CN116405644A - Remote control system and method for computer network equipment - Google Patents

Remote control system and method for computer network equipment Download PDF

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CN116405644A
CN116405644A CN202310628726.9A CN202310628726A CN116405644A CN 116405644 A CN116405644 A CN 116405644A CN 202310628726 A CN202310628726 A CN 202310628726A CN 116405644 A CN116405644 A CN 116405644A
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fishing
path
optimal
pasture
sea cucumber
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CN116405644B (en
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陆燕
杨秋芬
邝允新
焦鹏翔
胡赐元
李卓
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Hunan Open University Hunan Network Engineering Vocational College Hunan Cadre Education And Training Network College
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
    • A01K80/00—Harvesting oysters, mussels, sponges or the like
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
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    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V10/00—Arrangements for image or video recognition or understanding
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    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/05—Underwater scenes
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
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    • H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
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Abstract

The invention relates to the technical field of remote control analysis of network equipment, and particularly discloses a remote control system and a remote control method of computer network equipment.

Description

Remote control system and method for computer network equipment
Technical Field
The invention belongs to the technical field of equipment control analysis, and relates to a computer network equipment remote control system and a computer network equipment remote control method.
Background
Sea cucumbers have the effects of improving memory, resisting tumors and delaying gonad aging, are popular with masses, are caught one by one through artificial launching at present, have very bad working environments for catching sea cucumbers in the artificial launching, and seriously threaten the life safety of the catching staff, so that the importance of controlling the sea cucumber catching robot to catch sea cucumbers in the launching is highlighted.
At present, the control of sea cucumber fishing robots in the sea mainly controls path planning, and has certain disadvantages, and obviously, the control and management of the sea cucumber fishing robots at present have the following disadvantages: 1. at present, no accurate analysis is performed on the identification of the underwater object, the underwater environment is poor, the brightness is low, the red light attenuation is rapid, and the color cast is serious, so that the identification degree and the visible range of the underwater environment are low, the difficulty of identifying sea cucumbers and capturing is increased, the precision rate of capturing sea cucumbers is reduced to a certain extent, the capturing amount of sea cucumbers is reduced, the false capturing of non-target fishes cannot be avoided, and the influence on the marine ecological environment is not reduced.
2. At present, gray detection is not carried out on the underwater object image, and then the underwater object information cannot be accurately obtained, the development of the intelligent fishing industry cannot be promoted and realized, the autonomous use capacity of the sea cucumber fishing robot cannot be improved, and further the operation cannot be guaranteed to be more efficient, accurate and controllable.
3. At present, no accurate underwater object analysis is performed on the sea cucumber fishing robot, the intelligent level of the sea cucumber fishing robot is reduced to a certain extent, reliable data support cannot be provided for fishing of the sea cucumber fishing robot, and certain potential safety hazards exist.
Disclosure of Invention
In view of the above problems in the prior art, the present invention provides a remote control system and method for a computer network device, which are used for solving the above technical problems.
In order to achieve the above and other objects, the present invention adopts the following technical scheme: the invention provides a remote control system of computer network equipment, which comprises a fishing information acquisition module, a fishing cable control module, a fishing path generation module, a fishing path screening module, a fishing path monitoring module, a fishing path analysis module, a remote control terminal and an underwater database.
The fishing information acquisition module is used for acquiring the fishing information corresponding to the target fishing ship from the underwater database;
the fishing cable control module is used for accurately controlling the cable length corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship;
the fishing path generation module is used for extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and further generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture;
the fishing path screening module is used for obtaining actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing to obtain the fishing fault tolerance of the fishing paths corresponding to the sea pasture, and screening to obtain the optimal fishing path corresponding to the sea cucumber fishing robots according to the fishing fault tolerance;
the fishing path monitoring module is used for carrying out video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot;
the fishing path analysis module is used for analyzing each object in the optimal fishing path according to the monitoring video corresponding to the suspected object in the optimal fishing path and performing corresponding control;
the remote control terminal is used for accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot;
the underwater database is used for storing fishing information corresponding to a target fishing ship, reference names corresponding to each model diagram and reference RGB difference deviation coefficients, and is also used for storing pixel standard chromaticity values corresponding to each unitary gray entropy value, pixel standard gray levels corresponding to each gray level and brightness intervals corresponding to each gray level.
As a further improvement of the invention, the fishing information corresponding to the target fishing vessel comprises the area, the shape and the deepest sea depth of the region corresponding to the marine pasture, and further comprises basic parameters corresponding to the sea cucumber fishing robot, wherein the basic parameters comprise arm length, length and width.
As a further improvement of the invention, the cable length corresponding to the target fishing vessel is precisely controlled, and the specific control process is as follows: according to the deepest sea depth corresponding to the ocean pasture and the arm length corresponding to the sea cucumber catching robot, a calculation formula is utilized
Figure SMS_1
Calculating the cable length corresponding to the target fishing vessel>
Figure SMS_2
Wherein L is the arm length corresponding to the sea cucumber capturing robot, and +.>
Figure SMS_3
Expressed as the deepest sea depth corresponding to the marine ranch.
And feeding the cable length corresponding to the target fishing vessel back to the remote control terminal, so as to accurately control the cable length of the target fishing vessel.
As a further improvement of the invention, each fishing path corresponding to the sea cucumber fishing robot is generated, and the generation process is as follows: w1, extracting the shape of an area corresponding to the ocean pasture from the fishing information corresponding to the target fishing ship, substituting the shape into the model diagram, and further obtaining the boundary information corresponding to the ocean pasture, wherein the boundary information comprises the number of the boundary.
W2, selecting a sea pasture reference boundary line from sea pasture boundaries, taking the arm length of the sea cucumber fishing robot as a path distance, and making straight lines parallel to the sea pasture reference boundary line, so as to intercept straight lines parallel to the sea pasture reference boundary line and intersecting points of sea pasture contour boundary lines as endpoints, taking the straight lines as reference path straight lines, so as to obtain reference path straight lines corresponding to the sea pasture, and taking the reference path straight lines as fishing paths corresponding to the sea pasture reference boundary line.
And W3, analyzing the fishing paths corresponding to the side lines of the ocean pasture in a similar way according to the analysis mode of the fishing paths corresponding to the reference side lines of the ocean pasture, and marking the fishing paths corresponding to the side lines of the ocean pasture as the fishing paths corresponding to the ocean pasture.
As a further improvement of the invention, the screening method for the optimal fishing path corresponding to the sea cucumber fishing robot comprises the following specific screening process: q1, according to each fishing path of the marine pasture, obtaining the actual fishing area of each fishing path of the marine pasture, according to the basic parameters corresponding to the sea cucumber fishing robot, calculating the fishing movement area corresponding to the sea cucumber fishing robot by using a calculation formula, and recording the fishing movement area as
Figure SMS_4
Further utilize the calculation formula
Figure SMS_5
Calculating the fishing fault tolerance corresponding to each fishing path of the marine pasture>
Figure SMS_6
P is denoted by the number corresponding to each fishing path,/for each fishing path>
Figure SMS_7
，/>
Figure SMS_8
The actual fishing area of the p-th fishing path corresponding to the ocean pasture is expressed, a1 and a2 are respectively expressed as set fishing repetition areas and influence factors corresponding to non-fishing areas, and S is expressed as the area of the region corresponding to the ocean pasture.
Q2, arranging the fishing fault-tolerance rates corresponding to the fishing paths corresponding to the marine pastures in sequence from large to small, and further screening out the fishing paths with the first fishing fault-tolerance rate arrangement from the fishing fault-tolerance rates as the best fishing paths corresponding to the sea cucumber fishing robot.
As a further improvement of the invention, the objects in the optimal fishing path are analyzed, and the specific analysis process is as follows: and E1, extracting a monitoring image corresponding to the optimal fishing path from a monitoring video corresponding to each fishing path in the marine pasture, obtaining a monitoring image corresponding to each object in the optimal fishing path according to the monitoring image corresponding to the optimal fishing path, and performing filtering processing on the monitoring image corresponding to each object in the optimal fishing path to obtain pixel arrangement information corresponding to each line of each object in the optimal fishing path, wherein the pixel arrangement information comprises the pixel number of each line and the RGB value of each pixel in each line.
E2, calculating RGB values of the objects in the optimal capturing path corresponding to the pixel points in each row by using a calculation formula to obtain RGB difference values of the objects in the optimal capturing path corresponding to the pixel points in each row, and marking the RGB difference values as
Figure SMS_9
Wherein j is represented by the number corresponding to each object, < >>
Figure SMS_10
K is the number corresponding to each pixel point,>
Figure SMS_11
w is denoted by the number corresponding to each row, ">
Figure SMS_12
Further utilize the formula->
Figure SMS_13
Calculating the difference deviation coefficient of RGB of each pixel point of each object corresponding to each row in the optimal capturing path>
Figure SMS_14
，/>
Figure SMS_15
And comparing the RGB difference deviation coefficient of each pixel point corresponding to each line of each object in the optimal capturing path with the reference RGB difference deviation coefficient stored in the underwater database, if the RGB difference deviation coefficient of a certain suspected object corresponding to a certain line in a certain target path is larger than the reference RGB difference deviation coefficient, extracting the number of the pixel point corresponding to the suspected object in the optimal capturing path, and recording the number as the contour pixel point of the suspected object in the optimal capturing path, wherein each contour pixel point corresponding to each object in the optimal capturing path can be obtained according to the data processing mode, and therefore the contour corresponding to each object in the optimal capturing path is obtained.
And E3, carrying out grey-scale treatment on each monitoring image corresponding to the optimal capturing path, further obtaining grey-scale images corresponding to each object in the optimal capturing path, carrying out statistics to obtain brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path, comparing the brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path with brightness intervals corresponding to each grey level stored in the underwater database, and judging that the pixel point corresponding to a suspected object in the optimal capturing path is the grey level if the brightness value corresponding to a certain pixel point corresponding to a certain suspected object in the optimal capturing path is within the brightness interval corresponding to a certain grey level, thereby obtaining the grey level of each pixel point corresponding to each object in the optimal capturing path.
E4, screening the total number of pixel points corresponding to each object in the optimal catching path, and recording the total number as
Figure SMS_16
Wherein, according to the analytical formula->
Figure SMS_17
Calculating to obtain unitary gray level entropy value of each object corresponding to each pixel point in the optimal capturing path>
Figure SMS_18
，/>
Figure SMS_19
The number of the f pixel points corresponding to the jth suspected object in the optimal capturing path is represented, f is represented as the number corresponding to each pixel point, and +.>
Figure SMS_20
。
And E5, comparing the unitary gray entropy value of each object corresponding to each pixel point in the optimal fishing path with the standard color value of each pixel point corresponding to the unitary gray entropy value stored in the underwater database, further obtaining the standard color value of each object corresponding to each pixel point in the optimal fishing path, and comparing the gray level of each object corresponding to each pixel point in the optimal fishing path with the standard gray level of each pixel point corresponding to the color level stored in the underwater database, further obtaining the standard color level of each object corresponding to each pixel point in the optimal fishing path.
And E6, importing the outline corresponding to each object in the optimal fishing path, the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path into a background terminal platform, and filling the outline corresponding to each object in the optimal fishing path according to the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path, so as to obtain a reference model diagram corresponding to each object in the optimal fishing path.
As a further improvement of the present invention, the analyzing each object in the optimal fishing path, the specific analyzing process further includes: and comparing the reference model pictures corresponding to the objects in the optimal fishing path with the reference names corresponding to the model pictures stored in the underwater database to obtain the reference names corresponding to the objects in the optimal fishing path, and feeding back the reference names corresponding to the objects in the optimal fishing path to the remote control terminal.
If the reference name corresponding to a certain object in the optimal fishing path is sea cucumber, the sea cucumber fishing robot is controlled to catch, otherwise, the sea cucumber fishing robot is controlled to accurately avoid the obstacle.
A second aspect of the present invention provides a method for remotely controlling a computer network device, the method comprising the steps of: step one, acquiring fishing information, namely acquiring the fishing information corresponding to the target fishing ship from an underwater database.
And secondly, fishing cable control, namely accurately controlling the cable length corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship.
Step three, generating fishing paths, namely extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture.
And step four, screening the fishing paths, namely obtaining the actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing to obtain the fishing fault tolerance of the fishing paths corresponding to the sea pasture, and screening to obtain the optimal fishing path corresponding to the sea cucumber fishing robots according to the fishing fault tolerance.
Fifthly, monitoring the fishing path, and further performing video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot.
And step six, analyzing the fishing path, and further analyzing and correspondingly controlling each object in the optimal fishing path according to the monitoring video corresponding to the suspected object in the optimal fishing path.
And seventhly, accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot by the remote control terminal.
As described above, the system and method for remotely controlling computer network equipment provided by the invention have at least the following beneficial effects: (1) According to the remote control system and the remote control method for the computer network equipment, the fishing information corresponding to the target fishing ship is used for further generating the fishing path corresponding to the sea cucumber fishing robot, the optimal fishing path is screened out, further, the monitoring image in the optimal fishing path is accurately processed, the result is fed back to the background control terminal, further, remote control is achieved, the problem that the underwater recognition degree of the sea cucumber fishing robot is low in the prior art is effectively solved, the underwater object is accurately recognized and analyzed, the recognition degree of the underwater environment of the fishing robot is improved, the sea cucumber fishing difficulty is reduced to a certain extent, the sea cucumber fishing precision is improved, meanwhile, the false fishing of non-target fishes is effectively avoided, and the influence on the marine ecological environment is greatly reduced.
(2) According to the embodiment of the invention, the accuracy of the underwater object information is improved by carrying out grey detection on the underwater object image, so that the development of the intelligent fishing industry is greatly promoted, the autonomous use capacity of the sea cucumber fishing robot is improved, and the sea cucumber fishing operation is ensured to be more efficient, accurate and controllable.
(3) According to the embodiment of the invention, the underwater object accurate analysis is carried out on the sea cucumber fishing robot, so that the intelligent level of the sea cucumber fishing robot is improved to a certain extent, reliable data support can be effectively provided for fishing of the sea cucumber fishing robot, the occurrence of potential safety hazards is avoided, and meanwhile, the service life and the service time of the sea cucumber fishing robot are also improved to a certain extent.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the connection of the modules of the system of the present invention.
FIG. 2 is a schematic diagram showing the connection of the steps of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a remote control system for computer network equipment includes a fishing information acquisition module, a fishing cable control module, a fishing path generation module, a fishing path screening module, a fishing path monitoring module, a fishing path analysis module, a remote control terminal, and an underwater database.
The remote control terminal is connected with the fishing cable control module and the fishing path analysis module, the fishing information acquisition module is connected with the fishing cable control module and the fishing path generation module, the fishing path monitoring module is connected with the fishing path screening module and the fishing path analysis module, and the underwater database is connected with the fishing information acquisition module and the fishing path analysis module.
The fishing information acquisition module is used for acquiring the fishing information corresponding to the target fishing ship from the underwater database.
It should be further noted that the fishing information corresponding to the target fishing vessel includes an area, an area shape and a deepest sea depth corresponding to the marine pasture, and further includes basic parameters corresponding to the sea cucumber fishing robot, where the basic parameters include arm length, length and width.
The fishing cable control module is used for accurately controlling the length of the cable corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship.
It should be further noted that, the precise control is performed on the cable length corresponding to the target fishing vessel, and the specific control process is as follows: according to the deepest sea depth corresponding to the ocean pasture and the arm length corresponding to the sea cucumber catching robot, a calculation formula is utilized
Figure SMS_21
Calculating the cable length corresponding to the target fishing vessel>
Figure SMS_22
Wherein L is the arm length corresponding to the sea cucumber capturing robot, and +.>
Figure SMS_23
Expressed as the deepest sea depth corresponding to the marine ranch.
And feeding the cable length corresponding to the target fishing vessel back to the remote control terminal, so as to accurately control the cable length of the target fishing vessel.
The fishing path generation module is used for extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and further generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture.
It should be further noted that, the generating of each fishing path corresponding to the sea cucumber fishing robot includes the following steps: w1, extracting the shape of an area corresponding to the ocean pasture from the fishing information corresponding to the target fishing ship, substituting the shape into the model diagram, and further obtaining the boundary information corresponding to the ocean pasture, wherein the boundary information comprises the number of the boundary.
W2, selecting a sea pasture reference boundary line from sea pasture boundaries, taking the arm length of the sea cucumber fishing robot as a path distance, and making straight lines parallel to the sea pasture reference boundary line, so as to intercept straight lines parallel to the sea pasture reference boundary line and intersecting points of sea pasture contour boundary lines as endpoints, taking the straight lines as reference path straight lines, so as to obtain reference path straight lines corresponding to the sea pasture, and taking the reference path straight lines as fishing paths corresponding to the sea pasture reference boundary line.
And W3, analyzing the fishing paths corresponding to the side lines of the ocean pasture in a similar way according to the analysis mode of the fishing paths corresponding to the reference side lines of the ocean pasture, and marking the fishing paths corresponding to the side lines of the ocean pasture as the fishing paths corresponding to the ocean pasture.
The fishing path screening module is used for obtaining actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing and obtaining fishing fault tolerance rates of the fishing paths corresponding to the sea pasture, and screening and obtaining the optimal fishing paths corresponding to the sea cucumber fishing robots according to the fishing fault tolerance rates.
It should be further noted that, the screening sea cucumber fishing robot corresponds to an optimal fishing path, and the specific screening process is as follows: q1, according to each fishing path of the marine pasture, obtaining the actual fishing area of each fishing path of the marine pasture, according to the basic parameters corresponding to the sea cucumber fishing robot, calculating the fishing movement area corresponding to the sea cucumber fishing robot by using a calculation formula, and recording the fishing movement area as
Figure SMS_24
Further utilize the calculation formula
Figure SMS_25
Calculating the fishing fault tolerance corresponding to each fishing path of the marine pasture>
Figure SMS_26
P is denoted by the number corresponding to each fishing path,/for each fishing path>
Figure SMS_27
，/>
Figure SMS_28
The actual fishing area of the p-th fishing path corresponding to the ocean pasture is expressed, a1 and a2 are respectively expressed as set fishing repetition areas and influence factors corresponding to non-fishing areas, and S is expressed as the area of the region corresponding to the ocean pasture.
In a specific embodiment, the actual capturing area of each capturing path of the marine ranch is obtained, and the specific capturing process is as follows: substituting each fishing path of the ocean pasture into the ocean pasture three-dimensional model diagram to obtain the actual fishing area of each fishing path of the ocean pasture.
In a specific embodiment, the capturing movement area corresponding to the sea cucumber capturing robot is calculated by using a calculation formula, and the specific calculation process is as follows: according to the basic parameters corresponding to the sea cucumber capturing robot, extracting the arm length and the arm width corresponding to the sea cucumber capturing robot from the basic parameters, and utilizing a calculation formula
Figure SMS_29
Calculating the corresponding fishing movement area of the sea cucumber fishing robot>
Figure SMS_30
Wherein, K' represents the corresponding width of the sea cucumber catching robot.
Q2, arranging the fishing fault-tolerance rates corresponding to the fishing paths corresponding to the marine pastures in sequence from large to small, and further screening out the fishing paths with the first fishing fault-tolerance rate arrangement from the fishing fault-tolerance rates as the best fishing paths corresponding to the sea cucumber fishing robot.
The fishing path monitoring module is used for carrying out video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot.
And the fishing path analysis module is used for analyzing each object in the optimal fishing path according to the monitoring video corresponding to the suspected object in the optimal fishing path and performing corresponding control.
It should be further noted that, the analysis of each object in the optimal fishing path is performed by the following specific analysis process: and E1, extracting a monitoring image corresponding to the optimal fishing path from a monitoring video corresponding to each fishing path in the marine pasture, obtaining a monitoring image corresponding to each object in the optimal fishing path according to the monitoring image corresponding to the optimal fishing path, and performing filtering processing on the monitoring image corresponding to each object in the optimal fishing path to obtain pixel arrangement information corresponding to each line of each object in the optimal fishing path, wherein the pixel arrangement information comprises the pixel number of each line and the RGB value of each pixel in each line.
E2, calculating RGB values of the objects in the optimal capturing path corresponding to the pixel points in each row by using a calculation formula to obtain RGB difference values of the objects in the optimal capturing path corresponding to the pixel points in each row, and marking the RGB difference values as
Figure SMS_31
Wherein j is represented by the number corresponding to each object, < >>
Figure SMS_32
K is the number corresponding to each pixel point,>
Figure SMS_33
w is denoted by the number corresponding to each row, ">
Figure SMS_34
Further utilize the formula->
Figure SMS_35
Calculating the difference deviation coefficient of RGB of each pixel point of each object corresponding to each row in the optimal capturing path>
Figure SMS_36
，/>
Figure SMS_37
The standard RGB value expressed as the set kth pixel is used for comparing the RGB difference deviation coefficient of each pixel point of each line corresponding to each object in the optimal capturing path with the reference RGB difference deviation coefficient stored in the underwater database, if the RGB difference deviation coefficient of a certain suspected object corresponding to a certain pixel point of a certain line in a certain target path is larger than the reference RGB difference deviation coefficient, the serial number of the pixel point of the suspected object corresponding to the line in the optimal capturing path is extracted and recorded as the outline pixel point of the suspected object in the optimal capturing path, and each object in the optimal capturing path can be obtained according to the data processing modeAnd corresponding contour pixel points, so that the corresponding contour of each object in the optimal capturing path is obtained.
In a specific embodiment, the RGB difference values of the pixels in each row corresponding to each suspected object in the optimal capturing path are calculated by using a calculation formula, and the specific calculation process is as follows: obtaining RGB values of pixel points corresponding to each suspected object in the optimal capturing path in each row, selecting reference pixel points corresponding to each suspected object in the optimal capturing path in each row from the RGB values, obtaining RGB values of reference pixel points corresponding to each suspected object in the optimal capturing path in each row, and numbering the pixel points corresponding to each suspected object in the optimal capturing path except the reference pixel points as
Figure SMS_39
Further obtaining RGB values of pixels corresponding to the suspected objects in the optimal capturing path except the reference pixels in the lines, and marking the RGB values as the other pixels in the lines corresponding to the suspected objects in the optimal capturing path, and utilizing a calculation formula
Figure SMS_42
Calculating RGB difference values of reference pixel points in each line corresponding to each suspected object in the optimal capturing path>
Figure SMS_44
，/>
Figure SMS_40
Expressed as RGB values corresponding to reference pixel points in a w row corresponding to a jth suspected object in an s-th target path,/for>
Figure SMS_41
The value is expressed as RGB value corresponding to the x other pixel point in the w row corresponding to the j suspected object in the s target path, wherein j is expressed as the number corresponding to each suspected object, and>
Figure SMS_43
w is denoted by the number corresponding to each row, ">
Figure SMS_45
X is the number corresponding to each additional pixel point, +.>
Figure SMS_38
V is expressed as the total number of further pixels.
In a specific embodiment, the contour corresponding to each suspected object in the optimal capturing path is obtained by the following steps: substituting each contour pixel point corresponding to each suspected object in the optimal capturing path into a monitoring image corresponding to each suspected object in the optimal capturing path, obtaining the coordinate position corresponding to each contour pixel point corresponding to each obstacle in the optimal capturing path, calculating the distance between each contour pixel point corresponding to each obstacle in the optimal capturing path and other contour pixel points by using a coordinate difference formula, arranging the distances between each contour pixel point corresponding to each obstacle in the optimal capturing path and other contour pixel points in a descending order, screening out the number and the position corresponding to the nearest contour pixel point of each contour pixel point corresponding to each obstacle in the optimal capturing path, recording the nearest contour pixel point of each contour pixel point corresponding to each obstacle in the optimal capturing path as a first contour pixel point, and simultaneously connecting each contour pixel point corresponding to each obstacle in the optimal capturing path with the corresponding first contour pixel point.
And E3, carrying out grey-scale treatment on each monitoring image corresponding to the optimal capturing path, further obtaining grey-scale images corresponding to each object in the optimal capturing path, carrying out statistics to obtain brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path, comparing the brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path with brightness intervals corresponding to each grey level stored in the underwater database, and judging that the pixel point corresponding to a suspected object in the optimal capturing path is the grey level if the brightness value corresponding to a certain pixel point corresponding to a certain suspected object in the optimal capturing path is within the brightness interval corresponding to a certain grey level, thereby obtaining the grey level of each pixel point corresponding to each object in the optimal capturing path.
E4, screening the total number of pixel points corresponding to each object in the optimal catching path, and recording the total number as
Figure SMS_46
Wherein, according to the analytical formula->
Figure SMS_47
Calculating to obtain unitary gray level entropy value of each object corresponding to each pixel point in the optimal capturing path>
Figure SMS_48
，/>
Figure SMS_49
The number of the f pixel points corresponding to the jth suspected object in the optimal capturing path is represented, f is represented as the number corresponding to each pixel point, and +.>
Figure SMS_50
。
And E5, comparing the unitary gray entropy value of each object corresponding to each pixel point in the optimal fishing path with the standard color value of each pixel point corresponding to the unitary gray entropy value stored in the underwater database, further obtaining the standard color value of each object corresponding to each pixel point in the optimal fishing path, and comparing the gray level of each object corresponding to each pixel point in the optimal fishing path with the standard gray level of each pixel point corresponding to the color level stored in the underwater database, further obtaining the standard color level of each object corresponding to each pixel point in the optimal fishing path.
And E6, importing the outline corresponding to each object in the optimal fishing path, the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path into a background terminal platform, and filling the outline corresponding to each object in the optimal fishing path according to the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path, so as to obtain a reference model diagram corresponding to each object in the optimal fishing path.
According to the embodiment of the invention, the accuracy of the underwater object information is improved by carrying out grey detection on the underwater object image, so that the development of the intelligent fishing industry is greatly promoted, the autonomous use capacity of the sea cucumber fishing robot is improved, and the sea cucumber fishing operation is ensured to be more efficient, accurate and controllable.
It should be further noted that, the analyzing each object in the optimal fishing path, the specific analysis process further includes: and comparing the reference model pictures corresponding to the objects in the optimal fishing path with the reference names corresponding to the model pictures stored in the underwater database to obtain the reference names corresponding to the objects in the optimal fishing path, and feeding back the reference names corresponding to the objects in the optimal fishing path to the remote control terminal.
If the reference name corresponding to a certain object in the optimal fishing path is sea cucumber, the sea cucumber fishing robot is controlled to catch, otherwise, the sea cucumber fishing robot is controlled to accurately avoid the obstacle.
According to the embodiment of the invention, through carrying out accurate identification analysis on the underwater objects, the identification degree of the underwater environment of the fishing robot is improved, the difficulty in fishing sea cucumbers is reduced to a certain extent, the precision rate of fishing sea cucumbers is improved, meanwhile, the false fishing of non-target fishes can be effectively avoided, and the influence on the marine ecological environment is greatly reduced.
According to the embodiment of the invention, the underwater object accurate analysis is carried out on the sea cucumber fishing robot, so that the intelligent level of the sea cucumber fishing robot is improved to a certain extent, reliable data support can be effectively provided for fishing of the sea cucumber fishing robot, the occurrence of potential safety hazards is avoided, and meanwhile, the service life and the service time of the sea cucumber fishing robot are also improved to a certain extent.
The remote control terminal is used for accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot.
The underwater database is used for storing fishing information corresponding to a target fishing ship, reference names corresponding to each model diagram and reference RGB difference deviation coefficients, and is also used for storing pixel standard chromaticity values corresponding to each unitary gray entropy value, pixel standard gray levels corresponding to each gray level and brightness intervals corresponding to each gray level.
Referring to fig. 2, a remote control method for a computer network device includes the following steps: step one, acquiring fishing information, namely acquiring the fishing information corresponding to the target fishing ship from an underwater database.
And secondly, fishing cable control, namely accurately controlling the cable length corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship.
Step three, generating fishing paths, namely extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture.
And step four, screening the fishing paths, namely obtaining the actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing to obtain the fishing fault tolerance of the fishing paths corresponding to the sea pasture, and screening to obtain the optimal fishing path corresponding to the sea cucumber fishing robots according to the fishing fault tolerance.
Fifthly, monitoring the fishing path, and further performing video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot.
And step six, analyzing the fishing path, and further analyzing and correspondingly controlling each object in the optimal fishing path according to the monitoring video corresponding to the suspected object in the optimal fishing path.
And seventhly, accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot by the remote control terminal.

Claims (8)

1. A computer network device remote control system, characterized by: the system comprises:
the fishing information acquisition module is used for acquiring the fishing information corresponding to the target fishing ship from the underwater database; the fishing cable control module is used for accurately controlling the cable length corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship;
the fishing path generation module is used for extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and further generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture;
the fishing path screening module is used for obtaining actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing and obtaining the fishing fault tolerance of the fishing paths corresponding to the sea pasture, and screening and obtaining the optimal fishing paths corresponding to the sea cucumber fishing robots according to the fishing fault tolerance;
the fishing path monitoring module is used for carrying out video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot;
the fishing path analysis module is used for analyzing each object in the optimal fishing path according to the monitoring video corresponding to the suspected object in the optimal fishing path and performing corresponding control; the remote control terminal is used for accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot; the underwater database is used for storing fishing information corresponding to a target fishing ship, reference names corresponding to each model diagram and reference RGB difference deviation coefficients, and storing pixel standard chromaticity values corresponding to each unitary gray entropy value, pixel standard gray levels corresponding to each gray level and brightness intervals corresponding to each gray level.
2. A computer network device remote control system as claimed in claim 1, wherein: the fishing information corresponding to the target fishing ship comprises the area, the shape and the deepest sea depth of the area corresponding to the marine pasture, and also comprises basic parameters corresponding to the sea cucumber fishing robot, wherein the basic parameters comprise arm length, length and width.
3. A computer network device remote control system as claimed in claim 2, wherein: the cable length corresponding to the target fishing ship is precisely controlled, and the specific control process is as follows:
according to the deepest sea depth corresponding to the ocean pasture and the arm length corresponding to the sea cucumber catching robot, a calculation formula is utilized
Figure QLYQS_1
Calculating the cable length corresponding to the target fishing vessel>
Figure QLYQS_2
Wherein L is the arm length corresponding to the sea cucumber capturing robot, and +.>
Figure QLYQS_3
Expressed as the deepest sea depth corresponding to the marine ranch;
and feeding the cable length corresponding to the target fishing vessel back to the remote control terminal, so as to accurately control the cable length of the target fishing vessel.
4. A computer network device remote control system as claimed in claim 1, wherein: the sea cucumber fishing robot is used for generating each fishing path corresponding to the sea cucumber fishing robot, and the generation process is as follows:
w1, extracting the shape of an area corresponding to the ocean pasture from the fishing information corresponding to the target fishing ship, substituting the shape into a model diagram, and further obtaining the boundary information corresponding to the ocean pasture, wherein the boundary information comprises the number of the boundary;
w2, selecting a sea pasture reference boundary line from all the boundary lines of the sea pasture, taking the arm length of the sea cucumber fishing robot as a path distance, and making all straight lines parallel to the sea pasture reference boundary line, so as to intercept all straight lines parallel to the sea pasture reference boundary line and the intersecting point of the sea pasture profile boundary line as an endpoint, taking the straight lines as all reference path straight lines, so as to obtain all reference path straight lines corresponding to the sea pasture, and taking the straight lines as fishing paths corresponding to the sea pasture reference boundary line;
and W3, analyzing the fishing paths corresponding to the side lines of the ocean pasture in a similar way according to the analysis mode of the fishing paths corresponding to the reference side lines of the ocean pasture, and marking the fishing paths corresponding to the side lines of the ocean pasture as the fishing paths corresponding to the ocean pasture.
5. A computer network appliance remote control system as claimed in claim 4, wherein: the optimal fishing path corresponding to the sea cucumber fishing robot is screened, and the specific screening process is as follows:
q1, according to each fishing path of the marine pasture, obtaining the actual fishing area of each fishing path of the marine pasture, according to the basic parameters corresponding to the sea cucumber fishing robot, calculating the fishing movement area corresponding to the sea cucumber fishing robot by using a calculation formula, and recording the fishing movement area as
Figure QLYQS_4
Further utilize the formula->
Figure QLYQS_5
Calculating the fishing fault tolerance corresponding to each fishing path of the marine pasture>
Figure QLYQS_6
P is denoted by the number corresponding to each fishing path,/for each fishing path>
Figure QLYQS_7
，/>
Figure QLYQS_8
The actual fishing area of the p-th fishing path corresponding to the ocean pasture is represented, a1 and a2 are respectively represented as set fishing repetition areas and influence factors corresponding to non-fishing areas, and S is represented as the area of the region corresponding to the ocean pasture;
q2, arranging the fishing fault-tolerance rates corresponding to the fishing paths corresponding to the marine pastures in sequence from large to small, and further screening out the fishing paths with the first fishing fault-tolerance rate arrangement from the fishing fault-tolerance rates as the best fishing paths corresponding to the sea cucumber fishing robot.
6. A computer network device remote control system as claimed in claim 1, wherein: the method is characterized in that each object in the optimal fishing path is analyzed, and the specific analysis process is as follows:
e1, extracting a monitoring image corresponding to the optimal fishing path from a monitoring video corresponding to each fishing path in the marine pasture, obtaining a monitoring image corresponding to each object in the optimal fishing path according to the monitoring image corresponding to the optimal fishing path, and performing filtering processing on the monitoring image corresponding to each object in the optimal fishing path to obtain pixel arrangement information corresponding to each row of each object in the optimal fishing path, wherein the pixel arrangement information comprises the pixel number of each row and RGB value of each pixel in each row;
e2, calculating RGB values of the objects in the optimal capturing path corresponding to the pixel points in each row by using a calculation formula to obtain RGB difference values of the objects in the optimal capturing path corresponding to the pixel points in each row, and marking the RGB difference values as
Figure QLYQS_9
Wherein j is represented by the number corresponding to each object, < >>
Figure QLYQS_10
K is the number corresponding to each pixel point,>
Figure QLYQS_11
w is denoted by the number corresponding to each row, ">
Figure QLYQS_12
Further utilize the formula->
Figure QLYQS_13
Calculating the difference deviation coefficient of RGB of each pixel point of each object corresponding to each row in the optimal capturing path>
Figure QLYQS_14
，/>
Figure QLYQS_15
The standard RGB value of the kth pixel is set, the difference deviation coefficient of RGB of each pixel point of each line corresponding to each object in the optimal capturing path is compared with the reference RGB difference deviation coefficient stored in the underwater database, if the difference deviation coefficient of RGB of a certain pixel point of a certain suspected object corresponding to a certain line in a certain target path is larger than the reference RGB difference deviation coefficient, the serial number of the pixel point of the suspected object corresponding to the line in the optimal capturing path is extracted and recorded as the outline pixel point of the suspected object in the optimal capturing path, and each outline pixel point corresponding to each object in the optimal capturing path can be obtained according to the data processing mode, so that the outline corresponding to each object in the optimal capturing path is obtained;
e3, carrying out grey-scale treatment on each monitoring image corresponding to the optimal capturing path, further obtaining grey-scale images corresponding to each object in the optimal capturing path, carrying out statistics to obtain brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path, comparing the brightness values corresponding to each pixel point corresponding to each object in the optimal capturing path with brightness intervals corresponding to each grey level stored in an underwater database, and judging that the pixel point corresponding to a suspected object in the optimal capturing path is the grey level if the brightness value corresponding to a certain pixel point corresponding to a certain suspected object in the optimal capturing path is within the brightness interval corresponding to a certain grey level, thereby obtaining the grey level of each pixel point corresponding to each object in the optimal capturing path;
e4, screening the total number of pixel points corresponding to each object in the optimal catching path, and recording the total number as
Figure QLYQS_16
Wherein, according to the analytical formula->
Figure QLYQS_17
Calculating to obtain unitary gray level entropy value of each object corresponding to each pixel point in the optimal capturing path>
Figure QLYQS_18
，/>
Figure QLYQS_19
The number of the f pixel points corresponding to the jth suspected object in the optimal capturing path is represented, f is represented as the number corresponding to each pixel point, and +.>
Figure QLYQS_20
；
E5, comparing the unitary gray entropy value of each object corresponding to each pixel point in the optimal fishing path with the standard color value of each pixel point corresponding to the unitary gray entropy value stored in the underwater database, further obtaining the standard color value of each object corresponding to each pixel point in the optimal fishing path, and comparing the gray level of each object corresponding to each pixel point in the optimal fishing path with the standard gray level of each pixel point corresponding to each color level stored in the underwater database, further obtaining the standard color level of each object corresponding to each pixel point in the optimal fishing path;
and E6, importing the outline corresponding to each object in the optimal fishing path, the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path into a background terminal platform, and filling the outline corresponding to each object in the optimal fishing path according to the standard chromaticity value and the standard chromaticity grade corresponding to each pixel point of each object in the optimal fishing path, so as to obtain a reference model diagram corresponding to each object in the optimal fishing path.
7. A computer network appliance remote control system as claimed in claim 6, wherein: the method for analyzing the objects in the optimal fishing path comprises the following steps:
comparing the reference model pictures corresponding to the objects in the optimal fishing path with the reference names corresponding to the model pictures stored in the underwater database to obtain the reference names corresponding to the objects in the optimal fishing path, and feeding back the reference names corresponding to the objects in the optimal fishing path to the remote control terminal;
if the reference name corresponding to a certain object in the optimal fishing path is sea cucumber, the sea cucumber fishing robot is controlled to catch, otherwise, the sea cucumber fishing robot is controlled to accurately avoid the obstacle.
8. A computer network device remote control method, characterized in that: the method comprises the following steps:
step one, acquiring fishing information, namely acquiring the fishing information corresponding to a target fishing ship from an underwater database;
step two, fishing cable control, namely accurately controlling the cable length corresponding to the target fishing ship according to the fishing information corresponding to the target fishing ship;
step three, generating fishing paths, namely extracting the number of the side lines corresponding to the ocean pasture according to the fishing information corresponding to the target fishing ship, and generating each fishing path corresponding to the sea cucumber fishing robot according to each side line of the ocean pasture;
step four, screening fishing paths, namely obtaining actual fishing areas of the fishing paths corresponding to the sea pasture according to the fishing paths corresponding to the sea cucumber fishing robots, analyzing to obtain the fishing fault tolerance of the fishing paths corresponding to the sea pasture, and screening to obtain the optimal fishing path corresponding to the sea cucumber fishing robots according to the fishing fault tolerance;
fifthly, monitoring a fishing path, and further performing video monitoring on each object in the optimal fishing path according to a high-definition camera of the sea cucumber fishing robot;
step six, analyzing the fishing path, namely analyzing each object in the optimal fishing path according to a monitoring video corresponding to the suspected object in the optimal fishing path, and correspondingly controlling the objects;
and seventhly, accurately and remotely controlling the target fishing ship and the sea cucumber fishing robot by the remote control terminal.
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