Intelligent operation inspection method and system combined with photovoltaic cleaning robot

Abstract

The invention provides an intelligent operation inspection method and system combined with a photovoltaic cleaning robot, and relates to the field of photovoltaic technology, and the method comprises the steps: constructing a central coordination system; determining a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover; acquiring a first photovoltaic cleaning task and first photovoltaic operation inspection data corresponding to the first photovoltaic cleaning robot, and sharing the first photovoltaic cleaning task and the first photovoltaic operation inspection data to a second photovoltaic cleaning robot to generate a second photovoltaic cleaning task; and controlling the second photovoltaic cleaning robot to clean the current photovoltaic, and uploading second photovoltaic operation inspection data to the central coordination system. The technical problem that the operation and inspection efficiency is poor due to the fact that task allocation and handover of the photovoltaic cleaning robots are not flexible enough in the prior art can be solved, efficient cooperation and data sharing are carried out on the photovoltaic cleaning robots by constructing the central cooperation system, it is ensured that cleaning and operation and inspection tasks are continuously carried out, and the operation and inspection efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of photovoltaic technology, and in particular to an intelligent operation and maintenance method and system combined with a photovoltaic cleaning robot. Background Art

[0002] Photovoltaic operation and maintenance is to ensure the normal operation of a photovoltaic power station, improve power generation efficiency, and extend the service life of equipment. Through regular inspections, faults and potential problems of photovoltaic equipment can be detected in a timely manner. An intelligent photovoltaic cleaning robot ensures normal light irradiation by cleaning dust and dirt on the surface of photovoltaic modules, and checks and maintains connection equipment to reduce power loss, thereby improving power generation efficiency. The cleaning robot is usually equipped with advanced sensors and intelligent algorithms, which can autonomously complete the cleaning task, improve the cleaning efficiency, ensure the cleanliness of the photovoltaic panels while protecting the photovoltaic panels from damage. However, the task allocation and handover of existing photovoltaic operation and maintenance using cleaning robots may not be flexible enough to dynamically adjust the task allocation and handover plan according to real-time changes (such as robot battery power, faults, changes in cleaning requirements, etc.), resulting in insufficient resource utilization or low task execution efficiency. There may be situations of waiting or repeated cleaning during task execution, and some photovoltaic panels are not cleaned in a timely manner, thus affecting the overall operation and maintenance efficiency.

[0003] In summary, there is a technical problem in the prior art that the operation and maintenance efficiency is poor due to the inflexible task allocation and handover of photovoltaic cleaning robots. Summary of the Invention

[0004] The purpose of this application is to provide an intelligent operation and maintenance method and system combined with a photovoltaic cleaning robot to solve the technical problem in the prior art that the operation and maintenance efficiency is poor due to the inflexible task allocation and handover of photovoltaic cleaning robots.

[0005] In view of the above problems, this application provides an intelligent operation and maintenance method and system combined with a photovoltaic cleaning robot.

[0006] In a first aspect, the present application provides an intelligent operation and inspection method combined with a photovoltaic cleaning robot, and the intelligent operation and inspection method combined with the photovoltaic cleaning robot is implemented through an intelligent operation and inspection system combined with the photovoltaic cleaning robot. Among them, the intelligent operation and inspection method combined with the photovoltaic cleaning robot includes: constructing a central coordination system, which is used for handover coordination management of a group of photovoltaic cleaning robots, and data interaction between the group of photovoltaic cleaning robots; determining, according to the central coordination system, a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover, where the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover; obtaining a first photovoltaic cleaning task and first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, sharing the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generating a second photovoltaic cleaning task; controlling the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and uploading second photovoltaic operation and inspection data to the central coordination system.

[0007] In a second aspect, the present application further provides an intelligent operation and inspection system combined with a photovoltaic cleaning robot, which is used to execute the intelligent operation and inspection method combined with the photovoltaic cleaning robot as described in the first aspect. Among them, the intelligent operation and inspection system combined with the photovoltaic cleaning robot includes: a system construction module for constructing a central coordination system, which is used for handover coordination management of a group of photovoltaic cleaning robots, and data interaction between the group of photovoltaic cleaning robots; a robot determination module for determining, according to the central coordination system, a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover, where the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover; a task generation module for obtaining a first photovoltaic cleaning task and first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, sharing the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generating a second photovoltaic cleaning task; a cleaning control module for controlling the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and uploading second photovoltaic operation and inspection data to the central coordination system.

[0008] One or more technical solutions provided in the present application have at least the following technical effects or advantages: By constructing a central coordination system for handover coordination management of a group of photovoltaic cleaning robots and data interaction among the group of photovoltaic cleaning robots, determining, according to the central coordination system, a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover, where the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover; obtaining a first photovoltaic cleaning task and first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, sharing the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot to generate a second photovoltaic cleaning task; controlling the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task and uploading second photovoltaic operation and inspection data to the central coordination system. That is to say, by constructing a central coordination system for handover coordination management of a group of robots, when a certain robot needs to hand over tasks due to reasons such as insufficient power, the task area reaching the boundary, or a fault, a available robot is preferentially selected to ensure the timeliness and efficiency of the handover. At the same time, the central system shares the cleaning task data and operation and inspection data of the robot with the replacement robot in real time to ensure the complete handover of tasks and environmental information, so as to prevent the replacement robot from repeating operations or missing some cleaning areas. Dynamically adjusting task allocation according to the current state of the robot enables efficient utilization of resources, reduces the time of the robot in the standby or task-free state, ensures efficient interaction and collaborative operation among robots, and improves the overall operation and inspection efficiency.

[0009] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented according to the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically illustrates the specific embodiments of the present application. It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understandable through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings described below are only exemplary, and those of ordinary skill in the art can obtain other drawings according to the provided drawings without creative efforts.

[0011] Figure 1 It is a schematic flowchart of an intelligent operation and inspection method for a photovoltaic cleaning robot in combination with the present application; Figure 2This is a schematic structural diagram of an intelligent operation and maintenance system combined with a photovoltaic cleaning robot for this application.

[0012] Explanation of reference numerals: System construction module 11, robot determination module 12, task generation module 13, cleaning control module 14. Specific implementation manners

[0013] This application provides an intelligent operation and maintenance method and system combined with a photovoltaic cleaning robot, which solves the technical problem in the prior art that due to the inflexible task assignment and handover of photovoltaic cleaning robots, the operation and maintenance efficiency is poor. By constructing a central coordination system to conduct handover coordination management for a group of robots, when a certain robot needs to hand over tasks due to reasons such as insufficient power, the task area has reached the boundary, or a failure occurs, a available robot is preferentially selected to ensure the timeliness and efficiency of the handover. At the same time, the central coordination system shares the cleaning task data and operation and maintenance data of the robot with the replacing robot in real time to ensure the complete handover of task and environment information, so as to prevent the replacing robot from repeating operations or missing some cleaning areas. Dynamically adjust the task assignment according to the current state of the robot, so that resources are efficiently utilized, reduce the time of the robot in the standby or task-free state, ensure the efficient interaction and collaborative operation among robots, and improve the overall operation and maintenance efficiency.

[0014] Next, the technical solutions in this application will be described clearly and completely with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments of this application. It should be understood that this application is not limited by the example embodiments described here. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this application. Additionally, it should be noted that for the sake of description, only the parts related to this application are shown in the accompanying drawings rather than all of them.

[0015] Example 1, please refer to the attached Figure 1 , this application provides an intelligent operation and maintenance method combined with a photovoltaic cleaning robot. Among them, the intelligent operation and maintenance method combined with a photovoltaic cleaning robot is applied to an intelligent operation and maintenance system combined with a photovoltaic cleaning robot. The intelligent operation and maintenance method combined with a photovoltaic cleaning robot specifically includes the following steps: Step 1: Construct a central coordination system, which is used to conduct handover coordination management for a group of photovoltaic cleaning robots, and data interaction occurs among the group of photovoltaic cleaning robots.

[0016] Specifically, a central coordination system is constructed to control and coordinate the operations among multiple robots or devices. It is responsible for managing the cleaning task allocation, data exchange, status monitoring, and handover coordination of the robot swarm. At the same time, the central coordination system is also responsible for monitoring the status of all photovoltaic cleaning robots, including their task progress, location information, power level, and current task situation. Handover coordination management refers to the management and coordination work of the central coordination system during the task handover among multiple robots or task execution units to ensure task continuity and efficiency, including determining the robots that need to be handed over and sharing the current tasks and operation and inspection data of the robots to be handed over to another suitable replacement robot, and generating new cleaning tasks for execution. Real-time data exchange can be carried out among the photovoltaic cleaning robot swarm and between the robot swarm and the central coordination system, including information such as task status, cleaning progress, photovoltaic panel status, location information, cleaning progress, and fault reports. For example, assume that there are 10 photovoltaic cleaning robots in a photovoltaic power generation field. The central coordination system analyzes the power, cleaning progress, and dirtiness degree of each robot. When the power of robot A is low, robot B takes over its cleaning task, and the current position, cleaning progress, and photovoltaic panel status data of robot A are transmitted to robot B to ensure seamless task handover. Through the central coordination system, the robot swarm can complete tasks more efficiently, reduce the waiting time during task handover, optimize the utilization efficiency of robots, and ensure the continuity of cleaning tasks.

[0017] Step 2: According to the central coordination system, determine the first photovoltaic cleaning robot and the second photovoltaic cleaning robot for handover, where the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover.

[0018] Specifically, the central coordination system monitors the operating status of each photovoltaic cleaning robot in real time. When a certain photovoltaic cleaning robot triggers a handover condition, such as the need for handover due to insufficient power, the task area reaching the boundary, or a fault, the central coordination system marks it as the first photovoltaic cleaning robot, that is, the photovoltaic cleaning robot to be handed over. A handover allocation space is generated among the non-marked photovoltaic cleaning robots in the photovoltaic cleaning robot swarm, and a fitness analysis is performed on the marked photovoltaic cleaning robot, including cleaning function fitness, path consumption fitness, and task emergency status fitness, and the most suitable photovoltaic cleaning robot is selected. According to the results of the fitness analysis, the central coordination system selects the most suitable photovoltaic cleaning robot to take over the task and continue to execute as the second photovoltaic cleaning robot, that is, the photovoltaic cleaning robot for handover. By clarifying the robots to be handed over and the robots for handover, the central coordination system ensures seamless task handover among the robots and maintains the continuity of the cleaning work.

[0019] Step 3: Obtain the first photovoltaic cleaning task and the first photovoltaic operation and maintenance data corresponding to the first photovoltaic cleaning robot, share the first photovoltaic cleaning task and the first photovoltaic operation and maintenance data with the second photovoltaic cleaning robot, and generate a second photovoltaic cleaning task.

[0020] Specifically, obtaining the first photovoltaic cleaning task corresponding to the first photovoltaic cleaning robot means obtaining the photovoltaic cleaning tasks that the robot to be handed over is currently performing or about to perform, including detailed information such as the photovoltaic panel area, cleaning path, cleaning requirements, cleaning progress collaboration tasks, and related parameters. Call the photovoltaic operation and maintenance device, which is usually equipped with various sensors, such as cameras, temperature sensors, pollution sensors, etc., to obtain various data of the photovoltaic panels, including the pollution degree of the photovoltaic panels obtained from operation and maintenance, the surface temperature of the photovoltaic panels, and the cleaning effect of the photovoltaic panels. Evaluate the accumulation of dirt on the surface of the photovoltaic panels to judge whether the cleaning effect meets the expectations; detect the surface temperature of the photovoltaic panels, and abnormal temperature indicates that there is a hot spot (local overheating) fault in the photovoltaic panels; by comparing the pollution degree data before and after cleaning, evaluate whether the cleaning effect completed by the cleaning robot is qualified, and help identify areas that need to be repeatedly cleaned or maintained.

[0021] Decompose the first photovoltaic cleaning task to obtain the area to be cleaned, that is, the cleaning area that the first photovoltaic cleaning robot has not completed. Evaluate the pollution degree of the first photovoltaic operation and maintenance data, identify the areas with relatively serious pollution degree, and use them as the priority cleaning areas. Combine the area to be cleaned and the priority cleaning areas, and jointly adjust the initial second photovoltaic cleaning task of the second photovoltaic cleaning robot to obtain the second photovoltaic cleaning task. By sharing tasks and operation and maintenance data, the second photovoltaic cleaning robot can generate effective cleaning tasks on the basis of a comprehensive understanding of the current cleaning status, achieve seamless task handover, improve cleaning efficiency, reduce task overlap, and at the same time ensure the continuity and effectiveness of cleaning, so that the photovoltaic system remains in the best state.

[0022] Step 4: Control the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and upload the second photovoltaic operation and maintenance data to the central coordination system.

[0023] Specifically, the second photovoltaic cleaning robot receives and starts to execute the second photovoltaic cleaning task, including the area to be cleaned and the area to be cleaned preferentially, guiding the robot on how to allocate cleaning time and paths. The robot adjusts the cleaning equipment parameters (such as pressure, detergent concentration) and travel paths according to the specific requirements of the task. For example, in areas with severe pollution, the cleaning intensity may be increased, while in areas with mild pollution, the regular cleaning mode is maintained. The second photovoltaic cleaning robot performs cleaning operations according to the specified paths and parameters to ensure that the pollutants on the surface of the photovoltaic panels are effectively removed. During the cleaning process, the cleaning effect is monitored in real time and self-adjusted to ensure that the cleaning task is successfully completed as planned. During the cleaning process, the second photovoltaic cleaning robot uses the built-in sensor equipment to collect operation and inspection data, including information such as the surface state of the photovoltaic panels after cleaning, temperature changes, and pollution residue conditions. The operation and inspection data during the cleaning process is uploaded to the central coordination system to ensure that the latest cleaning status is recorded in the system. After receiving the operation and inspection data, the central coordination system integrates it into the overall data record to evaluate the status of the overall photovoltaic system and optimize future cleaning strategies. Based on the analysis results of the operation and inspection data, the central coordination system can decide whether to adjust future cleaning tasks or dispatch additional robots for supplementary cleaning. While completing the cleaning task, the second photovoltaic cleaning robot uploads the operation and inspection data to the central coordination system, realizing the closed-loop management of the cleaning task. This not only helps the system monitor the cleaning status of the photovoltaic panels but also provides data support for subsequent operation and maintenance and task scheduling, improving the overall cleaning efficiency and the operation and inspection efficiency.

[0024] Furthermore, step one of this application includes: The central coordination system includes a handover trigger module, where the handover trigger module at least includes power anomaly trigger, equipment failure trigger, and cleaning non-compliance trigger; monitor the real-time operating status of the group of photovoltaic cleaning robots by the central coordination system to obtain the operating status data of each photovoltaic cleaning robot; perform pattern recognition on the operating status data of each photovoltaic cleaning robot to obtain the identification photovoltaic cleaning robot for triggering the handover; the central coordination system performs cleaning handover allocation for the identification photovoltaic cleaning robot.

[0025] Specifically, the central coordination system includes a handover trigger module, which is used to monitor the operating status of the photovoltaic cleaning robot in real time and identify the conditions for triggering task handover, such as abnormal power, equipment failure, or unqualified cleaning. Abnormal power trigger: When the power of the robot is lower than the preset threshold, the task handover is triggered to ensure that the robot can return to charge without interrupting the entire cleaning process. Equipment failure trigger: When the robot detects that a certain component of itself fails and cannot work properly, the task handover is triggered to transfer the task to other robots. Unqualified cleaning trigger: When the robot detects that the cleaning effect does not meet the preset standard (such as detecting that the residual dirt is higher than the allowable range) after completing the cleaning task, the task handover is triggered, and other robots are required to clean again to ensure the cleaning quality.

[0026] The photovoltaic cleaning robot group is monitored in real time through sensors, communication modules, etc., and the operating status data of each robot is collected and obtained, including power, equipment status, cleaning effect, etc. Pattern recognition is performed on the operating status data of each photovoltaic cleaning robot to identify whether there are abnormal situations. Pattern recognition is a data analysis and machine learning technology used to identify specific patterns or rules from data. Data preprocessing and feature extraction are performed on the operating status data, and algorithms (such as classifiers, neural networks, support vector machines, etc. in machine learning) are used to learn the features to establish a model for identifying patterns. The trained model is used to screen out abnormal situations in real time. Based on the results of pattern recognition, once it is detected that the robot status does not meet the normal operating standard, this robot is marked as a robot that needs to perform task handover, that is, the photovoltaic cleaning robot is identified, and the specific trigger type (such as abnormal power, equipment failure, or unqualified cleaning) is recorded.

[0027] A suitable replacement robot is arranged for the identified trigger robot. Considering the fitness of the robot's cleaning function, the fitness of the path consumption, and the fitness of the task emergency status, the most suitable matching photovoltaic cleaning robot is selected, and the unfinished task data and operating status information of the identified robot are shared with the matching photovoltaic cleaning robot, and the matching photovoltaic cleaning robot is assigned to continue to complete the unfinished cleaning area. Through real-time monitoring, pattern recognition, and rapid response, the handover trigger module enables the central coordination system to discover and solve abnormal situations in the operation of the robot in the first time, ensuring the continuity of the cleaning task. By intelligently allocating replacement robots, it is ensured that the task can be completed efficiently and the operation interruption is reduced without affecting the cleaning effect.

[0028] Furthermore, the present application further includes the following steps: Obtain the unlabeled photovoltaic cleaning robots in the photovoltaic cleaning robot group; generate a handover allocation space according to the unlabeled photovoltaic cleaning robots, and perform fitness analysis on the labeled photovoltaic cleaning robots in the handover allocation space, and output the matching photovoltaic cleaning robots corresponding to the labeled photovoltaic cleaning robots respectively for handover; wherein, the fitness analysis includes robot cleaning function fitness, path consumption fitness, and task emergency state fitness.

[0029] Specifically, obtain the unlabeled photovoltaic cleaning robots in the photovoltaic cleaning robot group, that is, the photovoltaic cleaning robots that have not triggered the handover condition and are still in the normal working state. Identify those candidate robots that can be used for handover tasks, so as to construct a task replacement candidate pool. The candidate robots need to meet the conditions of normal state, completed tasks or in a standby state. The handover allocation space is a virtual selection space generated by the central coordination system for handover tasks, used to comprehensively consider the adaptability of each unlabeled photovoltaic cleaning robot, and contains information such as the positions, cleaning capabilities, and current task status of all unlabeled robots.

[0030] In the handover allocation space, perform multi-dimensional fitness analysis on the labeled photovoltaic cleaning robots to determine which unlabeled photovoltaic cleaning robots are most suitable for taking over the tasks of the labeled robots. The fitness analysis includes evaluating the cleaning function fitness, path consumption fitness, and task emergency state fitness. The cleaning function fitness refers to evaluating the adaptability of candidate robots in terms of cleaning functions, and matching the cleaning tool types, cleaning modes, and cleaning intensities of candidate robots according to the uncompleted cleaning task requirements of the labeled robots. For example, if the dirt degree in the task area of the labeled robot is relatively heavy, a candidate robot with higher cleaning ability is preferentially selected. The path consumption fitness is used to evaluate the path consumption when the candidate robot takes over the task, and calculate the path distance and estimated consumption (such as power or time) from the current position of the candidate robot to the task area. The higher the path consumption fitness of a robot, the less its path consumption, the less energy consumption and time required to take over the task, and it can complete the handover task more efficiently. The task emergency state fitness evaluates the urgency of the task of the labeled robot, such as the time limit of the task, the cleaning frequency requirements, etc. If the task of the labeled robot is an urgent task, a candidate robot with a higher fitness is preferentially selected for handover to ensure that the task is completed on time. Urgent tasks include situations where the task is about to expire or the cleanliness of the area has affected the power generation efficiency.

[0031] After performing fitness analysis on each unlabeled photovoltaic cleaning robot, sort the candidate robots according to the fitness scores from high to low, and select the candidate robot with the highest fitness as the matching photovoltaic cleaning robot, that is, the optimal successor. By comprehensively considering functions, paths, and urgency through fitness analysis, the task allocation becomes more intelligent and the execution efficiency of the handover task is improved.

[0032] Furthermore, step two of this application includes: Determine whether the first photovoltaic cleaning robot is in a collaborative relationship. If the first photovoltaic cleaning robot is in a collaborative relationship, identify the collaborative robot that has a collaborative relationship with the first photovoltaic cleaning robot; obtain the collaborative task between the first photovoltaic cleaning robot and the collaborative robot, and share the collaborative task with the second photovoltaic cleaning robot; update the second photovoltaic cleaning task according to the collaborative task.

[0033] Specifically, the central coordination system determines whether the current first photovoltaic cleaning robot has a collaborative relationship based on factors such as task type, cleaning area size, and cleaning time. A collaborative relationship generally means that two or more photovoltaic cleaning robots rely on each other or cooperate to complete a complex task when performing cleaning tasks. For example, cleaning a larger area of photovoltaic panels or a cleaning area with higher difficulty may require two or more photovoltaic cleaning robots to work together. If not, direct handover is performed. If the first photovoltaic cleaning robot is in a collaborative relationship, identify other photovoltaic cleaning robots that have a collaborative relationship with this photovoltaic cleaning robot, which are called collaborative robots. Extract the collaborative tasks between the collaborative robot, the first photovoltaic cleaning robot, and the collaborative robot from the task management database of the central coordination system (responsible for managing the task allocation of each photovoltaic cleaning robot), that is, the tasks jointly undertaken by multiple photovoltaic cleaning robots, which require coordination and cooperation between photovoltaic cleaning robots, including cleaning target areas, cleaning standards, path planning, cleaning frequencies, etc.

[0034] Share the collaborative task data with the second photovoltaic cleaning robot, that is, the handover photovoltaic cleaning robot. Update the second photovoltaic cleaning task of the second photovoltaic cleaning robot according to the collaborative task to ensure that the second robot not only takes over the task of the first robot but also can smoothly complete the collaborative task with the collaborative robot. If the collaborative task has specific cleaning strategies (such as cleaning in sequence, running synchronously, etc.), these strategies will also be updated to the task configuration of the second robot to achieve seamless cooperation. By identifying the collaborative relationship and task sharing, it is ensured that the collaborative task is not interrupted during the handover process, realizing seamless task connection.

[0035] Furthermore, step three of this application includes: Obtain a photovoltaic operation and maintenance device, which obtains first photovoltaic operation and maintenance data. The first photovoltaic operation and maintenance data includes the pollution degree of the photovoltaic panel obtained through operation and maintenance, the surface temperature of the photovoltaic panel, and the cleaning effect of the photovoltaic panel. The central coordination system inputs the first photovoltaic operation and maintenance data into a fault detection model, and based on the fault detection model, obtains a fault identification area. A reminder is sent to the upper computer of the central coordination system with the fault identification area, and based on the central coordination system, a maintenance handover is allocated for the first photovoltaic cleaning robot, and a first photovoltaic maintenance robot is output.

[0036] Specifically, a photovoltaic operation and maintenance device is retrieved. This is a device specifically used to detect the status of photovoltaic panels and usually includes various sensors (such as pollution sensors, temperature sensors), a communication module, and a data analysis module. The sensors are responsible for collecting various data of the photovoltaic panel. The communication module transmits the first photovoltaic operation and maintenance data to the data analysis module to process and analyze this data and evaluate the status of the photovoltaic panel. When the operation and maintenance device detects serious pollution on the surface of the photovoltaic panel or abnormal temperature, etc., it will transmit the data to the central coordination system, triggering relevant cleaning or maintenance tasks. The first photovoltaic operation and maintenance data is obtained from the photovoltaic operation and maintenance device, which is data on the current status of the photovoltaic panel, including the pollution degree of the photovoltaic panel, the surface temperature of the photovoltaic panel, and the cleaning effect of the photovoltaic panel. The pollution level of the photovoltaic panel surface is evaluated through a pollution sensor or image recognition technology; the surface temperature of the photovoltaic panel is measured through a temperature sensor to help judge the operation status and efficiency of the photovoltaic panel; by comparing the data before and after cleaning, the cleaning effect is evaluated to ensure the power generation efficiency of the photovoltaic panel.

[0037] The first photovoltaic operation and maintenance data is input into a fault detection model. The fault detection model is a tool that uses data analysis techniques (such as machine learning, statistical methods, or preset rules) to identify and locate potential faults in a system. For photovoltaic operation and maintenance, the goal of the fault detection model is to analyze the operation and maintenance data from the photovoltaic panel, identify abnormal situations, so as to take corresponding maintenance measures. Obtain the fault records of the photovoltaic operation and maintenance device in the past period, preprocess the data and extract features, and use machine learning algorithms (such as deep learning, support vector machines, etc.) to train and analyze the extracted features to identify existing faults. Evaluate the performance of the model through methods such as cross-validation, and adjust the parameters of the model according to the evaluation results to finally obtain a fault detection model with better accuracy. After preprocessing the first photovoltaic operation and maintenance data, key features are extracted and input into the fault prediction model to detect whether there are potential faults, identify the possible fault types and severity, and the areas with problems. After analyzing the operation and maintenance data by the fault detection model, the fault area is marked as the fault identification area, that is, the area of the photovoltaic panel where a fault may exist.

[0038] Upload the information of the fault identification area to the upper computer of the central coordination system for reminder, including the location of the fault identification area, the fault type (such as serious pollution, abnormal temperature, etc.), and other relevant parameters of the photovoltaic panels in this area. The upper computer usually refers to the advanced control center or operation interface of the monitoring system, which is used to receive and process information from the lower computer (such as photovoltaic cleaning robots). The central coordination system arranges the handover of the maintenance tasks for the first photovoltaic cleaning robot according to the detected fault identification area, selects a suitable photovoltaic maintenance robot, and assigns the maintenance tasks to this robot to ensure timely repair. Determine the most suitable maintenance robot (i.e., the first photovoltaic maintenance robot) according to the task priority and the fault situation, and assign it to be responsible for the repair work in the fault area. The maintenance tasks include cleaning the hot spot or the fault location, repairing the circuit, re-detecting the cleaning effect, etc. Through the operation and inspection data analysis and the fault detection model, automatically identify the faults of the photovoltaic panels and make rapid allocation, effectively reducing the workload of manual detection, automatically arranging the most suitable maintenance robot, ensuring the maintenance efficiency, and reducing the impact of the faults on the power generation efficiency.

[0039] Furthermore, the present application further includes the following steps: Decompose the first photovoltaic cleaning task to obtain the areas to be cleaned; evaluate the pollution degree through the first photovoltaic operation and inspection data to obtain the priority cleaning areas, where the priority cleaning areas are the areas with a pollution degree greater than a preset threshold; merge the areas to be cleaned and the priority cleaning areas to obtain the merged cleaning task; obtain the initial second photovoltaic cleaning task of the second photovoltaic cleaning robot; adjust the initial second photovoltaic cleaning task according to the merged cleaning task to generate the second photovoltaic cleaning task.

[0040] Specifically, decompose the first photovoltaic cleaning task, divide the entire photovoltaic cleaning task into different areas, and determine the cleaning requirements for each specific area. Task decomposition means decomposing the first photovoltaic cleaning task into smaller subtasks for easy management and execution. By decomposing the task, the areas to be cleaned on the photovoltaic panels are divided. The areas to be cleaned are the parts that need to be cleaned on the entire photovoltaic panel, ensuring full coverage without omission. By analyzing the first photovoltaic operation and inspection data, evaluate the pollution degree of each area. According to specific requirements, preset a preset threshold for judging which areas have a pollution degree exceeding the threshold and need to be marked as priority cleaning areas. Compare the pollution degree of each area with the preset threshold, and take the areas with a pollution degree greater than the preset threshold as the priority cleaning areas.

[0041] Merge the tasks of the area to be cleaned and the priority cleaning area to form a new cleaning task list, that is, the merged cleaning task. This task list includes all areas that need to be cleaned and highlights the priority cleaning areas, which is comprehensive and targeted. So that when executing, the key pollution areas can be cleaned first without missing other cleaning areas and without duplicate cleaning. Obtain the initial second photovoltaic cleaning task of the second photovoltaic cleaning robot, that is, the cleaning task of the second photovoltaic cleaning robot itself before handing over the current task. Adjust the initial second photovoltaic cleaning task according to the merged cleaning task, including adding new cleaning areas, adjusting the cleaning order or priority. Assign the merged cleaning task to the second photovoltaic cleaning robot to ensure that its tasks include the area to be cleaned and the priority cleaning area, avoiding duplication or omission. The adjusted task is the new second photovoltaic cleaning task, ensuring that the second photovoltaic cleaning robot gives priority to cleaning the highly polluted areas and covers all areas to be cleaned. For example, the area to be cleaned is A, and the priority cleaning area is B (pollution degree score: 0.85, threshold: 0.8). At this time, the merged cleaning task is A + B. The initial second photovoltaic cleaning task is area C. According to the merged cleaning task, the updated initial second photovoltaic cleaning task may be: clean B first, and then clean areas A and C. In specific practical applications, usually, factors such as the current specific cleaning path, robot efficiency, and collaborative operation also need to be considered to ensure the reasonable allocation of cleaning tasks. Through pollution degree assessment, it is ensured that the areas most in need are cleaned first, improving the cleaning efficiency. Merging the cleaning tasks and adjusting the second photovoltaic cleaning task ensure the reasonable allocation of cleaning resources and avoid duplicate work.

[0042] Furthermore, the present application further includes the following steps: Collect data according to the central coordination system to obtain the layout information of the photovoltaic power station, the arrangement mode of the photovoltaic panels, and the real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot; perform handover area optimization according to the layout information of the photovoltaic power station, the arrangement mode of the photovoltaic panels, and the real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to obtain an optimal solution for the handover area with a handover obstacle degree less than the preset value; send the optimal solution for the handover area to the central coordination system, and based on the central coordination system, control the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to perform handover in the optimal solution for the handover area.

[0043] Specifically, the central coordination system collects the layout information of the photovoltaic power station, including the arrangement of photovoltaic panels, the overall area distribution, the location of equipment, etc., to clarify the physical structure of the photovoltaic power station and the current state of the robot. The layout information of the photovoltaic power station includes the overall structure of the photovoltaic power station, the installation location and direction of the photovoltaic panels, the geographical features of the power station, etc. The arrangement of the photovoltaic panels refers to the specific layout and direction of the photovoltaic panels, whether they are in series or parallel, and the distance between the photovoltaic panels, etc. Real-time track the position information of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot, as well as the current motion state and direction. According to the layout of the photovoltaic power station and the arrangement of the photovoltaic panels, combined with the real-time position distribution of the two robots, calculate a suitable handover area. The preset handover obstacle degree is a standard set in advance to evaluate the feasibility and efficiency of the handover area, including factors such as the distance between robots, the arrangement of photovoltaic panels, obstacles in the power station, narrow passages, large slopes, etc. Areas with a lower obstacle degree can effectively reduce the time consumption and energy loss of robot movement. Through an optimization algorithm (such as a path planning algorithm based on obstacle degree or a heuristic algorithm), determine the optimal solution for the junction area. For example, the A* algorithm combines obstacle evaluation while seeking the shortest path to calculate the passing cost of different areas; by assigning obstacle degree weights to each potential handover area in the photovoltaic power station, the Dijkstra algorithm finds the lowest obstacle area that the two robots can reach smoothly, so as to be used as the handover optimal solution; the genetic algorithm simulates the handover positions in the photovoltaic power station as population individuals, and through multiple generations of fitness screening, evolves a handover area that meets the low obstacle degree. The fitness function includes obstacle degree, path length, robot energy consumption, etc., so that the final optimal solution has the characteristics of high handover efficiency and good safety.

[0044] The central coordination system collects data such as the obstacle degree and path accessibility of each area, and establishes a map of the handover area of the photovoltaic station. Select a suitable optimization algorithm according to the specific environment. For example, the A algorithm can be selected when the obstacles are stable, and D or particle swarm can be selected when the obstacles change dynamically. According to the obstacle degree and path information, calculate the handover scores of each area, and select the optimal area. Send the handover area with the smallest obstacle degree and low path consumption to the central coordination system as the optimal solution for the handover area. The central coordination system accordingly updates the respective task paths and position adjustment instructions of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to avoid resource waste or potential handover errors during the handover process.

[0045] Under the guidance of the central coordination system, the first photovoltaic cleaning robot and the second photovoltaic cleaning robot move to the optimal solution of the handover area. The selection of the handover area enables the robots to achieve handover under obstacle-free and optimal path conditions. Precisely control the positions and handover times of the two robots so that the handover is carried out in a specific area, complete the transfer of tasks and cleaning information, and complete the robot handover at the optimal solution of the handover area to make good connections for subsequent task cleaning.

[0046] For example, assume that the layout information of the photovoltaic power station is as follows: the area of the photovoltaic power station is 100,000 square meters, there are 10 rows in total, and each row has 20 photovoltaic panels. The real-time position of the first photovoltaic cleaning robot is near the 10th photovoltaic panel in the 5th row, and the real-time position of the second photovoltaic cleaning robot is near the 15th photovoltaic panel in the 8th row. The preset threshold of the handover obstacle degree is 10 (this is a hypothetical obstacle degree unit, and the smaller the value, the lower the obstacle degree). The genetic algorithm is used to optimize the handover area. Area 1: The distance of the first photovoltaic cleaning robot to this area is 50 meters, the distance of the second photovoltaic cleaning robot to this area is 70 meters, the terrain obstacle degree is 2, and the total handover obstacle degree is 12; Area 2: The distance of the first photovoltaic cleaning robot to this area is 30 meters, the distance of the second photovoltaic cleaning robot to this area is 60 meters, the terrain obstacle degree is 1, and the total handover obstacle degree is 9; Area 3: The distance of the first photovoltaic cleaning robot to this area is 45 meters, the distance of the second photovoltaic cleaning robot to this area is 80 meters, the terrain obstacle degree is 3, and the total handover obstacle degree is 14. The total handover obstacle degree of Area 2 is 9, which is less than the preset threshold of 10. Therefore, Area 2 is taken as the optimal solution of the handover area, and the coordinate information of Area 2 is sent to the central coordination system, and instructions are sent to the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to guide the two robots to move to Area 2 for handover. After receiving the instructions, the two robots move from their current positions to Area 2 for handover respectively. It takes 2 minutes for the first photovoltaic cleaning robot to move to the handover area; it takes 3 minutes for the second photovoltaic cleaning robot to move to the handover area; the handover process takes 5 minutes; after the handover is completed, the second photovoltaic cleaning robot continues to perform the remaining cleaning tasks. By optimizing the handover area, the time consumption and potential obstacles in the handover process are reduced, the handover efficiency is improved, it helps to avoid handover in complex or unsuitable areas, and the safety risk in the handover process is reduced.

[0047] In summary, the intelligent operation and maintenance method combining photovoltaic cleaning robots provided by the present application has the following technical effects: By constructing a central coordination system, the central coordination system is used to conduct handover coordination management for a group of photovoltaic cleaning robots, and data interaction occurs among the group of photovoltaic cleaning robots; according to the central coordination system, a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover are determined, wherein the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover; obtain the first photovoltaic cleaning task and the first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, share the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generate a second photovoltaic cleaning task; control the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and upload the second photovoltaic operation and inspection data to the central coordination system. That is to say, by constructing a central coordination system to conduct handover coordination management for the group of robots, when a certain robot needs to hand over tasks due to reasons such as insufficient power, the task area has reached the boundary, or a failure occurs, a available robot is preferentially selected to ensure the timeliness and efficiency of the handover. At the same time, the central system shares the cleaning task data and operation and inspection data of the robot with the replacement robot in real time to ensure the complete handover of task and environmental information, so as to avoid the replacement robot from repeating operations or missing some cleaning areas. Dynamically adjust the task allocation according to the current state of the robot, so that resources are efficiently utilized, the time of the robot in the standby or task-free state is reduced, the efficient interaction and collaborative operation among the robots are ensured, and the overall operation and inspection efficiency is improved.

[0048] Embodiment 2. Based on the same inventive concept as the intelligent operation and inspection method of the photovoltaic cleaning robot in the foregoing Embodiment 1, the present application further provides an intelligent operation and inspection system of the photovoltaic cleaning robot. Please refer to the attached Figure 2 , the intelligent operation and inspection system of the photovoltaic cleaning robot includes: The system construction module 11 is used to construct a central coordination system, and the central coordination system is used to conduct handover coordination management for a group of photovoltaic cleaning robots, and data interaction occurs among the group of photovoltaic cleaning robots.

[0049] The robot determination module 12 is used to determine a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover according to the central coordination system, wherein the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot for handover.

[0050] The task generation module 13 is used to obtain the first photovoltaic cleaning task and the first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, share the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generate a second photovoltaic cleaning task.

[0051] The cleaning control module 14 is used to control the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and upload the second photovoltaic operation and inspection data to the central coordination system.

[0052] Furthermore, the system construction module 11 in the intelligent operation and inspection system combined with the photovoltaic cleaning robot is also used for: The central coordination system includes a handover trigger module. Among them, the handover trigger module at least includes power anomaly trigger, equipment failure trigger, and cleaning non-compliance trigger; monitor the real-time operation status of the photovoltaic cleaning robot group according to the central coordination system, and obtain the operation status data of each photovoltaic cleaning robot; perform pattern recognition on the operation status data of each photovoltaic cleaning robot to obtain the identified photovoltaic cleaning robot for handover; the central coordination system performs cleaning handover allocation for the identified photovoltaic cleaning robot.

[0053] Furthermore, the system construction module 11 in the intelligent operation and inspection system combined with the photovoltaic cleaning robot is also used for: Obtain the non-identified photovoltaic cleaning robots in the photovoltaic cleaning robot group; generate a handover allocation space according to the non-identified photovoltaic cleaning robots, and perform fitness analysis on the identified photovoltaic cleaning robots in the handover allocation space, and output the matching photovoltaic cleaning robots corresponding to the identified photovoltaic cleaning robots for handover; among them, the fitness analysis includes robot cleaning function fitness, path consumption fitness, and task emergency status fitness.

[0054] Furthermore, the robot determination module 12 in the intelligent operation and inspection system combined with the photovoltaic cleaning robot is also used for: Judge whether the first photovoltaic cleaning robot is in a cooperative relationship. If the first photovoltaic cleaning robot is in a cooperative relationship, identify the cooperative robot that has a cooperative relationship with the first photovoltaic cleaning robot; obtain the cooperative task between the first photovoltaic cleaning robot and the cooperative robot, and share the cooperative task with the second photovoltaic cleaning robot; update the second photovoltaic cleaning task according to the cooperative task.

[0055] Furthermore, the task generation module 13 in the intelligent operation and inspection system combined with the photovoltaic cleaning robot is also used for: Obtain a photovoltaic operation and maintenance device. The photovoltaic operation and maintenance device obtains first photovoltaic operation and maintenance data, which includes the pollution degree of the photovoltaic panel obtained through operation and maintenance, the surface temperature of the photovoltaic panel, and the cleaning effect of the photovoltaic panel. The central coordination system inputs the first photovoltaic operation and maintenance data into a fault detection model, and according to the fault detection model, obtains a fault identification area. Remind the upper computer of the central coordination system with the fault identification area, and based on the central coordination system, allocate maintenance handover for the first photovoltaic cleaning robot, and output a first photovoltaic maintenance robot.

[0056] Furthermore, the task generation module 13 in the intelligent operation and maintenance system combined with the photovoltaic cleaning robot is further used for: Decompose the first photovoltaic cleaning task to obtain an area to be cleaned; through the pollution degree evaluation of the first photovoltaic operation and maintenance data, obtain a priority cleaning area, where the priority cleaning area is an area with a pollution degree greater than a preset threshold; merge the area to be cleaned and the priority cleaning area to obtain a merged cleaning task; obtain the initial second photovoltaic cleaning task of the second photovoltaic cleaning robot; adjust the initial second photovoltaic cleaning task according to the merged cleaning task to generate a second photovoltaic cleaning task.

[0057] Furthermore, the intelligent operation and maintenance system combined with the photovoltaic cleaning robot further includes a handover area determination module, which is used for: Collect data according to the central coordination system to obtain the layout information of the photovoltaic power station, the arrangement mode of the photovoltaic panels, and the real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot; perform handover area optimization according to the layout information of the photovoltaic power station, the arrangement mode of the photovoltaic panels, and the real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to obtain an optimal solution for the handover area with a handover obstacle degree less than the preset value; send the optimal solution for the handover area to the central coordination system, and based on the central coordination system, control the first photovoltaic cleaning robot and the second photovoltaic cleaning robot to perform handover in the optimal solution for the handover area.

[0058] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The foregoing Figure 1The intelligent operation and inspection method and specific examples in Embodiment 1 are equally applicable to the intelligent operation and inspection system of the combined photovoltaic cleaning robot in this embodiment. Through the detailed description of the intelligent operation and inspection method of the combined photovoltaic cleaning robot above, those skilled in the art can clearly know the intelligent operation and inspection system of the combined photovoltaic cleaning robot in this embodiment. Therefore, for the sake of simplicity of the specification, it will not be described in detail here. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the description of the method part.

[0059] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0060] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application also intends to include these changes and modifications.

Claims

1. An intelligent operation and inspection method combined with a photovoltaic cleaning robot, characterized in that: include: Constructing a central coordination system, the central coordination system is used to coordinate and manage the handover of the photovoltaic cleaning robot group, and the data exchange between the photovoltaic cleaning robot groups; According to the central coordination system, determining a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover, wherein the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over; Acquire a first photovoltaic cleaning task and first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, share the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generate a second photovoltaic cleaning task; According to the second photovoltaic cleaning task, the second photovoltaic cleaning robot is controlled to clean the current photovoltaic, and the second photovoltaic operation and inspection data is uploaded to the central coordination system.

2. The intelligent operation and inspection method combined with a photovoltaic cleaning robot according to claim 1, characterized in that: The central coordination system includes an intersection and triggering module, include: Wherein, the cross-connection trigger module at least includes power abnormality trigger, equipment failure trigger and cleaning failure trigger; Monitoring the real-time operating status of the photovoltaic cleaning robot group according to the central coordination system to obtain operating status data of each photovoltaic cleaning robot; Perform pattern recognition on the operating status data of each photovoltaic cleaning robot to obtain the photovoltaic cleaning robot that triggers the handover; The central coordination system performs cleaning handover allocation for the identified photovoltaic cleaning robot.

3. The intelligent operation and inspection method combined with a photovoltaic cleaning robot as claimed in claim 2, characterized in that: The central coordination system performs cleaning handover allocation for the identified photovoltaic cleaning robot, including: Acquire a non-identified photovoltaic cleaning robot from the photovoltaic cleaning robot group; Generate a handover allocation space according to the non-identified photovoltaic cleaning robot, perform fitness analysis on the identified photovoltaic cleaning robot in the handover allocation space, and output matching photovoltaic cleaning robots for handover corresponding to the identified photovoltaic cleaning robots; Among them, the fitness analysis includes the robot's cleaning function fitness, path consumption fitness and task emergency state fitness.

4. The intelligent operation and inspection method combined with a photovoltaic cleaning robot according to claim 1, characterized in that: According to the central coordination system, determining a first photovoltaic cleaning robot and a second photovoltaic cleaning robot for handover, further comprising: Determining whether the first photovoltaic cleaning robot is in a collaborative relationship, and if the first photovoltaic cleaning robot is in a collaborative relationship, identifying a collaborative robot that is in a collaborative relationship with the first photovoltaic cleaning robot; Acquire a collaborative task between the first photovoltaic cleaning robot and the collaborative robot, and share the collaborative task with the second photovoltaic cleaning robot; The second photovoltaic cleaning task is updated according to the collaborative task.

5. The intelligent operation and inspection method combined with a photovoltaic cleaning robot according to claim 1, characterized in that: Acquiring first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, including: Acquire a photovoltaic operation and inspection device, wherein the photovoltaic operation and inspection device acquires first photovoltaic operation and inspection data, wherein the first photovoltaic operation and inspection data includes a degree of contamination of photovoltaic panels, a surface temperature of photovoltaic panels, and a cleaning effect of photovoltaic panels obtained through operation and inspection; The central coordination system inputs the first photovoltaic operation and inspection data into a fault detection model, and acquires a fault identification area according to the fault detection model; The fault identification area is used to remind the host computer of the central coordination system, and based on the central coordination system, maintenance handover allocation is performed for the first photovoltaic cleaning robot, and the first photovoltaic maintenance robot is output.

6. The intelligent operation and inspection method combined with a photovoltaic cleaning robot according to claim 1, characterized in that: The first photovoltaic cleaning task and the first photovoltaic operation and inspection data are shared with the second photovoltaic cleaning robot to generate a second photovoltaic cleaning task, including: Decomposing the first photovoltaic cleaning task to obtain an area to be cleaned; By evaluating the pollution degree of the first photovoltaic operation and inspection data, a priority cleaning area is obtained, wherein the priority cleaning area is an area with a pollution degree greater than a preset threshold; Merge tasks of the to-be-cleaned area and the priority cleaning area to obtain a merged cleaning task; acquiring an initial second photovoltaic cleaning task of the second photovoltaic cleaning robot; The initial second photovoltaic cleaning task is adjusted according to the combined cleaning task to generate a second photovoltaic cleaning task.

7. The intelligent operation and inspection method combined with a photovoltaic cleaning robot according to claim 1, characterized in that: Also includes: Collecting data according to the central coordination system to obtain layout information of the photovoltaic power station, arrangement of photovoltaic panels, and real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot; According to the layout information of the photovoltaic power station, the arrangement of photovoltaic panels, and the real-time position distribution of the first photovoltaic cleaning robot and the second photovoltaic cleaning robot, an optimal solution for the handover area is obtained with a handover obstacle degree less than a preset handover obstacle degree; The optimal solution of the handover area is sent to the central coordination system, and based on the central coordination system, the first photovoltaic cleaning robot and the second photovoltaic cleaning robot are controlled to perform handover at the optimal solution of the handover area.

8. The intelligent operation and inspection system combined with the photovoltaic cleaning robot is characterized by: The steps for implementing the intelligent operation and inspection method combined with the photovoltaic cleaning robot according to any one of claims 1 to 7, wherein the intelligent operation and inspection system combined with the photovoltaic cleaning robot comprises: The system construction module is used to construct a central coordination system, and the central coordination system is used to coordinate and manage the handover of the photovoltaic cleaning robot group, and the data exchange between the photovoltaic cleaning robot groups; The robot determination module is used to determine the first photovoltaic cleaning robot and the second photovoltaic cleaning robot for handover according to the central coordination system, wherein the first photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over, and the second photovoltaic cleaning robot is the photovoltaic cleaning robot to be handed over; The task generation module is used to obtain a first photovoltaic cleaning task and first photovoltaic operation and inspection data corresponding to the first photovoltaic cleaning robot, share the first photovoltaic cleaning task and the first photovoltaic operation and inspection data with the second photovoltaic cleaning robot, and generate a second photovoltaic cleaning task; The cleaning control module is used to control the second photovoltaic cleaning robot to clean the current photovoltaic according to the second photovoltaic cleaning task, and upload the second photovoltaic operation and inspection data to the central coordination system.

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