Intelligent photovoltaic module self-cleaning device and method

Abstract

The invention provides an intelligent photovoltaic module self-cleaning device and method, and relates to the technical field of intelligent cleaning, and the device comprises a sensing detection module which is used for carrying out the intelligent recognition of a target photovoltaic module based on a preset sensor, and obtaining a first module image, determining component parameters and a component state parameter set of the target photovoltaic component based on the first component image; the cleaning planning module is used for performing parameter classification based on component state parameters in the component state parameter set and intelligently planning a corresponding cleaning path based on a parameter classification result; the strategy execution module is used for determining a self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the module state parameters, and self-cleaning the photovoltaic module based on the self-cleaning strategy; and the monitoring feedback module is used for monitoring self-cleaning key parameters of the photovoltaic module in real time and performing comprehensive evaluation in combination with a cleaning result so as to perform strategy optimization on a self-cleaning strategy. The self-cleaning of the target photovoltaic module can be more accurate and timely.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent cleaning, and particularly to an intelligent self-cleaning device and method for photovoltaic modules. Background Art

[0002] At present, in the field of photovoltaic energy, as the core of the power generation system, the surface cleanliness of photovoltaic modules is directly related to the power generation efficiency. However, photovoltaic modules are exposed outdoors for a long time and are easily covered by pollutants such as dust, bird droppings, and leaves, resulting in a significant decline in power generation efficiency. Traditional manual cleaning is not only time-consuming and laborious, but also difficult to ensure the cleaning effect, and there are also potential safety hazards.

[0003] In view of this, the self-cleaning technology of photovoltaic modules has attracted much attention in recent years. The self-cleaning in the existing technology mostly relies on preset strategies to automate the process.

[0004] However, the existing self-cleaning strategies are usually simple and fixed, and it is difficult to intelligently adjust according to the actual state of the module and the external environment, resulting in poor cleaning effects and even possible damage to the module.

[0005] Therefore, the present invention provides an intelligent self-cleaning device and method for photovoltaic modules. Summary of the Invention

[0006] The present invention provides an intelligent self-cleaning device and method for photovoltaic modules, which are used to plan the cleaning path of the photovoltaic module by combining the module parameters and the module state parameters of the photovoltaic module, so as to obtain a self-cleaning strategy, adjust and optimize the self-cleaning strategy, and then perform self-cleaning, and monitor the self-cleaning process in real time and optimize the strategy in a timely manner, which can make the self-cleaning of the target photovoltaic module more accurate and timely.

[0007] The present invention provides an intelligent self-cleaning device for photovoltaic modules, including: A sensing and detection module: used to intelligently identify the target photovoltaic module based on a preset sensor, obtain a first module image, and determine a set of module parameters and module state parameters of the target photovoltaic module based on the first module image; A cleaning planning module: used to classify the parameters based on the module state parameters in the set of module state parameters, and intelligently plan the corresponding cleaning path based on the parameter classification result; A strategy execution module: used to determine the self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the module state parameters, and perform self-cleaning of the photovoltaic module based on the self-cleaning strategy; A monitoring and feedback module: used to monitor the key parameters of the self-cleaning of the photovoltaic module in real time, and conduct a comprehensive evaluation in combination with the cleaning result, so as to optimize the self-cleaning strategy.

[0008] According to the sensing and detection module provided by the present invention, it includes: Image detection unit: used to intelligently identify the target photovoltaic module based on a preset sensor, and perform image display based on the intelligent identification result to obtain the first component image; Parameter extraction unit: used to extract the component parameters of the target photovoltaic module based on the first component image; Status determination unit: used to determine the set of status parameters of the target photovoltaic module from the first component image based on an image analysis method.

[0009] According to the cleaning planning module provided by the present invention, it includes: Parameter classification unit: used to classify each parameter in the set of component status parameters to obtain the first classification result; Classification judgment unit: used to judge the first classification result based on a preset dirt level to obtain the dirt result of the target photovoltaic module; Path planning unit: used to plan the component cleaning path matching the target photovoltaic module according to the dirt result of the target photovoltaic module in combination with the first classification result.

[0010] According to the path planning unit provided by the present invention, it includes: Information integration sub-unit: used to integrate the first classification result, dirt result, equipment cleaning performance of the target photovoltaic module and the component parameters of the target photovoltaic module to obtain the first integrated information; Path optimization sub-unit: used to input the first integrated information into a preset path optimization algorithm, and determine the first initial cleaning path of the target photovoltaic module in combination with the component cleaning requirements; Path verification sub-unit: used to perform path feasibility verification based on the first initial cleaning path. If the first initial cleaning path is feasible, then use the first initial cleaning path of the target photovoltaic module as the component cleaning path.

[0011] According to the strategy execution module provided by the present invention, it includes: Initial cleaning unit: used to screen the corresponding self-cleaning method based on the component status parameters of the target photovoltaic module to obtain the initial cleaning method and cleaning intensity of the target photovoltaic module; Strategy initial unit: used to integrate the component cleaning path with the initial cleaning method and cleaning intensity to obtain the initial cleaning strategy of the target photovoltaic module; First adjustment unit: used to adjust the initial cleaning strategy based on the component cleaning requirements of the target photovoltaic module to obtain the first cleaning strategy; First optimization unit: used to obtain the real-time external environment of the target photovoltaic module and judge whether the real-time external environment will affect the first cleaning strategy of the target photovoltaic module; If the real-time external environment is in a normal situation, then use the first cleaning strategy of the target photovoltaic module as the self-cleaning strategy; On the contrary, it is necessary to optimize the first cleaning strategy of the target photovoltaic module based on the real-time external environment to obtain the self-cleaning strategy of the target photovoltaic module; Self-cleaning unit: used to perform self-cleaning based on the self-cleaning strategy of the target photovoltaic module.

[0012] According to the monitoring and feedback module provided by the present invention, it includes: Parameter monitoring unit: used to, during the self-cleaning process, monitor the key cleaning parameters of the target photovoltaic module in real time based on preset sensors to obtain a set of key cleaning parameters of the target photovoltaic module; Parameter classification unit: used to classify the set of key cleaning parameters according to the parameter types of the key cleaning parameters to obtain a first classified parameter set; Curve fitting unit: used to input each first classified parameter subset in the first classified parameter set into the same coordinate system and perform curve fitting to obtain a cleaning parameter curve corresponding to each first classified parameter subset; First evaluation unit: used to obtain a first evaluation index for the target photovoltaic module based on the curve trend of the cleaning parameter curve; Second status unit: used to, after the self-cleaning of the target photovoltaic module is completed, obtain the second component image of the target photovoltaic module in real time and extract the second status parameters of the target photovoltaic module from the second component image based on a preset image analysis method; Second evaluation unit: used to combine the second status parameters of the target photovoltaic module with the component parameters of the target photovoltaic module to obtain a second evaluation index for the target photovoltaic module; Comprehensive evaluation unit: used to comprehensively evaluate the first evaluation index and the second evaluation index to obtain a comprehensive evaluation result for the target photovoltaic module; Evaluation and optimization unit: used to determine whether the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module, there is no need to optimize the self-cleaning strategy of the target photovoltaic module; On the contrary, it is necessary to optimize the self-cleaning strategy of the target photovoltaic module to obtain the optimal self-cleaning strategy.

[0013] According to the evaluation and optimization unit provided by the present invention, it includes: Evaluation and judgment subunit: used to determine whether the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is used as the optimal self-cleaning strategy; If the comprehensive evaluation result does not fall within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is used as the first self-cleaning strategy; Difference evaluation subunit: used to judge the difference between the first self-cleaning strategy and the corresponding boundary evaluation value of the standard evaluation range, so as to obtain the comprehensive difference of the first self-cleaning strategy; Strategy optimization subunit: used to screen the strategy optimization plan matching the first self-cleaning strategy from the preset strategy optimization database based on the comprehensive difference of the first self-cleaning strategy, and optimize the first self-cleaning strategy based on the strategy optimization plan to obtain the optimal self-cleaning strategy.

[0014] The present invention provides an intelligent self-cleaning method for photovoltaic modules, including: Step 1: Based on a preset sensor, perform intelligent identification on the target photovoltaic module to obtain a first component image, and determine the component parameters and the set of component state parameters of the target photovoltaic module based on the first component image; Step 2: Classify the component state parameters in the set of component state parameters, and intelligently plan the corresponding cleaning path based on the parameter classification result; Step 3: Determine the self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the component state parameters, and perform self-cleaning of the photovoltaic module based on the self-cleaning strategy; Step 4: Real-time monitor the key parameters of the self-cleaning of the photovoltaic module, and perform comprehensive evaluation in combination with the cleaning result, so as to optimize the self-cleaning strategy.

[0015] Compared with the prior art, the beneficial effects of the present invention are: an intelligent self-cleaning device and method for photovoltaic modules provided by the present invention plan the cleaning path of the photovoltaic module by combining the component parameters and the component state parameters of the photovoltaic module, so as to obtain a self-cleaning strategy, and adjust and optimize the self-cleaning strategy, so as to perform self-cleaning, and real-time monitor the self-cleaning process and perform strategy optimization in a timely manner, which can make the self-cleaning of the target photovoltaic module more accurate and timely. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions in the present invention 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 in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0017] Figure 1 It is a structural diagram of an intelligent self-cleaning device for photovoltaic modules provided by an embodiment of the present invention; Figure 2It is a flowchart of an intelligent self - cleaning method for photovoltaic modules provided by an embodiment of the present invention. Detailed implementation manners

[0018] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1: An embodiment of the present invention provides an intelligent self - cleaning device for photovoltaic modules, as Figure 1 shown, including: A sensing and detection module: used to intelligently identify a target photovoltaic module based on a preset sensor to obtain a first component image, and determine the component parameters and the set of component status parameters of the target photovoltaic module based on the first component image; A cleaning planning module: used to classify parameters based on the component status parameters in the set of component status parameters, and intelligently plan a corresponding cleaning path based on the parameter classification result; A strategy execution module: used to determine the self - cleaning strategy of the target photovoltaic module by combining the cleaning path with the component status parameters, and perform self - cleaning of the photovoltaic module based on the self - cleaning strategy; A monitoring and feedback module: used to monitor the key parameters of the self - cleaning of the photovoltaic module in real time, and conduct a comprehensive evaluation in combination with the cleaning result, so as to optimize the self - cleaning strategy.

[0020] In this embodiment, the preset sensor is a device pre - installed in a photovoltaic power station for monitoring and collecting data related to photovoltaic modules. The sensor may include a high - definition camera, an infrared sensor, a temperature sensor, a humidity sensor, etc., and can capture key information such as the image, temperature, and humidity of the photovoltaic module.

[0021] In this embodiment, intelligent identification uses technologies such as image processing and machine vision to automatically identify and analyze the images captured by the preset sensor to obtain information such as the accurate position and shape of the photovoltaic module.

[0022] In this embodiment, the first component image is a preliminary image of the photovoltaic module obtained through intelligent identification technology, and this image shows the appearance of the component and the possible dirt distribution.

[0023] In this embodiment, the component parameters are parameters describing the physical and electrical characteristics of the photovoltaic module, such as component size, power, voltage, current, etc.

[0024] In this embodiment, the set of component state parameters is a group of parameters that describe the current state of a photovoltaic component, which may include the degree of dirt, surface damage, temperature, humidity, etc.

[0025] In this embodiment, parameter classification is to classify the parameters in the set of component state parameters according to certain criteria. For example, they are classified into mild, moderate, and severe pollution according to the degree of dirt.

[0026] In this embodiment, intelligent planning is to use algorithms and intelligent technologies to automatically plan the optimal cleaning path based on the parameter classification results and information such as the layout of the photovoltaic components.

[0027] In this embodiment, the cleaning path is the trajectory of the cleaning equipment moving on the surface of the photovoltaic component, aiming to ensure that the cleaning process covers comprehensively and efficiently.

[0028] In this embodiment, the self-cleaning strategy is a specific plan formulated based on the cleaning path and component state parameters to guide the self-cleaning process of the photovoltaic component, including the cleaning method, cleaning intensity, cleaning frequency, etc.

[0029] In this embodiment, the self-cleaning of photovoltaic components is to use automated equipment and self-cleaning strategies to clean the photovoltaic components regularly or on demand to maintain the cleanliness and power generation efficiency of their surfaces.

[0030] In this embodiment, the key parameters are important parameters that need to be monitored in real time during the self-cleaning process of the photovoltaic component, such as the water consumption of the cleaning equipment, cleaning speed, and component temperature change.

[0031] In this embodiment, the comprehensive evaluation is to comprehensively analyze the cleaning results and the key parameters monitored in real time to evaluate the effectiveness of the self-cleaning strategy and the safety of the cleaning process.

[0032] In this embodiment, strategy optimization is to adjust and improve the self-cleaning strategy according to the comprehensive evaluation results to improve the cleaning efficiency, reduce costs, and extend the service life of the photovoltaic component.

[0033] The beneficial effects of the above technical solution are as follows: By combining the component parameters and component state parameters of the photovoltaic component to plan the cleaning path of the photovoltaic component, a self-cleaning strategy is obtained, and the self-cleaning strategy is adjusted and optimized, so as to perform self-cleaning, and the self-cleaning process is monitored in real time, and the strategy is optimized in time, which can make the self-cleaning of the target photovoltaic component more accurate and timely.

[0034] Embodiment 2: Based on Embodiment 1, the sensing and detection module includes: The image detection unit: It is used to perform intelligent identification on the target photovoltaic component based on a preset sensor, and perform image display based on the intelligent identification result to obtain the first component image; Parameter extraction unit: used to extract the component parameters of the target photovoltaic component based on the first component image; Status determination unit: used to determine the set of status parameters of the target photovoltaic component from the first component image based on the image analysis method.

[0035] In this embodiment, the preset sensor is a device pre-installed in the photovoltaic power station for monitoring and collecting data related to photovoltaic components. The sensor may include a high-definition camera, an infrared sensor, a temperature sensor, a humidity sensor, etc., and can capture key information such as the image, temperature, and humidity of the photovoltaic component.

[0036] In this embodiment, intelligent recognition uses technologies such as image processing and machine vision to automatically recognize and analyze the images captured by the preset sensor to obtain information such as the accurate position and shape of the photovoltaic component.

[0037] In this embodiment, the first component image is a preliminary image of the photovoltaic component obtained through intelligent recognition technology, and this image shows the appearance of the component and the possible dirt distribution.

[0038] In this embodiment, the component parameters are parameters describing the physical and electrical characteristics of the photovoltaic component, such as component size, power, voltage, current, etc.

[0039] In this embodiment, the set of component status parameters is a set of parameters describing the current status of the photovoltaic component, and may include dirt degree, surface damage, temperature, humidity, etc.

[0040] The beneficial effect of the above technical solution is that by obtaining the component parameters and component status parameters of the target photovoltaic component in real time, the judgment of the cleaning path of the target photovoltaic component can be made more accurate, so that the self-cleaning of the target photovoltaic component is more accurate.

[0041] Embodiment 3: Based on Embodiment 2, the cleaning planning module includes: Parameter classification unit: used to classify each parameter in the set of component status parameters to obtain the first classification result; Classification judgment unit: used to judge the first classification result based on the preset dirt level to obtain the dirt result of the target photovoltaic component; Path planning unit: used to plan the component cleaning path matching the target photovoltaic component according to the dirt result of the target photovoltaic component combined with the first classification result.

[0042] In this embodiment, the set of component status parameters is the parameter describing the current status of the photovoltaic component, including dirt degree, surface damage, temperature, humidity, dust type, snow accumulation (if applicable), and other factors that may affect the performance of the component.

[0043] In this embodiment, parameter classification is to classify each parameter in the set of component status parameters according to its specific criteria or thresholds. For example, the degree of fouling can be classified according to the thickness of the dirt, the coverage area, or the degree of its impact on the performance of the photovoltaic module.

[0044] In this embodiment, the preset fouling level is a set of predefined fouling level criteria used to evaluate the fouling degree of the photovoltaic module. It is based on factors such as the coverage area, thickness of the dirt, and the degree of impact on the power generation efficiency. The preset fouling level is an important basis for determining whether the component needs to be cleaned and the cleaning priority.

[0045] In this embodiment, the first classification result is the output of the parameter classification process, that is, the result of classifying each parameter in the set of component status parameters according to its specific criteria or thresholds. The first classification result transforms the status parameters of the component into a series of comparable and evaluable categories, providing a basis for subsequent determination of the fouling result and planning of the cleaning path.

[0046] In this embodiment, the fouling result of the target photovoltaic module is the conclusion obtained by judging the first classification result based on the preset fouling level. It describes the current fouling degree and level of the target photovoltaic module. The fouling result will directly affect the planning of the cleaning path and the formulation of the cleaning strategy.

[0047] In this embodiment, the component cleaning path is the trajectory of the cleaning equipment moving on the surface of the component planned according to the fouling result and the first classification result of the target photovoltaic module. The cleaning path aims to ensure that the cleaning equipment can fully cover the surface of the component while optimizing the cleaning efficiency according to the fouling degree and type. The planning of the cleaning path may involve comprehensive consideration of factors such as the component layout, dirt distribution, and cleaning equipment performance.

[0048] The beneficial effect of the above technical solution is that by judging the first classification result, the fouling result of the target photovoltaic module is obtained, and the component cleaning path is determined, which can make the judgment of the cleaning path of the target photovoltaic module more accurate, thus making the self-cleaning of the target photovoltaic module more accurate.

[0049] Embodiment 4: Based on Embodiment 3, the path planning unit includes: The information integration sub-unit: used to integrate the first classification result, the fouling result, the cleaning performance of the equipment, and the component parameters of the target photovoltaic module to obtain the first comprehensive information; The path optimization sub-unit: used to input the first comprehensive information into the preset path optimization algorithm and determine the first initial cleaning path of the target photovoltaic module in combination with the component cleaning requirements; Path verification subunit: used to verify the feasibility of the path based on the first initial cleaning path. If the first initial cleaning path is feasible, the first initial cleaning path of the target photovoltaic module is used as the module cleaning path.

[0050] In this embodiment, the dirt result is a conclusion obtained by judging the dirt degree of the target photovoltaic module based on a preset dirt level standard.

[0051] In this embodiment, the equipment cleaning performance refers to the capabilities or characteristics exhibited by the cleaning equipment when performing cleaning tasks, including cleaning efficiency, cleaning quality, equipment stability, energy consumption, etc. The quality of the equipment cleaning performance directly affects the planning of the cleaning path and the cleaning effect.

[0052] In this embodiment, the module parameters are parameters describing the physical and electrical characteristics of the target photovoltaic module, such as module size, power, voltage, current, etc.

[0053] In this embodiment, the first comprehensive information is the information obtained by integrating the first classification result, dirt result, equipment cleaning performance, and module parameters of the target photovoltaic module. The first comprehensive information provides comprehensive input data for the subsequent path optimization algorithm, which helps to generate a more reasonable and efficient cleaning path.

[0054] In this embodiment, the preset path optimization algorithm is a mathematical model or algorithm for planning the cleaning path. It automatically generates an optimal or sub-optimal cleaning path according to the input comprehensive information (such as the first comprehensive information) and cleaning requirements. The preset path optimization algorithm may involve various optimization strategies, such as minimizing the cleaning time and maximizing the cleaning efficiency.

[0055] In this embodiment, the first initial cleaning path is the preliminary cleaning path generated by the preset path optimization algorithm according to the first comprehensive information and cleaning requirements. The first initial cleaning path may need to be further verified and adjusted to ensure its feasibility and effectiveness.

[0056] In this embodiment, the path feasibility verification is the process of verifying the first initial cleaning path, aiming to confirm whether the cleaning path can effectively cover all areas to be cleaned without damaging the module. The path feasibility verification may involve comprehensive consideration of factors such as cleaning equipment performance, module layout, and dirt distribution.

[0057] In this embodiment, the module cleaning path is the final cleaning path of the target photovoltaic module determined after the path feasibility verification.

[0058] The beneficial effects of the above technical solution are: By optimizing and verifying the module cleaning path, the judgment of the cleaning path for the target photovoltaic module can be made more accurate, so that the self-cleaning of the target photovoltaic module is more accurate.

[0059] Example 5: Based on Example 3, the policy execution module includes: Initial cleaning unit: used to screen the corresponding self-cleaning method based on the component status parameters of the target photovoltaic component, and obtain the initial cleaning method and cleaning intensity of the target photovoltaic component; Policy initial unit: used to synthesize the component cleaning path with the initial cleaning method and cleaning intensity to obtain the initial cleaning policy of the target photovoltaic component; First adjustment unit: used to adjust the initial cleaning policy based on the component cleaning requirements of the target photovoltaic component to obtain the first cleaning policy; First optimization unit: used to obtain the real-time external environment of the target photovoltaic component and determine whether the real-time external environment will affect the first cleaning policy of the target photovoltaic component; If the real-time external environment is normal, then use the first cleaning policy of the target photovoltaic component as the self-cleaning policy; Otherwise, it is necessary to optimize the first cleaning policy of the target photovoltaic component based on the real-time external environment to obtain the self-cleaning policy of the target photovoltaic component; Self-cleaning unit: used to perform self-cleaning based on the self-cleaning policy of the target photovoltaic component.

[0060] In this embodiment, the self-cleaning method refers to the specific method or technology used to clean the photovoltaic component, such as water spray cleaning, mechanical brushing, ultrasonic cleaning, etc. Different self-cleaning methods are applicable to different component states and environmental conditions.

[0061] In this embodiment, the cleaning intensity refers to the magnitude of the force or energy used during the cleaning process, such as water flow rate, brushing intensity, ultrasonic power, etc. The cleaning intensity directly affects the cleaning effect and the safety of the component.

[0062] In this embodiment, the component cleaning path is the trajectory of the cleaning equipment moving on the surface of the photovoltaic component, aiming to ensure that the cleaning covers comprehensively and efficiently. The cleaning path is usually planned according to factors such as component layout and dirt distribution.

[0063] In this embodiment, the initial cleaning policy is the preliminary cleaning plan obtained by synthesizing the component cleaning path with the initial cleaning method and cleaning intensity.

[0064] In this embodiment, the component cleaning requirement refers to the cleaning requirement that the photovoltaic component needs to perform due to dirt accumulation, performance degradation, etc., and varies depending on factors such as component type, installation environment, and operation time.

[0065] In this embodiment, the first cleaning policy is the cleaning plan obtained by adjusting the initial cleaning policy based on the component cleaning requirements of the target photovoltaic component.

[0066] In this embodiment, the real-time external environment refers to the natural conditions such as weather, temperature, humidity, wind speed, etc. in which the target photovoltaic module is currently located. These conditions may affect the implementation and effectiveness of the cleaning strategy.

[0067] In this embodiment, strategy optimization is a process of adjusting and improving the first cleaning strategy according to the real-time external environment. The purpose of strategy optimization is to ensure that the cleaning strategy remains effective and safe under specific environmental conditions.

[0068] In this embodiment, the self-cleaning strategy is a final determined solution for guiding the self-cleaning process of the photovoltaic module after adjustment and optimization. It comprehensively considers various factors such as the component state, cleaning requirements, real-time external environment, etc.

[0069] In this embodiment, self-cleaning is to use automated equipment and the self-cleaning strategy to clean the photovoltaic module regularly or on demand to maintain the cleanliness and power generation efficiency of its surface.

[0070] The beneficial effects of the above technical solution are: By adjusting and optimizing the self-cleaning strategy and then performing self-cleaning, the self-cleaning of the target photovoltaic module can be made more accurate and timely.

[0071] Embodiment 6: Based on Embodiment 5, the monitoring and feedback module includes: Parameter monitoring unit: Used to, during the self-cleaning process, based on preset sensors, monitor the key cleaning parameters of the target photovoltaic module in real time to obtain a set of key cleaning parameters of the target photovoltaic module; Parameter classification unit: Used to classify the set of key cleaning parameters according to the parameter types of the key cleaning parameters to obtain a first classified parameter set; Curve fitting unit: Used to input each first classified parameter subset in the first classified parameter set into the same coordinate system and perform curve fitting to obtain a cleaning parameter curve corresponding to each first classified parameter subset; First evaluation unit: Used to obtain a first evaluation index for the target photovoltaic module based on the curve trend of the cleaning parameter curve; Second state unit: Used to, after the self-cleaning of the target photovoltaic module is completed, obtain the second component image of the target photovoltaic module in real time and extract the second state parameters of the target photovoltaic module from the second component image based on a preset image analysis method; Second evaluation unit: Used to combine the second state parameters of the target photovoltaic module with the component parameters of the target photovoltaic module to obtain a second evaluation index for the target photovoltaic module; Comprehensive evaluation unit: Used to comprehensively evaluate the first evaluation index and the second evaluation index to obtain a comprehensive evaluation result for the target photovoltaic module; Evaluation and Optimization Unit: used to determine whether the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result falls within the standard evaluation range of the target photovoltaic module, there is no need to optimize the self-cleaning strategy of the target photovoltaic module; Otherwise, it is necessary to optimize the self-cleaning strategy of the target photovoltaic module to obtain the optimal self-cleaning strategy.

[0072] In this embodiment, the self-cleaning process refers to the process of cleaning the photovoltaic module using automated equipment and a preset self-cleaning strategy. This process aims to keep the surface of the module clean to improve power generation efficiency.

[0073] In this embodiment, the preset sensors are devices installed in the photovoltaic power station for real-time monitoring of key parameters of the photovoltaic module during the cleaning process. These sensors may include temperature sensors, humidity sensors, pressure sensors, flow sensors, etc.

[0074] In this embodiment, the cleaning key parameters are parameters directly related to the cleaning effect of the target photovoltaic module, such as cleaning liquid flow rate, cleaning time, cleaning temperature, etc., which are real-time monitored by the preset sensors during the self-cleaning process.

[0075] In this embodiment, the set of cleaning key parameters is the set of all cleaning key parameters, which reflects the overall situation of the self-cleaning process.

[0076] In this embodiment, the first classification parameter set is a subset of parameters obtained by classifying the set of cleaning key parameters according to the parameter types of the cleaning key parameters (such as temperature, flow rate, time, etc.).

[0077] In this embodiment, the first classification parameter subset is the set of parameters belonging to the same parameter type in the first classification parameter set.

[0078] In this embodiment, curve fitting is to process the data points of the first classification parameter subset in the same coordinate system to obtain a curve that can best describe the corresponding distribution trend of the cleaning key parameters of each parameter type.

[0079] In this embodiment, the cleaning parameter curve is obtained through curve fitting and reflects the distribution trend of the data points of the first classification parameter subset.

[0080] In this embodiment, the first evaluation index is the preliminary evaluation result of the effect of the self-cleaning process based on the trend of the cleaning parameter curve.

[0081] In this embodiment, the second component image is the component image obtained in real time by the image acquisition device after the self-cleaning of the target photovoltaic module is completed.

[0082] In this embodiment, the preset image analysis method is a series of image processing techniques and algorithms for extracting target photovoltaic module status parameters from the second module image.

[0083] In this embodiment, the second status parameter is a set of parameters extracted from the second module image through the preset image analysis method, reflecting the status of the target photovoltaic module after cleaning.

[0084] In this embodiment, the second evaluation index is the comprehensive evaluation result of the cleaning effect by combining the second status parameter of the target photovoltaic module with its module parameters.

[0085] In this embodiment, the comprehensive evaluation result is obtained by combining the first evaluation index and the second evaluation index, and is the evaluation result of the overall effect of the self-cleaning process of the target photovoltaic module.

[0086] In this embodiment, the standard evaluation range is preset, and is a threshold or range for judging whether the comprehensive evaluation result is qualified.

[0087] In this embodiment, strategy optimization is a process of adjusting and improving the self-cleaning strategy of the target photovoltaic module according to the comprehensive evaluation result, aiming to improve the cleaning effect and efficiency.

[0088] In this embodiment, the optimal self-cleaning strategy is obtained through strategy optimization and is a self-cleaning scheme that can achieve the best cleaning effect under specific conditions.

[0089] The beneficial effects of the above technical solutions are: by monitoring the self-cleaning process in real time, performing strategy optimization in a timely manner, and obtaining the optimal self-cleaning strategy for photovoltaic module self-cleaning, the self-cleaning of the target photovoltaic module can be made more accurate and timely.

[0090] Embodiment 7: Based on Embodiment 6, the evaluation and optimization unit includes: Evaluation and judgment sub-unit: used to judge whether the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is used as the optimal self-cleaning strategy; If the comprehensive evaluation result is not within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is used as the first self-cleaning strategy; Difference evaluation sub-unit: used to judge the difference between the first self-cleaning strategy and the corresponding boundary evaluation value of the standard evaluation range, so as to obtain the comprehensive difference of the first self-cleaning strategy; Policy Optimization Subunit: It is used to screen a policy optimization solution that matches the first self-cleaning policy from a preset policy optimization database based on the comprehensive difference of the first self-cleaning policy, and perform policy optimization on the first self-cleaning policy based on the policy optimization solution to obtain the optimal self-cleaning policy.

[0091] In this embodiment, the comprehensive evaluation result: This is the conclusion obtained after a multi-faceted evaluation (such as cleaning effect, efficiency, energy consumption, etc.) of the self-cleaning process of the target photovoltaic module. It is usually a quantitative index or a set of indexes used to comprehensively reflect the actual effect of the cleaning policy.

[0092] In this embodiment, the standard evaluation range is preset and is a threshold range used to judge whether the comprehensive evaluation result is qualified. It may be a specific numerical interval or a combined judgment criterion based on multiple evaluation indexes.

[0093] In this embodiment, the optimal self-cleaning policy is determined after evaluation and optimization, and is a self-cleaning solution that can achieve the best balance of cleaning effect, efficiency, and energy consumption under specific conditions. It represents the optimal solution of the cleaning policy under the current conditions.

[0094] In this embodiment, the first self-cleaning policy is the self-cleaning policy that is retained as the basis for further optimization if the comprehensive evaluation result does not meet the standard evaluation range after preliminary evaluation. It may contain some effective cleaning methods and parameters, but needs to be adjusted to achieve a better effect.

[0095] In this embodiment, the corresponding boundary evaluation value is the boundary value of the standard evaluation range and is used to judge whether the comprehensive evaluation result or the first self-cleaning policy is close to or exceeds the qualified range. It helps to determine the direction and degree of optimization.

[0096] In this embodiment, the comprehensive difference of the first self-cleaning policy is the difference amount obtained by comparing the first self-cleaning policy with the corresponding boundary evaluation value of the standard evaluation range.

[0097] In this embodiment, the preset policy optimization database is a database that stores a variety of policy optimization solutions and corresponding conditions. These solutions are obtained based on historical data, expert experience, simulation and other methods, aiming to provide optimization suggestions for cleaning policies under different conditions.

[0098] In this embodiment, the policy optimization solution is a set of specific suggestions or measures screened from the preset policy optimization database that matches the first self-cleaning policy and can improve its comprehensive difference. It may include adjusting cleaning parameters, changing cleaning methods, adding auxiliary equipment, etc.

[0099] In this embodiment, policy optimization is the process of adjusting and improving the first self-cleaning policy based on the policy optimization solution.

[0100] The beneficial effects of the above technical solution are as follows: By monitoring the self-cleaning process in real time and conducting cleaning evaluation, the strategy can be optimized in a timely manner, and the optimal self-cleaning strategy can be obtained for the self-cleaning of photovoltaic modules, making the self-cleaning of the target photovoltaic modules more accurate and timely.

[0101] Example 8: An embodiment of the present invention provides an intelligent self-cleaning method for photovoltaic modules, as Figure 2 shown, including: Step 1: Based on a preset sensor, perform intelligent identification on the target photovoltaic module to obtain a first module image, and determine the module parameters and the set of module state parameters of the target photovoltaic module based on the first module image; Step 2: Classify the module state parameters in the set of module state parameters, and intelligently plan the corresponding cleaning path based on the parameter classification result; Step 3: Determine the self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the module state parameters, and perform self-cleaning of the photovoltaic module based on the self-cleaning strategy; Step 4: Monitor the key parameters of the self-cleaning of the photovoltaic module in real time, and conduct comprehensive evaluation in combination with the cleaning result, so as to optimize the self-cleaning strategy.

[0102] The beneficial effects of the above technical solution are as follows: By combining the module parameters and the module state parameters of the photovoltaic module to plan the cleaning path of the photovoltaic module, the self-cleaning strategy can be obtained, and the self-cleaning strategy can be adjusted and optimized, so as to perform self-cleaning, monitor the self-cleaning process in real time, and optimize the strategy in a timely manner, making the self-cleaning of the target photovoltaic module more accurate and timely.

[0103] 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 foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An intelligent self-cleaning device for photovoltaic modules, characterized in that, Including: Sensing and detection module: used to intelligently identify a target photovoltaic module based on a preset sensor, obtain a first module image, and determine the module parameters and the set of module status parameters of the target photovoltaic module based on the first module image; Cleaning planning module: used to classify parameters based on the module status parameters in the set of module status parameters, and intelligently plan a corresponding cleaning path based on the parameter classification result; Strategy execution module: used to determine the self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the module status parameters, and perform self-cleaning of the photovoltaic module based on the self-cleaning strategy; Monitoring and feedback module: used to monitor the key parameters of the self-cleaning of the photovoltaic module in real time, and conduct a comprehensive evaluation in combination with the cleaning result, so as to optimize the self-cleaning strategy.

2. The intelligent self-cleaning device for photovoltaic modules according to claim 1, wherein Sensing and detection module, including: Image detection unit: used to intelligently identify a target photovoltaic module based on a preset sensor, and perform image display based on the intelligent identification result to obtain a first module image; Parameter extraction unit: used to extract the module parameters of the target photovoltaic module based on the first module image; Status determination unit: used to determine the set of status parameters of the target photovoltaic module from the first module image based on an image analysis method.

3. The intelligent self-cleaning device for photovoltaic modules according to claim 2, characterized in that, Cleaning planning module, including: Parameter classification unit: used to classify each parameter in the set of module status parameters to obtain a first classification result; Classification judgment unit: used to judge the first classification result based on a preset dirt level to obtain the dirt result of the target photovoltaic module; Path planning unit: used to plan a module cleaning path matching the target photovoltaic module according to the dirt result of the target photovoltaic module in combination with the first classification result.

4. The intelligent self-cleaning device for photovoltaic modules according to claim 3, wherein, Path planning unit, including: Information integration sub-unit: used to integrate the first classification result, dirt result, equipment cleaning performance of the target photovoltaic module and the module parameters of the target photovoltaic module to obtain first comprehensive information; Path optimization sub-unit: used to input the first comprehensive information into a preset path optimization algorithm, and determine the first initial cleaning path of the target photovoltaic module in combination with the module cleaning requirements; Path verification sub-unit: used to perform path feasibility verification based on the first initial cleaning path. If the first initial cleaning path is feasible, the first initial cleaning path of the target photovoltaic module is used as the module cleaning path.

5. An intelligent self-cleaning device for photovoltaic modules according to claim 3, characterized in that, Strategy execution module, including: Initial cleaning unit: used to screen the corresponding self-cleaning method based on the module status parameters of the target photovoltaic module to obtain the initial cleaning method and cleaning intensity of the target photovoltaic module; Strategy initial unit: used to integrate the module cleaning path with the initial cleaning method and cleaning intensity to obtain the initial cleaning strategy of the target photovoltaic module; First adjustment unit: used to adjust the initial cleaning strategy based on the module cleaning requirements of the target photovoltaic module to obtain a first cleaning strategy; First optimization unit: used to obtain the real-time external environment of the target photovoltaic module and judge whether the real-time external environment will affect the first cleaning strategy of the target photovoltaic module; If the real-time external environment is in a normal situation, the first cleaning strategy of the target photovoltaic module is used as the self-cleaning strategy; Conversely, it is necessary to optimize the first cleaning strategy of the target photovoltaic module based on the real-time external environment to obtain the self-cleaning strategy of the target photovoltaic module; Self-cleaning unit: for self-cleaning based on the self-cleaning strategy of the target photovoltaic module.

6. The intelligent self-cleaning device for photovoltaic modules according to claim 5, characterized in that, Monitoring and feedback module, including: Parameter monitoring unit: for, during the self-cleaning process, based on preset sensors, to monitor in real time the key cleaning parameters of the target photovoltaic module, and obtain the set of key cleaning parameters of the target photovoltaic module; Parameter classification unit: for classifying the set of key cleaning parameters according to the parameter types of the key cleaning parameters, to obtain the first classified parameter set; Curve fitting unit: for inputting each first classified parameter subset in the first classified parameter set into the same coordinate system and performing curve fitting, so as to obtain the cleaning parameter curve corresponding to each first classified parameter subset; First evaluation unit: for obtaining the first evaluation index of the target photovoltaic module based on the curve trend of the cleaning parameter curve; Second status unit: for, after the self-cleaning of the target photovoltaic module is completed, to obtain in real time the second component image of the target photovoltaic module, and based on a preset image analysis method, to extract the second status parameters of the target photovoltaic module from the second component image; Second evaluation unit: for combining the second status parameters of the target photovoltaic module with the component parameters of the target photovoltaic module, so as to obtain the second evaluation index of the target photovoltaic module; Comprehensive evaluation unit: for comprehensively evaluating the first evaluation index and the second evaluation index, so as to obtain the comprehensive evaluation result of the target photovoltaic module; Evaluation and optimization unit: for judging whether the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module, there is no need to optimize the self-cleaning strategy of the target photovoltaic module; Conversely, it is necessary to optimize the self-cleaning strategy of the target photovoltaic module to obtain the optimal self-cleaning strategy.

7. An intelligent self-cleaning device for photovoltaic modules according to claim 6, characterized in that, Evaluation and optimization unit, including: Evaluation and judgment sub-unit: for judging whether the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module; If the comprehensive evaluation result is within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is taken as the optimal self-cleaning strategy; If the comprehensive evaluation result is not within the standard evaluation range of the target photovoltaic module, the self-cleaning strategy of the target photovoltaic module is taken as the first self-cleaning strategy; Difference evaluation sub-unit: for judging the difference between the first self-cleaning strategy and the corresponding boundary evaluation value of the standard evaluation range, so as to obtain the comprehensive difference of the first self-cleaning strategy; Strategy optimization sub-unit: for screening, based on the comprehensive difference of the first self-cleaning strategy, a strategy optimization plan that matches the first self-cleaning strategy from a preset strategy optimization database, and optimizing the first self-cleaning strategy based on the strategy optimization plan to obtain the optimal self-cleaning strategy.

8. An intelligent self-cleaning method for photovoltaic modules, characterized in that, Including: Step 1: Based on preset sensors, perform intelligent identification on the target photovoltaic module to obtain the first component image, and based on the first component image, determine the component parameters and the set of component status parameters of the target photovoltaic module; Step 2: Classify the parameters based on the component status parameters in the component status parameter set, and intelligently plan the corresponding cleaning path based on the parameter classification results; Step 3: Determine the self-cleaning strategy of the target photovoltaic module by combining the cleaning path with the component status parameters, and perform self-cleaning of the photovoltaic module based on the self-cleaning strategy; Step 4: Real-time monitor the key parameters of the self-cleaning of the photovoltaic module, and conduct a comprehensive evaluation in combination with the cleaning results, so as to optimize the self-cleaning strategy.

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