AI photovoltaic module series welding machine system

Abstract

The invention discloses an AI photovoltaic module series welding machine system, and belongs to the technical field of photovoltaic module production, the AI photovoltaic module series welding machine system comprises an image acquisition module, a welding module, a data processing module, a control module and the like, and through the image acquisition module and an image identification unit, the position, specification and welding abnormal conditions of a photovoltaic module can be accurately identified and timely processed; the data analysis unit establishes a welding quality and parameter relation model to determine optimal parameters, the production quality is improved, the welding module adopts an advanced heating technology and the cost optimization module to intelligently sort and adjust parameters, the welding efficiency is improved, and the cost optimization module can also reduce the use amount of welding flux, reduce the energy consumption of equipment and achieve cost minimization. The system has a self-learning function, can automatically adjust an algorithm and a strategy, communicates with external equipment to realize remote monitoring and management, improves the production management convenience and the intelligent degree, realizes intelligent production, and improves the quality, the efficiency, the cost and the intelligence of photovoltaic module production in multiple aspects.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic module production, and in particular relates to an AI photovoltaic module string welding machine system. Background Art

[0002] As the global demand for renewable energy continues to grow, the photovoltaic industry has developed rapidly. As the core component of the photovoltaic power generation system, the production quality and efficiency of photovoltaic modules are crucial.

[0003] In the production process of photovoltaic modules, the string welding machine is one of the key equipment. Traditional photovoltaic module string welding machines mainly rely on manual operation and experience for welding, which has problems such as unstable welding quality, low production efficiency, and high cost. At the same time, traditional string welding machines are difficult to detect and handle abnormal conditions in the welding process in a timely manner, which can easily lead to product quality problems and production interruptions.

[0004] With the continuous development of artificial intelligence technology, applying artificial intelligence technology to photovoltaic module string welding machine systems to realize the automation and intelligence of the welding process has become an important way to improve the production quality and efficiency of photovoltaic modules and reduce production costs. To this end, we propose an AI photovoltaic module string welding machine system to solve the above-mentioned problems. Summary of the invention

[0005] The purpose of the present invention is to provide an AI photovoltaic module string welding machine system to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an AI photovoltaic module string welding machine system, characterized in that it includes:

[0007] Image acquisition module, used to collect image information of photovoltaic modules and welding process. The image acquisition module consists of high-resolution industrial cameras and is installed in multiple key locations, including but not limited to the photovoltaic module feed, above the welding station and the discharge. It can collect clear images at different angles and frequencies to ensure comprehensive monitoring of all parts of the photovoltaic module and the welding process.

[0008] The welding module performs welding operations on photovoltaic modules. The welding module includes a welding platform, a solder conveying device and a temperature control unit. The welding platform adopts advanced heating technology, which can quickly reach the required welding temperature, and the temperature is uniform and stable. The solder conveying device can accurately control the output of solder, ensuring the welding quality while reducing solder waste. The temperature control unit monitors the welding temperature in real time and automatically adjusts it according to different photovoltaic module materials and welding requirements.

[0009] The data processing module receives the image information from the image acquisition module and performs analysis and processing. The data processing module includes a high-performance processor and a large-capacity storage device, which can quickly process a large amount of image data. The processor runs advanced image recognition algorithms and data analysis software to perform real-time analysis on the collected image information, identify the location and specifications of the photovoltaic modules, and abnormal conditions during the welding process, and generate corresponding processing solutions. The storage device is used to store historical image data and analysis results for subsequent data analysis and system optimization.

[0010] The control module is connected to the image acquisition module, the welding module and the data processing module, and controls the welding module according to the analysis results of the data processing module. The control module adopts an advanced programmable logic controller (PLC) and the latest processing system GPU, which can achieve precise control of the welding module. According to the instructions of the data processing module, the control module can adjust the position, temperature, solder delivery speed and other parameters of the welding platform to ensure the stability and efficiency of the welding process. At the same time, the control module can also coordinate and control the entire system to achieve automated production.

[0011] Preferably, the data processing module includes:

[0012] The image recognition unit is used to identify the location and specifications of photovoltaic modules and abnormal conditions during welding. The image recognition unit uses deep learning algorithms to extract and classify features of the collected images. It can accurately identify photovoltaic modules of different specifications, including the size, shape, color and other characteristics of the cells. At the same time, for abnormal conditions during welding, such as welding ribbon offset, photovoltaic cell damage, poor welding, etc., the image recognition unit can quickly detect and locate the problem, providing accurate information for subsequent processing.

[0013] The data analysis unit analyzes the collected image information and determines the optimization plan for welding parameters. The data analysis unit conducts statistics and analysis on a large amount of historical image data by connecting to big data Ai, and establishes a relationship model between welding quality and welding parameters. According to the real-time collected image information and model prediction, the optimal welding parameters are determined, including welding temperature, solder amount, welding time, etc. At the same time, the data analysis unit can also monitor and analyze the operating status of the system, discover potential problems in a timely manner, and propose corresponding improvement measures.

[0014] Preferably, the abnormal conditions that the image recognition unit can identify include but are not limited to welding ribbon offset, photovoltaic cell damage, and poor welding. For welding ribbon offset, the image recognition unit can detect the relative position deviation between the welding ribbon and the cell, and calculate the offset. When the offset exceeds the set threshold, the system will automatically issue an alarm and notify the control module to make adjustments. For photovoltaic cell damage, the image recognition unit can detect defects such as cracks and missing corners on the surface of the cell, and evaluate the degree of damage. Based on the evaluation results, the system can decide whether to continue the welding operation or remove the damaged cell. For poor welding, the image recognition unit can detect the quality of the welding point, including welding strength, appearance, etc. If poor welding is found, the system will automatically rework or notify the operator to check and repair.

[0015] Preferably, when the data processing module identifies an abnormal situation, the control module can adjust the welding parameters or suspend the welding operation in time and issue an alarm. The control module takes different treatment measures according to the type and severity of the abnormal situation. For minor abnormal situations, such as a slight deviation of the welding ribbon, the control module can automatically adjust the position and angle of the welding platform for real-time correction. For more serious abnormal situations, such as damaged battery cells or poor welding, the control module will suspend the welding operation, issue an alarm, and notify the operator to handle it. At the same time, the control module will also record the time, type and treatment results of the abnormal situation for subsequent data analysis and system optimization.

[0016] Preferably, the system further comprises a cost optimization module, which reduces production costs by:

[0017] Optimize welding parameters and reduce solder usage. The cost optimization module automatically adjusts the solder delivery speed and welding time according to the welding parameter optimization plan provided by the data analysis unit to ensure that the amount of solder used is reduced while ensuring the welding quality. At the same time, the cost optimization module will also monitor and analyze the waste of solder to promptly discover and solve the problem of solder waste.

[0018] Intelligently sort PV modules to improve welding efficiency and reduce equipment energy consumption. The cost optimization module intelligently sorts incoming PV modules according to the specifications and welding requirements of the PV modules. By rationally arranging the welding sequence, welding efficiency can be improved, equipment idling time can be reduced, and equipment energy consumption can be reduced. At the same time, the cost optimization module will also monitor and analyze the operating status of the equipment, adjust the operating parameters of the equipment according to the actual situation, and further reduce energy consumption.

[0019] Preferably, the cost optimization module automatically adjusts the welding parameters according to different specifications of photovoltaic modules and welding requirements to achieve the best cost-effectiveness. The cost optimization module establishes a welding parameter database for photovoltaic modules of different specifications, and continuously updates and optimizes the parameters in the database according to actual production conditions. When the system receives new photovoltaic module specifications and welding requirements, the cost optimization module will automatically query the corresponding welding parameters from the database and make appropriate adjustments to ensure that the cost is minimized while meeting the welding quality requirements.

[0020] Preferably, the control module uses an artificial intelligence algorithm to continuously optimize the welding process control strategy based on historical welding data and real-time image information. The control module uses a machine learning algorithm to analyze and learn historical welding data and establish a welding quality prediction model. Based on the real-time collected image information and model prediction, the control module can automatically adjust the welding parameters and control strategy to improve welding quality and production efficiency. At the same time, the control module can also monitor and analyze the operating status of the system in real time, promptly discover and solve potential problems, and ensure the stable operation of the system.

[0021] Preferably, the system can communicate with external devices to achieve remote monitoring and management. The system is equipped with a communication interface that can communicate with the factory's central control system or cloud server. Through the communication interface, operators can remotely monitor the operating status of the system, view real-time image information and production data, and remotely control and manage the system. At the same time, the system can also upload production data to the cloud server for big data analysis and processing to provide support for the company's production decisions.

[0022] Preferably, the system has a self-learning function and can automatically adjust the image recognition algorithm and welding control strategy according to different production environments and photovoltaic module characteristics. During operation, the system will continuously collect new image data and production data, and analyze and learn these data. According to the learning results, the system can automatically adjust the image recognition algorithm and welding control strategy to adapt to different production environments and photovoltaic module characteristics. At the same time, the system can also communicate with external devices to obtain the latest technology and knowledge, and continuously improve its performance and intelligence level.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. Improve production quality

[0025] Through the image acquisition module and the image recognition unit in the data processing module, the position and specifications of the photovoltaic modules and abnormal conditions in the welding process, such as welding ribbon deviation, photovoltaic cell damage, poor welding, etc., can be accurately identified and handled in a timely manner, effectively improving the welding quality of the product.

[0026] The data analysis unit establishes a relationship model between welding quality and welding parameters based on a large amount of historical image data, determines the optimal welding parameters, and further ensures the stability of welding quality.

[0027] 2. Improve production efficiency

[0028] The welding platform of the welding module adopts advanced heating technology, which can quickly reach the required welding temperature, the temperature is uniform and stable, and the welding speed is improved.

[0029] The cost optimization module intelligently sorts photovoltaic modules, arranges the welding sequence reasonably, reduces equipment idling time, and improves welding efficiency.

[0030] The control module adopts advanced programmable logic controller (PLC) and the latest processing system GPU and artificial intelligence algorithm, which can achieve precise control of the welding module, ensure the stability and efficiency of the welding process, and thus improve overall production efficiency.

[0031] 3. Reduce production costs

[0032] The cost optimization module automatically adjusts the solder delivery speed and welding time according to the welding parameter optimization plan provided by the data analysis unit, reduces the solder usage, and monitors and analyzes the solder waste, thereby reducing the raw material cost.

[0033] By monitoring and analyzing the operating status of the equipment, the equipment operating parameters are adjusted, the equipment energy consumption is reduced, and energy costs are saved.

[0034] The cost optimization module automatically adjusts welding parameters according to different specifications of PV modules and welding requirements to minimize costs.

[0035] 4. Realize intelligent production

[0036] The system has a self-learning function and can automatically adjust the image recognition algorithm and welding control strategy according to different production environments and photovoltaic module characteristics, continuously improving its performance and intelligence level.

[0037] It can communicate with external devices to achieve remote monitoring and management. Operators can remotely view system operation status, real-time image information and production data, and perform remote control and management, which improves the convenience and intelligence of production management. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present invention will be described clearly and completely below in combination with 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.

[0039] The present invention provides an AI photovoltaic module string welding machine system, comprising:

[0040] Image acquisition module, used to collect image information of photovoltaic modules and welding process. The image acquisition module consists of high-resolution industrial cameras and is installed in multiple key locations, including but not limited to the photovoltaic module feed, above the welding station and the discharge. It can collect clear images at different angles and frequencies to ensure comprehensive monitoring of all parts of the photovoltaic module and the welding process.

[0041] The welding module performs welding operations on photovoltaic modules. The welding module includes a welding platform, a solder conveying device and a temperature control unit. The welding platform adopts advanced heating technology, which can quickly reach the required welding temperature, and the temperature is uniform and stable. The solder conveying device can accurately control the output of solder, ensuring the welding quality while reducing solder waste. The temperature control unit monitors the welding temperature in real time and automatically adjusts it according to different photovoltaic module materials and welding requirements.

[0042] The data processing module receives the image information from the image acquisition module and performs analysis and processing. The data processing module includes a high-performance processor and a large-capacity storage device, which can quickly process a large amount of image data. The processor runs advanced image recognition algorithms and data analysis software to perform real-time analysis on the collected image information, identify the location and specifications of the photovoltaic modules, and abnormal conditions during the welding process, and generate corresponding processing solutions. The storage device is used to store historical image data and analysis results for subsequent data analysis and system optimization.

[0043] The control module is connected with the image acquisition module, welding module and data processing module, and controls the welding module according to the analysis results of the data processing module. The control module adopts advanced programmable logic controller (PLC) and the latest processing system GPU, which can realize precise control of the welding module. According to the instructions of the data processing module, the control module can adjust the position, temperature, solder delivery speed and other parameters of the welding platform to ensure the stability and efficiency of the welding process. At the same time, the control module can also coordinate and control the entire system to realize automated production.

[0044] Furthermore, the high-resolution industrial camera in the image acquisition module performs image acquisition at multiple key locations, which can monitor the photovoltaic modules and welding process comprehensively and from multiple angles. This not only helps to detect potential problems in a timely manner, but also provides accurate image information for subsequent analysis and processing. The welding module adopts advanced welding platform heating technology, solder conveying device and temperature control unit to ensure the rapid stabilization of welding temperature and precise control of solder output, thereby improving welding quality and reducing solder waste. The data processing module uses high-performance processors and large-capacity storage devices to quickly process large amounts of image data, and runs advanced image recognition algorithms and data analysis software to analyze image information in real time, identify abnormal situations and generate processing solutions, providing strong support for the intelligent operation of the system. The control module adopts an advanced programmable logic controller, which can accurately control the welding module according to the analysis results of the data processing module, realize the automated production of the entire system, and improve production efficiency and stability.

[0045] In this embodiment, the data processing module includes:

[0046] The image recognition unit is used to identify the location and specifications of photovoltaic modules and abnormal conditions during welding. The image recognition unit uses deep learning algorithms to extract and classify features of the collected images. It can accurately identify photovoltaic modules of different specifications, including the size, shape, color and other characteristics of the cells. At the same time, for abnormal conditions during welding, such as welding ribbon offset, photovoltaic cell damage, poor welding, etc., the image recognition unit can quickly detect and locate the problem, providing accurate information for subsequent processing.

[0047] The data analysis unit analyzes the collected image information and determines the optimization plan for welding parameters. The data analysis unit conducts statistics and analysis on a large amount of historical image data by connecting to big data Ai, and establishes a relationship model between welding quality and welding parameters. According to the real-time collected image information and model prediction, the optimal welding parameters are determined, including welding temperature, solder amount, welding time, etc. At the same time, the data analysis unit can also monitor and analyze the operating status of the system, discover potential problems in a timely manner, and propose corresponding improvement measures.

[0048] Furthermore, the image recognition unit uses a deep learning algorithm to accurately identify the location, specifications, and various abnormalities of the photovoltaic modules during welding. For photovoltaic modules of different specifications, it can achieve accurate classification and positioning by identifying the size, shape, color, and other features of the cells. During the welding process, for abnormalities such as welding strip offset, photovoltaic cell damage, and poor welding, the image recognition unit can quickly detect and locate the problem and provide accurate information for subsequent processing. This greatly improves the stability of product quality and reduces the production of defective products due to abnormal conditions not being discovered in time. The data analysis unit establishes a relationship model between welding quality and welding parameters through statistical analysis of a large amount of historical image data. According to the real-time collected image information and model prediction, the optimal welding parameters, including welding temperature, solder amount, welding time, etc., are determined. This not only ensures the optimal welding quality, but also can be dynamically adjusted according to different production conditions, improving the adaptability and flexibility of the system.

[0049] In this embodiment, the abnormal conditions that the image recognition unit can identify include but are not limited to welding ribbon offset, photovoltaic cell damage, and poor welding. For welding ribbon offset, the image recognition unit can detect the relative position deviation between the welding ribbon and the cell and calculate the offset. When the offset exceeds the set threshold, the system will automatically issue an alarm and notify the control module to make adjustments. For photovoltaic cell damage, the image recognition unit can detect defects such as cracks and missing corners on the surface of the cell and evaluate the degree of damage. Based on the evaluation results, the system can decide whether to continue the welding operation or remove the damaged cell. For poor welding, the image recognition unit can detect the quality of the welding point, including welding strength, appearance, etc. If poor welding is found, the system will automatically rework or notify the operator to check and repair.

[0050] Furthermore, for the offset of the welding strip, the image recognition unit can detect the relative position deviation between the welding strip and the cell and calculate the offset. When the offset exceeds the set threshold, the system will automatically sound an alarm and notify the control module to make adjustments. This effectively avoids welding quality problems caused by welding strip offset and improves the consistency and reliability of the product. For the damage of photovoltaic cells, the image recognition unit can detect defects such as cracks and missing corners on the surface of the cell and evaluate the degree of damage. According to the evaluation results, the system can decide whether to continue the welding operation or remove the damaged cell. This not only ensures the quality of the product, but also reduces the waste of resources caused by the participation of damaged cells in welding. For poor welding, the image recognition unit can detect the quality of the welding point, including welding strength, appearance, etc. If poor welding is found, the system will automatically rework or notify the operator to check and repair. This ensures the quality of each welding point and improves the overall performance and service life of the product.

[0051] In this embodiment, when the data processing module identifies an abnormal situation, the control module can adjust the welding parameters or suspend the welding operation in time and issue an alarm. The control module takes different treatment measures according to the type and severity of the abnormal situation. For minor abnormal situations, such as a slight deviation of the welding ribbon, the control module can automatically adjust the position and angle of the welding platform for real-time correction. For more serious abnormal situations, such as damaged battery cells or poor welding, the control module will suspend the welding operation and issue an alarm to notify the operator to handle it. At the same time, the control module will also record the time, type and treatment results of the abnormal situation for subsequent data analysis and system optimization.

[0052] Furthermore, the control module takes different treatment measures according to the type and severity of the abnormal situation. For minor abnormal situations, such as a slight deviation of the welding ribbon, the control module can automatically adjust the position and angle of the welding platform and make real-time corrections. This not only improves production efficiency, but also reduces production interruptions caused by minor problems. For more serious abnormal situations, such as broken battery cells or poor welding, the control module will suspend the welding operation and issue an alarm to notify the operator to handle it. This effectively avoids further expansion of the problem and ensures product quality and production safety. At the same time, the control module will also record the occurrence time, type and treatment results of the abnormal situation for subsequent data analysis and system optimization. This provides valuable data support for the continuous improvement of the system, enabling the system to continuously adapt to different production environments and requirements and improve overall performance and stability.

[0053] In this embodiment, the system also includes a cost optimization module, which reduces production costs by:

[0054] Optimize welding parameters and reduce solder usage. The cost optimization module automatically adjusts the solder delivery speed and welding time according to the welding parameter optimization plan provided by the data analysis unit to ensure that the amount of solder used is reduced while ensuring the welding quality. At the same time, the cost optimization module will also monitor and analyze the waste of solder to promptly discover and solve the problem of solder waste.

[0055] Intelligently sort PV modules to improve welding efficiency and reduce equipment energy consumption. The cost optimization module intelligently sorts incoming PV modules according to the specifications and welding requirements of the PV modules. By rationally arranging the welding sequence, welding efficiency can be improved, equipment idling time can be reduced, and equipment energy consumption can be reduced. At the same time, the cost optimization module will also monitor and analyze the operating status of the equipment, adjust the operating parameters of the equipment according to the actual situation, and further reduce energy consumption.

[0056] Furthermore, first, optimize the welding parameters and reduce the amount of solder used. The cost optimization module automatically adjusts the solder delivery speed and welding time according to the welding parameter optimization scheme provided by the data analysis unit. Under the premise of ensuring the welding quality, the amount of solder used is reduced, and the cost of raw materials is reduced. At the same time, the waste of solder is monitored and analyzed, and the problem of solder waste is discovered and solved in time, further improving the resource utilization rate. Secondly, the photovoltaic components are intelligently sorted to improve welding efficiency and reduce equipment energy consumption. The cost optimization module intelligently sorts the incoming photovoltaic components according to the specifications and welding requirements of the photovoltaic components. By reasonably arranging the welding sequence, the welding efficiency can be improved, the idling time of the equipment can be reduced, and the energy consumption of the equipment can be reduced. At the same time, the operating status of the equipment is monitored and analyzed, and the operating parameters of the equipment are adjusted according to the actual situation, which further reduces energy consumption and saves energy costs. This series of measures effectively reduces production costs and improves the economic benefits and market competitiveness of enterprises.

[0057] In this embodiment, the cost optimization module automatically adjusts the welding parameters according to the different specifications of photovoltaic modules and welding requirements to achieve the best cost-effectiveness. The cost optimization module establishes a welding parameter database for photovoltaic modules of different specifications, and continuously updates and optimizes the parameters in the database according to actual production conditions. When the system receives new photovoltaic module specifications and welding requirements, the cost optimization module will automatically query the corresponding welding parameters from the database and make appropriate adjustments to ensure that the cost is minimized while meeting the welding quality requirements.

[0058] Furthermore, the cost optimization module automatically adjusts welding parameters according to different specifications of photovoltaic modules and welding requirements, which has significant advantages. First of all, this function establishes a welding parameter database for photovoltaic modules of different specifications, providing a scientific basis for production. In actual production, when the system receives new photovoltaic module specifications and welding requirements, it can quickly query the corresponding welding parameters from the database and make appropriate adjustments to ensure that costs are minimized while meeting welding quality requirements. This greatly improves the flexibility and adaptability of production. Whether it is facing different sizes of cells or special welding requirements, the system can respond quickly.

[0059] For enterprises, this means that they can respond to changes in market demand more efficiently. With the continuous development of the photovoltaic industry, customers' specifications and performance requirements for photovoltaic modules are also constantly changing. The automatic adjustment function of the cost optimization module allows enterprises to quickly adjust production parameters to meet customer needs without increasing too much cost. At the same time, by continuously updating and optimizing the parameters in the database, the system can continuously improve welding quality and reduce costs, bringing long-term economic benefits to enterprises. In addition, automatic adjustment of welding parameters can also reduce the impact of human factors on production, improve production stability and consistency, reduce product defective rate, and further enhance the competitiveness of enterprises.

[0060] In this embodiment, the control module uses artificial intelligence algorithms to continuously optimize the welding process control strategy based on historical welding data and real-time image information. The control module uses machine learning algorithms to analyze and learn historical welding data and establish a welding quality prediction model. Based on the real-time collected image information and model prediction, the control module can automatically adjust the welding parameters and control strategies to improve welding quality and production efficiency. At the same time, the control module can also monitor and analyze the operating status of the system in real time, promptly discover and solve potential problems, and ensure the stable operation of the system.

[0061] Furthermore, the control module uses artificial intelligence algorithms, which brings many huge advantages to the photovoltaic module string welding machine system. First, the machine learning algorithm is used to analyze and learn historical welding data to establish a welding quality prediction model, which enables the system to predict welding quality more intelligently. By mining a large amount of historical data, the system can identify the potential relationship between different welding parameters and welding quality, so that in actual production, according to the real-time collected image information and model prediction, the welding parameters and control strategies are automatically adjusted, which greatly improves welding quality and production efficiency.

[0062] In the actual production process, this function can monitor various changes in the welding process in real time and make timely adjustments. For example, if the welding temperature is found to be too high or too low, the system can automatically adjust the temperature of the welding platform to ensure stable welding quality. At the same time, the real-time monitoring and analysis of the system operation status by the control module can promptly discover and solve potential problems to ensure the stable operation of the system. This not only reduces the risk of production interruption, but also reduces the cost of equipment maintenance. In addition, the application of artificial intelligence algorithms can continuously optimize the welding process control strategy, adapt to different production environments and photovoltaic module characteristics, and improve the adaptability and reliability of the system. For enterprises, this means that more efficient and stable production can be achieved, product quality can be improved, production costs can be reduced, and market competitiveness can be enhanced.

[0063] In this embodiment, the system can communicate with external devices to achieve remote monitoring and management. The system is equipped with a communication interface that can communicate with the factory's central control system or cloud server. Through the communication interface, operators can remotely monitor the operating status of the system, view real-time image information and production data, and remotely control and manage the system. At the same time, the system can also upload production data to the cloud server for big data analysis and processing to provide support for the company's production decisions.

[0064] Furthermore, the system can communicate with external devices to achieve remote monitoring and management, which brings many important benefits to photovoltaic module production. First, by equipping the communication interface to communicate with the factory's central control system or cloud server, operators can remotely monitor the operating status of the system. This means that no matter where the operator is, they can always understand key information such as production progress, equipment status and welding quality, which greatly improves the convenience and flexibility of production management.

[0065] Remote monitoring can also promptly detect abnormal conditions in system operation, such as poor welding, equipment failure, etc., and quickly take corresponding measures. Operators can remotely view real-time image information and production data, accurately judge and analyze problems, and thus conduct remote control and management in a timely manner. This not only reduces the workload of on-site operators, but also improves the efficiency of problem handling and reduces the risk of production interruption. At the same time, the system uploads production data to the cloud server for big data analysis and processing, which can provide strong support for the company's production decision-making. Through the analysis of a large amount of production data, enterprises can understand the bottlenecks and optimization space in the production process, formulate more scientific and reasonable production plans and management strategies, further improve production efficiency and quality, and reduce production costs. In addition, the remote communication function also facilitates the upgrade and maintenance of the system. Technicians can remotely update the system software and troubleshoot, which improves the maintainability and reliability of the system.

[0066] In this embodiment, the system has a self-learning function and can automatically adjust the image recognition algorithm and welding control strategy according to different production environments and photovoltaic module characteristics. During operation, the system will continuously collect new image data and production data, and analyze and learn these data. According to the learning results, the system can automatically adjust the image recognition algorithm and welding control strategy to adapt to different production environments and photovoltaic module characteristics. At the same time, the system can also communicate with external devices to obtain the latest technology and knowledge, and continuously improve its performance and intelligence level.

[0067] Furthermore, the system has a self-learning function, which can automatically adjust the image recognition algorithm and welding control strategy according to different production environments and photovoltaic module characteristics, bringing strong adaptability and continuous optimization capabilities to the photovoltaic module string welding machine system. In actual production, different production environments may have various effects on the welding process, such as changes in temperature, humidity, dust and other factors. At the same time, photovoltaic modules of different specifications and characteristics also require different welding parameters and control strategies. The system's self-learning function can automatically adapt to these changes, continuously adjust the image recognition algorithm and welding control strategy, and ensure that the system can maintain good performance under various circumstances.

[0068] During the implementation of the self-learning function, the system will continuously collect new image data and production data, and analyze and learn these data. Through the accumulation and analysis of a large amount of data, the system can gradually improve its ability to recognize different production environments and photovoltaic module characteristics, and optimize the accuracy and speed of image recognition. At the same time, the welding control strategy can be automatically adjusted according to the learning results to better adapt to different welding requirements and improve welding quality and production efficiency. In addition, the system communicates with external devices to obtain the latest technology and knowledge, and further improves its performance and intelligence level. This enables the system to keep up with the pace of industry development, continuously introduce new technologies and methods, and provide enterprises with more advanced and efficient production solutions. For enterprises, this means that they can reduce their reliance on manual experience, improve the stability and reliability of production, and also provide strong support for the sustainable development of enterprises.

[0069] The working principle and use process of the present invention:

[0070] During the production process of photovoltaic modules, the AI ​​photovoltaic module string welding machine system starts to start. First, the high-resolution industrial cameras in the image acquisition module start working at multiple key positions such as the photovoltaic module feed, above the welding station and the discharge. These cameras collect clear images at different angles and frequencies, and comprehensively monitor all parts of the photovoltaic modules that are about to enter the welding process. At the same time, they also continue to record every detail of the welding process. Then, the photovoltaic modules enter the welding module. The welding platform adopts advanced heating technology to quickly reach the required welding temperature. The temperature is uniform and stable, ready for welding. The solder conveying device, in cooperation with the temperature control unit, accurately controls the output of the solder according to the preset parameters to ensure the welding quality while reducing Solder waste. At the same time, the image information collected by the image acquisition module is transmitted to the data processing module. The high-performance processor and large-capacity storage device in the data processing module begin to play a role. The image recognition unit uses a deep learning algorithm to extract and classify the image features, accurately identify the location and specifications of the photovoltaic components and abnormal conditions in the welding process, such as welding ribbon offset, photovoltaic cell damage, poor welding, etc. The data analysis unit performs statistical analysis on a large amount of historical image data, establishes a relationship model between welding quality and welding parameters, and determines the optimal welding parameters, including welding temperature, solder amount, welding time, etc., based on the real-time collected image information and model prediction. The control module determines the optimal welding parameters according to the analysis of the data processing module. The analysis results are used to precisely control the welding module. If the data processing module identifies an abnormality, the control module will take different treatment measures according to the type and severity of the abnormality. For minor abnormalities, such as slight deviation of the welding strip, the control module automatically adjusts the position and angle of the welding platform for real-time correction; for serious abnormalities, such as damaged cells or poor welding, the control module suspends the welding operation and issues an alarm to notify the operator to handle it, while recording the occurrence time, type and treatment results of the abnormality. In addition, the cost optimization module also plays an important role in the whole process, by optimizing welding parameters, reducing the amount of solder used, monitoring and analyzing the solder waste, and at the same time, according to the specifications and welding requirements of the photovoltaic modules, The incoming photovoltaic modules are intelligently sorted to improve welding efficiency and reduce equipment energy consumption. The cost optimization module will automatically adjust welding parameters according to different specifications of photovoltaic modules and welding requirements to achieve the best cost-effectiveness. The system also has the ability to communicate with external devices and can communicate with the factory's central control system or cloud server. Operators can remotely monitor the system's operating status, view real-time image information and production data, and perform remote control and management. The system continuously collects new image data and production data during operation, has a self-learning function, and can automatically adjust image recognition algorithms and welding control strategies according to different production environments and photovoltaic module characteristics, continuously improving its performance and intelligence level.

[0071] The electronic components and modules used in the invention can be parts commonly used in the market and can realize the specific functions in this case, and the specific models and sizes can be selected and adjusted according to actual needs.

[0072] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. An AI photovoltaic module string welding machine system, characterized in that: include: Image acquisition module, used to collect image information of photovoltaic modules and welding process. The image acquisition module consists of high-resolution industrial cameras and is installed in multiple key locations, including but not limited to the photovoltaic module feed, above the welding station and the discharge. It can collect clear images at different angles and frequencies to ensure comprehensive monitoring of all parts of the photovoltaic module and the welding process. The welding module performs welding operations on photovoltaic modules. The welding module includes a welding platform, a solder conveying device and a temperature control unit. The welding platform adopts advanced heating technology, which can quickly reach the required welding temperature, and the temperature is uniform and stable. The solder conveying device can accurately control the output of solder, ensuring the welding quality while reducing solder waste. The temperature control unit monitors the welding temperature in real time and automatically adjusts it according to different photovoltaic module materials and welding requirements. The data processing module receives the image information from the image acquisition module and performs analysis and processing. The data processing module includes a high-performance processor and a large-capacity storage device, which can quickly process a large amount of image data. The processor runs advanced image recognition algorithms and data analysis software to perform real-time analysis on the collected image information, identify the location and specifications of the photovoltaic modules, and abnormal conditions during the welding process, and generate corresponding processing solutions. The storage device is used to store historical image data and analysis results for subsequent data analysis and system optimization. The control module is connected to the image acquisition module, the welding module and the data processing module, and controls the welding module according to the analysis results of the data processing module. The control module adopts an advanced programmable logic controller (PLC) and the latest processing system GPU, which can achieve precise control of the welding module. According to the instructions of the data processing module, the control module can adjust the position, temperature, solder delivery speed and other parameters of the welding platform to ensure the stability and efficiency of the welding process. At the same time, the control module can also coordinate and control the entire system to achieve automated production.

2. The AI ​​photovoltaic module string welding machine system according to claim 1, characterized in that: The data processing module comprises: The image recognition unit is used to identify the location and specifications of photovoltaic modules and abnormal conditions during welding. The image recognition unit uses deep learning algorithms to extract and classify features of the collected images. It can accurately identify photovoltaic modules of different specifications, including the size, shape, color and other characteristics of the cells. At the same time, for abnormal conditions during welding, such as welding ribbon offset, photovoltaic cell damage, poor welding, etc., the image recognition unit can quickly detect and locate the problem, providing accurate information for subsequent processing. The data analysis unit analyzes the collected image information and determines the optimization plan for welding parameters. The data analysis unit conducts statistics and analysis on a large amount of historical image data by connecting to big data Ai, and establishes a relationship model between welding quality and welding parameters. According to the real-time collected image information and model prediction, the optimal welding parameters are determined, including welding temperature, solder amount, welding time, etc. At the same time, the data analysis unit can also monitor and analyze the operating status of the system, discover potential problems in a timely manner, and propose corresponding improvement measures.

3. The AI ​​photovoltaic module string welding machine system according to claim 2 is characterized in that: The abnormal conditions that the image recognition unit can identify include but are not limited to welding ribbon offset, photovoltaic cell damage, and poor welding. For welding ribbon offset, the image recognition unit can detect the relative position deviation between the welding ribbon and the cell and calculate the offset. When the offset exceeds the set threshold, the system will automatically issue an alarm and notify the control module to make adjustments. For photovoltaic cell damage, the image recognition unit can detect defects such as cracks and missing corners on the surface of the cell and evaluate the degree of damage. Based on the evaluation results, the system can decide whether to continue the welding operation or remove the damaged cell. For poor welding, the image recognition unit can detect the quality of the welding point, including welding strength, appearance, etc. If poor welding is found, the system will automatically rework or notify the operator to check and repair.

4. The AI ​​photovoltaic module string welding machine system according to claim 1, characterized in that: When the data processing module identifies an abnormal situation, the control module can adjust the welding parameters or suspend the welding operation in time and issue an alarm. The control module takes different treatment measures according to the type and severity of the abnormal situation. For minor abnormal situations, such as slight deviation of the welding strip, the control module can automatically adjust the position and angle of the welding platform for real-time correction. For more serious abnormal situations, such as damaged battery cells or poor welding, the control module will suspend the welding operation and issue an alarm to notify the operator to handle it. At the same time, the control module will also record the occurrence time, type and treatment results of the abnormal situation for subsequent data analysis and system optimization.

5. The AI ​​photovoltaic module string welding machine system according to claim 1, characterized in that: The system also includes a cost optimization module that reduces production costs by: Optimize welding parameters and reduce solder usage. The cost optimization module automatically adjusts the solder delivery speed and welding time according to the welding parameter optimization plan provided by the data analysis unit to ensure that the amount of solder used is reduced while ensuring the welding quality. At the same time, the cost optimization module will also monitor and analyze the waste of solder to promptly discover and solve the problem of solder waste. Intelligently sort PV modules to improve welding efficiency and reduce equipment energy consumption. The cost optimization module intelligently sorts incoming PV modules according to the specifications and welding requirements of the PV modules. By rationally arranging the welding sequence, welding efficiency can be improved, equipment idling time can be reduced, and equipment energy consumption can be reduced. At the same time, the cost optimization module will also monitor and analyze the operating status of the equipment, adjust the operating parameters of the equipment according to the actual situation, and further reduce energy consumption.

6. The AI ​​photovoltaic module string welding machine system according to claim 5, characterized in that: The cost optimization module automatically adjusts the welding parameters according to different specifications of photovoltaic modules and welding requirements to achieve the best cost-effectiveness. The cost optimization module establishes a welding parameter database for photovoltaic modules of different specifications, and continuously updates and optimizes the parameters in the database according to actual production conditions. When the system receives new photovoltaic module specifications and welding requirements, the cost optimization module will automatically query the corresponding welding parameters from the database and make appropriate adjustments to ensure that the cost is minimized while meeting the welding quality requirements.

7. The AI ​​photovoltaic module string welding machine system according to claim 1, characterized in that: The control module uses artificial intelligence algorithms to continuously optimize the welding process control strategy based on historical welding data and real-time image information. The control module uses machine learning algorithms to analyze and learn historical welding data and establish a welding quality prediction model. Based on the real-time collected image information and model prediction, the control module can automatically adjust the welding parameters and control strategy to improve welding quality and production efficiency. At the same time, the control module can also monitor and analyze the operating status of the system in real time, promptly discover and solve potential problems, and ensure the stable operation of the system.

8. The AI ​​photovoltaic module string welding machine system according to claim 1, characterized in that: The system can communicate with external devices to achieve remote monitoring and management. The system is equipped with a communication interface that can communicate with the factory's central control system or cloud server. Through the communication interface, operators can remotely monitor the system's operating status, view real-time image information and production data, and remotely control and manage the system. At the same time, the system can also upload production data to the cloud server for big data analysis and processing to provide support for the company's production decisions.

9. The AI ​​photovoltaic module string welding machine system according to any one of claims 1 to 8, characterized in that: The system has a self-learning function and can automatically adjust the image recognition algorithm and welding control strategy according to different production environments and photovoltaic module characteristics. During operation, the system will continuously collect new image data and production data, and analyze and learn from these data. According to the learning results, the system can automatically adjust the image recognition algorithm and welding control strategy to adapt to different production environments and photovoltaic module characteristics. At the same time, the system can also communicate with external devices to obtain the latest technology and knowledge, and continuously improve its performance and intelligence level.