A method and system for automatically cleaning photovoltaic panels

Abstract

The present application relates to the field of solar photovoltaic power generation operation and maintenance, and discloses a photovoltaic panel automatic cleaning method and system, through constructing a multi-dimensional perception network, real-time synchronous collection and preprocessing of environmental meteorological data, photovoltaic component operation data and cleaning equipment state data are realized; based on data comprehensive evaluation, the current pollution level of the photovoltaic component is determined, and based on meteorological operation condition analysis, the suitability index is obtained, and then the operation time window is determined; the thermal shock safety of the component surface temperature state is analyzed, the component thermal shock safety margin is calculated, and the safety of the equipment contact operation is judged accordingly; based on the judgment result, the pollution level and the time window, the cleaning operation mode and the initial operation parameters are matched and selected from the preset strategy library; finally, the running parameters are adaptively corrected combined with real-time data, the correction factor and the instruction set are generated, the cleaning equipment is controlled to execute adaptive accurate cleaning action, and the safety and efficiency of the operation can be significantly improved.

Description

Technical Field

[0001] This invention relates to the field of solar photovoltaic power generation operation and maintenance technology, and more specifically to an automatic cleaning method and system for photovoltaic panels. Background Technology

[0002] Currently, automatic cleaning technology for photovoltaic modules has been widely applied in the daily maintenance of photovoltaic power plants. Existing technical solutions typically integrate data acquisition modules and execution control modules, which can continuously monitor the operating conditions of photovoltaic modules and changes in the external environment. Based on ambient temperature, irradiance, module backsheet temperature, and the status information of the equipment itself, these systems establish corresponding calculation models to assess the necessity and timing of cleaning operations, and then control the cleaning equipment to perform automated cleaning operations such as roller brush cleaning or water rinsing, so as to achieve timely removal of contaminants on the surface of photovoltaic panels and maintain power generation efficiency.

[0003] However, existing automatic cleaning methods for photovoltaic panels still have certain limitations in practical applications: On the one hand, existing cleaning control logic focuses on the suitability of environmental meteorological conditions, often ignoring the thermal shock effect generated when the cleaning medium comes into contact with the surface of the photovoltaic module. It lacks quantitative safety analysis of the surface temperature of the module and environmental thermal stress, which may damage the photovoltaic module due to excessive thermal stress caused by sudden changes in the surface temperature of the module when cleaning is carried out under specific working conditions such as high temperature or strong radiation. On the other hand, the existing cleaning operation process is usually controlled by preset fixed parameters, lacking a mechanism to adaptively correct the cleaning operation parameters according to real-time environmental meteorological data and the operating status of the module during the operation. This results in poor dynamic adaptability of the cleaning strategy, making it difficult to ensure both cleaning efficiency and equipment operation safety under complex and ever-changing actual working conditions. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an automatic cleaning method and system for photovoltaic panels to solve the problems existing in the background art.

[0005] This invention provides the following technical solution: an automatic cleaning method for photovoltaic panels, comprising: S1: By constructing a multi-dimensional sensing network, environmental meteorological data, photovoltaic module operation data and cleaning equipment status data are collected in real time and synchronously, and the collected data is preprocessed. S2: Based on the pre-processed environmental meteorological data, photovoltaic module operation data and cleaning equipment status data, a comprehensive analysis and evaluation of the surface pollution level of the photovoltaic module is conducted to determine the current pollution level of the photovoltaic module; S3: Based on the preprocessed environmental meteorological data, conduct meteorological operation suitability analysis on meteorological operation conditions to obtain the meteorological suitability index, and determine the operation time window based on the meteorological suitability index; S4: Based on the pre-processed photovoltaic module operating data, perform thermal shock safety analysis on the module surface temperature state to obtain the module thermal shock safety margin. Based on the module thermal shock safety margin, determine the safety of equipment contact operations and obtain the module contact safety determination result. S5: Based on the component contact safety judgment result, the current pollution level and the operation time window, perform a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value, and select the corresponding cleaning operation mode and initial operation parameters. S6: Based on the initial operating parameters, pre-processed photovoltaic module operating data, and pre-processed environmental meteorological data, adaptive correction analysis is performed on the cleaning operating parameters to obtain the operating correction factor, generate a cleaning execution instruction set, and control the cleaning equipment to perform cleaning actions.

[0006] Preferably, step S1 involves constructing a multi-dimensional sensing network to synchronously collect environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time, including: A multi-dimensional sensing network is constructed by deploying an environmental monitoring sensor array, a photovoltaic string monitoring unit, and an onboard sensing terminal for cleaning equipment. The Internet of Things (IoT) communication protocol is used to synchronize time and interact data between nodes in the network, and to collect environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time. The environmental meteorological data includes wind speed, ambient temperature, air humidity, and solar irradiance. The photovoltaic module operation data includes module output current, output voltage, output power, and module surface temperature. The cleaning equipment status data includes the real-time position coordinates of the equipment, operating attitude angle, remaining battery power, and actuator status. The preprocessing of the collected data specifically includes: outlier removal, noise filtering, timestamp alignment, and normalization of the raw data.

[0007] Preferably, step S2 involves a comprehensive analysis and evaluation of the surface contamination level of the photovoltaic module to determine its current contamination level, specifically including: Based on the solar irradiance and ambient temperature in the preprocessed environmental meteorological data, the theoretical output power is calculated in combination with the preset photoelectric conversion parameters of the photovoltaic module; the actual output power is calculated based on the output current and output voltage in the preprocessed photovoltaic module operating data; the theoretical output power is dynamically corrected by combining the preset module aging attenuation coefficient and line transmission loss parameters to obtain the corrected benchmark output power; the difference between the corrected benchmark output power and the actual output power is calculated to obtain the real-time pollution index; the real-time pollution index is compared with the preset pollution level threshold range to determine the numerical range of the real-time pollution index, and the pollution level corresponding to the numerical range is determined as the current pollution level of the photovoltaic module.

[0008] Preferably, step S3 involves performing a meteorological suitability analysis on the meteorological operation conditions to obtain a meteorological suitability index, and determining the operation time window based on the meteorological suitability index, specifically including: Based on the wind speed, ambient temperature, air humidity and solar irradiance in the preprocessed environmental meteorological data, each meteorological parameter is substituted into the preset meteorological suitability membership function for calculation to obtain the corresponding wind speed suitability score, temperature suitability score, humidity suitability score and light suitability score. Based on the degree of influence of each meteorological parameter on the safety and effectiveness of cleaning operations, corresponding weight coefficients are set, and the suitability scores are weighted and summed to obtain the meteorological suitability index. The meteorological suitability index is compared with the preset operation threshold in chronological order to identify the start and end times when the meteorological suitability index continuously meets the operation conditions. The duration between the start and end times is calculated. When the duration meets the preset minimum operation time requirement, the time period between the start and end times is determined as the operation time window.

[0009] Preferably, step S4 performs a thermal shock safety analysis on the surface temperature of the component to obtain the component's thermal shock safety margin, and makes a safety judgment on the equipment contact operation based on the component's thermal shock safety margin to obtain the component contact safety judgment result, specifically including: Based on the module surface temperature in the pre-processed photovoltaic module operation data, the absolute temperature difference between the module surface temperature and the preset cleaning medium reference temperature is calculated; the solar irradiance in the pre-processed environmental meteorological data is obtained, and the corresponding environmental thermal stress correction coefficient is determined according to the mapping relationship between solar irradiance and the preset irradiance-correction coefficient; the absolute temperature difference is multiplied by the environmental thermal stress correction coefficient to obtain the equivalent thermal shock impact value. The ratio of the equivalent thermal shock impact value to the preset thermal shock safety threshold is calculated to obtain the thermal shock utilization ratio; the difference between the preset unit value and the thermal shock utilization ratio is used to calculate the component thermal shock safety margin. The component thermal shock safety margin is compared with the preset safety margin judgment threshold. If the component thermal shock safety margin is greater than the preset safety margin judgment threshold, the component contact operation is judged to be safe and a component contact safety judgment result that allows the operation is generated. Otherwise, the component contact operation is judged to be unsafe and a component contact safety judgment result that prohibits the operation is generated.

[0010] Preferably, step S5 performs a comprehensive matching analysis on the preset cleaning strategy library to obtain a cleaning strategy matching value, and selects the corresponding cleaning operation mode and initial operation parameters, specifically including: If the component contact safety assessment result indicates that operation is permitted, then candidate cleaning strategies that meet the current safety status are selected from the preset cleaning strategy library; for each candidate cleaning strategy, the matching degree between the current pollution level and the preset applicable pollution level of the candidate cleaning strategy is calculated to obtain a pollution matching score; the matching degree between the duration of the operation time window and the preset operation time requirement of the candidate cleaning strategy is calculated to obtain a time matching score; according to the preset weight allocation coefficient, the pollution matching score and the time matching score are weighted and summed to obtain the cleaning strategy matching value corresponding to each candidate cleaning strategy; the candidate cleaning strategy with the highest cleaning strategy matching value is determined as the target cleaning strategy, and the cleaning operation mode and initial operation parameters corresponding to the target cleaning strategy are extracted; If the component contact safety determination result indicates that operation is prohibited, the comprehensive matching analysis process of the preset cleaning strategy library will be terminated, and the photovoltaic module automatic cleaning system will be controlled to maintain the current non-operational state or enter the safety waiting mode based on the component contact safety determination result of prohibited operation.

[0011] Preferably, step S6 involves adaptively correcting the cleaning operation parameters to obtain an operation correction factor, generating a cleaning execution instruction set, and controlling the cleaning equipment to perform cleaning actions. Specifically, this includes: The initial operating parameters are obtained, and combined with the real-time wind speed and real-time irradiance fluctuations in the pre-processed environmental meteorological data, as well as the real-time temperature of the photovoltaic modules in the pre-processed photovoltaic module operation data, the operating condition deviation between the current actual operating conditions and the preset standard operating conditions corresponding to the initial operating parameters is calculated. The operating condition deviation value is input into a preset adaptive parameter correction model to calculate the operation correction factor; the operation correction factor is used to dynamically compensate for the cleaning operation intensity and speed. The initial operation parameters are corrected and updated item by item using the operation correction factor to generate a cleaning execution instruction set that includes the target cleaning pressure, target travel speed and target water spray volume. The cleaning execution instruction set is sent to the control terminal of the cleaning equipment to drive the cleaning equipment to perform real-time adaptive cleaning actions according to the cleaning execution instruction set and the cleaning operation mode.

[0012] To achieve the above objectives, the present invention provides the following technical solution: an automatic photovoltaic panel cleaning system, comprising implementing the above-mentioned automatic photovoltaic panel cleaning method, including: Multi-dimensional sensing data acquisition module: By constructing a multi-dimensional sensing network, it synchronously collects environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time, and preprocesses the collected data; Pollution Level Analysis and Assessment Module: Based on pre-processed environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data, this module comprehensively analyzes and assesses the degree of surface pollution of photovoltaic modules to determine the current pollution level of the photovoltaic modules. Meteorological suitability analysis module: Based on preprocessed environmental meteorological data, it performs meteorological operation suitability analysis on meteorological operation conditions, obtains meteorological suitability index, and determines operation time window based on meteorological suitability index; Thermal shock safety assessment module: Based on the pre-processed photovoltaic module operating data, the module surface temperature state is analyzed for thermal shock safety to obtain the module thermal shock safety margin. Based on the module thermal shock safety margin, the safety of equipment contact operations is assessed to obtain the module contact safety assessment result. Cleaning strategy matching module: Based on the component contact safety judgment result, the current pollution level and the operation time window, the module performs a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value and select the corresponding cleaning operation mode and initial operation parameters. Adaptive correction module for operating parameters: Based on initial operating parameters, pre-processed photovoltaic module operating data and pre-processed environmental meteorological data, adaptive correction analysis is performed on the cleaning operating parameters to obtain the operating correction factor, generate a cleaning execution instruction set and control the cleaning equipment to perform cleaning actions.

[0013] The technical effects and advantages of this invention are as follows: By analyzing the thermal shock safety of the module surface temperature based on preprocessed photovoltaic module operating data, the thermal shock safety margin of the module is obtained. Based on the thermal shock safety margin of the module, the safety of equipment contact operations is judged. This achieves a quantitative safety assessment of the thermal shock effect generated when the cleaning medium comes into contact with the module. It effectively avoids damage to the photovoltaic module caused by excessive thermal stress due to sudden changes in module surface temperature under specific working conditions such as high temperature or strong radiation, and significantly improves the module safety during operation.

[0014] By performing adaptive correction analysis on cleaning operation parameters based on initial operating parameters, pre-processed photovoltaic module operating data, and pre-processed environmental meteorological data, an operating correction factor is obtained, a cleaning execution instruction set is generated, and the cleaning equipment is controlled to perform cleaning actions. A dynamic parameter adjustment mechanism based on real-time operating conditions is established during the operation, overcoming the shortcomings of existing technologies that use preset fixed parameters for control, which result in poor dynamic adaptability. It can flexibly adjust the cleaning intensity and speed under complex and ever-changing actual operating conditions, thereby ensuring both the efficiency of cleaning operations and the safety of equipment operation. Attached Figure Description

[0015] Figure 1 This is a diagram illustrating the method steps of the present invention.

[0016] Figure 2 This is a system structure block diagram of the present invention. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The automatic cleaning method and system for photovoltaic panels involved in the present invention are not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 The embodiment shown provides an automatic cleaning method for photovoltaic panels, including: S1: By constructing a multi-dimensional sensing network, environmental meteorological data, photovoltaic module operation data and cleaning equipment status data are collected in real time and synchronously, and the collected data is preprocessed.

[0019] In this embodiment, step S1, by constructing a multi-dimensional sensing network, synchronously collects environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time, including: A multi-dimensional sensing network is constructed by deploying an environmental monitoring sensor array, a photovoltaic string monitoring unit, and an onboard sensing terminal for cleaning equipment. The Internet of Things (IoT) communication protocol is used to synchronize time and interact data between nodes in the network, and to collect environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time. The environmental meteorological data includes wind speed, ambient temperature, air humidity, and solar irradiance. The photovoltaic module operation data includes module output current, output voltage, output power, and module surface temperature. The cleaning equipment status data includes the real-time position coordinates of the equipment, operating attitude angle, remaining battery power, and actuator status. The preprocessing of the collected data specifically includes: outlier removal, noise filtering, timestamp alignment, and normalization of the raw data.

[0020] It should be specifically explained that a multi-dimensional sensing network is constructed by deploying an environmental monitoring sensor array, a photovoltaic string monitoring unit, and an onboard sensing terminal for the cleaning equipment. The network time protocol is synchronized between nodes using the Internet of Things (IoT) communication protocol to achieve time synchronization and data interaction. This allows for real-time synchronous collection of environmental meteorological data, photovoltaic module operating data, and cleaning equipment status data. Specifically, environmental meteorological data, including wind speed, ambient temperature, air humidity, and solar irradiance, is obtained through anemometers, temperature and humidity sensors, and irradiance meters. Photovoltaic module operating data, including output current, output voltage, output power, and module surface temperature, is obtained through current and voltage transformers and thermocouples. Cleaning equipment status data is obtained through a positioning module, inertial measurement unit, and power monitoring. The circuit acquires the real-time position coordinates, operating attitude angle, remaining battery power, and actuator status of the equipment. When preprocessing the collected data, the mean and standard deviation of the data sequence are first calculated based on the Raida criterion. Data points that deviate from the mean by more than three times the standard deviation are identified as outliers and removed. Then, a five-point cubic smoothing filter algorithm is used to smooth the time series data to suppress high-frequency noise interference. Next, the linear interpolation method is used to align the timestamps of multi-source data streams with different sampling frequencies, and all data are resampled to the same time reference axis. Finally, the minimum-maximum normalization algorithm is used to linearly map the data values ​​of each dimension to the interval between 0 and 1, thereby eliminating the influence of dimensions and generating a standardized dataset for subsequent analysis.

[0021] S2: Based on preprocessed environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data, a comprehensive analysis and assessment of the surface contamination level of the photovoltaic module is conducted to determine the current contamination level of the photovoltaic module.

[0022] In this embodiment, step S2 performs a comprehensive analysis and evaluation of the surface contamination level of the photovoltaic module to determine the current contamination level of the photovoltaic module, specifically including: Based on the solar irradiance and ambient temperature in the preprocessed environmental meteorological data, the theoretical output power is calculated in combination with the preset photoelectric conversion parameters of the photovoltaic module; the actual output power is calculated based on the output current and output voltage in the preprocessed photovoltaic module operating data; the theoretical output power is dynamically corrected by combining the preset module aging attenuation coefficient and line transmission loss parameters to obtain the corrected benchmark output power; the difference between the corrected benchmark output power and the actual output power is calculated to obtain the real-time pollution index; the real-time pollution index is compared with the preset pollution level threshold range to determine the numerical range of the real-time pollution index, and the pollution level corresponding to the numerical range is determined as the current pollution level of the photovoltaic module.

[0023] It needs to be specifically explained that, using pre-processed solar irradiance and ambient temperature, combined with the preset photoelectric conversion parameters of the photovoltaic module, the theoretical output power of the module under the current environment is calculated through a power model under standard test conditions. This calculation process corrects the nominal power of the module based on the irradiance proportionality coefficient and temperature coefficient. Next, the collected output current value is multiplied by the output voltage value to calculate the actual output power of the photovoltaic module. To ensure the accuracy of the evaluation benchmark, preset module aging degradation coefficient and line transmission loss parameters are further introduced to correct the theoretical output power. Specifically, the theoretical output power is multiplied by (1 minus the module aging degradation coefficient) and the power value corresponding to the line transmission loss is subtracted to obtain the corrected benchmark output power. This benchmark value represents the ideal output capability of the module under pollution-free conditions but considering aging and line losses. Subsequently, the power difference between the corrected benchmark output power and the actual output power is calculated, and this power difference is divided by the corrected benchmark output power. The result is the real-time pollution index. The data, expressed as a percentage, visually represents the degree of power generation efficiency loss caused by surface contamination. Finally, the calculated real-time pollution index is compared with a preset pollution level threshold range. This preset threshold range is based on the percentage of relative power generation loss caused by surface contamination of photovoltaic modules, and its setting process must comprehensively consider the economic efficiency of photovoltaic power plant operation and maintenance, as well as the module's operating efficiency requirements. Specifically, based on historical dust accumulation experimental data, typical power loss distributions corresponding to different pollution levels are statistically analyzed, and the break-even point between the energy consumption cost of a single cleaning operation and the revenue from power generation loss is used to determine the critical value of the range. For example, a real-time pollution index value between 0% and 5% is set as Level 1 (light pollution), indicating that the current pollution has a relatively small impact on power generation and no cleaning operation is required; a range between 5% and 15% is set as Level 2 (moderate pollution), indicating that the pollution level has reached the standard requiring cleaning; and a range greater than 15% is set as Level 3 (heavy pollution), indicating that power generation efficiency is severely impaired and cleaning operations must be initiated immediately. If the current pollution level of the photovoltaic module is determined to be in the Level 1 light pollution range, the system will terminate the subsequent cleaning operation process and control the photovoltaic module automatic cleaning system to maintain the current monitoring waiting state; if the current pollution level of the photovoltaic module is determined to be in the Level 2 moderate pollution range or the Level 3 heavy pollution range, step S3 will continue to be executed. By using the threshold range determined based on the cost-benefit analysis, the accuracy and economy of the cleaning decision can be ensured.

[0024] S3: Based on the preprocessed environmental meteorological data, conduct meteorological operation suitability analysis on meteorological operation conditions to obtain the meteorological suitability index, and determine the operation time window based on the meteorological suitability index.

[0025] In this embodiment, step S3 performs a meteorological suitability analysis on the meteorological operation conditions to obtain a meteorological suitability index, and determines the operation time window based on the meteorological suitability index, specifically including: Based on the wind speed, ambient temperature, air humidity and solar irradiance in the preprocessed environmental meteorological data, each meteorological parameter is substituted into the preset meteorological suitability membership function for calculation to obtain the corresponding wind speed suitability score, temperature suitability score, humidity suitability score and light suitability score. Based on the degree of influence of each meteorological parameter on the safety and effectiveness of cleaning operations, corresponding weight coefficients are set, and the suitability scores are weighted and summed to obtain the meteorological suitability index. The meteorological suitability index is compared with the preset operation threshold in chronological order to identify the start and end times when the meteorological suitability index continuously meets the operation conditions. The duration between the start and end times is calculated. When the duration meets the preset minimum operation time requirement, the time period between the start and end times is determined as the operation time window.

[0026] It should be specifically explained that by acquiring preprocessed environmental meteorological data, including wind speed, ambient temperature, air humidity, and solar irradiance, the real-time values ​​of each parameter are substituted into a preset meteorological suitability membership function to calculate a suitability score. This membership function is based on trapezoidal distribution logic and is used to map parameter values ​​to values ​​between 0 and 1, where a value of 1 represents that the meteorological conditions are within the optimal cleaning range, and a value of 0 represents that the meteorological conditions do not meet the operational requirements. For example, the highest score is output when the wind speed is within the range of 0 to 5 meters per second, and the score decreases linearly outside this range. Subsequently, weighting coefficients are set according to the degree of influence of each meteorological parameter on the safety and quality of the cleaning operation, for example... To ensure safety, a maximum wind speed weighting coefficient is set. A weighted summation algorithm is used to synthesize the wind speed suitability score, temperature suitability score, humidity suitability score, and light suitability score to obtain the meteorological suitability index. Finally, the calculated meteorological suitability index is compared with the preset operation threshold in real time according to the time series. The start and end times when the meteorological suitability index is continuously greater than the operation threshold are identified. The difference between the two times is calculated as the duration. When the duration is greater than or equal to the preset minimum operation time, the continuous time period from the start time to the end time is officially determined as the operation time window, thereby ensuring that the cleaning equipment has enough time to complete the complete cleaning operation process.

[0027] S4: Based on the pre-processed photovoltaic module operating data, perform thermal shock safety analysis on the module surface temperature state to obtain the module thermal shock safety margin. Based on the module thermal shock safety margin, determine the safety of equipment contact operations and obtain the module contact safety determination result.

[0028] In this embodiment, step S4 performs a thermal shock safety analysis on the surface temperature of the component to obtain the component's thermal shock safety margin. Based on the component's thermal shock safety margin, it performs a safety assessment of the equipment contact operation to obtain the component contact safety assessment result, specifically including: Based on the module surface temperature in the pre-processed photovoltaic module operation data, the absolute temperature difference between the module surface temperature and the preset cleaning medium reference temperature is calculated; the solar irradiance in the pre-processed environmental meteorological data is obtained, and the corresponding environmental thermal stress correction coefficient is determined according to the mapping relationship between solar irradiance and the preset irradiance-correction coefficient; the absolute temperature difference is multiplied by the environmental thermal stress correction coefficient to obtain the equivalent thermal shock impact value. The ratio of the equivalent thermal shock impact value to the preset thermal shock safety threshold is calculated to obtain the thermal shock utilization ratio; the difference between the preset unit value and the thermal shock utilization ratio is used to calculate the component thermal shock safety margin. The component thermal shock safety margin is compared with the preset safety margin judgment threshold. If the component thermal shock safety margin is greater than the preset safety margin judgment threshold, the component contact operation is judged to be safe and a component contact safety judgment result that allows the operation is generated. Otherwise, the component contact operation is judged to be unsafe and a component contact safety judgment result that prohibits the operation is generated.

[0029] Specifically, the real-time surface temperature of the photovoltaic modules is extracted from the pre-processed operational data and combined with a preset reference temperature for the cleaning medium, such as ambient temperature or the standard temperature of the cleaning solution, to calculate the absolute temperature difference between the two. Next, the solar irradiance from the pre-processed environmental meteorological data is obtained. By consulting a preset irradiance-correction coefficient mapping table, the environmental thermal stress correction coefficient corresponding to the current irradiance is determined. This coefficient characterizes the cumulative effect of light intensity on the module's heat load, and is typically set to a larger correction coefficient for higher irradiance. Finally, the calculated absolute temperature difference is multiplied by the environmental thermal stress correction coefficient to obtain the equivalent thermal shock impact value, which quantifies the potential impact of the cleaning operation. The comprehensive thermal shock intensity is calculated; the ratio of the equivalent thermal shock impact value to the preset thermal shock safety threshold is used to obtain the thermal shock utilization ratio, where the preset thermal shock safety threshold represents the maximum thermal shock limit that the photovoltaic module material can withstand; then, the module thermal shock safety margin is calculated by subtracting the thermal shock utilization ratio from the value 1, which reflects the remaining space between the current operating condition and the safety limit; finally, the module thermal shock safety margin is compared with the preset safety margin judgment threshold. If the module thermal shock safety margin is greater than the judgment threshold, the equipment contact operation is determined to be safe and the module contact safety judgment result of allowing operation is output; if it is less than or equal to the judgment threshold, it is determined to be unsafe and the module contact safety judgment result of prohibiting operation is output.

[0030] S5: Based on the component contact safety judgment results, the current pollution level and the operation time window, perform a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value, and select the corresponding cleaning operation mode and initial operation parameters.

[0031] In this embodiment, step S5 performs a comprehensive matching analysis on the preset cleaning strategy library to obtain a cleaning strategy matching value, and selects the corresponding cleaning operation mode and initial operation parameters, specifically including: If the component contact safety assessment result indicates that operation is permitted, then candidate cleaning strategies that meet the current safety status are selected from the preset cleaning strategy library; for each candidate cleaning strategy, the matching degree between the current pollution level and the preset applicable pollution level of the candidate cleaning strategy is calculated to obtain a pollution matching score; the matching degree between the duration of the operation time window and the preset operation time requirement of the candidate cleaning strategy is calculated to obtain a time matching score; according to the preset weight allocation coefficient, the pollution matching score and the time matching score are weighted and summed to obtain the cleaning strategy matching value corresponding to each candidate cleaning strategy; the candidate cleaning strategy with the highest cleaning strategy matching value is determined as the target cleaning strategy, and the cleaning operation mode and initial operation parameters corresponding to the target cleaning strategy are extracted; If the component contact safety determination result indicates that operation is prohibited, the comprehensive matching analysis process of the preset cleaning strategy library will be terminated, and the photovoltaic module automatic cleaning system will be controlled to maintain the current non-operational state or enter the safety waiting mode based on the component contact safety determination result of prohibited operation.

[0032] Specifically, the process first obtains the component contact safety determination result. If the result indicates that operation is prohibited, the matching process is terminated directly, and the equipment is controlled to maintain a non-operational state or enter a safety waiting mode. If the result indicates that operation is permitted, all candidate cleaning strategies that meet the current safety conditions are retrieved from the preset cleaning strategy library. For each candidate cleaning strategy, its preset applicable pollution level range is compared with the current pollution level. The pollution matching score is determined by calculating the position or deviation of the current level within the applicable range. At the same time, the preset operation time requirement of the strategy is subtracted from the duration of the operation time window, and the remaining time is used to determine the score. The time matching score is calculated proportionally; if the window duration is less than the required duration, the time matching score is zero. Next, based on the preset weighting coefficients, the calculated pollution matching score and time matching score are weighted and summed to obtain the comprehensive score of the candidate cleaning strategy, i.e., the cleaning strategy matching value. The cleaning strategy matching values ​​of all candidate cleaning strategies are sorted or compared one by one, and the one with the highest value is determined as the target cleaning strategy. The cleaning operation mode and initial operation parameters bound to this strategy are read, including the cleaning type such as dry sweeping or wet washing, as well as specific values ​​such as equipment operating speed and cleaning pressure, for subsequent execution.

[0033] S6: Based on the initial operating parameters, pre-processed photovoltaic module operating data, and pre-processed environmental meteorological data, adaptive correction analysis is performed on the cleaning operating parameters to obtain the operating correction factor, generate a cleaning execution instruction set, and control the cleaning equipment to perform cleaning actions.

[0034] In this embodiment, step S6 performs adaptive correction analysis on the cleaning operation parameters to obtain the operation correction factor, generates a cleaning execution instruction set, and controls the cleaning equipment to perform cleaning actions, specifically including: The initial operating parameters are obtained, and combined with the real-time wind speed and real-time irradiance fluctuations in the pre-processed environmental meteorological data, as well as the real-time temperature of the photovoltaic modules in the pre-processed photovoltaic module operation data, the operating condition deviation between the current actual operating conditions and the preset standard operating conditions corresponding to the initial operating parameters is calculated. The operating condition deviation value is input into a preset adaptive parameter correction model to calculate the operation correction factor; the operation correction factor is used to dynamically compensate for the cleaning operation intensity and speed. The initial operation parameters are corrected and updated item by item using the operation correction factor to generate a cleaning execution instruction set that includes the target cleaning pressure, target travel speed and target water spray volume. The cleaning execution instruction set is sent to the control terminal of the cleaning equipment to drive the cleaning equipment to perform real-time adaptive cleaning actions according to the cleaning execution instruction set and the cleaning operation mode.

[0035] It needs to be specifically explained that the initial operating parameters are obtained, including the baseline cleaning pressure, baseline travel speed, and baseline water spray volume. These parameters are then combined with real-time wind speed and real-time irradiance fluctuations from pre-processed environmental meteorological data, and real-time module temperature from pre-processed photovoltaic module operating data. The operating condition deviation between the current actual operating conditions and the preset standard operating conditions corresponding to the initial operating parameters is calculated. Specifically, the wind speed deviation is obtained by subtracting the standard wind speed value from the real-time wind speed value; the irradiance deviation is obtained by subtracting the standard fluctuation threshold from the real-time irradiance fluctuation amplitude; and the temperature deviation is obtained by subtracting the standard module temperature from the real-time module temperature. Subsequently, the calculated operating condition deviation values ​​are input into a preset adaptive parameter correction model. This model, based on a built-in transfer function algorithm, calculates and outputs operating correction factors for dynamic compensation based on the sign and magnitude of the deviation values. For example, when detecting… When the wind speed deviation is positive, the model calculates a pressure correction factor greater than 1 to enhance cleaning power and resist wind resistance. When a positive temperature deviation is detected, the model calculates a corresponding flow correction factor to increase the water spray volume and prevent rapid evaporation of the water film. Next, the initial operating parameters are corrected and updated item by item using the operating correction factor. Specifically, the baseline cleaning pressure is multiplied by the pressure correction factor to obtain the target cleaning pressure, the baseline travel speed is multiplied by the speed correction factor to obtain the target travel speed, and the baseline water spray volume is multiplied by the flow correction factor to obtain the target water spray volume. Finally, the data containing the updated target cleaning pressure, target travel speed, and target water spray volume are packaged to generate a cleaning execution instruction set, which is then sent to the control terminal of the cleaning equipment through the communication network to drive the cleaning equipment to perform real-time adaptive cleaning actions according to the instruction set and the cleaning operation mode.

[0036] like Figure 2 This embodiment provides an implementation system for an automatic cleaning method for photovoltaic panels, including a multi-dimensional sensing data acquisition module, a pollution level analysis and assessment module, a meteorological suitability analysis module, a thermal shock safety determination module, a cleaning strategy matching module, and an adaptive correction module for operating parameters. The multi-dimensional sensing data acquisition module is connected to the pollution level analysis and assessment module, the meteorological suitability analysis module, and the thermal shock safety determination module. The pollution level analysis and assessment module, the meteorological suitability analysis module, and the thermal shock safety determination module are all connected to the cleaning strategy matching module. The multi-dimensional sensing data acquisition module and the cleaning strategy matching module are all connected to the adaptive correction module for operating parameters.

[0037] The multi-dimensional sensing data acquisition module collects environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time and synchronously by constructing a multi-dimensional sensing network, and preprocesses the collected data. The pollution level analysis and assessment module comprehensively analyzes and assesses the degree of surface pollution of photovoltaic modules based on preprocessed environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data, and determines the current pollution level of the photovoltaic modules. The meteorological suitability analysis module analyzes the meteorological suitability of meteorological operation conditions based on preprocessed environmental meteorological data, obtains a meteorological suitability index, and determines the operation time window based on the meteorological suitability index. The thermal shock safety assessment module analyzes the thermal shock safety of the module surface temperature based on the pre-processed photovoltaic module operating data, obtains the module thermal shock safety margin, and assesses the safety of equipment contact operations based on the module thermal shock safety margin, thus obtaining the module contact safety assessment result. The cleaning strategy matching module performs a comprehensive matching analysis on the preset cleaning strategy library based on the component contact safety judgment result, the current pollution level and the operation time window, obtains the cleaning strategy matching value, and selects the corresponding cleaning operation mode and initial operation parameters. The adaptive correction module for operating parameters performs adaptive correction analysis on the cleaning operating parameters based on the initial operating parameters, pre-processed photovoltaic module operating data, and pre-processed environmental meteorological data, obtains the operating correction factor, generates a cleaning execution instruction set, and controls the cleaning equipment to perform cleaning actions.

[0038] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0039] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An automatic cleaning method for photovoltaic panels, characterized in that, include: S1: By constructing a multi-dimensional sensing network, environmental meteorological data, photovoltaic module operation data and cleaning equipment status data are collected in real time and synchronously, and the collected data is preprocessed. S2: Based on the pre-processed environmental meteorological data, photovoltaic module operation data and cleaning equipment status data, a comprehensive analysis and evaluation of the surface pollution level of the photovoltaic module is conducted to determine the current pollution level of the photovoltaic module; S3: Based on the preprocessed environmental meteorological data, conduct meteorological operation suitability analysis on meteorological operation conditions to obtain the meteorological suitability index, and determine the operation time window based on the meteorological suitability index; S4: Based on the pre-processed photovoltaic module operating data, perform thermal shock safety analysis on the module surface temperature state to obtain the module thermal shock safety margin. Based on the module thermal shock safety margin, determine the safety of equipment contact operations and obtain the module contact safety determination result. S5: Based on the component contact safety judgment result, the current pollution level and the operation time window, perform a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value, and select the corresponding cleaning operation mode and initial operation parameters. S6: Based on the initial operating parameters, pre-processed photovoltaic module operating data, and pre-processed environmental meteorological data, adaptive correction analysis is performed on the cleaning operating parameters to obtain the operating correction factor, generate a cleaning execution instruction set, and control the cleaning equipment to perform cleaning actions.

2. The automatic cleaning method for photovoltaic panels according to claim 1, characterized in that, The S1 constructs a multi-dimensional sensing network to synchronously collect environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time, including: A multi-dimensional sensing network is constructed by deploying an environmental monitoring sensor array, a photovoltaic string monitoring unit, and an onboard sensing terminal for cleaning equipment. The Internet of Things (IoT) communication protocol is used to synchronize time and interact data between nodes in the network, and to collect environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time. The environmental meteorological data includes wind speed, ambient temperature, air humidity, and solar irradiance. The photovoltaic module operation data includes module output current, output voltage, output power, and module surface temperature. The cleaning equipment status data includes the real-time position coordinates of the equipment, operating attitude angle, remaining battery power, and actuator status. The preprocessing of the collected data specifically includes: outlier removal, noise filtering, timestamp alignment, and normalization of the raw data.

3. The automatic cleaning method for photovoltaic panels according to claim 2, characterized in that, S2 performs a comprehensive analysis and evaluation of the surface contamination level of the photovoltaic module to determine the current contamination level of the photovoltaic module, specifically including: Based on the solar irradiance and ambient temperature in the preprocessed environmental meteorological data, the theoretical output power is calculated in combination with the preset photoelectric conversion parameters of the photovoltaic module; the actual output power is calculated based on the output current and output voltage in the preprocessed photovoltaic module operating data; the theoretical output power is dynamically corrected by combining the preset module aging attenuation coefficient and line transmission loss parameters to obtain the corrected benchmark output power; the difference between the corrected benchmark output power and the actual output power is calculated to obtain the real-time pollution index; the real-time pollution index is compared with the preset pollution level threshold range to determine the numerical range of the real-time pollution index, and the pollution level corresponding to the numerical range is determined as the current pollution level of the photovoltaic module.

4. The automatic cleaning method for photovoltaic panels according to claim 3, characterized in that, S3 performs a meteorological suitability analysis on meteorological operation conditions to obtain a meteorological suitability index, and determines the operation time window based on the meteorological suitability index, specifically including: Based on the wind speed, ambient temperature, air humidity and solar irradiance in the preprocessed environmental meteorological data, each meteorological parameter is substituted into the preset meteorological suitability membership function for calculation to obtain the corresponding wind speed suitability score, temperature suitability score, humidity suitability score and light suitability score. Based on the degree of influence of each meteorological parameter on the safety and effectiveness of cleaning operations, corresponding weight coefficients are set, and the suitability scores are weighted and summed to obtain the meteorological suitability index. The meteorological suitability index is compared with the preset operation threshold in chronological order to identify the start and end times when the meteorological suitability index continuously meets the operation conditions. The duration between the start and end times is calculated. When the duration meets the preset minimum operation duration requirement, the time period between the start and end times is determined as the operation time window.

5. The automatic cleaning method for photovoltaic panels according to claim 4, characterized in that, S4 performs a thermal shock safety analysis on the surface temperature of the component to obtain the component's thermal shock safety margin. Based on the component's thermal shock safety margin, it assesses the safety of equipment contact operations to obtain the component contact safety assessment result, which specifically includes: Based on the module surface temperature in the pre-processed photovoltaic module operation data, the absolute temperature difference between the module surface temperature and the preset cleaning medium reference temperature is calculated; the solar irradiance in the pre-processed environmental meteorological data is obtained, and the corresponding environmental thermal stress correction coefficient is determined according to the mapping relationship between solar irradiance and the preset irradiance-correction coefficient; the absolute temperature difference is multiplied by the environmental thermal stress correction coefficient to obtain the equivalent thermal shock impact value. The ratio of the equivalent thermal shock impact value to the preset thermal shock safety threshold is calculated to obtain the thermal shock utilization ratio; the difference between the preset unit value and the thermal shock utilization ratio is used to calculate the component thermal shock safety margin. The component thermal shock safety margin is compared with the preset safety margin judgment threshold. If the component thermal shock safety margin is greater than the preset safety margin judgment threshold, the component contact operation is judged to be safe and a component contact safety judgment result that allows the operation is generated. Otherwise, the component contact operation is judged to be unsafe and a component contact safety judgment result that prohibits the operation is generated.

6. The automatic cleaning method for photovoltaic panels according to claim 5, characterized in that, S5 performs a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value, and selects the corresponding cleaning operation mode and initial operation parameters, specifically including: If the component contact safety assessment result indicates that operation is permitted, then candidate cleaning strategies that meet the current safety status are selected from the preset cleaning strategy library; for each candidate cleaning strategy, the matching degree between the current pollution level and the preset applicable pollution level of the candidate cleaning strategy is calculated to obtain a pollution matching score; the matching degree between the duration of the operation time window and the preset operation time requirement of the candidate cleaning strategy is calculated to obtain a time matching score; according to the preset weight allocation coefficient, the pollution matching score and the time matching score are weighted and summed to obtain the cleaning strategy matching value corresponding to each candidate cleaning strategy; the candidate cleaning strategy with the highest cleaning strategy matching value is determined as the target cleaning strategy, and the cleaning operation mode and initial operation parameters corresponding to the target cleaning strategy are extracted; If the component contact safety determination result indicates that operation is prohibited, the comprehensive matching analysis process of the preset cleaning strategy library will be terminated, and the photovoltaic module automatic cleaning system will be controlled to maintain the current non-operational state or enter the safety waiting mode based on the component contact safety determination result of prohibited operation.

7. The automatic cleaning method for photovoltaic panels according to claim 6, characterized in that, S6 performs adaptive correction analysis on the cleaning operation parameters to obtain the operation correction factor, generates a cleaning execution instruction set, and controls the cleaning equipment to perform cleaning actions, specifically including: The initial operating parameters are obtained, and combined with the real-time wind speed and real-time irradiance fluctuations in the pre-processed environmental meteorological data, as well as the real-time temperature of the photovoltaic modules in the pre-processed photovoltaic module operation data, the operating condition deviation value between the current actual operating conditions and the preset standard operating conditions corresponding to the initial operating parameters is calculated. The operating condition deviation value is input into a preset adaptive parameter correction model to calculate the operation correction factor; the operation correction factor is used to dynamically compensate for the cleaning operation intensity and speed. The initial operation parameters are corrected and updated item by item using the operation correction factor to generate a cleaning execution instruction set that includes the target cleaning pressure, target travel speed and target water spray volume. The cleaning execution instruction set is sent to the control terminal of the cleaning equipment to drive the cleaning equipment to perform real-time adaptive cleaning actions according to the cleaning execution instruction set and the cleaning operation mode.

8. An automatic photovoltaic panel cleaning system, implementing the automatic photovoltaic panel cleaning method as described in any one of claims 1-7, characterized in that, include: Multi-dimensional sensing data acquisition module: By constructing a multi-dimensional sensing network, it synchronously collects environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data in real time, and preprocesses the collected data; Pollution Level Analysis and Assessment Module: Based on pre-processed environmental meteorological data, photovoltaic module operation data, and cleaning equipment status data, this module comprehensively analyzes and assesses the degree of surface pollution of photovoltaic modules to determine the current pollution level of the photovoltaic modules. Meteorological suitability analysis module: Based on preprocessed environmental meteorological data, it performs meteorological operation suitability analysis on meteorological operation conditions, obtains meteorological suitability index, and determines operation time window based on meteorological suitability index; Thermal shock safety assessment module: Based on the pre-processed photovoltaic module operating data, the module surface temperature state is analyzed for thermal shock safety to obtain the module thermal shock safety margin. Based on the module thermal shock safety margin, the safety of equipment contact operations is assessed to obtain the module contact safety assessment result. Cleaning strategy matching module: Based on the component contact safety judgment result, the current pollution level and the operation time window, the module performs a comprehensive matching analysis on the preset cleaning strategy library to obtain the cleaning strategy matching value and select the corresponding cleaning operation mode and initial operation parameters. Adaptive correction module for operating parameters: Based on initial operating parameters, pre-processed photovoltaic module operating data and pre-processed environmental meteorological data, adaptive correction analysis is performed on the cleaning operating parameters to obtain the operating correction factor, generate a cleaning execution instruction set and control the cleaning equipment to perform cleaning actions.

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