Photovoltaic area inspection method and device, storage medium and electronic device

Abstract

The invention discloses a photovoltaic area inspection method, and the method comprises the steps: determining a to-be-detected photovoltaic area; dividing the photovoltaic area based on the number of the unmanned aerial vehicles and the endurance condition of the unmanned aerial vehicles, and determining a plurality of photovoltaic sub-areas and the unmanned aerial vehicles corresponding to the plurality of photovoltaic sub-areas; aiming at any photovoltaic sub-region in the plurality of photovoltaic sub-regions, determining an inspection route of the aiming photovoltaic sub-region; and based on the unmanned aerial vehicle corresponding to the targeted photovoltaic sub-region and the routing inspection route, routing inspection is carried out on the targeted photovoltaic sub-region. The problem that the inspection efficiency of the photovoltaic area is low in the inspection process of the photovoltaic area in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of photovoltaic technology, and more specifically, to a photovoltaic area inspection method and device, a storage medium, and an electronic device. Background Art

[0002] As the global demand for clean energy grows, the scale of photovoltaic power stations is gradually expanding, and their operation and maintenance has become an important and complex task. The operation and maintenance of photovoltaic power stations includes but is not limited to regular inspection and maintenance of photovoltaic modules, inverters, combiner boxes and other equipment to ensure the normal operation and efficient power generation of the power station. Traditional operation and maintenance methods, such as manual inspections, face many problems such as low efficiency, high labor costs, and limited coverage, especially in large-capacity photovoltaic power stations, where these defects are more obvious.

[0003] In recent years, the application of drone technology in photovoltaic area inspection has gradually increased. The high mobility and automation capabilities of drones can be used to conduct rapid inspections over a large area. However, the existing drone inspection technology still has shortcomings: the endurance of a single drone is limited. For large photovoltaic areas, frequent charging and replacement and return interrupt the continuity of the inspection and reduce the inspection efficiency. When multiple drones conduct collaborative inspections, the coordination and communication mechanisms between drones are often poorly designed, lacking real-time information sharing and dynamic task adjustment, which not only affects the efficiency of the inspection, but also reduces the overall reliability of the system.

[0004] Therefore, there is an urgent need for a drone inspection method to overcome the above-mentioned defects and achieve more efficient, reliable and comprehensive photovoltaic area inspection. Summary of the invention

[0005] An embodiment of the present application provides a method for inspecting a photovoltaic area, so as to at least solve the problem of low efficiency of drone inspection in related technologies.

[0006] According to one embodiment of the embodiments of the present application, a method for inspecting a photovoltaic area is provided, including: determining a photovoltaic area to be inspected; dividing the photovoltaic area based on the number of drones and the endurance of drones, determining a plurality of photovoltaic sub-areas, and drones corresponding to the plurality of photovoltaic sub-areas respectively; for any one of the plurality of photovoltaic sub-areas, determining an inspection route for the targeted photovoltaic sub-area; and inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0007] In an exemplary embodiment, the photovoltaic area is divided based on the number of drones and the endurance of the drones to determine a plurality of photovoltaic sub-areas, including: determining the center point of the photovoltaic area according to the distribution positions of the photovoltaic equipment in the photovoltaic area; taking the center point as the origin, dividing the photovoltaic area based on the number of drones and the endurance of the drones to obtain a plurality of photovoltaic sub-areas.

[0008] In an exemplary embodiment, determining the inspection route for the photovoltaic sub-area includes: hierarchically dividing the boundary contour according to the camera performance parameters of the drone to obtain a hierarchical division result; and determining the inspection route for the photovoltaic sub-area according to the hierarchical division result.

[0009] In an exemplary embodiment, the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: determining a boundary contour of the targeted photovoltaic sub-area, and determining an edge position point from the boundary contour; taking the edge position point as a starting point, inspecting the targeted photovoltaic sub-area based on the drone and the inspection route.

[0010] In an exemplary embodiment, the training route includes inspection points; the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: when the drone arrives at the inspection point, obtaining an inspection image taken by the drone; performing fuzziness analysis on the inspection image to obtain a fuzziness index of the inspection image; when the fuzziness index reaches a set fuzziness threshold, determining that the inspection image is in a fuzzy state; when it is determined that the inspection image is in a fuzzy state, instructing the drone to re-take the inspection image, and returning to the step of performing fuzziness analysis on the inspection image to obtain the fuzziness index of the inspection image, until stopping when a preset condition is met.

[0011] In an exemplary embodiment, the training route includes inspection points; the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: when the drone of the targeted photovoltaic sub-area arrives at the inspection point, determining the positioning coordinates of the drone in the targeted photovoltaic sub-area; performing error calculation between the positioning coordinates of the drone in the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a first position deviation; when the first position deviation does not meet the first position deviation condition, performing positioning detection on the current position of the drone to obtain a positioning detection result.

[0012] In an exemplary embodiment, the positioning detection is performed on the current position of the drone to obtain a positioning detection result, including: calculating the error between the positioning coordinates of the drone in other photovoltaic sub-areas except the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a second position deviation, and determining multiple relative positions of the drone in the targeted photovoltaic sub-area and drones in other photovoltaic sub-areas except the targeted photovoltaic sub-area; when the second position deviation satisfies the second position deviation condition and the multiple relative positions satisfy the relative position condition, it is determined that the positioning of the drone is correct.

[0013] According to another embodiment of the embodiment of the present application, a patrol device for a photovoltaic area is also provided, including: a first determination module, used to determine a photovoltaic area to be inspected; a division module, used to divide the photovoltaic area based on the number of drones and the endurance of the drones, and determine multiple photovoltaic sub-areas, and drones corresponding to the multiple photovoltaic sub-areas respectively; a second determination module, used to determine an inspection route for any one of the multiple photovoltaic sub-areas; and an inspection module, used to inspect the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0014] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, in which a computer program is stored, wherein the computer program is configured to execute the above method when running.

[0015] According to another aspect of the embodiments of the present application, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the method through the computer program.

[0016] According to another aspect of the embodiments of the present application, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0017] In an embodiment of the present application, a photovoltaic area to be inspected is determined; the photovoltaic area is divided based on the number of drones and the endurance of the drone, and multiple photovoltaic sub-areas and drones corresponding to the multiple photovoltaic sub-areas are determined. By scientifically dividing the photovoltaic area into multiple sub-areas based on the number of drones and the endurance, each drone can focus on a specific sub-area for inspection, avoiding frequent round-trip charging due to endurance limitations during single-machine inspections, and significantly improving the continuity and overall efficiency of inspections. For any of the multiple photovoltaic sub-areas, the inspection route of the targeted photovoltaic sub-area is determined, so as to plan the inspection route for each sub-area, ensuring that each photovoltaic sub-area can be accurately covered, reducing inspection blind spots and repeated work caused by unreasonable route planning, and improving the comprehensiveness and accuracy of inspections. The targeted photovoltaic sub-area is inspected based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route. Through refined photovoltaic area division and inspection route planning, combined with multi-machine collaboration, the inspection efficiency and quality of photovoltaic areas have been effectively improved, the operation and maintenance costs have been reduced, the stability of the system and the ability to cope with complex environments have been enhanced, providing strong technical support for the efficient operation and maintenance of photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 It is a hardware structure block diagram of a computer terminal of a photovoltaic area inspection method according to an embodiment of the present application;

[0020] Figure 2 is a flow chart of a method for inspecting a photovoltaic area according to an embodiment of the present application;

[0021] Figure 3 is a schematic structural diagram of a photovoltaic sub-region according to an embodiment of the present application;

[0022] Figure 4 is a schematic diagram of the structure after the route of the photovoltaic sub-area is determined according to the embodiment of the present application;

[0023] Figure 5 It is a structural block diagram of a photovoltaic area inspection device according to an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0025] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices; "plurality" means two or more.

[0026] The method embodiments provided in the embodiments of the present application can be executed in a computer terminal or similar computing device or cloud platform or independent physical server or software platform, wherein the above software platform is run through one or more servers. Taking running on a computer terminal as an example, Figure 1 1 is a hardware structure diagram of a photovoltaic area inspection method according to an embodiment of the present application. Figure 1 As shown, the computer terminal may include one or more ( Figure 1 Only one is shown in the figure) processor 102 and memory 104 for storing data. In an exemplary embodiment, the computer terminal may also include a transmission device 106 for communication functions and an input and output device 108, wherein the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA. A person skilled in the art can understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above-mentioned computer terminal. Figure 1 More or fewer components as shown, or with Figure 1 Equivalent functions or comparisons shown Figure 1 A different configuration with more features is shown.

[0027] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as computer programs corresponding to the methods in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, that is, the above-mentioned methods are implemented. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the computer terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of a computer terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] In this embodiment, a photovoltaic area inspection method is provided, which is applied to the above-mentioned computer terminal. Figure 2 : is a flow chart of a method for inspecting a photovoltaic area according to an embodiment of the present application, the process comprising the following steps:

[0030] Step S202: determining a photovoltaic area to be detected.

[0031] Step S204, dividing the photovoltaic area based on the number of drones and the endurance of the drones, determining a plurality of photovoltaic sub-areas and drones corresponding to the plurality of photovoltaic sub-areas respectively;

[0032] It is understandable that when dividing the photovoltaic sub-areas, a detailed performance evaluation is conducted on all drones participating in the inspection, with an emphasis on their endurance. Endurance can be determined by factors such as the drone's battery capacity, flight speed, and load weight. The endurance or range of each drone will serve as an important reference indicator when assigning tasks. The photovoltaic area is then divided into multiple photovoltaic sub-areas based on the number and endurance of the drones. Assuming that there are n drones in the station, and the endurance ratio of each drone is s1:s2:…sn, the photovoltaic area can be divided into equal parts of su=sum(s1:sn). The principle of division is to ensure that the area of ​​the sub-area and the amount of inspection tasks assigned to each drone match its endurance to avoid waste of resources or task overload.

[0033] After the sub-area division and task allocation are completed, each drone will receive a detailed inspection plan for the sub-area it is responsible for, including parameters such as the starting point, end point, flight altitude, and flight speed. At the same time, a communication mechanism is established between drones to share status information and inspection data in real time to coordinate actions and optimize inspection efficiency. This communication mechanism can also dynamically adjust and relay tasks between drones to ensure the continuity and integrity of inspection tasks.

[0034] In an exemplary embodiment, the photovoltaic area is divided based on the number of drones and the endurance of the drones to determine a plurality of photovoltaic sub-areas, including: determining the center point of the photovoltaic area according to the distribution positions of the photovoltaic equipment in the photovoltaic area; taking the center point as the origin, dividing the photovoltaic area based on the number of drones and the endurance of the drones to obtain a plurality of photovoltaic sub-areas.

[0035] It is understandable that in the planning of drone-based PV area inspection routes, ensuring the efficient use and optimal coverage of drones is the key. Through high-precision satellite maps, aerial images or ground measurement data, the distribution locations of all PV equipment (such as PV panels, inverters, junction boxes, etc.) in the PV area are accurately captured. These data need to be detailed to the specific coordinate information of each device for subsequent center point calculation and area division.

[0036] Using the above-collected distribution locations of photovoltaic devices, the center point of the photovoltaic area is calculated using the geometric center algorithm, centroid algorithm or a more complex weighted average algorithm. The determination of the center point is crucial for the subsequent division of sub-areas. As the origin, it can help evenly distribute drone inspection tasks.

[0037] Evaluate the performance of all the drones involved in the inspection in detail, especially their endurance. Endurance is determined by many factors such as the drone's battery capacity, flight mode, and load weight, which directly affects the drone's flight time and coverage in the photovoltaic area. Assume that there are n drones in the station, and the endurance ratio of each drone is s1, s2, ..., sn. Taking the center point O as the origin, divide the photovoltaic area into equal parts of su = sum (s1: s2: ...: sn) according to the ratio of the drone's endurance s1: s2: ...: sn. When dividing, consider evenly dividing the area along the center point so that the angle range or area responsible for each drone matches its endurance. Assign each sub-area to the corresponding drone according to the endurance ratio. The drone with the strongest endurance will be assigned to the sub-area with the largest or most complex coverage area, while the drone with weaker endurance will be assigned to the smaller or relatively simple sub-area.

[0038] Step S206: for any one photovoltaic sub-region among the plurality of photovoltaic sub-regions, determine an inspection route for the photovoltaic sub-region.

[0039] It should be noted that for each photovoltaic sub-area, a corresponding inspection route is allocated.

[0040] In an exemplary embodiment, determining the inspection route for the photovoltaic sub-area includes: hierarchically dividing the boundary contour according to the camera performance parameters of the drone to obtain a hierarchical division result; and determining the inspection route for the photovoltaic sub-area according to the hierarchical division result.

[0041] Among them, the camera performance parameters of the drones performing inspection tasks are evaluated in detail. These parameters include but are not limited to the camera's resolution, viewing angle, focal length, focusing ability, and image processing speed. The camera performance parameters will directly affect the quality and coverage of the drone inspection images, and are an important basis for planning inspection routes.

[0042] Secondly, satellite images, aerial images or laser radar are used to identify and determine the boundary contour of each photovoltaic sub-area. The boundary contour is the edge line of the photovoltaic sub-area, including the edge of the photovoltaic panel area, the obstacle boundary, the dividing line between photovoltaic sub-areas, etc. The boundary contour is hierarchically divided according to the camera performance parameters of the drone. The purpose of hierarchical division is to divide the boundary contour into several layers, each layer corresponding to a different flight altitude and camera angle, so as to ensure that the drone can fully cover the photovoltaic sub-area during inspection and the image quality meets the inspection requirements. For example, a camera with a higher resolution may be able to obtain a clear image at a lower flight altitude, while a camera with a lower resolution needs to fly at a higher altitude. The hierarchical division can take into account the camera angle and coverage of the drone to ensure that the drone can complete the comprehensive coverage of the boundary contour at different flight altitudes while avoiding repeated shooting. When dividing, it can be reduced layer by layer from the outside to the inside, that is, starting from the outer boundary of the photovoltaic sub-area, and gradually dividing to the central area, and the width and flight altitude of each layer are dynamically adjusted according to the camera performance parameters.

[0043] In some embodiments, the outermost boundary of the photovoltaic sub-area is used as the initial inspection route, and the drone first inspects along this boundary to ensure that the edge of the photovoltaic sub-area is fully covered. According to the hierarchical division results, an independent inspection route is designed for each level. Route planning should take into account the flight direction, speed and flight altitude of the drone at each level to ensure that the drone can gradually go from the outer boundary to the inside in accordance with the preset hierarchical order during the inspection process to achieve full coverage of the photovoltaic sub-area. After determining the inspection route, it is necessary to ensure task synchronization between drones to avoid conflicts or missed inspections during the inspection process. Drones should inspect according to the preset hierarchical route. At the same time, they should share location information and inspection progress in real time through the communication mechanism to dynamically adjust the inspection route to ensure efficient execution of the inspection task.

[0044] Through the above steps, the inspection route for each photovoltaic sub-area can be scientifically planned, which not only fully considers the camera performance of the drone and ensures the image quality, but also optimizes the inspection route through the hierarchical division of the boundary contour, reduces the invalid distance of the drone flight, and improves the efficiency and accuracy of the inspection. In addition, the hierarchical route planning can also adapt to the terrain and equipment distribution of the photovoltaic sub-area, further improving the flexibility and applicability of the inspection route.

[0045] Step S208: inspect the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0046] In some exemplary embodiments, the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: determining a boundary contour of the targeted photovoltaic sub-area, and determining an edge position point from the boundary contour; taking the edge position point as a starting point, inspecting the targeted photovoltaic sub-area based on the drone and the inspection route.

[0047] In some embodiments, during the inspection of photovoltaic sub-areas based on drones, it is crucial to ensure the comprehensiveness and accuracy of the inspection results. Detailed topographic information of the photovoltaic sub-area is obtained through high-precision satellite images, aerial images or laser radar scanning and other technical means, and then the boundary contour of the photovoltaic sub-area is determined. The boundary contour should clearly define the scope of the photovoltaic sub-area, including the boundaries of the photovoltaic panel area, channel, obstacles, etc. After determining the boundary contour of the photovoltaic sub-area, the key edge position point is selected from the boundary as the starting point of the drone inspection. The selection of edge position points should be based on the needs of inspection route planning to ensure that the drone can start the inspection from the edge of the photovoltaic sub-area and cover the entire area. Taking the edge position point as the starting point, the initial inspection route is set in combination with the drone's endurance, camera performance parameters and preset inspection route hierarchical division. The initial route is usually carried out along the outer boundary of the photovoltaic sub-area to ensure full coverage of the edge area. The drone takes off from the preset edge position point and inspects according to the set initial inspection route. The drone should remain within the planned height and speed range during the flight to ensure image quality and inspection efficiency. The camera carried by the drone will automatically capture images within the photovoltaic area. These images will be transmitted back to the ground control station in real time. The control station's software will perform pre-processing and preliminary analysis to check the image quality and the drone's positioning accuracy to ensure the accuracy and effectiveness of the inspection results.

[0048] In some embodiments, during the inspection process, the drone will dynamically adjust the inspection route based on real-time environmental factors (such as wind speed, lighting conditions, etc.) and its own status (such as battery power, equipment operating status, etc.). For example, when the battery power is lower than the preset threshold, the drone will automatically plan a route back to the charging point, or after completing the inspection in other areas, the drone with sufficient remaining power will continue to perform the unfinished task. Through the communication mechanism between multiple drones, drones share inspection progress, image data and location information in real time to coordinate the inspection routes of each drone, avoid repeated coverage and missed areas, and ensure the efficient execution of inspection tasks.

[0049] In a specific application, after the inspection is completed, all the data and images collected by the drone will be summarized, and through data analysis and image processing technology, an inspection report of the photovoltaic sub-area will be generated. The report should include any abnormal conditions found during the inspection, such as photovoltaic panel damage, equipment failure or areas that need maintenance, as well as the execution of the inspection route and the operating status of the drone.

[0050] Through the above embodiments, each drone can conduct efficient and comprehensive inspections for the photovoltaic sub-area it is responsible for, ensuring the normal operation of photovoltaic equipment and the safe management of photovoltaic sites. At the same time, through dynamic route adjustment and multi-machine information sharing, the flexibility of the inspection system and its ability to cope with complex environments have been significantly improved, providing an innovative solution for the operation and maintenance management of large-capacity photovoltaic power stations in the future.

[0051] In some exemplary embodiments, the training route includes inspection points; the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: when the drone arrives at the inspection point, obtaining an inspection image taken by the drone; performing fuzziness analysis on the inspection image to obtain a fuzziness index of the inspection image; when the fuzziness index reaches a set fuzziness threshold, determining that the inspection image is in a fuzzy state; when it is determined that the inspection image is in a fuzzy state, instructing the drone to re-take the inspection image, and returning to the step of performing fuzziness analysis on the inspection image to obtain the fuzziness index of the inspection image, until stopping when a preset condition is met.

[0052] It is understandable that when the UAV flies according to the training route and arrives at the preset inspection point, it will automatically trigger the camera to capture the inspection image. Image acquisition should be performed when the UAV is hovering stably or flying at a moderate speed to ensure image quality. Obtain inspection images taken by the UAV, including images of key equipment such as photovoltaic panels, inverters, and trackers. Use image processing algorithms to perform blur analysis on the inspection images and calculate the blur index of the image. The blur index is an indicator of image clarity, usually calculated based on features such as the sharpness, contrast, and clarity of the image edges. The blur threshold is used to determine whether the inspection image meets acceptable clarity standards. The setting of the threshold should take into account factors such as inspection needs, image analysis technology, and equipment conditions.

[0053] When the fuzziness index reaches or exceeds the set fuzziness threshold, it is determined that the inspection image is blurred, that is, the image quality does not meet the inspection requirements. After determining that the inspection image is blurred, the ground control station will send instructions to the drone in real time, instructing the drone to retake the inspection image at the current point. The number and method of reshooting should be flexibly adjusted according to actual conditions to avoid energy waste and reduced inspection efficiency of the drone due to frequent reshoots. After the drone reshoots the image according to the instructions, it returns to the fuzziness analysis step, that is, the fuzziness index of the newly taken image is calculated again to determine whether the image is clear. This process will be executed repeatedly until the image quality of all inspection points reaches the set threshold.

[0054] In some embodiments, when the image quality of all inspection points meets the preset conditions, the inspection process ends. At this time, the ground control station will collect all clear and effective inspection images, conduct further data analysis and processing, and generate an inspection report, which should include information such as the evaluation of the condition of the photovoltaic equipment, the location of the fault point, and necessary maintenance recommendations.

[0055] Through the above embodiments, the high quality of images and the accuracy of inspection results during drone inspections are ensured, the omission of inspection information due to blurred images is effectively avoided, and the efficiency of photovoltaic equipment inspections and the intelligent level of photovoltaic power station operation and maintenance are improved.

[0056] In some embodiments, the training route includes inspection points; the inspection of the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: when the drone of the targeted photovoltaic sub-area arrives at the inspection point, determining the positioning coordinates of the drone in the targeted photovoltaic sub-area; performing error calculation between the positioning coordinates of the drone in the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a first position deviation; when the first position deviation does not meet the first position deviation condition, performing positioning detection on the current position of the drone to obtain a positioning detection result.

[0057] It is understandable that the ground control station sets a training route based on the layout, equipment distribution and inspection requirements of the photovoltaic sub-area, and the route includes a series of preset inspection points. The inspection points should cover the key equipment and inspection points in the photovoltaic sub-area to ensure the comprehensiveness and pertinence of the inspection. When the drone flies according to the training route and arrives at the preset inspection point, the flight control system of the drone will automatically record the current positioning coordinates, that is, the actual position of the drone in the targeted photovoltaic sub-area. The real-time positioning coordinates of the drone in the photovoltaic sub-area are obtained by using the GPS, Beidou or other satellite positioning systems carried by the drone. The positioning coordinates of the drone should be accurate enough to complete the inspection task, such as meters or finer. The actual positioning coordinates of the drone are compared with the preset inspection point coordinates, and the position deviation between the two coordinates is calculated to obtain the first position deviation. The size of the position deviation can reflect the accuracy of the drone positioning.

[0058] Among them, the first position deviation condition is the maximum allowable deviation range. This condition should be determined according to the inspection requirements, the accuracy of the drone positioning system and the distribution of photovoltaic equipment to ensure that the drone can accurately reach each inspection point.

[0059] In the case where the first position deviation does not meet the first position deviation condition, that is, the current position of the drone deviates too much from the preset inspection point position, the system will trigger the drone's self-check mechanism and perform a more detailed positioning test on the current position of the drone to confirm the cause of the positioning deviation. Among them, the positioning test results will help analyze the specific reasons for the drone's positioning deviation, including but not limited to positioning system failure, external interference (such as electromagnetic interference, building obstruction, etc.), weather conditions or unstable flight status of the drone.

[0060] In some embodiments, based on the positioning detection results, the system will take corresponding countermeasures to ensure that the drone can accurately perform the inspection task. This may include adjusting the flight parameters of the drone (such as flight speed and altitude), replanning the inspection route, correcting the positioning system parameters, or performing self-correction of the drone. After the positioning deviation is resolved, the drone will continue to perform the inspection task according to the training route. This process will be repeated until all preset inspection points are accurately covered. The inspection results will be transmitted back to the ground control station in real time for image analysis and data processing, and a detailed inspection report will be generated for reference by the photovoltaic power station operation and maintenance personnel.

[0061] Through the above embodiments, it is possible to ensure high-precision positioning of drones during photovoltaic area inspections and accurate execution of inspection tasks, effectively improve the inspection quality and efficiency, and provide reliable technical support for the intelligent operation and maintenance of photovoltaic power stations.

[0062] In some embodiments, the positioning detection is performed on the current position of the drone to obtain a positioning detection result, including: calculating the error between the positioning coordinates of the drone in other photovoltaic sub-areas except the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a second position deviation, and determining multiple relative positions of the drone in the targeted photovoltaic sub-area and drones in other photovoltaic sub-areas except the targeted photovoltaic sub-area; when the second position deviation satisfies the second position deviation condition and the multiple relative positions satisfy the relative position condition, it is determined that the positioning of the drone is correct.

[0063] It should be noted that when the drone performs inspection tasks, the positioning coordinates of the drone's GPS, Beidou or other positioning systems are obtained in real time. These coordinate data include not only the drone currently being inspected, but also the positioning coordinates of drones performing tasks in other photovoltaic sub-areas within the photovoltaic station, which provides a comprehensive data basis for subsequent position deviation analysis. The positioning coordinates of the drone in all other photovoltaic sub-areas except the targeted photovoltaic sub-area are calculated with the actual point position of the current inspection point to obtain the second position deviation. The calculation of the second position deviation is helpful for comparative analysis to determine whether the current drone's positioning deviation is abnormal.

[0064] Determine multiple relative positions between the current UAV (the UAV targeting the PV sub-area) and other UAVs. Relative position analysis can not only verify the positioning accuracy of the current UAV, but also ensure the coordination and safety of multiple UAVs during collaboration to avoid collisions or repeated inspections. Set the second position deviation condition, that is, the maximum allowable deviation range. This condition should be determined based on the accuracy of the UAV positioning system, the distribution of PV equipment, and the inspection requirements to ensure that the positioning deviation of UAVs other than the targeted PV sub-area remains within a reasonable range. Set relative position conditions, that is, the reasonable minimum safety distance between UAVs and the relative position relationship during collaborative inspections. The setting of relative position conditions helps to maintain the smooth progress of multi-machine collaborative operations and avoid interference between UAVs.

[0065] Second position deviation judgment: Check whether all second position deviations meet the second position deviation conditions. If the second position deviations of all drones are within the allowed range, this indicates that the positioning of other drones except the current inspection point is accurate. Check whether multiple relative positions between the current drone and other drones meet the relative position conditions. If all relative positions are within the set reasonable range, this indicates that the cooperation between drones is correct and the positioning relationship is correct.

[0066] When the second position deviation and multiple relative positions meet the corresponding conditions, the system will determine that the current positioning of the drone is correct. If there is an abnormal deviation or the relative position does not meet the conditions, the system will trigger the drone's self-check mechanism to perform positioning correction or re-plan the inspection route to ensure the normal execution of subsequent inspection tasks.

[0067] Through the above-mentioned embodiments, it is possible to ensure the precise positioning of the UAV in the photovoltaic sub-area inspection task through comprehensive position deviation analysis and relative position judgment, effectively avoiding invalid inspection data or collaboration conflicts between UAVs due to positioning deviation, and providing technical guarantee for realizing efficient and safe photovoltaic site inspection.

[0068] Through the above steps S202-S208, the photovoltaic area to be inspected is determined; the photovoltaic area is divided based on the number of drones and the endurance of the drone, and multiple photovoltaic sub-areas and drones corresponding to the multiple photovoltaic sub-areas are determined. By scientifically dividing the photovoltaic area into multiple sub-areas based on the number of drones and the endurance, each drone can focus on a specific sub-area for inspection, avoiding frequent round-trip charging due to endurance limitations during single-machine inspection, and significantly improving the continuity and overall efficiency of the inspection. For any one of the multiple photovoltaic sub-areas, the inspection route of the photovoltaic sub-area is determined, so as to plan the inspection route for each sub-area, ensuring that each photovoltaic sub-area can be accurately covered, reducing the inspection blind spots and repeated work caused by unreasonable route planning, and improving the comprehensiveness and accuracy of the inspection. The photovoltaic sub-area is inspected based on the drone corresponding to the photovoltaic sub-area and the inspection route. Through refined photovoltaic area division and inspection route planning, combined with multi-machine collaboration, the inspection efficiency and quality of photovoltaic areas have been effectively improved, the operation and maintenance costs have been reduced, the stability of the system and the ability to cope with complex environments have been enhanced, providing strong technical support for the efficient operation and maintenance of photovoltaic power stations.

[0069] In order to better understand the process of the inspection method of the photovoltaic area, the inspection method of the photovoltaic area is described below in combination with an optional embodiment, but it is not used to limit the technical solution of the embodiment of the present application.

[0070] For large-capacity photovoltaic power stations, due to the limited endurance of current drones, the efficiency of single-machine inspections using a back-and-forth charging and battery replacement method is low. Some use drone inspections, but there are problems such as unreasonable inspection routes, incomplete coverage, and poor multi-machine collaboration. Existing drone inspection route planning methods may not take multi-machine collaboration into account, or the coordination is poor when multiple machines collaborate, which can easily lead to missing certain areas or repeated inspections.

[0071] refer to Figure 3As shown in the figure, it is a schematic diagram of several photovoltaic sub-areas. Assuming that there are n drones in the station, if the endurance ratio of the middle drone is s1:s2:…:sn, with the center point O of the photovoltaic station as the center point, the photovoltaic station is divided into su=sum(s1:sn) equal parts, and then the angle values ​​of the inspection areas responsible for each are allocated s1:s2:…:sn parts according to the endurance. The inspection area division can also be divided according to the inspection area integral and the endurance.

[0072] Based on the divided photovoltaic sub-areas, the inspection routes are divided, refer to Figure 4 The figure shows the inspection route diagram for each PV sub-area. Multiple drones start from the edge of the sub-area s1:s2:…:sn at the same time, and gradually zoom inwards according to a certain contour for inspection. Each drone enters the far edge position b1~bn of each area according to its own complex area, that is, the edge position point, and begins to inspect layer by layer according to the edge contour of the station.

[0073] During the inspection process, drones share information in real time through communication mechanisms and coordinate actions to avoid repeated inspections and missed areas. Each time each drone completes an inspection point, it first automatically checks the image of the inspection point: first, confirm whether there is a large deviation between the current drone's single Beidou positioning coordinates and the actual planned route point position, and the judgment standard is based on the empirical deviation threshold; if it exceeds the deviation position, then the single Beidou positioning error of other drones is compared synchronously. If the deviation size is close, the relative position of each drone is planned at the same time. If it is found that there is no large deviation in the relative position, it can be determined that the inspection drone positioning is fine at this time. The positioning error may be caused by other problems such as the ionosphere, and the inspection will continue; if it is found that the relative position is inconsistent with the expected plan, the inspection position of the drone is adjusted. Secondly, check the quality of the drone inspection picture, and check the blurriness of the inspection picture quality of each point through various forms such as drone flight control and onboard computer. If a blurry state is found, re-collect the image at this position.

[0074] Adjust inspection routes and strategies based on real-time monitoring data to ensure full coverage and accurate inspection of the photovoltaic area. For example, if a drone's performance or various reasons cause the drone's inspection to be too slow and fail to complete the inspection route on time, other drones that have completed the inspection route first can take over the relevant inspection route tasks to achieve task adjustment and ensure inspection efficiency.

[0075] Through the description of the above implementation methods, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of each embodiment of the present application.

[0076] The present application also provides a structural block diagram of a photovoltaic area inspection device. Figure 5 is a structural block diagram of a photovoltaic area inspection device according to an embodiment of the present application; Figure 5 As shown, including:

[0077] A first determination module 52 is used to determine a photovoltaic area to be detected;

[0078] A division module 54 is used to divide the photovoltaic area based on the number of drones and the endurance of the drones, and determine a plurality of photovoltaic sub-areas and drones corresponding to the plurality of photovoltaic sub-areas respectively;

[0079] A second determination module 56 is used to determine an inspection route for any one of the plurality of photovoltaic sub-regions;

[0080] The inspection module 58 is used to inspect the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0081] In an exemplary embodiment, the division module 54 is also used to determine the center point of the photovoltaic area according to the distribution positions of the photovoltaic equipment in the photovoltaic area; taking the center point as the origin, the photovoltaic area is divided based on the number of drones and the endurance of the drones to obtain multiple photovoltaic sub-areas.

[0082] In an exemplary embodiment, the second determination module 56 is further used to hierarchically divide the boundary contour according to the camera performance parameters of the drone to obtain a hierarchical division result; and determine the inspection route of the photovoltaic sub-area according to the hierarchical division result.

[0083] In an exemplary embodiment, the inspection module 58 is also used to determine the boundary contour of the targeted photovoltaic sub-area and determine the edge position point from the boundary contour; taking the edge position point as the starting point, the targeted photovoltaic sub-area is inspected based on the drone and the inspection route.

[0084] In an exemplary embodiment, the training route includes inspection points; the inspection module 58 obtains the inspection image taken by the drone when the drone arrives at the inspection point; performs fuzziness analysis on the inspection image to obtain the fuzziness index of the inspection image; determines that the inspection image is in a fuzzy state when the fuzziness index reaches a set fuzziness threshold; when it is determined that the inspection image is in a fuzzy state, instructs the drone to re-take the inspection image, and returns to the step of performing fuzziness analysis on the inspection image to obtain the fuzziness index of the inspection image, until stopping when a preset condition is met.

[0085] In an exemplary embodiment, the training route includes inspection points; the inspection module 58 is also used to determine the positioning coordinates of the drone in the targeted photovoltaic sub-area when the drone in the targeted photovoltaic sub-area arrives at the inspection point; perform error calculation between the positioning coordinates of the drone in the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a first position deviation; when the first position deviation does not meet the first position deviation condition, perform positioning detection on the current position of the drone to obtain a positioning detection result.

[0086] In an exemplary embodiment, the inspection module 58 is also used to perform positioning detection on the current position of the drone to obtain a positioning detection result, including: calculating the error between the positioning coordinates of the drone in other photovoltaic sub-areas except the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a second position deviation, and determining multiple relative positions of the drone in the targeted photovoltaic sub-area and drones in other photovoltaic sub-areas except the targeted photovoltaic sub-area; when the second position deviation satisfies the second position deviation condition and the multiple relative positions satisfy the relative position condition, it is determined that the positioning of the drone is correct.

[0087] Through the above device, the photovoltaic area to be inspected is determined; the photovoltaic area is divided based on the number of drones and the endurance of the drone, and multiple photovoltaic sub-areas and drones corresponding to the multiple photovoltaic sub-areas are determined. By scientifically dividing the photovoltaic area into multiple sub-areas based on the number of drones and the endurance, each drone can focus on a specific sub-area for inspection, avoiding frequent round-trip charging due to endurance limitations during single-machine inspection, and significantly improving the continuity and overall efficiency of the inspection. For any photovoltaic sub-area among the multiple photovoltaic sub-areas, the inspection route of the photovoltaic sub-area is determined, so as to plan the inspection route for each sub-area, ensuring that each photovoltaic sub-area can be accurately covered, reducing the inspection blind spots and repeated work caused by unreasonable route planning, and improving the comprehensiveness and accuracy of the inspection. The photovoltaic sub-area targeted is inspected based on the drone corresponding to the photovoltaic sub-area and the inspection route. Through refined photovoltaic area division and inspection route planning, combined with multi-machine collaboration, the inspection efficiency and quality of photovoltaic areas have been effectively improved, the operation and maintenance costs have been reduced, the stability of the system and the ability to cope with complex environments have been enhanced, providing strong technical support for the efficient operation and maintenance of photovoltaic power stations.

[0088] An embodiment of the present application further provides a storage medium, which includes a stored program, wherein the program executes any of the above methods when it is run.

[0089] Optionally, in this embodiment, the storage medium may be configured to store program codes for executing the following steps:

[0090] S1, determining the photovoltaic area to be detected;

[0091] S2, dividing the photovoltaic area based on the number of drones and the endurance of the drones, determining a plurality of photovoltaic sub-areas and drones corresponding to the plurality of photovoltaic sub-areas respectively;

[0092] S3, determining an inspection route for any one of the plurality of photovoltaic sub-areas;

[0093] S4, inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0094] An embodiment of the present application further provides an electronic device, including a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute the steps in any one of the above method embodiments.

[0095] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0096] Optionally, in this embodiment, the processor may be configured to perform the following steps through a computer program:

[0097] S1, determining the photovoltaic area to be detected;

[0098] S2, dividing the photovoltaic area based on the number of drones and the endurance of the drones, determining a plurality of photovoltaic sub-areas and drones corresponding to the plurality of photovoltaic sub-areas respectively;

[0099] S3, determining an inspection route for any one of the plurality of photovoltaic sub-areas;

[0100] S4, inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

[0101] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0102] An embodiment of the present application further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps in any one of the above method embodiments are implemented.

[0103] An embodiment of the present application further provides another computer program product, including a non-volatile computer-readable storage medium, wherein the non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0104] An embodiment of the present application also provides a computer program, which includes computer instructions stored in a computer-readable storage medium; a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps in any one of the above method embodiments.

[0105] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0106] Obviously, those skilled in the art should understand that the above modules or steps of the present application can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that herein, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present application is not limited to any specific combination of hardware and software.

[0107] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A photovoltaic area inspection method, characterized in that: include: Determining the photovoltaic area to be inspected; Dividing the photovoltaic area based on the number of drones and the endurance of the drones, determining a plurality of photovoltaic sub-areas, and drones corresponding to the plurality of photovoltaic sub-areas respectively; For any one photovoltaic sub-area among the multiple photovoltaic sub-areas, determining an inspection route for the photovoltaic sub-area; The targeted photovoltaic sub-area is inspected based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

2. The method according to claim 1, characterized in that The photovoltaic area is divided based on the number of drones and the endurance of the drones to determine a plurality of photovoltaic sub-areas, including: Determining the center point of the photovoltaic area according to the distribution positions of the photovoltaic devices in the photovoltaic area; Taking the center point as the origin, the photovoltaic area is divided based on the number of drones and the endurance of the drones to obtain a plurality of photovoltaic sub-areas.

3. The method according to claim 1, characterized in that The step of determining the inspection route of the photovoltaic sub-area includes: According to the camera performance parameters of the UAV, the boundary contour is divided into levels to obtain a level division result; According to the hierarchical division result, an inspection route for the photovoltaic sub-area is determined.

4. The method according to claim 1, characterized in that: The inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: Determine a boundary contour of the targeted photovoltaic sub-region, and determine edge position points from the boundary contour; Taking the edge position point as a starting point, the targeted photovoltaic sub-area is inspected based on the drone and the inspection route.

5. The method according to claim 1, characterized in that The training route includes inspection points; The inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: When the drone arrives at the inspection point, obtaining the inspection image taken by the drone; Performing fuzziness analysis on the inspection image to obtain a fuzziness index of the inspection image; When the fuzziness index reaches a set fuzziness threshold, determining that the inspection image is in a fuzzy state; When it is determined that the inspection image is blurred, the drone is instructed to re-shoot the inspection image and return to the step of performing blur analysis on the inspection image to obtain a blur index of the inspection image, and the process stops when a preset condition is met.

6. The method according to claim 1, characterized in that The training route includes inspection points; The inspecting the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route includes: When the drone of the targeted photovoltaic sub-area arrives at the inspection point, determining the positioning coordinates of the drone in the targeted photovoltaic sub-area; Performing error calculation between the positioning coordinates of the UAV in the targeted photovoltaic sub-area and the actual position of the inspection point to obtain a first position deviation; When the first position deviation does not satisfy the first position deviation condition, a positioning detection is performed on the current position of the UAV to obtain a positioning detection result.

7. The method according to claim 6, characterized in that The performing positioning detection on the current position of the drone to obtain a positioning detection result includes: Performing error calculation between the positioning coordinates of the drone in other photovoltaic sub-areas except the targeted photovoltaic sub-area and the actual point position of the inspection point to obtain a second position deviation, and determining multiple relative positions of the drone in the targeted photovoltaic sub-area and the drones in other photovoltaic sub-areas except the targeted photovoltaic sub-area; When the second position deviation satisfies the second position deviation condition and the multiple relative positions satisfy the relative position condition, it is determined that the positioning of the drone is correct.

8. A photovoltaic area inspection device, characterized in that: include: A first determination module is used to determine a photovoltaic area to be detected; A division module, used to divide the photovoltaic area based on the number of drones and the endurance of the drones, and determine a plurality of photovoltaic sub-areas and drones corresponding to the plurality of photovoltaic sub-areas respectively; A second determination module is used to determine an inspection route for any one of the plurality of photovoltaic sub-areas; The inspection module is used to inspect the targeted photovoltaic sub-area based on the drone corresponding to the targeted photovoltaic sub-area and the inspection route.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein the program executes the method described in any one of claims 1 to 7 when executed.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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