Intelligent control system and method for photovoltaic panel production and processing

Abstract

The invention discloses an intelligent control system and method for production and processing of a photovoltaic panel, and relates to the technical field of intelligent control. During operation of the system, key data involved in the production process of the photovoltaic panel is collected, a light-transmitting effect data set, a durability data set and an electrical performance data set are generated and preprocessed, and a multivariate data analysis algorithm is applied to obtain a data set; and after calculation, obtaining a quality index Zlzs of the agricultural photovoltaic panel, receiving an analysis result of the data processing module, and performing real-time quality monitoring on the photovoltaic panel subjected to production process optimization by adjusting a material ratio, production environment parameters and a process flow. A quality index Zlzs of an agricultural photovoltaic panel is compared with a preset first quality qualification threshold value M and a preset second quality qualification threshold value N, potential quality problems in production are identified, a quality evaluation strategy scheme is obtained, an evaluation result is fed back to a production process optimization module and a data processing module, and a closed-loop control system is formed. And continuous optimization and quality improvement are realized.

Description

Technical Field

[0001] The present invention relates to the field of intelligent control technology, and in particular to an intelligent control system and method for photovoltaic panel production and processing. Background Art

[0002] With the continuous development of renewable energy technology, photovoltaic power generation has been widely used around the world. Especially in the field of agriculture, photovoltaic panels are widely used in scenarios such as agricultural photovoltaic complementarity and photovoltaic greenhouses. These photovoltaic panels not only need to meet the basic needs of power generation, but also have good weather resistance and durability to cope with the challenges of long-term exposure to extreme environments. However, due to external factors such as ultraviolet radiation, humidity, heat, salt spray, and mechanical shock in the agricultural environment, the long-term performance of photovoltaic panels is often affected to varying degrees. Therefore, an intelligent control system for the production and processing of photovoltaic panels came into being to ensure the long-term stability and high efficiency of photovoltaic panels in agricultural applications.

[0003] In the traditional photovoltaic panel production process, there is often a lack of real-time monitoring and optimization of the production process, resulting in unstable quality of photovoltaic panels and difficulty in achieving expected performance indicators. In addition, the existing quality monitoring and environmental adaptability testing methods are relatively limited and cannot fully identify and solve potential quality and weather resistance problems. Through the above-mentioned photovoltaic panel production and processing intelligent control system, real-time data collection and processing can be carried out during the production process, the production process can be optimized, and the quality and adaptability of photovoltaic panels can be fully monitored and evaluated, thereby effectively improving the reliability and service life of photovoltaic panels in agricultural applications.

[0004] In the current production and application of photovoltaic panels, due to the lack of effective quality control and environmental adaptability testing, photovoltaic panels have problems such as performance degradation, insufficient weather resistance and reduced mechanical durability in long-term use. These problems are mainly caused by the unstable process of photovoltaic panels during the production process and the failure to fully consider the extreme conditions in the agricultural environment. When these problems are not discovered and solved in time, the power generation efficiency of photovoltaic panels will decrease, the maintenance cost will increase, and even the stability and benefits of the entire agricultural production system will be affected. Therefore, there is an urgent need for a comprehensive and intelligent control system to ensure the reliability and durability of photovoltaic panels under various extreme conditions. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention provides an intelligent control system and method for photovoltaic panel production and processing, which solves the problems mentioned in the background technology.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent control system for photovoltaic panel production and processing, including a data acquisition module, a data processing module, a production process optimization module, a quality monitoring module, an environmental adaptability testing module and a feedback module;

[0007] The data acquisition module is used to collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate a light transmission effect data set, a durability performance data set and an electrical performance data set, and transmit them to the data processing module;

[0008] The data processing module is used to clean, filter and pre-process the light transmission effect data set, the durability performance data set and the electrical performance data set transmitted by the data acquisition module, and apply the multivariate data analysis algorithm to calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel;

[0009] The production process optimization module is used to receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time;

[0010] The quality monitoring module is used to perform real-time quality monitoring of photovoltaic panels after production process optimization, and compares the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualified threshold M and second quality qualified threshold N, identifies potential quality problems in production, obtains quality assessment strategy solutions, and feeds back to the production process optimization module for timely adjustment. It uses online detection equipment and sensors to automatically collect quality data of photovoltaic panels at various stages of the production process and generate a quality monitoring report;

[0011] The environmental adaptability test module is used to simulate various external conditions in agricultural environments, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, with a focus on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural-photovoltaic complementary and photovoltaic greenhouse applications;

[0012] The feedback module is used to summarize the working results of each module, including feedback information of data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed back the evaluation results to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0013] Preferably, the data acquisition module includes an optical performance acquisition unit, a durability acquisition unit and an electrical performance acquisition unit;

[0014] The optical performance acquisition unit is used to collect optical performance data of the photovoltaic panel through a high-precision spectrum analyzer, a light scattering analyzer, a material light absorption measuring instrument, a surface profiler and a reflectivity tester to obtain: light transmittance T avg , light scattering rate H, light absorption coefficient K abs , the thickness of the photovoltaic panel d, the surface roughness Rs and the effectiveness of anti-reflective coatings arc , forming a light transmission effect data set;

[0015] The durability collection unit is used to collect durability data of photovoltaic panels through ultraviolet aging test box, salt spray test box, universal test machine, damp heat test box and impact test machine to obtain: ultraviolet aging rate U UV , UV irradiation time t UV , reference radiation intensity U ref 、Salt spray concentration C salt , Mechanical strength F mech , mechanical strength threshold F thres , Resistance change ΔR caused by humidity and heat cycle dh , initial resistance value R0, impact force I imp and the maximum allowable impact force I max , generate a durability performance data set;

[0016] The electrical performance acquisition unit is used to collect the electrical performance data of the photovoltaic panel through a solar simulator, an IV tester and a resistance measuring instrument to obtain: the photoelectric conversion efficiency η PV , open circuit voltage V oc , reference voltage V ref , short circuit current I sc , reference current I ref , fill factor FF and resistance loss R s and the maximum allowable resistance value R max , generate electrical performance data set.

[0017] Preferably, the data processing module includes a data preprocessing unit and a data calculation unit;

[0018] The data preprocessing unit is used to perform preliminary processing on the light transmittance effect data set, durability performance data set and electrical performance data set transmitted from the data acquisition module, and perform data cleaning: remove noise, outliers and redundant data, select key parameters related to the quality of photovoltaic panels for data filtering, integrate data sets of different categories, and perform normalization processing.

[0019] Preferably, the data calculation unit is used to apply a multivariate data analysis algorithm to perform in-depth analysis on the preprocessed data set, analyze the relationship between the light transmittance effect, durability and electrical performance, extract key performance indicators, and calculate the transmittance coefficient Tgx, durability coefficient Njx and electrical conversion efficiency coefficient Dzxxl according to the set calculation model. Based on the calculation results of the above coefficients, the quality index Zlzs of the agricultural photovoltaic panel is finally obtained, and the calculation results and related analysis data are fed back to the production process optimization module.

[0020] Preferably, the production process optimization module includes a material ratio optimization unit and a process adjustment unit;

[0021] The material ratio optimization unit is used to adjust the material selection and ratio in the production of photovoltaic panels according to the preset optimization goals. Through experiments and calculations, the optimal material formula is determined to ensure that the physical and chemical properties of the materials meet the production requirements. Through experiments and calculations, the optimal material formula is determined to ensure that the physical and chemical properties of the materials meet the production requirements;

[0022] The process adjustment unit is used to adjust the key environmental parameters involved in the production process, including temperature, humidity, and light intensity, optimize and refine the management of the production process, ensure that each process step is strictly implemented, and make fine-tuning according to real-time data. Through the real-time monitoring system, continuously track the immediate impact of production process changes on the performance of photovoltaic panels.

[0023] Preferably, the quality monitoring module includes an on-line quality monitoring unit and a quality evaluation unit;

[0024] The on-line quality monitoring unit is used to use on-line detection equipment and sensors to collect the quality data of photovoltaic panels in the production process in real time, covering the key indicators of light transmittance performance, durability, and electrical performance, monitor each stage of production in real time, compare the collected quality data with the preset standards, and identify possible quality problems in production.

[0025] Preferably, the quality evaluation unit is used to compare the real-time monitoring data with the preset first quality qualification threshold M and second quality qualification threshold N according to the quality index Zlzs of agricultural photovoltaic panels. Through analysis and evaluation, a quality evaluation strategy plan is generated, adjustment suggestions are provided, a detailed quality monitoring report is automatically generated, recording the quality status and evaluation results of each production stage, and providing data support for the further optimization of the production process;

[0026] When Zlzs > N, the overall quality of the photovoltaic panel meets the standard, but there is a deviation of ±10%, resulting in a slight performance decay during long-term use. For the minor deviations in optical performance or electrical performance, optimize the particle size distribution or purity of the raw materials, and ensure that these fine-tuning can improve the light transmittance and electrical conversion efficiency of the photovoltaic panel on the light absorption material and the conductive coating;

[0027] When M < Zlzs ≤ N, the photovoltaic panel has quality problems, affecting its application effect in agriculture. In terms of long-term durability, replace the substrate material or improve the formula of the anti-ultraviolet coating to enhance durability. The specific operations include replacing the plating material with stronger corrosion resistance and improving the anti-reflection performance of the photovoltaic panel surface;

[0028] When Zlzs≤M, the quality problems of photovoltaic panels have reached a serious level and immediate measures must be taken to correct them. Otherwise, the products will not be able to meet the application requirements in the agricultural field. The main materials must be completely replaced and anti-aging additives must be introduced.

[0029] Preferably, the environmental adaptability test module includes an extreme environment simulation unit and a mechanical performance test unit;

[0030] The extreme environment simulation unit is used to simulate various extreme conditions in the agricultural environment, including high-intensity ultraviolet radiation, hot and humid environment and salt spray corrosion. Through precise control equipment, it reproduces the challenges that photovoltaic panels may face under different climatic conditions, focuses on testing the weather resistance of photovoltaic panels under these extreme conditions, evaluates their anti-aging and corrosion resistance, and ensures the stability of photovoltaic panels in agricultural photovoltaic complementarity and photovoltaic greenhouse application scenarios;

[0031] The mechanical performance testing unit is used to test the mechanical durability of photovoltaic panels in an agricultural environment by applying mechanical shock, vibration and pressure, simulating the mechanical stress and shock encountered during actual use, including the impact of wind loads and hail.

[0032] Preferably, the feedback module is used to summarize each module and includes a comprehensive data summary unit and an evaluation result feedback unit;

[0033] The comprehensive data summary unit is used to collect and summarize the feedback data and results from each module, uniformly organize and analyze the working results of each module, form a comprehensive performance data set, and generate a comprehensive performance report, covering the performance of the photovoltaic panel in different aspects, including optical performance, durability, electrical performance and environmental adaptability;

[0034] The evaluation result feedback unit is used to analyze the overall quality and performance of the photovoltaic panel based on the comprehensive performance report, identify the existing room for improvement, and feed back the evaluation results to the relevant modules to guide the subsequent production process adjustment and data processing optimization to form a closed-loop control system.

[0035] A photovoltaic panel production and processing intelligent control method comprises the following steps:

[0036] Step 1: Collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate light transmittance effect data set, durability performance data set and electrical performance data set, and transmit them to the data processing module;

[0037] Step 2: Clean, filter and pre-process the light transmission effect data set, durability performance data set and electrical performance data set transmitted by the data acquisition module, apply the multivariate data analysis algorithm, and calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel;

[0038] Step 3: Receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time;

[0039] Step 4: Conduct real-time quality monitoring of photovoltaic panels that have undergone production process optimization, compare the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualification threshold M and second quality qualification threshold N, identify potential quality problems in production, obtain quality assessment strategy solutions, and feed back to the production process optimization module for timely adjustment. Use online detection equipment and sensors to automatically collect quality data of photovoltaic panels at various stages of the production process and generate quality monitoring reports;

[0040] Step 5: Simulate various external conditions in the agricultural environment, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, focusing on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural photovoltaic complementary and photovoltaic greenhouse applications;

[0041] Step 6: Summarize the work results of each module, including feedback information from data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed back the evaluation results to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0042] The present invention provides an intelligent control system and method for photovoltaic panel production and processing, which has the following beneficial effects:

[0043] (1) When the system is running, it collects key data involved in the production process of photovoltaic panels, generates a light transmittance effect data set, a durability performance data set, and an electrical performance data set, and performs preprocessing. It applies a multivariate data analysis algorithm to calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel, receives the analysis results of the data processing module, and performs real-time quality monitoring of the photovoltaic panels after production process optimization by adjusting the material ratio, production environment parameters, and process flow. By comparing the quality index Zlzs of the agricultural photovoltaic panel with the preset first quality qualified threshold M and the second quality qualified threshold N, it identifies potential quality problems in production, obtains a quality assessment strategy, and feeds the assessment results back to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0044] (2) By introducing data acquisition, data processing, production process optimization, quality monitoring, environmental adaptability testing and feedback modules, the system can achieve all-round monitoring and optimization of the photovoltaic panel production process. The optical performance, durability and electrical performance data collected by the data acquisition module ensure that the key indicators involved in the production process can be accurately monitored; the data processing module cleans and analyzes these data, calculates key performance indicators, and provides a scientific basis for subsequent process adjustments. Based on the data processing results, the production process optimization module adjusts the material ratio and process parameters in real time to ensure that the photovoltaic panels are always in the best state during the production process, thereby improving product quality and performance.

[0045] (3) The quality monitoring module can detect potential quality problems in a timely manner by real-time monitoring of photovoltaic panels during the production process, and generate detailed quality monitoring reports to ensure that the photovoltaic panels produced meet the preset quality standards. The environmental adaptability test module tests the weather resistance and mechanical durability of photovoltaic panels by simulating various extreme agricultural environments to ensure their long-term stability in practical applications. Finally, the feedback module summarizes the working results of each module, generates a comprehensive performance report, and feeds back the evaluation results to the relevant modules to form a closed-loop control, continuously optimize the production process and product quality, and achieve the purpose of improving the performance of photovoltaic panels, extending their service life, and reducing maintenance costs.

[0046] (4) Compared with the traditional photovoltaic panel production process, the photovoltaic panel production and processing intelligent control system has achieved significant technological breakthroughs in many aspects. First, real-time data acquisition and processing have greatly improved the accuracy and efficiency of the production process; second, the introduction of production process optimization and quality monitoring modules has significantly improved production quality, and the product qualification rate and stability have been significantly improved; finally, through environmental adaptability testing, the system can foresee and avoid potential performance degradation problems, ensuring the reliability of photovoltaic panels in various agricultural environments. Overall, the application of this intelligent control system not only improves the overall performance of photovoltaic panels, but also enhances its application value in the agricultural field, promoting the wider application and development of photovoltaic technology in agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a block diagram of an intelligent control system for photovoltaic panel production and processing according to the present invention;

[0048] Figure 2 The present invention is a schematic diagram of the steps of an intelligent control method for photovoltaic panel production and processing. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] Example 1

[0051] The present invention provides an intelligent control system for photovoltaic panel production and processing. Figure 1 , including data acquisition module, data processing module, production process optimization module, quality monitoring module, environmental adaptability test module and feedback module;

[0052] The data acquisition module is used to collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate a light transmission effect data set, a durability performance data set and an electrical performance data set, and transmit them to the data processing module;

[0053] The data processing module is used to clean, filter and pre-process the light transmission effect data set, the durability performance data set and the electrical performance data set transmitted by the data acquisition module, and apply the multivariate data analysis algorithm to calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel;

[0054] The production process optimization module is used to receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time;

[0055] The quality monitoring module is used to perform real-time quality monitoring of photovoltaic panels after production process optimization, and compare the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualified threshold M and second quality qualified threshold N, identify potential quality problems in production, obtain quality assessment strategy solutions, and feed back to the production process optimization module for timely adjustment. Online detection equipment and sensors are used to automatically collect quality data of photovoltaic panels at various stages of the production process and generate quality monitoring reports;

[0056] The environmental adaptability test module is used to simulate various external conditions in agricultural environments, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, with a focus on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural-photovoltaic complementary and photovoltaic greenhouse applications;

[0057] The feedback module is used to summarize the working results of each module, including feedback information of data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed back the evaluation results to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0058] In this embodiment, key data involved in the production process of photovoltaic panels are collected, and a light transmittance effect data set, a durability performance data set, and an electrical performance data set are generated and preprocessed. A multivariate data analysis algorithm is applied to obtain the quality index Zlzs of the agricultural photovoltaic panel after calculation, and the analysis result of the data processing module is received. By adjusting the material ratio, production environment parameters, and process flow, the photovoltaic panels with optimized production process are monitored in real time. The quality index Zlzs of the agricultural photovoltaic panel is compared with the preset first quality qualified threshold M and the second quality qualified threshold N to identify potential quality problems in production, obtain a quality assessment strategy, and feed the assessment results back to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0059] Example 2

[0060] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data acquisition module includes an optical performance acquisition unit, a durability acquisition unit and an electrical performance acquisition unit;

[0061] The optical performance acquisition unit is used to collect optical performance data of the photovoltaic panel through a high-precision spectrum analyzer, a light scattering analyzer, a material light absorption measuring instrument, a surface profiler and a reflectivity tester to obtain: light transmittance T avg , light scattering rate H, light absorption coefficient K abs , the thickness of the photovoltaic panel d, the surface roughness R s and the effectiveness of anti-reflective coatings arc , forming a light transmission effect data set;

[0062] The durability collection unit is used to collect durability data of photovoltaic panels through ultraviolet aging test box, salt spray test box, universal test machine, damp heat test box and impact test machine to obtain: ultraviolet aging rate U UV , UV irradiation time t UV , reference radiation intensity U ref 、Salt spray concentration C salt , Mechanical strength F mech , mechanical strength threshold F thres , Resistance change ΔR caused by humidity and heat cycle dh , initial resistance value R0, impact force I imp and the maximum allowable impact force Imax , generate a durability performance data set;

[0063] The electrical performance acquisition unit is used to collect the electrical performance data of the photovoltaic panel through a solar simulator, an IV tester and a resistance measuring instrument to obtain: the photoelectric conversion efficiency η PV , open circuit voltage V oc , reference voltage V ref , short circuit current I sc , reference current I ref , fill factor FF and resistance loss R s and the maximum allowable resistance value R max , generate electrical performance data set.

[0064] In this embodiment, the data acquisition module in the intelligent control system for photovoltaic panel production and processing accurately collects various performance data of photovoltaic panels through optical performance acquisition units, durability acquisition units and electrical performance acquisition units. The optical performance acquisition unit uses high-precision instruments to obtain key data such as light transmittance Tavg, light scattering rate H, light absorption coefficient Kabs, photovoltaic panel thickness d, surface roughness Rs and anti-reflective coating effectiveness Earc, thereby comprehensively evaluating the light transmission effect of photovoltaic panels. The durability acquisition unit collects data such as ultraviolet aging rate UUV, mechanical strength Fmech, and wet heat cycle resistance change ΔRdh through a series of rigorous environmental simulation tests to ensure the long-term durability of photovoltaic panels in various environments. The electrical performance acquisition unit accurately monitors the electrical performance of photovoltaic panels through indicators such as photoelectric conversion efficiency, open circuit voltage Voc, and short-circuit current Isc, providing reliable data support for subsequent process optimization.

[0065] Example 3

[0066] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the data processing module includes a data preprocessing unit and a data calculation unit;

[0067] The data preprocessing unit is used to perform preliminary processing on the light transmission effect data set, the durability performance data set and the electrical performance data set transmitted from the data acquisition module, and perform data cleaning: remove noise, outliers and redundant data, select key parameters related to the quality of photovoltaic panels for data filtering, integrate data sets of different categories, and perform normalization processing;

[0068] The data calculation unit is used to apply a multivariate data analysis algorithm to perform in-depth analysis on the preprocessed data set, analyze the relationship between light transmittance effect, durability and electrical performance, extract key performance indicators, and calculate the transmittance coefficient Tgx, durability coefficient Njx and electrical conversion efficiency coefficient Dzxxl according to the set calculation model. Based on the calculation results of the above coefficients, the quality index Zlzs of the agricultural photovoltaic panel is finally obtained, and the calculation results and related analysis data are fed back to the production process optimization module;

[0069] The transmittance coefficient Tgx is calculated by the following formula:

[0070]

[0071] Where, T avg represents light transmittance, H represents light scattering rate, K abs represents the light absorption coefficient, d represents the thickness of the photovoltaic panel, R s Indicates surface roughness, E arc Indicates the effectiveness of the anti-reflective coating;

[0072] This formula takes into account the transmittance, light scattering, material absorption, thickness and surface characteristics of the photovoltaic panel. avg , light scattering rate H, surface roughness R s and the effectiveness of anti-reflective coatings arc The amount of light transmitted is reduced, and the light absorption coefficient K abs And the thickness d of the photovoltaic panel controls the loss of light.

[0073] The durability coefficient Njx is calculated by the following formula:

[0074]

[0075] Where U UV Indicates the UV aging rate, t UV Indicates the UV exposure time, U ref Indicates the reference radiation intensity, C salt Indicates the salt spray concentration, F mech Indicates mechanical strength, F thres Indicates the threshold value of mechanical strength, ΔR dh represents the resistance change caused by the wet heat cycle, R0 represents the initial resistance value, I imp Indicates the impact force, I max Indicates the maximum allowable impact force.

[0076] The formula takes into account five main factors: UV aging, salt spray corrosion, mechanical strength, moisture and heat stability, and impact resistance, to ensure that the durability assessment of photovoltaic panels is comprehensive and accurate. Each parameter is based on the actual performance indicators of photovoltaic panels under specific conditions to reflect their reliability in long-term use.

[0077] The electrical conversion efficiency coefficient Dzxxl is calculated by the following formula:

[0078]

[0079] Where η PV Represents the photoelectric conversion efficiency, V oc Indicates the open circuit voltage, V ref Indicates the reference voltage, I sc Indicates the short-circuit current, I ref represents reference current, FF represents fill factor, R s Represents the resistance loss, R max Indicates the maximum allowable resistance value.

[0080] The formula comprehensively measures the electrical performance of photovoltaic panels through key parameters such as photoelectric conversion efficiency, open circuit voltage, short circuit current, fill factor and resistance loss. It reflects the energy conversion efficiency of photovoltaic panels in actual operation and directly affects their power generation capacity.

[0081] The quality index Zlzs of the agricultural photovoltaic panel is calculated by the following formula:

[0082]

[0083] Wherein, Tgx represents the transmittance coefficient, Njx represents the durability coefficient, and Dzxxl represents the electrical conversion efficiency coefficient.

[0084] The agricultural photovoltaic panel quality index Zlzs is the geometric mean of the transmittance coefficient Tgx, the durability coefficient Njx and the electrical conversion efficiency coefficient Dzxxl. The geometric mean method can reflect the relative importance of each coefficient in a balanced way, avoiding the deviation of the final result caused by a certain coefficient being too large or too small. In this way, Zlzs can comprehensively evaluate the overall performance of photovoltaic panels in the agricultural field, ensuring that they meet high standards in terms of light transmittance, durability and electrical conversion efficiency.

[0085] In this embodiment, the data processing module comprehensively processes the light transmission effect data set, durability performance data set and electrical performance data set transmitted from the data acquisition module through the data preprocessing unit and the data calculation unit. The data preprocessing unit cleans the original data, removes noise, outliers and redundant data, and ensures the accuracy of subsequent calculations. Through normalization and data filtering, the data preprocessing unit integrates data of different categories into a format with more analytical value, laying the foundation for further analysis. The data calculation unit applies a multivariate data analysis algorithm to conduct an in-depth analysis of the preprocessed data, extract key performance indicators, and calculate the transmittance coefficient Tgx, durability coefficient Njx and electrical conversion efficiency coefficient Dzxxl through the set calculation model, and finally obtains the quality index Zlzs of the agricultural photovoltaic panel. The application of this module effectively improves the accuracy and efficiency of photovoltaic panel performance analysis and ensures the scientific nature of various decisions in the production process.

[0086] Example 4

[0087] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the production process optimization module includes a material ratio optimization unit and a process adjustment unit;

[0088] The material ratio optimization unit is used to adjust the material selection and ratio in the production of photovoltaic panels according to the preset optimization target, determine the best material formula through experiments and calculations, and ensure that the physical and chemical properties of the materials meet the production requirements;

[0089] The process adjustment unit is used to adjust key environmental parameters involved in the production process, including temperature, humidity and light intensity, to optimize and refine the production process, to ensure that each process step is strictly executed, and to make fine adjustments based on real-time data. Through the real-time monitoring system, the immediate impact of production process changes on photovoltaic panel performance is continuously tracked.

[0090] The quality monitoring module includes an online quality monitoring unit and a quality assessment unit;

[0091] The online quality monitoring unit is used to use online detection equipment and sensors to collect quality data of photovoltaic panels in the production process in real time, covering key indicators of light transmittance, durability and electrical performance, monitor each stage of production in real time, compare the collected quality data with preset standards, and identify quality problems that may occur in production;

[0092] The quality assessment unit is used to compare the real-time monitoring data with the preset first quality qualification threshold M and second quality qualification threshold N according to the quality index Zlzs of the agricultural photovoltaic panel. Through analysis and evaluation, it generates a quality assessment strategy plan, provides adjustment suggestions, automatically generates a detailed quality monitoring report, records the quality status and evaluation results of each production stage, and provides data support for the further optimization of the production process;

[0093] When Zlzs > N, the overall quality of the photovoltaic panel meets the standard, but there is a deviation of ±10%, resulting in mild performance attenuation during long-term use. For the minor deviations in optical or electrical performance, optimize the particle size distribution or purity of the raw materials. On the light-absorbing material and conductive coating, ensure that these fine-tuning can improve the light transmittance and electrical conversion efficiency of the photovoltaic panel. Re-evaluate the key temperature control points or pressurization stages in the production process, and adjust the heating curve or pressing intensity to eliminate the minor defects that may be introduced during the production process. It is recommended to increase the regular monitoring frequency, especially in the initial stage of production and during the material replacement stage, to ensure that the effects after material and process adjustments are verified;

[0094] When M < Zlzs ≤ N, the photovoltaic panel has quality problems, which affect its application effect in agriculture. In terms of long-term durability, replace the substrate material or improve the formula of the anti-ultraviolet coating to enhance durability. The specific operations include replacing the plating material with stronger corrosion resistance and improving the anti-reflection performance of the photovoltaic panel surface. Optimize the temperature and humidity control in the production environment to ensure that the humidity fluctuation in the production environment is maintained within a stricter range, reducing the material stress and performance attenuation caused by humidity changes. Strengthen the supervision and real-time adjustment of key process nodes. For example, in the lamination and encapsulation stages, use higher-precision measurement and control equipment to ensure the uniformity and tightness of each layer of material;

[0095] When Zlzs ≤ M, the quality problem of the photovoltaic panel has reached a serious level, and measures must be taken immediately to correct it, otherwise the product will not meet the application requirements in the agricultural field. Completely replace the main materials, introduce anti-aging additives, conduct a comprehensive evaluation and re-design of the entire production process. It may be necessary to redefine the parameters of each production stage, including temperature, pressure, time, etc., to ensure that each step can be strictly controlled within the optimal range. Introduce a more stringent quality control system and add multiple inspection links at each key node of production to ensure that problems can be detected and corrected at an early stage;

[0096] The environmental adaptability test module includes an extreme environment simulation unit and a mechanical performance test unit;

[0097] The extreme environment simulation unit is used to simulate various extreme conditions in the agricultural environment, including high-intensity ultraviolet radiation, hot and humid environment and salt spray corrosion. Through precise control equipment, it reproduces the challenges that photovoltaic panels may face under different climatic conditions, focuses on testing the weather resistance of photovoltaic panels under these extreme conditions, evaluates their anti-aging and corrosion resistance, and ensures the stability of photovoltaic panels in agricultural photovoltaic complementarity and photovoltaic greenhouse application scenarios;

[0098] The mechanical performance testing unit is used to test the mechanical durability of photovoltaic panels in an agricultural environment by applying mechanical shock, vibration and pressure, simulating the mechanical stress and shock encountered during actual use, including the impact of wind loads and hail.

[0099] The feedback module is used to summarize each module and includes a comprehensive data summary unit and an evaluation result feedback unit;

[0100] The comprehensive data summary unit is used to collect and summarize the feedback data and results from each module, uniformly organize and analyze the working results of each module, form a comprehensive performance data set, and generate a comprehensive performance report, covering the performance of the photovoltaic panel in different aspects, including optical performance, durability, electrical performance and environmental adaptability;

[0101] The evaluation result feedback unit is used to analyze the overall quality and performance of the photovoltaic panel based on the comprehensive performance report, identify the existing room for improvement, and feed back the evaluation results to the relevant modules to guide the subsequent production process adjustment and data processing optimization to form a closed-loop control system.

[0102] In this embodiment, the production process optimization module and the quality monitoring module effectively improve the production quality and efficiency of agricultural photovoltaic panels. This optimization method, supported by real-time data, not only improves the optical performance, durability and electrical performance of photovoltaic panels, but also reduces the errors that may occur in the production process, thereby achieving a more efficient and reliable production process. The quality monitoring module further ensures the final quality of photovoltaic panels. The online quality monitoring unit accurately tracks the quality status of each stage of production through real-time data acquisition, while the quality assessment unit comprehensively assesses the quality of the product based on the quality index Zlzs of the agricultural photovoltaic panel. This module can identify and warn of quality problems that may occur in production. By generating detailed quality monitoring reports and quality assessment strategies, the system can quickly make adjustment suggestions, avoid product performance attenuation or failure due to quality problems, and ensure the long-term application of photovoltaic panels in agriculture. The feedback module forms a closed-loop control system through comprehensive data aggregation and evaluation result feedback, which further improves the intelligence level of the system.

[0103] Example 5

[0104] A photovoltaic panel production and processing intelligent control method, please refer to Figure 2 , specifically: including the following steps:

[0105] Step 1: Collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate light transmittance effect data set, durability performance data set and electrical performance data set, and transmit them to the data processing module;

[0106] Step 2: Clean, filter and pre-process the light transmission effect data set, durability performance data set and electrical performance data set transmitted by the data acquisition module, apply the multivariate data analysis algorithm, and calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel;

[0107] Step 3: Receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time;

[0108] Step 4: Conduct real-time quality monitoring of photovoltaic panels that have undergone production process optimization, compare the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualification threshold M and second quality qualification threshold N, identify potential quality problems in production, obtain quality assessment strategy solutions, and feed back to the production process optimization module for timely adjustment. Use online detection equipment and sensors to automatically collect quality data of photovoltaic panels at various stages of the production process and generate quality monitoring reports;

[0109] Step 5: Simulate various external conditions in the agricultural environment, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, focusing on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural photovoltaic complementary and photovoltaic greenhouse applications;

[0110] Step 6: Summarize the work results of each module, including feedback information from data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed the evaluation results back to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

[0111] In this embodiment, through the above six steps, the designed photovoltaic panel production and monitoring system significantly improves the overall performance and production efficiency of photovoltaic panels. First, the system accurately obtains the key performance data of photovoltaic panels in the production process through comprehensive data collection and processing, ensures the accuracy and relevance of the data, and lays a solid foundation for subsequent analysis and optimization. Through the in-depth analysis and processing of these data, the system can not only calculate the quality index Zlzs of photovoltaic panels in real time, but also optimize the optical performance, durability and electrical conversion efficiency of photovoltaic panels according to this index, thereby improving the overall quality of photovoltaic panels. Through the close cooperation and closed-loop feedback of each module, the system realizes all-round intelligent control from data collection, analysis and processing, production optimization to quality monitoring. The working results of each module support each other, forming a highly integrated closed-loop control system, so that every link in the production process of photovoltaic panels is optimally adjusted. This not only greatly improves production efficiency and product quality, but also makes the application effect of photovoltaic panels in the agricultural field reach the best, and realizes performance breakthroughs and technological innovations in agricultural photovoltaic applications.

[0112] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent control system for photovoltaic panel production and processing, characterized in that: It includes data acquisition module, data processing module, production process optimization module, quality monitoring module, environmental adaptability test module and feedback module; The data acquisition module is used to collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate a light transmission effect data set, a durability performance data set and an electrical performance data set, and transmit them to the data processing module; The data processing module is used to clean, filter and pre-process the light transmission effect data set, the durability performance data set and the electrical performance data set transmitted by the data acquisition module, and apply the multivariate data analysis algorithm to calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel; The production process optimization module is used to receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time; The quality monitoring module is used to perform real-time quality monitoring of photovoltaic panels after production process optimization, and compares the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualified threshold M and second quality qualified threshold N, identifies potential quality problems in production, obtains quality assessment strategy solutions, and feeds back to the production process optimization module for timely adjustment. It uses online detection equipment and sensors to automatically collect quality data of photovoltaic panels at various stages of the production process and generate a quality monitoring report; The environmental adaptability test module is used to simulate various external conditions in agricultural environments, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, with a focus on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural-photovoltaic complementary and photovoltaic greenhouse applications; The feedback module is used to summarize the working results of each module, including feedback information of data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed back the evaluation results to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

2. The photovoltaic panel production and processing intelligent control system according to claim 1 is characterized by: The data acquisition module includes an optical performance acquisition unit, a durability acquisition unit and an electrical performance acquisition unit; The optical performance acquisition unit is used to collect optical performance data of the photovoltaic panel through a high-precision spectrum analyzer, a light scattering analyzer, a material light absorption measuring instrument, a surface profiler and a reflectivity tester to obtain: light transmittance T avg , light scattering rate H, light absorption coefficient K abs , the thickness of the photovoltaic panel d, the surface roughness R s and the effectiveness of anti-reflective coatings arc , forming a light transmission effect data set; The durability collection unit is used to collect durability data of photovoltaic panels through ultraviolet aging test box, salt spray test box, universal test machine, damp heat test box and impact test machine to obtain: ultraviolet aging rate U UV , UV irradiation time t UV , reference radiation intensity U ref 、Salt spray concentration C salt , Mechanical strength F mech , mechanical strength threshold F thres , Resistance change ΔR caused by humidity and heat cycle dh , initial resistance value R0, impact force I imp and the maximum allowable impact force I max , generate a durability performance data set; The electrical performance acquisition unit is used to collect the electrical performance data of the photovoltaic panel through a solar simulator, an IV tester and a resistance measuring instrument to obtain: the photoelectric conversion efficiency η PV , open circuit voltage V oc , reference voltage V ref , short circuit current I sc , reference current I ref , fill factor FF and resistance loss R s and the maximum allowable resistance value R max , generate electrical performance data set.

3. The photovoltaic panel production and processing intelligent control system according to claim 2 is characterized by: The data processing module includes a data preprocessing unit and a data calculation unit; The data preprocessing unit is used to perform preliminary processing on the light transmittance effect data set, durability performance data set and electrical performance data set transmitted from the data acquisition module, and perform data cleaning: remove noise, outliers and redundant data, select key parameters related to the quality of photovoltaic panels for data filtering, integrate data sets of different categories, and perform normalization processing.

4. The photovoltaic panel production and processing intelligent control system according to claim 3 is characterized by: The data calculation unit is used to apply multivariate data analysis algorithms to deeply analyze the preprocessed data set, analyze the mutual relationships among the light transmission effect, durability performance, and electrical performance, extract key performance indicators, calculate the light transmittance coefficient Tgx, durability coefficient Njx, and electrical conversion efficiency coefficient Dzxxl based on the set calculation model, and finally obtain the quality index Zlzs of the agricultural photovoltaic panel based on the calculation results of the above coefficients. The calculation results and relevant analysis data are fed back to the production process optimization module.

5. The photovoltaic panel production and processing intelligent control system according to claim 1 is characterized by: The production process optimization module includes a material ratio optimization unit and a process adjustment unit; The material ratio optimization unit is used to adjust the material selection and ratio in the production of photovoltaic panels according to the preset optimization objectives. Through experiments and calculations, the optimal material formula is determined to ensure that the physical and chemical properties of the materials meet the production requirements. Through experiments and calculations, the optimal material formula is determined to ensure that the physical and chemical properties of the materials meet the production requirements; The process adjustment unit is used to adjust the key environmental parameters involved in the production process, including temperature, humidity, and light intensity, optimize and refine the management of the production process, ensure that each process step is strictly executed, and make fine-tuning according to real-time data. Through the real-time monitoring system, continuously track the immediate impact of production process changes on the performance of photovoltaic panels.

6. The photovoltaic panel production and processing intelligent control system according to claim 1, characterized in that: The quality monitoring module includes an on-line quality monitoring unit and a quality assessment unit; The on-line quality monitoring unit is used to use on-line detection equipment and sensors to collect the quality data of photovoltaic panels in the production process in real time, covering the key indicators of light transmission performance, durability, and electrical performance, monitor each stage of production in real time, compare the collected quality data with the preset standards, and identify quality problems occurring in production.

7. The photovoltaic panel production and processing intelligent control system according to claim 6, characterized in that: The quality assessment unit is used to compare the real-time monitoring data with the preset first quality qualification threshold M and second quality qualification threshold N based on the quality index Zlzs of the agricultural photovoltaic panel. Through analysis and evaluation, generate a quality assessment strategy plan, provide adjustment suggestions, automatically generate a detailed quality monitoring report, record the quality status and evaluation results of each production stage, and provide data support for the further optimization of the production process; When Zlzs > N, the overall quality of the photovoltaic panel meets the standards, but there is a deviation of ±10%, resulting in a slight performance decay during long-term use. For the minor deviations in optical performance or electrical performance, optimize the particle size distribution or purity of the raw materials, and ensure that these fine-tuning can improve the light transmittance and electrical conversion efficiency of the photovoltaic panel on the light absorption material and conductive coating; When M < Zlzs ≤ N, the photovoltaic panel has quality problems, affecting its application effect in agriculture. In terms of long-term durability, replace the substrate material or improve the formula of the anti-ultraviolet coating to enhance durability. The specific operations include replacing the plating material with stronger corrosion resistance and improving the anti-reflection performance of the photovoltaic panel surface; When Zlzs≤M, the quality problems of photovoltaic panels have reached a serious level and immediate measures must be taken to correct them. Otherwise, the products will not be able to meet the application requirements in the agricultural field. The main materials must be completely replaced and anti-aging additives must be introduced.

8. The photovoltaic panel production and processing intelligent control system according to claim 1 is characterized by: The environmental adaptability test module includes an extreme environment simulation unit and a mechanical performance test unit; The extreme environment simulation unit is used to simulate various extreme conditions in the agricultural environment, including high-intensity ultraviolet radiation, hot and humid environment and salt spray corrosion. Through precise control equipment, it reproduces the challenges faced by photovoltaic panels under different climatic conditions, focuses on testing the weather resistance of photovoltaic panels under these extreme conditions, evaluates their anti-aging and corrosion resistance, and ensures the stability of photovoltaic panels in agricultural photovoltaic complementarity and photovoltaic greenhouse application scenarios; The mechanical performance testing unit is used to test the mechanical durability of photovoltaic panels in an agricultural environment by applying mechanical shock, vibration and pressure, simulating the mechanical stress and shock encountered during actual use, including the impact of wind loads and hail.

9. The photovoltaic panel production and processing intelligent control system according to claim 1, characterized in that: The feedback module is used to summarize each module and includes a comprehensive data summary unit and an evaluation result feedback unit; The comprehensive data summary unit is used to collect and summarize the feedback data and results from each module, uniformly organize and analyze the working results of each module, form a comprehensive performance data set, and generate a comprehensive performance report, covering the performance of the photovoltaic panel in different aspects, including optical performance, durability, electrical performance and environmental adaptability; The evaluation result feedback unit is used to analyze the overall quality and performance of the photovoltaic panel based on the comprehensive performance report, identify the existing room for improvement, and feed back the evaluation results to the relevant modules to guide the subsequent production process adjustment and data processing optimization to form a closed-loop control system.

10. A photovoltaic panel production and processing intelligent control method, applied to a photovoltaic panel production and processing intelligent control system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Step 1: Collect key data involved in the photovoltaic panel production process, including optical performance, durability and electrical performance, generate light transmittance effect data set, durability performance data set and electrical performance data set, and transmit them to the data processing module; Step 2: Clean, filter and pre-process the light transmission effect data set, durability performance data set and electrical performance data set transmitted by the data acquisition module, apply the multivariate data analysis algorithm, and calculate and obtain: the quality index Zlzs of the agricultural photovoltaic panel; Step 3: Receive the analysis results of the data processing module, optimize the optical performance, durability and electrical conversion efficiency of the photovoltaic panel by adjusting the material ratio, production environment parameters and process flow, and monitor the impact of production process changes on the performance of the photovoltaic panel in real time; Step 4: Conduct real-time quality monitoring of photovoltaic panels that have undergone production process optimization, compare the quality index Zlzs of agricultural photovoltaic panels with the preset first quality qualification threshold M and second quality qualification threshold N, identify potential quality problems in production, obtain quality assessment strategy solutions, and feed back to the production process optimization module for timely adjustment. Use online detection equipment and sensors to automatically collect quality data of photovoltaic panels at various stages of the production process and generate quality monitoring reports; Step 5: Simulate various external conditions in the agricultural environment, including ultraviolet radiation, humidity, heat, salt spray and mechanical shock, to test the performance of photovoltaic panels in extreme environments, focusing on testing the weather resistance and durability of photovoltaic panels to ensure their long-term stability in agricultural photovoltaic complementary and photovoltaic greenhouse applications; Step 6: Summarize the work results of each module, including feedback information from data processing, production process optimization, quality monitoring and environmental adaptability testing, generate a comprehensive performance report, and feed the evaluation results back to the production process optimization module and the data processing module to form a closed-loop control system to achieve continuous optimization and quality improvement.

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