Distributed photovoltaic power station unmanned aerial vehicle inspection line planning method and device

Abstract

The invention discloses a distributed photovoltaic power station unmanned aerial vehicle inspection route planning method and device. The method comprises the following steps: acquiring a three-dimensional image of a photovoltaic power station, and generating a three-dimensional model of the photovoltaic power station; defining a basic area according to the three-dimensional model, and dividing at least two sub-areas on the basis of the basic area; performing equidistant outward expansion on each sub-region to generate an expanded sub-region; determining the number of unmanned aerial vehicles for routing inspection according to the division condition of the expanded sub-regions; and carrying out waypoint planning on each expanded sub-region, and generating an unmanned aerial vehicle inspection route covering the current sub-region. According to the technical scheme, the three-dimensional image is adopted to generate the model, the basic area and the sub-area are accurately divided, and the distributed photovoltaic power station is quickly inspected by the unmanned aerial vehicle through equidistant outward expansion and automatic waypoint planning, so that the comprehensive coverage is ensured, the inspection time is shortened, and the operation efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drone inspection, and in particular to a method and device for planning a distributed photovoltaic power station drone inspection route. Background Art

[0002] In recent years, with the rapid development of photovoltaic power generation technology, distributed photovoltaic power stations have been widely promoted due to their flexibility and proximity to users. However, distributed photovoltaic power stations are often located in areas with complex and scattered terrain. Traditional manual inspection methods have defects such as low inspection efficiency, high cost, and easy missed inspections. Although existing drone inspection technologies have been partially applied to photovoltaic power stations, most of them rely on manual route planning, resulting in a long planning cycle and difficulty in fully covering all monitoring areas. At the same time, due to the uneven distribution of photovoltaic strings, existing solutions are prone to monitoring blind spots when handling inspection tasks, affecting the timely maintenance and safe operation of equipment. Therefore, how to implement drone inspection route planning so that it can achieve comprehensive and efficient inspections of distributed photovoltaic power stations has become an urgent issue to be solved in the industry. Summary of the invention

[0003] The present invention provides a method and device for planning a distributed photovoltaic power station drone inspection route, which is used to accurately divide and expand sub-areas through three-dimensional image and model building technology, thereby improving the efficiency and accuracy of distributed photovoltaic power station drone rapid inspection.

[0004] According to a first aspect of the present invention, a method for planning a distributed photovoltaic power station drone inspection route is provided, and the method for planning a distributed photovoltaic power station drone inspection route comprises:

[0005] Acquire a three-dimensional image of the photovoltaic power station and generate a three-dimensional model of the photovoltaic power station;

[0006] Delineating a basic area according to the three-dimensional model, and dividing the basic area into at least two sub-areas, each of which contains at least two photovoltaic strings;

[0007] Expanding each sub-region outward at equal intervals to generate expanded sub-regions;

[0008] According to the division of the expanded sub-areas, the number of drones used for inspection is determined to ensure that each sub-area can be effectively covered;

[0009] Waypoint planning is performed for each expanded sub-area, and a drone inspection route covering the current sub-area is generated to achieve a comprehensive and rapid inspection of the entire PV power station.

[0010] In one embodiment, the step of acquiring a three-dimensional image of a photovoltaic power station includes:

[0011] Use drone photography or satellite remote sensing to obtain three-dimensional images of photovoltaic power plants;

[0012] The three-dimensional image is preprocessed to generate a three-dimensional model.

[0013] In one embodiment, dividing the basic area into at least two sub-areas includes:

[0014] The basic area is further divided into a plurality of sub-areas, wherein the distance between the photovoltaic strings contained in each sub-area is less than a preset threshold.

[0015] In one embodiment, the sub-areas are equidistantly extended outwards, including:

[0016] Uniformly expand the boundaries of each sub-region;

[0017] Control the area of ​​the expanded sub-area so that it is within the flight range of a single drone.

[0018] In one embodiment, generating a drone inspection route covering the current sub-area includes:

[0019] Automatically generate inspection waypoints for each sub-area after coverage expansion;

[0020] The inspection waypoints are transmitted to the flight control module of the corresponding UAV to perform the inspection task.

[0021] In one embodiment, it further includes:

[0022] During the drone inspection process, the monitoring module collects real-time monitoring data from sensors;

[0023] The monitoring data is transmitted to a cloud server through a network module for data storage, analysis or calling.

[0024] According to a second aspect of the present invention, a distributed photovoltaic power station drone inspection route planning device is provided, comprising:

[0025] An acquisition module, used to acquire a three-dimensional image of the photovoltaic power station and generate a three-dimensional model of the photovoltaic power station;

[0026] A division module, used for demarcating a basic area according to the three-dimensional model, and dividing the basic area into at least two sub-areas, each of which contains at least two photovoltaic strings;

[0027] An expansion module, used for expanding each sub-region outward at equal intervals to generate expanded sub-regions;

[0028] A determination module, used to determine the number of drones used for inspection according to the division of the expanded sub-areas, so as to ensure that each sub-area can be effectively covered;

[0029] The planning module is used to plan waypoints for each expanded sub-area and generate a drone inspection route covering the current sub-area to achieve a comprehensive and rapid inspection of the entire PV power station.

[0030] According to a third aspect of the present invention, there is provided an electronic device, the electronic device comprising: a communication interface, a processor, and a memory;

[0031] Among them, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, any of the above-mentioned distributed photovoltaic power station drone inspection route planning methods is implemented.

[0032] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, on which computer program instructions are stored. When the computer program instructions are executed by a computer (for example, a processor in a computer), any of the above-mentioned distributed photovoltaic power station drone inspection route planning methods is implemented.

[0033] In summary, the present invention provides a method and device for planning a route for UAV inspection of a distributed photovoltaic power station, the method comprising: obtaining a three-dimensional image of a photovoltaic power station and generating a three-dimensional model of the photovoltaic power station; demarcating a basic area according to the three-dimensional model, and dividing at least two sub-areas on the basis of the basic area, each sub-area containing at least two photovoltaic strings; expanding each sub-area outward at equal intervals to generate an expanded sub-area; determining the number of UAVs used for inspection according to the division of the expanded sub-area to ensure that each sub-area can be effectively covered; planning waypoints for each expanded sub-area to generate a UAV inspection route covering the current sub-area, so as to achieve a comprehensive and rapid inspection of the entire photovoltaic power station. The technical solution of the present application adopts a three-dimensional image generation model to accurately divide the basic area and sub-areas, and realizes UAV rapid inspection of distributed photovoltaic power stations through equal-distance outward expansion and automatic waypoint planning, which not only ensures comprehensive coverage, but also shortens the inspection time and significantly improves the operation efficiency.

[0034] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0035] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for planning a distributed photovoltaic power station drone inspection route provided by an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of the connection relationship between the monitoring drone, the monitoring sub-area, the monitoring module, the network module and the cloud server provided in an embodiment of the present invention;

[0039] Figure 3 A schematic diagram of an expanded sub-area and a drone inspection route provided for an embodiment of the present invention;

[0040] Figure 4 A structural diagram of a distributed photovoltaic power station drone inspection route planning device provided by an embodiment of the present invention;

[0041] Figure 5 A structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The features and exemplary embodiments of various aspects of the present application will be described in detail below. In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain the present application, rather than to limit the present application. For those skilled in the art, the present application can be implemented without the need for some of these specific details. The following description of the embodiments is only to provide a better understanding of the present application by illustrating the examples of the present application.

[0043] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0044] like Figure 1 As shown, the present invention provides a method for planning a route for a distributed photovoltaic power station drone inspection, the method comprising:

[0045] In step S11, a three-dimensional image of the photovoltaic power station is acquired to generate a three-dimensional model of the photovoltaic power station;

[0046] In step S12, a basic area is delineated according to the three-dimensional model, and at least two sub-areas are divided based on the basic area, each sub-area contains at least two photovoltaic strings;

[0047] In step S13, each sub-region is expanded outward at equal intervals to generate an expanded sub-region;

[0048] In step S14, the number of drones used for inspection is determined according to the division of the expanded sub-areas to ensure that each sub-area can be effectively covered;

[0049] In step S15, waypoint planning is performed for each expanded sub-area to generate a drone inspection route covering the current sub-area, so as to achieve a comprehensive and rapid inspection of the entire photovoltaic power station.

[0050] In one embodiment, three-dimensional image processing, area division, waypoint automatic planning and Internet of Things data transmission technology are used to achieve comprehensive, fast and accurate inspection of photovoltaic power stations. The distributed photovoltaic power station drone inspection route planning method based on the Internet of Things includes the following components: monitoring drones, monitoring sub-areas, monitoring modules, network modules and cloud servers, as shown in the attached Figure 2As shown in the figure, the cloud server realizes signal transmission through the network module and the monitoring module; the monitoring module is used to collect and process the monitoring data collected by the sensor in real time; the network module is used to transmit the processed monitoring data to the cloud server, which is responsible for data storage, analysis and call; the monitoring drone is used to patrol the photovoltaic modules in the monitoring sub-area.

[0051] Use drone photography or satellite remote sensing technology to obtain three-dimensional image data of the photovoltaic power station area. To ensure that the image data meets the accuracy requirements of subsequent model construction, the acquisition equipment should have the characteristics of high resolution and high dynamic range. The drone is equipped with a high-definition camera to shoot the photovoltaic power station from multiple angles according to the preset flight altitude and route; or use satellite remote sensing technology to obtain image data covering the target area. The collected image data is processed by the pre-processing module for denoising, geometric distortion correction, image stitching and orthorectification to generate a processed image with accurate scale and geographic location information. Subsequently, based on three-dimensional reconstruction algorithms such as multi-view stereo vision or structured light, a three-dimensional model reflecting the actual distribution of photovoltaic power station equipment and environment is constructed. This three-dimensional model provides a data basis for subsequent regional division.

[0052] After the three-dimensional model is constructed, the entire photovoltaic power station is initially segmented using the region division algorithm to form a basic region, which is further divided into at least two sub-regions, such as the attached Figure 3 As shown. Based on the spatial distribution characteristics of photovoltaic strings in the three-dimensional model and combined with geographic information data, the power station as a whole is preliminarily zoned. The delineation of basic areas takes into account factors such as the arrangement of power station equipment and site topography to ensure that the equipment in each basic area is relatively concentrated and spatially continuous. In each basic area, according to the actual distribution of photovoltaic strings, the photovoltaic strings are grouped using a preset distance threshold, so that each sub-area contains at least two photovoltaic strings, and the distance between each photovoltaic string is less than the preset value. This process can be implemented using a clustering algorithm (such as K-means or DBSCAN) to classify photovoltaic strings with spatial proximity into the same sub-area, thereby ensuring the rationality and scientificity of the segmentation results.

[0053] Due to the complex actual environment of distributed photovoltaic power stations, in order to avoid inspection blind spots caused by missing edge areas during area division, the system performs equidistant outward expansion processing on each initially divided sub-area. The buffer zone algorithm (Buffer Zone Algorithm) is used to expand the boundaries of each sub-area equidistantly, that is, a certain distance is uniformly increased outward on the basis of the original boundaries of the sub-area. The selection of the expansion distance is based on parameters such as the endurance of the drone, flight speed, and environmental risk assessment, to ensure that the expanded area can cover all target equipment without exceeding the flight range of a single drone. The area of ​​the expanded sub-area is automatically calculated by the system and compared with the preset endurance range of a single drone to ensure that the expanded area is within the range that the drone can cover in a single mission. If the expanded area exceeds the endurance, the sub-area can be further subdivided or the expansion distance can be adjusted until the inspection requirements are met.

[0054] Based on the division of the expanded sub-areas, the system automatically determines the number of drones required to ensure that each expanded sub-area can be effectively inspected. The system automatically calculates the optimal number of drones based on the area of ​​each expanded sub-area, the complexity of the inspection, and the flight parameters of the drone (such as flight speed, endurance, maximum flight distance, etc.). In general, one drone is assigned to each expanded sub-area; however, in areas with large areas or complex inspection tasks, multi-machine collaborative operations can also be used. The task scheduling module sends the inspection tasks and the generated waypoint data to each drone separately. After receiving the task, each drone automatically loads the pre-planned inspection route and performs the flight inspection task on time and according to the task requirements. During the task allocation process, manual intervention is also supported to adjust special areas or emergency situations.

[0055] In order to achieve comprehensive coverage of each expanded sub-area, the present invention uses an automatic waypoint planning algorithm to generate a drone inspection route. First, the expanded sub-area is divided into several grids (Grid) according to the geometric shape, and then a greedy algorithm or a heuristic search algorithm is used to generate a waypoint sequence covering all grids. This method takes into account factors such as the turning radius of the drone flight, the distance between waypoints and the flight speed, and tries to shorten the overall flight path while ensuring the inspection coverage rate. After generating the preliminary waypoint sequence, the system optimizes the route. During the optimization process, the steering angle between the waypoints, the continuity of the flight path and the overlapping area are adjusted to ensure that the inspection route can avoid repeated flights and effectively cover all key areas. The optimization algorithm can use a genetic algorithm, an ant colony algorithm or other optimization methods suitable for path planning. The optimized waypoint data is transmitted to the flight control module of the corresponding drone in an encrypted manner through the network module. After the flight control module receives and parses the waypoint information, the drone automatically performs the inspection task according to the predetermined route. The whole process does not require human intervention, and the fully automated execution of the inspection task is achieved.

[0056] In the process of the drone performing the inspection task, in order to monitor the operating status of the photovoltaic power station in real time, the present embodiment integrates a monitoring module on the drone. The drone is equipped with a variety of sensors, including high-definition cameras, infrared thermal imagers, laser rangefinders, and environmental temperature and humidity sensors. During the inspection process, the monitoring module collects the operating data of the photovoltaic string in real time, such as temperature, current, voltage, local hot spot images, etc., and performs preliminary processing and compression on the collected data. The collected monitoring data is transmitted to the cloud server through the built-in network module (wireless communication, cellular network or other short-distance communication technology can be used). The cloud server is equipped with a big data processing platform to store, analyze and display the transmitted data, and identify and warn abnormal information through data mining and machine learning algorithms. Encryption technology is used in the data transmission process to ensure data security and integrity. The cloud server feeds back the processing results to the monitoring center and the human-computer interaction interface (HMI) in real time, so that maintenance personnel can intuitively understand the inspection progress, equipment status and potential failure risks. For the detected abnormal data, the system can automatically trigger the early warning mechanism and promptly notify relevant personnel to conduct on-site inspection and processing to ensure the safe operation of the photovoltaic power station.

[0057] In order to achieve the organic coordination of the above steps, the present invention constructs a patrol inspection system based on the Internet of Things architecture, and its main components include: UAV platform, task scheduling module, image processing and waypoint planning module, monitoring module, data communication module and cloud server. The UAV is the executor of the patrol inspection task, equipped with image acquisition device, sensor and flight control system. The UAV platform supports autonomous flight and multi-machine collaborative operation, can complete the preset patrol inspection task according to the waypoint information issued by the task scheduling module, and collect environmental data in real time during the flight. The task scheduling module is responsible for the allocation and management of the overall patrol inspection task. The module automatically calculates the required number of UAVs based on the expanded sub-area division, the UAV endurance and the real-time task status, and dynamically schedules each UAV task. During the scheduling process, the system supports manual intervention, which is convenient for flexible adjustment for special areas or emergencies. The image processing module is responsible for preprocessing the collected images and constructing a three-dimensional model, providing basic data for the division of basic areas and sub-areas. The waypoint planning module automatically generates patrol inspection waypoint data based on the area coverage algorithm and the path optimization algorithm, and sends it to the UAV flight control system through the standard interface to realize automatic patrol inspection path planning. The data communication module realizes data transmission between the drone and the cloud server through wireless networks, cellular networks or other communication methods. The cloud server has powerful data storage, processing and analysis capabilities. It not only monitors the inspection data in real time, but also uses big data and artificial intelligence algorithms to predict and analyze the status of photovoltaic power station equipment to assist maintenance decisions. In order to facilitate maintenance personnel to monitor inspection tasks in real time, this embodiment also designs a friendly graphical human-computer interaction interface. The interface content includes inspection area maps, real-time location of drones, waypoint trajectories, sensor data curves, and abnormal warning information. Users can monitor the task status, query data, and make necessary manual adjustments through this interface to improve overall operation and maintenance efficiency.

[0058] In the process of waypoint planning and task scheduling, various emergencies that may occur in the actual flight environment are fully considered, and corresponding algorithm optimization and dynamic adjustment mechanisms are designed. After the initial generation of the waypoint sequence, the system adaptively optimizes the route, considers the flight parameters of the drone (such as turning radius, flight speed and energy consumption) and environmental obstacle information, and uses genetic algorithms or ant colony algorithms to fine-tune the path to ensure that the inspection path can cover all target areas and avoid unnecessary energy consumption and safety hazards. For large-scale distributed photovoltaic power stations, it is difficult for a single-machine inspection to complete the task within the specified time. The present invention introduces a multi-machine collaboration mechanism. The system realizes real-time information sharing between drones through wireless communication, dynamically adjusts the progress of each task, avoids repeated inspections and route conflicts, and further improves the overall inspection efficiency. During the inspection process, if the drone deviates from the predetermined route due to sudden weather, signal interruption or other external interference, the system can receive the location information and sensor data of the drone in real time, and automatically start the backup communication link or adjust the flight path. Through real-time data feedback, the system dynamically corrects the route in the cloud to ensure that the inspection task is not interrupted and continues to run efficiently.

[0059] The technical solution in this embodiment can automatically generate inspection routes and task scheduling mechanisms, significantly reducing the time required for traditional manual planning and inspections. In actual applications, the inspection time can be shortened by more than 50%. The regional division based on the three-dimensional model and the equidistant outward expansion technology are adopted to ensure that the inspection path fully covers the power station area and avoid the safety hazards caused by missed inspections at the edge. The integration of multiple sensors and cloud data analysis platforms realizes real-time monitoring and fault warning of the operating status of photovoltaic power station equipment, ensuring the safe and stable operation of the power station. Parameters can be adjusted according to photovoltaic power stations of different scales and environments, and a variety of drone platforms and flight control systems are supported to achieve wide applicability. Through redundant data transmission, encrypted communication and real-time exception handling mechanisms, the stable operation of inspection tasks in complex environments is ensured, and data loss or information leakage is effectively prevented.

[0060] The distributed photovoltaic power station drone inspection route planning method realizes the full automation and intelligence of the inspection task through the preprocessing of the three-dimensional image of the photovoltaic power station, the precise division of the basic area and sub-area, the equidistant outward expansion of the sub-area and the automatic waypoint planning. It not only significantly improves the inspection efficiency and data accuracy, but also greatly reduces the cost of manual inspection, and ensures the safe and stable operation of the photovoltaic power station through real-time monitoring and early warning mechanisms. After field test verification, it can still maintain efficient and stable operation in complex environments, and has broad application prospects and promotion value. In summary, this embodiment describes in detail the various steps of the distributed photovoltaic power station drone inspection route planning method and its implementation method. The steps are closely connected, which not only makes full use of advanced three-dimensional image processing and area division technology, but also combines efficient waypoint planning algorithms and real-time monitoring data transmission technology to form a complete set of intelligent inspection systems.

[0061] The technical solution in this embodiment adopts a three-dimensional image generation model to accurately divide the basic area and sub-area, and through equidistant outward expansion and automatic waypoint planning, it realizes the rapid inspection of distributed photovoltaic power stations by drones, which not only ensures comprehensive coverage, but also shortens the inspection time and significantly improves operational efficiency.

[0062] In one embodiment, Figure 4 FIG. 1 is a block diagram of a distributed photovoltaic power station drone inspection route planning device according to an exemplary embodiment. Figure 4 As shown, the distributed photovoltaic power station drone inspection route planning device includes an acquisition module 41, a division module 42, an expansion module 43, a determination module 44 and a planning module 45.

[0063] The acquisition module 41 is used to acquire a three-dimensional image of the photovoltaic power station and generate a three-dimensional model of the photovoltaic power station;

[0064] The division module 42 is used to define a basic area according to the three-dimensional model, and divide the basic area into at least two sub-areas, each of which contains at least two photovoltaic strings;

[0065] The expansion module 43 is used to expand each sub-region outward at equal intervals to generate expanded sub-regions;

[0066] The determination module 44 is used to determine the number of drones used for inspection according to the division of the expanded sub-areas to ensure that each sub-area can be effectively covered;

[0067] The planning module 45 is used to plan waypoints for each expanded sub-area and generate a drone inspection route covering the current sub-area to achieve a comprehensive and rapid inspection of the entire photovoltaic power station.

[0068] The acquisition module 41, the division module 42, the expansion module 43, the determination module 44 and the planning module 45 included in the block diagram of the distributed photovoltaic power station drone inspection route planning device are controlled to execute the distributed photovoltaic power station drone inspection route planning method described in any of the above embodiments.

[0069] like Figure 5 As shown, the present invention provides an electronic device 500, the electronic device comprising: a communication interface, a processor 501, and a memory 502;

[0070] Among them, the memory 502 is used to store program instructions. When the program instructions are executed by the processor 501 that is communicatively connected to the memory 502 through the communication interface, a three-dimensional image of the photovoltaic power station is obtained to generate a three-dimensional model of the photovoltaic power station; a basic area is delineated according to the three-dimensional model, and at least two sub-areas are divided on the basis of the basic area, and each sub-area contains at least two photovoltaic strings; each sub-area is equidistantly expanded outward to generate expanded sub-areas; according to the division of the expanded sub-areas, the number of drones used for inspection is determined to ensure that each sub-area can be effectively covered; waypoint planning is performed for each expanded sub-area, and a drone inspection route covering the current sub-area is generated to achieve a comprehensive and rapid inspection of the entire photovoltaic power station.

[0071] The present invention provides a computer-readable storage medium, on which computer program instructions are stored. When the computer program instructions are executed by a processor, a three-dimensional image of a photovoltaic power station is acquired to generate a three-dimensional model of the photovoltaic power station; a basic area is delineated according to the three-dimensional model, and at least two sub-areas are divided on the basis of the basic area, each sub-area containing at least two photovoltaic strings; each sub-area is equidistantly expanded outward to generate extended sub-areas; according to the division of the extended sub-areas, the number of drones used for inspection is determined to ensure that each sub-area can be effectively covered; waypoint planning is performed on each extended sub-area, and a drone inspection route covering the current sub-area is generated to achieve a comprehensive and rapid inspection of the entire photovoltaic power station.

[0072] It should be understood that the specific features, operations and details described hereinabove about the method of the present invention may also be similarly applied to the device and system of the present invention, or, vice versa. In addition, each step of the method of the present invention described above may be performed by the corresponding parts or units of the device or system of the present invention.

[0073] It should be understood that each module / unit of the device of the present invention can be implemented in whole or in part by software, hardware, firmware or a combination thereof. Each module / unit can be embedded in the processor of the computer device in the form of hardware or firmware or independent of the processor, or can be stored in the memory of the computer device in the form of software for the processor to call to perform the operation of each module / unit. Each module / unit can be implemented as an independent component or module, or two or more modules / units can be implemented as a single component or module.

[0074] In one embodiment, a computer device is provided, which includes a memory and a processor, and the memory stores computer instructions executable by the processor, and the computer instructions instruct the processor to execute each step of the method of the embodiment of the present invention when executed by the processor. The computer device can be a server, a terminal, or any other electronic device with necessary computing and / or processing capabilities in a broad sense. In one embodiment, the computer device may include a processor, a memory, a network interface, a communication interface, etc. connected through a system bus. The processor of the computer device can be used to provide necessary computing, processing and / or control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. An operating system, a computer program, etc. may be stored in or on the non-volatile storage medium. The internal memory can provide an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface and the communication interface of the computer device can be used to connect and communicate with external devices through a network. The steps of the method of the present invention are executed by the processor.

[0075] The present invention may be implemented as a computer-readable storage medium having a computer program stored thereon, which causes the steps of the method of an embodiment of the present invention to be executed when executed by a processor. In one embodiment, the computer program is distributed on a plurality of computer devices or processors coupled to a network so that the computer program is stored, accessed, and executed in a distributed manner by one or more computer devices or processors. A single method step / operation, or two or more method steps / operations, may be performed by a single computer device or processor or by two or more computer devices or processors. One or more method steps / operations may be performed by one or more computer devices or processors, and one or more other method steps / operations may be performed by one or more other computer devices or processors. One or more computer devices or processors may perform a single method step / operation, or perform two or more method steps / operations.

[0076] It will be understood by those skilled in the art that the method steps of the present invention can be completed by instructing related hardware such as a computer device or a processor through a computer program, and the computer program can be stored in a non-temporary computer-readable storage medium, and the steps of the present invention are executed when the computer program is executed. Depending on the circumstances, any reference to memory, storage, database or other media herein may include non-volatile and / or volatile memory. Examples of non-volatile memory include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (using a three-dimensional image generation model to accurately divide basic areas and sub-areas, and through equidistant outward expansion and automatic waypoint planning, to achieve UAV rapid inspection of distributed photovoltaic power stations, which not only ensures comprehensive coverage, but also shortens the inspection time and significantly improves operating efficiency PROM), flash memory, magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk, etc. Examples of volatile memory include random access memory (RAM), external cache memory, etc.

[0077] The various technical features described above can be combined arbitrarily. Although all possible combinations of these technical features are not described, any combination of these technical features should be considered to be covered by this specification as long as there is no contradiction in such combination.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning a distributed photovoltaic power station drone inspection route, characterized in that: include: Acquire a three-dimensional image of the photovoltaic power station and generate a three-dimensional model of the photovoltaic power station; Delineating a basic area according to the three-dimensional model, and dividing the basic area into at least two sub-areas, each of which contains at least two photovoltaic strings; Expanding each sub-region outward at equal intervals to generate expanded sub-regions; According to the division of the expanded sub-areas, the number of drones used for inspection is determined to ensure that each sub-area can be effectively covered; Waypoint planning is performed for each expanded sub-area, and a drone inspection route covering the current sub-area is generated to achieve a comprehensive and rapid inspection of the entire PV power station.

2. The distributed photovoltaic power station drone inspection route planning method according to claim 1 is characterized in that: The step of acquiring a three-dimensional image of a photovoltaic power station comprises: Use drone photography or satellite remote sensing to obtain three-dimensional images of photovoltaic power plants; The three-dimensional image is preprocessed to generate a three-dimensional model.

3. The distributed photovoltaic power station drone inspection route planning method according to claim 1 is characterized in that: The dividing the basic area into at least two sub-areas comprises: The basic area is further divided into a plurality of sub-areas, wherein the distance between the photovoltaic strings contained in each sub-area is less than a preset threshold.

4. The distributed photovoltaic power station drone inspection route planning method according to claim 1 is characterized in that: The sub-areas extend outwards at equal intervals and include: Uniformly expand the boundaries of each sub-region; Control the area of ​​the expanded sub-area so that it is within the flight range of a single drone.

5. The distributed photovoltaic power station drone inspection route planning method according to claim 1 is characterized in that: The generating of the drone inspection route covering the current sub-area includes: Automatically generate inspection waypoints for each sub-area after coverage expansion; The inspection waypoints are transmitted to the flight control module of the corresponding UAV to perform the inspection task.

6. The distributed photovoltaic power station drone inspection route planning method according to claim 1 is characterized in that: Also includes: During the drone inspection process, the monitoring module collects real-time monitoring data from sensors; The monitoring data is transmitted to a cloud server through a network module for data storage, analysis or calling.

7. A distributed photovoltaic power station drone inspection route planning device, characterized in that: include: An acquisition module, used to acquire a three-dimensional image of the photovoltaic power station and generate a three-dimensional model of the photovoltaic power station; A division module, used for demarcating a basic area according to the three-dimensional model, and dividing the basic area into at least two sub-areas, each of which contains at least two photovoltaic strings; An expansion module, used for expanding each sub-region outward at equal intervals to generate expanded sub-regions; A determination module, used to determine the number of drones used for inspection according to the division of the expanded sub-areas, so as to ensure that each sub-area can be effectively covered; The planning module is used to plan waypoints for each expanded sub-area and generate a drone inspection route covering the current sub-area to achieve a comprehensive and rapid inspection of the entire PV power station.

8. The distributed photovoltaic power station drone inspection route planning device according to claim 7, characterized in that: The acquisition module, the division module, the expansion module, the determination module and the planning module are controlled to execute the distributed photovoltaic power station drone inspection route planning method according to any one of claims 1-6.

9. An electronic device, characterized in that: include: Communication interface, processor, memory; Among them, the memory is used to store program instructions, and when the program instructions are executed by the processor that is communicatively connected to the memory through the communication interface, the electronic device implements the distributed photovoltaic power station drone inspection route planning method as described in any one of claims 1 to 6.

10. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a computer, the computer implements the distributed photovoltaic power station drone inspection route planning method as described in any one of claims 1 to 6.

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