CN113568405B - Network equipment signal lamp visual identification system and method based on inspection robot - Google Patents
Network equipment signal lamp visual identification system and method based on inspection robot Download PDFInfo
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- CN113568405B CN113568405B CN202110799052.XA CN202110799052A CN113568405B CN 113568405 B CN113568405 B CN 113568405B CN 202110799052 A CN202110799052 A CN 202110799052A CN 113568405 B CN113568405 B CN 113568405B
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- 238000007689 inspection Methods 0.000 title claims abstract description 143
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- 230000000007 visual effect Effects 0.000 title claims abstract description 31
- 230000005540 biological transmission Effects 0.000 claims abstract description 25
- 230000007613 environmental effect Effects 0.000 claims abstract description 17
- 238000013480 data collection Methods 0.000 claims abstract description 12
- 238000012545 processing Methods 0.000 claims description 15
- 238000004422 calculation algorithm Methods 0.000 claims description 14
- 230000003044 adaptive effect Effects 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 10
- 238000013528 artificial neural network Methods 0.000 claims description 9
- 238000007906 compression Methods 0.000 claims description 9
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- 238000000354 decomposition reaction Methods 0.000 claims description 9
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- 239000000523 sample Substances 0.000 claims description 7
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Classifications
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
- G05D1/0246—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means using a video camera in combination with image processing means
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0219—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory ensuring the processing of the whole working surface
Abstract
The invention discloses a network equipment signal lamp visual identification system and a method thereof based on a patrol robot, wherein the technical scheme is as follows: the wireless transmission system comprises a patrol robot module, wherein a wireless transmission module is arranged at the output end of the patrol robot module, a terminal control module is arranged at the connecting end of the wireless transmission module, a control chip module is arranged at the connecting end of the patrol robot module, a conventional identification module and a self-adaptive identification module are respectively arranged at the connecting end of the control chip module, a magnetic track navigation module and an RFID positioning module are respectively arranged at the connecting end of the conventional identification module, an environmental data collection module is arranged at the connecting end of the RFID positioning module, and the beneficial effects of the invention are that: information collection is carried out on the surrounding environment and the signal lamp through the inspection robot, interference of information collection is avoided, the visual recognition success rate is high, and the inspection robot can independently inspect and reduce the manual inspection cost.
Description
Technical Field
The invention relates to the field of inspection robots, in particular to a network equipment signal lamp visual identification system and method based on the inspection robot.
Background
The robot is an intelligent machine capable of semi-autonomous or fully autonomous working, has the basic characteristics of sensing, decision making, executing and the like, can assist or even replace human beings to complete dangerous, heavy and complex work, improves the working efficiency and quality, serves human life, expands or extends the activity and capacity range of the human beings, is a robot device capable of automatically executing work, can accept human command, can run a preset program and can act according to the principle schema formulated by an artificial intelligence technology, and has the task of assisting or replacing the work of the human beings, such as the industry, the building industry or dangerous work, and people also use the robot to replace the human beings to carry out inspection in some special places nowadays, and the robot is called an inspection robot.
The prior art has the following defects: the signal lamp in the existing traffic system has become an indispensable safety indication tool in traffic driving safety and traffic junction, the existing acquisition image unit acquires all environmental information in the view finding range, the acquisition data are more, the image processing unit is more in interference in the data processing process, the recognition efficiency is lower, the recognition success rate is lower, meanwhile, the defect of inconvenient carrying exists, the user requirement cannot be well met, and the visual recognition is performed through manual remote control operation and cannot be performed independently.
Therefore, the invention is based on the network equipment signal lamp visual identification system and method of the inspection robot.
Disclosure of Invention
Therefore, the invention provides the network equipment signal lamp visual recognition system and the method based on the inspection robot, which aim to solve the problems that the acquired data are more, the image processing unit is more disturbed in the process of processing the data, the recognition efficiency is lower, the recognition success rate is lower, the carrying inconvenience is caused, the user requirement cannot be well met, and the visual recognition is performed manually and the environment data collection cannot be performed autonomously.
In order to achieve the above object, the present invention provides the following technical solutions: the network equipment signal lamp visual identification system based on the inspection robot comprises an inspection robot module, wherein a wireless transmission module is arranged at the output end of the inspection robot module, a terminal control module is arranged at the connecting end of the wireless transmission module, a control chip module is arranged at the connecting end of the inspection robot module, a conventional identification module and an adaptive identification module are respectively arranged at the connecting end of the control chip module, a magnetic track navigation module and an RFID positioning module are respectively arranged at the connecting end of the conventional identification module, an environmental data collection module is arranged at the connecting end of the RFID positioning module, an inertial navigation module, a map matching module and a sound collection module are respectively arranged at the connecting end of the adaptive identification module, and the output ends of the inertial navigation module, the map matching module and the sound collection module are electrically connected with the input end of the environmental data collection module;
the wireless transmission module comprises a wireless transmission module, a safety communication module is arranged at the connecting end of the wireless transmission module, a wireless receiving module is arranged at the connecting end of the safety communication module, a signal encryption module is arranged at the input end of the safety communication module, a signal collecting module is arranged at the connecting end of the wireless receiving module, and an intranet switch module is arranged at the connecting end of the signal collecting module.
Preferably, the terminal control module connecting end is provided with an information interaction module, the input end of the information interaction module is provided with a signal lamp module, the input end of the signal lamp module is provided with a signal synchronization module, and the input end of the signal synchronization module is provided with a signal lamp processing module.
Preferably, the terminal control module connecting end is provided with a data storage module and a data processing module, the data processing module connecting end is provided with a computer algorithm simulation module, the computer algorithm simulation module connecting end is respectively provided with an environment model creation module and a path model creation module, the environment model creation module and the path model creation module connecting end are provided with a model sending module, and the output end of the model sending module is electrically connected with the input end of the inspection robot module.
Preferably, the inspection robot module connecting end is provided with a self-state monitoring module and an alarm module respectively, and the self-state monitoring module connecting end is provided with a speed detection module, an energy detection module and an autonomous charging module respectively.
Preferably, the inspection robot module connecting end is provided with a visual identification module, the visual identification module connecting end is respectively provided with a high-definition camera module, a wireless sensor positioning module, an infrared sensor module and a laser radar module, and the output ends of the high-definition camera module, the wireless sensor positioning module, the infrared sensor module and the laser radar module are electrically connected with the input end of the environmental data collection module.
Preferably, the inspection robot module comprises an inspection robot shell, a protective cover is fixedly arranged inside the inspection robot shell, a motor is arranged inside the protective cover, a third bevel gear is fixedly connected to the output end of the motor, a threaded rod is arranged at the top of the third bevel gear and penetrates through the protective cover and is connected with the protective cover through a bearing, two ends of the threaded rod are connected with the side wall of the inspection robot module through a bearing, a first bevel gear is fixedly sleeved outside the threaded rod, and the first bevel gear is meshed with the third bevel gear.
Preferably, the two sides of the first bevel gear are respectively provided with a screw rod, the screw rods penetrate through the protective cover and are connected with the protective cover through bearings, the end parts of the screw rods are connected with the side wall of the inspection robot module through bearings, one end of each screw rod is fixedly connected with a second bevel gear, and the second bevel gears are meshed with the first bevel gear.
Preferably, the screw rod and the screw rod are both sleeved with threaded blocks, the threaded blocks are respectively connected with the screw rod and the screw rod through threads, one side of each threaded block is fixedly connected with a connecting frame, one end of each connecting frame is fixedly connected with a camera probe, and the camera probes penetrate through the shell of the inspection robot.
Preferably, the both sides of inspection robot module bottom all are equipped with the removal wheel, inspection robot module outside is equipped with the baffle, inspection robot module passes through pivot swing joint with the baffle.
Preferably, the specific steps are as follows:
s1, conventional inspection identification: when all the targets to be inspected are conventional inspection targets, the overall path planning of the inspection robot only needs to be based on the total station environment and the equipment map, and the magnetic navigation map is used for obtaining the optimal inspection path, so that the robot can reach an information acquisition point of the inspection target only by means of a method based on magnetic guide rail navigation and RFID positioning, and meanwhile, the conventional inspection can directly receive signal lamp information;
s2, self-adaptive inspection identification: when the robot can not reach the information acquisition point of the inspection target by means of the magnetic guide rail navigation and RFID positioning method, the inspection target is an adaptive inspection target, global inspection path planning is still carried out by the substation robot global path planning based on the magnetic guide rail and the magnetic navigation of the RFID positioning beacon in order to ensure the positioning and the navigation accuracy and the reliability of the substation robot, a proper RFID positioning beacon disengaging point is selected according to the position of the adaptive inspection target, the substation robot is disengaged from the magnetic guide rail at the point, autonomous positioning and navigation are carried out by means of a vision system, inertial navigation, map matching and the like, and a proper RFID return point is selected after the adaptive target inspection task is finished;
s3, terminal control: the inspection robot transmits inspection information to the terminal through wireless transmission, the terminal stores the obtained information, the terminal synchronizes with the signal lamp, the terminal obtains the signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and the model is sent to the inspection robot again, the computer algorithm is based on a deep neural network architecture, the method based on parameter pruning and sharing is focused on a redundant part in exploring model parameters through a deep neural network compression technology based on methods such as parameter pruning and sharing, low rank decomposition, migration, compression convolution filter and knowledge refining, and the like, and attempts to remove redundant and unimportant parameters, the method based on the low rank decomposition technology uses a matrix and tensor decomposition to estimate parameters with the most information quantity in the deep neural network, the method based on the migration and compression convolution filter designs a convolution filter with special structure to reduce complexity of storage and calculation, and the knowledge refining and learning refining model;
s4, collecting environment data: the visual recognition system can shoot the environment through a high-definition camera on the inspection robot, collect environmental information, send real-time positions by utilizing a wireless sensor, detect the surroundings by utilizing an infrared sensor and a radar, shoot a signal lamp by utilizing the high-definition camera, detect the speed and the energy condition of the inspection robot by the inspection robot, control the inspection robot to charge, collect sound of the surrounding environment, and send the environmental information and the signal lamp information in the vision to the terminal.
The beneficial effects of the invention are as follows:
1. according to the invention, the environment is photographed and imaged through the high-definition camera on the inspection robot by the visual recognition system, the environment information is collected, the wireless sensor is used for transmitting real-time positions, the infrared sensor and the radar are used for detecting the surroundings, the high-definition camera is used for photographing the signal lamp, the inspection robot can detect the speed and the energy condition of the inspection robot, the inspection robot is controlled to charge, the surrounding environment is collected in sound, and the environment information and the signal lamp information in the vision are transmitted to the terminal;
2. according to the invention, the inspection information is transmitted to the terminal through the inspection robot in a wireless way, the terminal stores the obtained information, the terminal synchronizes with the signal lamp, so that the terminal obtains the signal lamp information, the terminal processes the inspection information, the environment model and the path model are created through the computer algorithm, the model is sent to the inspection robot again, the next path planning of the inspection robot is facilitated, the inspection robot collects the information of the surrounding environment and the signal lamp, the interference of information collection is avoided, the vision recognition success rate is high, and the inspection robot can carry out inspection autonomously, so that the manual inspection cost is reduced.
Drawings
FIG. 1 is a block diagram of an overall system provided by the present invention;
fig. 2 is a system structure diagram of a wireless transmission module provided by the invention;
FIG. 3 is a system configuration diagram of a terminal control module provided by the present invention;
FIG. 4 is a block diagram of a system of inspection robot modules provided by the present invention;
FIG. 5 is a cross-sectional view of a patrol robot housing provided by the present invention;
fig. 6 is a top cross-sectional view of the inspection robot housing provided by the present invention.
In the figure: the system comprises a 1 inspection robot module, a 2 wireless transmission module, a 3 terminal control module, a 4 control chip module, a 5 conventional identification module, a 6 magnetic track navigation module, a 7RFID positioning module, an 8 environment data collection module, a 9 self-adaptive identification module, a 10 inertial navigation module, a 11 map matching module, a 12 sound collection module, a 13 signal encryption module, a 14 wireless transmission module, a 15 safety communication module, a 16 wireless receiving module, a 17 signal collection module, a 18 intranet switch module, a 19 information interaction module, a 20 data storage module, a 21 data processing module, a 22 computer algorithm simulation module, a 23 environment model creation module, a 24 model transmission module, a 25 path model creation module, a 26 signal lamp module, a 27 signal synchronization module, a 28 signal lamp processing module, a 29 speed detection module, a 30 energy detection module, a 31 self-adaptive charging module, a 32 self-state monitoring module, a 33 alarm module, a 34 visual identification module, a 35 high-definition camera module, a 36 wireless sensor positioning module, a 37 infrared sensor module, a 38 laser radar module, a 39 inspection robot shell, a 40 baffle, a 41 connection frame, a 42 camera probe, a 43 screw thread protection cover, a 44, a 45 motor, a 45 first bevel gear, a 46, a second bevel gear, a 49, a third bevel gear and a 51.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Referring to fig. 1-6, the network equipment signal lamp visual identification system based on the inspection robot and the method thereof provided by the invention comprise an inspection robot module 1, wherein the output end of the inspection robot module 1 is provided with a wireless transmission module 2, the connection end of the wireless transmission module 2 is provided with a terminal control module 3, the connection end of the inspection robot module 1 is provided with a control chip module 4, the connection end of the control chip module 4 is respectively provided with a conventional identification module 5 and a self-adaptive identification module 9, the connection end of the conventional identification module 5 is respectively provided with a magnetic track navigation module 6 and an RFID positioning module 7, the connection end of the RFID positioning module 7 is provided with an environmental data collection module 8, the connection end of the self-adaptive identification module 9 is respectively provided with an inertial navigation module 10, a map matching module 11 and a sound collection module 12, and the output ends of the inertial navigation module 10, the map matching module 11 and the sound collection module 12 are electrically connected with the input end of the environmental data collection module 8;
the wireless transmission module 2 comprises a wireless transmission module 14, a safety communication module 15 is arranged at the connecting end of the wireless transmission module 14, a wireless receiving module 16 is arranged at the connecting end of the safety communication module 15, a signal encryption module 13 is arranged at the input end of the safety communication module 15, a signal collecting module 17 is arranged at the connecting end of the wireless receiving module 16, and an intranet switch module 18 is arranged at the connecting end of the signal collecting module 17.
Further, the connection end of the terminal control module 3 is provided with an information interaction module 19, the input end of the information interaction module 19 is provided with a signal lamp module 26, the input end of the signal lamp module 26 is provided with a signal synchronization module 27, and the input end of the signal synchronization module 27 is provided with a signal lamp processing module 28.
Further, a data storage module 20 and a data processing module 21 are arranged at the connecting end of the terminal control module 3, a computer algorithm simulation module 22 is arranged at the connecting end of the data processing module 21, an environment model creation module 23 and a path model creation module 25 are respectively arranged at the connecting end of the computer algorithm simulation module 22, a model sending module 24 is arranged at the connecting end of the environment model creation module 23 and the path model creation module 25, and the output end of the model sending module 24 is electrically connected with the input end of the inspection robot module 1.
Further, the connection end of the inspection robot module 1 is respectively provided with a self-state monitoring module 32 and an alarm module 33, and the connection end of the self-state monitoring module 32 is respectively provided with a speed detection module 29, an energy detection module 30 and an autonomous charging module 31.
Further, the connection end of the inspection robot module 1 is provided with a visual identification module 34, the connection end of the visual identification module 34 is respectively provided with a high-definition camera module 35, a wireless sensor positioning module 36, an infrared sensor module 37 and a laser radar module 38, and the output ends of the high-definition camera module 35, the wireless sensor positioning module 36, the infrared sensor module 37 and the laser radar module 38 are electrically connected with the input end of the environmental data collection module 8.
Further, the inspection robot module 1 comprises an inspection robot shell 39, a protection cover 47 is fixedly arranged inside the inspection robot shell 39, a motor 50 is arranged inside the protection cover 47, a third bevel gear 51 is fixedly connected to the output end of the motor 50, a threaded rod 44 is arranged at the top of the third bevel gear 51, the threaded rod 44 penetrates through the protection cover 47 and is connected with the protection cover 47 through a bearing, two ends of the threaded rod 44 are connected with the side wall of the inspection robot module 1 through bearings, a first bevel gear 45 is fixedly sleeved outside the threaded rod 44, and the first bevel gear 45 is meshed with the third bevel gear 51.
Further, two sides of the first bevel gear 45 are respectively provided with a screw rod 49, the screw rods 49 penetrate through the protective cover 47 and are connected with the protective cover 47 through bearings, the end parts of the screw rods 49 are connected with the side wall of the inspection robot module 1 through bearings, one end of each screw rod 49 is fixedly connected with a second bevel gear 46, and the second bevel gears 46 are meshed with the first bevel gear 45.
Further, the screw rod 49 and the screw rod 44 are both sleeved with a thread block 43, the thread block 43 is respectively connected with the screw rod 44 and the screw rod 49 through threads, one side of the thread block 43 is fixedly connected with a connecting frame 41, one end of the connecting frame 41 is fixedly connected with a camera probe 42, and the camera probe 42 penetrates through the inspection robot shell 39.
Further, moving wheels 48 are respectively arranged on two sides of the bottom of the inspection robot module 1, a baffle 40 is arranged on the outer side of the inspection robot module 1, and the inspection robot module 1 is movably connected with the baffle 40 through a rotating shaft.
Further, the specific steps are as follows:
s1, conventional inspection identification: when all the targets to be inspected are conventional inspection targets, the overall path planning of the inspection robot only needs to be based on the total station environment and the equipment map, and the magnetic navigation map is used for obtaining the optimal inspection path, so that the robot can reach an information acquisition point of the inspection target only by means of a method based on magnetic guide rail navigation and RFID positioning, and meanwhile, the conventional inspection can directly receive signal lamp information;
s2, self-adaptive inspection identification: when the robot can not reach the information acquisition point of the inspection target by means of the magnetic guide rail navigation and RFID positioning method, the inspection target is an adaptive inspection target, global inspection path planning is still carried out by the substation robot global path planning based on the magnetic guide rail and the magnetic navigation of the RFID positioning beacon in order to ensure the positioning and the navigation accuracy and the reliability of the substation robot, a proper RFID positioning beacon disengaging point is selected according to the position of the adaptive inspection target, the substation robot is disengaged from the magnetic guide rail at the point, autonomous positioning and navigation are carried out by means of a vision system, inertial navigation, map matching and the like, and a proper RFID return point is selected after the adaptive target inspection task is finished;
s3, terminal control: the inspection robot transmits inspection information to the terminal through wireless transmission, the terminal stores the obtained information, the terminal synchronizes with the signal lamp, the terminal obtains the signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and the model is sent to the inspection robot again, the computer algorithm is based on a deep neural network architecture, the method based on parameter pruning and sharing is focused on a redundant part in exploring model parameters through a deep neural network compression technology based on methods such as parameter pruning and sharing, low rank decomposition, migration, compression convolution filter and knowledge refining, and the like, and attempts to remove redundant and unimportant parameters, the method based on the low rank decomposition technology uses a matrix and tensor decomposition to estimate parameters with the most information quantity in the deep neural network, the method based on the migration and compression convolution filter designs a convolution filter with special structure to reduce complexity of storage and calculation, and the knowledge refining and learning refining model;
s4, collecting environment data: the visual recognition system can shoot the environment through a high-definition camera on the inspection robot, collect environmental information, send real-time positions by utilizing a wireless sensor, detect the surroundings by utilizing an infrared sensor and a radar, shoot a signal lamp by utilizing the high-definition camera, detect the speed and the energy condition of the inspection robot by the inspection robot, control the inspection robot to charge, collect sound of the surrounding environment, and send the environmental information and the signal lamp information in the vision to the terminal.
The above description is of the preferred embodiments of the present invention, and any person skilled in the art may modify the present invention or make modifications to the present invention with the technical solutions described above. Therefore, any simple modification or equivalent made according to the technical solution of the present invention falls within the scope of the protection claimed by the present invention.
Claims (9)
1. A network equipment signal lamp visual identification method based on a patrol robot is characterized in that: the network equipment signal lamp visual identification system comprises a patrol robot module (1), wherein a wireless transmission module (2) is arranged at the output end of the patrol robot module (1), a terminal control module (3) is arranged at the connecting end of the wireless transmission module (2), a control chip module (4) is arranged at the connecting end of the patrol robot module (1), a conventional identification module (5) and an adaptive identification module (9) are respectively arranged at the connecting end of the control chip module (4), a magnetic track navigation module (6) and an RFID positioning module (7) are respectively arranged at the connecting end of the conventional identification module (5), an environmental data collection module (8) is arranged at the connecting end of the RFID positioning module (7), an inertial navigation module (10), a map matching module (11) and a sound collection module (12) are respectively arranged at the connecting end of the adaptive identification module (9), and the output ends of the inertial navigation module (10), the map matching module (11) and the sound collection module (12) are electrically connected with the input end of the environmental data collection module (8);
the wireless transmission module (2) comprises a wireless transmission module (14), a safety communication module (15) is arranged at the connecting end of the wireless transmission module (14), a wireless receiving module (16) is arranged at the connecting end of the safety communication module (15), a signal encryption module (13) is arranged at the input end of the safety communication module (15), a signal collection module (17) is arranged at the connecting end of the wireless receiving module (16), and an intranet switch module (18) is arranged at the connecting end of the signal collection module (17);
the visual recognition method of the network equipment signal lamp comprises the following steps:
s1, conventional inspection identification: when all the targets to be inspected are conventional inspection targets, the overall path planning of the inspection robot only needs to be based on the total station environment and the equipment map, and the magnetic navigation map is used for obtaining the optimal inspection path, so that the robot can reach an information acquisition point of the inspection target only by means of a method based on magnetic guide rail navigation and RFID positioning, and meanwhile, the conventional inspection can directly receive signal lamp information;
s2, self-adaptive inspection identification: when the robot can not reach the information acquisition point of the inspection target by means of the magnetic guide rail navigation and RFID positioning method, the inspection target is an adaptive inspection target, global inspection path planning is still carried out by the substation robot global path planning based on the magnetic guide rail and the magnetic navigation of the RFID positioning beacon in order to ensure the positioning and the navigation accuracy and the reliability of the substation robot, a proper RFID positioning beacon disengaging point is selected according to the position of the adaptive inspection target, the substation robot is disengaged from the magnetic guide rail at the point, autonomous positioning and navigation are carried out by means of a vision system, inertial navigation and a map matching method, and a proper RFID return point is selected after the inspection task of the adaptive target is finished;
s3, terminal control: the inspection robot transmits inspection information to the terminal through wireless transmission, the terminal stores the obtained information, the terminal synchronizes with the signal lamp, the terminal obtains the signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and the model is sent to the inspection robot again, the computer algorithm is based on a deep neural network architecture, the method based on parameter pruning and sharing is focused on a redundant part in exploring model parameters and tries to remove redundant and unimportant parameters through a deep neural network compression technology based on parameter pruning and sharing, low rank decomposition, migration, compression convolution filter and knowledge refining methods, the method based on the low rank decomposition technology uses a matrix and tensor decomposition to estimate parameters with the most information quantity in the deep neural network, the method based on migration and compression convolution filter designs a convolution filter with special structure to reduce complexity of storage and calculation, and the knowledge refining and learning the refining model;
s4, collecting environment data: the visual recognition system can shoot the environment through a high-definition camera on the inspection robot, collect environmental information, send real-time positions by utilizing a wireless sensor, detect the surroundings by utilizing an infrared sensor and a radar, shoot a signal lamp by utilizing the high-definition camera, detect the speed and the energy condition of the inspection robot by the inspection robot, control the inspection robot to charge, collect sound of the surrounding environment, and send the environmental information and the signal lamp information in the vision to the terminal.
2. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 1, wherein the method comprises the following steps of: the terminal control module (3) is characterized in that an information interaction module (19) is arranged at the connecting end of the terminal control module (3), a signal lamp module (26) is arranged at the input end of the information interaction module (19), a signal synchronization module (27) is arranged at the input end of the signal lamp module (26), and a signal lamp processing module (28) is arranged at the input end of the signal synchronization module (27).
3. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 1, wherein the method comprises the following steps of: the intelligent inspection robot comprises a terminal control module (3), wherein a data storage module (20) and a data processing module (21) are arranged at the connecting end of the terminal control module (3), a computer algorithm simulation module (22) is arranged at the connecting end of the data processing module (21), an environment model creation module (23) and a path model creation module (25) are respectively arranged at the connecting end of the computer algorithm simulation module (22), a model sending module (24) is arranged at the connecting end of the environment model creation module (23) and the path model creation module (25), and the output end of the model sending module (24) is electrically connected with the input end of the inspection robot module (1).
4. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 1, wherein the method comprises the following steps of: the inspection robot module (1) is characterized in that a self-state monitoring module (32) and an alarm module (33) are respectively arranged at the connecting end of the inspection robot module (1), and a speed detection module (29), an energy detection module (30) and an autonomous charging module (31) are respectively arranged at the connecting end of the self-state monitoring module (32).
5. The inspection robot-based network equipment signal lamp visual identification method according to claim 4 is characterized in that: the inspection robot module (1) link is equipped with vision recognition module (34), vision recognition module (34) link is equipped with high definition digtal camera module (35), wireless sensor positioning module (36), infrared sensor module (37) and laser radar module (38) respectively, the output and the environmental data collection module (8) input electric connection of high definition digtal camera module (35), wireless sensor positioning module (36), infrared sensor module (37) and laser radar module (38).
6. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 1, wherein the method comprises the following steps of: the utility model provides a patrol robot module (1) is including patrol robot casing (39), patrol robot casing (39) inside is fixed to be equipped with safety cover (47), inside being equipped with of safety cover (47), inside motor (50) that are equipped with of safety cover (47), motor (50) output fixedly connected with third bevel gear (51), third bevel gear (51) top is equipped with threaded rod (44), threaded rod (44) run through safety cover (47) and pass through bearing connection with safety cover (47), threaded rod (44) both ends are connected through bearing with patrol robot module (1) lateral wall, threaded rod (44) outside fixed cover is equipped with first bevel gear (45), first bevel gear (45) mesh with third bevel gear (51).
7. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 6, wherein the method comprises the following steps of: the utility model discloses a robot inspection device, including first bevel gear (45), safety cover (47), lead screw (49), protection cover (47) are run through to lead screw (49), lead screw (49) both sides all are equipped with lead screw (49), lead screw (49) run through safety cover (47) and are connected through the bearing with safety cover (47), lead screw (49) tip and inspection robot module (1) lateral wall are connected through the bearing, lead screw (49) one end fixedly connected with second bevel gear (46), second bevel gear (46) meshes with first bevel gear (45).
8. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 7, wherein the method comprises the following steps of: screw rod (49) and threaded rod (44) outside all overlap and are equipped with screw thread piece (43), screw thread piece (43) are respectively with threaded rod (44) and screw rod (49) through threaded connection, screw thread piece (43) one side fixedly connected with link (41), link (41) one end fixedly connected with probe (42) of making a video recording, probe (42) of making a video recording runs through inspection robot casing (39).
9. The inspection robot-based visual recognition method for network equipment signal lamps according to claim 6, wherein the method comprises the following steps of: the inspection robot module (1) is characterized in that moving wheels (48) are arranged on two sides of the bottom of the inspection robot module (1), a baffle (40) is arranged on the outer side of the inspection robot module (1), and the inspection robot module (1) is movably connected with the baffle (40) through a rotating shaft.
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