Photovoltaic array multi-angle antireflection optimization method and system

Abstract

The invention provides a photovoltaic array multi-angle antireflection optimization method and system. Belongs to the photovoltaic technical field. The method comprises the steps of collecting corresponding data of a photovoltaic module in real time through a high-precision sensor, and meanwhile, obtaining key information of an airplane through airspace management data; based on the collected data, establishing an antireflection model including multi-factor influence; the multiple factors comprise a photovoltaic module material, an antireflection film structure, solar incident angle and azimuth angle changes, and view angle changes when the airplane takes off and lands; according to an output result of the model, an advanced algorithm is adopted to optimize an anti-reflection scheme; through a real-time monitoring system, the power generation efficiency and reflectivity of the photovoltaic array and the change of meteorological conditions are monitored. Real-time data is fed back to the antireflection model, and the model is optimized and adjusted according to the feedback data. According to the technical scheme, the influence of various factors on the reflection characteristics of the photovoltaic array is comprehensively considered, and multi-angle antireflection optimization is achieved.

Description

Technical Field

[0001] The invention provides a photovoltaic array multi-angle anti-reflection optimization method and system, belonging to the field of photovoltaic technology. Background Art

[0002] As an important part of solar power generation, the power generation efficiency of photovoltaic arrays directly affects the performance and economy of the entire system. In the design of photovoltaic arrays, the application of anti-reflection film is an important means to improve the photoelectric conversion efficiency of photovoltaic modules. However, traditional anti-reflection methods usually only optimize for a certain fixed angle, ignoring the impact of changes in the solar incident angle and the inclination angle of the photovoltaic module layout on the reflectivity. In addition, photovoltaic arrays are usually installed in open areas, and the surrounding environmental factors, such as aircraft takeoff and landing, may also affect the reflective characteristics of the photovoltaic array. Summary of the invention

[0003] The present invention provides a photovoltaic array multi-angle anti-reflection optimization method and system to solve the problems mentioned in the above background technology: The present invention proposes a photovoltaic array multi-angle anti-reflection optimization method, the method comprising: S1. Collect the corresponding data of photovoltaic modules in real time through high-precision sensors, and obtain key information of aircraft through airspace management data; S2. Based on the collected data, an anti-reflection model including the influence of multiple factors is established; the multiple factors include photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth angle, and changes in viewing angle during aircraft takeoff and landing; S3. According to the output results of the model, the anti-reflection scheme is optimized using advanced algorithms; S4. Monitor the power generation efficiency and reflectivity of the photovoltaic array and changes in meteorological conditions through a real-time monitoring system; feed the real-time data back to the anti-reflection model, which is then optimized and adjusted based on the feedback data.

[0004] Furthermore, the S1 includes: S11. Install multiple types of high-precision sensors at key locations of the photovoltaic array, including tilt sensors, azimuth sensors, sun tracking sensors, and weather stations; S12, integrated airspace management system data interface, real-time acquisition of flight information of nearby airports, the flight information including flight path, altitude and time of take-off and landing of aircraft; S13. Based on the Internet of Things technology, the data collected by each sensor is transmitted to the central data processing center in real time, and the data is pre-processed by the central data processing center; S14. Conduct time series analysis on meteorological data, predict weather trends in the future, and provide a basis for model prediction based on the prediction results; S15. Combine flight information with the spatial layout of the photovoltaic array to analyze the visibility of photovoltaic modules from the perspective of the aircraft.

[0005] Furthermore, the S13 includes: Build a multi-path transmission network, use different communication links as backup or parallel transmission channels, and dynamically adjust the transmission path according to the real-time network status; Based on intelligent load balancing algorithm, data traffic is dynamically allocated according to the real-time load, delay and packet loss rate of each transmission path; Build a distributed data preprocessing pipeline, using cloud computing or edge computing resources to distribute preprocessing tasks to multiple nodes for parallel processing; In the preprocessing stage, machine learning algorithms are introduced to quickly extract features and detect anomalies from sensor data. Based on streaming data processing technology, real-time data is processed instantly. Based on the cache and buffer mechanism, data that cannot be processed immediately is cached and processed when the processing resources are idle, and the cache strategy is optimized.

[0006] Furthermore, the S14 includes: Integrate the raw data collected by the weather station, conduct quality assessment on the integrated data, detect outliers and missing values ​​through statistical methods, and perform correction or interpolation processing; Through time series analysis technology, meteorological data is modeled to capture the time dependence and periodicity in the data; and key features in the time series are extracted; Based on the extracted features, a multivariate prediction model is constructed to predict the meteorological parameters in the future. Through cross-validation or rolling forecast, the prediction results are verified to evaluate the prediction accuracy and generalization ability of the model. According to the verification results, the parameters of the prediction model are adjusted or the structure is optimized; Combining historical meteorological data with actual observation data, the prediction results are post-processed and applied to the anti-reflection optimization model of the photovoltaic array to provide the model with meteorological conditions input for a period of time in the future; Based on the feedback mechanism, the actual observed meteorological data are compared with the forecast results, the source of the forecast error is analyzed, and the forecast model is continuously improved and optimized accordingly.

[0007] Furthermore, the S2 includes: S21. Based on the material properties of photovoltaic modules, the basic framework of the anti-reflection model is established, the structural parameters of the anti-reflection film are introduced, and the dynamic reflectivity prediction part of the anti-reflection model is constructed in combination with the changes in the solar incident angle and azimuth angle; S22. Based on the changes in the aircraft's flight path and altitude, the interference degree of the photovoltaic modules on the pilot's vision at different flight stages is evaluated through three-dimensional visual simulation technology, and the visual image evaluation model of the anti-reflection model is improved based on the evaluation results; S23. Verify the accuracy of the anti-reflection model through simulation, and adjust the anti-reflection model parameters based on the verification results; and train the anti-reflection model through a machine learning algorithm.

[0008] Furthermore, the S21 includes: Analyze the main material properties of photovoltaic modules and establish the basic framework of anti-reflection model based on the material property analysis; Based on the known optical properties of different anti-reflection film materials, the structural parameters of the anti-reflection film are designed, and the optimal film layer combination is found through the optimization algorithm; The designed anti-reflection film structural parameters are introduced into the anti-reflection model, and the reflectivity under different film layer combinations is calculated through numerical simulation, and compared with experimental data for verification; Establish a mathematical relationship model between the solar incident angle and azimuth and the reflectivity of the photovoltaic module surface. Use a solar tracking sensor to measure the changes in the solar incident angle and azimuth in real time. Input these data into the anti-reflection model to calculate the reflectivity of the photovoltaic module at different times and seasons. Based on the dynamic reflectivity prediction algorithm, combined with historical meteorological data and sun track prediction, the reflectivity in the future period is predicted; The reflectivity data of photovoltaic modules under different conditions were collected and compared with the model prediction results for verification. Based on the verification results, the parameters of the anti-reflection model were adjusted.

[0009] Furthermore, the S3 includes: S31. Based on the output results of the anti-reflection model, apply the principles of material science to screen the best anti-reflection film material combination and optimize the film structure to minimize the reflectivity at different sun angles; S32. Based on the intelligent coating technology, the anti-reflection film is adaptively adjusted, wherein the adaptive adjustment includes automatically adjusting the refractive index of the film layer according to the position of the sun; S33. For a specific period of time, an algorithm is used to predict the overlapping area between the flight path and the photovoltaic array, and based on the prediction results, the inclination angle and azimuth angle of the photovoltaic module layout in the affected area are dynamically adjusted; S34. Through the intelligent dispatching system, the overall layout of the photovoltaic array is automatically adjusted according to real-time meteorological data and flight information.

[0010] Furthermore, the S33 includes: Integrate the airspace management system and the airport flight information system to obtain real-time flight path data for a specific period of time, including flight altitude, speed and direction; Based on geographic information system (GIS) and 3D modeling technology, an accurate 3D model of the photovoltaic array is constructed. By combining flight path data and the photovoltaic array model, spatial analysis algorithms are used to predict the overlap area between the aircraft and the photovoltaic array in a specific period of time. Based on the overlap area prediction results, the photovoltaic module areas affected by aircraft flight, that is, potential visual interference areas, are identified. Based on material science and optical principles, the reflectivity and photovoltaic conversion efficiency of photovoltaic modules at different inclination and azimuth angles are analyzed. Through the dynamic adjustment algorithm, the dynamic adjustment plan of the photovoltaic modules in the affected area is calculated according to the prediction results and the optimal combination of inclination and azimuth.

[0011] Furthermore, the S4 includes: S41. Use high-precision photoelectric conversion efficiency monitoring equipment to record the power generation efficiency of the photovoltaic array in real time, and use a reflectivity measuring instrument to continuously monitor the reflectivity of the photovoltaic module surface; S42. Based on the data feedback mechanism, the real-time monitoring data is input into the anti-reflection model, and dynamic adjustments are made. The reinforcement learning algorithm is used to continuously optimize the anti-reflection strategy. S43. Regularly evaluate the impact of optimization measures on the environment, including reducing light pollution and improving energy efficiency, and continuously adjust the optimization strategy based on the evaluation results.

[0012] The present invention proposes a system for realizing a multi-angle anti-reflection optimization method for a photovoltaic array, the system comprising: Data acquisition module: collects the corresponding data of photovoltaic modules in real time through high-precision sensors, and obtains key information of aircraft through airspace management data; Model building module: Based on the collected data, an anti-reflection model is established that includes the influence of multiple factors; the multiple factors include photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth angle, and changes in viewing angle during aircraft takeoff and landing; Solution optimization module: Based on the output results of the model, the anti-reflection solution is optimized using advanced algorithms; Optimization and adjustment module: Through the real-time monitoring system, the power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions are monitored; the real-time data is fed back to the anti-reflection model, and the model is optimized and adjusted based on the feedback data.

[0013] Beneficial effects of the invention: The technical solution of the invention comprehensively considers the influence of various factors on the reflection characteristics of the photovoltaic array, and realizes multi-angle anti-reflection optimization; dynamically adjusts the anti-reflection scheme according to changes in real-time data and meteorological conditions to ensure that the photovoltaic array can maintain the best anti-reflection effect and photovoltaic conversion efficiency under different conditions; reduces the interference of the photovoltaic array on the environment, especially in special scenarios such as aircraft takeoff and landing, reducing interference with the pilot's perspective. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a step diagram of the method of the present invention; Figure 2 This is a system module diagram of the present invention. DETAILED DESCRIPTION

[0015] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0016] One embodiment of the present invention, as Figure 1 As shown, a photovoltaic array multi-angle anti-reflection optimization method, the method comprising: S1. Install high-precision sensors at the locations of photovoltaic arrays to collect real-time information on the inclination and azimuth of photovoltaic modules, the incident angle and azimuth of the sun, and local meteorological conditions. At the same time, obtain key information such as the flight path, altitude and time of aircraft takeoff and landing through airspace management data. S2. Based on the collected data, establish an anti-reflection model that includes multiple factors such as photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth, and changes in viewing angle during aircraft takeoff and landing; S3. Optimize the anti-reflection scheme based on the output of the model; for example, adjust the material and thickness of the anti-reflection film to minimize the reflectivity at different solar incidence angles and azimuths; at the same time, adjust the layout inclination and azimuth of the photovoltaic modules for special situations such as aircraft takeoff and landing to reduce interference with the pilot's vision; S4. The power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions, are monitored through a real-time monitoring system. The real-time data is fed back to the anti-reflection model, which is then optimized and adjusted based on the feedback data to ensure that the photovoltaic array can maintain the best anti-reflection effect and photovoltaic conversion efficiency under different conditions.

[0017] The working principle of the above technical solution is: through high-precision sensors, key data is collected in real time at the layout position of the photovoltaic array, including the layout inclination and azimuth of the photovoltaic module, the incident angle and azimuth of the sun, and local meteorological conditions (such as temperature, humidity, wind speed, etc.). At the same time, key information such as the flight path, altitude and time of aircraft takeoff and landing is obtained through airspace management data. Then, based on the collected data, an anti-reflection model with multiple factors is established. The anti-reflection model not only considers conventional factors such as photovoltaic module materials, anti-reflection film structure, solar incident angle and azimuth changes, but also considers the special factor of the change in viewing angle when the aircraft takes off and lands. Then, according to the output results of the anti-reflection model, the algorithm is used to optimize the anti-reflection scheme, including adjusting the material and thickness of the anti-reflection film to minimize the reflectivity at different solar incident angles and azimuths, thereby improving the photovoltaic conversion efficiency. At the same time, for special situations such as aircraft takeoff and landing, the layout inclination and azimuth of the photovoltaic module are adjusted to reduce interference with the pilot's viewing angle and ensure flight safety. Finally, the power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions, are monitored through a real-time monitoring system. The real-time data is fed back to the anti-reflection model, which is then optimized and adjusted based on the feedback data.

[0018] The effect of the above technical solution is: by collecting data in real time through high-precision sensors, the changes in the solar incident angle and azimuth angle can be accurately understood, so that the material and thickness of the anti-reflection film can be adjusted according to these changes. This targeted optimization can minimize the reflectivity, so that more sunlight can be absorbed by the photovoltaic modules, thereby improving the photovoltaic conversion efficiency; by real-time monitoring of local meteorological conditions, such as temperature, humidity, etc., these factors will affect the conversion efficiency of photovoltaic modules. According to the changes in meteorological conditions, the model can adjust the anti-reflection strategy in real time to ensure that the photovoltaic array can maintain the best conversion efficiency under different weather conditions; by obtaining key information such as the flight path, altitude and time of aircraft takeoff and landing, and combining the layout inclination and azimuth of photovoltaic modules, the degree of interference of light reflection on the pilot's perspective can be accurately calculated. In response to this situation, the solution can adjust the layout of photovoltaic modules to reduce interference with the pilot's perspective, thereby ensuring flight safety; through the real-time monitoring system, problems in the photovoltaic array in terms of power generation efficiency and reflectivity can be discovered in time, and real-time data can be fed back to the anti-reflection model. The model is then optimized and adjusted based on the feedback data. Through the closed-loop feedback mechanism, it can ensure that the photovoltaic array can maintain the best anti-reflection effect and photovoltaic conversion efficiency under different conditions, thereby improving the stability and reliability of the entire photovoltaic system. Through real-time monitoring and optimization, problems in the photovoltaic array can be discovered and resolved in a timely manner, reducing operation and maintenance costs and extending the service life of the photovoltaic array. The optimized photovoltaic array can more effectively utilize solar energy resources, improve energy utilization efficiency, and help promote the sustainable development of the photovoltaic industry.

[0019] In one embodiment of the present invention, the S1 includes: S11. Install multiple types of high-precision sensors at key locations of the photovoltaic array, including tilt sensors, azimuth sensors, sun tracking sensors (for measuring the sun's incident angle and azimuth), and meteorological stations (for monitoring wind speed, wind direction, temperature, humidity, etc.); S12, integrated airspace management system data interface, real-time acquisition of flight information of nearby airports, the flight information including flight path, altitude and time of take-off and landing of aircraft; S13. Based on the Internet of Things technology, the data collected by each sensor is transmitted to the central data processing center in real time, and the data is pre-processed by the central data processing center; S14. Conduct time series analysis on meteorological data, predict weather trends in the future, and provide a basis for model prediction based on the prediction results; S15. Combine flight information with the spatial layout of the photovoltaic array to analyze the visibility of photovoltaic modules from the perspective of the aircraft.

[0020] The working principle of the above technical solution is: deploy a variety of high-precision sensors at key locations of the photovoltaic array to comprehensively collect relevant information. The sensors include: Tilt sensor: accurately measure the tilt angle of photovoltaic modules; Azimuth sensor: real-time monitoring of the azimuth of photovoltaic modules; Sun tracking sensor: obtains the sun's incident angle and azimuth in real time through precise measurement technology; Weather station: comprehensive monitoring of wind speed, wind direction, temperature, humidity and other meteorological conditions; Through the data interface integration with the airspace management system, real-time flight information of nearby airports can be obtained, including: Flight Path: Understand the aircraft’s flight path to analyze the potential interference of the PV array to the pilot’s view.

[0021] Altitude and time: Know the specific time and altitude of aircraft takeoff and landing to provide a reference for the time window and altitude range for anti-reflection optimization.

[0022] Based on the Internet of Things technology, the data collected by each sensor is transmitted to the central data processing center in real time. After the data arrives, the central data processing center performs pre-processing and conducts time series analysis on the data collected by the meteorological station, aiming to predict weather trends in the future. Combine flight information with the spatial layout of the photovoltaic array for comprehensive analysis. The core of this step is to evaluate the visibility of photovoltaic modules from the perspective of the aircraft, that is, to analyze whether the photovoltaic array will interfere with the pilot's perspective during takeoff and landing. Based on this analysis, special situational considerations can be provided for anti-reflection optimization, such as adjusting the layout inclination and azimuth of the photovoltaic modules to reduce interference with the pilot's perspective.

[0023] The effect of the above technical solution is: by installing multiple types of high-precision sensors at key positions of the photovoltaic array, key data such as the layout inclination and azimuth of the photovoltaic modules, the incident angle and azimuth of the sun, and local meteorological conditions can be collected in real time and accurately. The central data processing center pre-processes the data collected by the sensors, including data cleaning and denoising, which effectively improves the accuracy and reliability of the data and reduces the model prediction errors caused by data errors or noise interference; through the data interface of the integrated airspace management system, it can obtain flight information of nearby airports in real time, including the flight path, altitude and time of take-off and landing of the aircraft, providing timely and accurate data support for evaluating the visibility of photovoltaic modules from the perspective of the aircraft; combining flight information with the spatial layout of the photovoltaic array, analyzing the visibility of photovoltaic modules from the perspective of the aircraft, providing special situational considerations for anti-reflection optimization, and optimizing the anti-reflection effect of the photovoltaic array under the premise of ensuring flight safety; based on the Internet of Things technology, the data collected by each sensor can be transmitted to the central data processing center in real time, realizing rapid data processing and response, and improving the monitoring and management efficiency of the photovoltaic array; Internet of Things technology It enables different types of data to be integrated for analysis, so as to draw more comprehensive and accurate conclusions, and provide a more scientific basis for the anti-reflection optimization of photovoltaic arrays; time series analysis of meteorological data can predict weather change trends in the future, providing a time window for the anti-reflection optimization of photovoltaic arrays, so that strategies can be adjusted in advance before adverse weather conditions arrive; the prediction results based on meteorological data can provide a basis for model prediction, which helps to more accurately evaluate the performance of photovoltaic arrays under different weather conditions, so as to formulate more reasonable anti-reflection strategies; through precise data collection and processing, as well as optimized anti-reflection strategies, the photovoltaic conversion efficiency of photovoltaic arrays can be significantly improved, the cost of photovoltaic power generation can be reduced, and the economic benefits can be improved; combining flight information with the spatial layout of photovoltaic arrays for analysis can reduce the interference of photovoltaic arrays on the pilot's perspective, thereby ensuring flight safety.

[0024] In one embodiment of the present invention, the S13 includes: Build a multi-path transmission network, use different communication links (such as satellite communication, 4G / 5G, Wi-Fi, etc.) as backup or parallel transmission channels, and dynamically adjust the transmission path according to the real-time network status; the real-time network status is calculated by the following formula:

[0025] Where n and m represent the number of transmission paths and the number of node processing capabilities, respectively; and Represent the weight coefficients of i and j respectively; , , , , , Indicates the weight of each factor, which represents the importance of load, delay, packet loss rate, preprocessing capability, bandwidth utilization and node processing capability in each path or node; , , , , , , The points are the current load, maximum load capacity, current delay, current packet loss rate, actual preprocessing speed, current bandwidth usage and actual processing speed of node j of path i; , , , , They represent the maximum delay, maximum packet loss rate, ideal preprocessing speed, maximum bandwidth, and ideal node processing speed in all paths respectively; , , , , , Represents the adjustment coefficient.

[0026] Based on intelligent load balancing algorithm, data traffic is dynamically allocated according to factors such as real-time load, delay and packet loss rate of each transmission path; Build a distributed data preprocessing pipeline, using cloud computing or edge computing resources to distribute preprocessing tasks to multiple nodes for parallel processing; In the preprocessing stage, machine learning algorithms are introduced to quickly extract features and detect anomalies from sensor data. Based on streaming data processing technology, real-time data is processed instantly. Based on the cache and buffer mechanism, data that cannot be processed immediately is cached and processed when the processing resources are idle, and the cache strategy is optimized.

[0027] The working principle of the above technical solution is as follows: In order to ensure the real-time and reliability of data, the system builds a multi-path transmission network, including the use of satellite communications, 4G / 5G mobile communication networks, Wi-Fi and other communication links as backup or parallel transmission channels for data. The system will dynamically adjust the transmission path according to the real-time network conditions (such as bandwidth, delay, stability, etc.), and select the optimal or suboptimal path for data transmission; the system has a built-in intelligent load balancing algorithm, which monitors the load, delay and packet loss rate of each transmission path in real time. Key indicators. According to these indicators, the algorithm will dynamically allocate data traffic to avoid overloading a single path, thereby optimizing the overall transmission efficiency; the system builds a distributed data preprocessing pipeline, using cloud computing or edge computing resources to distribute preprocessing tasks to multiple nodes for parallel processing. Each node will perform specific preprocessing tasks, such as data cleaning, format conversion, etc.; in the preprocessing stage, the system introduces machine learning algorithms (such as autoencoders or convolutional neural networks CNN) to quickly extract features and detect anomalies from sensor data. The system uses streaming data processing technology to process data that arrives in real time. Once the data arrives at the system, it will be processed and analyzed immediately, without waiting for all the data to be collected before batch processing. For data that cannot be processed immediately, the system will cache it. When the processing resources are idle, the data will be processed. At the same time, the system will optimize the cache strategy to ensure the efficiency and effectiveness of the cache.

[0028] The effects of the above technical solution are as follows: by using different communication links (such as satellite communication, 4G / 5G, Wi-Fi, etc.) as backup or parallel transmission channels, the impact of single path failure on data transmission is effectively reduced; the transmission path is dynamically adjusted according to the real-time network status to ensure the continuity and stability of data transmission; data traffic is dynamically allocated according to factors such as the real-time load, delay and packet loss rate of each transmission path to avoid the problem of single path overload; the overall transmission efficiency is improved to ensure the timeliness and accuracy of data transmission; cloud computing or edge computing resources are used to disperse the preprocessing tasks to multiple nodes for parallel processing, which significantly improves the efficiency of data preprocessing; reduces processing delays, so that data can enter the subsequent analysis and decision-making stages more quickly; in preprocessing In the processing stage, machine learning algorithms such as autoencoders or convolutional neural networks (CNNs) are introduced to quickly extract features and detect anomalies from sensor data; noise and invalid data are effectively removed, data quality is improved, and more accurate data support is provided for subsequent analysis and model building; real-time data is processed immediately instead of waiting for all data to be collected before batch processing; processing delays are significantly reduced, and the response speed of the system is improved, allowing the system to make decisions and react faster; data that cannot be processed immediately is cached and processed when processing resources are idle; resource utilization is optimized to avoid processing delays or data loss due to insufficient resources; and the overall performance and cost-effectiveness of the system are further improved through the optimization of caching strategies. The above formula can more accurately reflect the real-time status of the network through weighted integration of multiple network factors (such as load, delay, packet loss rate, etc.), evaluate and help select the optimal transmission path, and improve the stability and reliability of data transmission; the real-time calculated network status allows the system to dynamically adjust the transmission path according to the changing network environment, ensuring the optimal performance of data transmission and reducing the impact caused by link congestion or failure; combined with the real-time load situation, the algorithm can effectively distribute data traffic to different paths, avoid overloading a single path, and improve the overall network throughput and response speed; by using cloud computing and edge computing resources, data processing tasks can be dispersed to multiple nodes, thereby improving processing efficiency, reducing latency, and adapting to the real-time processing needs of large-scale sensor data; this design allows flexible adjustment according to real-time needs and network conditions, enhances the elasticity and scalability of the system, and adapts to changing application scenarios and user needs.

[0029] In one embodiment of the present invention, the S14 includes: Integrate the raw data collected by the weather station, conduct quality assessment on the integrated data, detect outliers and missing values ​​through statistical methods, and perform correction or interpolation processing; Through time series analysis technology, meteorological data is modeled to capture the time dependence and periodicity in the data; and key features in the time series are extracted; Based on the extracted features, a multivariate prediction model is constructed to predict the meteorological parameters in the future. Through cross-validation or rolling forecast, the prediction results are verified to evaluate the prediction accuracy and generalization ability of the model. According to the verification results, the parameters of the prediction model are adjusted or the structure is optimized; Combining historical meteorological data with actual observation data, post-processing the prediction results, including smoothing or trend correction, and applying the prediction results to the anti-reflection optimization model of the photovoltaic array to provide the model with meteorological condition input for a period of time in the future; Based on the feedback mechanism, the actual observed meteorological data are compared with the forecast results, the source of the forecast error is analyzed, and the forecast model is continuously improved and optimized accordingly.

[0030] The working principle of the above technical solution is: integrate the raw data collected by the meteorological station, and the raw data includes key meteorological parameters such as wind speed, wind direction, temperature and humidity. Subsequently, the quality of the integrated data is evaluated, and the outliers and missing values ​​in the data are detected using statistical methods (such as mean, standard deviation, box plot, etc.). For the detected outliers, they are corrected or eliminated according to their specific circumstances; for missing values, interpolation methods (such as linear interpolation, Lagrange interpolation, etc.) are used to fill them; meteorological data are modeled through time series analysis techniques, such as ARIMA (autoregressive integrated moving average) model or LSTM (long short-term memory) neural network. The model can capture the time dependence and periodicity in the data, thereby extracting the key features in the time series, including trend terms, seasonal terms and random terms. Based on the extracted time series features, a multivariate prediction model is constructed, such as multivariate linear regression, support vector machine (SVM) or random forest. The model can comprehensively consider the mutual influence between multiple meteorological parameters and predict meteorological parameters in the future. The prediction results include key meteorological parameters such as wind speed, wind direction, temperature and humidity, which provide necessary input information for the anti-reflection optimization model of the photovoltaic array; the prediction results are verified by cross-validation or rolling prediction methods to evaluate the prediction accuracy and generalization ability of the model. According to the verification results, the prediction model is parameter adjusted or the structure is optimized to improve the prediction performance of the model. Combining historical meteorological data with actual observation data, the prediction results are post-processed, including smoothing or trend correction, to further improve the accuracy and availability of the prediction results. Subsequently, the processed prediction results are applied to the anti-reflection optimization model of the photovoltaic array to provide the model with meteorological conditions input for a period of time in the future. By simulating and predicting the reflectivity and power generation efficiency of photovoltaic modules under different weather conditions, a scientific basis is provided for the operation, maintenance and optimization of the photovoltaic array; based on the feedback mechanism, the actual observed meteorological data is compared with the prediction results to analyze the source and cause of the prediction error. According to the analysis results, the prediction model is continuously improved and optimized to improve the prediction accuracy and generalization ability of the model.

[0031] The effects of the above technical solutions are as follows: by integrating the original data collected by the meteorological station, the comprehensiveness and completeness of the data are ensured; statistical methods are used to evaluate the quality of the data, effectively detect and correct outliers and missing values, and improve the accuracy and reliability of the data; time series analysis technologies such as ARIMA model or LSTM neural network are used to model the meteorological data, successfully capturing the time dependence and periodicity in the data; key features in the time series, such as trend terms, seasonal terms and random terms, are extracted, providing an important basis for subsequent predictions; based on the extracted features, multivariate prediction models such as multivariate linear regression, support vector machine (SVM) or random forest are constructed, which can comprehensively consider the mutual influence between multiple meteorological parameters and accurately predict meteorological parameters in the future; the prediction results are verified by methods such as cross-validation or rolling prediction to ensure the prediction accuracy and generalization ability of the model ; According to the verification results, the prediction model is adjusted in parameters or optimized in structure to further improve the prediction performance of the model; the prediction results are applied to the anti-reflection optimization model of the photovoltaic array, providing the model with meteorological conditions input for a period of time in the future; this enables the model to simulate and predict the reflectivity and power generation efficiency of photovoltaic modules under different weather conditions, providing a scientific basis for the operation and optimization of the photovoltaic array; by optimizing the anti-reflection performance of the photovoltaic array, the light absorption efficiency of the photovoltaic modules is improved; thereby improving the power generation efficiency and economic benefits of the photovoltaic power station and reducing the operation and maintenance costs; a feedback mechanism based on actual observation data is established to compare the actual observed meteorological data with the prediction results; based on the feedback results, the prediction model is continuously improved and optimized to improve the adaptability and accuracy of the model; the model is able to better adapt to different meteorological conditions and the operation conditions of the photovoltaic array, providing strong support for the efficient use of photovoltaic energy.

[0032] In one embodiment of the present invention, the S2 includes: S21. Based on the material properties of photovoltaic modules (such as silicon wafer type, surface treatment, etc.), the basic framework of the anti-reflection model is established, the structural parameters of the anti-reflection film (such as film material, thickness, refractive index, etc.) are introduced, and the dynamic reflectivity prediction part of the anti-reflection model is constructed in combination with the changes in the solar incident angle and azimuth angle; S22. Based on the changes in the aircraft's flight path and altitude, the interference degree of the photovoltaic modules on the pilot's vision at different flight stages is evaluated through three-dimensional visual simulation technology, and the visual image evaluation model of the anti-reflection model is improved based on the evaluation results; S23. Verify the accuracy of the anti-reflection model through simulation, and adjust the anti-reflection model parameters based on the verification results; and train the anti-reflection model through a machine learning algorithm.

[0033] The working principle of the above technical solution is as follows: according to the material properties of photovoltaic modules (such as silicon wafer type, surface treatment, etc.), the basic framework of the anti-reflection model is constructed. The material properties have an important influence on the reflection, absorption and transmission behavior of light, and are the basis for model construction; then, the structural parameters of the anti-reflection film (such as film material, thickness, refractive index, etc.) are introduced into the model. The parameters determine the optical properties of the anti-reflection film, which in turn affects the reflectivity of the photovoltaic module; combined with the changes in the solar incident angle and azimuth, the dynamic reflectivity prediction part of the anti-reflection model is constructed. Since the position of the sun changes over time, the intensity and angle of solar radiation received by the photovoltaic modules will also change, so the model needs to be able to dynamically predict the reflectivity under different sun positions; based on the changes in the aircraft's flight path and altitude, use three-dimensional visual simulation technology to simulate the degree of interference of photovoltaic modules on the pilot's line of sight at different flight stages, including simulating factors such as the pilot's perspective, flight altitude, speed, and the layout and size of photovoltaic modules; based on the simulation results, evaluate the degree of interference of photovoltaic modules on the pilot's line of sight at different flight stages, and improve the visual image evaluation model of the anti-reflection model based on the evaluation results, including adjusting the model parameters to reduce interference, or proposing improvement suggestions to reduce the potential impact of photovoltaic modules on flight safety; verify the accuracy of the anti-reflection model through simulation, including comparing the model prediction results with experimental data or actual observation data to evaluate the model's prediction performance; based on the verification results, adjust the parameters of the anti-reflection model, including adjusting the film material, thickness, refractive index and other structural parameters, and optimizing the prediction algorithm of the solar incidence angle and azimuth, etc., to improve the prediction accuracy of the model; use machine learning algorithms to train the anti-reflection model. Through training, the model can learn more about the material properties of photovoltaic modules, the structural parameters of anti-reflection films, and the influence of the sun's position on reflectivity, thereby improving the model's prediction accuracy and generalization ability. During the training process, supervised learning or unsupervised learning methods can be used to select appropriate algorithms and parameters based on specific application scenarios and data characteristics.

[0034] The effects of the above technical solution are as follows: by introducing the material properties of photovoltaic modules and the structural parameters of the anti-reflection film, combined with the changes in the solar incident angle and azimuth, the dynamic reflectivity prediction part of the anti-reflection model is constructed, so that the model can accurately predict the reflectivity of photovoltaic modules under different lighting conditions, providing a scientific basis for optimizing the anti-reflection performance of photovoltaic modules; based on the prediction results of the model, the anti-reflection design of photovoltaic modules can be optimized. For example, the film material, thickness, refractive index and other parameters are adjusted to reduce the reflectivity and improve the light absorption efficiency of photovoltaic modules, thereby increasing the power generation and economic benefits of photovoltaic power stations; through three-dimensional visual simulation technology, the interference degree of photovoltaic modules on the pilot's line of sight in different flight stages is evaluated, potential safety hazards are identified, and guidance is provided for the layout and planning of photovoltaic power stations to ensure flight safety; based on the evaluation results, the visual image evaluation model of the anti-reflection model is improved. The model can more accurately predict the degree of interference of photovoltaic modules on the pilot's vision at different flight stages, providing more reliable technical support for flight safety assessment; the accuracy of the anti-reflection model is verified through simulation, which helps to find potential problems in the model and adjust the model parameters to improve the prediction accuracy of the model; the anti-reflection model is trained using a machine learning algorithm. Through training, the model can learn more about the material properties of photovoltaic modules, the structural parameters of the anti-reflection film, and the influence of the change in the position of the sun on the reflectivity, improve the prediction accuracy and generalization ability of the model, and enable the model to better adapt to different lighting conditions and photovoltaic module types; the application of this technical solution will promote the innovation and development of photovoltaic module anti-reflection technology. By continuously optimizing model parameters and design solutions, it will help improve the performance and efficiency of photovoltaic modules, reduce production costs, and promote the sustainable development of the photovoltaic industry; with the improvement of photovoltaic module performance and the reduction of costs, the photovoltaic industry will be more competitive.

[0035] In one embodiment of the present invention, the S21 includes: Analyze the main material properties of photovoltaic modules, including silicon wafer types (such as monocrystalline silicon, polycrystalline silicon), surface treatment processes (such as acid etching, alkaline etching, polishing, etc.) and surface microstructures (such as pyramid structures, nanowires, etc.). Based on the material property analysis, establish the basic framework of the anti-reflection model, including the optical transmission matrix model, Fresnel reflection coefficient calculation, etc.; Based on the known optical properties of different anti-reflection film materials (such as silicon dioxide, silicon nitride, titanium oxide, etc.), including refractive index, extinction coefficient, etc., as well as the transmittance and reflectivity of these materials at different wavelengths, the structural parameters of the anti-reflection film are designed, including the number of film layers, the thickness of each film layer, the refractive index, etc., and the optimal film layer combination is found through optimization algorithms (such as genetic algorithm, particle swarm optimization, etc.); The designed anti-reflection film structural parameters are introduced into the anti-reflection model, and the reflectivity under different film layer combinations is calculated by numerical simulation and compared with the experimental data for verification; the reflectivity of the multilayer film system is calculated by the following formula:

[0036] in, represents the interface reflection coefficient between the jth film and air or substrate; represents the phase thickness of the j-th film, where represents the refractive index of the j-th film; Indicates the thickness of the film; Indicates wavelength; represents the reflection coefficient of the incident medium; It represents the reflection coefficient of the interface between the topmost film and air; N represents the number of film layers; represents an imaginary unit; A mathematical relationship model between the solar incident angle and azimuth and the reflectivity of the photovoltaic module surface is established. The changes in the solar incident angle and azimuth are measured in real time by solar tracking sensors. These data are input into the anti-reflection model to calculate the reflectivity of the photovoltaic module at different times and seasons. The reflectivity changes with the solar incident angle. The change in is calculated using the following formula:

[0037] in, The incident angle is The reflectivity at represents the refractive index of the incident medium (air); Represents the effective refractive efficiency of the photovoltaic module surface (taking into account the effect of anti-reflection film); Represents the relative refractive index; Based on the dynamic reflectivity prediction algorithm, combined with historical meteorological data and sun track prediction, the reflectivity in the future period is predicted; The reflectivity data of photovoltaic modules under different conditions were collected and compared with the model prediction results for verification. Based on the verification results, the parameters of the anti-reflection model were adjusted.

[0038] The working principle of the above technical solution is as follows: conduct an in-depth analysis of the main material properties of photovoltaic modules, including silicon wafer types (such as monocrystalline silicon, polycrystalline silicon), surface treatment processes (such as acid etching, alkali etching, polishing, etc.) and surface microstructures (such as pyramid structures, nanowires, etc.). The properties have an important influence on the reflection, absorption and transmission behavior of light; based on the material property analysis, build the basic framework of the anti-reflection model, including the optical transmission matrix model, which is used to describe the transmission process of light in multi-layer media; and the Fresnel reflection coefficient calculation, which is used to calculate the reflection coefficient of light on the interface of different media; analyze the optical properties of different anti-reflection film materials (such as silicon dioxide, silicon nitride, titanium oxide, etc.), including refractive index, extinction coefficient, etc., as well as the transmittance and reflectivity of these materials at different wavelengths; based on the known optical properties, design the structural parameters of the anti-reflection film, including the number of film layers, the thickness of each film layer, the refractive index, etc.; use optimization algorithms (such as genetic algorithms, particle swarm optimization, etc.) to find the optimal film layer combination to achieve the lowest reflectivity; The anti-reflection film structural parameters are introduced into the anti-reflection model. The reflectivity under different film layer combinations is calculated through numerical simulation, and compared with experimental data to ensure the accuracy of the model. Considering the impact of the incident angle and polarization state of sunlight on the reflectivity, a mathematical relationship model between the solar incident angle and azimuth and the reflectivity of the photovoltaic module surface is established. The changes in the solar incident angle and azimuth are measured in real time by the solar tracking sensor, and these data are input into the anti-reflection model to calculate the reflectivity of the photovoltaic module at different times and seasons. Based on the dynamic reflectivity prediction algorithm, combined with historical meteorological data and solar trajectory prediction, the reflectivity in the future is predicted. It helps to provide real-time guidance for the anti-reflection optimization of the photovoltaic array; through laboratory measurements and outdoor experiments, the reflectivity data of photovoltaic modules under different conditions are collected and compared with the model prediction results. Based on the verification results, the parameters of the anti-reflection model are adjusted, including material characteristic parameters, film layer structure parameters, and mathematical model parameters of solar incident angle and azimuth.

[0039] The effects of the above technical solutions are as follows: through in-depth analysis of the main material properties of photovoltaic modules, including silicon wafer type, surface treatment process and surface microstructure, the basic framework of the anti-reflection model is established, which helps to more accurately describe the reflection, absorption and transmission behavior of light on the surface of photovoltaic modules, thereby improving the prediction accuracy of the model; based on the optical properties of different anti-reflection film materials, reasonable anti-reflection film structure parameters are designed. The optimal film layer combination is found through the optimization algorithm to achieve the lowest reflectivity, thereby improving the light absorption efficiency of photovoltaic modules; a mathematical relationship model between the solar incident angle and azimuth angle and the reflectivity of the photovoltaic module surface is established, and the influence of the incident angle and polarization state of sunlight on the reflectivity is considered. The changes in the solar incident angle and azimuth angle are measured in real time by the solar tracking sensor, and the reflectivity in the future is predicted in combination with the dynamic reflectivity prediction algorithm, which provides real-time guidance for the anti-reflection optimization of the photovoltaic array and helps to increase the power generation of photovoltaic power stations; through laboratory measurements and outdoor experiments, the reflectivity data of photovoltaic modules under different conditions are collected, and compared with the model prediction results for verification. Based on the verification results, the parameters of the anti-reflection model were adjusted, including material property parameters, film structure parameters, and mathematical model parameters of the solar incident angle and azimuth, etc., which improved the accuracy and generalization ability of the model, helped the long-term stable operation of photovoltaic power stations, and improved economic benefits; the application of this technical solution promoted the innovation and development of photovoltaic module anti-reflection technology. By continuously optimizing the design and preparation process of anti-reflection films, the performance and efficiency of photovoltaic modules have been improved. At the same time, it also provides strong technical support for the upgrading and transformation of the photovoltaic industry; in the implementation of the technical solution, cross-disciplinary and cross-field cooperation and communication are needed. The reflectivity formula of the above-mentioned multi-layer film system takes into account the superposition effect of multi-layer anti-reflection films, and can analyze and calculate the overall reflectivity of photovoltaic modules with multi-layer structures; by introducing phase thickness, the interference effect of different film layers on light can be accurately calculated to ensure that the designed film layer can effectively reduce reflection; by accurately calculating the reflectivity of multi-layer films, it helps to design more efficient photovoltaic modules and reduce light loss; by deeply understanding the interference phenomenon between film layers, it helps to improve the overall light absorption capacity of the module; by accurately adjusting the film thickness, the energy conversion efficiency of photovoltaic modules can be significantly improved. The above-mentioned calculation formula for the change of reflectivity with the solar incident angle θ mainly considers the influence of the incident angle on the reflectivity, and is suitable for the performance analysis of photovoltaic modules in dynamic scenes; through the introduction of the effective refractive index, the influence of the anti-reflection film on light can be considered, which is suitable for the reflection behavior analysis of photovoltaic modules at different angles in practical applications; it can quickly evaluate the reflection of photovoltaic modules at different solar angles and improve the adaptability of the modules; the formula is relatively simple, which is convenient for rapid preliminary evaluation of optical performance, saving time and computing resources; it provides reliable predictions for the performance of photovoltaic modules under actual operating conditions and helps optimize installation angles and directions.

[0040] In one embodiment of the present invention, S3 includes: S31. Based on the output results of the anti-reflection model, apply the principles of material science to screen the best anti-reflection film material combination and optimize the film structure to minimize the reflectivity at different solar angles.

[0041] S32. Based on the intelligent coating technology, the anti-reflection film is adaptively adjusted, wherein the adaptive adjustment includes automatically adjusting the refractive index of the film layer according to the position of the sun; S33. For specific time periods, such as aircraft takeoff and landing, the overlap area between the flight path and the photovoltaic array is predicted through an algorithm, and the inclination and azimuth angles of the photovoltaic modules in the affected area are dynamically adjusted based on the prediction results; S34. Through the intelligent dispatching system, the overall layout of the photovoltaic array is automatically adjusted according to real-time meteorological data and flight information.

[0042] The working principle of the above technical solution is as follows: Based on the output results of the anti-reflection model, this step uses the principles of material science to screen different anti-reflection film materials. This includes considering the refractive index, extinction coefficient, stability, and transmission and reflection characteristics of the material at different wavelengths; through the optimization algorithm, the film structure is refined, including the number of film layers, the thickness of each film layer, and the adjustment of parameters such as the refractive index to minimize the reflectivity at different solar angles. Using intelligent coating technology, the anti-reflection film can be adaptively adjusted according to the changes in the position of the sun, including real-time detection of the sun's position through built-in sensors, and automatic adjustment of the refractive index of the film layer based on these data; the purpose of adaptive adjustment is to ensure that the anti-reflection film can maintain the best anti-reflection effect at different solar angles, thereby further improving the light absorption efficiency of photovoltaic modules; for specific time periods, such as aircraft takeoff and landing, this step predicts the overlapping area of ​​the flight path and the photovoltaic array through an algorithm. This includes considering factors such as the aircraft's flight altitude, speed, route, and the geographical location and layout of the photovoltaic array; based on the prediction results, the inclination and azimuth of the photovoltaic module layout in the affected area are dynamically adjusted. This step aims to reduce the interference of the photovoltaic array on the flight line of sight and ensure flight safety. This step uses the intelligent scheduling system to automatically adjust the overall layout of the photovoltaic array based on real-time meteorological data and flight information, including factors such as wind speed, wind direction, cloud thickness, solar radiation intensity, and flight density. The goal of the intelligent scheduling system is to maximize the photovoltaic conversion efficiency while ensuring flight safety. By adjusting the layout of the photovoltaic array in real time, the angle and area of ​​sunlight reception can be optimized, thereby improving the power generation efficiency of photovoltaic modules.

[0043] The effects of the above technical solutions are as follows: by applying the principles of material science, screening out the best anti-reflection film material combination and optimizing the film structure, the reflectivity of photovoltaic modules at different solar angles can be significantly reduced, thereby improving the photoelectric conversion efficiency; the optimized anti-reflection film material combination not only has excellent anti-reflection performance, but also may have better weather resistance, corrosion resistance and wear resistance, thereby extending the service life of photovoltaic modules; based on intelligent coating technology, the anti-reflection film can be adaptively adjusted according to changes in the position of the sun, and the refractive index of the film layer can be automatically adjusted to ensure that the best anti-reflection effect is maintained in different time periods; by responding to changes in the position of the sun in real time, the intelligent coating technology can capture sunlight to the maximum extent, Improve the energy efficiency of photovoltaic modules, thereby improving the power generation capacity of the entire photovoltaic system; for specific time periods (such as aircraft takeoff and landing), the overlap area between the flight path and the photovoltaic array is predicted through algorithms, and the inclination and azimuth of the photovoltaic module layout in the affected area are dynamically adjusted, which can significantly reduce the interference of the photovoltaic array on the flight line of sight and ensure flight safety; by reducing visual interference, this technical solution helps to improve the public's acceptance and satisfaction with photovoltaic power stations, and create favorable conditions for the promotion and popularization of photovoltaic power stations; through the intelligent scheduling system, the overall layout of the photovoltaic array is automatically adjusted according to real-time meteorological data and flight information, which can maximize the photovoltaic conversion efficiency while ensuring flight safety. This intelligent scheduling method achieves dual protection of flight safety and photovoltaic efficiency; the intelligent scheduling system can flexibly adjust the layout of the photovoltaic array according to actual conditions, so as to adapt to different meteorological conditions and flight requirements. This flexibility helps to improve the stability and reliability of energy production.

[0044] In one embodiment of the present invention, the S33 includes: Integrate the airspace management system and the airport flight information system to obtain real-time flight path data of aircraft takeoff, landing and other specific periods, including flight altitude, speed and direction; Based on geographic information system (GIS) and 3D modeling technology, an accurate 3D model of the photovoltaic array is constructed, including the size, position, inclination, azimuth and other parameters of the photovoltaic modules. Combined with flight path data and photovoltaic array model, spatial analysis algorithms are used to predict the overlap area between the aircraft and the photovoltaic array within a specific period of time, including the overlap time, position, range, etc. Based on the overlap area prediction results, the photovoltaic module areas affected by aircraft flight, that is, potential visual interference areas, are identified. Based on material science and optical principles, the reflectivity and photovoltaic conversion efficiency of photovoltaic modules at different inclination and azimuth angles are analyzed. Through the dynamic adjustment algorithm, the dynamic adjustment plan of the photovoltaic modules in the affected area is calculated according to the prediction results and the optimal combination of inclination and azimuth.

[0045] The working principle of the above technical solution is as follows: by connecting with the interface of the airspace management system and the airport flight information system, the flight path data of the aircraft during specific periods such as take-off and landing can be obtained in real time; the flight path data includes key information such as the aircraft's flight altitude, speed, direction, and expected take-off and landing time; using GIS technology, combined with the geographical location information of the photovoltaic power station, an accurate three-dimensional model of the photovoltaic array is constructed; the three-dimensional model describes in detail the key parameters such as the size, position, inclination, and azimuth of the photovoltaic components, providing a basis for subsequent spatial analysis; combining the flight path data and the photovoltaic array model, using the spatial analysis algorithm, predicting the overlapping area between the aircraft and the photovoltaic array within a specific period of time; the overlapping area prediction includes detailed information such as the overlapping time, position, and range, providing a basis for subsequent dynamic adjustments; based on the overlapping area prediction results, the photovoltaic components affected by the aircraft flight are identified area, that is, the potential visual interference area; use material science and optical principles to analyze the reflectivity and photovoltaic conversion efficiency of photovoltaic modules at different inclinations and azimuths; under the premise of ensuring flight safety, determine the optimal inclination and azimuth combination that maximizes photovoltaic conversion efficiency through comparative analysis; according to the prediction results and the optimal inclination and azimuth combination, use the dynamic adjustment algorithm to calculate the dynamic adjustment plan of photovoltaic modules in the affected area; the dynamic adjustment plan includes detailed information such as the time, amplitude, and sequence of adjustment to ensure that the photoelectric conversion efficiency of photovoltaic modules is maximized without affecting flight safety; in the actual implementation process, the inclination and azimuth of photovoltaic modules are dynamically adjusted through the intelligent control system to achieve real-time coordination with the flight path; at the same time, collect data during the adjustment process, such as the power generation efficiency and reflectivity changes of photovoltaic modules, which are used to verify and optimize the adjustment plan.

[0046] The effects of the above technical solutions are: by integrating the airspace management system and the airport flight information system, the flight path data of the aircraft during specific periods such as takeoff and landing can be obtained in real time, including flight altitude, speed and direction, providing an accurate data basis for subsequent spatial analysis and overlapping area prediction; based on the geographic information system (GIS) and three-dimensional modeling technology, an accurate three-dimensional model of the photovoltaic array is constructed, including the size, position, inclination, azimuth and other parameters of the photovoltaic modules. This accuracy helps to more accurately predict the overlap between the aircraft and the photovoltaic array; combining the flight path data and the photovoltaic array model, using the spatial analysis algorithm, it is possible to predict the overlap area between the aircraft and the photovoltaic array within a specific period of time, including the overlap time, location, range, etc. This provides an important basis for identifying the photovoltaic module area affected by the aircraft flight; based on the overlapping area prediction results, the photovoltaic module area affected by the aircraft flight, that is, the potential visual interference area, is identified. Through the analysis of material science and optical principles, the optimal combination of inclination and azimuth angles that maximizes photovoltaic conversion efficiency while ensuring flight safety can be determined, thereby improving the overall power generation efficiency of the photovoltaic array. Through the dynamic adjustment algorithm, based on the prediction results and the optimal combination of inclination and azimuth angles, the dynamic adjustment plan of photovoltaic modules in the affected area is calculated, including the adjustment time, amplitude, sequence, etc., to ensure that the photovoltaic array can maintain a safe and efficient operating state during the flight of the aircraft.

[0047] In one embodiment of the present invention, the S4 includes: S41. Use high-precision photoelectric conversion efficiency monitoring equipment to record the power generation efficiency of the photovoltaic array in real time, and use a reflectivity measuring instrument to continuously monitor the reflectivity of the photovoltaic module surface; S42. Based on the data feedback mechanism, the real-time monitoring data is input into the anti-reflection model, and dynamic adjustments are made. The reinforcement learning algorithm is used to continuously optimize the anti-reflection strategy. S43. Regularly evaluate the impact of optimization measures on the environment, including reducing light pollution and improving energy efficiency, and continuously adjust the optimization strategy based on the evaluation results.

[0048] The working principle of the above technical solution is: through high-precision photoelectric conversion efficiency monitoring equipment, the power generation efficiency of the photovoltaic array is recorded in real time and accurately. At the same time, the reflectivity of the surface of the photovoltaic module is continuously monitored by a reflectivity meter. Reflectivity is an important indicator for measuring the anti-reflection effect of photovoltaic modules, which directly affects the power generation efficiency of the photovoltaic array; combined with the monitoring data of power generation efficiency and reflectivity, the effectiveness of the implemented anti-reflection measures is evaluated. Potential problems are discovered and solved in a timely manner to ensure that the photovoltaic array always maintains the best state; based on the data feedback mechanism, the real-time monitoring data is input into the anti-reflection model. This step realizes the real-time synchronization between the model and the actual situation, providing data support for subsequent dynamic adjustments; the anti-reflection model is continuously optimized by using a reinforcement learning algorithm. The reinforcement learning algorithm is an advanced machine learning algorithm that can find the optimal solution through continuous trial and error and learning. Here, it can help the model evolve itself and adapt to different environmental conditions to ensure that the photovoltaic array always maintains the optimal anti-reflection effect and power generation efficiency; regularly evaluate the impact of optimization measures on the environment, including reducing light pollution and improving energy utilization efficiency. According to the evaluation results, the optimization strategy is continuously adjusted and improved. This helps ensure that PV arrays can improve efficiency while minimizing adverse impacts on the environment.

[0049] The effects of the above technical solutions are as follows: by real-time monitoring of the power generation efficiency of the photovoltaic array, the problem of performance degradation can be discovered and solved in time, thereby ensuring that the photovoltaic array always maintains the best working condition; the effective evaluation and optimization of anti-reflection measures can further reduce the reflection loss of light on the surface of the photovoltaic module and improve the photoelectric conversion efficiency; the use of reinforcement learning algorithms to dynamically adjust and optimize the anti-reflection model can automatically find the optimal anti-reflection strategy and reduce unnecessary material waste and manual intervention; this not only improves the performance of the photovoltaic array, but also reduces the operation and maintenance costs and enhances the economy of photovoltaic power stations; regular evaluation of the impact of optimization measures on the environment helps to ensure that the photovoltaic industry can minimize the adverse impact on the environment while improving efficiency; by reducing light pollution and improving energy utilization efficiency, it helps to promote the photovoltaic industry to develop in a more environmentally friendly and sustainable direction; by combining high-precision monitoring equipment, data feedback mechanism, reinforcement learning algorithm and other advanced technologies, the intelligent management and optimization of the photovoltaic array is realized, the operation and maintenance efficiency of photovoltaic power stations is improved, and the competitiveness of the photovoltaic industry in the market is enhanced.

[0050] One embodiment of the present invention, as Figure 2 As shown, a system for implementing a multi-angle anti-reflection optimization method for a photovoltaic array, the system comprising: Data acquisition module: collects the corresponding data of photovoltaic modules in real time through high-precision sensors, and obtains key information of aircraft through airspace management data; Model building module: Based on the collected data, an anti-reflection model is established that includes the influence of multiple factors; the multiple factors include photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth angle, and changes in viewing angle during aircraft takeoff and landing; Solution optimization module: Based on the output results of the model, the anti-reflection solution is optimized using advanced algorithms; Optimization and adjustment module: Through the real-time monitoring system, the power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions are monitored; the real-time data is fed back to the anti-reflection model, and the model is optimized and adjusted based on the feedback data.

[0051] The working principle of the above technical solution is: through high-precision sensors, key data is collected in real time at the layout position of the photovoltaic array, including the layout inclination and azimuth of the photovoltaic module, the incident angle and azimuth of the sun, and local meteorological conditions (such as temperature, humidity, wind speed, etc.). At the same time, key information such as the flight path, altitude and time of aircraft takeoff and landing is obtained through airspace management data. Then, based on the collected data, an anti-reflection model with multiple factors is established. The anti-reflection model not only considers conventional factors such as photovoltaic module materials, anti-reflection film structure, solar incident angle and azimuth changes, but also considers the special factor of the change in viewing angle when the aircraft takes off and lands. Then, according to the output results of the anti-reflection model, the algorithm is used to optimize the anti-reflection scheme, including adjusting the material and thickness of the anti-reflection film to minimize the reflectivity at different solar incident angles and azimuths, thereby improving the photovoltaic conversion efficiency. At the same time, for special situations such as aircraft takeoff and landing, the layout inclination and azimuth of the photovoltaic module are adjusted to reduce interference with the pilot's viewing angle and ensure flight safety. Finally, the power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions, are monitored through a real-time monitoring system. The real-time data is fed back to the anti-reflection model, which is then optimized and adjusted based on the feedback data.

[0052] The effect of the above technical solution is: by collecting data in real time through high-precision sensors, the changes in the solar incident angle and azimuth angle can be accurately understood, so that the material and thickness of the anti-reflection film can be adjusted according to these changes. This targeted optimization can minimize the reflectivity, so that more sunlight can be absorbed by the photovoltaic modules, thereby improving the photovoltaic conversion efficiency; by real-time monitoring of local meteorological conditions, such as temperature, humidity, etc., these factors will affect the conversion efficiency of photovoltaic modules. According to the changes in meteorological conditions, the model can adjust the anti-reflection strategy in real time to ensure that the photovoltaic array can maintain the best conversion efficiency under different weather conditions; by obtaining key information such as the flight path, altitude and time of aircraft takeoff and landing, and combining the layout inclination and azimuth of photovoltaic modules, the degree of interference of light reflection on the pilot's perspective can be accurately calculated. In response to this situation, the solution can adjust the layout of photovoltaic modules to reduce interference with the pilot's perspective, thereby ensuring flight safety; through the real-time monitoring system, problems in the photovoltaic array in terms of power generation efficiency and reflectivity can be discovered in time, and real-time data can be fed back to the anti-reflection model. The model is then optimized and adjusted based on the feedback data. Through the closed-loop feedback mechanism, it can ensure that the photovoltaic array can maintain the best anti-reflection effect and photovoltaic conversion efficiency under different conditions, thereby improving the stability and reliability of the entire photovoltaic system. Through real-time monitoring and optimization, problems in the photovoltaic array can be discovered and resolved in a timely manner, reducing operation and maintenance costs and extending the service life of the photovoltaic array. The optimized photovoltaic array can more effectively utilize solar energy resources, improve energy utilization efficiency, and help promote the sustainable development of the photovoltaic industry.

[0053] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A photovoltaic array multi-angle anti-reflection optimization method, characterized in that: The method comprises: S1. Collect the corresponding data of photovoltaic modules in real time through high-precision sensors, and obtain key information of aircraft through airspace management data; S2. Based on the collected data, an anti-reflection model including the influence of multiple factors is established; the multiple factors include photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth angle, and changes in viewing angle during aircraft takeoff and landing; S3. Optimizing the anti-reflection scheme through an optimization algorithm according to the output results of the model; S4. Monitor the power generation efficiency and reflectivity of the photovoltaic array and changes in meteorological conditions through a real-time monitoring system; feed the real-time data back to the anti-reflection model, which is then optimized and adjusted based on the feedback data.

2. According to claim 1, a photovoltaic array multi-angle anti-reflection optimization method is characterized in that: Said S1 comprises: S11. Install multiple types of high-precision sensors at key locations of the photovoltaic array, including tilt sensors, azimuth sensors, sun tracking sensors, and weather stations; S12, integrated airspace management system data interface, real-time acquisition of flight information of nearby airports, the flight information including flight path, altitude and time of take-off and landing of aircraft; S13. Based on the Internet of Things technology, the data collected by each sensor is transmitted to the central data processing center in real time, and the data is pre-processed by the central data processing center; S14. Conduct time series analysis on meteorological data, predict weather trends in the future, and provide a basis for model prediction based on the prediction results; S15. Combine flight information with the spatial layout of the photovoltaic array to analyze the visibility of photovoltaic modules from the perspective of the aircraft.

3. According to claim 2, a photovoltaic array multi-angle anti-reflection optimization method is characterized in that: The S13 comprises: Build a multi-path transmission network, use different communication links as backup or parallel transmission channels, and dynamically adjust the transmission path according to the real-time network status; Based on intelligent load balancing algorithm, data traffic is dynamically allocated according to the real-time load, delay and packet loss rate of each transmission path; Build a distributed data preprocessing pipeline, using cloud computing or edge computing resources to distribute preprocessing tasks to multiple nodes for parallel processing; In the preprocessing stage, machine learning algorithms are introduced to quickly extract features and detect anomalies from sensor data. Based on streaming data processing technology, real-time data is processed instantly. Based on the cache and buffer mechanism, data that cannot be processed immediately is cached and processed when the processing resources are idle, and the cache strategy is optimized.

4. According to claim 2, a photovoltaic array multi-angle anti-reflection optimization method is characterized in that: The S14 comprises: Integrate the raw data collected by the weather station, conduct quality assessment on the integrated data, detect outliers and missing values ​​through statistical methods, and perform correction or interpolation processing; Through time series analysis technology, meteorological data is modeled to capture the time dependence and periodicity in the data; and key features in the time series are extracted; Based on the extracted features, a multivariate prediction model is constructed to predict the meteorological parameters in the future. Through cross-validation or rolling forecast, the prediction results are verified to evaluate the prediction accuracy and generalization ability of the model. According to the verification results, the parameters of the prediction model are adjusted or the structure is optimized; Combining historical meteorological data with actual observation data, the prediction results are post-processed and applied to the anti-reflection optimization model of the photovoltaic array to provide the model with meteorological conditions input for a period of time in the future; Based on the feedback mechanism, the actual observed meteorological data are compared with the forecast results, the source of the forecast error is analyzed, and the forecast model is continuously improved and optimized accordingly.

5. A photovoltaic array multi-angle anti-reflection optimization method according to claim 1, characterized in that: The S2 comprises: S21. Based on the material properties of photovoltaic modules, the basic framework of the anti-reflection model is established, the structural parameters of the anti-reflection film are introduced, and the dynamic reflectivity prediction part of the anti-reflection model is constructed in combination with the changes in the solar incident angle and azimuth angle; S22. Based on the changes in the aircraft's flight path and altitude, the interference degree of the photovoltaic modules on the pilot's vision at different flight stages is evaluated through three-dimensional visual simulation technology, and the visual image evaluation model of the anti-reflection model is improved based on the evaluation results; S23. Verify the accuracy of the anti-reflection model through simulation, and adjust the anti-reflection model parameters based on the verification results; and train the anti-reflection model through a machine learning algorithm.

6. A photovoltaic array multi-angle anti-reflection optimization method according to claim 5, characterized in that: The S21 includes: Analyze the main material properties of photovoltaic modules and establish the basic framework of anti-reflection model based on the material property analysis; Based on the known optical properties of different anti-reflection film materials, the structural parameters of the anti-reflection film are designed, and the optimal film layer combination is found through the optimization algorithm; The designed anti-reflection film structural parameters are introduced into the anti-reflection model, and the reflectivity under different film layer combinations is calculated through numerical simulation, and compared with experimental data for verification; Establish a mathematical relationship model between the solar incident angle and azimuth and the reflectivity of the photovoltaic module surface. Use a solar tracking sensor to measure the changes in the solar incident angle and azimuth in real time. Input these data into the anti-reflection model to calculate the reflectivity of the photovoltaic module at different times and seasons. Based on the dynamic reflectivity prediction algorithm, combined with historical meteorological data and sun track prediction, the reflectivity in the future period is predicted; The reflectivity data of photovoltaic modules under different conditions were collected and compared with the model prediction results for verification. Based on the verification results, the parameters of the anti-reflection model were adjusted.

7. A photovoltaic array multi-angle anti-reflection optimization method according to claim 1, characterized in that: The S3 includes: S31. Based on the output of the anti-reflection model, apply the principles of material science to screen the best anti-reflection film material combination and optimize the film structure to minimize the reflectivity at different sun angles; S32. Based on the intelligent coating technology, the anti-reflection film is adaptively adjusted, wherein the adaptive adjustment includes automatically adjusting the refractive index of the film layer according to the position of the sun; S33. For a specific period of time, an algorithm is used to predict the overlapping area between the flight path and the photovoltaic array, and based on the prediction results, the inclination angle and azimuth angle of the photovoltaic module layout in the affected area are dynamically adjusted; S34. Through the intelligent dispatching system, the overall layout of the photovoltaic array is automatically adjusted according to real-time meteorological data and flight information.

8. A photovoltaic array multi-angle anti-reflection optimization method according to claim 1, characterized in that: The S33 comprises: Integrate the airspace management system and the airport flight information system to obtain real-time flight path data for a specific period of time, including flight altitude, speed and direction; Based on geographic information system (GIS) and 3D modeling technology, an accurate 3D model of the photovoltaic array is constructed. By combining flight path data and the photovoltaic array model, spatial analysis algorithms are used to predict the overlap area between the aircraft and the photovoltaic array in a specific period of time. Based on the overlap area prediction results, the photovoltaic module areas affected by aircraft flight, that is, potential visual interference areas, are identified. Based on material science and optical principles, the reflectivity and photovoltaic conversion efficiency of photovoltaic modules at different inclination and azimuth angles are analyzed. Through the dynamic adjustment algorithm, the dynamic adjustment plan of the photovoltaic modules in the affected area is calculated according to the prediction results and the optimal combination of inclination and azimuth.

9. A photovoltaic array multi-angle anti-reflection optimization method according to claim 1, characterized in that: The S4 comprises: S41. Use high-precision photoelectric conversion efficiency monitoring equipment to record the power generation efficiency of the photovoltaic array in real time, and use a reflectivity measuring instrument to continuously monitor the reflectivity of the photovoltaic module surface; S42. Based on the data feedback mechanism, the real-time monitoring data is input into the anti-reflection model, and dynamic adjustments are made. The reinforcement learning algorithm is used to continuously optimize the anti-reflection strategy. S43. Regularly evaluate the impact of optimization measures on the environment, including reducing light pollution and improving energy efficiency, and continuously adjust the optimization strategy based on the evaluation results.

10. A system for implementing the photovoltaic array multi-angle anti-reflection optimization method according to claim 1, characterized in that: The system comprises: Data acquisition module: collects the corresponding data of photovoltaic modules in real time through high-precision sensors, and obtains key information of aircraft through airspace management data; Model building module: Based on the collected data, an anti-reflection model is established that includes the influence of multiple factors; the multiple factors include photovoltaic module materials, anti-reflection film structure, changes in solar incident angle and azimuth angle, and changes in viewing angle during aircraft takeoff and landing; Solution optimization module: Based on the output results of the model, the anti-reflection solution is optimized using advanced algorithms; Optimization and adjustment module: Through the real-time monitoring system, the power generation efficiency and reflectivity of the photovoltaic array, as well as changes in meteorological conditions are monitored; the real-time data is fed back to the anti-reflection model, and the model is optimized and adjusted based on the feedback data.

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