CN112270373A - Automatic three-remote change identification method for scheduling master station based on image identification technology - Google Patents
Automatic three-remote change identification method for scheduling master station based on image identification technology Download PDFInfo
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Abstract
The invention discloses an automatic identification method for three-remote change of a scheduling master station based on an image identification technology, which comprises the following steps: the method comprises the steps of periodically scanning an SVG operation diagram of a background picture of a scheduling system, identifying and classifying graphs in the SVG operation diagram by adopting a static image identification technology, and dividing the graphs in the SVG operation diagram into known type images and unknown type images; continuously acquiring image data of the known type of image by adopting a dynamic identification mode, and identifying three remote changes of the image data of the known type of image on an SVG operation chart of a background picture of the scheduling system; carrying out image definition on the unknown type image by adopting an auxiliary means and converting the unknown type image into a known type image; dynamically recognizing the converted known type image by reusing a dynamic recognition mode; the invention replaces the traditional manual mode of checking whether the check point position changes after issuing the control operation, and can achieve the purpose of automatic test only by being matched with the automatic operation issuing.
Description
Technical Field
The invention relates to the technical field of image recognition, in particular to an automatic recognition method for three-remote change of a scheduling master station based on an image recognition technology.
Background
In the comprehensive automation transformation project of the transformer substation, the three remotes of the transformer substation need to be checked and accepted, in one example, the three remotes comprise remote signaling, remote measurement and remote control of the transformer substation, and the transformer substation can be put into use only if the three remotes are checked and accepted.
The remote signaling is a remote signal, and is a state quantity of primary equipment, the separation position and the closing position of the primary equipment are transmitted to a monitoring background, once the actual position changes, the remote signaling is displayed on a primary graph of the monitoring background, or when the primary equipment is separated, the remote signaling of the monitoring background is prompted, specifically, the state quantity of the primary equipment comprises a switch state, a disconnecting link state, a transformer tap signal, a primary equipment alarm signal, a protection trip signal, a forenotice signal, various equipment signals (such as an SF6 low-pressure alarm signal and an oil-pressure low-pressure alarm signal) and the like; remote measurement refers to remote measurement, which is collected operation parameters of the transformer substation, including various electrical quantities (voltage, current, power and other quantities on a line), load flow and the like; remote control refers to remote control, and refers to remote control commands received and executed by equipment in a substation, mainly including opening and closing operations, and remote control of remote switch control equipment.
The conventional method is to adopt a mode of manually selecting and making a table, but the traditional three-remote inspection acceptance efficiency is low in the implementation process.
Disclosure of Invention
The invention aims to provide an automatic identification method for three-remote change of a scheduling main station based on an image identification technology, and the method is used for solving the technical problem that the traditional three-remote acceptance check efficiency is low in the prior art.
In order to solve the technical problems, the invention specifically provides the following technical scheme:
an automatic identification method for three-remote change of a scheduling master station based on image identification comprises the following steps:
step 100, regularly scanning an SVG operation graph of a background picture of a scheduling system, and performing static image recognition and classification on the graph in the SVG operation graph to obtain a known type image and an unknown type image;
step 200, carrying out continuous dynamic identification on the image data of the known type image, and acquiring three-remote changes of the image data of the known type image on an SVG operation chart of a background picture of the dispatching system;
step 300, performing image definition on the unknown type image, and converting the unknown type image into a known type image;
and step 400, dynamically identifying the converted image of the known type.
Optionally, in step 100, the SVG operation graph of the background picture of the periodic scanning scheduling system includes:
step 101, regularly acquiring the SVG operation diagram from the scheduling system in a file transmission mode, and archiving a time and version number mark of each time of acquiring the SVG operation diagram;
102, establishing an image matching library according to different types of equipment, wherein the image matching comprises the SVG operation graph and corresponding primitive descriptions, and the primitive descriptions comprise three-remote data change corresponding to each type of equipment and action categories corresponding to the three-remote data change;
step 103, scanning the SVG operation diagram, carrying out static image recognition on the SVG operation diagram, extracting each graphic element in the SVG operation diagram, matching each graphic element one by one in the image matching library, classifying the matched graphic elements to be corresponding to the SVG operation diagram, and classifying the matched graphic elements to be known type images and the rest image elements to be unknown type images.
Optionally, the unknown type image includes a completely unmatched unknown pattern and a partially matched blurred pattern.
Optionally, the step 200 further includes the user executing remote control command issuing;
when a user executes a remote control command and issues the remote control command, a remote control dialog box needs to be opened first, and the change of the remote control operation state of the corresponding equipment is analyzed by identifying the form change of the known type image in the remote control dialog box.
Optionally, the remote control operation state includes remote control presetting, remote control issuing, and remote control returning.
Optionally, in the step 200, the continuously and dynamically identifying the image data of the known type of image includes:
dynamically identifying the description information of each known type image, intercepting the point number and point location description of the description information of each known type image, and perfecting the three-remote-point operation information of the corresponding equipment;
the dynamically identifying the description information of each image of the known type comprises:
capturing the change data of the known type image by adopting a KVM (keyboard video mouse) mode, and continuously transmitting the image data of the known type image through an HDMI (high-definition multimedia interface);
capturing the image data of the known type of image according to fixed interval time, and decomposing and comparing the graphic elements of the captured image each time;
when the screenshot has image change, extracting the changed graphic elements, acquiring the equipment type and the specific issued value through the surrounding text description, and distinguishing and identifying remote signaling change and remote sensing change.
Optionally, in the step 300, the image defining the unknown type image includes:
step 301, decomposing the graphic elements of the unknown type image, comparing the graphic elements with the graphic matching library, and dividing the unknown type image into a completely unmatched unknown image and a partially matched fuzzy image;
step 302, processing the fuzzy graph by adopting a fuzzy state processing method, determining a matching degree parameter partially matched with the fuzzy graph, and determining a known type image corresponding to the fuzzy graph according to the matching degree parameter;
step 303, defining the unknown graph by adopting a machine learning method, and re-editing and saving the unknown graph to the image matching library.
Optionally, in step 303, the graph type in the image matching library is first used as a deep learning data pool; defining the unknown pattern by matching the specific features of each pattern type; and finally, storing the data characteristics of the unknown graph into the image matching library to convert the unknown graph into a known type image.
Optionally, in the step 302, when the matching degree parameter between the blurred pattern and the pattern in the image matching library is greater than or equal to 80%, it is determined that the blurred pattern is of a known type.
Compared with the prior art, the invention has the following beneficial effects:
the method replaces the traditional manual mode that whether the check point position changes or not is checked after control operation is issued, the purpose of automatic testing can be achieved only by matching with automatic operation and issuing through the novel method, operation of testing personnel is not needed in the whole process, operation errors are reduced, testing efficiency is improved, and manpower and material resources are saved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a technical schematic diagram of an automatic identification method for three remote changes of a scheduling master station based on an image identification technology according to an embodiment of the present invention;
fig. 2 is a flowchart of an automatic identification method for three remote changes of a scheduling master station based on an image identification technology according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the 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.
As shown in fig. 1, the invention provides an automatic identification method for three remote changes of a scheduling master station based on image identification, which comprises the following steps:
and step 100, periodically scanning an SVG operation drawing of a background picture of the scheduling system, and performing static image recognition and classification on the drawing in the SVG operation drawing to obtain a known type image and an unknown type image.
In step 100, the specific implementation method of the SVG operation diagram for periodically scanning the background picture of the scheduling system is as follows:
step 101, obtaining the SVG operation diagram from the scheduling system periodically in a file transmission mode, and archiving the time and version number mark of obtaining the SVG operation diagram each time.
In the step, an SVG operation graph is periodically acquired from a scheduling system in a file transmission mode, and the time and version number of each acquired SVG operation graph are marked and archived;
102, establishing an image matching library according to different types of equipment, wherein the image matching comprises an SVG operation graph and corresponding primitive descriptions, and the primitive descriptions comprise three-remote data change corresponding to each type of equipment and action categories corresponding to the three-remote data change.
In the step, an image matching library is established according to the SVG operation diagram of each device and the corresponding primitive description, wherein the primitive description is used for integrally classifying the specific action of each device in the corresponding three-remote data change;
and 103, scanning the SVG operation diagrams, performing static image recognition on the SVG operation diagrams, extracting each graphic element in each SVG operation diagram, matching each graphic element one by one in an image matching library, classifying the SVG operation diagrams corresponding to the matched graphic element classification into known type images, and classifying the rest image elements into unknown type images.
Wherein, SVG: is an image file format, its english language is called Scalable Vector Graphics, meaning Scalable Vector Graphics. It is realized based on XML (extensible Markup language) and is a standard open vector graphics language, which can be opened and read by any word processing tool.
Step 200, continuously and dynamically identifying the image data of the known type image, and acquiring three remote changes of the image data of the known type image on an SVG operation chart of a background picture of the scheduling system.
In step 200, when the user executes the remote control command issue, the remote control dialog box needs to be opened first, and the remote control operation state change of the corresponding device is analyzed by identifying the morphological change of the known type image in the remote control dialog box, wherein the remote control operation state comprises remote control presetting, remote control issue and remote control return correction.
In step 200, identifying the description information of each known type image by using a dynamic identification mode, and intercepting the three-remote-point operation information of the corresponding device in which the point number and the point location description are perfected by using the description information of each known type image, wherein the specific implementation method for identifying the description information of each known type image by using the dynamic identification mode is as follows:
capturing the change data of the known type of image by adopting a KVM (keyboard video mouse) mode, and continuously transmitting the image data of the known type of image through an HDMI (high-definition multimedia interface);
screenshot is carried out on image data of images of known types according to fixed interval time, and graphic elements of the screenshot images at each time are decomposed and compared;
when the screenshot has image change, extracting the changed graphic elements, obtaining the equipment type and the specific issued value through the surrounding text description, and distinguishing and identifying remote signaling change and remote measuring change.
KVM: is an abbreviation of Keyboard Video Mouse, and can access and control a computer by directly connecting a Keyboard, a Video and Mouse (KVM) port, and the technology directly transmits image and sound data through an HDMI interface. Plays an important role in remote scheduling monitoring. The KVM technology can send various data information in the scheduling information network to the remote terminal, provides convenience for the next-level scheduling mechanism, and realizes information sharing.
And step 300, carrying out image definition on the unknown type image, and converting the unknown type image into the known type image.
In step 300, an auxiliary means is used to perform image definition on the unknown type image, and the specific implementation method is as follows:
step 301, decomposing the graphic elements of the unknown type image, comparing the graphic elements with a graphic matching library, and dividing the unknown type image into a completely unmatched unknown image and a partially matched fuzzy image;
step 302, processing the fuzzy graph by adopting a fuzzy state processing method, determining a matching degree parameter partially matched with the fuzzy graph, and determining a known type image corresponding to the fuzzy graph according to the matching degree parameter; when the matching degree parameter between the fuzzy pattern and the pattern in the image matching library is more than or equal to 80 percent, the fuzzy pattern is judged to belong to the known type.
Step 303, defining an unknown graph by adopting a machine learning method, and re-editing and storing the unknown graph to an image matching library; firstly, the graph type in an image matching library is used as a data pool for deep learning; defining an unknown graph by matching the specific characteristics of each graph type; and finally, storing the data characteristics of the unknown graph into an image matching library to convert the data characteristics into the known type image.
The machine learning is based on the preliminary knowledge of data and the analysis of learning purposes, a proper mathematical model is selected, hyper-parameters are drawn up, sample data is input, the model is trained by using a proper learning algorithm according to a certain strategy, and finally the trained model is used for analyzing and predicting the data.
And step 400, dynamically identifying the converted known type image.
In this step, the dynamic recognition mode of step 200 is reused to dynamically recognize the converted image of the known type.
The method replaces the traditional manual mode of checking whether the check point position changes after control operation is issued, can achieve the purpose of automatic testing only by being matched with automatic operation issuing, does not need operation of testing personnel in the whole process, reduces operation error, improves testing efficiency and saves manpower and material resources.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (9)
1. An automatic identification method for three-remote change of a scheduling master station based on image identification is characterized by comprising the following steps:
step 100, regularly scanning an SVG operation graph of a background picture of a scheduling system, and performing static image recognition and classification on the graph in the SVG operation graph to obtain a known type image and an unknown type image;
step 200, carrying out continuous dynamic identification on the image data of the known type image, and acquiring three-remote changes of the image data of the known type image on an SVG operation chart of a background picture of the dispatching system;
step 300, performing image definition on the unknown type image, and converting the unknown type image into a known type image;
and step 400, dynamically identifying the converted image of the known type.
2. The method for automatically identifying three-remote change of a dispatching master station based on image identification as claimed in claim 1, wherein in the step 100, the SVG operation chart for periodically scanning the background picture of the dispatching system comprises:
step 101, regularly acquiring the SVG operation diagram from the scheduling system in a file transmission mode, and archiving a time and version number mark of each time of acquiring the SVG operation diagram;
102, establishing an image matching library according to different types of equipment, wherein the image matching comprises the SVG operation graph and corresponding primitive descriptions, and the primitive descriptions comprise three-remote data change corresponding to each type of equipment and action categories corresponding to the three-remote data change;
step 103, scanning the SVG operation diagram, carrying out static image recognition on the SVG operation diagram, extracting each graphic element in the SVG operation diagram, matching each graphic element one by one in the image matching library, classifying the matched graphic elements to be corresponding to the SVG operation diagram, and classifying the matched graphic elements to be known type images and the rest image elements to be unknown type images.
3. The image recognition-based scheduling master station three-remote change automatic recognition method of claim 2, wherein the unknown type image comprises an unknown pattern which is completely unmatched and a fuzzy pattern which is partially matched.
4. The method for automatically identifying three remote changes of a scheduling master station based on image identification as claimed in claim 1, wherein the step 200 further comprises the steps of a user executing remote control command issuing;
when a user executes a remote control command and issues the remote control command, a remote control dialog box needs to be opened first, and the change of the remote control operation state of the corresponding equipment is analyzed by identifying the form change of the known type image in the remote control dialog box.
5. The image-recognition-based automatic three-remote change identification method for the scheduling master station according to claim 4, wherein the remote operation state comprises remote presetting, remote issuing and remote correcting.
6. The method of claim 4, wherein the step 200 of continuously and dynamically identifying the image data of the known type of image comprises:
dynamically identifying the description information of each known type image, intercepting the point number and point location description of the description information of each known type image, and perfecting the three-remote-point operation information of the corresponding equipment;
the dynamically identifying the description information of each image of the known type comprises:
capturing the change data of the known type image by adopting a KVM (keyboard video mouse) mode, and continuously transmitting the image data of the known type image through an HDMI (high-definition multimedia interface);
capturing the image data of the known type of image according to fixed interval time, and decomposing and comparing the graphic elements of the captured image each time;
when the screenshot has image change, extracting the changed graphic elements, acquiring the equipment type and the specific issued value through the surrounding text description, and distinguishing and identifying remote signaling change and remote sensing change.
7. The method according to claim 3, wherein the step 300 of image defining the image of the unknown type comprises:
step 301, decomposing the graphic elements of the unknown type image, comparing the graphic elements with the graphic matching library, and dividing the unknown type image into a completely unmatched unknown image and a partially matched fuzzy image;
step 302, processing the fuzzy graph by adopting a fuzzy state processing method, determining a matching degree parameter partially matched with the fuzzy graph, and determining a known type image corresponding to the fuzzy graph according to the matching degree parameter;
step 303, defining the unknown graph by adopting a machine learning method, and re-editing and saving the unknown graph to the image matching library.
8. The method according to claim 7, wherein in step 303, the image type in the image matching library is used as a deep learning data pool; defining the unknown pattern by matching the specific features of each pattern type; and finally, storing the data characteristics of the unknown graph into the image matching library to convert the unknown graph into a known type image.
9. The method as claimed in claim 7, wherein in step 302, when the matching degree parameter between the blurred pattern and the pattern in the image matching library is greater than or equal to 80%, it is determined that the blurred pattern is of a known type.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113269281A (en) * | 2021-07-21 | 2021-08-17 | 广东电网有限责任公司东莞供电局 | Image-based scheduling master station three-remote change identification method and system |
| CN113807342A (en) * | 2021-09-17 | 2021-12-17 | 广东电网有限责任公司 | Method and related device for acquiring equipment information based on image |
| CN114612079A (en) * | 2022-05-16 | 2022-06-10 | 国网江西省电力有限公司电力科学研究院 | Automatic checking method for monitoring information graph library of centralized control station |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3638188A (en) * | 1969-10-17 | 1972-01-25 | Westinghouse Electric Corp | Classification method and apparatus for pattern recognition systems |
| CN105843125A (en) * | 2016-05-10 | 2016-08-10 | 国网山东省电力公司德州供电公司 | Transformer station sequence operation system and method based on state identification technology |
| CN108573268A (en) * | 2017-03-10 | 2018-09-25 | 北京旷视科技有限公司 | Image-recognizing method and device, image processing method and device and storage medium |
| CN109242439A (en) * | 2018-09-21 | 2019-01-18 | 国家电网有限公司 | Feature extraction recognition methods based on substation equipment associated data |
-
2020
- 2020-11-06 CN CN202011232988.6A patent/CN112270373A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US3638188A (en) * | 1969-10-17 | 1972-01-25 | Westinghouse Electric Corp | Classification method and apparatus for pattern recognition systems |
| CN105843125A (en) * | 2016-05-10 | 2016-08-10 | 国网山东省电力公司德州供电公司 | Transformer station sequence operation system and method based on state identification technology |
| CN108573268A (en) * | 2017-03-10 | 2018-09-25 | 北京旷视科技有限公司 | Image-recognizing method and device, image processing method and device and storage medium |
| CN109242439A (en) * | 2018-09-21 | 2019-01-18 | 国家电网有限公司 | Feature extraction recognition methods based on substation equipment associated data |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113269281A (en) * | 2021-07-21 | 2021-08-17 | 广东电网有限责任公司东莞供电局 | Image-based scheduling master station three-remote change identification method and system |
| CN113807342A (en) * | 2021-09-17 | 2021-12-17 | 广东电网有限责任公司 | Method and related device for acquiring equipment information based on image |
| CN114612079A (en) * | 2022-05-16 | 2022-06-10 | 国网江西省电力有限公司电力科学研究院 | Automatic checking method for monitoring information graph library of centralized control station |
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