number of the monitoring and analyzing subsystems 2 is the same as that of the spectral image acquisition device 1 and the substation equipment, the monitoring and analyzing subsystems 2 are concentrated in the cloud server 3, and each monitoring and analyzing subsystem 2 is used for processing and storing part of substation equipment monitoring data and uploading the monitoring data to the data center library 4 through the internet.
Specifically, in this structure, a plurality of monitoring and analyzing subsystems 2 are centralized in the cloud server 3, and each monitoring and analyzing subsystem 2 corresponds to one substation device, and when the acquired images are transmitted to the cloud server 3, the monitoring and analyzing subsystem 2 monitors and analyzes the images, processes and stores part of the monitoring data, and transmits the monitoring data to the data center library 4 through the network.
Further, the data center library 4 is configured to receive and process the monitoring data uploaded from the cloud server 3, and perform deep learning analysis and result visualization AR processing, and the data center library 4 includes a data processing and receiving module 10 and a deep learning module 11, where the data processing and receiving module 10 is connected to the deep learning module 11.
Specifically, in this configuration, the data center library 4 can receive and process the pictures, the data center library 4 has the data processing and receiving module 10 and the deep learning module 11 therein, the deep learning module 11 can build a neural network in the data center library 4, perform deep learning on the acquired pictures, and perform AR technology processing, and the data processing and receiving module 10 performs visualization processing on the received AR pictures.
Further, the display module 5 is configured to display the visualized analysis result through the display device, and transmit the visualized analysis result to the mobile terminal device 6 through the internet, where the mobile terminal device 6 implements AR processing of the visualized result based on AR application.
Specifically, in this structure, the display module 5 can display the AR image subjected to the visualization processing through the display device, and upload the screen displayed by the display device to the mobile terminal device 6, and the mobile terminal device 6 is provided with an AR application for AR screen display in advance, so as to perform AR display on the screen in the substation device on the mobile terminal device 6.
Further, the monitoring device 7 and the early warning device 8 display early warning information on the monitoring device 7 in real time through an AR interface based on the AR application running on the mobile terminal device 6, and can trigger corresponding maintenance or processing early warning operation.
Specifically, in this structure, the monitoring device 7 and the early warning device 8 monitor based on the displayed AR screen, and once an abnormal situation occurs, early warning information is displayed on the monitoring device 7 in real time through the AR interface, and a corresponding maintenance or processing early warning operation can be triggered.
Further, the data mining and fault prediction module 9 performs mining and fault prediction on historical monitoring data of the substation equipment, and provides basis for equipment maintenance.
Specifically, in this structure, the data center library 4 stores the historical monitoring data of the substation equipment, and when an abnormality occurs at the substation equipment, the data mining and fault prediction module 9 performs mining and fault prediction on the historical monitoring data of the substation equipment, thereby providing a good basis for the subsequent maintenance operation of the staff.
The specific principle of the structure is as follows:
firstly, spectrum image acquisition equipment 1 is installed at four corners of transformer substation equipment to acquire spectrum images of the transformer substation equipment, a plurality of monitoring and analyzing subsystems 2 are stored in a cloud server 3 to respectively analyze and detect images acquired by corresponding transformer substations, and part of transformer substation equipment monitoring data is processed and stored and uploaded to a data center library 4;
the data center library 4 receives the picture information from the transformer substation equipment through the data processing and receiving module 10 and carries out technical processing on the picture information, a neural network is established based on the deep learning module 11, and after the feature extraction is carried out on the processed picture, the picture information is imported into the neural network for deep learning analysis training and AR visualization processing;
the visual analysis result is displayed through the display device by utilizing the display module 5 and is transmitted to the mobile terminal device 6 through the Internet, the AR application is installed on the mobile terminal device 6, and the picture is displayed by utilizing the AR application on the mobile terminal device 6;
the monitoring equipment 7 and the early warning equipment 8 are utilized to display early warning information on the monitoring equipment 7 in real time through an AR interface based on AR application running on the mobile terminal equipment 6, corresponding maintenance or early warning operation processing can be triggered, the data center library 4 stores historical monitoring data of the transformer substation equipment, and when the transformer substation equipment is abnormal, the data mining and fault prediction module 9 performs mining and fault prediction on the historical monitoring data of the transformer substation equipment, so that good basis is provided for subsequent maintenance operation of staff.
In a word, the invention carries out multispectral image acquisition inside the transformer substation equipment, carries out technical processing on the acquired images, carries out advanced training analysis by adopting a neural network, carries out AR visual processing, displays the visual AR images by adopting the AR application on the mobile terminal equipment 6, can achieve AR visual monitoring and analysis inside the transformer substation equipment, combines the monitoring equipment 7 and the early warning equipment 8, displays early warning information on the monitoring equipment 7 in real time through an AR interface based on the AR application running on the mobile terminal equipment 6, can trigger corresponding maintenance or processing early warning operation, and carries out mining and fault prediction on the historical monitoring data of the transformer substation equipment through the data mining and fault prediction module 9 so as to provide basis for subsequent maintenance of the equipment by staff.
EXAMPLE 2,
The embodiment provides a deep learning substation equipment monitoring and analyzing method based on an AR technology, which uses the deep learning substation equipment monitoring and analyzing system based on the AR technology, and comprises the following steps:
s1, respectively installing spectrum image acquisition equipment 1 at four corners of each transformer substation equipment, acquiring spectrum images of the transformer substation equipment, uploading the acquired images to a cloud server 3, and carrying out monitoring analysis processing and storage on monitoring data of part of transformer substation equipment by the cloud server 3 based on a monitoring and analysis subsystem 2 in the cloud server, and uploading the monitoring data to a data center library 4;
s2, a neural network is established based on a deep learning module 11 in the data center library 4, and the acquired spectrum image acquisition picture is subjected to technical processing through a data processing and receiving module 10;
s3, extracting features of the spectrum images processed by the technology, importing the spectrum images into a neural network model for training and deep learning analysis, and performing visual AR processing on the results of the deep learning analysis;
s4, an AR application is installed on the mobile terminal equipment 6, the analysis result of the visual AR processing is displayed and analyzed based on the AR application on the mobile terminal equipment 6, and the monitoring equipment 7 and the early warning equipment 8 are utilized to display early warning information on the monitoring equipment 7 in real time through an AR interface based on the AR application running on the mobile terminal equipment 6, and corresponding maintenance or processing early warning operation can be triggered;
s5, mining and fault prediction are carried out by adopting a data mining and fault prediction module 9 according to historical monitoring data of substation equipment stored in the system, so that a basis is provided for equipment maintenance.
When the acquired picture is subjected to technical processing, a radiation correction technology is adopted to leave the spectrum characteristics of the target, the geometric correction is adopted to obtain the optimal image resolution and spectrum information, and the trained neural network model comprises an input layer, a convolution layer, an excitation layer, a pooling layer, a full-connection layer and an output layer; the mobile terminal device 6 includes, but is not limited to, tablet, notebook, computer and cell phone, AR applications are based on running use on the mobile terminal device 6 and AR applications trained and visualizing AR processed pictures on the mobile terminal device 6 are displayed.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (10)
1. AR technology-based deep learning substation equipment monitoring and analyzing system comprises a plurality of spectrum image acquisition equipment (1) installed inside the substation equipment, and is characterized in that: the system is characterized in that the spectrum image acquisition equipment (1) is connected with a monitoring and analyzing subsystem (2), the monitoring and analyzing subsystem (2) is connected with a cloud server (3), the cloud server (3) is connected with a data center database (4), the data center database (4) is connected with a display module (5), the display module (5) is connected with a mobile terminal equipment (6), the mobile terminal equipment (6) is connected with a monitoring equipment (7), the monitoring equipment (7) is connected with an early warning equipment (8), and the early warning equipment (8) is connected with a data mining and fault prediction module (9).
2. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: four spectrum image acquisition devices (1) are arranged in each transformer substation device, and the four spectrum image acquisition devices (1) are respectively distributed at four corners of the transformer substation device and are used for carrying out multispectral image acquisition on the transformer substation device.
3. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: the monitoring and analyzing subsystems (2) are the same in number as the spectrum image acquisition equipment (1) and the substation equipment, the monitoring and analyzing subsystems (2) are concentrated in the cloud server (3), and each monitoring and analyzing subsystem (2) is used for processing and storing part of substation equipment monitoring data and uploading the monitoring data to the data center database (4) through the Internet.
4. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: the data center library (4) is used for receiving and processing monitoring data uploaded from the cloud server (3) and performing deep learning analysis and result visualization AR processing, the data center library (4) comprises a data processing and receiving module (10) and a deep learning module (11), and the data processing and receiving module (10) is connected with the deep learning module (11).
5. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: the display module (5) is used for displaying the visualized analysis result through the display device and transmitting the visualized analysis result to the mobile terminal device (6) through the Internet, and the mobile terminal device (6) realizes AR processing of the visualized result based on AR application.
6. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: the monitoring equipment (7) and the early warning equipment (8) display early warning information on the monitoring equipment (7) in real time through an AR interface based on the AR application running on the mobile terminal equipment (6), and can trigger corresponding maintenance or early warning operation processing.
7. The AR technology-based deep learning substation equipment monitoring and analysis system according to claim 1, wherein: the data mining and fault prediction module (9) is used for mining and predicting faults of historical monitoring data of substation equipment, and provides basis for equipment maintenance.
8. An AR technology-based deep learning substation equipment monitoring and analyzing method, using the AR technology-based deep learning substation equipment monitoring and analyzing system according to any one of claims 1 to 7, characterized in that: the method comprises the following steps:
s1, respectively installing spectrum image acquisition equipment (1) at four corners of each transformer substation equipment, acquiring spectrum images of the transformer substation equipment, uploading the acquired images to a cloud server (3), and carrying out monitoring analysis processing and storage on monitoring data of part of transformer substation equipment by the cloud server (3) based on a monitoring and analysis subsystem (2) in the cloud server, and uploading the monitoring data to a data center library (4);
s2, a neural network is established based on a deep learning module (11) in a data center library (4), and the acquired spectrum image acquisition picture is subjected to technical processing through a data processing and receiving module (10);
s3, extracting features of the spectrum images processed by the technology, importing the spectrum images into a neural network model for training and deep learning analysis, and performing visual AR processing on the results of the deep learning analysis;
s4, an AR application is installed on the mobile terminal equipment (6), the analysis result of the visual AR processing is displayed and analyzed based on the AR application on the mobile terminal equipment (6), and the monitoring equipment (7) and the early warning equipment (8) are utilized to display early warning information on the monitoring equipment (7) in real time through an AR interface based on the AR application running on the mobile terminal equipment (6), so that corresponding maintenance or processing early warning operation can be triggered;
s5, mining and fault prediction are carried out by adopting a data mining and fault prediction module (9) according to historical monitoring data of substation equipment stored in the system, so that a basis is provided for equipment maintenance.
9. The AR technology-based deep learning substation equipment monitoring and analysis method according to claim 8, wherein: when the acquired picture is technically processed, the spectral characteristics of the target are reserved by adopting a radiation correction technology, the optimal image resolution and spectral information are acquired by adopting geometric correction, and the trained neural network model comprises an input layer, a convolution layer, an excitation layer, a pooling layer, a full-connection layer and an output layer.
10. The AR technology-based deep learning substation equipment monitoring and analysis method according to claim 8, wherein: the mobile terminal device (6) includes, but is not limited to, a tablet, a notebook, a computer, and a cell phone, and the AR application is based on running use on the mobile terminal device (6) and displaying the trained and visualized AR application with an AR processing picture on the mobile terminal device (6).
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| CN111784026A (en) * | 2020-05-28 | 2020-10-16 | 国网信通亿力科技有限责任公司 | Cloud-side cooperative sensing-based all-dimensional physical examination system for electrical equipment of transformer substation |
| CN112101375A (en) * | 2020-08-11 | 2020-12-18 | 安徽新力电业科技咨询有限责任公司 | Intelligent substation equipment state detection method and system based on deep learning |
| CN113962659A (en) * | 2021-10-25 | 2022-01-21 | 国网山东省电力公司平邑县供电公司 | Cloud computing-based smart power grid big data information management system and method |
| CN115510253A (en) * | 2022-08-26 | 2022-12-23 | 国网山东省电力公司枣庄供电公司 | Transformer substation equipment AR auxiliary maintenance method and terminal based on three-dimensional knowledge graph |
| CN116824859A (en) * | 2023-07-21 | 2023-09-29 | 佛山市新基建科技有限公司 | Intelligent traffic big data analysis system based on Internet of things |
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