CN112264419A - Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence - Google Patents
Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence Download PDFInfo
- Publication number
- CN112264419A CN112264419A CN202011064471.0A CN202011064471A CN112264419A CN 112264419 A CN112264419 A CN 112264419A CN 202011064471 A CN202011064471 A CN 202011064471A CN 112264419 A CN112264419 A CN 112264419A
- Authority
- CN
- China
- Prior art keywords
- cleaning robot
- cleaning
- photovoltaic
- cell panel
- photovoltaic cell
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
- 238000004140 cleaning Methods 0.000 title claims abstract description 151
- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 26
- 238000001514 detection method Methods 0.000 claims abstract description 29
- 238000012545 processing Methods 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 7
- 238000007689 inspection Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 239000002699 waste material Substances 0.000 abstract description 5
- 239000002689 soil Substances 0.000 abstract description 4
- 238000012549 training Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008447 perception Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000000712 assembly Effects 0.000 description 3
- 238000000429 assembly Methods 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 101100075995 Schizosaccharomyces pombe (strain 972 / ATCC 24843) fma2 gene Proteins 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 238000012360 testing method Methods 0.000 description 2
- 101150064138 MAP1 gene Proteins 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B08—CLEANING
- B08B—CLEANING IN GENERAL; PREVENTION OF FOULING IN GENERAL
- B08B13/00—Accessories or details of general applicability for machines or apparatus for cleaning
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/94—Investigating contamination, e.g. dust
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02S—GENERATION OF ELECTRIC POWER BY CONVERSION OF INFRARED RADIATION, VISIBLE LIGHT OR ULTRAVIOLET LIGHT, e.g. USING PHOTOVOLTAIC [PV] MODULES
- H02S40/00—Components or accessories in combination with PV modules, not provided for in groups H02S10/00 - H02S30/00
- H02S40/10—Cleaning arrangements
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/50—Photovoltaic [PV] energy
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
Abstract
The invention belongs to the technical field of artificial intelligence, and particularly relates to a multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence. The method comprises the following steps: acquiring second image information of a second photovoltaic cell panel by using a first cleaning robot carrying a first camera; detecting whether the second image information has stains to obtain a second stain detection result; feeding back the second soil detection result to a second cleaning robot carrying a second camera; the second cleaning robot is used for cleaning a second photovoltaic cell panel, and the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel; and the second cleaning robot executes preset cleaning operation according to the second stain detection result and collects first image information of the first photovoltaic cell panel. The embodiment of the application can improve the cleaning efficiency and avoid the waste of cleaning resources.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence.
Background
The conventional method for scheduling the cleaning robots of the planar photovoltaic power station is that each cleaning robot is responsible for cleaning the photovoltaic cell assemblies in one area and is responsible for cleaning all positions in the area in a fixed form at a fixed time. The method has low cleaning efficiency and wastes cleaning resources.
Shoot the image through unmanned aerial vehicle usually among the prior art, discern the spot position and clean, patrol and examine the mode of scheduling cleaning robot through unmanned aerial vehicle and be difficult to fix automatic orbit of patrolling and examining and increased the waste of the energy.
Disclosure of Invention
In order to solve the problems, the invention provides a cooperative scheduling method of a plurality of photovoltaic cleaning robots based on artificial intelligence, which comprises the following specific schemes:
in a first aspect, an embodiment of the present invention provides an artificial intelligence-based collaborative scheduling method for multiple photovoltaic cleaning robots, including the following steps:
acquiring second image information of a second photovoltaic cell panel by using a first cleaning robot carrying a first camera;
detecting whether the second image information has stains to obtain a second stain detection result;
feeding back the second soil detection result to a second cleaning robot carrying a second camera; the second cleaning robot is used for cleaning a second photovoltaic cell panel, and the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel;
and the second cleaning robot executes preset cleaning operation according to the second stain detection result and collects first image information of the first photovoltaic cell panel.
Preferably, the step of detecting whether the second image information contains the stain includes processing the second image information according to a deep neural network to obtain a bounding frame of the stain and a position of the stain.
Preferably, the coordinated scheduling method further includes the step of constructing a triad by three adjacent photovoltaic cleaning robots, and performing mutual information feedback between the adjacent photovoltaic cleaning robots in the triad.
Preferably, the coordinated scheduling further includes a step of setting a time for which the first cleaning robot starts to operate.
In a second aspect, another embodiment of the present invention further includes a system for artificial intelligence-based collaborative scheduling of multiple photovoltaic cleaning robots, the system including the following modules:
the information acquisition module is used for acquiring second image information of the second photovoltaic cell panel by using a first cleaning robot carrying a first camera;
the image processing module is used for detecting whether the second image information contains stains or not to obtain a second stain detection result;
the inspection cleaning module is used for feeding back the second stain detection result to a second cleaning robot carrying a second camera, the second cleaning robot is used for cleaning a second photovoltaic cell panel, the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel, the second cleaning robot executes preset cleaning operation according to the second stain detection result, and first image information of the first photovoltaic cell panel is collected;
a control module to send scheduling instructions to the first cleaning robot and the second cleaning robot.
Preferably, the information collecting module further includes:
a time presetting unit for setting a time for the first cleaning robot to start operating.
Preferably, the image processing module further includes:
and the stain detection unit is used for processing the second image information according to the deep neural network and positioning the stain position according to the surrounding frame coordinate.
Preferably, the system further comprises:
the triple construction unit is used for constructing a triple by using three adjacent photovoltaic cleaning robots, and mutual information feedback is carried out between the adjacent photovoltaic cleaning robots in the triple.
Preferably, the control module further comprises:
and the instruction sending unit is used for sending the scheduling instruction to the corresponding cleaning robot by the server.
The invention has the beneficial effects that:
the embodiment of the invention provides an artificial intelligence-based collaborative scheduling method for multiple photovoltaic cleaning robots, wherein a first cleaning robot provided with a first camera is used for collecting second image information of a second photovoltaic cell panel, whether stains exist in the second image information is detected, a second stain detection result is obtained, the second stain detection result is fed back to a second cleaning robot provided with a second camera, the second cleaning robot is used for cleaning the second photovoltaic cell panel, the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel, the second cleaning robot executes preset cleaning operation according to the second stain detection result and collects first image information of the first photovoltaic cell panel, the technical problem that the polling cleaning efficiency of a single robot in a fixed area is low is solved, the collaborative scheduling method can improve the cleaning efficiency of the photovoltaic cell panel through the collaboration of adjacent robots, the waste of clean resources is avoided.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a cooperative scheduling method of a multi-photovoltaic cleaning robot based on artificial intelligence according to an embodiment of the present invention;
FIG. 2 is a perspective view of a cleaning robot capturing images in accordance with one embodiment of the present invention;
fig. 3 is a block diagram illustrating a cooperative scheduling system of multiple photovoltaic cleaning robots based on artificial intelligence according to an embodiment of the present invention;
fig. 4 is a flowchart of an image processing module of a cooperative scheduling system of multiple photovoltaic cleaning robots based on artificial intelligence according to an embodiment of the present invention.
Detailed Description
To further illustrate the technical means and effects of the present invention adopted to achieve the predetermined object, the following detailed description of the method and apparatus for collaborative scheduling of multiple photovoltaic cleaning robots based on artificial intelligence according to the present invention with reference to the accompanying drawings and the preferred embodiments thereof will be made as follows. In the following description, different "one embodiment" or "another embodiment" refers to not necessarily the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Referring to fig. 1, fig. 1 is a flowchart illustrating a cooperative scheduling method of multiple photovoltaic cleaning robots based on artificial intelligence according to an embodiment of the present invention, the method including the following steps:
and step S1, collecting second image information of the second photovoltaic cell panel by using the first cleaning robot provided with the first camera.
Specifically, this embodiment is applicable to under the scene of many photovoltaic cell boards of tiled.
Specifically, the tracks are arranged between the adjacent photovoltaic cell panels, and certain gaps exist, so that the camera can conveniently acquire complete image information of the photovoltaic cell panels.
Specifically, the working mode of the cleaning robot is conventional single-row cleaning, the control end equipment of the cleaning robot is arranged on the track, the position of the cleaning robot can be adjusted to clean each row of battery assemblies of the photovoltaic cell panel, the cleaning end of the cleaning robot is connected with the control end, and the cleaning robot can extend forwards along the row line of the photovoltaic cell panel to clean the whole row of assemblies.
Specifically, each cleaning robot is provided with a camera, the camera is a wide-angle RGB camera, the shot image comprises four corner points of a photovoltaic cell panel, the camera is arranged right above the cleaning robot, the initial visual angle is the direction of the photovoltaic cell panel, the holder is fixed and can only rotate 180 degrees, and the visual angle is opposite to the initial visual angle.
As shown in fig. 2, in the embodiment, a horizontal viewing angle of the camera is 60 to 80 degrees, and a viewing distance is 3 to 5 meters, wherein the camera may be a wide-angle camera with a pan-tilt, and after the cleaning robot with the camera 1001 is moved to an end point of a middle row of the track 1002, the camera 1001 may collect a complete image of the adjacent cell panel 1003.
Further, the rule for acquiring the image is as follows:
firstly, after a system starts to operate, a first track cleaning robot starts to clean from a middle row of a solar panel, and when the end point of the row is reached, an image of a second photovoltaic cell panel is shot;
and step two, after the second track cleaning robot receives the control command, shooting a first photovoltaic cell panel image at the center of the track before cleaning, moving to the middle row of the cell panels after cleaning or when no cleaning task is performed, and shooting a third photovoltaic cell panel image when the end point of the row is reached.
Step S2, detecting whether the second image information has stains, and obtaining a second stain detection result.
Referring to fig. 4, a flow of detecting a position of a stain is shown, two neural networks are used to process a panel image, which are a first branch network and a second branch network, respectively, the first branch network senses four corner points of a photovoltaic panel, and the second branch network senses the stain of the photovoltaic panel, in which a DNN neural network may be used in an embodiment.
Specifically, the input of the first branch network is an image shot by a camera, the image is subjected to Feature extraction through a key point perception Encoder (Encoder1), a first Feature map (Feature map1) is output, the first Feature map is used as an input and sent to a key point perception Decoder (Decoder1) for upsampling, and then thermodynamic diagrams (heatmaps) of four types of key points of the photovoltaic cell panel are output according to channels, the four types of key points are four corner points of the photovoltaic cell panel, and the thermodynamic diagrams are processed to obtain image coordinates of the four corner points of the photovoltaic cell panel.
The training set of the first branch network selects images acquired by a plurality of cameras, and the training set can be amplified by image rotation and zooming. The method is characterized in that the method is marked as hot spots generated by Gaussian blur by taking key points as centers, the key points are four corner points of the photovoltaic cell panel, and a loss function adopted in training is a mean square error loss function.
Specifically, the input of the second branch network is an image shot by a camera, the shot image is subjected to Feature extraction through a stain perception Encoder (Encoder2), a second Feature map (Feature map2) is output, the Feature map2 is used as an input and sent to a stain perception Decoder (Decoder2), and regression results of a bounding box, specifically, a first corner point of the bounding box and the length and width of the bounding box, are output.
The specific content of the second branch network training is as follows: the training set selects a plurality of cameras to acquire images, and the training set can be amplified by image rotation, zooming and other methods. The first corner coordinates x, y of the bounding box labeled as soil and the length and width of the bounding box, the second branch network training used a penalty function of Smooth L1 penalty function.
The data processing processes of the first branch network and the second branch network are finished in an image processing layer, a plane coordinate system of the photovoltaic cell panel can be constructed according to the obtained image coordinates of four angular points of the photovoltaic cell panel, the lower left corner is taken as a coordinate origin, the coordinates of the four angular points in the plane of the photovoltaic cell panel are easy to obtain, and homography matrixes are calculated through a four-point method; according to the obtained image coordinates of the two end points of the bottom edge of the surrounding frame, the center point coordinate between the two points represents the spot position, and the coordinate of the spot position in the plane of the photovoltaic cell panel is obtained through the calculated homography matrix, so that the subsequent cleaning work can be conveniently positioned.
Step S3, feeding back the second soil detection result to the second cleaning robot equipped with the second camera; the second cleaning robot is used for cleaning a second photovoltaic cell panel, and the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel.
And step S4, the second cleaning robot executes preset cleaning operation according to the second stain detection result and collects first image information of the first photovoltaic cell panel.
Specifically, a triple is constructed by every three adjacent cleaning robots, the number of the triples is the same as that of the cleaning robots, and the feedback of the stain detection result is only performed in the triple.
The steps of scheduling and feedback between triplets are specifically explained by taking two adjacent photovoltaic cell panels as an example, the two adjacent photovoltaic cell panels are respectively a first photovoltaic cell panel and a second photovoltaic cell panel, the first photovoltaic cell panel corresponds to a first cleaning robot, the second photovoltaic cell panel corresponds to a second cleaning robot, and the corresponding steps of scheduling and feedback are as follows:
s101, the first cleaning robot moves to a middle row of the first photovoltaic cell panel, a cleaning mode is not started to move to a destination of the row, then image information of the second photovoltaic cell panel is collected, then the second cleaning robot is dispatched, and processing results of the image information are divided into two types, wherein one type is free of stains, and the other type is stained;
s102, after receiving the scheduling information, the second cleaning robot shoots the image information of the first photovoltaic cell panel, feeds the image information back to the first cleaning robot after processing, moves to the middle row of the second photovoltaic cell panel when no dirt exists, does not start the cleaning mode to travel to the end point of the middle row, moves to the corresponding row of the dirt position of the second photovoltaic cell panel when the dirt exists, and starts the cleaning mode to travel to the corresponding end point of the dirt position.
S103, after cleaning is finished, the second cleaning robot moves to the middle row of the second photovoltaic cell panel, and the cleaning mode is not started to move to the middle row end point.
And S104, after the first cleaning robot receives the feedback information, the first cleaning robot moves to a row corresponding to the spot position of the first photovoltaic cell panel, and the cleaning mode is started to move to the end point of the row. And then moving to the middle row of the first photovoltaic cell panel, and not starting the cleaning mode to move to the end point of the row.
Specifically, scheduling and feedback among the triplets are performed according to scheduling steps, when the scheduling information and the feedback information are both free of stains or when stains at a certain position are detected for more than three times, the triplet task is ended, a next triplet task is performed, and the positions of the stains detected repeatedly are stored and transmitted to operation and maintenance management personnel.
In summary, in the embodiment of the present invention, a first cleaning robot equipped with a first camera is used to collect second image information of a second photovoltaic cell panel, detect whether there is a stain in the second image information, obtain a second stain detection result, and feed the second stain detection result back to a second cleaning robot equipped with a second camera, where the second cleaning robot is used to clean the second photovoltaic cell panel, the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel, and the second cleaning robot performs a predetermined cleaning operation according to the second stain detection result and collects the first image information of the first photovoltaic cell panel, so that a situation that a single robot performs polling cleaning in a fixed area is avoided. According to the embodiment of the invention, the cleaning efficiency of the photovoltaic cell panel can be improved through the cooperative inspection of the adjacent robots, and the waste of cleaning resources is avoided.
Based on the same inventive concept as the method embodiment, another embodiment of the invention further provides a cooperative dispatching system of the multi-photovoltaic cleaning robot based on artificial intelligence.
Referring to fig. 3, a block diagram of a cooperative dispatching system of a multi-photovoltaic cleaning robot based on artificial intelligence is shown, wherein the system comprises an information acquisition module 101, an image processing module 102, an information sharing module 103 and a control module 104.
The information acquisition module 101 is configured to acquire second image information of the second photovoltaic cell panel by using the first cleaning robot equipped with the first camera. The image processing module 102 is configured to detect whether the second image information contains stains, and obtain a second stain detection result. Patrol and examine clean module 103 and be used for feeding back the second cleaning robot who carries the second camera with second spot testing result, and second cleaning robot is used for clean second photovoltaic cell board, and first photovoltaic cell board is adjacent with second photovoltaic cell board, and second cleaning robot carries out predetermined cleaning operation according to second spot testing result to gather first photovoltaic cell board's first image information. The control module 104 is configured to send scheduling instructions to the first cleaning robot and the second cleaning robot.
Preferably, the information collecting module 101 further includes a time presetting unit for setting a time for the first cleaning robot to start operating.
Preferably, the image processing module 102 further includes a stain detection unit, configured to process the second image information according to a deep neural network, so as to obtain a bounding frame of the stain and a position of the stain.
Preferably, the inspection and cleaning module 103 further includes a triple construction unit, configured to construct a triple with three adjacent photovoltaic cleaning robots, where information between adjacent photovoltaic cleaning robots in the triple is fed back to each other.
Preferably, the control module 104 further includes an instruction transmitting unit for transmitting a scheduling instruction to the camera device by the server.
It should be noted that: the precedence order of the above embodiments of the present invention is only for description, and does not represent the merits of the embodiments. And specific embodiments thereof have been described above. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may also be possible or may be advantageous.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (9)
1. A multi-photovoltaic cleaning robot cooperative scheduling method based on artificial intelligence is characterized by comprising the following steps:
acquiring second image information of a second photovoltaic cell panel by using a first cleaning robot carrying a first camera;
detecting whether the second image information has stains to obtain a second stain detection result;
feeding back the second stain detection result to a second cleaning robot carrying a second camera, wherein the second cleaning robot is used for cleaning a second photovoltaic cell panel, and the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel;
and the second cleaning robot executes preset cleaning operation according to the second stain detection result and collects first image information of the first photovoltaic cell panel.
2. The artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling method according to claim 1, wherein the step of detecting whether stains exist in the second image information includes:
and processing the second image information according to the deep neural network to obtain a dirty surrounding frame and a dirty position.
3. The artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling method according to claim 1, wherein the collaborative scheduling method further comprises the following steps:
three adjacent photovoltaic cleaning robots construct a triad, and mutual information feedback is carried out between the adjacent photovoltaic cleaning robots in the triad.
4. The artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling method according to claim 1, wherein the collaborative scheduling further includes:
setting a time for the first cleaning robot to start operating.
5. The system for the cooperative scheduling of the multiple photovoltaic cleaning robots based on the artificial intelligence is characterized by further comprising the following modules:
the information acquisition module is used for acquiring second image information of the second photovoltaic cell panel by using a first cleaning robot carrying a first camera;
the image processing module is used for detecting whether the second image information contains stains or not to obtain a second stain detection result;
the inspection cleaning module is used for feeding back the second stain detection result to a second cleaning robot carrying a second camera, the second cleaning robot is used for cleaning a second photovoltaic cell panel, the first photovoltaic cell panel is adjacent to the second photovoltaic cell panel, the second cleaning robot executes preset cleaning operation according to the second stain detection result, and first image information of the first photovoltaic cell panel is collected;
a control module to send scheduling instructions to the first cleaning robot and the second cleaning robot.
6. The system for collaborative scheduling of multiple photovoltaic cleaning robots based on artificial intelligence of claim 5, wherein the information collection module further comprises:
a time presetting unit for setting a time for the first cleaning robot to start operating.
7. The system for artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling according to claim 5, wherein the image processing module further includes:
and the stain detection unit is used for processing the second image information according to the deep neural network to obtain a stain surrounding frame and a stain position.
8. The system for artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling according to claim 5, further comprising:
the triple construction unit is used for constructing a triple by using three adjacent photovoltaic cleaning robots, and mutual information feedback is carried out between the adjacent photovoltaic cleaning robots in the triple.
9. The system for artificial intelligence based multi-photovoltaic cleaning robot collaborative scheduling according to claim 5, wherein the control module further includes:
and the instruction sending unit is used for sending the scheduling instruction to the corresponding cleaning robot by the server.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011064471.0A CN112264419A (en) | 2020-09-30 | 2020-09-30 | Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011064471.0A CN112264419A (en) | 2020-09-30 | 2020-09-30 | Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112264419A true CN112264419A (en) | 2021-01-26 |
Family
ID=74338765
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011064471.0A Withdrawn CN112264419A (en) | 2020-09-30 | 2020-09-30 | Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112264419A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113888632A (en) * | 2021-09-14 | 2022-01-04 | 上海景吾智能科技有限公司 | Method and system for positioning stains in pool by combining RGBD image |
| AU2022203164B2 (en) * | 2021-05-14 | 2024-02-15 | Ihi Corporation | Solar panel cleaning system and cleaning path generation device |
-
2020
- 2020-09-30 CN CN202011064471.0A patent/CN112264419A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2022203164B2 (en) * | 2021-05-14 | 2024-02-15 | Ihi Corporation | Solar panel cleaning system and cleaning path generation device |
| CN113888632A (en) * | 2021-09-14 | 2022-01-04 | 上海景吾智能科技有限公司 | Method and system for positioning stains in pool by combining RGBD image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112264419A (en) | Multi-photovoltaic cleaning robot cooperative scheduling method and system based on artificial intelligence | |
| CN106734010B (en) | Photovoltaic power station cleaning method and system | |
| CN108769578B (en) | Real-time panoramic imaging system and method based on multiple cameras | |
| WO2017024975A1 (en) | Unmanned aerial vehicle portable ground station processing method and system | |
| CN111339893B (en) | Pipeline detection system and method based on deep learning and unmanned aerial vehicle | |
| CN110033103A (en) | A kind of photovoltaic panel cruising inspection system and method for inspecting | |
| CN205693769U (en) | A kind of motion cameras positioning capturing quick to panorama target system | |
| CN111985352B (en) | AI front-end substation inspection video real-time identification method and system | |
| CN108960134A (en) | A kind of patrol UAV image mark and intelligent identification Method | |
| CN112449147B (en) | Video cluster monitoring system of photovoltaic power station and image processing method thereof | |
| CN110160587B (en) | Capacitance detection method and system | |
| CN107438152A (en) | A kind of motion cameras is to panorama target fast positioning method for catching and system | |
| WO2024093420A1 (en) | Patrol method and apparatus for cooperative operation of unmanned aerial vehicle and ground patrol robot | |
| CN112184650A (en) | Photovoltaic cell panel batch grid breakage detection method and system based on artificial intelligence | |
| CN117161004A (en) | Laser cleaning path planning method and system | |
| CN103707300A (en) | Manipulator device | |
| CN117519291A (en) | Photovoltaic panel inspection system based on multi-unmanned aerial vehicle path planning | |
| CN117876308A (en) | Photovoltaic panel detection method based on image analysis | |
| CN115049322A (en) | Container management method and system for container yard | |
| CN207013315U (en) | The flange quality control system of view-based access control model detection | |
| CN112260402B (en) | Monitoring method for state of intelligent substation inspection robot based on video monitoring | |
| CN112184651A (en) | Photovoltaic power station part looseness detection system and method based on artificial intelligence | |
| CN207923179U (en) | Intelligent patrol detection vehicle | |
| CN203636826U (en) | Robot device | |
| CN115622231A (en) | Photovoltaic power generation station intelligence operation and maintenance system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210126 |