CN113568405A - 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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Abstract
The invention discloses a network equipment signal lamp vision identification system based on a patrol robot and a method thereof, and the technical scheme is as follows: the intelligent 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 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, and an environmental data collection module is arranged at the connecting end of the RFID positioning module, so that the intelligent inspection robot has the beneficial effects that: carry out information collection to surrounding environment and signal lamp through patrolling and examining the robot, avoid information collection to appear disturbing, and visual identification success rate is high, and it can independently patrol and examine, reduce the manual work and patrol and examine the cost to patrol and examine the robot.
Description
Technical Field
The invention relates to the field of inspection robots, in particular to a network equipment signal lamp visual identification system and a network equipment signal lamp visual identification method based on an inspection robot.
Background
The robot is an intelligent machine capable of working semi-autonomously or fully autonomously, has basic characteristics of perception, decision, execution and the like, can assist or even replace human beings to finish dangerous, heavy and complex work, improves work efficiency and quality, serves human life, enlarges or extends the activity and capacity range of the human beings, is a robot device for automatically executing work, can accept human commands, run pre-arranged programs, and can also perform actions according to principles formulated by artificial intelligence technology, the task of the robot device is to assist or replace the work of the human beings, such as production industry, building industry or dangerous work, nowadays, in some special places, people also use the robot to replace the human beings for inspection, and the robot is called an inspection robot.
The prior art has the following defects: signal lamp among the current traffic system has become the essential safety indicator in traffic driving safety and the traffic hub, the environmental information that present collection image unit can find a view the within range carries out whole collection, the data acquisition is more, make image processing unit disturb more in the data processing process, make recognition efficiency lower, the while discernment success rate is lower, still there is the inconvenient defect of carrying simultaneously, can not be fine satisfy the user demand, and visual identification carries out remote control operation through the manual work, can't independently carry out the environmental data collection.
Therefore, it is necessary to invent a system and a method for identifying signal lights of network equipment based on an inspection robot.
Disclosure of Invention
Therefore, the invention provides a network equipment signal lamp visual identification system based on an inspection robot and a method thereof, which aim to solve the problems that the collected data is more, the interference of an image processing unit in the data processing process is more, the identification efficiency is lower, the identification success rate is lower, the carrying is inconvenient, the user requirements cannot be well met, and the visual identification is realized by manual remote control operation and the environmental data collection cannot be carried out autonomously.
In order to achieve the above purpose, the invention provides the following technical scheme: a network equipment signal lamp visual identification system based on an inspection robot and a method thereof comprise an inspection robot module, the output end of the inspection robot module is provided with a wireless transmission module, the connecting end of the wireless transmission module is provided with a terminal control module, the connecting end of the inspection robot module is provided with a control chip module, the connecting end of the control chip module is respectively provided with a conventional identification module and a self-adaptive identification module, the connecting end of the conventional identification module is respectively provided with a magnetic track navigation module and an RFID positioning module, the connecting end of the RFID positioning module is provided with an environmental data collecting module, the connecting end of the self-adaptive identification module is respectively provided with an inertial navigation module, a map matching module and a sound collecting module, 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 environment data collection module;
the wireless transmission module comprises a wireless transmitting module, a wireless transmitting module connecting end is provided with a safe communication module, a safe communication module connecting end is provided with a wireless receiving module, a signal encryption module is arranged at the input end of the safe communication module, a signal collecting module is arranged at the wireless receiving module connecting end, and an internal network switch module is arranged at the signal collecting module connecting end.
Preferably, the terminal control module connecting end is equipped with information interaction module, the information interaction module input is equipped with the signal lamp module, the signal lamp module input is equipped with signal synchronization module, signal synchronization module input is equipped with 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 establishing module and a path model establishing module, the environment model establishing module and the path model establishing 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 module link of patrolling and examining robot is equipped with from state monitoring module and alarm module respectively, be equipped with speed detection module, energy detection module and the module of independently charging respectively from state monitoring module link.
Preferably, the module link of patrolling and examining robot is equipped with the vision identification module, the vision identification module link is equipped with high definition camera module, wireless sensor orientation module, infrared ray sensor module and laser radar module respectively, high definition camera module, wireless sensor orientation module, infrared ray sensor module and laser radar module's output and environmental data collection module input electric connection.
Preferably, patrol and examine the robot module and including patrolling and examining the robot housing, it is equipped with the safety cover to patrol and examine the inside fixed safety cover that is equipped with of robot housing, the inside motor that is equipped with of safety cover, motor output fixedly connected with third bevel gear, third bevel gear top is equipped with the threaded rod, the threaded rod runs through the safety cover and passes through the bearing with the safety cover and be connected, the threaded rod both ends pass through the bearing with the module lateral wall of patrolling and examining the robot and be connected, the outside fixed cover of threaded rod is equipped with first bevel gear, first bevel gear meshes with third bevel gear mutually.
Preferably, first bevel gear both sides all are equipped with the lead screw, the lead screw runs through the safety cover and passes through the bearing with the safety cover and be connected, the lead screw tip passes through the bearing with the module lateral wall of inspection robot and is connected, lead screw one end fixedly connected with second bevel gear, second bevel gear meshes with first bevel gear mutually.
Preferably, the screw rod and the outside screw rod all overlap and are equipped with the screw thread piece, the screw thread piece passes through threaded connection with screw rod and screw rod respectively, screw thread piece one side fixedly connected with link, link one end fixedly connected with camera probe, camera probe runs through and patrols and examines the robot casing.
Preferably, it all is equipped with the removal wheel to patrol and examine robot module bottom both sides, it is equipped with the baffle to patrol and examine the robot module outside, it passes through pivot swing joint with the baffle to patrol and examine robot module.
Preferably, the specific steps are as follows:
s1, conventional inspection identification: when all the objects to be inspected are conventional inspection objects, the overall path planning of the inspection robot only needs to obtain an optimal inspection path based on a total station environment and an equipment map and a magnetic navigation map, namely, the robot can reach an information acquisition point of the inspection object only by 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 only by means of methods based on magnetic guide rail navigation and RFID positioning, the target to be inspected is a self-adaptive inspection target, in order to ensure the positioning and navigation accuracy and reliability of the substation robot, the global path planning of the substation robot still carries out global inspection path planning based on the magnetic guide rails and the magnetic navigation of the RFID positioning beacons, a proper RFID positioning beacon separation point is selected according to the position of the self-adaptive inspection target, the substation robot is separated from the magnetic guide rails at the point, autonomous positioning and navigation are carried out based on methods such as a visual system, inertial navigation and map matching, and a proper RFID return point is selected after the self-adaptive target inspection task is finished;
s3, terminal control: the inspection robot transmits inspection information to a terminal in a wireless mode, the terminal stores the acquired information, the terminal synchronizes with a signal lamp to enable the terminal to acquire signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and are transmitted to the inspection robot again, the computer algorithm is based on a deep neural network architecture, a method based on parameter pruning and sharing focuses on searching redundant parts in model parameters and tries to remove redundant and unimportant parameters through a deep neural network compression technology based on methods of parameter pruning and sharing, low-rank decomposition, migration, compression convolution filter, knowledge refining and the like, the method based on the low-rank decomposition technology uses matrix and tensor decomposition to estimate parameters with the most information amount in a deep neural network, designing a convolution filter with a special structure based on a migration and compression convolution filter method to reduce the complexity of storage and calculation, and learning a refined model through knowledge refining;
s4, collecting environmental data: the vision recognition system can shoot the environment through patrolling and examining the high definition camera on the robot and make a video recording to environmental information collects, utilize wireless sensor to send real-time position simultaneously, and utilize infrared sensor and radar to survey around, high definition camera can shoot the signal lamp simultaneously, and it can oneself detect self speed to patrol and examine the robot, the energy condition, and control is patrolled and examined the robot and is charged, and carry out sound collection to the surrounding environment, and send environmental information and the signal lamp information in the vision for the terminal.
The invention has the beneficial effects that:
1. according to the invention, a visual identification system can photograph and shoot an environment through a high-definition camera on the inspection robot, collect environment information, send a real-time position by using a wireless sensor, detect the surroundings by using an infrared sensor and a radar, photograph a signal lamp by using the high-definition camera, detect the speed and energy conditions of the inspection robot, control the inspection robot to charge, collect the sound of the surroundings, and send the environment information in vision and the signal lamp information to a terminal for passing;
2. according to the invention, the inspection information is wirelessly transmitted to the terminal by the inspection robot, the terminal stores the obtained information, and meanwhile, the terminal synchronizes with the signal lamp, so that the terminal obtains the signal lamp information, and meanwhile, the terminal processes the inspection information, and meanwhile, the environment model and the path model are created by a computer algorithm and are sent to the inspection robot again, so that 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 the information collection is avoided, the visual identification success rate is high, and the inspection robot can autonomously perform inspection and reduce the manual inspection cost.
Drawings
FIG. 1 is a block diagram of the overall system provided by the present invention;
fig. 2 is a structural diagram of a wireless transmission module system provided by the present invention;
FIG. 3 is a system configuration diagram of a terminal control module according to the present invention;
FIG. 4 is a block diagram of a module system of the inspection robot according to the present invention;
FIG. 5 is a cross-sectional view of a housing of the inspection robot 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: 1 patrol robot module, 2 wireless transmission module, 3 terminal control module, 4 control chip module, 5 conventional identification module, 6 magnetic track navigation module, 7RFID positioning module, 8 environment data collection module, 9 self-adaptive identification module, 10 inertial navigation module, 11 map matching module, 12 sound collection module, 13 signal encryption module, 14 wireless transmission module, 15 safety communication module, 16 wireless receiving module, 17 signal collection module, 18 intranet switch module, 19 information interaction module, 20 data storage module, 21 data processing module, 22 computer algorithm simulation module, 23 environment model creation module, 24 model transmission module, 25 path model creation module, 26 signal lamp module, 27 signal synchronization module, 28 signal lamp processing module, 29 speed detection module, 30 energy detection module, 31 autonomous charging module, The system comprises a self-condition monitoring module 32, an alarm module 33, a visual recognition module 34, a high-definition camera module 35, a wireless sensor positioning module 36, an infrared sensor module 37, a laser radar module 38, a routing inspection robot shell 39, a baffle 40, a connecting frame 41, a camera probe 42, a threaded block 43, a threaded rod 44, a first bevel gear 45, a second bevel gear 46, a protective cover 47, a moving wheel 48, a screw rod 49, a motor 50 and a third bevel gear 51.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
Referring to the attached drawings 1-6, the inspection robot-based network equipment signal lamp visual identification system and method provided by the invention comprises an inspection robot module 1, wherein the output end of the inspection robot module 1 is provided with a wireless transmission module 2, the connecting end of the wireless transmission module 2 is provided with a terminal control module 3, the connecting end of the inspection robot module 1 is provided with a control chip module 4, the connecting end of the control chip module 4 is respectively provided with a conventional identification module 5 and an adaptive identification module 9, the connecting end of the conventional identification module 5 is respectively provided with a magnetic track navigation module 6 and an RFID positioning module 7, the connecting end of the RFID positioning module 7 is provided with an environmental data collection module 8, the connecting end of the adaptive identification module 9 is respectively provided with an inertial navigation module 10, a map matching module 11 and a sound collection module 12, the inertial navigation module 10, the map matching module 11 and the sound collection module 12, The output ends of the map matching module 11 and the sound collecting module 12 are electrically connected with the input end of the environmental data collecting module 8;
Further, the terminal control module 3 connecting end is equipped with information interaction module 19, the information interaction module 19 input is equipped with signal lamp module 26, the signal lamp module 26 input is equipped with signal synchronization module 27, the signal synchronization module 27 input is equipped with signal lamp processing module 28.
Further, the terminal control module 3 link is equipped with data storage module 20 and data processing module 21, the data processing module 21 link is equipped with computer algorithm simulation module 22, computer algorithm simulation module 22 link is equipped with environment model and establishes module 23 and route model and establish module 25 respectively, environment model establishes module 23 and route model and establishes module 25 link and is equipped with model sending module 24, model sending module 24 output and patrolling and examining robot module 1 input electric connection.
Further, the 1 link of robot module patrols and examines is equipped with from state monitoring module 32 and alarm module 33 respectively, be equipped with speed detection module 29, energy detection module 30 and the module 31 of independently charging respectively from state monitoring module 32 link.
Further, it is equipped with vision identification module 34 to patrol and examine robot module 1 link, vision identification module 34 link is equipped with high definition camera module 35, wireless sensor orientation module 36, infrared ray sensor module 37 and laser radar module 38 respectively, high definition camera module 35, wireless sensor orientation module 36, infrared ray sensor module 37 and laser radar module 38's output and the 8 input electric connection of environmental data collection module.
Further, it includes robot housing 39 to patrol and examine robot module 1, it is equipped with safety cover 47 to patrol and examine robot housing 39 inside fixed, safety cover 47 is inside to be equipped with, the inside motor 50 that is equipped with of safety cover 47, motor 50 output end fixedly connected with third bevel gear 51, third bevel gear 51 top is equipped with threaded rod 44, threaded rod 44 runs through safety cover 47 and passes through the bearing with safety cover 47 and be connected, threaded rod 44 both ends with patrol and examine robot module 1 lateral wall and pass through the bearing and be connected, the outside fixed cover of threaded rod 44 is equipped with first bevel gear 45, first bevel gear 45 meshes with third bevel gear 51 mutually.
Further, first bevel gear 45 both sides all are equipped with lead screw 49, lead screw 49 runs through safety cover 47 and passes through the bearing with safety cover 47 and be connected, lead screw 49 tip passes through the bearing with the lateral wall of inspection robot module 1 and is connected, lead screw 49 one end fixedly connected with second bevel gear 46, second bevel gear 46 meshes with first bevel gear 45 mutually.
Further, the screw rod 49 and the outside screw rod 44 all overlap and are equipped with screw block 43, screw block 43 passes through threaded connection with screw rod 44 and screw rod 49 respectively, screw block 43 one side fixedly connected with link 41, link 41 one end fixedly connected with camera probe 42, camera probe 42 runs through and patrols and examines robot housing 39.
Further, it all is equipped with removal wheel 48 to patrol and examine 1 bottom both sides of robot module, it is equipped with baffle 40 to patrol and examine the robot module 1 outside, it passes through pivot swing joint with baffle 40 to patrol and examine robot module 1.
Further, the specific steps are as follows:
s1, conventional inspection identification: when all the objects to be inspected are conventional inspection objects, the overall path planning of the inspection robot only needs to obtain an optimal inspection path based on a total station environment and an equipment map and a magnetic navigation map, namely, the robot can reach an information acquisition point of the inspection object only by 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 only by means of methods based on magnetic guide rail navigation and RFID positioning, the target to be inspected is a self-adaptive inspection target, in order to ensure the positioning and navigation accuracy and reliability of the substation robot, the global path planning of the substation robot still carries out global inspection path planning based on the magnetic guide rails and the magnetic navigation of the RFID positioning beacons, a proper RFID positioning beacon separation point is selected according to the position of the self-adaptive inspection target, the substation robot is separated from the magnetic guide rails at the point, autonomous positioning and navigation are carried out based on methods such as a visual system, inertial navigation and map matching, and a proper RFID return point is selected after the self-adaptive target inspection task is finished;
s3, terminal control: the inspection robot transmits inspection information to a terminal in a wireless mode, the terminal stores the acquired information, the terminal synchronizes with a signal lamp to enable the terminal to acquire signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and are transmitted to the inspection robot again, the computer algorithm is based on a deep neural network architecture, a method based on parameter pruning and sharing focuses on searching redundant parts in model parameters and tries to remove redundant and unimportant parameters through a deep neural network compression technology based on methods of parameter pruning and sharing, low-rank decomposition, migration, compression convolution filter, knowledge refining and the like, the method based on the low-rank decomposition technology uses matrix and tensor decomposition to estimate parameters with the most information amount in a deep neural network, designing a convolution filter with a special structure based on a migration and compression convolution filter method to reduce the complexity of storage and calculation, and learning a refined model through knowledge refining;
s4, collecting environmental data: the vision recognition system can shoot the environment through patrolling and examining the high definition camera on the robot and make a video recording to environmental information collects, utilize wireless sensor to send real-time position simultaneously, and utilize infrared sensor and radar to survey around, high definition camera can shoot the signal lamp simultaneously, and it can oneself detect self speed to patrol and examine the robot, the energy condition, and control is patrolled and examined the robot and is charged, and carry out sound collection to the surrounding environment, and send environmental information and the signal lamp information in the vision for the terminal.
The above description is only a preferred embodiment of the present invention, and any person skilled in the art may modify the present invention or modify it into an equivalent technical solution by using the technical solution described above. Therefore, any simple modifications or equivalent substitutions made in accordance with the technical solution of the present invention are within the scope of the claims of the present invention.
Claims (10)
1. Network equipment signal lamp vision identification system based on robot patrols and examines, including patrolling and examining robot module (1), its characterized in that: the output end of the inspection robot module (1) is provided with a wireless transmission module (2), the connecting end of the wireless transmission module (2) is provided with a terminal control module (3), the connecting end of the inspection robot module (1) is provided with a control chip module (4), the connecting end of the control chip module (4) is respectively provided with a conventional identification module (5) and a self-adaptive identification module (9), the connecting end of the conventional identification module (5) is respectively provided with a magnetic track navigation module (6) and an RFID positioning module (7), an environment data collection module (8) is arranged at the connecting end of the RFID positioning module (7), the connecting 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), 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);
wireless transmission module (2) are including wireless transmitting module (14), wireless transmitting module (14) link is equipped with safety communication module (15), safety communication module (15) link is equipped with wireless receiving module (16), safety communication module (15) input is equipped with signal encryption module (13), wireless receiving module (16) link is equipped with signal collection module (17), signal collection module (17) link is equipped with intranet switch module (18).
2. The inspection robot-based network equipment signal lamp visual identification system according to claim 1, wherein: the terminal control module (3) link is equipped with information interaction module (19), information interaction module (19) input is equipped with signal lamp module (26), signal lamp module (26) input is equipped with signal synchronization module (27), signal synchronization module (27) input is equipped with signal lamp processing module (28).
3. The inspection robot-based network equipment signal lamp visual identification system according to claim 1, wherein: terminal control module (3) link is equipped with data storage module (20) and data processing module (21), data processing module (21) link is equipped with computer algorithm simulation module (22), computer algorithm simulation module (22) link is equipped with environment model respectively and establishes module (23) and route model and establish module (25), environment model establishes module (23) and route model and establishes module (25) link and is equipped with model sending module (24), model sending module (24) output and inspection robot module (1) input electric connection.
4. The inspection robot-based network equipment signal lamp visual identification system according to claim 1, wherein: patrol and examine robot module (1) link and be equipped with respectively from state monitoring module (32) and alarm module (33), be equipped with speed detection module (29), energy detection module (30) and independently charge module (31) respectively from state monitoring module (32) link.
5. The inspection robot-based network equipment signal lamp visual identification system according to claim 4, wherein: patrol and examine robot module (1) link and be equipped with vision identification module (34), vision identification module (34) link is equipped with high definition camera module (35), wireless sensor orientation module (36), infrared ray sensor module (37) and laser radar module (38) respectively, the output and the environmental data collection module (8) input electric connection of high definition camera module (35), wireless sensor orientation module (36), infrared ray sensor module (37) and laser radar module (38).
6. The inspection robot-based network equipment signal lamp visual identification system according to claim 1, wherein: patrol and examine robot module (1) including patrolling and examining robot housing (39), it is equipped with safety cover (47) to patrol and examine robot housing (39) inside fixed, safety cover (47) inside is equipped with, inside motor (50) that is equipped with of safety cover (47), motor (50) output end 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 are connected through the bearing with safety cover (47), threaded rod (44) both ends pass through the bearing with patrolling and examining robot module (1) lateral wall and being connected, 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 network equipment signal lamp visual identification system according to claim 6, wherein: first bevel gear (45) both sides all are equipped with lead screw (49), lead screw (49) run through safety cover (47) and pass through the bearing with safety cover (47) and be connected, lead screw (49) tip passes through the bearing with the lateral wall of patrolling and examining robot module (1) and is connected, lead screw (49) one end fixedly connected with second bevel gear (46), second bevel gear (46) mesh mutually with first bevel gear (45).
8. The inspection robot-based network equipment signal lamp visual identification system according to claim 7, wherein: lead screw (49) and the outside screw rod (44) all overlap and be equipped with screw block (43), screw block (43) pass through threaded connection with screw rod (44) and lead screw (49) respectively, screw block (43) one side fixedly connected with link (41), link (41) one end fixedly connected with camera probe (42), camera probe (42) run through and patrol and examine robot housing (39).
9. The inspection robot-based network equipment signal lamp visual identification system according to claim 6, wherein: patrol and examine robot module (1) bottom both sides and all be equipped with removal wheel (48), it is equipped with baffle (40) to patrol and examine robot module (1) outside, it passes through pivot swing joint with baffle (40) to patrol and examine robot module (1).
10. The inspection robot-based identification method of the network equipment signal lamp vision identification system according to claim 1, characterized in that: the method comprises the following specific steps:
s1, conventional inspection identification: when all the objects to be inspected are conventional inspection objects, the overall path planning of the inspection robot only needs to obtain an optimal inspection path based on a total station environment and an equipment map and a magnetic navigation map, namely, the robot can reach an information acquisition point of the inspection object only by 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 only by means of methods based on magnetic guide rail navigation and RFID positioning, the target to be inspected is a self-adaptive inspection target, in order to ensure the positioning and navigation accuracy and reliability of the substation robot, the global path planning of the substation robot still carries out global inspection path planning based on the magnetic guide rails and the magnetic navigation of the RFID positioning beacons, a proper RFID positioning beacon separation point is selected according to the position of the self-adaptive inspection target, the substation robot is separated from the magnetic guide rails at the point, autonomous positioning and navigation are carried out based on methods such as a visual system, inertial navigation and map matching, and a proper RFID return point is selected after the self-adaptive target inspection task is finished;
s3, terminal control: the inspection robot transmits inspection information to a terminal in a wireless mode, the terminal stores the acquired information, the terminal synchronizes with a signal lamp to enable the terminal to acquire signal lamp information, the terminal processes the inspection information, an environment model and a path model are created through a computer algorithm and are transmitted to the inspection robot again, the computer algorithm is based on a deep neural network architecture, a method based on parameter pruning and sharing focuses on searching redundant parts in model parameters and tries to remove redundant and unimportant parameters through a deep neural network compression technology based on methods of parameter pruning and sharing, low-rank decomposition, migration, compression convolution filter, knowledge refining and the like, the method based on the low-rank decomposition technology uses matrix and tensor decomposition to estimate parameters with the most information amount in a deep neural network, designing a convolution filter with a special structure based on a migration and compression convolution filter method to reduce the complexity of storage and calculation, and learning a refined model through knowledge refining;
s4, collecting environmental data: the vision recognition system can shoot the environment through patrolling and examining the high definition camera on the robot and make a video recording to environmental information collects, utilize wireless sensor to send real-time position simultaneously, and utilize infrared sensor and radar to survey around, high definition camera can shoot the signal lamp simultaneously, and it can oneself detect self speed to patrol and examine the robot, the energy condition, and control is patrolled and examined the robot and is charged, and carry out sound collection to the surrounding environment, and send environmental information and the signal lamp information in the vision for the terminal.
Priority Applications (1)
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