CN114723675A - Photovoltaic module detection method, device, equipment and storage medium - Google Patents
Photovoltaic module detection method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention belongs to the technical field of unmanned aerial vehicles, and discloses a photovoltaic module detection method, device, equipment and storage medium. The method comprises the following steps: acquiring infrared image information according to the infrared camera; determining a target photovoltaic module image according to the infrared image information; and carrying out target detection on the target photovoltaic module image, and determining a target hot spot. In this way, use unmanned aerial vehicle to patrol and examine and combine infrared imaging technique and target detection technique to carry out automatic identification to the photovoltaic module hot spot, improved photovoltaic equipment's fortune dimension efficiency.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicles, in particular to a photovoltaic module detection method, device, equipment and storage medium.
Background
At present, the operation and maintenance of a photovoltaic power station mainly depends on the voltage and current and other electrical characteristics of a photovoltaic inverter, but is limited by the installation modes of the inverter and a combiner box, the electrical operation and maintenance can only be accurate to a group string, the electrical operation and maintenance is difficult to be accurate to a specific assembly, the influence of weather is large, and the fault diagnosis precision is not high. The operation and maintenance of specific photovoltaic modules mainly depend on manual inspection, the distribution environment of a large photovoltaic power station is complex, the coverage area is large, the photovoltaic power station is influenced by the terrain, the clutter and the dispersity are further presented, and the manual inspection mode is very time-consuming and labor-consuming. Such as mountain power stations, water power stations and the like, bring great challenges to manual inspection.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention mainly aims to provide a photovoltaic module detection method, a photovoltaic module detection device, equipment and a storage medium, and aims to solve the technical problem that the photovoltaic module inspection difficulty is high in the prior art.
In order to achieve the above object, the present invention provides a method for detecting a photovoltaic module, the method comprising the steps of:
acquiring infrared image information according to the infrared camera;
determining a target photovoltaic module image according to the infrared image information;
and carrying out target detection on the target photovoltaic module image, and determining a target hot spot.
Optionally, the performing target detection on the target photovoltaic module to determine a target hot spot includes:
inputting the target photovoltaic module image into a preset target detection model to obtain a target detection result;
and determining the target hot spot according to the target detection result.
Optionally, before the target photovoltaic module image is input into a preset target detection model and a target detection result is obtained, the method further includes:
acquiring a training sample, wherein the training sample at least comprises a normal photovoltaic module image sample, a hot spot photovoltaic module image sample and a reflective photovoltaic module image sample;
and training a preset initial neural network model according to the training samples to obtain a preset target detection model.
Optionally, the performing target detection on the target photovoltaic module image and after determining the target hot spot further includes:
acquiring positioning information of the unmanned aerial vehicle;
monitoring relative position information of the target hot spot;
and determining the geographic position of the target photovoltaic module according to the positioning information of the unmanned aerial vehicle and the relative position information.
Optionally, after the target detection is performed on the target photovoltaic module image and the target hot spot is determined, the method further includes:
determining a pixel area of the target hot spot;
determining the pixel area of the photovoltaic component corresponding to the target hot spot;
and determining the actual area of the target hot spot according to the pixel area of the target hot spot, the pixel area of the photovoltaic module corresponding to the target hot spot and preset photovoltaic module product information, and determining the fault condition of the corresponding photovoltaic module according to the actual area of the target hot spot.
Optionally, after acquiring the infrared image information according to the infrared camera, the method further includes:
detecting the image quality of the infrared image;
when the image quality of the infrared image is lower than the preset image quality, generating a flight adjustment instruction value;
and adjusting the flight parameters of the unmanned aerial vehicle according to the flight adjustment instruction until the image quality is no longer lower than the preset image quality.
Optionally, the adjusting the flight parameters of the unmanned aerial vehicle according to the flight adjustment instruction until the image quality is no longer lower than a preset image quality includes:
adjusting the flying height of the unmanned aerial vehicle according to the flying adjustment instruction;
the completion the flying height adjustment of unmanned aerial vehicle, just when image quality is less than preset image quality, the adjustment unmanned aerial vehicle's airspeed no longer is less than preset image quality until image quality.
In addition, in order to achieve the above object, the present invention further provides a photovoltaic module detection apparatus, including:
the acquisition module is used for acquiring infrared image information according to the infrared camera;
the processing module is used for determining a target photovoltaic module image according to the infrared image information;
the processing module is further used for carrying out target detection on the target photovoltaic assembly image to determine a target hot spot.
In addition, in order to achieve the above object, the present invention further provides a photovoltaic module detection apparatus, including: a memory, a processor, and a photovoltaic module detection program stored on the memory and executable on the processor, the photovoltaic module detection program configured to implement the steps of the photovoltaic module detection method as described above.
In addition, in order to achieve the above object, the present invention further provides a storage medium, where a photovoltaic module detection program is stored, and when the photovoltaic module detection program is executed by a processor, the steps of the photovoltaic module detection method as described above are implemented.
According to the invention, infrared image information is acquired by the infrared camera; determining a target photovoltaic module image according to the infrared image information; and carrying out target detection on the target photovoltaic module image, and determining a target hot spot. In this way, use unmanned aerial vehicle to patrol and examine and combine infrared imaging technique and target detection technique to carry out automatic identification to the photovoltaic module hot spot, improved photovoltaic equipment's fortune dimension efficiency.
Drawings
FIG. 1 is a schematic structural diagram of a photovoltaic module detection device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of a photovoltaic module inspection method according to the present invention;
FIG. 3 is a schematic flow chart of a photovoltaic module inspection method according to a second embodiment of the present invention;
fig. 4 is a block diagram of a first embodiment of the photovoltaic module inspection apparatus according to the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a photovoltaic module detection device in a hardware operating environment according to an embodiment of the present invention.
As shown in fig. 1, the photovoltaic module inspection apparatus may include: a processor 1001, such as a Central Processing Unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a Wireless interface (e.g., a Wireless-Fidelity (Wi-Fi) interface). The Memory 1005 may be a Random Access Memory (RAM) Memory, or may be a Non-Volatile Memory (NVM), such as a disk Memory. The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the configuration shown in fig. 1 does not constitute a limitation of the photovoltaic assembly detection apparatus and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a storage medium, may include therein an operating system, a network communication module, a user interface module, and a photovoltaic module detection program.
In the photovoltaic module detection apparatus shown in fig. 1, the network interface 1004 is mainly used for data communication with a network server; the user interface 1003 is mainly used for data interaction with a user; the processor 1001 and the memory 1005 of the photovoltaic module detection apparatus of the present invention may be disposed in the photovoltaic module detection apparatus, and the photovoltaic module detection apparatus calls the photovoltaic module detection program stored in the memory 1005 through the processor 1001 and executes the photovoltaic module detection method provided by the embodiment of the present invention.
An embodiment of the present invention provides a photovoltaic module detection method, and referring to fig. 2, fig. 2 is a schematic flow diagram of a first embodiment of a photovoltaic module detection method according to the present invention.
In this embodiment, the photovoltaic module detection method includes the following steps:
step S10: and acquiring infrared image information according to the infrared camera.
It should be noted that, the execution main body of the embodiment is an unmanned aerial vehicle, an infrared camera is disposed on the unmanned aerial vehicle, the unmanned aerial vehicle may be an unmanned aerial vehicle, and may also be other devices having the same or similar functions as the unmanned aerial vehicle.
It should be noted that, this embodiment is applied to the in-process of patrolling and examining to photovoltaic power plant and photovoltaic equipment, because at present the operation and maintenance to specific photovoltaic module mainly relies on the manual work to patrol and examine, and large-scale photovoltaic power plant distribution environment is complicated, and the coverage area is huge, receives the topography influence, still presents clutter and dispersibility, adopts the mode of manual work to patrol and examine very to waste time and energy. Such as mountain power stations, water power stations and the like, bring great challenges to manual inspection. Therefore, this embodiment proposes to patrol and examine through unmanned aerial vehicle carrier this because patrol and examine through unmanned aerial vehicle, receives the topography restriction little, and the field of vision is wide, and is high-efficient, nimble, safe, the photovoltaic power plant operation and maintenance of greatly making things convenient for. Further this embodiment still proposes to take images through the infrared camera that unmanned aerial vehicle carried to whether quick affirmation according to photovoltaic module's temperature distribution condition breaks down and the position and the condition of trouble, this is because infrared camera can detect photovoltaic module's surface temperature accurately, does not cause the interference to the power station operation. The method has the advantages that faults can be detected according to the surface temperature condition of the photovoltaic module, potential safety hazards are eliminated, and the influence of the faults on the power generation efficiency is roughly estimated. In contrast to visible light cameras, infrared cameras can detect failures caused by internal defects of the photovoltaic module. However, the visible light camera can only detect macroscopic faults such as shielding, glass fragmentation and the like. Therefore, the unmanned aerial vehicle is adopted to carry the infrared camera to patrol the photovoltaic power station, the patrol efficiency can be improved, the safety is improved, the operation and maintenance cost is reduced, the operation and maintenance pressure of the power station is greatly reduced, and the unmanned aerial vehicle has very important significance in ensuring the stable operation of the photovoltaic power station.
It should be noted that the photovoltaic module mainly includes a solar panel, a power converter, an energy storage device, an energy transmission device, and the like, which is not limited in this embodiment.
It can be understood that the infrared image information is an image obtained by rendering temperature information acquired by the infrared camera, temperature information is directly detected by a sensor of a general infrared camera, and then the temperature information is rendered into an image through an algorithm. The rendering modes are various, and if the lava mode is adopted, the rendering algorithm of the lava mode can render the image information which is acquired currently in a manner similar to equalization.
It should be noted that, when the photovoltaic module is, for example: when the solar panel breaks down, the area of trouble can obviously be different from the area of normal work around, for example: if the solar panel is damaged or covered by a shield, the heat absorption efficiency will change significantly, and a significant hot spot will appear in the infrared image, for example: defects or line faults occur inside the photovoltaic module, and obvious hot spots also occur in the infrared image.
In the present embodiment, the image quality of the infrared image is detected; when the image quality of the infrared image is lower than the preset image quality, generating a flight adjustment instruction value; and adjusting the flight parameters of the unmanned aerial vehicle according to the flight adjustment instruction until the image quality is no longer lower than the preset image quality.
It should be noted that the image quality indicates whether the acquired infrared image is clear, and due to the influence of the environment, the distance and the device model, the definition of the acquired image does not necessarily reach the standard for identification, because the infrared camera is not focused correctly, and among them, the flying speed of the unmanned aerial vehicle is too fast, which causes the imaging blur of the infrared image and interferes with the detection of the photovoltaic assembly and the hot spot. The problem is solved by adding an image quality judgment method after the unmanned aerial vehicle acquires the image.
In this embodiment, the flying height of the drone is adjusted according to the flight adjustment instruction; the completion the flying height adjustment of unmanned aerial vehicle, just when image quality is less than preset image quality, the adjustment unmanned aerial vehicle's airspeed no longer is less than preset image quality until image quality.
In concrete realization, carry out image quality on line and judge after unmanned aerial vehicle gathers the image, if the image is fuzzy, then try automatic adjustment unmanned aerial vehicle flying height earlier, try automatic adjustment unmanned aerial vehicle flying speed again, continue to carry out flight and image acquisition, processing on next step after the image that unmanned aerial vehicle gathered is clear again.
Further, this embodiment proposes a flight control's preferred scheme to guarantee that unmanned aerial vehicle can reach the image quality of demand as fast as possible, for example: the flight adjustment instruction adjusts the flight height of the unmanned aerial vehicle, firstly flies to a preset first ground clearance calibrated in advance, gradually decreases to a second ground clearance, and stops when the image quality meets the requirement in the descending process. If the second ground clearance is reached and the fuzzy state still exists, the flying speed is gradually reduced until the picture is clear.
Step S20: and determining a target photovoltaic assembly image according to the infrared image information.
It should be noted that in the inspection process, the photovoltaic module needs to be identified first and then hot spot detection is performed, so that the photovoltaic module in the environmental image can be detected by using the deep learning neural network first to lock the image in the target frame where the photovoltaic module is located, and the image in the target frame is the target photovoltaic module image.
In specific implementation, the deep learning neural network for detecting the photovoltaic module can be trained through image samples with the photovoltaic module and without the photovoltaic module and only environment images, during training, the whole photovoltaic module is used as a sample, the sample is marked as a positive sample, and useless environment images are used as negative samples for training.
Step S30: and carrying out target detection on the target photovoltaic module image, and determining a target hot spot.
It should be noted that after the target photovoltaic module image is determined, the fault point needs to be further confirmed, and in different infrared images, the same temperature may correspond to different colors. Observe unmanned aerial vehicle and patrol and examine the video and can discover, when appearing very bright region in the infrared camera field of vision, other regions all can have the darkening of certain degree.
The embodiment performs recognition through an object detection model. For example: a deep learning neural network is formed by using 12 residual assemblies and 1001 convolution kernels to carry out photovoltaic assembly and hot spot detection, and a basic framework of the deep learning neural network can be divided into 4 parts, namely Input, Back bone, neutral and Prediction. The Input part enriches the data set by splicing data enhancement, and has low requirement on hardware equipment and low calculation cost. The Backbone part mainly comprises CSP modules, and feature extraction is carried out through CSPDarknet 53. FPN and path aggregation networks (PANet) are used in Neck to aggregate the image features at this stage. Finally, the network performs target prediction and outputs through prediction. The above neural network model is only used for explaining the structure of the neural network, and is not used for limiting the present solution.
It can be understood that a deep learning neural network can be used for detecting a photovoltaic module and also can be used for detecting a hot spot at a position of the photovoltaic module. The two functions can be combined and carried out in two steps, namely, the photovoltaic module is detected, and then the hot spot is detected on the photovoltaic module, so that the photovoltaic module with the hot spot and the position of the hot spot can be detected.
In the embodiment, positioning information of the unmanned aerial vehicle is obtained; monitoring relative position information of the target hot spot; and determining the geographic position of the target photovoltaic module according to the positioning information of the unmanned aerial vehicle and the relative position information.
It should be noted that after the hot spots are detected, the unmanned aerial vehicle GPS positioning information corresponding to the images is combined to determine which photovoltaic module has the hot spots, and then further corresponding processing is performed. This is because, after the target hotspot is confirmed, information about the target hotspot needs to be acquired to provide an information basis for subsequent maintenance. The specific position information of the hot spot needs to be determined, the accurate geographic position can be determined by means of a GPS (global positioning system) of the unmanned aerial vehicle, the approximate position of the photovoltaic assembly with the problem can be determined through the GPS, and further the photovoltaic station with the problem can be known so that maintenance personnel can go to the station. But even if the rough position is known, the specific position of the photovoltaic module is generally required to be identified according to the actual image shot by the unmanned aerial vehicle. In view of the situation, the orientation information of the hot spot can be calculated through the image acquired by the unmanned aerial vehicle, and a more accurate position of the hot spot can be determined according to the current position of the unmanned aerial vehicle and the relative position (distance and azimuth angle) of the hot spot and the unmanned aerial vehicle.
In this embodiment, the pixel area of the target hot spot is determined; determining the pixel area of the photovoltaic component corresponding to the target hot spot; and determining the actual area of the target hot spot according to the pixel area of the target hot spot, the pixel area of the photovoltaic module corresponding to the target hot spot and preset photovoltaic module product information, and determining the fault condition of the corresponding photovoltaic module according to the actual area of the target hot spot.
It should be noted that, after the target hot spot is confirmed, a further hot spot confirmation condition is needed to help the maintenance staff prepare a maintenance tool and strategy, specifically, after the hot spot on the photovoltaic module and the photovoltaic module is detected, a ratio between a hot spot target frame area and a photovoltaic module target frame area is calculated, which can be used as a reference value of the hot spot area size to further help the maintenance staff to guess the fault type and scale, because this step also needs to confirm the frame surface or the surface of each type of module as a reference object, it also needs to collect the size parameter of each type of module in advance, that is, preset photovoltaic module product information, and in addition, it also needs to identify the photovoltaic module according to the target photovoltaic module image to determine the type of the reference object, for example: the hot spots appear in the solar cell panel, the solar cell panel where the hot spots are located can be identified, the model or the type of the solar cell panel is determined, the frame size of the corresponding solar cell panel is found by combining preset photovoltaic module product information, and then the size of the target hot spots is calculated. Therefore, the photovoltaic modules need to be labeled for identifying the photovoltaic modules, samples of various types and sizes need to be collected and labeled, and the neural network needs to be trained, so that various types of targets can be detected during detection.
The embodiment acquires infrared image information according to the infrared camera; determining a target photovoltaic module image according to the infrared image information; and carrying out target detection on the target photovoltaic module image, and determining a target hot spot. In this way, use unmanned aerial vehicle to patrol and examine and combine infrared imaging technique and target detection technique to carry out automatic identification to the photovoltaic module hot spot, improved photovoltaic equipment's fortune dimension efficiency.
Referring to fig. 3, fig. 3 is a schematic flow chart of a photovoltaic module inspection method according to a second embodiment of the present invention.
Based on the first embodiment, in step S30, the method for detecting a photovoltaic module in this embodiment includes:
step S31: and inputting the target photovoltaic module image into a preset target detection model to obtain a target detection result.
It should be noted that the hot spot detection needs to be obtained by identifying a pre-trained preset target detection model, where the preset target detection model may be a neural network model, specifically, a deep neural network in which a deep learning neural network is composed of 1001 convolution kernels and performs photovoltaic module and hot spot detection may be used as the neural network model, and a basic framework of the deep learning neural network may be divided into 4 parts, i.e., Input, backhaul, neutral and Prediction. The Input part enriches the data set by splicing data enhancement, and has low requirement on hardware equipment and low calculation cost. The Backbone part mainly comprises CSP modules, and feature extraction is carried out through CSPDarknet 53. FPN and path aggregation networks (PANet) are used in Neck to aggregate the image features at this stage. And finally, the network carries out target prediction and obtains a final detection result through prediction output, wherein the detection result comprises whether hot spots exist or not, and if yes, the position of the hot spots in the target photovoltaic module image is output.
The deep learning neural network can be used for detecting one photovoltaic module and also can be used for detecting the hot spot at the position of the photovoltaic module. The two functions can be combined and carried out in two steps, namely, the photovoltaic module is detected, and then the hot spot is detected on the photovoltaic module, so that the photovoltaic module with the hot spot and the position of the hot spot can be detected.
In this embodiment, training samples are obtained, where the training samples at least include a normal photovoltaic module image sample, a hot spot photovoltaic module image sample, and a reflective photovoltaic module image sample; and training a preset initial neural network model according to the training samples to obtain a preset target detection model.
It should be noted that, in the hot spot detection process, the most susceptible place of the neural network to the erroneous judgment is the situation that the photovoltaic module may reflect light, and when the photovoltaic module generates a reflected image of the sun, the other photovoltaic module areas may become dark. In order to cope with such a phenomenon, when detecting hot spots and reflection light, the brightness contrast between the hot spots or the reflection light regions and the surrounding regions and the shape information of the hot spots or the reflection light regions can be used. In the infrared image, the color development state of the reflective image and the color development state of the hot spot are very similar, and the only difference is that the edge brightness change condition of the reflective image is less obvious compared with the hot spot, namely the hot spot has more obvious boundary sense, and then the mixed sample with the reflective photovoltaic module image sample and the hot spot photovoltaic module image sample can be used as a difficult sample to train the neural network model in the model training process so as to improve the distinguishing capability of the preset neural network model on the reflective image and the hot spot image. Because the solar panel actually has a reflection phenomenon, the infrared image and the hot spot are similar to each other, and interference is caused. When a sample is marked, the reflection and the hot spots can be distinguished according to the characteristics that the edge of the reflection spot is gradually changed and the like, and then a type of reflection is added in the sample training process. When the hot spot is detected by the unmanned plane, the reflected light and the hot spot are simultaneously detected, so that the reflected light and the hot spot are distinguished.
Step S32: and determining the target hot spot according to the target detection result.
It can be understood that when the detected hot spot information and the position of the hot spot exist in the target result, the target photovoltaic can be known to have the target hot spot, and after the hot spot is locked, other hot spot information can be further obtained according to the target hot spot.
The embodiment inputs the target photovoltaic module image into a preset target detection model to obtain a target detection result; and determining the target hot spot according to the target detection result. By adding the difficult sample during model training, the discrimination of the hot spots and the reflected light image is improved, the effective identification of the hot spots is realized, and the accuracy of hot spot identification is improved.
In addition, an embodiment of the present invention further provides a storage medium, where a photovoltaic module detection program is stored on the storage medium, and when executed by a processor, the photovoltaic module detection program implements the steps of the photovoltaic module detection method described above.
Referring to fig. 4, fig. 4 is a block diagram of a first embodiment of the photovoltaic module detection apparatus according to the present invention.
As shown in fig. 4, a photovoltaic module detection apparatus provided in an embodiment of the present invention includes:
and the acquisition module 10 is used for acquiring infrared image information according to the infrared camera.
And the processing module 20 is configured to determine an image of the target photovoltaic module according to the infrared image information.
The processing module 20 is further configured to perform target detection on the target photovoltaic module image to determine a target hot spot.
It should be understood that the above is only an example, and the technical solution of the present invention is not limited in any way, and in a specific application, a person skilled in the art may set the technical solution as needed, and the present invention is not limited thereto.
The embodiment acquiring module 10 acquires infrared image information according to the infrared camera; the processing module 20 determines a target photovoltaic module image according to the infrared image information; the processing module 20 performs target detection on the target photovoltaic module image to determine a target hot spot. In this way, use unmanned aerial vehicle to patrol and examine and combine infrared imaging technique and target detection technique to carry out automatic identification to the photovoltaic module hot spot, improved photovoltaic equipment's fortune dimension efficiency.
In this embodiment, the processing module 20 is further configured to input an image of the target photovoltaic module into a preset target detection model to obtain a target detection result;
and determining the target hot spot according to the target detection result.
In this embodiment, the processing module 20 is further configured to obtain a training sample, where the training sample at least includes a normal photovoltaic module image sample, a hot spot photovoltaic module image sample, and a reflective photovoltaic module image sample;
and training a preset initial neural network model according to the training samples to obtain a preset target detection model.
In this embodiment, the processing module 20 is further configured to obtain positioning information of the unmanned aerial vehicle;
monitoring relative position information of the target hot spot;
and determining the geographic position of the target photovoltaic module according to the positioning information of the unmanned aerial vehicle and the relative position information.
In this embodiment, the processing module 20 is further configured to determine a pixel area of the target hot spot;
determining the pixel area of the photovoltaic component corresponding to the target hot spot;
and determining the actual area of the target hot spot according to the pixel area of the target hot spot, the pixel area of the photovoltaic module corresponding to the target hot spot and preset photovoltaic module product information, and determining the fault condition of the corresponding photovoltaic module according to the actual area of the target hot spot.
In this embodiment, the processing module 20 is further configured to detect an image quality of an infrared image;
when the image quality of the infrared image is lower than the preset image quality, generating a flight adjustment instruction value;
and adjusting the flight parameters of the unmanned aerial vehicle according to the flight adjustment instruction until the image quality is no longer lower than the preset image quality.
In this embodiment, the processing module 20 is further configured to adjust the flying height of the unmanned aerial vehicle according to the flight adjustment instruction;
the completion the flying height adjustment of unmanned aerial vehicle, just when image quality is less than preset image quality, the adjustment unmanned aerial vehicle's airspeed no longer is less than preset image quality until image quality.
It should be noted that the above-described work flows are only exemplary, and do not limit the scope of the present invention, and in practical applications, a person skilled in the art may select some or all of them to achieve the purpose of the solution of the embodiment according to actual needs, and the present invention is not limited herein.
In addition, the technical details that are not described in detail in this embodiment can be referred to the method for detecting a photovoltaic module provided in any embodiment of the present invention, and are not described herein again.
Further, it is to be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention or portions thereof that contribute to the prior art may be embodied in the form of a software product, where the computer software product is stored in a storage medium (e.g. Read Only Memory (ROM)/RAM, magnetic disk, optical disk), and includes several instructions for enabling a terminal device (e.g. a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. The photovoltaic module detection method is characterized in that the photovoltaic module is detected by an unmanned aerial vehicle, an infrared camera is arranged on the unmanned aerial vehicle, and the photovoltaic module detection method comprises the following steps:
acquiring infrared image information according to the infrared camera;
determining a target photovoltaic module image according to the infrared image information;
and carrying out target detection on the target photovoltaic assembly image, and determining a target hot spot.
2. The method of claim 1, wherein the target detection of the target photovoltaic module, determining a target hot spot, comprises:
inputting the target photovoltaic module image into a preset target detection model to obtain a target detection result;
and determining the target hot spot according to the target detection result.
3. The method of claim 2, wherein before inputting the target photovoltaic module image into a preset target detection model and obtaining a target detection result, the method further comprises:
acquiring a training sample, wherein the training sample at least comprises a normal photovoltaic module image sample, a hot spot photovoltaic module image sample and a reflective photovoltaic module image sample;
and training a preset initial neural network model according to the training samples to obtain a preset target detection model.
4. The method of claim 1, wherein the target detection of the target photovoltaic module image, after determining the target hotspot, further comprises:
acquiring positioning information of the unmanned aerial vehicle;
monitoring relative position information of the target hot spot;
and determining the geographic position of the target photovoltaic module according to the positioning information of the unmanned aerial vehicle and the relative position information.
5. The method of claim 1, wherein the target detection of the target photovoltaic module image, after determining the target hotspot, further comprises:
determining a pixel area of the target hot spot;
determining the pixel area of the photovoltaic component corresponding to the target hot spot;
and determining the actual area of the target hot spot according to the pixel area of the target hot spot, the pixel area of the photovoltaic module corresponding to the target hot spot and preset photovoltaic module product information, and determining the fault condition of the corresponding photovoltaic module according to the actual area of the target hot spot.
6. The method of claim 1, wherein after acquiring infrared image information from the infrared camera, further comprising:
detecting the image quality of the infrared image;
when the image quality of the infrared image is lower than the preset image quality, generating a flight adjustment instruction value;
and adjusting the flight parameters of the unmanned aerial vehicle according to the flight adjustment instruction until the image quality is no longer lower than the preset image quality.
7. The method of claim 6, wherein said adjusting flight parameters of the drone according to the flight adjustment instructions until the image quality is no longer below a preset image quality comprises:
adjusting the flight height of the unmanned aerial vehicle according to the flight adjustment instruction;
the completion the flying height adjustment of unmanned aerial vehicle, just when image quality is less than preset image quality, the adjustment unmanned aerial vehicle's airspeed no longer is less than preset image quality until image quality.
8. A photovoltaic module detection apparatus, comprising:
the acquisition module is used for acquiring infrared image information according to the infrared camera;
the processing module is used for determining a target photovoltaic module image according to the infrared image information;
the processing module is further used for carrying out target detection on the target photovoltaic assembly image to determine a target hot spot.
9. A photovoltaic module inspection apparatus, the apparatus comprising: a memory, a processor, and a photovoltaic module detection program stored on the memory and executable on the processor, the photovoltaic module detection program configured to implement the steps of the photovoltaic module detection method of any one of claims 1 to 7.
10. A storage medium having a photovoltaic module detection program stored thereon, wherein the photovoltaic module detection program, when executed by a processor, implements the steps of the photovoltaic module detection method according to any one of claims 1 to 7.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115586792A (en) * | 2022-09-30 | 2023-01-10 | 三峡大学 | Iron tower parameter-based unmanned aerial vehicle power inspection system and method |
| CN116152195A (en) * | 2023-02-20 | 2023-05-23 | 北京御航智能科技有限公司 | Hot spot detection method and device for photovoltaic cell panel and electronic equipment |
| CN116896320A (en) * | 2023-03-30 | 2023-10-17 | 淮南市国家电投新能源有限公司 | Water-land-air intelligent operation and maintenance method applied to water surface photovoltaic power station |
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- 2022-03-17 CN CN202210262405.7A patent/CN114723675A/en active Pending
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115586792A (en) * | 2022-09-30 | 2023-01-10 | 三峡大学 | Iron tower parameter-based unmanned aerial vehicle power inspection system and method |
| CN115586792B (en) * | 2022-09-30 | 2023-06-27 | 三峡大学 | Unmanned aerial vehicle power inspection system and method based on iron tower parameters |
| CN116152195A (en) * | 2023-02-20 | 2023-05-23 | 北京御航智能科技有限公司 | Hot spot detection method and device for photovoltaic cell panel and electronic equipment |
| CN116896320A (en) * | 2023-03-30 | 2023-10-17 | 淮南市国家电投新能源有限公司 | Water-land-air intelligent operation and maintenance method applied to water surface photovoltaic power station |
| CN116896320B (en) * | 2023-03-30 | 2024-04-05 | 淮南市国家电投新能源有限公司 | Water-land-air intelligent operation and maintenance method applied to water surface photovoltaic power station |
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