Intelligent inspection system and method for photovoltaic module of photovoltaic power generation system

Abstract

The invention discloses an intelligent inspection system and method for a photovoltaic module of a photovoltaic power generation system, and relates to the technical field of intelligent inspection. The method comprises the steps that a high-definition camera and an infrared imager carried by an unmanned aerial vehicle automatically inspect a photovoltaic panel, real-time operation data are acquired and transmitted to a preprocessing module, the preprocessing module optimizes an image through denoising, graying, contrast enhancement and geometric correction, a Unet algorithm is utilized to accurately segment strings and assemblies in the image, and the photovoltaic panel is obtained. And a defect identification module analyzes the processed image, calculates the average temperature, compares the average temperature with a threshold value, identifies hot spot defects, and finally outputs specific information, fault types and maintenance suggestions of a defective component by the system, and generates an inspection report. According to the technical scheme, a fault source can be quickly positioned, and cloud storage and an AI analysis tool are used for managing data and predicting potential maintenance requirements.

Description

Technical Field

[0001] The present invention relates to the field of intelligent inspection technology, and in particular to an intelligent inspection system and method for photovoltaic components of a photovoltaic power generation system. Background Art

[0002] Photovoltaic modules may encounter a variety of faults during their use, which not only affect power generation efficiency and service life, but may also bring safety hazards and increase maintenance costs. In order to prevent and reduce the occurrence of these faults, manufacturers should strengthen the quality control of raw material procurement and production processes, and users should also regularly inspect and maintain photovoltaic modules.

[0003] With the continued growth of renewable energy, especially solar power generation, the effective management and maintenance of photovoltaic power generation systems has become increasingly important. Traditional manual inspection methods are costly and have limited coverage for vast photovoltaic power plants. It is difficult to detect and locate all faults in a timely and accurate manner, resulting in reduced power plant efficiency. Summary of the invention

[0004] The purpose of the present invention is to provide a photovoltaic module intelligent inspection system and method for a photovoltaic power generation system to solve the problems of limited coverage and difficulty in timely and accurate detection and positioning of faults mentioned in the above background technology.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: an intelligent inspection system for photovoltaic components of a photovoltaic power generation system, comprising a drone, the drone equipped with a camera and an infrared imager, the drone autonomously inspects within the distribution range of the photovoltaic components, the camera and the infrared imager collect image data of the photovoltaic components, the camera and the infrared imager are connected to the image preprocessing module signal, the image preprocessing module is connected to the defect recognition module and the database signal, the database is connected to the fault prediction module signal, the fault prediction module and the defect recognition module are connected to the work platform signal.

[0006] A photovoltaic component intelligent inspection method for a photovoltaic power generation system comprises the following steps: S1: The drone conducts automatic inspections of the photovoltaic panels of the photovoltaic power station within the distribution range of the photovoltaic modules. The high-definition camera and infrared imager carried by the drone obtain the real-time operation data of the photovoltaic modules to be inspected, take appearance images and temperature distribution images, and transmit the captured image signals to the image preprocessing module; S2: The collected image data is preprocessed through the preprocessing module. First, data cleaning is performed, and filters are used to remove random noise in the image, smooth the image, and improve the accuracy of subsequent processing; then grayscale processing is performed to convert the color image into a grayscale image containing only brightness information, reducing computational complexity and improving processing speed; then contrast enhancement is performed to make important features in the image more obvious by adjusting the contrast of the image; finally, illumination correction and geometric transformation are performed to eliminate shadows and bright spots in the image caused by uneven illumination and correct the geometric distortion of the image to ensure the accuracy of the object morphology in the image; S3: The preprocessing module segments the preprocessed image. The preprocessing module has a built-in Unet segmentation algorithm. The Unet segmentation algorithm is used to segment the acquired infrared image, and the strings and individual components of the photovoltaic modules are automatically distinguished and extracted from the image background. The Unet algorithm can automatically learn the target segmentation features, improve the segmentation accuracy, and improve the computing performance. The segmented image is binarized, and the connected domain detection algorithm is used to identify and calculate the area where each string and module are located. S4: The defect recognition module recognizes the image data to calculate the component information, and identifies whether the photovoltaic component has a fault by comparing it with a preset threshold. For example, the average temperature of each string and component area is calculated, and the obtained average temperature is compared with a preset threshold. The photovoltaic components suspected of having a thermal defect are identified according to the comparison result. If the average temperature of a component is higher than the set threshold, it is determined that there may be a hot spot defect. S5: The defect recognition module outputs the pixel position coordinates and temperature value related information of the suspected defective photovoltaic modules in the detection results, outputs the fault type and recommended maintenance measures, and generates a corresponding inspection report.

[0007] Preferably, the defect identification module determines that a photovoltaic module is suspected of having a hot spot defect based on the average temperature of the module exceeding a preset threshold, wherein the threshold is set based on the temperature range and historical data of the photovoltaic module during normal operation; the report includes the fault diagnosis results obtained by analyzing the AI ​​fault identification algorithm, which can quickly locate the source of the fault and shorten the response time and repair time.

[0008] Preferably, the Unet segmentation algorithm improves the performance and accuracy of the original Unet algorithm in the infrared image segmentation scenario by deepening the network structure or optimizing the training strategy.

[0009] Preferably, the connected domain detection algorithm adopts the Two-Pass algorithm. During the first scan, the image is traversed from left to right and from top to bottom, and an initial label is assigned to each valid pixel. If the left or upper neighbor of a pixel has a label, then the pixel will inherit this label; if not, a new label is assigned. During the second scan, the label conflict problem is resolved to ensure that all connected pixels have the same label. The main function of the connected domain algorithm is to identify and segment regions composed of pixels with the same pixel value and adjacent positions, providing a basis for image recognition.

[0010] Preferably, the method further comprises a feedback mechanism, wherein the feedback adjustment module is used to adjust the preset threshold value according to the output detection of the suspected defective component and the subsequent actual inspection results, so as to optimize the accuracy of defect detection.

[0011] A photovoltaic component intelligent inspection system for a photovoltaic power generation system, comprising a photovoltaic component, a drone and a server, characterized in that the drone is equipped with two sensors, a high-definition camera and an infrared thermal imager, the drone has a built-in navigation and positioning module, a route planning module, an image acquisition module and a flight control module, and is detachably connected to the drone, the flight control module can receive signals from the navigation and positioning module and the route planning module, the high-definition camera and the infrared imager are controlled by the image acquisition module, the server has an image preprocessing module, a defect recognition module, a result output module, a fault prediction module, and a database module, and the high-definition camera and the infrared thermal imager signals are connected to the server; The route planning module is responsible for developing one or more optimal flight paths for the UAV based on mission requirements, flight environment and other relevant factors; The positioning module uses GPS, GLONASS, and BeiDou global navigation satellite systems to provide accurate location information for the drone; The flight control module is responsible for controlling the flight attitude, speed, and direction of the drone. It receives instructions from the route planning module and the navigation and positioning module, and controls the motor and steering components of the drone according to these instructions, so that the drone flies according to the predetermined path and speed; The image acquisition module is used to control the infrared camera carried by the drone to acquire high-definition images of the photovoltaic modules; The image preprocessing module is used to perform image preprocessing, segmentation and connected domain detection; The defect identification module is used to calculate the average temperature of each component and compare it with a threshold value to identify components suspected of having thermal defects; The result output module is used to organize and output relevant information of suspected defective components so that operators can conduct further analysis and processing; The fault prediction module is used to predict possible future faults of the equipment based on historical data and real-time data; The database module is used to store and manage various types of data generated during the drone inspection process, provide data support, help manage and utilize data, improve inspection efficiency, and reduce operating costs.

[0012] A photovoltaic component intelligent inspection system for a photovoltaic power generation system also includes a feedback adjustment module for receiving input from an operator and dynamically adjusting a threshold value for defect detection according to actual inspection results.

[0013] Preferably, the result output module generates a detailed inspection report based on the performance score and AI fault identification results, and the report includes the number, location, performance score, fault type and recommended maintenance measures of the photovoltaic module.

[0014] Preferably, the route planning module includes sensor data to provide geographic and environmental information. Through the processor and algorithm, the module can quickly process real-time data to ensure the timeliness and accuracy of route planning. The user generates the optimal route by inputting specific mission parameters. The module also provides an automatic adjustment function to avoid weather restricted areas and other flight obstacles. The module dynamically adjusts the flight plan according to real-time flight conditions and mission requirements to respond to emergencies.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. The invention can quickly cover large areas of photovoltaic panels, reduce costs, and safely access inaccessible areas, reducing safety risks for personnel.

[0016] 2. The invention also includes a result output module and a fault prediction module, which can quickly locate the source of the fault by analyzing the data collected by the drone, and use cloud storage and AI analysis tools to manage the data and predict potential maintenance needs.

[0017] 3. The image preprocessing module of the invention has a built-in Unet segmentation algorithm, which can fully automatically and efficiently segment and locate possible defects in infrared images and clearly identify the components to which they belong. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1is a flow chart of a photovoltaic component intelligent inspection method of a photovoltaic power generation system of the present invention; Figure 2 It is a structural diagram of a drone and related modules of a photovoltaic module intelligent inspection system of a photovoltaic power generation system of the present invention; Figure 3 It is a structural diagram of a photovoltaic component intelligent inspection system server and related modules of the photovoltaic power generation system of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] The embodiment of the present invention provides a photovoltaic component intelligent inspection method of the photovoltaic power generation system of the present invention, which can quickly locate the fault source, manage data and predict potential maintenance needs. The embodiment of the present invention also provides a corresponding photovoltaic component intelligent inspection system of the photovoltaic power generation system. The following are detailed descriptions.

[0022] See also Figure 1 , Figure 2 and Figure 3 The photovoltaic module intelligent inspection system of the present invention mainly includes a drone, a server and related software modules. Photovoltaic modules, drones and servers, the drone is equipped with two sensors, a high-definition camera and an infrared thermal imager, the drone has a built-in navigation and positioning module, a route planning module, an image acquisition module and a flight control module and is detachably connected to the drone, the flight control module can receive signals from the navigation and positioning module and the route planning module, the high-definition camera and the infrared imager are controlled by the image acquisition module, the server has an image preprocessing module, a defect recognition module, a result output module, a fault prediction module, and a database module, and the high-definition camera and the infrared thermal imager signals are connected to the server; This system has a wide range of application scenarios, including but not limited to daily maintenance, fault detection and preventive maintenance of large photovoltaic power plants. Its advantage lies in the efficient and automated inspection process, which greatly reduces manpower requirements and inspection time, while improving the accuracy and safety of detection. In addition, using data analysis and machine learning technology, the system can predict potential failures, thereby reducing maintenance costs and extending the service life of equipment; The route planning module is responsible for developing one or more optimal flight paths for the drone based on mission requirements, flight environment and other relevant factors. The module contains sensor data to provide geographic and environmental information, and quickly processes real-time data through processors and algorithms to ensure the immediacy and accuracy of route planning. Users generate the optimal route by inputting specific mission parameters, and the module also provides automatic adjustment functions to avoid weather restricted areas and other flight obstacles; The positioning module uses GPS, GLONASS, and Beidou global navigation satellite systems to provide accurate location information for the drone. The flight control module is responsible for controlling the flight attitude, speed, and direction of the drone. It receives instructions from the route planning module and the navigation and positioning module, and controls the motor and steering components of the drone according to these instructions, so that the drone flies according to the predetermined path and speed; The image acquisition module is used to control the infrared camera on the drone to acquire high-definition images of photovoltaic modules. The image preprocessing module is used to perform image preprocessing, segmentation, and connected domain detection. The defect recognition module is used to calculate the average temperature of each component and compare it with the threshold to identify components suspected of having thermal defects; The feedback adjustment module is used to receive input from operators and dynamically adjust the threshold of defect detection according to the actual inspection results. The result output module generates a detailed inspection report based on the performance score and AI fault identification results. The report contains the number, location, performance score, fault type and recommended maintenance measures of the photovoltaic module; The fault prediction module is used to predict possible future equipment failures based on historical data and real-time data. The database module is used to store and manage various types of data generated during the drone inspection process, provide data support, help manage and utilize data, improve inspection efficiency, and reduce operating costs. The present invention integrates drone technology, image processing technology, and data analysis algorithms to achieve efficient and automated inspections of photovoltaic power stations. The system not only improves inspection efficiency and accuracy, but also reduces labor costs and safety risks. It is an important tool for the maintenance and management of photovoltaic power stations.

[0023] The implementation steps of the present invention are: S1: The drone conducts automatic inspections of the photovoltaic panels of the photovoltaic power station within the distribution range of the photovoltaic modules. The high-definition camera and infrared imager carried by the drone obtain the real-time operation data of the photovoltaic modules to be inspected, take appearance images and temperature distribution images, and transmit the captured image signals to the image preprocessing module; S2: The collected image data is preprocessed through the preprocessing module. First, data cleaning is performed, and filters are used to remove random noise in the image, smooth the image, and improve the accuracy of subsequent processing; then grayscale processing is performed to convert the color image into a grayscale image containing only brightness information, reducing computational complexity and improving processing speed; then contrast enhancement is performed to make important features in the image more obvious by adjusting the contrast of the image; finally, illumination correction and geometric transformation are performed to eliminate shadows and bright spots in the image caused by uneven illumination and correct the geometric distortion of the image to ensure the accuracy of the object morphology in the image; S3: The preprocessing module segments the preprocessed image. The preprocessing module has a built-in Unet segmentation algorithm. The Unet segmentation algorithm is used to segment the acquired infrared image, and the strings and individual components of the photovoltaic modules are automatically distinguished and extracted from the image background. The Unet algorithm can automatically learn the target segmentation features, improve the segmentation accuracy, and improve the computing performance. The segmented image is binarized, and the connected domain detection algorithm is used to identify and calculate the area where each string and module are located. S4: The defect recognition module recognizes the image data to calculate the component information, and identifies whether the photovoltaic component has a fault by comparing it with a preset threshold. For example, the average temperature of each string and component area is calculated, and the obtained average temperature is compared with a preset threshold. The photovoltaic components suspected of having a thermal defect are identified according to the comparison result. If the average temperature of a component is higher than the set threshold, it is determined that there may be a hot spot defect. S5: The defect recognition module outputs the pixel position coordinates and temperature value related information of the suspected defective photovoltaic modules in the detection results, outputs the fault type and recommended maintenance measures, and generates a corresponding inspection report.

[0024] The specific process of defect detection is as follows: 1. Sort the average temperatures of all strings and take the median value. Set the temperature difference threshold between the string and the mean value as △T1.

[0025] 2. Traverse all strings, subtract each string from the string median, and obtain △Ts. When △Ts≤△T1, the current string can be considered to be a large-area normal string. Otherwise, the string is considered to be an abnormal string with large-area defects.

[0026] 3. Traverse the components of each string in turn. If the current traversal is a normal string, use the temperature mean of the component to make a difference with the components of the string; if the current traversal is an abnormal string, use the median of the string to make a difference with the components of the string, and the difference is △Tp.

[0027] 4. Assume that the component temperature difference threshold is △T2. When △Tp≤△T2, the current component is considered to be a normal component, otherwise it is considered to be an abnormal component.

[0028] 5. Repeat steps 1-4 for the infrared image of the drone inspection until no new defective components are identified, and output all components identified as defective and the strings they belong to.

[0029] The connected domain detection algorithm uses a Two-Pass algorithm. During the first scan, the image is traversed from left to right and from top to bottom, and an initial label is assigned to each valid pixel. If the left or upper neighbor of a pixel has a label, the pixel will inherit the label; if not, a new label is assigned. During the second scan, the label conflict problem is resolved to ensure that all connected pixels have the same label. The main function of the connected domain algorithm is to identify and segment regions composed of pixels with the same pixel value and adjacent positions, providing a basis for image recognition.

[0030] The feedback adjustment module is responsible for receiving the suspected defective component information provided by the result output module, and analyzing and processing this data according to the subsequent actual inspection results. Its core function is to dynamically adjust the detection threshold preset in the defect identification module to optimize the accuracy of the system for defect detection. The feedback mechanism is as follows: 1. The result output module outputs relevant information of the suspected defective components, such as component number, location, performance score, and fault type, to the feedback adjustment module.

[0031] 2. The operator or professional maintenance team conducts on-site inspection based on the information provided by the result output module. This step is the key to verifying the system test results, and it is necessary to record the actual test findings in detail and compare them with the system test results.

[0032] 3. After receiving the actual test results, the feedback adjustment module compares and analyzes them with the preliminary system test results. This step is to identify possible false positives or false negatives in the system.

[0033] 4. Based on the results of data comparison and analysis, the feedback adjustment module automatically calculates and adjusts the detection threshold in the defect identification module. If it is found that the system has many false alarms, the threshold for determining defects will be appropriately increased, otherwise it will be reduced.

[0034] 5. The adjusted threshold is applied to the defect recognition module for subsequent detection processes to improve the system recognition accuracy and reduce false positives and false negatives. The feedback adjustment module will continue to monitor the effect of the adjustment to ensure that the accuracy of defect detection continues to improve. This process may require multiple iterations to ensure the optimization of system performance.

[0035] The test report contains the fault diagnosis results obtained by the AI ​​fault identification algorithm analysis, which can quickly locate the source of the fault, shorten the response time and repair time. The prediction report enables the maintenance team to intervene before the problem actually occurs, reducing the need for emergency repairs, thereby achieving a more efficient and lower-cost maintenance strategy. When the PV power station staff receives the test report and prediction report, the staff will first review the test report and prediction report in detail to understand the data provided in the report, the fault diagnosis results, and the predicted potential problems. Based on the information in the test report, the staff will conduct an on-site inspection of the components marked as having potential problems to confirm the existence of the fault and accurately locate the faulty component. Once the fault is confirmed, the staff will formulate a repair or replacement plan based on the recommendations of the report and the severity of the fault. For potential future problems mentioned in the prediction report, the staff will evaluate and enhance the maintenance frequency of the components or perform necessary preventive measures such as system upgrades to reduce the risk of future failures. After repair or replacement, the staff will closely monitor the recovery and performance of the system to ensure that all repaired components are working properly and the system returns to the optimal operating state.

[0036] It should be noted that, since the information interaction, execution process and other contents between the units in the above-mentioned devices and systems are based on the same concept as the embodiments of the method of the present invention, the specific contents can be found in the description of the embodiments of the method of the present invention and will not be repeated here.

[0037] The above is a detailed introduction to the intelligent inspection system and method for photovoltaic components of a photovoltaic power generation system provided by an embodiment of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for intelligent inspection of photovoltaic components of a photovoltaic power generation system, characterized in that: The method comprises the following steps: S1: The drone conducts automatic inspections of the photovoltaic panels of the photovoltaic power station within the distribution range of the photovoltaic modules. The high-definition camera and infrared imager carried by the drone obtain the real-time operation data of the photovoltaic modules to be inspected, take appearance images and temperature distribution images, and transmit the captured image signals to the image preprocessing module; S2: The collected image data is preprocessed through the preprocessing module. First, data cleaning is performed, and filters are used to remove random noise in the image and smooth the image. Then, grayscale processing is performed to convert the color image into a grayscale image containing only brightness information. Then, contrast enhancement is performed to make the important features in the image more obvious by adjusting the contrast of the image. Finally, illumination correction and geometric transformation are performed to eliminate shadows and bright spots in the image caused by uneven illumination and correct the geometric distortion of the image to ensure the accuracy of the object morphology in the image. S3: The preprocessed image is segmented through the preprocessing module. The preprocessing module has a built-in Unet segmentation algorithm. The Unet segmentation algorithm is used to segment the collected infrared image, automatically distinguish and extract the photovoltaic module strings and individual components from the image background. The Unet algorithm can automatically learn the target segmentation features and improve the segmentation accuracy. The segmented image is binarized, and the connected domain detection algorithm is used to identify and calculate the area where each string and component is located. S4: The defect recognition module recognizes the image data to calculate the component information, and identifies whether the photovoltaic component is faulty by comparing it with the preset threshold value, calculates the average temperature of each string and component area, and compares the obtained average temperature with the preset threshold value, and identifies the photovoltaic components suspected of having thermal defects according to the comparison results, wherein if the average temperature of a component is higher than the set threshold value, it is determined that there may be a hot spot defect; S5: The defect recognition module outputs the pixel position coordinates and temperature value related information of the suspected defective photovoltaic modules in the detection results, outputs the fault type and recommended maintenance measures, and generates a corresponding inspection report.

2. The method according to claim 1, characterized in that The defect identification module determines whether a photovoltaic module is suspected of having a hot spot defect based on the average temperature of the module exceeding a preset threshold, wherein the threshold is set based on the temperature range and historical data of the photovoltaic module during normal operation; and the report includes the fault diagnosis results obtained by analyzing the AI ​​fault identification algorithm.

3. The method according to claim 1, characterized in that The Unet segmentation algorithm improves the performance and accuracy of the original Unet algorithm in infrared image segmentation scenarios by deepening the network structure or optimizing the training strategy.

4. The method according to claim 1, characterized in that: The connected domain detection algorithm adopts the Two-Pass algorithm. During the first scan, the image is traversed from left to right and from top to bottom, and an initial label is assigned to each valid pixel. If the left or upper neighbor of a pixel has a label, then the pixel will inherit this label; if not, a new label is assigned. During the second scan, the label conflict problem is resolved to ensure that all connected pixels have the same label. The main function of the connected domain algorithm is to identify and segment areas composed of pixels with the same pixel value and adjacent positions, providing a basis for image recognition.

5. The method according to claim 1, characterized in that The method also includes a feedback mechanism, wherein the feedback adjustment module is used to adjust the preset threshold value according to the output of the detection of the suspected defective component and the subsequent actual inspection results to optimize the accuracy of the defect detection.

6. A photovoltaic module intelligent inspection system for a photovoltaic power generation system, comprising a photovoltaic module, a drone and a server, characterized in that: The drone is equipped with two sensors, a high-definition camera and an infrared thermal imager. The drone has a built-in navigation and positioning module, a route planning module, an image acquisition module and a flight control module, which are detachably connected to the drone. The flight control module can receive signals from the navigation and positioning module and the route planning module. The high-definition camera and the infrared imager are controlled by the image acquisition module. The server has an image preprocessing module, a defect recognition module, a result output module, a fault prediction module and a database module. The high-definition camera and the infrared thermal imager signals are connected to the server. The route planning module is responsible for developing one or more optimal flight paths for the UAV according to mission requirements and flight environment; The positioning module uses GPS, GLONASS, and BeiDou global navigation satellite systems to provide accurate location information for the drone; The flight control module is responsible for controlling the flight attitude, speed and direction of the drone. It receives instructions from the route planning module and the navigation and positioning module, and controls the motor and steering components of the drone according to these instructions, so that the drone flies according to the predetermined path and speed; The image acquisition module is used to control the infrared camera carried by the drone to acquire high-definition images of the photovoltaic modules; The image preprocessing module is used to perform image preprocessing, segmentation and connected domain detection; The defect identification module is used to calculate the average temperature of each component and compare it with a threshold value to identify components suspected of having thermal defects; The result output module is used to organize and output relevant information of suspected defective components so that operators can conduct further analysis and processing; The fault prediction module is used to predict possible future faults of the equipment based on historical data and real-time data; The database module is used to store and manage various types of data generated during the drone inspection process, provide data support, and help manage and utilize data.

7. The system according to claim 6, characterized in that The system also includes a feedback adjustment module for receiving input from an operator and dynamically adjusting the threshold of defect detection according to actual inspection results.

8. The system according to claim 6, characterized in that The result output module generates a detailed inspection report based on the performance score and AI fault identification results. The report includes the number, location, performance score, fault type and recommended maintenance measures of the photovoltaic module.

9. The system according to claim 6, characterized in that: The route planning module contains sensor data to provide geographic and environmental information. Through the processor and algorithm, the module can quickly process real-time data to ensure the timeliness and accuracy of route planning. The user generates the optimal route by inputting specific mission parameters. At the same time, the module also provides automatic adjustment functions to avoid weather restricted areas and other flight obstacles. The module dynamically adjusts the flight plan according to real-time flight conditions and mission requirements to respond to emergencies.

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