CN113569714A - Intelligent image recognition system of transformer substation based on unmanned aerial vehicle patrols and examines - Google Patents
Intelligent image recognition system of transformer substation based on unmanned aerial vehicle patrols and examines Download PDFInfo
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Abstract
The invention discloses a transformer substation intelligent image recognition system based on unmanned aerial vehicle inspection, which comprises an unmanned aerial vehicle monitoring module and a remote monitoring terminal, wherein the remote monitoring terminal comprises a storage module, an image analysis module, a display module and an alarm module; the unmanned aerial vehicle monitoring module collects image information and uploads the image information to the storage module through a special network; the image analysis module processes and analyzes the image information received by the storage module, compares, analyzes and judges the image to be matched with the template image to obtain conclusion information of the running state of the monitoring equipment, and sends the conclusion information to the storage module for storage. The image recognition system can rapidly recognize the appearance image of the equipment by using the unmanned aerial vehicle for routing inspection, the accuracy rate of the appearance image recognition of the equipment reaches more than 80 percent by training an image recognition algorithm, the safe and reliable operation of the equipment is ensured, the regional power supply reliability is improved, the storage and management of monitoring information are standard and complete, the dispatching and the use are convenient, and the working efficiency is greatly improved.
Description
Technical Field
The invention relates to a transformer substation inspection system, in particular to a transformer substation intelligent image identification system based on unmanned aerial vehicle inspection.
Background
In recent years, the number of transformer substations is gradually increased, meanwhile, the number of transformer operation and maintenance personnel is basically kept unchanged, and the contradiction between the expansion of equipment scale and the number of personnel is increasingly highlighted. The traditional method depends on manual inspection and manual recording, and the method can not ensure that inspection personnel can complete standard operation according to quality on time, can not ensure the accuracy and comprehensiveness of recording, seriously hinders the improvement of the working quality and efficiency of safety production, and gradually can not meet the current requirements on safe and reliable operation of a power grid. Therefore, it is of great significance to develop an intelligent identification system suitable for the transformer substation.
Disclosure of Invention
In order to overcome the defects, the invention aims to provide the transformer substation intelligent image identification system based on unmanned aerial vehicle routing inspection.
In order to achieve the purpose, the invention adopts the following technical scheme:
a transformer substation intelligent image recognition system based on unmanned aerial vehicle routing inspection comprises an unmanned aerial vehicle monitoring module and a remote monitoring terminal, wherein the remote monitoring terminal comprises a storage module, an image analysis module, a display module and an alarm module;
the unmanned aerial vehicle monitoring module acquires image information and transmits the image information to the unmanned aerial vehicle remote controller through a private network, the unmanned aerial vehicle remote controller transmits the image information to the unmanned aerial vehicle nest through the in-station local area network, and the unmanned aerial vehicle nest uploads the image information to the storage module through the private network;
the image analysis module processes and analyzes the image information received by the storage module, compares the image to be matched with the template image, analyzes and judges the image to be matched to obtain conclusion information of the running state of the monitoring equipment, and sends the conclusion information to the storage module for storage; sending the abnormal equipment information to an alarm module for alarming, and generating a defect report for operation and maintenance personnel to overhaul and call the equipment;
the display module obtains the conclusion information of the running state of the monitoring equipment, which is obtained by the processing of the image analysis module, and displays the conclusion information in real time in a dynamic curve, chart or image mode.
Preferably, unmanned aerial vehicle monitoring module is including setting up visible light camera and the infrared thermal imaging camera on unmanned aerial vehicle, and the instrument pointer direction and reading, switch on-off state, the card turning over ware image are shot to the visible light camera, and the main transformer image is shot to the infrared thermal imaging camera for measure main transformer temperature.
Preferably, the display module adopts an OLED display screen, an LED display screen, an LCD display screen, a smart phone, a notebook or a desktop computer.
Preferably, the template image is a defect image artificially marked and polluted.
Preferably, the image analysis processing module processes the image to be matched and the template image into three layers of pyramid images, then performs feature point extraction on each layer of pyramid image by using a Harris feature point detection algorithm with the same parameter setting to obtain a feature point pyramid of the image to be matched and the template image, and finally performs NCC algorithm matching from the top layer of the pyramid of the feature points of the image to be matched and the template image to the bottom layer step by step.
Preferably, the three-layer pyramid image processing method includes: and performing two-layer decomposition on the image to be matched and the template image by Haar wavelet transform, wherein the subgraph with the minimum resolution is used as the top layer of the pyramid, and the original image is used as the bottom layer of the pyramid to form a three-layer pyramid image.
The invention has the following positive beneficial effects:
1. the substation intelligent image recognition system based on unmanned aerial vehicle inspection can rapidly recognize the appearance image of the equipment by using unmanned aerial vehicle inspection, and trains an image recognition algorithm to ensure that the accuracy of the appearance image recognition of the equipment reaches more than 80%, so that the equipment can operate safely and reliably, the regional power supply reliability is improved, the monitoring information storage management is standard and complete, the dispatching and the use are convenient, the problem that a large number of pictures are shot on site and need to be checked manually one by one is solved, operation and maintenance personnel are liberated from the repeated work of machinery, the substation inspection efficiency can be improved, and the working pressure of the operation and maintenance personnel is reduced.
2. The intelligent unmanned aerial vehicle inspection system is popularized and applied to substation unmanned aerial vehicle intelligent inspection, and is combined with local area network base station construction and AI (artificial intelligence) technical application, so that intelligent transformation and upgrading of a substation are further performed, and operation and maintenance mode change of an intelligent operation inspection control center, a team centralized control station and an intelligent station of a company is promoted.
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FIG. 1 is a structural block diagram of a transformer substation intelligent image recognition system based on unmanned aerial vehicle inspection according to the invention;
FIG. 2 is a flow chart of the image analysis processing module according to the present invention.
Detailed Description
The invention will be further illustrated with reference to some specific examples.
Example 1
Referring to fig. 1, an intelligent image recognition system of transformer substation based on unmanned aerial vehicle patrols and examines, including unmanned aerial vehicle monitoring module and remote monitor terminal, remote monitor terminal includes storage module, image analysis module, display module and alarm module, unmanned aerial vehicle monitoring module is including setting up visible light camera and the infrared thermal imaging camera on unmanned aerial vehicle, instrument pointer direction and reading are shot to visible light camera, switch deciliter state, card turning over ware image, infrared thermal imaging camera shoots main transformer image, be used for measuring main transformer temperature, unmanned aerial vehicle monitoring module gathers image information, and transmit to the unmanned aerial vehicle remote controller through private network, the unmanned aerial vehicle remote controller passes through the in-station LAN transmission to the unmanned aerial vehicle nest, unmanned aerial vehicle nest passes through private network and uploads to storage module again.
Further, the image analysis module processes and analyzes the image information received by the storage module, compares, analyzes and judges the image to be matched with the template image to obtain conclusion information of the running state of the monitoring equipment, and sends the conclusion information to the storage module for storage, wherein the template image is a defect image which is artificially marked and polluted; sending the abnormal equipment information to an alarm module for alarming, generating a defect report, and exporting the defect report according to a preset standard template for operation and maintenance personnel to overhaul and call the equipment;
the display module acquires the conclusion information of the running state of the monitoring equipment, which is processed by the image analysis module, and displays the conclusion information in real time in a dynamic curve, chart or image mode; the display module adopts an OLED display screen, an LED display screen, an LCD display screen, a smart phone, a notebook computer or a desktop computer.
Furthermore, the image analysis processing module processes the image to be matched and the template image into three layers of pyramid images, and the three layers of pyramid image processing method comprises the following steps: performing two-layer decomposition on the image to be matched and the template image by Haar wavelet transform, wherein a subgraph with the minimum resolution is used as the top layer of a pyramid, and an original image is used as the bottom layer of the pyramid to form a three-layer pyramid image, and the detailed description is as follows:
l represents a low-pass filter, H represents a high-pass filter, the rows and the columns of the image are respectively convoluted, 2-to-1 subsampling is carried out, and the original image is decomposed into 4 sub-bands LL1, LH1, HL1 and HH 1; where LL1 reflects the low frequency components of the original image, the subbands are obtained by low pass filters in both the horizontal and vertical directions; LH1 reflects the horizontal edge details of the original image, being the subbands obtained by a horizontal low-pass filter and a vertical high-pass filter; HL1 is a subband obtained by high frequency in the horizontal direction and low frequency in the vertical direction; HH1 is a subband obtained from the horizontal direction high frequency and the vertical direction high frequency; setting the resolution as 1/2 of the original image, and further decomposing the sub-band LL1 to obtain 4 sub-bands of LL2, LH2, HL2 and HH 2; the resolution is set to 1/4 of the original image, the sub-band LL2 is further decomposed to obtain 4 sub-bands of LL3, LH3, HL3 and HH3, and the wavelet transform three-level decomposition of the embodiment is completed.
Referring to fig. 2, a Harris feature point detection algorithm with the same parameter setting is adopted to extract feature points of images of each layer of pyramid, feature point pyramids of the images to be matched and the template images are obtained, and finally, NCC algorithm matching is performed from the top layers of the pyramid of the features of the images to be matched and the template images to the bottom layers gradually.
Wherein, the NCC algorithm is as follows:
wherein: s1(x, y) is the mean of the images to be matched, S2(x, y) is the variance of the image to be matched, x, y are the size of the reference image, m, n are the size of the template, A (i, j) is the gray value of the pixel in the matching area in the real-time image, (x, y) is the matching point, B (i-x, i-y) is the gray value of the pixel in the template,
is the average of the gray levels of the templates,
is the mean, σ, of the matching region in the imageBIs the variance of the template image;
wherein the content of the first and second substances,
taking the average value and the variance of the template image, the accumulated sum and the square accumulated sum of the images to be matched to obtain the point with the highest matching degree C (x, y) as the top-layer optimal matching point; after the top-layer optimal matching point is obtained, the top-layer optimal matching point is used as a central point of lower-layer image matching, searching is carried out again in the neighborhood of the central points of the image to be matched and the template image, and NCC algorithm matching is carried out to obtain the lower-layer optimal matching point; gradually matching to the bottommost layer, gradually limiting the search range of cross-correlation matching along with the improvement of resolution, gradually improving the precision of the matching points, gradually mapping to the best matching points of the bottommost layer, and judging whether the image to be matched is different from the original image or not from the best matching points of the bottommost layer, so that whether the image to be matched is a defect image or not can be judged.
The design of the image recognition system of the invention takes 'advancement, reliability, practicability, economy and expansibility' as basic principles, and the system adopts the latest artificial intelligence technology to intelligently analyze the collected image models in real time, explore potential dangerous situations and perform real-time early warning and disposal. The system hardware adopts a carrier-grade server and professional equipment, redundancy backup measures are taken for key equipment, and the software adopts a modularized and hierarchical isolation design concept to ensure the long-term stable operation of the whole system. The monitoring system which can solve the industrial pain point and meet the actual requirements is developed by combining the actual requirements of electric power safety monitoring, and the monitoring system is powerful in function, excellent in performance, friendly in interface, high in intelligent achievement and easy to learn and use, and various operations of the system are simple. The system design fully considers the economy, can realize remote maintenance, and has the characteristics of simple maintenance operation and less maintenance workload. The system adopts a modular design, has strong expansibility, and can conveniently realize scale expansion and service extension. The system adopts hardware equipment which is mainstream in the industry, provides a standard protocol, has good compatibility and a universal software and hardware interface, can be fully compatible with equipment of mainstream manufacturers, and can provide interfaces for other systems.
Finally, the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and other modifications or equivalent substitutions made by the technical solutions of the present invention by those of ordinary skill in the art should be covered within the scope of the claims of the present invention as long as they do not depart from the spirit and scope of the technical solutions of the present invention.
Claims (6)
1. A transformer substation intelligent image recognition system based on unmanned aerial vehicle inspection is characterized by comprising an unmanned aerial vehicle monitoring module and a remote monitoring terminal, wherein the remote monitoring terminal comprises a storage module, an image analysis module, a display module and an alarm module;
the unmanned aerial vehicle monitoring module acquires image information and transmits the image information to the unmanned aerial vehicle remote controller through a private network, the unmanned aerial vehicle remote controller transmits the image information to the unmanned aerial vehicle nest through the in-station local area network, and the unmanned aerial vehicle nest uploads the image information to the storage module through the private network;
the image analysis module processes and analyzes the image information received by the storage module, compares the image to be matched with the template image, analyzes and judges the image to be matched to obtain conclusion information of the running state of the monitoring equipment, and sends the conclusion information to the storage module for storage; sending the abnormal equipment information to an alarm module for alarming, and generating a defect report for operation and maintenance personnel to overhaul and call the equipment;
the display module obtains the conclusion information of the running state of the monitoring equipment, which is obtained by the processing of the image analysis module, and displays the conclusion information in real time in a dynamic curve, chart or image mode.
2. The intelligent image recognition system of transformer substation based on unmanned aerial vehicle patrols and examines of claim 1, characterized in that, unmanned aerial vehicle monitoring module includes visible light camera and the infrared thermal imaging camera of setting on unmanned aerial vehicle, and visible light camera shoots instrument pointer direction and reading, switch on-off state, the tablet turnover ware image, and the infrared thermal imaging camera shoots main transformer image for measure main transformer temperature.
3. The unmanned aerial vehicle inspection-based substation intelligent image recognition system according to claim 1, wherein the display module is an OLED display screen, an LED display screen, an LCD display screen, a smart phone, a notebook or a desktop computer.
4. The unmanned aerial vehicle inspection tour-based substation intelligent image identification system of claim 1, wherein the template image is a manually labeled contaminated defect image.
5. The unmanned aerial vehicle inspection-based substation intelligent image recognition system according to claim 1, wherein the image analysis processing module processes the image to be matched and the template image into three layers of pyramid images, then performs feature point extraction on each layer of pyramid image by using a Harris feature point detection algorithm with the same parameter setting to obtain feature point pyramids of the image to be matched and the template image, and finally performs NCC algorithm matching from the top layers of the feature point pyramids of the image to be matched and the template image to gradually match the top layers of the feature point pyramids to the bottom layers.
6. The unmanned aerial vehicle inspection tour-based substation intelligent image recognition system of claim 5, wherein the three-layer pyramid image processing method is as follows: and performing two-layer decomposition on the image to be matched and the template image by Haar wavelet transform, wherein the subgraph with the minimum resolution is used as the top layer of the pyramid, and the original image is used as the bottom layer of the pyramid to form a three-layer pyramid image.
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