CN118012100B - Unmanned aerial vehicle intelligent photovoltaic power station inspection method and system - Google Patents
Unmanned aerial vehicle intelligent photovoltaic power station inspection method and system Download PDFInfo
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Abstract
The application relates to an intelligent inspection method and system for a photovoltaic power station of an unmanned aerial vehicle, which relate to the technical field of photovoltaic power stations and comprise the following steps: obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations; obtaining the inspection task amount based on the coverage area of each photovoltaic power station; matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station; transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying; receiving images acquired by an unmanned aerial vehicle; judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform.
Description
Technical Field
The application relates to the technical field of photovoltaic power stations, in particular to an intelligent unmanned aerial vehicle photovoltaic power station inspection method and system.
Background
Photovoltaic power generation is a technology that uses the photovoltaic effect of a semiconductor interface to directly convert light energy into electrical energy. The solar energy power generation system mainly comprises three parts of a solar panel (assembly), a controller and an inverter, wherein the main parts are composed of electronic components. The solar cells are packaged and protected after being connected in series to form a large-area solar cell module, and then the solar cell module is matched with components such as a power controller and the like to form the photovoltaic power generation device.
Solar energy is taken as a clean energy source with wide sources, and is fully paid attention in China in recent years, and the photovoltaic power generation industry reaches a certain scale and becomes an important component for the structural adjustment of national energy sources.
With the increasing scale of photovoltaic power stations, the operation and maintenance of photovoltaic panels has also presented great difficulties. The photovoltaic panel is used as a core component of the photovoltaic power generation system, is exposed to the natural environment for a long time in daily operation, and inevitably generates various defects such as shade shading, photovoltaic panel breakage and the like, so that the defects of the panel are found in time and the manual intervention is significant for guaranteeing the power generation efficiency of the power station.
Disclosure of Invention
In order to at least partially solve the technical problems, the application provides an unmanned aerial vehicle intelligent photovoltaic power station inspection method and system.
In a first aspect, the unmanned aerial vehicle intelligent photovoltaic power station inspection method provided by the application adopts the following technical scheme.
The unmanned aerial vehicle intelligent photovoltaic power station inspection method comprises the following steps:
obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations;
Obtaining the inspection task amount based on the coverage area of each photovoltaic power station;
matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station;
transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying;
Receiving images acquired by an unmanned aerial vehicle;
judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform.
By adopting the technical scheme, a plurality of inspection routes can be planned by analyzing the specific positions of each photovoltaic power station, so that the unmanned aerial vehicle can cover all the power stations to be inspected; according to the land area occupied by each photovoltaic power station, the workload required by inspection can be estimated, and the unmanned aerial vehicle resource and time can be reasonably distributed; the number of unmanned aerial vehicles required for finishing the inspection can be calculated by combining the inspection task amount, the planned route, the cruising ability of the unmanned aerial vehicles and the running time of the photovoltaic power station; inputting the inspection route and the area information into a control system of the unmanned aerial vehicle, so that the unmanned aerial vehicle can automatically take off and carry out inspection according to a preset route; in the process of inspection, no one can shoot real-time images of the photovoltaic power station and transmit the images back to a control center; through analyzing the collected images, the problems which possibly affect the normal operation of the photovoltaic power station, such as equipment damage, weed shielding and the like, can be timely found, and are timely reported to the management platform, so that the inspection efficiency is improved, and the labor cost and the time cost are reduced.
Optionally, the route is patrolled and examined based on the distribution condition of a plurality of photovoltaic power stations, including:
Step 201, selecting a photovoltaic power station closest to a deployment position of an unmanned aerial vehicle as an initial planning point;
Step 202, in the nth route, starting from the initial power station, judging whether an unvisited photovoltaic power station adjacent to the current photovoltaic power station exists or not;
Step 2021, if there is an adjacent unvisited photovoltaic power plant, adding it to the current route, marking as visited and as current photovoltaic power plant; re-jump to step 202;
step 2022, if there is no adjacent unviewed photovoltaic power station, obtaining a patrol route based on the photovoltaic power station marked as visited in the nth route; jump to step 203;
Step 203, adding 1 to N; selecting the unvisited photovoltaic power station closest to the initial planning point as the initial power station of the updated Nth route; and jumps back to step 202;
Step 204, repeating steps 202 to 203 until all photovoltaic power stations are accessed.
Optionally, the factors affecting operation of the photovoltaic power plant include shade shielding;
judging whether tree shadow shielding exists or not based on the image, comprising:
Using an edge monitoring algorithm to find the edge of the photovoltaic module;
superposing the image of the photovoltaic module and the background image to extract a shadow area in the photovoltaic module;
comparing the pixel value of the shadow area with a preset shadow threshold value; and if the pixel value of the shadow area is larger than the shadow threshold value, judging that tree shadow shielding exists.
Optionally, the method further comprises photovoltaic hot spot detection; the unmanned aerial vehicle comprises an infrared camera module; the photovoltaic hot spot detection comprises:
acquiring infrared image data of a photovoltaic panel;
Creating a pruned U2-Net model and a pruned Mob i l eNetV model;
Dividing the infrared image of the photovoltaic panel by using the pruned U2-Net model to obtain an infrared image of the photovoltaic module;
And carrying out hot spot detection on the infrared image of the photovoltaic module by using the Mob i l eNetV model after pruning, and obtaining a final hot spot detection result according to the detection result.
Optionally, creating the pruned U2-Net model and the pruned Mob i l eNetV model includes:
Loading a pre-trained U2-Net model;
evaluating the accuracy and the cross ratio of the model by using a verification set or a test set;
selecting a pruning strategy of structured pruning;
Evaluating the importance of each neuron or weight to find the neurons that have the least impact on final performance;
Pruning is started from the least important neurons or weights, and the model after pruning is retrained until the preset requirement is met.
Optionally, after receiving the image acquired by the unmanned aerial vehicle, the method further includes:
Calibrating to obtain unmanned aerial vehicle camera parameters and distortion correction parameters according to the internal structure of the unmanned aerial vehicle camera and the established distortion model, and obtaining a mapping relation from the acquired image to the distortion correction image;
detecting and calibrating corner distribution points in all acquired images;
According to the ideal angular point position obtained by the photovoltaic power station layout and the calibrated angular point position obtained by detection, projecting the acquired image to the same coordinate system to become a bird's-eye view, and establishing a mapping relation from the corrected image to the bird's-eye view;
and performing characteristic point matching on the overlapping area in the aerial view, and splicing all aerial views into the panoramic aerial view of the power station.
In a second aspect, the intelligent photovoltaic power station inspection method of the unmanned aerial vehicle provided by the application adopts the following technical scheme.
Unmanned aerial vehicle intelligence photovoltaic power plant inspection system includes:
a first processing module for: obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations;
a second processing module for: obtaining the inspection task amount based on the coverage area of each photovoltaic power station;
A third processing module for: matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station;
A fourth processing module for: transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying;
a fifth processing module for: receiving images acquired by an unmanned aerial vehicle;
a sixth processing module for: judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform.
In a third aspect, the application discloses an electronic device comprising a memory and a processor, the memory having stored thereon a computer program to be loaded by the processor and to perform any of the methods described above.
In a fourth aspect, the present application discloses a computer readable storage medium storing a computer program capable of being loaded by a processor and performing any of the methods described above.
Drawings
FIG. 1 is a flow chart of an intelligent photovoltaic power station inspection method for a unmanned aerial vehicle according to an embodiment of the application;
FIG. 2 is a system block diagram of an intelligent photovoltaic power station inspection system of the unmanned aerial vehicle in an embodiment of the application;
In the figure, 201, a first processing module; 202. a second processing module; 203. a third processing module; 204. a fourth processing module; 205. a fifth processing module; 206. and a sixth processing module.
Detailed Description
The application is further illustrated by the following description of the embodiments in conjunction with the accompanying figures 1-2:
First, what needs to be described here is: in the description of the present application, terms such as "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," "outer," and the like are used for convenience of description only as regards orientation or positional relationship as shown in the accompanying drawings, and do not denote or imply that the apparatus or element in question must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application; moreover, the numerical terms such as the terms "first," "second," "third," etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. In addition, unless explicitly stated or limited otherwise, the terms "mounted," "connected," and "connected" should be construed broadly, and may be, for example, a fixed connection, a releasable connection, an interference fit, a transition fit, or an integral connection; can be directly connected or indirectly connected through an intermediate medium; the specific meaning of the above terms in the present application will be understood by those skilled in the art according to the specific circumstances.
The embodiment of the application discloses an intelligent inspection method for a photovoltaic power station of an unmanned aerial vehicle. Referring to fig. 1, as an embodiment of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, the unmanned aerial vehicle intelligent photovoltaic power station inspection method includes the steps of:
And step 101, obtaining a routing inspection route based on the distribution condition of a plurality of photovoltaic power stations.
And 102, obtaining the inspection task amount based on the coverage area of each photovoltaic power station.
Step 103, matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicle and the total patrol time length; and the total inspection time length corresponds to the daily operation time length of the photovoltaic power station.
And 104, transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying.
And 105, receiving images acquired by the unmanned aerial vehicle.
And step 106, judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform.
Specifically, a plurality of inspection routes can be planned by analyzing the specific positions of each photovoltaic power station, so that the unmanned aerial vehicle can cover all the power stations to be inspected; according to the land area occupied by each photovoltaic power station, the workload required by inspection can be estimated, and the unmanned aerial vehicle resource and time can be reasonably distributed; the number of unmanned aerial vehicles required for finishing the inspection can be calculated by combining the inspection task amount, the planned route, the cruising ability of the unmanned aerial vehicles and the running time of the photovoltaic power station; inputting the inspection route and the area information into a control system of the unmanned aerial vehicle, so that the unmanned aerial vehicle can automatically take off and carry out inspection according to a preset route; in the process of inspection, no one can shoot real-time images of the photovoltaic power station and transmit the images back to a control center; through analyzing the collected images, the problems which possibly affect the normal operation of the photovoltaic power station, such as equipment damage, weed shielding and the like, can be timely found, and are timely reported to the management platform, so that the inspection efficiency is improved, and the labor cost and the time cost are reduced.
As a specific implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, an inspection route is obtained based on the distribution condition of a plurality of photovoltaic power stations, and the method comprises the following steps:
Step 201, selecting a photovoltaic power station closest to a deployment position of an unmanned aerial vehicle as an initial planning point;
Step 202, in the nth route, starting from the initial power station, judging whether an unvisited photovoltaic power station adjacent to the current photovoltaic power station exists or not;
Step 2021, if there is an adjacent unvisited photovoltaic power plant, adding it to the current route, marking as visited and as current photovoltaic power plant; re-jump to step 202;
step 2022, if there is no adjacent unviewed photovoltaic power station, obtaining a patrol route based on the photovoltaic power station marked as visited in the nth route; jump to step 203;
Step 203, adding 1 to N; selecting the unvisited photovoltaic power station closest to the initial planning point as the initial power station of the updated Nth route; and jumps back to step 202;
Step 204, repeating steps 202 to 203 until all photovoltaic power stations are accessed.
Specifically, the photovoltaic power station closest to the deployment position of the unmanned aerial vehicle is selected as an initial planning point, so that the difficulty and cost of unmanned aerial vehicle deployment can be reduced. In the nth route, it is determined from the starting power station whether there is an unvisited photovoltaic power station adjacent to the current photovoltaic power station. If there is an adjacent unvisited photovoltaic power plant, it is added to the current route, marked as visited and as current photovoltaic power plant. Continuous inspection of adjacent photovoltaic power stations can be guaranteed, and missing inspection is avoided. If no adjacent unviewed photovoltaic power stations exist, a patrol route is obtained based on the photovoltaic power stations marked as visited in the Nth route. And then adding 1 to N, and selecting the unviewed photovoltaic power station closest to the initial planning point as the initial power station of the updated Nth route. The continuity and the integrity of the inspection route are guaranteed, and all photovoltaic power stations are accessed.
The efficiency and the accuracy of photovoltaic power station inspection can be effectively improved.
As a specific implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, factors influencing the operation of the photovoltaic power station comprise shade shielding;
judging whether tree shadow shielding exists or not based on the image, comprising:
Using an edge monitoring algorithm to find the edge of the photovoltaic module;
superposing the image of the photovoltaic module and the background image to extract a shadow area in the photovoltaic module;
comparing the pixel value of the shadow area with a preset shadow threshold value; and if the pixel value of the shadow area is larger than the shadow threshold value, judging that tree shadow shielding exists.
Specifically, an edge monitoring algorithm is used to find the edge of the photovoltaic module, so that the position and the shape of the photovoltaic module can be accurately identified. And superposing the image of the photovoltaic module and the background image to extract a shadow area in the photovoltaic module, comparing the pixel value of the shadow area with a preset shadow threshold value, and if the pixel value of the shadow area is larger than the shadow threshold value, judging that a tree exists, so that whether the tree shadow is blocked or not can be effectively judged, and timely processing is performed.
As a specific implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, the method further comprises photovoltaic hot spot detection; the unmanned aerial vehicle comprises an infrared camera module; the photovoltaic hot spot detection comprises:
acquiring infrared image data of a photovoltaic panel;
Creating a pruned U2-Net model and a pruned Mobi l eNetV model;
Dividing the infrared image of the photovoltaic panel by using the pruned U2-Net model to obtain an infrared image of the photovoltaic module;
and carrying out hot spot detection on the infrared image of the photovoltaic module by using the Mobi l eNetV model after pruning, and obtaining a final hot spot detection result according to the detection result.
As one implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, creating a pruned U2-Net model and a pruned Mobi l eNetV model, including:
Loading a pre-trained U2-Net model;
evaluating the accuracy and the cross ratio of the model by using a verification set or a test set;
selecting a pruning strategy of structured pruning;
Evaluating the importance of each neuron or weight to find the neurons that have the least impact on final performance;
Pruning is started from the least important neurons or weights, and the model after pruning is retrained until the preset requirement is met.
As one implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection method, calibrating according to the internal structure of the unmanned aerial vehicle camera and the established distortion model to obtain unmanned aerial vehicle camera parameters and distortion correction parameters, and obtaining a mapping relation from the acquired image to the distortion correction image;
detecting and calibrating corner distribution points in all acquired images;
According to the ideal angular point position obtained by the photovoltaic power station layout and the calibrated angular point position obtained by detection, projecting the acquired image to the same coordinate system to become a bird's-eye view, and establishing a mapping relation from the corrected image to the bird's-eye view;
and performing characteristic point matching on the overlapping area in the aerial view, and splicing all aerial views into the panoramic aerial view of the power station.
The application also provides an unmanned aerial vehicle intelligent photovoltaic power station inspection system, which is one implementation mode of the unmanned aerial vehicle intelligent photovoltaic power station inspection system, and comprises the following steps:
a first processing module 201, configured to: obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations;
a second processing module 202 for: obtaining the inspection task amount based on the coverage area of each photovoltaic power station;
A third processing module 203, configured to: matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station;
a fourth processing module 204 for: transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying;
a fifth processing module 205, configured to: receiving images acquired by an unmanned aerial vehicle;
a sixth processing module 206, configured to: judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform.
The embodiment of the application also discloses electronic equipment.
Specifically, the device comprises a memory and a processor, wherein the memory stores a computer program which can be loaded by the processor and execute any unmanned aerial vehicle intelligent photovoltaic power station inspection method.
The embodiment of the application also discloses a computer readable storage medium. In particular, the computer readable storage medium stores a computer program that can be loaded by a processor and that performs any of the unmanned aerial vehicle intelligent photovoltaic power plant inspection methods described above, for example, the computer readable storage medium comprising: a usb disk, a removable hard disk, a Read-On-y Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: although the present application has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that the present application may be modified or equivalent thereto without departing from the spirit and scope of the application, and all such modifications and improvements thereof are intended to be included within the scope of the appended claims.
Claims (8)
1. The unmanned aerial vehicle intelligent photovoltaic power station inspection method is characterized by comprising the following steps of:
obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations;
Obtaining the inspection task amount based on the coverage area of each photovoltaic power station;
matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station;
transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying;
Receiving images acquired by an unmanned aerial vehicle;
judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform;
Obtain route of patrolling and examining based on the distribution condition of a plurality of photovoltaic power plant, include:
Step 201, selecting a photovoltaic power station closest to a deployment position of an unmanned aerial vehicle as an initial planning point;
Step 202, in the nth route, starting from the initial power station, judging whether an unvisited photovoltaic power station adjacent to the current photovoltaic power station exists or not;
Step 2021, if there is an adjacent unvisited photovoltaic power plant, adding it to the current route, marking as visited and as current photovoltaic power plant; re-jump to step 202;
step 2022, if there is no adjacent unviewed photovoltaic power station, obtaining a patrol route based on the photovoltaic power station marked as visited in the nth route; jump to step 203;
Step 203, adding 1 to N; selecting the unvisited photovoltaic power station closest to the initial planning point as the initial power station of the updated Nth route; and jumps back to step 202;
Step 204, repeating steps 202 to 203 until all photovoltaic power stations are accessed.
2. The unmanned aerial vehicle intelligent photovoltaic power plant inspection method of claim 1, wherein the factors affecting the operation of the photovoltaic power plant include shade shielding;
judging whether tree shadow shielding exists or not based on the image, comprising:
Using an edge monitoring algorithm to find the edge of the photovoltaic module;
Superposing the image of the photovoltaic module and the background image to extract a shadow area in the photovoltaic module;
comparing the pixel value of the shadow area with a preset shadow threshold value; and if the pixel value of the shadow area is larger than the shadow threshold value, judging that tree shadow shielding exists.
3. The unmanned aerial vehicle intelligent photovoltaic power station inspection method of claim 2, further comprising photovoltaic hot spot detection; the unmanned aerial vehicle comprises an infrared camera module; the photovoltaic hot spot detection comprises:
acquiring infrared image data of a photovoltaic panel;
Creating a pruned U2-Net model and a pruned Mobi leNetV model;
Dividing the infrared image of the photovoltaic panel by using the pruned U2-Net model to obtain an infrared image of the photovoltaic module;
And carrying out hot spot detection on the infrared image of the photovoltaic module by using the MobileNetV model after pruning, and obtaining a final hot spot detection result according to the detection result.
4. A method of unmanned aerial vehicle intelligent photovoltaic power plant inspection as claimed in claim 3, wherein creating a pruned U2-Net model and a pruned Mobi leNetV model comprises:
Loading a pre-trained U2-Net model;
evaluating the accuracy and the cross ratio of the model by using a verification set or a test set;
selecting a pruning strategy of structured pruning;
Evaluating the importance of each neuron or weight to find the neurons that have the least impact on final performance;
Pruning is started from the least important neurons or weights, and the model after pruning is retrained until the preset requirement is met.
5. The method of claim 4, further comprising, after receiving the image collected by the drone:
Calibrating to obtain unmanned aerial vehicle camera parameters and distortion correction parameters according to the internal structure of the unmanned aerial vehicle camera and the established distortion model, and obtaining a mapping relation from the acquired image to the distortion correction image;
detecting and calibrating corner distribution points in all acquired images;
According to the ideal angular point position obtained by the photovoltaic power station layout and the calibrated angular point position obtained by detection, projecting the acquired image to the same coordinate system to become a bird's-eye view, and establishing a mapping relation from the corrected image to the bird's-eye view;
and performing characteristic point matching on the overlapping area in the aerial view, and splicing all aerial views into the panoramic aerial view of the power station.
6. Unmanned aerial vehicle intelligence photovoltaic power plant inspection system, its characterized in that includes:
a first processing module for: obtaining a routing inspection route based on the distribution situation of a plurality of photovoltaic power stations;
a second processing module for: obtaining the inspection task amount based on the coverage area of each photovoltaic power station;
A third processing module for: matching a plurality of unmanned aerial vehicles based on the patrol task amount, the patrol route, the flight capacity of the unmanned aerial vehicles and the total patrol time length; the total inspection time length corresponds to the current day operation time length of the photovoltaic power station;
A fourth processing module for: transmitting a control system of the unmanned aerial vehicle based on the regional information of the photovoltaic power station in the inspection route to trigger the unmanned aerial vehicle to start flying;
a fifth processing module for: receiving images acquired by an unmanned aerial vehicle;
a sixth processing module for: judging whether factors influencing the operation of the photovoltaic power station exist or not based on the image, and if so, sending a prompt signal to a management platform:
Obtain route of patrolling and examining based on the distribution condition of a plurality of photovoltaic power plant, include:
Step 201, selecting a photovoltaic power station closest to a deployment position of an unmanned aerial vehicle as an initial planning point;
Step 202, in the nth route, starting from the initial power station, judging whether an unvisited photovoltaic power station adjacent to the current photovoltaic power station exists or not;
Step 2021, if there is an adjacent unvisited photovoltaic power plant, adding it to the current route, marking as visited and as current photovoltaic power plant; re-jump to step 202;
step 2022, if there is no adjacent unviewed photovoltaic power station, obtaining a patrol route based on the photovoltaic power station marked as visited in the nth route; jump to step 203;
Step 203, adding 1 to N; selecting the unvisited photovoltaic power station closest to the initial planning point as the initial power station of the updated Nth route; and jumps back to step 202;
Step 204, repeating steps 202 to 203 until all photovoltaic power stations are accessed.
7. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program for loading and executing by the processor the method of any of claims 1 to 5.
8. A computer readable storage medium, characterized in that a computer program is stored which can be loaded by a processor and which performs the method according to any of claims 1 to 5.
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| CN113867400A (en) * | 2021-10-28 | 2021-12-31 | 中电(沈阳)能源投资有限公司 | Unmanned aerial vehicle-based photovoltaic power generation equipment patrol processing method and system |
| CN117519291A (en) * | 2023-12-04 | 2024-02-06 | 北京信息科技大学 | A photovoltaic panel inspection system based on multi-UAV path planning |
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