Method, device and control system for cleaning polycrystalline silicon photovoltaic panel by unmanned aerial vehicle

Abstract

The invention provides a method and device for cleaning a polycrystalline silicon photovoltaic panel by an unmanned aerial vehicle and a control system, and relates to the technical field of cleaning robots, and the method comprises the steps: recognizing the position state information of the polycrystalline silicon photovoltaic panel and surrounding obstacles in a target photovoltaic region through a detection sensor on unmanned aerial vehicle equipment, and generating a three-dimensional photovoltaic space map; performing dynamic path planning on the unmanned aerial vehicle equipment according to the three-dimensional photovoltaic space map to generate an optimal cleaning path; the precursor flight state of the unmanned aerial vehicle equipment is monitored, and the optimal flight state is predicted; panel temperature change data of the surface of the polycrystalline silicon photovoltaic panel is collected through an inductive sensor on the unmanned aerial vehicle equipment, and the optimal cleaning strength of the unmanned aerial vehicle equipment is determined; and on the basis of the optimal cleaning path, the optimal flight state and the optimal cleaning strength, unmanned aerial vehicle equipment is controlled to clean the polycrystalline silicon photovoltaic panel, so that all-directional efficient cleaning of the photovoltaic panel can be realized, the energy utilization rate is increased, the operation and maintenance cost is reduced, and the safety of a photovoltaic power station is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of cleaning robots, and in particular to a method, device and control system for cleaning polysilicon photovoltaic panels by unmanned aerial vehicles. Background Art

[0002] As the global demand for clean energy grows, solar power generation systems have been widely used. However, effective cleaning of photovoltaic panels has become the key to improving energy conversion efficiency.

[0003] In the existing technology, traditional manual cleaning methods are not only costly and inefficient, but also difficult to ensure safety in complex terrain or high-altitude operations. At present, although the existing fixed-track photovoltaic panel cleaning robots have solved some cleaning problems to a certain extent, their adaptability and flexibility are limited when facing large photovoltaic arrays with irregular shapes or changing environments, resulting in low cleaning coverage and high installation and maintenance costs.

[0004] Therefore, it is necessary to provide a method, device and control system for cleaning polysilicon photovoltaic panels by drones to solve the above technical problems. Summary of the invention

[0005] In order to solve the above technical problems, the present invention provides a method, device and control system for cleaning polycrystalline silicon photovoltaic panels by drones, which are used to solve the problems that traditional manual cleaning methods are not only costly and inefficient, but also difficult to ensure safety in complex terrain or high-altitude operations. Although the existing fixed-track photovoltaic panel cleaning robots have solved some cleaning problems to a certain extent, their adaptability and flexibility are limited when facing large photovoltaic arrays with irregular shapes or changing environments, resulting in low cleaning coverage and high installation and maintenance costs.

[0006] The method for cleaning a polycrystalline silicon photovoltaic panel by a drone provided by the present invention comprises: Based on the target photovoltaic area, the detection sensor on the drone equipment is used to identify the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generate a corresponding three-dimensional photovoltaic space map; Performing dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV equipment; Monitoring the precursor flight state of the UAV device, and predicting the optimal flight state of the UAV device based on the precursor flight state; The panel temperature change data of the surface of the polysilicon photovoltaic panel is collected by the induction sensor on the drone device, and the optimal cleaning intensity of the drone device is determined according to the panel temperature change data; Based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, the drone equipment is controlled to perform cleaning operations on the polycrystalline silicon photovoltaic panel.

[0007] Preferably, based on the target photovoltaic area, the detection sensor on the drone equipment is used to identify the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generate a corresponding three-dimensional photovoltaic space map, which specifically includes: Identifying the positions of the polysilicon photovoltaic panel and surrounding obstacles by using a visual navigation sensor on the drone device to generate three-dimensional photovoltaic position data of the target photovoltaic area; Scanning the status of the polycrystalline silicon photovoltaic panel and surrounding obstacles by using the laser radar on the drone equipment to generate three-dimensional photovoltaic status data of the target photovoltaic area; Performing registration and reconstruction processing on the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map; Wherein, the detection sensor includes the visual navigation sensor and the laser radar.

[0008] Preferably, the registering and reconstructing the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map specifically includes: The three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data are used as the three-dimensional photovoltaic source point cloud, and the three-dimensional photovoltaic source point cloud is subjected to point cloud registration processing, that is, the three-dimensional photovoltaic target point cloud corresponding to the three-dimensional photovoltaic source point cloud is determined, and the error distance between the points in the three-dimensional photovoltaic source point cloud and the corresponding points in the three-dimensional photovoltaic target point cloud is minimized by adjusting the rotation matrix and translation vector during the point cloud registration processing. The corresponding calculation formula is as follows: Where R represents the rotation matrix during point cloud registration processing, that is, the rotation operation required to rotate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; t represents the translation vector during point cloud registration processing, that is, the translation operation required to translate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; represents the sum of squares of error distances between points in the 3D PV source point cloud and corresponding points in the 3D PV target point cloud; It represents the value of the rotation matrix R and the translation vector t when the sum of the squares of the error distances reaches the minimum; N represents the total number of points in the three-dimensional photovoltaic source point cloud; represents the nth point in the three-dimensional photovoltaic source point cloud; Represents the nth corresponding point in the 3D photovoltaic target point cloud; Based on the surface reconstruction algorithm, the three-dimensional photovoltaic space map is obtained according to the three-dimensional photovoltaic source point cloud after point cloud registration processing.

[0009] Preferably, the step of performing dynamic path planning on the UAV equipment according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV equipment specifically includes: Setting a cleaning path starting point and a cleaning path end point of the UAV equipment; Based on the shortest path algorithm, the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path is iteratively updated, and the corresponding calculation formula is as follows: In the formula, represents the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path; F represents the set of all cleaning paths of the UAV equipment; represents the shortest path estimation value between the predecessor node u corresponding to the map node v and the starting point of the cleaning path; Indicates the weight of the path formed by the map node v and the corresponding predecessor node u; min indicates the minimum value operation; When the map node v is updated to the end point of the cleaning path, the iterative update process of the shortest path estimation value is stopped to obtain the optimal cleaning path of the UAV equipment.

[0010] Preferably, monitoring the precursor flight state of the UAV device and predicting the optimal flight state of the UAV device based on the precursor flight state specifically includes: Based on the state transfer equation, the optimal solution of the current flight state of the UAV device is calculated according to the predecessor flight state of the UAV device. The corresponding calculation formula is as follows: In the formula, represents the optimal solution for the current flight state i of the UAV device; It represents the optimal solution of the predecessor flight state j corresponding to the current flight state i of the UAV device; It represents the cost of the UAV equipment transferring from the predecessor flight state j to the current flight state i; Indicates the set of predecessor flight states corresponding to the current flight state i of the drone device; min indicates the minimum value operation; The optimal flight state is determined according to the optimal solution of the current flight state of the UAV device.

[0011] Preferably, the collecting of panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel by the induction sensor on the drone equipment, and determining the optimal cleaning intensity of the drone equipment according to the panel temperature change data, specifically includes: The panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel is collected by the induction sensor on the drone device, and the panel pollution data corresponding to the polycrystalline silicon photovoltaic panel is evaluated based on the panel temperature change data; The optimal cleaning intensity of the drone device is determined according to the panel contamination data, and based on the optimal cleaning intensity, the pressure data of the cleaning pressure pump and the rotation frequency data of the cleaning brush head on the drone device are dynamically adjusted.

[0012] Preferably, after determining the optimal cleaning intensity, real-time weather conditions are obtained, and an optimal cleaning time window for the drone equipment is selected based on the real-time weather conditions.

[0013] Preferably, the controlling the UAV equipment to perform a cleaning operation on the polycrystalline silicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity specifically includes: Based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, constructing a target energy consumption function corresponding to the UAV device; By adjusting the weights corresponding to the optimal cleaning path, the optimal flight state, and the optimal cleaning intensity, the target energy consumption function is minimized to generate the optimal cleaning strategy for the UAV equipment. The calculation formula of the target energy consumption function is as follows: Where, E represents the target energy consumption function; Indicates the start cleaning time; Indicates the end of cleaning time; represents the power function; represents the optimal cleaning path; Represents the weight corresponding to the optimal cleaning path; Indicates the optimal flight state; Indicates the weight corresponding to the optimal flight state; Indicates the optimal cleaning intensity; represents the weight corresponding to the optimal cleaning intensity; Z represents the cleaning time of the UAV equipment; Based on the optimal cleaning strategy, the drone equipment is controlled to perform cleaning operations on the polysilicon photovoltaic panel.

[0014] A device for cleaning polycrystalline silicon photovoltaic panels by a drone, the device comprising: A map construction module is used to identify the position status information of polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area through the detection sensors on the drone equipment, and generate a corresponding three-dimensional photovoltaic space map; A path planning module, used to perform dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map, and generate an optimal cleaning path for the UAV equipment; A state prediction module, used to monitor the precursor flight state of the UAV device and predict the optimal flight state of the UAV device based on the precursor flight state; An intensity determination module, used to collect panel temperature change data on the surface of the polysilicon photovoltaic panel through the induction sensor on the drone device, and determine the optimal cleaning intensity of the drone device according to the panel temperature change data; The panel cleaning module is used to control the UAV equipment to perform cleaning operations on the polysilicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity.

[0015] A control system for a drone to clean polycrystalline silicon photovoltaic panels includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

[0016] Compared with the related art, the method, device and control system for cleaning polycrystalline silicon photovoltaic panels by drone provided by the present invention have the following beneficial effects: The present invention can identify the position status information of the polycrystalline silicon photovoltaic panel and the surrounding obstacles in the target photovoltaic area through the detection sensors on the drone equipment, and generate a corresponding three-dimensional photovoltaic space map; perform dynamic path planning on the drone equipment according to the three-dimensional photovoltaic space map, and generate an optimal cleaning path for the drone equipment; monitor the precursor flight state of the drone equipment, and predict the optimal flight state of the drone equipment based on the precursor flight state; collect the panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel through the inductive sensors on the drone equipment, and determine the optimal cleaning intensity of the drone equipment according to the panel temperature change data; based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, control the drone equipment to perform cleaning operations on the polycrystalline silicon photovoltaic panel, thereby solving the problem that the fixed track cleaning robot cannot flexibly adapt to the irregularly distributed or complexly laid out photovoltaic panels, and the high construction and maintenance costs of the fixed track system, and further realizing all-round and efficient cleaning of the photovoltaic panels, improving energy utilization, reducing operation and maintenance costs, and enhancing the safety of the photovoltaic power station.

[0017] The present invention adopts a collaborative working mode of UAV equipment and intelligent control mechanism, which can achieve all-round and efficient cleaning of photovoltaic panels under any layout, thereby improving energy utilization and operation and maintenance management level; the present invention performs shortest path planning for UAV equipment, predicts and optimizes its flight status, and adaptively adjusts the intensity of the cleaning device on the UAV equipment, thereby significantly reducing long-term operating costs and enhancing the reliability and economic feasibility of system operation and maintenance; at the same time, the present invention can reduce the number and risks of manual intervention by intelligently controlling the autonomous flight of UAVs, thereby ensuring the convenience and safety of daily maintenance of large-scale photovoltaic power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flow chart of a method for cleaning polycrystalline silicon photovoltaic panels by a drone provided in an embodiment of the present invention; Figure 2 A system block diagram of a device for cleaning polycrystalline silicon photovoltaic panels using a drone provided by an embodiment of the present invention; Figure 3 A schematic diagram of the hardware structure of a control system for cleaning polycrystalline silicon photovoltaic panels using a drone provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] like Figure 1 FIG. 1 is a flow chart of a method for cleaning polycrystalline silicon photovoltaic panels by a drone according to an embodiment of the present invention. Figure 1 The execution subject of the method shown may be a software and / or hardware device. The execution subject of the present application may include but is not limited to at least one of the following: user equipment, network equipment, etc. Among them, user equipment may include but is not limited to computers, smart phones, personal digital assistants (PDA) and the electronic devices mentioned above. Network equipment may include but is not limited to a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of computers or network servers, wherein cloud computing is a type of distributed computing, a super virtual computer composed of a group of loosely coupled computers. This embodiment does not limit this. It includes steps S1 to S5, as follows: S1, based on the target photovoltaic area, identifying the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area through the detection sensor on the drone equipment, and generating a corresponding three-dimensional photovoltaic space map; Among them, the target photovoltaic area refers to the area where the polycrystalline silicon photovoltaic panels that need to be cleaned are located, and its area range is the maximum activity range of the drone equipment to perform cleaning operations; the detection sensor refers to the sensor carried on the drone for detecting the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles; the three-dimensional photovoltaic space map refers to the regional map of the photovoltaic area in three-dimensional space, specifically including the position distribution and real-time status characteristics of the polycrystalline silicon photovoltaic panels and surrounding obstacles in three-dimensional space.

[0021] It is understandable that in the photovoltaic area, drone equipment equipped with high-precision detection sensors can be used to accurately identify the location and status of polycrystalline silicon photovoltaic panels and surrounding obstacles in the photovoltaic area, and then generate a three-dimensional photovoltaic space map corresponding to the photovoltaic area. This map not only accurately depicts the layout of photovoltaic panels in the photovoltaic area, but also accurately marks the location status information of all potential obstacles.

[0022] S2, performing dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV equipment; It should be noted that based on the constructed three-dimensional photovoltaic space map, a dynamic path planning algorithm can be used to optimize the flight trajectory of the drone equipment according to the distribution density of photovoltaic panels, obstacle avoidance requirements and the drone's own maneuverability, and generate an efficient and safe optimal cleaning path, thereby maximizing the cleaning efficiency of the drone equipment and reducing its energy consumption.

[0023] S3, monitoring the precursor flight state of the UAV device, and predicting the optimal flight state of the UAV device based on the precursor flight state; In practical applications, before the UAV equipment performs cleaning tasks, its front-end flight status can be monitored in real time, including key parameters such as speed, altitude, and attitude. Moreover, based on these real-time data, the prediction model algorithm can be used to estimate the optimal flight status of the UAV equipment in the future, thereby ensuring that the UAV equipment can operate stably in a complex and changing environment.

[0024] S4, collecting panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel through the induction sensor on the drone device, and determining the optimal cleaning intensity of the drone device according to the panel temperature change data; Among them, the drone equipment is equipped with a highly sensitive inductive sensor, which can collect temperature change data on the surface of polycrystalline silicon photovoltaic panels in real time to evaluate the cleaning needs of the photovoltaic panels.

[0025] Since the temperature changes of photovoltaic panels may directly affect the cleaning effect of drone equipment, before the drone equipment performs the cleaning task, the optimal cleaning intensity that the drone equipment needs to adopt can be intelligently determined, thereby ensuring the cleaning quality of the photovoltaic panels and avoiding damage that may be caused by excessive cleaning.

[0026] S5, based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, controlling the UAV equipment to perform cleaning operations on the polysilicon photovoltaic panel.

[0027] In practical applications, based on the precisely planned optimal cleaning path, the accurately predicted optimal flight state, and the optimal cleaning intensity determined according to the panel temperature change, the drone equipment can be intelligently controlled to perform efficient, accurate, and safe cleaning operations on polycrystalline silicon photovoltaic panels, thereby significantly improving the power generation efficiency and operation and maintenance management level of photovoltaic power stations.

[0028] In the specific implementation process, based on the target photovoltaic area, the detection sensor on the drone equipment is used to identify the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generate a corresponding three-dimensional photovoltaic space map, which specifically includes: Identifying the positions of the polysilicon photovoltaic panel and surrounding obstacles by using a visual navigation sensor on the drone device to generate three-dimensional photovoltaic position data of the target photovoltaic area; Scanning the status of the polycrystalline silicon photovoltaic panel and surrounding obstacles by using the laser radar on the drone equipment to generate three-dimensional photovoltaic status data of the target photovoltaic area; Performing registration and reconstruction processing on the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map; Wherein, the detection sensor includes the visual navigation sensor and the laser radar.

[0029] It is understandable that we can first use the visual navigation sensors carried by drone equipment, such as high-definition cameras combined with image processing algorithms, to perform high-precision identification of the edge contours of polycrystalline silicon photovoltaic panels and the specific positions of surrounding obstacles. By capturing and analyzing image data, we can obtain three-dimensional photovoltaic position data of the photovoltaic area, providing a basic framework for the subsequent construction of a three-dimensional photovoltaic space map.

[0030] Secondly, the laser radar on the drone can be used to perform non-contact scanning to measure the depth and evaluate the status of the polycrystalline silicon photovoltaic panel and its surrounding obstacles. Specifically, the laser radar emits laser pulses and receives reflected signals, and calculates the distance based on the time difference, so as to accurately depict the three-dimensional shape and obstacle status of the photovoltaic area and generate corresponding three-dimensional photovoltaic status data.

[0031] Furthermore, the acquired three-dimensional photovoltaic position data and three-dimensional photovoltaic status data can be registered and fused to ensure accurate matching of position information and status information, and finally integrated to form a complete and accurate three-dimensional photovoltaic space map.

[0032] It should be noted that the detection sensors specifically include visual navigation sensors and lidars, which work together to collect and analyze the entire three-dimensional spatial information, thereby enabling intelligent management of the entire photovoltaic area and reducing maintenance costs.

[0033] The registering and reconstructing the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map specifically includes: The three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data are used as the three-dimensional photovoltaic source point cloud, and the three-dimensional photovoltaic source point cloud is subjected to point cloud registration processing, that is, the three-dimensional photovoltaic target point cloud corresponding to the three-dimensional photovoltaic source point cloud is determined, and the error distance between the points in the three-dimensional photovoltaic source point cloud and the corresponding points in the three-dimensional photovoltaic target point cloud is minimized by adjusting the rotation matrix and translation vector during the point cloud registration processing. The corresponding calculation formula is as follows: Where R represents the rotation matrix during point cloud registration processing, that is, the rotation operation required to rotate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; t represents the translation vector during point cloud registration processing, that is, the translation operation required to translate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; represents the sum of squares of error distances between points in the 3D PV source point cloud and corresponding points in the 3D PV target point cloud; It represents the value of the rotation matrix R and the translation vector t when the sum of the squares of the error distances reaches the minimum; N represents the total number of points in the three-dimensional photovoltaic source point cloud; represents the nth point in the three-dimensional photovoltaic source point cloud; Represents the nth corresponding point in the 3D photovoltaic target point cloud; Based on the surface reconstruction algorithm, the three-dimensional photovoltaic space map is obtained according to the three-dimensional photovoltaic source point cloud after point cloud registration processing.

[0034] Among them, the collected 3D photovoltaic position data and 3D photovoltaic status data can be integrated to form an initial 3D photovoltaic source point cloud. Then, these 3D photovoltaic source point clouds can be subjected to point cloud registration processing. Specifically, the target point cloud corresponding to the 3D photovoltaic source point cloud can be determined first, and then the optimal alignment between the source point cloud and the target point cloud can be achieved by adjusting the rotation matrix and the translation vector.

[0035] It should be noted that the rotation matrix represents a series of rotation operations used to rotate the source point cloud to a direction that completely matches the target point cloud; while the translation vector represents a translation operation used to ensure that the position of the source point cloud in three-dimensional space matches the target point cloud.

[0036] In order to achieve the best registration effect, an error minimization strategy can be adopted. By calculating the sum of the squares of the error distances between the points in the 3D photovoltaic source point cloud and the corresponding points in the 3D photovoltaic target point cloud, the combination of the rotation matrix and the translation vector that minimizes the sum of the squares can be determined, thereby achieving accurate alignment between the point clouds.

[0037] Furthermore, the surface reconstruction algorithm can be used to process the accurately registered source point cloud to generate a continuous and smooth three-dimensional surface model, and finally obtain the required three-dimensional photovoltaic space map.

[0038] The step of dynamically planning a path for the UAV device according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV device specifically includes: Setting a cleaning path starting point and a cleaning path end point of the UAV equipment; Based on the shortest path algorithm, the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path is iteratively updated, and the corresponding calculation formula is as follows: In the formula, represents the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path; F represents the set of all cleaning paths of the UAV equipment; represents the shortest path estimation value between the predecessor node u corresponding to the map node v and the starting point of the cleaning path; Indicates the weight of the path formed by the map node v and the corresponding predecessor node u; min indicates the minimum value operation; When the map node v is updated to the end point of the cleaning path, the iterative update process of the shortest path estimation value is stopped to obtain the optimal cleaning path of the UAV equipment.

[0039] First, the starting point and end point of the cleaning path of the drone equipment can be clearly set. Then, the shortest path algorithm can be used to iteratively update and optimize the shortest path estimate between each node in the map and the starting point of the cleaning path within the framework of the three-dimensional photovoltaic space map.

[0040] When the nodes in the map are updated to the end of the cleaning path, the iterative update process of the shortest path estimation value stops. At this time, the optimal path result for the drone equipment to perform the cleaning task can be output, thereby improving the cleaning efficiency of the drone equipment.

[0041] The monitoring of the precursor flight state of the UAV device and predicting the optimal flight state of the UAV device based on the precursor flight state specifically includes: Based on the state transfer equation, the optimal solution of the current flight state of the UAV device is calculated according to the predecessor flight state of the UAV device. The corresponding calculation formula is as follows: In the formula, represents the optimal solution for the current flight state i of the UAV device; It represents the optimal solution of the predecessor flight state j corresponding to the current flight state i of the UAV device; It represents the cost of the UAV equipment transferring from the predecessor flight state j to the current flight state i; Indicates the set of predecessor flight states corresponding to the current flight state i of the drone device; min indicates the minimum value operation; The optimal flight state is determined according to the optimal solution of the current flight state of the UAV device.

[0042] It should be noted that, firstly, based on the state transfer equation and combined with the predecessor flight state data of the UAV equipment, the optimal solution of the current flight state of the UAV equipment can be solved, that is, at the current moment, the performance of the UAV equipment reaches the optimal flight parameter configuration.

[0043] Finally, based on the calculated optimal solution of the current flight state of the UAV equipment, the optimal flight state of the UAV equipment can be further determined and output, which is helpful for the subsequent flight control and optimization of the UAV equipment.

[0044] The method of collecting panel temperature change data on the surface of the polysilicon photovoltaic panel through the induction sensor on the drone device and determining the optimal cleaning intensity of the drone device according to the panel temperature change data specifically includes: The panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel is collected by the induction sensor on the drone device, and the panel pollution data corresponding to the polycrystalline silicon photovoltaic panel is evaluated based on the panel temperature change data; The optimal cleaning intensity of the drone device is determined according to the panel contamination data, and based on the optimal cleaning intensity, the pressure data of the cleaning pressure pump and the rotation frequency data of the cleaning brush head on the drone device are dynamically adjusted.

[0045] It is understandable that the temperature change data on the surface of polycrystalline silicon photovoltaic panels can be systematically collected through the precisely configured inductive sensors on the drone equipment.

[0046] Specifically, these sensors are highly sensitive and accurate, and can capture the temperature change characteristics of the photovoltaic panel surface due to different pollution levels in real time. Then, based on the collected panel temperature change data, the complex relationship between the temperature change of the photovoltaic panel surface and the pollution level can be determined, and the panel pollution data corresponding to the polycrystalline silicon photovoltaic panel can be evaluated, thereby ensuring the accuracy and reliability of the pollution data obtained.

[0047] Furthermore, based on the obtained panel contamination data, the optimal cleaning intensity of the UAV equipment when performing cleaning tasks can be determined by weighing and optimizing multiple factors such as cleaning efficiency, energy consumption, and panel damage risk.

[0048] It should be noted that once the optimal cleaning intensity is determined, the dynamic adjustment mechanism can be immediately activated to precisely control the key cleaning components on the drone equipment. Specifically, by adjusting the pressure data of the cleaning pressure pump, it can be ensured that the cleaning fluid acts on the surface of the photovoltaic panel with appropriate force, thereby effectively removing pollutants on the panel surface. At the same time, the rotation frequency of the cleaning brush head can be adjusted to match the pressure output of the high-pressure pump, thereby maximizing the cleaning effect.

[0049] Through the above method, polycrystalline silicon photovoltaic panels can be cleaned efficiently and accurately by drone equipment, effectively improving the power generation efficiency and service life of photovoltaic panels.

[0050] After determining the optimal cleaning intensity, real-time weather conditions are obtained, and an optimal cleaning time window for the drone equipment is selected according to the real-time weather conditions.

[0051] It should be noted that after obtaining the optimal cleaning intensity parameters, it is possible to further obtain real-time meteorological condition data of the current environment, including key factors such as temperature, humidity, wind speed and precipitation probability.

[0052] Then, based on these real-time weather conditions information, the optimal cleaning time window for the drone equipment can be accurately selected, thereby ensuring that the cleaning effect of the drone equipment is optimal and ensuring the safety and efficiency of the drone equipment operation.

[0053] The controlling the UAV device to perform a cleaning operation on the polycrystalline silicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity specifically includes: Based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, constructing a target energy consumption function corresponding to the UAV device; By adjusting the weights corresponding to the optimal cleaning path, the optimal flight state, and the optimal cleaning intensity, the target energy consumption function is minimized to generate the optimal cleaning strategy for the UAV equipment. The calculation formula of the target energy consumption function is as follows: Where, E represents the target energy consumption function; Indicates the start cleaning time; Indicates the end of cleaning time; represents the power function; represents the optimal cleaning path; Represents the weight corresponding to the optimal cleaning path; Indicates the optimal flight state; Indicates the weight corresponding to the optimal flight state; Indicates the optimal cleaning intensity; represents the weight corresponding to the optimal cleaning intensity; Z represents the cleaning time of the UAV equipment; Based on the optimal cleaning strategy, the drone equipment is controlled to perform cleaning operations on the polysilicon photovoltaic panel.

[0054] It can be understood that first, a target energy consumption function model of the UAV equipment can be constructed based on the optimal cleaning path, optimal flight state and optimal cleaning intensity to comprehensively evaluate the energy efficiency of the UAV equipment when performing cleaning tasks.

[0055] Furthermore, by adjusting the weight coefficients corresponding to the optimal cleaning path, optimal flight state, and optimal cleaning intensity, the optimization algorithm can be used to minimize the target energy consumption function and generate the optimal cleaning strategy for the UAV equipment.

[0056] Specifically, the optimal cleaning strategy can ensure that the energy consumption of the drone equipment is minimized while meeting the panel cleaning quality. Finally, based on the obtained optimal cleaning strategy, the drone equipment can be accurately controlled to perform comprehensive and efficient cleaning operations on the polycrystalline silicon photovoltaic panels according to the established path, flight state and cleaning intensity.

[0057] Through the above method, the photovoltaic panels can be cleaned in an all-round and efficient manner by drone equipment, thereby improving the energy utilization rate of the drone equipment, significantly reducing the cost of long-term operation and maintenance, and enhancing the reliability and safety of the photovoltaic power station.

[0058] like Figure 2 , which is a system block diagram of a device for cleaning polycrystalline silicon photovoltaic panels by a drone provided by an embodiment of the present invention, the device comprises: A map construction module is used to identify the position status information of polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area through the detection sensors on the drone equipment, and generate a corresponding three-dimensional photovoltaic space map; A path planning module, used to perform dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map, and generate an optimal cleaning path for the UAV equipment; A state prediction module, used to monitor the precursor flight state of the UAV device and predict the optimal flight state of the UAV device based on the precursor flight state; An intensity determination module, used to collect panel temperature change data on the surface of the polysilicon photovoltaic panel through the induction sensor on the drone device, and determine the optimal cleaning intensity of the drone device according to the panel temperature change data; The panel cleaning module is used to control the UAV equipment to perform cleaning operations on the polysilicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity.

[0059] Figure 2 The apparatus of the embodiment shown can be used to perform Figure 1 The implementation principles and technical effects of the steps in the method embodiment shown are similar and will not be repeated here.

[0060] A control system for a drone to clean polycrystalline silicon photovoltaic panels includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the steps of the above method are implemented.

[0061] like Figure 3 FIG. 1 is a schematic diagram of the hardware structure of a control system for cleaning polycrystalline silicon photovoltaic panels provided by an unmanned aerial vehicle according to an embodiment of the present invention. The control system 30 for cleaning polycrystalline silicon photovoltaic panels provided by the unmanned aerial vehicle comprises: a processor 31, a memory 32 and a computer program; The memory 32 is used to store the computer program, which may also be a flash memory. The computer program is, for example, an application program, a functional module, etc. for implementing the above method.

[0062] The processor 31 is used to execute the computer program stored in the memory to implement each step performed by the device in the above method. For details, please refer to the relevant description in the above method embodiment.

[0063] Optionally, the memory 32 may be independent or integrated with the processor 31 .

[0064] When the memory 32 is a device independent of the processor 31, the device may further include: The bus 33 is used to connect the memory 32 and the processor 31 .

[0065] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement the methods provided by the various embodiments described above.

[0066] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (Application Specific Integrated Circuits, referred to as: ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0067] The present invention also provides a program product, which includes an execution instruction, which is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements the methods provided in the above various embodiments.

[0068] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly implemented as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0069] Through the introduction of the above embodiments, the present invention uses a method, device and control system for cleaning polycrystalline silicon photovoltaic panels by drones. Based on the target photovoltaic area, the detection sensor on the drone equipment identifies the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generates a corresponding three-dimensional photovoltaic space map; the drone equipment is dynamically planned according to the three-dimensional photovoltaic space map to generate the optimal cleaning path of the drone equipment; the precursor flight state of the drone equipment is monitored, and the optimal flight state of the drone equipment is predicted based on the precursor flight state; the panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel is collected through the induction sensor on the drone equipment, and the optimal cleaning intensity of the drone equipment is determined according to the panel temperature change data; based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, the drone equipment is controlled to perform cleaning operations on the polycrystalline silicon photovoltaic panel, thereby solving the problem that the fixed track cleaning robot cannot flexibly adapt to irregularly distributed or complexly laid out photovoltaic panels, and the high construction and maintenance costs of the fixed track system, thereby achieving all-round and efficient cleaning of the photovoltaic panel, improving energy utilization, reducing operation and maintenance costs, and enhancing the safety of the photovoltaic power station.

[0070] The present invention adopts a collaborative working mode of UAV equipment and intelligent control mechanism, which can realize all-round and efficient cleaning of photovoltaic panels under any layout, thereby improving energy utilization and operation and maintenance management level; the present invention performs shortest path planning for UAV equipment, predicts and optimizes its flight status, and adaptively adjusts the strength of the cleaning device on the UAV equipment, which can greatly reduce long-term operating costs and enhance the reliability and economic feasibility of system operation and maintenance; at the same time, the present invention can reduce the number and risks of manual intervention by intelligently controlling the autonomous flight of UAVs, thereby ensuring the convenience and safety of daily maintenance of large-scale photovoltaic power stations.

[0071] 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 cleaning polycrystalline silicon photovoltaic panels by drone, characterized in that: The method comprises: Based on the target photovoltaic area, the detection sensor on the drone equipment is used to identify the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generate a corresponding three-dimensional photovoltaic space map; Performing dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV equipment; Monitoring the precursor flight state of the UAV device, and predicting the optimal flight state of the UAV device based on the precursor flight state; The panel temperature change data of the surface of the polysilicon photovoltaic panel is collected by the induction sensor on the drone device, and the optimal cleaning intensity of the drone device is determined according to the panel temperature change data; Based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, the drone equipment is controlled to perform cleaning operations on the polycrystalline silicon photovoltaic panel.

2. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 1, characterized in that: Based on the target photovoltaic area, the detection sensor on the drone equipment is used to identify the position status information of the polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area, and generate a corresponding three-dimensional photovoltaic space map, specifically including: Identifying the positions of the polysilicon photovoltaic panel and surrounding obstacles by using a visual navigation sensor on the drone device to generate three-dimensional photovoltaic position data of the target photovoltaic area; Scanning the status of the polycrystalline silicon photovoltaic panel and surrounding obstacles by using the laser radar on the drone equipment to generate three-dimensional photovoltaic status data of the target photovoltaic area; Performing registration and reconstruction processing on the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map; Wherein, the detection sensor includes the visual navigation sensor and the laser radar.

3. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 2, characterized in that: The registering and reconstructing the three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data to obtain the three-dimensional photovoltaic space map specifically includes: The three-dimensional photovoltaic position data and the three-dimensional photovoltaic state data are used as the three-dimensional photovoltaic source point cloud, and the three-dimensional photovoltaic source point cloud is subjected to point cloud registration processing, that is, the three-dimensional photovoltaic target point cloud corresponding to the three-dimensional photovoltaic source point cloud is determined, and the error distance between the points in the three-dimensional photovoltaic source point cloud and the corresponding points in the three-dimensional photovoltaic target point cloud is minimized by adjusting the rotation matrix and translation vector during the point cloud registration processing. The corresponding calculation formula is as follows: Where R represents the rotation matrix during point cloud registration processing, that is, the rotation operation required to rotate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; t represents the translation vector during point cloud registration processing, that is, the translation operation required to translate the 3D photovoltaic source point cloud to align with the 3D photovoltaic target point cloud; represents the sum of squares of error distances between points in the 3D PV source point cloud and corresponding points in the 3D PV target point cloud; It represents the value of the rotation matrix R and the translation vector t when the sum of the squares of the error distances reaches the minimum; N represents the total number of points in the three-dimensional photovoltaic source point cloud; represents the nth point in the three-dimensional photovoltaic source point cloud; Represents the nth corresponding point in the 3D photovoltaic target point cloud; Based on the surface reconstruction algorithm, the three-dimensional photovoltaic space map is obtained according to the three-dimensional photovoltaic source point cloud after point cloud registration processing.

4. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 1, characterized in that: The step of dynamically planning a path for the UAV device according to the three-dimensional photovoltaic space map to generate an optimal cleaning path for the UAV device specifically includes: Setting a cleaning path starting point and a cleaning path end point of the UAV equipment; Based on the shortest path algorithm, the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path is iteratively updated, and the corresponding calculation formula is as follows: In the formula, represents the shortest path estimation value between the map node v in the three-dimensional photovoltaic space map and the starting point of the cleaning path; F represents the set of all cleaning paths of the UAV equipment; represents the shortest path estimation value between the predecessor node u corresponding to the map node v and the starting point of the cleaning path; Indicates the weight of the path formed by the map node v and the corresponding predecessor node u; min indicates the minimum value operation; When the map node v is updated to the end point of the cleaning path, the iterative update process of the shortest path estimation value is stopped to obtain the optimal cleaning path of the UAV equipment.

5. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 1, characterized in that: The monitoring of the precursor flight state of the UAV device and predicting the optimal flight state of the UAV device based on the precursor flight state specifically includes: Based on the state transfer equation, the optimal solution of the current flight state of the UAV device is calculated according to the predecessor flight state of the UAV device. The corresponding calculation formula is as follows: In the formula, represents the optimal solution for the current flight state i of the UAV device; It represents the optimal solution of the predecessor flight state j corresponding to the current flight state i of the UAV device; represents the cost of the UAV equipment transferring from the predecessor flight state j to the current flight state i; Indicates the set of predecessor flight states corresponding to the current flight state i of the drone device; min indicates the minimum value operation; The optimal flight state is determined according to the optimal solution of the current flight state of the UAV device.

6. The method for cleaning polysilicon photovoltaic panels by using a drone according to claim 1, characterized in that: The method of collecting panel temperature change data on the surface of the polysilicon photovoltaic panel through the induction sensor on the drone device and determining the optimal cleaning intensity of the drone device according to the panel temperature change data specifically includes: The panel temperature change data on the surface of the polycrystalline silicon photovoltaic panel is collected by the induction sensor on the drone device, and the panel pollution data corresponding to the polycrystalline silicon photovoltaic panel is evaluated based on the panel temperature change data; The optimal cleaning intensity of the drone device is determined according to the panel contamination data, and based on the optimal cleaning intensity, the pressure data of the cleaning pressure pump and the rotation frequency data of the cleaning brush head on the drone device are dynamically adjusted.

7. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 1, characterized in that: After determining the optimal cleaning intensity, real-time weather conditions are obtained, and an optimal cleaning time window for the drone equipment is selected according to the real-time weather conditions.

8. The method for cleaning polycrystalline silicon photovoltaic panels by using a drone according to claim 1, characterized in that: The controlling the UAV device to perform a cleaning operation on the polycrystalline silicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity specifically includes: Based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity, constructing a target energy consumption function corresponding to the UAV device; By adjusting the weights corresponding to the optimal cleaning path, the optimal flight state, and the optimal cleaning intensity, the target energy consumption function is minimized to generate the optimal cleaning strategy for the UAV equipment. The calculation formula of the target energy consumption function is as follows: Where, E represents the target energy consumption function; Indicates the start cleaning time; Indicates the end of cleaning time; represents the power function; represents the optimal cleaning path; Represents the weight corresponding to the optimal cleaning path; Indicates the optimal flight state; Indicates the weight corresponding to the optimal flight state; Indicates the optimal cleaning intensity; represents the weight corresponding to the optimal cleaning intensity; Z represents the cleaning time of the UAV equipment; Based on the optimal cleaning strategy, the drone equipment is controlled to perform cleaning operations on the polysilicon photovoltaic panel.

9. A device for cleaning polycrystalline silicon photovoltaic panels by using a drone, applied to the method for cleaning polycrystalline silicon photovoltaic panels by using a drone as claimed in any one of claims 1 to 8, the device comprising: A map construction module is used to identify the position status information of polycrystalline silicon photovoltaic panels and surrounding obstacles in the target photovoltaic area through the detection sensors on the drone equipment, and generate a corresponding three-dimensional photovoltaic space map; A path planning module, used to perform dynamic path planning for the UAV equipment according to the three-dimensional photovoltaic space map, and generate an optimal cleaning path for the UAV equipment; A state prediction module, used to monitor the precursor flight state of the UAV device and predict the optimal flight state of the UAV device based on the precursor flight state; An intensity determination module, used to collect panel temperature change data on the surface of the polysilicon photovoltaic panel through the induction sensor on the drone device, and determine the optimal cleaning intensity of the drone device according to the panel temperature change data; The panel cleaning module is used to control the UAV equipment to perform cleaning operations on the polysilicon photovoltaic panel based on the optimal cleaning path, the optimal flight state and the optimal cleaning intensity.

10. A control system for cleaning polysilicon photovoltaic panels by a drone, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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