Large-scale photovoltaic power station automatic operation and maintenance method and system based on Beidou and SLAM fusion

Abstract

The invention provides a large photovoltaic power station automatic operation and maintenance method and system based on Beidou and SLAM fusion, and belongs to the technical field of photovoltaic power station automatic operation and maintenance. Fusing the Beidou positioning data and the positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; determining a task allocation cost function according to the fusion positioning result of each photovoltaic module in combination with the health degree of the photovoltaic module and the distance from the robot to the photovoltaic module, and solving the task allocation of the minimum task allocation cost function through a Hungary algorithm; according to the distributed task, an improved A * algorithm is adopted to determine an inspection path; and controlling a robot to inspect each photovoltaic module according to the inspection path. The problems of positioning drift, low multi-machine cooperation efficiency, insufficient fault detection real-time performance and the like in a complex environment are solved, and the inspection efficiency and the inspection precision are greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic operation and maintenance of photovoltaic power stations, and in particular to a method and system for automatic operation and maintenance of large-scale photovoltaic power stations based on Beidou and SLAM. Background Art

[0002] A large-scale photovoltaic power station is a facility that uses concentrated photovoltaic panels installed in open areas, such as deserts, Gobi deserts, water surfaces, wastelands, and mountainous areas, to generate electricity using the abundant solar energy resources in these areas. Large-scale photovoltaic power stations typically have installed capacities ranging from tens to hundreds of megawatts (MW). These stations generate large amounts of power and can be directly connected to the public grid. These stations are centrally distributed through high-voltage transmission systems, providing stable power to users.

[0003] Inspections of large-scale photovoltaic power plants are crucial for ensuring safe, stable, and efficient operation. Key inspections include: checking the surfaces of photovoltaic modules for obstructions such as dirt, dust, bird droppings, and leaves; inspecting modules for damage, such as broken glass, deformed frames, or hidden cracks in the cells; and checking the stability of photovoltaic mountings to ensure they are free of rust, looseness, or deformation. Obstructions, module damage, and the stability of mountings can affect the modules' ability to receive sunlight, reducing power generation efficiency.

[0004] However, large-scale photovoltaic power stations usually cover a vast area (tens to hundreds of hectares) and have a huge number of photovoltaic modules. Traditional manual inspections for fault detection in large photovoltaic power stations have problems such as low efficiency, high cost, high missed detection rate, and poor environmental adaptability. It is not only time-consuming and labor-intensive, but also difficult to achieve real-time response.

[0005] Existing automated operation and maintenance systems for large-scale photovoltaic power plants mostly rely on a single navigation method, such as pure Beidou / GPS navigation systems, pure SLAM navigation technology, and single-robot inspections. However, achieving accurate positioning in complex and changing environments has always been a major challenge in inspection operations. Pure Beidou / GPS navigation systems rely on satellite signals, which are susceptible to signal interference in complex terrain (such as mountains and obstructed areas). This often leads to positioning drift, reducing positioning accuracy and affecting operational efficiency. Pure SLAM technology relies on LiDAR or vision sensors to build local maps, but this can lead to accumulated errors over long periods of operation and struggles to operate stably in featureless environments. Single-robot inspections have limited coverage and are unable to meet the high-frequency operation and maintenance requirements of ultra-large power plants. In scenarios where multiple inspection robots collaborate, information sharing and collaborative operation between them are crucial. However, traditional multi-robot collaborative methods often suffer from information transmission delays and low data processing efficiency, making it difficult to improve the efficiency of multi-robot collaboration. Summary of the Invention

[0006] In order to address the shortcomings of the existing technology, the present invention provides an automatic operation and maintenance method and system for large-scale photovoltaic power stations based on Beidou and SLAM. Through the deep integration of Beidou navigation and positioning and SLAM navigation and positioning, it solves the problems of positioning drift in complex environments, low efficiency of multi-machine collaboration, and insufficient real-time fault detection, thereby greatly improving the inspection efficiency and accuracy.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions: In a first aspect, the present invention provides a large-scale photovoltaic power station automatic operation and maintenance method based on the integration of Beidou and SLAM, including the following process: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; According to the fused positioning results of each photovoltaic module, the task allocation cost function is determined in combination with the health of the photovoltaic module and the distance between the inspection robot and the photovoltaic module, and the task allocation with the minimum task allocation cost function is solved by the Hungarian algorithm; According to the assigned tasks, the improved A* algorithm is used to determine the inspection path. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. The inspection robot is controlled according to the inspection path to inspect each photovoltaic component.

[0008] As a further limitation of the first aspect of the present invention, the improved A* algorithm is used, and the cost function is: , g ( n ) is the starting point to the node n The actual cost, h ( n ) is the inspiration cost, Risk ( n ) is the terrain risk value, and λ is the weight value.

[0009] As a further limitation of the first aspect of the present invention, the temperature of the photovoltaic module in the infrared image obeys a normal distribution. ; Define temperature anomaly threshold : ;in, represents the mean of the normal distribution of the photovoltaic module temperature in the infrared image, Represents the standard deviation of the normal distribution of the PV module temperature in the infrared image.

[0010] TemperatureT The area exceeding the abnormal threshold is segmented using morphology to calculate the proportion of hot spot area: ; in, is the temperature value of the photovoltaic module; The fusion coordinates of the inspection robot ( x , y ) to component number ( Row , Col ): , ; in, d row and d col are the row spacing and column spacing of the photovoltaic array, Indicates the reference ordinate of the photovoltaic array, Indicates the reference horizontal coordinate of the photovoltaic array.

[0011] As a further limitation of the first aspect of the present invention, it is assumed that the power station includes N PV panels, define the task allocation cost function : ; in, d ij For inspection robots i to photovoltaic panels j Distance, Health j ∈[0,1] is the health of the photovoltaic module, α and β is the weight coefficient (calibrated by the particle swarm optimization algorithm), and the Hungarian algorithm is used to solve the task allocation with the minimum total cost.

[0012] In a second aspect, the present invention provides an automatic operation and maintenance system for a large photovoltaic power station based on the integration of Beidou and SLAM, comprising: A data acquisition unit is configured to: acquire Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, construct a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; A positioning fusion unit is configured to: fuse the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; The task allocation unit is configured to: determine a task allocation cost function based on the fused positioning results of each photovoltaic module, in combination with the health of the photovoltaic module and the distance between the robot and the photovoltaic module, and solve the task allocation that minimizes the task allocation cost function using the Hungarian algorithm; The path acquisition unit is configured to: determine the inspection path based on the assigned task using an improved A* algorithm; wherein the cost function of the improved A* algorithm is a weighted sum of the actual cost, the heuristic cost, and the terrain risk value; The automatic inspection unit is configured to control the inspection robot to inspect each photovoltaic module according to the inspection path.

[0013] As a further limitation of the second aspect of the present invention, in the automatic inspection unit, the temperature of the photovoltaic module in the infrared image obeys a normal distribution: , define the temperature anomaly threshold : ;in, represents the mean of the normal distribution of the photovoltaic module temperature in the infrared image, Represents the standard deviation of the normal distribution of the PV module temperature in the infrared image.

[0014] Morphological segmentation is used for areas where the temperature T exceeds the temperature anomaly threshold, and the proportion of the hot spot area is calculated: ; in, is the temperature value of the photovoltaic module; The fusion coordinates of the inspection robot ( x , y ) to component number ( Row , Col ): , ; in, d row and d col are the row spacing and column spacing of the photovoltaic array, Indicates the reference ordinate of the photovoltaic array, Indicates the reference horizontal coordinate of the photovoltaic array.

[0015] As a further limitation of the second aspect of the present invention, in the task allocation unit, it is assumed that the power station includes N PV panels, define the task allocation cost function : ; in, d ij For robots i to photovoltaic panelsj Distance, Health j ∈[0,1] is the health of the photovoltaic module, α and β is the weight coefficient (calibrated by the particle swarm optimization algorithm), and the Hungarian algorithm is used to solve the task allocation with the minimum total cost.

[0016] In a third aspect, the present invention provides a computer device comprising: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for automatic operation and maintenance of a large photovoltaic power station based on the integration of Beidou and SLAM as described in the first aspect of the present invention is implemented.

[0017] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, wherein the computer program is suitable for being loaded by a processor and executing the automatic operation and maintenance method of a large photovoltaic power station based on the fusion of Beidou and SLAM as described in the first aspect of the present invention.

[0018] In a fifth aspect, the present invention provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the automatic operation and maintenance method of a large photovoltaic power station based on the fusion of Beidou and SLAM as described in the first aspect of the present invention.

[0019] Compared with the prior art, the present invention has the following beneficial effects: By deeply integrating Beidou navigation positioning with SLAM (Simultaneous Localization and Mapping) navigation positioning, positioning drift is avoided, stable absolute position information is provided for the inspection robot, and positioning accuracy is improved; by constructing environmental maps in real time through SLAM and dynamically adjusting according to environmental changes, the accuracy of relative position is further ensured; the combination of the two enables the inspection miracle man to achieve continuous, stable and precise positioning in complex environments.

[0020] Through the deep integration of Beidou navigation and positioning and SLAM navigation and positioning, each inspection robot can share location information and environmental maps in real time, thereby achieving closer and more efficient collaborative operations; improving inspection efficiency, each inspection robot can flexibly adjust its operation strategy according to actual conditions, further optimizing the inspection process; and can accurately control the relative positions of multiple inspection robots, avoiding operation conflicts or omissions caused by position deviations.

[0021] Through the deep integration of Beidou navigation and positioning and SLAM navigation and positioning, the inspection robot can obtain its own position and environmental information in real time, and perform intelligent analysis in combination with the preset fault detection algorithm; once an abnormal situation is detected, the inspection robot can immediately issue an alarm and perform preliminary processing, thereby greatly improving the real-time and accuracy of fault detection. This not only reduces the impact of equipment failures on inspection operations, but also provides strong data support for subsequent maintenance and repair work.

[0022] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0024] Figure 1 This is a flow chart of the automatic operation and maintenance method for a large photovoltaic power station based on Beidou and SLAM as described in Example 1 of the present invention.

[0025] Figure 2 This is a functional principle block diagram of the large-scale photovoltaic power station automatic operation and maintenance system based on Beidou and SLAM as described in Example 2 of the present invention.

[0026] Figure 3 This is a functional principle block diagram of the computer device described in Example 3 of the present invention. DETAILED DESCRIPTION

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0029] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other. Example

[0030] Example 1 In this embodiment 1, a method for automatic operation and maintenance of large-scale photovoltaic power stations based on Beidou and SLAM is proposed. The automatic operation and maintenance of large-scale photovoltaic power stations based on Beidou and SLAM is an innovative operation and maintenance model that combines the advantages of the Beidou satellite navigation system and SLAM (Simultaneous Localization and Mapping) technology, aiming to improve the operation and maintenance efficiency and intelligence level of photovoltaic power stations.

[0031] The BeiDou and SLAM-based automated operation and maintenance method for large-scale photovoltaic power stations described in this embodiment utilizes the high-precision positioning capabilities of the BeiDou system, combined with the power station environment map constructed using SLAM technology. Inspection robots can accurately locate equipment within the photovoltaic power station and automatically plan optimal routes based on equipment location and operation and maintenance requirements, thereby improving operation and maintenance efficiency. The inspection robots are equipped with various sensors that can monitor the operating status of equipment within the photovoltaic power station in real time. They use infrared thermal imagers to detect abnormalities such as hot spots and use lidar sensors to detect dirt or damage on the equipment surface. The detection data is then transmitted in real time to the operation and maintenance system data center for analysis and processing by operation and maintenance personnel. The BeiDou and SLAM-based automated operation and maintenance method for large-scale photovoltaic power stations described in this embodiment, combined with weather forecasts and lighting information provided by the BeiDou system, allows the operation and maintenance system to intelligently adjust the power station's operating mode to improve power generation efficiency. Based on the inspection results and troubleshooting data from the inspection robots, operation and maintenance tasks and optimization suggestions are automatically generated to guide operation and maintenance personnel in performing precise operation and maintenance.

[0032] like Figure 1 As shown, the automatic operation and maintenance method of a large photovoltaic power station based on Beidou and SLAM described in this embodiment includes the following processes: S1: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; S2: Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; S4: determining a task allocation cost function based on the fused positioning results of each photovoltaic module, the health of the photovoltaic module, and the distance between the robot and the photovoltaic module, and solving the task allocation with the minimum task allocation cost function using the Hungarian algorithm; S5: Based on the assigned tasks, the inspection path is determined using the improved A* algorithm. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. S6: Control the robot to inspect each photovoltaic module according to the inspection path.

[0033] In this embodiment, a dual-frequency receiver is used to output the locations (L, B, H) of key equipment in the photovoltaic power station in the WGS-84 coordinate system, where L represents longitude, B represents latitude, and H represents altitude. This is used to obtain LiDAR data and visual data from the inspection robot to construct a local grid map. Convert BeiDou's WGS-84 coordinates to the local UTM coordinate system: (1); in, , is the UTM scale factor; , is the radius of curvature of the Maoyou circle, The semi-major axis of the ellipse related to the radius of curvature of the Maoyou circle; is the meridian arc length formula, L0 represents the longitude of the central meridian.

[0034] Assume that the UTM coordinates at the time of SLAM initialization are , define the origin of the local coordinate system, and pass the quaternion q=[ q w , q x , q y , q z ]Describe the rotation relationship: q w represents the real part of the quaternion, q x 、 q y and q z They represent the imaginary part of the quaternion respectively.

[0035] (2); Where R(q) is the rotation matrix generated by the quaternion, t is the translation vector, and x, y, and z represent the coordinate values ​​in the local coordinate system.

[0036] definition k The state vector of the inspection robot at time , including the position (x, y) and moving heading angle of the inspection robot , moving linear speed and angular velocity , T represents the transpose of the vector.

[0037] The state transition model is: (3); Among them, k represents the kth moment, and the process noise , its covariance matrix is: (4); in, 、 、 As a noise parameter, it can be adaptively adjusted according to the robot's motion acceleration.

[0038] Determine BeiDou observation values: , after conversion to the local coordinate system: ; Determine SLAM observations: ; where x SLAM Indicates the x coordinate value in the SLAM local coordinate system, which is used to describe the horizontal position of the target or robot in this coordinate system; y SLAM Represents the y-coordinate value in the SLAM local coordinate system, which is used to describe the vertical position of the target or robot in this coordinate system; It represents the attitude angle in the SLAM local coordinate system, which can usually be the heading angle, etc., used to describe the orientation information of the target or robot. T represents the transpose of the vector.

[0039] Joint observation equation: (5); The observation matrix H is: (6); Observation noise ,in: (7); ρ is the observation correlation coefficient between BeiDou and SLAM.

[0040] According to the acceleration of the inspection robot a Dynamically adjust noise parameters, including: (8); Among them, σ0 is the basic noise, a max is the maximum permissible acceleration.

[0041] Adjust based on Beidou signal quality (carrier-to-noise ratio C / N0) ; (9); in, .

[0042] Power station includes N PV panels, define the task allocation cost function: (10); in, d ij For robots i to photovoltaic panels j Distance, Health j ∈[0,1] is the health of the sub-array (historical failure rate), α and β is the weight coefficient (calibrated by the particle swarm optimization algorithm), and the Hungarian algorithm is used to solve the task allocation with the minimum total cost.

[0043] Using the improved A* algorithm, the cost function is: (11); in, g ( n ) is the starting point to the node n The actual cost, h ( n ) is the heuristic cost (Euclidean distance), Risk( n ) is the terrain risk value (calculated according to the obstacle density of the SLAM map).

[0044] In this embodiment, the inspection robot is equipped with an infrared sensor and uses the collected infrared images to perform fault detection on photovoltaic modules, including: The temperature of photovoltaic modules in infrared images follows a normal distribution: , define the temperature anomaly threshold as: (12); Morphological segmentation is used for areas where the temperature T exceeds the threshold, and the proportion of the hot spot area is calculated: (13); Convert the inspection robot's fusion coordinates (x, y) to component numbers ( Row , Col ): , (14) ; (15) in, d row and d col are the row spacing and column spacing of the photovoltaic array, Indicates the reference ordinate of the photovoltaic array, Indicates the reference horizontal coordinate of the photovoltaic array.

[0045] In this embodiment, the above method is deployed in a 50-hectare photovoltaic power station, and the positioning errors under different scenarios are compared, as shown in Table 1 below.

[0046] Table 1

[0047] After 12 hours of continuous operation, the cumulative error of SLAM reached 2.1m, and the fusion system limited the error to within 0.2m through Beidou correction.

[0048] The fault detection performance is shown in Table 2 below.

[0049] Table 2

[0050] In summary, the automatic operation and maintenance method of a large photovoltaic power station based on Beidou and SLAM described in this embodiment can align the global coordinate system and the local coordinate system in real time, eliminate the cumulative error of SLAM, and improve the accuracy and efficiency of inspection and maintenance.

[0051] Example 2 like Figure 2 As shown, this embodiment 2 provides a large-scale photovoltaic power station automatic operation and maintenance system based on the integration of Beidou and SLAM, including: A data acquisition unit is configured to: acquire Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, construct a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; A positioning fusion unit is configured to: fuse the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; The task allocation unit is configured to: determine a task allocation cost function based on the fused positioning results of each photovoltaic module, in combination with the health of the photovoltaic module and the distance between the robot and the photovoltaic module, and solve the task allocation that minimizes the task allocation cost function using the Hungarian algorithm; The path acquisition unit is configured to: determine the inspection path based on the assigned task using an improved A* algorithm; wherein the cost function of the improved A* algorithm is a weighted sum of the actual cost, the heuristic cost, and the terrain risk value; The automatic inspection unit is configured to: control the robot to inspect each photovoltaic module according to the inspection path It is understandable that each of the above-mentioned units can be separately or completely combined into one or several other units to constitute, or one (or some) of the units can be further divided into multiple functionally smaller units to constitute, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present application. The above-mentioned units are divided based on logical functions. In actual applications, the functions of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present application, the system may also include other units. In actual applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0052] According to another embodiment of the present application, the system described in this embodiment can be constructed and the method of Example 1 of the present application can be implemented by running a computer program (including program code) capable of executing the steps involved in the corresponding method described in Example 1 on a general-purpose computing device such as a computer, which includes processing elements and storage elements such as a central processing unit (CPU), random access memory (RAM), and read-only memory (ROM). The computer program can be recorded on, for example, a computer-readable recording medium, and loaded into the above-mentioned computing device through the computer-readable recording medium and run therein.

[0053] Example 3 like Figure 3 As shown, in this embodiment 3, an electronic device is provided, which includes a processor, a communication interface, and a computer-readable storage medium. The processor, the communication interface, and the computer-readable storage medium may be connected via a bus or other means.

[0054] Among them, the communication interface is used to receive and send data, the computer-readable storage medium can be stored in the memory of the electronic device, the computer-readable storage medium is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer-readable storage medium.

[0055] A processor (or CPU (Central Processing Unit)) is the computing and control core of an electronic device. It is suitable for implementing one or more instructions, specifically for loading and executing one or more instructions to implement corresponding method processes or corresponding functions.

[0056] The processor is configured to execute the following process: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; According to the fused positioning results of each photovoltaic module, the task allocation cost function is determined in combination with the health of the photovoltaic module and the distance between the robot and the photovoltaic module, and the task allocation with the minimum task allocation cost function is solved by the Hungarian algorithm; According to the assigned tasks, the improved A* algorithm is used to determine the inspection path. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. The robot is controlled according to the inspection path to inspect each photovoltaic component.

[0057] The detailed working process is described in Example 1 and will not be repeated here.

[0058] Example 4 In this fourth embodiment, a computer-readable storage medium (Memory) is provided. This computer-readable storage medium is a memory device within an electronic device, used to store programs and data. It is understood that the computer-readable storage medium herein may include both built-in storage media within the electronic device and, of course, extended storage media supported by the electronic device. The computer-readable storage medium provides storage space, which stores the processing system of the electronic device.

[0059] Furthermore, the storage space also stores one or more instructions suitable for being loaded and executed by the processor. These instructions may be one or more computer programs (including program code). It should be noted that the computer-readable storage medium herein may be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage device; alternatively, it may be at least one computer-readable storage medium located remotely from the processor.

[0060] In one embodiment, the computer-readable storage medium stores one or more instructions; the processor loads and executes the one or more instructions stored in the computer-readable storage medium to implement the following process: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; According to the fused positioning results of each photovoltaic module, the task allocation cost function is determined in combination with the health of the photovoltaic module and the distance between the robot and the photovoltaic module, and the task allocation with the minimum task allocation cost function is solved by the Hungarian algorithm; According to the assigned tasks, the improved A* algorithm is used to determine the inspection path. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. The robot is controlled according to the inspection path to inspect each photovoltaic component.

[0061] The detailed working process is described in Example 1 and will not be repeated here.

[0062] Example 5 In this embodiment 5, a computer program product or computer program is provided. The computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of an electronic device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the electronic device to perform the following process: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; According to the fused positioning results of each photovoltaic module, the task allocation cost function is determined in combination with the health of the photovoltaic module and the distance between the robot and the photovoltaic module, and the task allocation with the minimum task allocation cost function is solved by the Hungarian algorithm; According to the assigned tasks, the improved A* algorithm is used to determine the inspection path. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. The robot is controlled according to the inspection path to inspect each photovoltaic component.

[0063] The detailed working process is described in Example 1 and will not be repeated here.

[0064] Those skilled in the art will appreciate that the units and algorithmic steps of each example described in conjunction with the embodiments disclosed in this application can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional technical personnel may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0065] The above-described embodiments of the present invention can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. A computer program product comprises one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions according to the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted via a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, optical fiber, digital line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data processing device such as a server or data center that integrates one or more available media. Available media can include magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0066] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A large-scale photovoltaic power station automatic operation and maintenance method based on the integration of Beidou and SLAM, characterized in that: The following processes are included: Obtain Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, build a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; Fusing the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; According to the fused positioning results of each photovoltaic module, the task allocation cost function is determined in combination with the health of the photovoltaic module and the distance between the inspection robot and the photovoltaic module, and the task allocation with the minimum task allocation cost function is solved by the Hungarian algorithm; According to the assigned tasks, the improved A* algorithm is used to determine the inspection path. The cost function of the improved A* algorithm is the weighted sum of the actual cost, the heuristic cost, and the terrain risk value. The inspection robot is controlled according to the inspection path to inspect each photovoltaic component.

2. The automatic operation and maintenance method for a large photovoltaic power station based on the integration of Beidou and SLAM as claimed in claim 1, characterized in that: In the improved A* algorithm, the cost function is: , g ( n ) is the starting point to the node n The actual cost, h ( n ) is the heuristic cost, Risk( n ) is the terrain risk value, and λ is the weight value.

3. The automatic operation and maintenance method for a large photovoltaic power station based on the integration of Beidou and SLAM as claimed in claim 1, characterized in that: The temperature of the photovoltaic module in the infrared image follows a normal distribution: , define the temperature anomaly threshold : ； in, represents the mean of the normal distribution of the photovoltaic module temperature in the infrared image, Represents the standard deviation of the normal distribution of the PV module temperature in the infrared image; Morphological segmentation is used for areas where the temperature exceeds the temperature anomaly threshold, and the proportion of hot spot area is calculated: ； in, is the temperature value of the photovoltaic module; Convert the inspection robot's fusion coordinates (x, y) to component numbers ( Row , Col ): ， ； in, d row and d col Respectively represent the row spacing and column spacing of the photovoltaic array, Indicates the reference ordinate of the photovoltaic array, Indicates the reference horizontal coordinate of the photovoltaic array.

4. The automatic operation and maintenance method for a large photovoltaic power station based on the integration of Beidou and SLAM as claimed in claim 1, characterized in that: Assume that the power station contains N PV panels, define the task allocation cost function : ； in, d ij For robots i to photovoltaic panels j Distance, Health j ∈[0,1] is the health of the photovoltaic module, α and β The weight coefficients are calibrated by the particle swarm optimization algorithm, and the task allocation with the minimum total cost is solved by the Hungarian algorithm.

5. A large-scale photovoltaic power station automatic operation and maintenance system based on the integration of Beidou and SLAM, characterized by: include: A data acquisition unit is configured to: acquire Beidou positioning data of the photovoltaic modules to be inspected and lidar data of the inspection robot, construct a SLAM map based on the lidar data, and determine the SLAM positioning data of each photovoltaic module based on the SLAM map; A positioning fusion unit is configured to: fuse the Beidou positioning data and the SLAM positioning data of the photovoltaic modules in the SLAM map to obtain a fused positioning result of each photovoltaic module; The task allocation unit is configured to: determine a task allocation cost function based on the fused positioning results of each photovoltaic module, in combination with the health of the photovoltaic module and the distance between the inspection robot and the photovoltaic module, and solve the task allocation that minimizes the task allocation cost function using the Hungarian algorithm; The path acquisition unit is configured to: determine the inspection path based on the assigned task using an improved A* algorithm; wherein the cost function of the improved A* algorithm is a weighted sum of the actual cost, the heuristic cost, and the terrain risk value; The automatic inspection unit is configured to control the inspection robot to inspect each photovoltaic module according to the inspection path.

6. The large-scale photovoltaic power station automatic operation and maintenance system based on the integration of Beidou and SLAM as claimed in claim 5 is characterized in that: In the automatic inspection unit, the temperature of the photovoltaic modules in the infrared image follows a normal distribution: , define the temperature anomaly threshold : ； in, represents the mean of the normal distribution of the photovoltaic module temperature in the infrared image, Represents the standard deviation of the normal distribution of the PV module temperature in the infrared image; Morphological segmentation is used for areas where the temperature exceeds the temperature anomaly threshold, and the proportion of hot spot area is calculated: ； in, is the temperature value of the photovoltaic module; Convert the inspection robot's fusion coordinates (x, y) to component numbers ( Row , Col ): ， ； in, d row and d col Respectively represent the row spacing and column spacing of the photovoltaic array, Indicates the reference ordinate of the photovoltaic array, Indicates the reference horizontal coordinate of the photovoltaic array.

7. The large-scale photovoltaic power station automatic operation and maintenance system based on the integration of Beidou and SLAM as claimed in claim 5, characterized in that: In the task allocation unit, it is assumed that the power station contains N PV panels, define the task allocation cost function : ； in, d ij For robots i to photovoltaic panels j Distance, Health j ∈[0,1] is the health of the photovoltaic module, α and β The weight coefficients are calibrated by the particle swarm optimization algorithm, and the task allocation with the minimum total cost is solved by the Hungarian algorithm.

8. A computer device, characterized in that: include: a processor and a computer-readable storage medium; a processor adapted to execute a computer program; A computer-readable storage medium having a computer program stored therein, wherein when the computer program is executed by the processor, the method for automatic operation and maintenance of a large photovoltaic power station based on the integration of Beidou and SLAM is implemented as described in any one of claims 1 to 4.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is suitable for being loaded by a processor and executing the large-scale photovoltaic power station automatic operation and maintenance method based on Beidou and SLAM fusion as described in any one of claims 1 to 4.

10. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, it implements the large-scale photovoltaic power station automatic operation and maintenance method based on the integration of Beidou and SLAM as described in any one of claims 1 to 4.

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