CN114609158A - Substation equipment target and defect intelligent detection equipment based on deep learning - Google Patents
Substation equipment target and defect intelligent detection equipment based on deep learning Download PDFInfo
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Abstract
The invention discloses a power transformation equipment target and defect intelligent detection device based on deep learning, which relates to the technical field of power transformation equipment detection and comprises five parts, namely a multi-state quantity sensor, a multi-channel signal synchronous acquisition system, a high-performance control and data processing system, a multi-mode data communication system, a high-reliability power supply system and the like. The device can realize comprehensive monitoring and automatic analysis of key state quantities such as visible light images, infrared thermal imaging, partial discharge, sound, temperature, humidity and the like, and is a set of omnibearing, full-time-period and full-synergetic device state multi-parameter perception early warning device. According to the invention, data and edge calculation analysis results can be uploaded to the access node through Ethernet, WIFI, LoRa and other modes, or sent to the cloud platform server through 4G mobile communication network and other modes, and related users can remotely monitor through a mobile phone terminal APP or a PC browser, and can timely master equipment states and early warning information.
Description
Technical Field
The invention relates to the technical field of substation equipment detection, in particular to a substation equipment target and defect intelligent detection device based on deep learning.
Background
The transformer substation is a place for converting voltage and current, receiving electric energy and distributing electric energy in an electric power system, and is a boosting transformer substation in a power plant and has the function of boosting the electric energy generated by a generator and then feeding the boosted electric energy to a high-voltage power grid.
In the routine maintenance and inspection process of the traditional power grid, the defects are discovered, identified and classified by people, and the problems of negligence omission, low efficiency and the like exist.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a transformation equipment target and defect intelligent detection equipment based on deep learning, which solves the problem that background personnel cannot know the defects frequently when equipment in a transformer substation works for a long time.
In order to realize the purpose, the invention is realized by the following technical scheme: a power transformation equipment target and defect intelligent detection device based on deep learning comprises a background terminal and a holder, wherein a camera terminal is mounted on the holder and comprises a visible light collector and an infrared thermal imager;
the camera terminal also comprises a multi-state quantity sensor, an industrial personal computer and a power supply system, and is used for realizing comprehensive monitoring and automatic analysis of key state quantities of visible light images, infrared thermal imaging, partial discharge, sound, temperature and humidity. The camera terminal uploads the data and the edge calculation analysis result to the access node through Ethernet/WIFI/LoRa or sends the data and the edge calculation analysis result to the cloud platform server in a 4G mobile communication network mode, and related users can remotely monitor through a mobile phone terminal APP or a PC browser and timely master the equipment state and early warning information.
Further, the industrial personal computer comprises a data acquisition system, a data storage module, an image processing module, a display module, an early warning module and an error correction module, wherein the data acquisition system is used for acquiring image data detected by the thermal imager, the data storage module is used for storing the image data acquired by the data acquisition system, the image processing module is used for preprocessing the acquired image data, the display module is used for displaying the preprocessed image data, the error correction module is used for correcting the displayed image data, a temperature range value of normal work of equipment is set in the early warning module, and when the detected range value exceeds a preset value, the early warning module generates an early warning signal and then wirelessly transmits the early warning signal to a background terminal to notify background personnel.
Furthermore, when the image processing module preprocesses the image data, high-frequency components in the image are removed through a low-pass filter to achieve the noise reduction effect, then high-frequency component blocking is performed through averaging of partial regions of the image, secondary filtering processing is performed on the image, then a gray level histogram of the image is changed through a gray level mapping function, the fact that each gray level has the same pixel point is guaranteed, and preprocessing work of the image is completed.
Further, when the error correction module corrects the image data, an average value of at least five sets of acquired data is used.
Furthermore, the temperature sensor is used for collecting the temperature of the working environment of the thermal imager and transmitting the collected signals to the single chip microcomputer, and the single chip microcomputer controls the fan and the temperature control lamp to work respectively.
Furthermore, the single chip microcomputer comprises a temperature presetting module, and the temperature presetting module is used for setting threshold values of the maximum limit ambient temperature and the minimum limit ambient temperature of the thermal imager during working.
Further, the system also comprises a video image processing module;
the visible light collector collects a target image of the equipment; the video image processing module marks defects by using a pre-trained deep learning model, sends the defect information to the display module and sends the defect information to the staff through the wireless module;
the deep learning model training process comprises the following steps:
s101, collecting a defect sample of an equipment target;
s102, manually labeling the defect sample: in order to obtain enough convolutional neural network training samples, the following two methods are adopted:
(1) inputting the defects confirmed by the workers in the actual detection into a deep learning model for repeated training;
(2) Increasing a sample set by a random transformation method, wherein the method comprises the steps of whitening a training sample set, randomly turning left and right images and randomly transforming the contrast of the images;
s103, constructing a multilayer convolutional neural network;
and S104, performing steepest descent optimization on the error gradient of the multilayer convolutional neural network, and performing off-line training to construct the multilayer convolutional neural network.
The top of the infrared thermal imager is provided with a concave shell through a bolt, and the top of the concave shell is provided with a lens protection assembly; the inboard horizontally connected of concave shell has the baffle, a plurality of ventilation hole has evenly been seted up on the surface of baffle, the inboard below position department that is located the baffle of concave shell installs temperature sensor, the inboard top position department horizontally mounted that is located the baffle of concave shell has the temperature control lamp, and just fan and singlechip are installed respectively to the inboard top of concave shell.
The camera lens protective assembly is including a spacing section of thick bamboo, the front end downside of a spacing section of thick bamboo is rotated and is installed in the upside intermediate position department of concave shell, and the rear end of a spacing section of thick bamboo installs servo motor, servo motor's output shaft is located the internal position department horizontally connect of a spacing section of thick bamboo has the screw rod, the outside cover of screw rod is equipped with the threaded sleeve rather than the looks adaptation, threaded sleeve's front end is connected with the limiting plate perpendicularly, the lower extreme of limiting plate is provided with the apron, the position of apron is corresponding with the camera lens position on the thermal imager, and the front side of apron installs motor, motor's output shaft has the mounting panel, a side of mounting panel bonds there is the foam-rubber cushion, the apron is for covering the back with camera lens on the thermal imager, the foam-rubber cushion just in time contacts with the lens of camera lens.
The upper side of the rear end of the limiting cylinder is hinged with an adjusting block, the upper part of one side of the concave shell is provided with an electric push rod, and an output shaft of the electric push rod is hinged to one end of the adjusting block.
Advantageous effects
The invention provides intelligent detection equipment for the defects of a substation equipment target, which has the following beneficial effects compared with the prior art:
1. this transformer equipment target and defect intellectual detection system based on degree of depth study, can carry out real-time on-line measuring to the defect of equipment in the transformer substation and the overheated condition, when there is the defect in equipment or take place overheated phenomenon, ensure that backstage personnel know very first time to timely corresponding measure of making reduces unnecessary economic loss, and the accuracy that detects moreover is high.
2. This substation equipment target and defect intelligent detection equipment based on degree of depth study, when thermal imaging system is detecting, through carrying out real-time monitoring and control to the temperature that thermal imaging system work produced, prevent that the ambient temperature of thermal imaging system work from surpassing high temperature limit value or low temperature limit value, and then ensure the work that thermal imaging system can be stable, improve the accuracy nature of its detection.
Drawings
FIG. 1 is a schematic view of example 1 of the present invention;
FIG. 2 is a schematic structural view of the present invention;
FIG. 3 is a schematic block diagram of a system of an industrial personal computer according to the present invention;
FIG. 4 is an internal schematic view of the concave shell structure of the present invention;
FIG. 5 is a schematic block diagram of the system control of the single-chip microcomputer of the present invention;
fig. 6 is a schematic structural diagram of a lens protection assembly according to the present invention.
In the figure: 1. a background terminal; 2. mounting a rod; 3. mounting blocks; 4. mounting a shell; 5. an industrial personal computer; 51. a data acquisition system; 52. a data storage module; 53. an image processing module; 54. a display module; 55. an early warning module; 56. an error correction module; 6. a holder; 7. a thermal imager; 8. a concave shell; 81. a partition plate; 82. a vent hole; 83. a temperature sensor; 84. a temperature control lamp; 85. a fan; 86. a single chip microcomputer; 861. a temperature presetting module; 9. a lens protection component; 91. a limiting cylinder; 92. a servo motor; 93. a screw; 94. a threaded sleeve; 95. a limiting plate; 96. a cover plate; 97. a motor; 98. mounting a plate; 99. a sponge cushion; 910. an adjusting block; 911. an electric push rod.
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.
The first embodiment,
Referring to fig. 1, the present invention provides a technical solution, an intelligent detection device for a defect of a target of a power transformation device, including a background terminal 1 and a cloud deck 6, wherein a camera terminal is installed on the cloud deck 6, and the camera terminal includes a visible light collector and an infrared thermal imager;
the camera terminal also comprises a multi-state quantity sensor, an industrial personal computer and a power supply system, and is used for realizing comprehensive monitoring and automatic analysis of visible light images, infrared thermal imaging, partial discharge, sound, temperature and humidity key state quantities;
the camera terminal uploads the data and the edge calculation analysis result to the access node through Ethernet/WIFI/LoRa or sends the data and the edge calculation analysis result to the cloud platform server in a 4G mobile communication network mode, and related users can remotely monitor through a mobile phone terminal APP or a PC browser and timely master the equipment state and early warning information.
Example II,
This embodiment is different from embodiment 1 in that,
the industrial personal computer 5 comprises a data acquisition system 51, a data storage module 52, an image processing module 53, a display module 54, an early warning module 55 and an error correction module 56, the data acquisition system 51 is used for acquiring image data detected by the thermal imager 7, the data storage module 52 is used for storing the image data acquired by the data acquisition system 51, the image processing module 53 is configured to perform pre-processing on the acquired image data, the display module 54 is configured to display the pre-processed image data, the error correction module 56 is used to correct the displayed image data, and first, the temperature range value for normal operation of the device is set in the early warning module 55, when the detected range value exceeds the preset value, the early warning module 55 generates an early warning signal, and then wirelessly transmits the early warning signal to the background terminal 1 to notify background personnel.
When the image processing module 53 preprocesses the image data, high-frequency components in the image are removed through a low-pass filter to achieve a noise reduction effect, then high-frequency component blocking is performed by averaging partial regions of the image, secondary filtering processing is performed on the image, then a gray level histogram of the image is changed through a gray level mapping function, it is ensured that each gray level has the same pixel point, and the preprocessing work of the image is completed.
When the error correction module 56 corrects the image data, an average value of at least five sets of acquired data is taken.
The thermal imager is characterized by further comprising a temperature sensor 83, wherein the temperature sensor 83 is used for collecting the temperature of the working environment of the thermal imager and transmitting collected signals to the single chip microcomputer 86, and the single chip microcomputer 86 controls the fan 85 and the temperature control lamp 84 to work respectively.
The single chip microcomputer 86 comprises a temperature presetting module 861, and the temperature presetting module 861 is used for setting threshold values of the maximum limit ambient temperature and the minimum limit ambient temperature of the thermal imager during working.
The device also comprises a video image processing module;
the visible light collector collects a device target image; the video image processing module marks defects by using a pre-trained deep learning model, sends the defect information to the display module and sends the defect information to the staff through the wireless module;
The deep learning model training process comprises the following steps:
s101, collecting a defect sample of an equipment target;
s102, manually labeling the defect sample: in order to obtain enough convolutional neural network training samples, the following two methods are adopted:
1, inputting the defects confirmed by the workers in the actual detection into a deep learning model for repeated training;
2, increasing the sample set by a random transformation method, including whitening the training sample set, randomly turning the image left and right, and randomly transforming the contrast of the image;
s103, constructing a multilayer convolutional neural network;
and S104, performing steepest descent optimization on the error gradient of the multilayer convolutional neural network, and performing off-line training to construct the multilayer convolutional neural network.
Example 3:
this embodiment and embodiment 1 and 2 difference lie in, including backstage terminal 1 and installation pole 2, the pot head of installation pole 2 is equipped with installation piece 3, and one side of installation piece 3 is provided with installation shell 4, and the internally mounted of installation shell 4 has industrial computer 5, and cloud platform 6 is installed to the front side of installation piece 3, installs thermal imager on cloud platform 6, and concave shell 8 is installed through the bolt in thermal imager's top, and the top of concave shell 8 is provided with camera lens protection component 9.
Referring to fig. 2, in the embodiment of the present invention, an industrial personal computer 5 includes a data acquisition system 51, a data storage module 52, an image processing module 53, a display module 54, an early warning module 55, and an error correction module 56, where the data acquisition system 51 is configured to acquire image data detected by a thermal imager, the data storage module 52 is configured to store the image data acquired by the data acquisition system 51, the image processing module 53 is configured to pre-process the acquired image data, the display module 54 is configured to display the pre-processed image data, the error correction module 56 is configured to correct the displayed image data, a temperature range value for normal operation of a device is set in the early warning module 55, when a detected range value exceeds a preset value, the early warning module 55 generates an early warning signal, and then wirelessly transmits the early warning signal to a background terminal 1 to notify background personnel, when the image processing module 53 preprocesses the image data, high-frequency components in the image are removed through a low-pass filter to achieve a noise reduction effect, then high-frequency component blocking is performed by averaging partial regions of the image, secondary filtering processing is performed on the image, then a gray histogram of the image is changed through a gray mapping function to ensure that each gray has the same pixel point, preprocessing work of the image is completed, and when the error correction module 56 corrects the image data, an average value of at least five groups of acquired data is taken, so that errors are reduced, and the detection accuracy is improved.
Referring to fig. 3-4, in the embodiment of the present invention, a partition plate 81 is horizontally connected to an inner side of a concave shell 8, a plurality of vent holes 82 are uniformly formed in a surface of the partition plate 81, a temperature sensor 83 is installed at a position, below the partition plate 81, of the inner side of the concave shell 8, a temperature control lamp 84 is horizontally installed at a position, above the partition plate 81, of the inner side of the concave shell 8, a fan 85 and a single chip 86 are respectively installed at a top portion of the inner side of the concave shell 8, the temperature sensor 83 is configured to collect a temperature of a working environment of the thermal imager 7, transmit a collected signal to the single chip 86, the single chip 86 respectively controls the fan 85 and the temperature control lamp 84 to work, the single chip 86 includes a temperature preset module 861, the temperature preset module 861 is configured to set threshold values of a maximum ambient temperature and a minimum ambient temperature when the thermal imager works, the maximum ambient temperature and the minimum ambient temperature when the thermal imager works are preset by the temperature preset module 861, when the temperature sensor 83 monitors that the maximum working environment temperature of the thermal imager is higher than the preset value, the single chip 86 enables the fan 85 to perform heat dissipation work on the thermal imager, and when the temperature sensor 83 monitors that the minimum working environment temperature of the thermal imager is lower than the preset value, the single chip 86 enables the single chip 86 to perform temperature control work on the temperature control lamp 84, so that the working environment temperature of the thermal imager 7 is recovered to be normal.
Referring to fig. 5, the lens protection assembly 9 includes a limiting cylinder 91, a lower side of a front end of the limiting cylinder 91 is rotatably installed at a middle position of an upper side of the concave housing 8, a servo motor 92 is installed at a rear end of the limiting cylinder 91, a screw 93 is horizontally connected to an output shaft of the servo motor 92 at an inner position of the limiting cylinder 91, a threaded sleeve 94 adapted to the screw 93 is sleeved outside the screw 93, a limiting plate 95 is vertically connected to a front end of the threaded sleeve 94, a cover plate 96 is disposed at a lower end of the limiting plate 95, the position of the cover plate 96 corresponds to a position of a lens on the thermal imager 7, a motor 97 is installed at a front side of the cover plate 96, an output shaft of the motor 97 is connected to an installation plate 98, a foam cushion 99 is adhered to one side surface of the installation plate 98, after the cover plate 96 covers the lens on the thermal imager 7, the foam cushion 99 just contacts with a lens of the lens, an adjustment block 910 is hinged to an upper side of the rear end of the limiting cylinder 91, an electric push rod 911 is installed on the upper portion of one side of the concave shell 8, an output shaft of the electric push rod 911 is hinged at one end of an adjusting block 910, a screw 93 is driven by a servo motor 92 to rotate, the screw 93 drives a threaded sleeve 94 to slide in a limiting cylinder 91, the threaded sleeve 94 drives a cover plate 96 at the lower end of a limiting plate 95 to move, the cover plate 96 moves towards the position of a lens on the thermal imager 7 to cover the lens, at the moment, the thermal imager 7 is protected from dust, meanwhile, a motor 97 drives a mounting plate 98 to rotate, a sponge pad 99 on the mounting plate 98 wipes and cleans the lens of the lens, when the thermal imager 7 is detected, the cover plate 96 returns to the initial position, then, the electric push rod 911 drives the adjusting block 910 to move, the adjusting block 910 drives the limiting cylinder 91 to rotate, the limiting cylinder 91 drives the threaded sleeve 94 to move, the threaded sleeve 94 drives the cover plate 96 at the lower end of the limiting plate 95 to move, the cover plate 96 is moved to one side, and at this time, under the operation of the cradle head 6, the thermal imaging camera 7 performs overheat detection work on the equipment in the substation.
The working principle is as follows: the temperature presetting module 861 is used for presetting the maximum and minimum environmental temperatures of the thermal imager 7 during working, when the temperature sensor 83 monitors that the maximum environmental temperature of the thermal imager 7 during working is higher than a preset value, the single chip microcomputer 86 enables the fan 85 to perform heat dissipation work on the thermal imager 7, and when the temperature sensor 83 monitors that the minimum environmental temperature of the thermal imager 7 during working is lower than a preset value, the single chip microcomputer 86 enables the single chip microcomputer 86 to perform temperature control work on the temperature control lamp 84, so that the environmental temperature of the thermal imager 7 during working is recovered to be normal;
further, the servo motor 92 drives the screw 93 to rotate, the screw 93 drives the threaded sleeve 94 to slide in the limiting cylinder 91, the threaded sleeve 94 drives the cover plate 96 at the lower end of the limiting plate 95 to move, so that the cover plate 96 moves to the position of the lens on the thermal imager 7 to cover the lens, and at this time, the thermal imager 7 is dustproof, meanwhile, the motor 97 drives the mounting plate 98 to rotate, the sponge pad 99 on the mounting plate 98 can wipe and clean the lens of the lens, when the thermal imager 7 performs detection, the cover plate 96 returns to the initial position, then the electric push rod 911 drives the adjusting block 910 to move, the adjusting block 910 drives the limiting cylinder 91 to rotate, so that the limiting cylinder 91 drives the threaded sleeve 94 to move, the threaded sleeve 94 drives the cover plate 96 at the lower end of the limiting plate 95 to move, so that the cover plate 96 moves all the way, at this time, under the work of the cradle head 6, the thermal imager 7 can perform overheat detection work on equipment in the transformer substation;
Further, the data acquisition system 51 acquires image data detected by the thermal imager, the data storage module 52 stores the image data acquired by the data acquisition system 51, the image data acquired by the image processing module 53 is preprocessed, high-frequency components in the image are removed through a low-pass filter to achieve a noise reduction effect, then high-frequency component blocking is performed by averaging partial regions of the image to perform secondary filtering processing on the image, then a gray level histogram of the image is changed through a gray level mapping function to ensure that each gray level has the same pixel point, then the display module 54 displays the preprocessed image data, the error correction module 56 corrects the displayed image data, average values of at least five groups of acquired data are taken, a normal working temperature range value of the device is set in the early warning module 55, when the range value detected by the thermal imager exceeds the preset value, the early warning module 55 generates an early warning signal, and then wirelessly transmits the early warning signal to the background terminal 1 to notify background personnel.
And those not described in detail in this specification are well within the skill of those in the art.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Claims (10)
1. The intelligent substation equipment target and defect detection equipment based on deep learning is characterized by comprising a background terminal (1) and a cloud deck (6), wherein a camera terminal is mounted on the cloud deck (6), and the camera terminal comprises a visible light collector and an infrared thermal imager;
the camera terminal also comprises a multi-state quantity sensor, an industrial personal computer and a power supply system, and is used for realizing comprehensive monitoring and automatic analysis of visible light images, infrared thermal imaging, partial discharge, sound, temperature and humidity key state quantities;
the camera terminal uploads the data and the edge calculation analysis result to the access node through Ethernet/WIFI/LoRa or sends the data and the edge calculation analysis result to the cloud platform server in a 4G mobile communication network mode, and related users can remotely monitor through a mobile phone terminal APP or a PC browser and timely master the equipment state and early warning information.
2. The intelligent substation equipment target and defect detection equipment based on deep learning according to claim 1,
the industrial personal computer (5) comprises a data acquisition system (51), a data storage module (52), an image processing module (53), a display module (54), an early warning module (55) and an error correction module (56), wherein the data acquisition system (51) is used for acquiring image data detected by the thermal imager (7), the data storage module (52) is used for storing the image data acquired by the data acquisition system (51), the image processing module (53) is used for preprocessing the acquired image data, the display module (54) is used for displaying the preprocessed image data, the error correction module (56) is used for correcting the displayed image data, a normal working temperature range value of equipment is set in the early warning module (55), and when the detected range value exceeds a preset value, the early warning module (55) generates an early warning signal, then wirelessly transmits the information to a background terminal (1) to inform background personnel.
3. The intelligent substation equipment target and defect detection equipment based on deep learning according to claim 2, characterized in that: when the image processing module (53) preprocesses the image data, high-frequency components in the image are removed through a low-pass filter to achieve the noise reduction effect, then high-frequency component blocking is carried out by taking the average value of partial areas of the image, secondary filtering processing is carried out on the image, then the gray level histogram of the image is changed through a gray level mapping function, the same pixel points of each gray level are guaranteed, and the preprocessing work of the image is completed.
4. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 1, wherein: and when the error correction module (56) corrects the image data, the average value of at least five groups of acquired data is taken.
5. The intelligent substation equipment target and defect detection equipment based on deep learning of claim, characterized in that: still include temperature sensor (83), temperature sensor (83) are used for gathering thermal imaging system operational environment's temperature, transmit the signal of gathering for singlechip (86), singlechip (86) control fan (85) and temperature control lamp (84) work respectively.
6. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 5, characterized in that: the single chip microcomputer (86) comprises a temperature preset module (861), and the temperature preset module (861) is used for setting threshold values of the maximum limit ambient temperature and the minimum limit ambient temperature of the thermal imager during working.
7. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 1, characterized in that: the device also comprises a video image processing module;
the visible light collector collects a device target image; the video image processing module marks defects by using a pre-trained deep learning model, sends the defect information to the display module and sends the defect information to the staff through the wireless module;
the deep learning model training process comprises the following steps:
s101, collecting a defect sample of an equipment target;
s102, manually labeling the defect sample: in order to obtain enough convolutional neural network training samples, the following two methods are adopted:
(1) inputting the defects confirmed by the workers in the actual detection into a deep learning model for repeated training;
(2) increasing a sample set by a random transformation method, wherein the method comprises the steps of whitening a training sample set, randomly turning left and right images and randomly transforming the contrast of the images;
S103, constructing a multilayer convolutional neural network;
and S104, performing steepest descent optimization on the error gradient of the multilayer convolutional neural network, and performing off-line training to construct the multilayer convolutional neural network.
8. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 1, wherein a concave shell (8) is installed on the top of the infrared thermal imager through a bolt, and a lens protection assembly (9) is arranged on the top of the concave shell (8); the inboard horizontally connected of concave shell (8) has baffle (81), a plurality of ventilation hole (82) have evenly been seted up on the surface of baffle (81), the inboard below position department that is located baffle (81) of concave shell (8) installs temperature sensor (83), the inboard top position department horizontally mounted that is located baffle (81) of concave shell (8) has temperature control lamp (84), and installs fan (85) and singlechip (86) respectively at the inboard top of concave shell (8).
9. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 8, wherein: the lens protection assembly (9) comprises a limiting barrel (91), the lower side of the front end of the limiting barrel (91) is rotatably installed at the middle position of the upper side of the concave shell (8), the rear end of the limiting barrel (91) is provided with a servo motor (92), the output shaft of the servo motor (92) is located at the inner position of the limiting barrel (91) and is horizontally connected with a screw rod (93), the outer sleeve of the screw rod (93) is provided with a threaded sleeve (94) matched with the screw rod, the front end of the threaded sleeve (94) is vertically connected with a limiting plate (95), the lower end of the limiting plate (95) is provided with a cover plate (96), the position of the cover plate (96) corresponds to the position of a lens on the thermal imaging instrument (7), a motor (97) is installed at the front side of the cover plate (96), the output shaft of the motor (97) is connected with a mounting plate (98), one side face of the mounting plate (98) is bonded with a sponge pad (99), after the lens on the thermal imaging instrument (7) is covered by the cover plate (96), the sponge cushion (99) is just in contact with the lens of the lens.
10. The intelligent substation equipment target and defect detection equipment based on deep learning of claim 9, wherein: the upper side of the rear end of the limiting cylinder (91) is hinged with an adjusting block (910), an electric push rod (911) is installed on the upper portion of one side of the concave shell (8), and an output shaft of the electric push rod (911) is hinged to one end of the adjusting block (910).
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| CN117347279A (en) * | 2023-12-06 | 2024-01-05 | 歌尔股份有限公司 | Detection mechanism |
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