Method and system for production process optimization of photovoltaic modules

Abstract

The application discloses a production process optimization method and system for a photovoltaic module, and relates to the technical field of photovoltaic production. The method comprises the following steps: front welding is performed on a target photovoltaic module, an infrared thermal image is used to monitor the thermal distribution of a welding head and a welding seam area, and a welding seam peak temperature distribution is obtained by analysis; the weldability of a plurality of tail ends after front welding is identified according to a preset index, and a tail end weldability distribution is obtained; based on the welding seam peak temperature distribution and the tail end weldability distribution, back stringing is positioned, compensated and matched with a secondary reflow heat curve, and a back stringing process optimization scheme is formed; and the back stringing is completed according to the scheme. The technical problems of unstable back stringing quality and electrical performance loss caused by the mismatch of front welding and back stringing process parameters are solved, the technical effect of realizing accurate positioning and heat process matching of back stringing through the data linkage of welding heat distribution and tail end weldability is achieved, and the stringing quality and the component yield are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of photovoltaic production, in particular to a production process optimization method and system for photovoltaic modules. BACKGROUND

[0002] With the rapid development of the photovoltaic power generation industry, the production efficiency and welding quality of photovoltaic modules have become key factors affecting the performance and service life of the modules. The production process of traditional photovoltaic modules usually includes cell front welding, inspection, back stringing, final inspection and other links. Among them, the front welding process is responsible for electrically connecting the solder strip and the cell main grid line, while the back stringing process connects several cells through the solder strip to form a module. There is a significant process correlation between the two, especially in terms of welding temperature, weldability state, alignment accuracy and mechanical stress transmission.

[0003] In the prior art, front welding process parameters such as welding head temperature, moving speed, pressure, etc. are usually set by experience, lacking real-time monitoring and data feedback of welding heat distribution, resulting in uneven weld temperature, large differences in weld strength, etc. This not only affects the weldability of the cell and the positioning accuracy during back stringing, but also easily causes defects such as virtual welding, broken strip, hidden cracks, etc., reducing the electrical performance and reliability of the module. In addition, the two processes of front welding and back stringing are usually run independently in the existing production line, lacking effective data linkage and process compensation mechanism, and unable to dynamically optimize the back stringing parameters according to the front welding state, resulting in difficulty in ensuring the quality of back stringing. SUMMARY

[0004] The present application provides a production process optimization method and system for photovoltaic modules, which solves the technical problem of unstable back stringing quality and electrical performance loss caused by mismatching of front welding and back stringing process parameters.

[0005] In a first aspect, the present application provides a production process optimization method for photovoltaic modules, the method comprising:

[0006] front welding of a target photovoltaic module, and monitoring the heat distribution of the welding head and the weld area of the welding area using an infrared thermal image, and analyzing to obtain the peak temperature distribution of the weld; identifying the weldability of the tail end of the front welding according to a preset tail end index, and obtaining the weldability distribution of the tail end; based on the peak temperature distribution of the weld and the weldability distribution of the tail end, positioning compensation and secondary reflow heat curve linkage matching of the back stringing of the target photovoltaic module are performed, and a back stringing process optimization scheme is obtained; and back stringing of the target photovoltaic module according to the back stringing process optimization scheme.

[0007] In a second aspect, the present application provides a production process optimization system for photovoltaic modules, the system comprising:

[0008] The temperature monitoring module: front welding is performed on the target photovoltaic module, and the temperature distribution of the welding head and the welding seam area of the welding area is monitored by using infrared thermal imaging, and the peak temperature distribution of the welding seam is obtained after analysis; the weldability identification module: the weldability of the plurality of welding tail ends after front welding is identified according to the preset tail end index, and the weldability distribution of the welding tail end is obtained; the process scheme obtaining module: based on the peak temperature distribution of the welding seam and the weldability distribution of the welding tail end, the back connection of the target photovoltaic module is positioned and compensated and the secondary reflow heat curve is matched, and the back connection process optimization scheme is obtained; the back connection module: according to the back connection process optimization scheme, the back connection of the target photovoltaic module is performed.

[0009] One or more technical solutions provided in the present application have at least the following technical effects or advantages:

[0010] First, the target photovoltaic module is front welded, and the temperature distribution of the welding area is monitored in real time by using infrared thermal imaging technology, so as to obtain the peak temperature distribution characteristics of the welding seam. Then, the plurality of welding tail ends after front welding are detected, and the weldability thereof is identified and evaluated according to the preset tail end shape and geometric index, so as to obtain the weldability distribution. Then, the welding seam temperature distribution and the weldability distribution are comprehensively analyzed, the back connection process is positioned and compensated, and the secondary reflow heat curve is matched, so as to generate the optimized back connection process scheme. Finally, the back connection operation is performed according to the optimization scheme, so as to realize the parameter linkage of the front and back processes and the overall improvement of the welding quality. DETAILED DESCRIPTION

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0012] Figure 1 The production process optimization method flowchart for the photovoltaic module provided by the embodiments of the present application.

[0013] Figure 2 The production process optimization system structure schematic diagram for the photovoltaic module provided by the embodiments of the present application.

[0014] Legend: temperature monitoring module 11, weldability identification module 12, process scheme obtaining module 13, back connection module 14. DETAILED DESCRIPTION

[0015] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined object of the present application, the specific embodiments, structures, features and effects thereof according to the present application are described in detail below in conjunction with the drawings and preferred embodiments.

[0016] As shown in Embodiment One, Figure 1 The present application provides a production process optimization method for a photovoltaic module, the method comprising:

[0017] The target photovoltaic module is front-welded, and the thermal distribution of the welding head and the weld area of the welding area is monitored by using an infrared thermal image, and the peak temperature distribution of the weld is obtained after analysis.

[0018] In the embodiments of the present application, first, the target photovoltaic module to be welded is placed on a positioning and adsorbing platform to ensure that the cell remains flat and stable during the welding process. Then, the infrared thermal image acquisition system is started to calibrate the background thermal field of the welding area to eliminate environmental temperature interference, and the process parameters such as the working temperature, welding pressure and moving speed of the welding head are set, and the welding head is controlled to move along the main grid line of the cell to weld and fix the solder strip and the main grid line under constant pressure. Subsequently, the infrared thermal image acquisition system controls the infrared thermal image camera installed above the welding area to collect the thermal radiation information of the welding head and the weld area in real time, and marks the welding head position coding information in each frame to generate a sequence of thermal image frames calibrated by temperature. Finally, the thermal distribution of the sequence of thermal image frames is analyzed to obtain the peak temperature distribution of the weld to reflect the uniformity of thermal input and local overheating at different positions during the welding process, thereby providing a basis for subsequent judgment of the formation quality of the welding point and the thermal matching of the back surface stringing.

[0019] Further, the target photovoltaic module is front-welded, and the thermal distribution of the welding head and the weld area of the welding area is monitored by using an infrared thermal image, and the peak temperature distribution of the weld is obtained after analysis, comprising:

[0020] The cell to be welded of the target photovoltaic module is placed on a positioning and adsorbing platform, and the infrared thermal image acquisition system is started to calibrate the background stability; a welding start instruction is obtained, the welding head moves along the main grid line of the cell to be welded and applies constant pressure, and front-welding is performed according to the preset welding head temperature and the preset moving speed; during the front-welding process, the infrared thermal image acquisition system synchronously collects the thermal image frames of the welding head and the weld area of the welding area to obtain a sequence of thermal image frames, wherein each thermal image frame includes welding head position coding information; the thermal distribution of the sequence of thermal image frames is analyzed to obtain the peak temperature distribution of the weld.

[0021] Preferably, the target photovoltaic module to be welded is first accurately placed on the positioning and adsorbing platform, and the position of the cell is stabilized and flat without warping during the welding process through vacuum adsorption. Then, the infrared thermal image acquisition system is started to calibrate the background thermal field of the welding environment. Specifically, the infrared thermal image camera is fixedly installed about 200 mm above the welding area to ensure that the lens is vertically aligned with the center area of the welding head and the weld. Then, the infrared thermal image camera and the infrared thermal image acquisition system are started to run continuously for 2-3 minutes in a static state to make the detector temperature reach thermal equilibrium, effectively reduce the initial thermal drift, and improve the stability of subsequent temperature measurement. After calibration is completed and a welding start instruction is received, the welding head starts to move along the main grid line direction of the cell and applies a constant pressure while front welding is performed according to the preset temperature and movement speed to achieve sufficient thermal fusion between the solder strip and the main grid line. Due to the thermal conduction loss and material thermal reflection difference between the set temperature of the welding head and the actual temperature of the welding contact area, the real temperature distribution of the weld needs to be obtained through infrared temperature measurement during the welding process. Specifically, the infrared thermal image acquisition system is used to control the infrared thermal image camera to synchronously acquire thermal image frame sequences of the welding head and the weld area at a fixed frame rate, such as 50 frames per second. Each frame of image records the current position encoding information of the welding head for subsequent temperature and position correspondence analysis. Generally, the infrared thermal image waveband is set to 8-14 μm, and the emissivity parameter is set to silver paste 0.92 and solder strip 0.80, respectively, according to the surface characteristics of the materials to ensure the temperature measurement accuracy. Then, the preset temperature calibration coefficient table is used to convert the acquired thermal image frame sequences to obtain calibrated thermal image frame sequences. Then, the effective pixel interval of the weld area is identified, the highest temperature points in each frame of image are extracted, and the weld peak temperature distribution is formed in combination with the position encoding of the welding head. This weld peak temperature distribution not only reflects the actual heat input during the welding process, but also provides a key basis for the subsequent back stringing process. By mastering the thermal distribution characteristics during front welding, the linkage optimization of the secondary reflow thermal curve can be realized during the back stringing process, thereby avoiding the accumulation of thermal stress and improving the welding reliability and electrical performance consistency.

[0022] Further, the thermal distribution of the thermal image frame sequence is analyzed to obtain the weld peak temperature distribution, including:

[0023] According to the preset temperature calibration coefficient table, the temperature values of the thermal image frame sequences are converted respectively to obtain calibrated thermal image frame sequences. The weld position set sequence is obtained by traversing the calibrated thermal image frame sequences for weld position detection. Based on the weld position set sequence and the calibrated thermal image frame sequences, the weld peak temperature distribution is obtained by analyzing the weld peak temperature.

[0024] Preferably, in order to accurately obtain the actual temperature distribution of the welding area, first, the collected thermal image frame sequence is subjected to temperature value conversion according to a preset temperature calibration coefficient table, which is obtained by standard blackbody radiation source calibration and determines the linear mapping relationship between the pixel gray value and the actual temperature through the gray response relationship at different temperature points. For each pixel point (c, y) in the thermal image frame, the original gray thermal image data can be converted into a calibrated thermal image frame sequence reflecting the true heat distribution according to the formula: T(c, y) = a x G(c, y) + b, wherein T(c, y) is the actual temperature value corresponding to the pixel; G(c, y) is the gray value of the corresponding pixel in the infrared image; a and b are linear calibration coefficients obtained through blackbody calibration experiment. Subsequently, the calibrated thermal image frame sequence is subjected to traversal analysis, for each thermal image frame traversed, the frame is bound with the corresponding welding head position code to form a synchronous data pair of time and position, and the welding head position codes belonging to the same thermal image frame are stored in a set to form a welding seam position set sequence, which reflects the spatial movement trajectory and morphological change of the welding seam in different time frames. Then, based on the welding seam position set sequence and the calibrated thermal image frame sequence, welding peak temperature analysis is performed, that is, the local highest temperature point in each welding seam position region is searched, and then all the welding seam highest temperature points are summarized and spatially mapped to form a complete welding peak temperature distribution, which provides accurate data support for subsequent heat curve matching and welding stress compensation in the back stringing process, thereby realizing closed-loop optimization and traceable control of the photovoltaic module welding process.

[0025] Further, based on the welding seam position set sequence and the calibrated thermal image frame sequence, welding peak temperature analysis is performed to obtain the welding peak temperature distribution, including:

[0026] Based on the welding seam position set sequence, an effective peak temperature extraction region set sequence is obtained by identifying the effective peak temperature extraction region of the calibrated thermal image frame sequence; combined with the effective peak temperature extraction region set sequence and the calibrated thermal image frame sequence, a mapping highest temperature value search is performed to determine a region highest temperature value set sequence; combined with the welding head position code information, the region highest temperature value set sequence is classified into the same welding head, and the maximum temperature value in the classification result is extracted to obtain the welding peak temperature distribution.

[0027] Optionally, first, based on the obtained sequence of sets of weld seam positions, an effective temperature extraction area of the weld seam is identified in each frame of the calibrated thermal image, in this process, by analyzing the temperature gradient change around the center line of the weld seam, a pixel area with a temperature value higher than a set threshold, such as 70% of the average temperature of the welding head, is determined as the effective peak temperature extraction area of the weld seam, and then a continuous area identification result is formed by frame-by-frame scanning to obtain a sequence of sets of effective peak temperature extraction areas, which can effectively exclude non-welding areas and background thermal interference and ensure the accuracy of subsequent temperature extraction. Subsequently, the sequence of sets of effective peak temperature extraction areas is spatially mapped with the sequence of calibrated thermal image frames, in each identified effective area, the highest temperature value point of the area is searched in a pixel traversal manner, and its pixel coordinates, frame sequence number and corresponding temperature value are recorded. By sequentially arranging all the highest temperature points of the frames, a sequence of sets of area maximum temperature values is formed, which fully reflects the temperature variation trend and hot spot distribution characteristics of the weld seam along the time and space directions. Then, combined with the welding head position coding information recorded during the welding process, the sequence of sets of area maximum temperature values is classified with the welding head, that is, according to the real-time position coding of the welding head, each maximum temperature point is grouped according to the corresponding welding position, so as to ensure that the peak temperature data of each weld seam corresponds to the specific welding path one by one. For each classification result, the maximum value of the temperature value is extracted, which represents the true thermal peak response when the weld seam is formed. Finally, after spatial sorting of all the maximum temperature values corresponding to the welding head, the peak temperature distribution of the weld seam is obtained, which provides accurate thermal data basis for subsequent back-to-back thermal curve matching and process compensation, and realizes intelligent optimization and quality traceability control of the welding process.

[0028] The weldability of the plurality of welding tail ends after front welding is identified according to a preset tail end index to obtain a weldability distribution of the welding tail ends.

[0029] In one embodiment, the target photovoltaic module with completed front-side welding is first conveyed to an automatic optical detection station, and image acquisition is performed on each welding tail end region by an automatic optical detection system. The automatic optical detection system adopts a linear array or area array camera structure and cooperates with a ring light source or a side light source to eliminate reflection interference and ensure that the edge of the welding ribbon, the tail shape, and the welding point contour are clearly visible. After the welding tail end image is acquired, feature recognition and weldability evaluation are performed on the acquired welding tail end image according to the preset tail end index, the weldability coefficient of each welding tail end is calculated, and the weldability coefficients of all tail ends are two-dimensionally mapped in combination with the spatial coordinate information of each welding tail end on the cell, to form a welding tail end weldability distribution. The welding tail end weldability distribution can directly show the quality state of each welding tail end and the distribution law thereof in the module, improve the automation and accuracy of welding tail end detection, and provide effective support for quality control and process optimization of the photovoltaic module welding process.

[0030] Further, the weldability of the plurality of welding tail ends after front-side welding is identified according to the preset tail end index, and a welding tail end weldability distribution is obtained, including:

[0031] The plurality of welding tail ends are traversed by an automatic optical detection system to perform high-definition image acquisition, and a plurality of welding tail end images are obtained. The weldability of the plurality of welding tail end images is identified according to the preset tail end index, and a plurality of welding tail end weldability coefficients are obtained. In combination with the positions of the plurality of welding tail ends, the plurality of welding tail end weldability coefficients are identified for distribution, and the welding tail end weldability distribution is obtained.

[0032] Preferably, the plurality of solder tails are first traversed for image acquisition by an automatic optical inspection system, which employs a high-resolution camera to capture high-definition images of features including the overall shape, length, and position of the solder tails, to form a plurality of solder tail images, providing basic data for subsequent solderability analysis. Subsequently, each solder tail image is identified and analyzed according to preset tail indicators, in the process, the solder ribbon edge in the image is detected by a Canny edge detection algorithm, the starting point and end point of the solder ribbon are extracted, and the length of the solder tail is calculated. The actual position of the solder tail is identified by morphological analysis methods such as dilation and erosion operations, and the deviation between the solder tail and the designed position is calculated. The angle between the solder tail and the main grid line of the battery sheet is detected by a straight line fitting algorithm. The warping degree of the solder tail is measured by a plane fitting algorithm. Then, the extracted features are divided by the corresponding solderability standard values, and the weighted sum of all the calculated quotients is obtained to obtain the solderability coefficient of each solder tail, each solderability coefficient is between 0 and 1, 0 indicates that the solder tail is not weldable, and 1 indicates that the solder tail completely meets the predetermined standard. After obtaining the solderability coefficients of all the solder tails, the solderability coefficients of each solder tail are combined with their positions on the battery sheet, and the solderability coefficients are displayed by a two-dimensional coordinate system to form a solder tail solderability distribution map, which can intuitively show the quality status of each solder tail, and further reveal possible welding quality problems in the entire photovoltaic module, such as excessive tail deviation or excessive warping degree, so that accurate process compensation or optimization can be performed in the back stringing process.

[0033] Further, the preset tail indicators include tail length, position, angle, and warping degree.

[0034] Optionally, the preset tail indicators are key parameters for evaluating whether the solder tail meets the welding requirements, including tail length, position, angle, and warping degree, wherein the tail length refers to the length of the solder ribbon extending from the starting point of the main grid line to the solder tail, and appropriate tail length ensures sufficient heat input during welding, avoiding incomplete coverage of the grid line of the battery sheet, thereby affecting the reliability of electrical connection. The position of the solder tail reflects the deviation of the solder tail end relative to the designed position of the battery sheet, and the position deviation may cause misalignment of welding, thereby affecting the welding effect and the performance of the battery sheet. The angle refers to the angle between the solder tail and the main grid line, and appropriate angle helps the solder evenly distribute in the welding area, ensuring the stability of the welding contact point. The warping degree refers to the degree of warping or bending of the solder tail due to temperature gradient or other external forces, and if the tail warping is too large during welding, it will affect the welding alignment accuracy, and may even cause poor contact between the solder ribbon and the battery sheet.

[0035] Based on the weld peak temperature distribution and the weld tail end weldability distribution, the back surface stringing of the target photovoltaic module is positioned and compensated, and the secondary reflow heat curve is matched in linkage to obtain a back surface stringing process optimization scheme.

[0036] In one embodiment, after obtaining the weld peak temperature distribution and the weld tail end weldability distribution, double-project laminating process connection authentication is performed according to the two distribution information. If the authentication is passed, the secondary reflow heat curve is matched in linkage according to the weld peak temperature distribution, and positioning adaptive compensation is performed for the weld tail end weldability of different positions to ensure the accurate docking of the solder strip and the previous welding area. By combining positioning compensation with secondary reflow heat curve matching, the system can realize the optimization of the back surface stringing process, ensure the stability of the welding area, the consistency of the welding points, and the reliability of the electrical performance. Finally, the back surface stringing process optimization scheme obtained based on the linkage optimization automatically adjusts the back surface stringing process to the best state to realize the efficient production and high-quality output of photovoltaic modules.

[0037] Further, based on the weld peak temperature distribution and the weld tail end weldability distribution, the back surface stringing of the target photovoltaic module is positioned and compensated, and the secondary reflow heat curve is matched in linkage to obtain a back surface stringing process optimization scheme, including:

[0038] The weld peak temperature distribution and the weld tail end weldability distribution are authenticated for double-project laminating process connection. If the authentication is passed, the secondary reflow heat curve is matched in linkage according to the weld peak temperature distribution to obtain a matched secondary reflow heat curve set, and positioning compensation is performed based on the weld tail end weldability distribution to obtain a positioning compensation distribution. The pre-set back surface stringing process is optimized by combining the matched secondary reflow heat curve set and the positioning compensation distribution to obtain the back surface stringing process optimization scheme. If the authentication is not passed, an early warning information is obtained.

[0039] Preferably, the weld peak temperature distribution is first superimposed with the weld tail weldability distribution, and matching verification is performed through interface authentication to ensure that the weld temperature and the weldability of the weld tail have a reasonable matching relationship in space and time. In the authentication process, the average temperature and the temperature standard deviation are calculated according to the weld peak temperature distribution. When the average temperature is within the temperature standard interval and the temperature standard deviation is less than the standard deviation threshold, it indicates that the temperature is uniformly distributed within the standard interval, and the first layer authentication passes. Then, according to the weld tail weldability distribution, the number of tail ends with a weldability coefficient below the threshold is counted. When the number of tail ends is less than the tail end number threshold, it indicates that the number of poor welds does not exceed the allowed deviation, and the double-item superposition process interface authentication passes. On the contrary, if any of the standards is not met, such as the overall temperature being too low or the uniformity being poor, or there are too many poor welds, the double-item superposition process interface authentication fails, and the system triggers a warning message, directly generates a warning message containing specific unqualified items, and notifies the operator to intervene or guide it to the repair station. For the authenticated one, a set of adaptive secondary reflow heat curves is determined according to the weld peak temperature distribution, combined with the material allowable heat input threshold and the standard reflow heat curve, to ensure that the heat input of the welding area is more uniform and stable when back-to-back stringing. Then, based on the weld tail weldability distribution, the compensation rules for each position are matched. For the weldability coefficient within the optimal interval, such as [0.9, 1.0], no compensation adjustment is needed; for the weldability coefficient within the good interval, such as [0.7, 0.9], XY fine tuning is performed, that is, 1 is subtracted from the weldability coefficient, and the difference is multiplied by the X fine tuning standard value (such as 0.1 mm) and the Y fine tuning standard value (such as 0.05 mm) respectively to obtain the X and Y compensation amounts; for the weldability coefficient within the medium interval, such as [0.5, 0.7], Z-θ three-dimensional compensation is performed, that is, 0.7 is subtracted from the weldability coefficient, and the difference is multiplied by the angle adjustment standard value (such as 3°) and the Z adjustment standard value (such as 0.1 mm) respectively to obtain the θ and Z compensation amounts; for the weldability coefficient within the poor interval, such as [0, 0.5], there are usually irreparable defects that cannot be repaired by compensation, and forced welding will result in virtual welding, which needs to be skipped and handled by subsequent processes. After positioning compensation is completed, a positioning compensation distribution can be obtained, which shows the weldability state and position of the weld tail after compensation, helping to understand which areas have been improved and which areas still have potential problems. Then, according to the matched secondary reflow heat curve set and the positioning compensation distribution, the process optimization analyzer is used to perform optimization analysis on the preset back-to-back stringing process to obtain the final back-to-back stringing process optimization scheme. This back-to-back stringing process optimization scheme can realize accurate welding position adjustment and heat input control, ensure the accurate docking of the weld joint and the weld tail during back-to-back stringing, and improve the electrical performance and mechanical strength of the welds.

[0040] Further, according to the weld peak temperature distribution, a secondary reflow heat curve linkage matching is performed to obtain a matched secondary reflow heat curve set, including:

[0041] Based on the material information of the to-be-welded battery piece, a material allowable heat input threshold is determined; a standard reflow heat curve of back surface stringing is obtained, wherein the standard reflow heat curve includes predicted area temperature and time, peak temperature and time of the heating area, and cooling area temperature; based on the weld peak temperature distribution and the material allowable heat input threshold, a heat input margin analysis is performed to obtain a heat input margin distribution; based on the heat input margin distribution, the standard reflow heat curve is mapped to obtain a matched secondary reflow heat curve set.

[0042] Optionally, first, based on the material information of the battery piece to be welded, such as material type, thermal conductivity, etc., the material allowed heat input threshold is matched from the battery piece information library. This material allowed heat input threshold is usually the total energy of the front welding heat input and the back stringing heat input. Then, the standard reflow heat curve of the back stringing station is called. This standard reflow heat curve is set according to actual process experience and standard requirements, including preheating zone temperature, temperature peak in temperature rising zone, cooling zone temperature, etc. The preheating zone temperature ranges from 150 to 180°C, and the time is 3-6 seconds. The purpose of the preheating zone is to preheat the welding area to avoid cold welding and help the uniform flow of the solder. The temperature peak in the temperature rising zone is between 260-300°C, and the time is 1-3 seconds. The temperature rising zone is used to make the welding reach the optimal temperature to ensure the full fusion of the welding part. The cooling zone temperature is below 100°C. The purpose of the cooling zone is to ensure that the welding area cools quickly to form a stable welding point and avoid excessive thermal stress. Then, according to the temperature of each position in the welding seam peak temperature distribution, the actual heat input value absorbed by each position in the front welding process is calculated to form an actual front heat input distribution. Then, the material allowed heat input threshold is subtracted from the actual heat input value of each position in the actual front heat input distribution to obtain the heat input margin of each position, which constitutes the heat input margin distribution. When the heat input margin is greater than 0, it means that there is still heat input margin at this position, which can be used for back stringing. On the contrary, it means that the front heat input has reached or exceeded the total budget, and the back stringing needs to be careful and the heat input must be reduced. Then, according to the obtained heat input margin distribution, the standard reflow heat curve is matched and adjusted. Specifically, the temperature matching adjustment coefficient and the time matching adjustment coefficient set according to the empirical value are obtained. Then, the temperature peak in the temperature rising zone is reduced by the product of the temperature matching adjustment coefficient and the heat input margin to map the heat input margin back to the temperature dimension. The time corresponding to the temperature peak in the temperature rising zone is added to the product of the time matching adjustment coefficient and the heat input margin mapped back to the temperature to map the heat input margin back to the time dimension. Then, the welding points on the photovoltaic module are divided into strings. Since the welding points in the same string have similarity in production process, the adjusted temperature peak and temperature rising time of all welding point positions in the same string are averaged to obtain the matching temperature peak and matching temperature rising time corresponding to the string. Then, the matching temperature peak and matching temperature rising time of each string are combined with the unadjusted preheating zone temperature, preheating time, cooling zone temperature, etc. to form a new secondary reflow heat curve data package. This data package records in detail the temperature-time parameters of each stage in the back stringing process corresponding to each string. Finally, all the secondary reflow heat curve data packages corresponding to the strings are integrated together to form a matched secondary reflow heat curve set. Each curve in this set is optimized and adjusted according to the actual situation of different strings of the photovoltaic module, which can better adapt to the heat input demand in actual production and improve the quality and reliability of the back stringing.

[0043] Further, the preset back stringing process is optimized in combination with the matched secondary reflow thermal curve set and the positioning compensation distribution to obtain a back stringing process optimization scheme, including:

[0044] An process optimization analyzer is obtained, wherein the process optimization analyzer is obtained after training based on a framework of a feedforward neural network; the process optimization analyzer is used to analyze the preset back stringing process in combination with the matched secondary reflow thermal curve set and the positioning compensation distribution to obtain the back stringing process optimization scheme.

[0045] Optionally, when the back stringing process is optimized, a process optimization analyzer is first constructed, which is obtained based on training of a feedforward neural network architecture. The training data used includes historical matched secondary reflow thermal curves, historical positioning distribution, a preset back stringing process and a historical back stringing process scheme, and the training steps involved include forward propagation, loss calculation, back propagation, parameter optimization and the like. Subsequently, the obtained matched secondary reflow thermal curve set and positioning compensation distribution are input into the process optimization analyzer. The process optimization analyzer combines the received matched secondary reflow thermal curve and positioning information with the internally preset back stringing process, identifies the best matching relationship of each welding area and welding tail end according to the learned rules in the training process, evaluates the influence of different welding parameters, and automatically generates an optimal back stringing process optimization scheme. The back stringing process optimization scheme includes welding head position, heat input, cooling rate, welding sequence, welding strip tension and the like, ensuring the quality and reliability of each welding point and improving overall production efficiency.

[0046] The target photovoltaic module is back-stitched according to the back stringing process optimization scheme.

[0047] In one embodiment, after obtaining the back stringing process optimization scheme, the welding equipment is automatically adjusted according to the back stringing process optimization scheme, including welding head position, welding temperature, pressure and movement speed and the like, to ensure that the welding process can respond to process requirements in real time. During the back stringing process, the system controls the temperature of the heating, holding and cooling stages in real time to ensure uniform heat input of the welding area, to ensure that the quality of each welding point meets the requirements, and to adjust and optimize the process parameters in real time when any abnormality occurs. When all the welding points are completed, the back stringing process is completed. The photovoltaic module undergoes this efficient and precise welding process, ensuring stable connection between the cell pieces, and has high electrical performance and mechanical strength.

[0048] In summary, the embodiments of the present application have at least the following technical effects:

[0049] Firstly, the front surface of the target photovoltaic module is welded, and the infrared thermal image is used to monitor the heat distribution of the welding head and the welding seam area of the welding area, and the welding peak temperature distribution is obtained after analysis. Secondly, the weldability of the plurality of welding tail ends after the front surface welding is identified according to the preset tail end index, and the weldability distribution of the welding tail end is obtained. Then, based on the welding peak temperature distribution and the weldability distribution of the welding tail end, the back surface stringing of the target photovoltaic module is positioned and compensated, and the second reflow heat curve is matched, and the back surface stringing process optimization scheme is obtained. Finally, the back surface stringing of the target photovoltaic module is carried out according to the back surface stringing process optimization scheme. The technical problems of unstable back surface stringing quality and loss of electrical performance caused by the mismatch of front surface welding and back surface stringing process parameters are solved, and the technical effects of precise positioning and heat process matching of back surface stringing through the data linkage of welding heat distribution and tail end weldability, improving the quality of stringing and the yield of the module are achieved.

[0050] In the embodiment two, based on the same inventive concept as the production process optimization method for photovoltaic modules in the foregoing embodiments, as shown in the embodiment two, Figure 2 The production process optimization system for photovoltaic modules provided by the present application comprises:

[0051] The temperature monitoring module 11: the front surface of the target photovoltaic module is welded, and the infrared thermal image is used to monitor the heat distribution of the welding head and the welding seam area of the welding area, and the welding peak temperature distribution is obtained after analysis. The weldability identification module 12: the weldability of the plurality of welding tail ends after the front surface welding is identified according to the preset tail end index, and the weldability distribution of the welding tail end is obtained. The process scheme obtaining module 13: based on the welding peak temperature distribution and the weldability distribution of the welding tail end, the back surface stringing of the target photovoltaic module is positioned and compensated, and the second reflow heat curve is matched, and the back surface stringing process optimization scheme is obtained. The back surface stringing module 14: according to the back surface stringing process optimization scheme, the back surface stringing of the target photovoltaic module is carried out.

[0052] Further, the temperature monitoring module 11 is used to execute the following method:

[0053] The target photovoltaic module to be welded is placed on the positioning and adsorbing platform, and the infrared thermal image acquisition system is started to calibrate the background stability. The welding start instruction is obtained, the welding head moves along the main grid line of the to-be-welded cell and applies a constant pressure, and the front surface welding is carried out according to the preset welding head temperature and the preset moving speed. During the front surface welding, the infrared thermal image acquisition system synchronously acquires the thermal image frames of the welding head and the welding seam area of the welding area, and a sequence of thermal image frames is obtained, wherein each thermal image frame includes welding head position encoding information. The thermal distribution of the thermal image frame sequence is analyzed, and the welding peak temperature distribution is obtained.

[0054] Further, the temperature monitoring module 11 is used to execute the following method:

[0055] Placing the to-be-welded cell of the target photovoltaic module on a positioning adsorption platform, starting an infrared thermal image acquisition system to perform background stable calibration; obtaining a welding start instruction, moving a welding head along a main grid line of the to-be-welded cell and applying a constant pressure, and performing front welding according to a preset welding head temperature and a preset moving speed; in the process of front welding, the infrared thermal image acquisition system synchronously acquires thermal image frames of the welding head and the weld area of the welding area, and obtains a thermal image frame sequence, wherein each thermal image frame includes welding head position coding information; performing thermal distribution analysis on the thermal image frame sequence to obtain a weld peak temperature distribution.

[0056] Further, the temperature monitoring module 11 is configured to perform the following method:

[0057] Based on the sequence of the set of weld positions, the sequence of the set of effective peak temperature extraction areas is obtained by performing effective peak temperature extraction area identification on the sequence of the calibration thermal image frames; the sequence of the set of regional maximum temperature values is determined by performing mapping maximum temperature value searching on the sequence of the set of effective peak temperature extraction areas and the sequence of the calibration thermal image frames; the sequence of the set of regional maximum temperature values is classified with the welding head based on the welding head position coding information, and the maximum temperature value in the classification result is extracted to obtain the weld peak temperature distribution.

[0058] Further, the weldability identification module 12 is configured to perform the following method:

[0059] The automatic optical detection system is used to traverse the plurality of welding tail ends to perform high-definition image acquisition, and a plurality of welding tail end images are obtained; the plurality of welding tail end images are identified for weldability according to the preset tail end indicators, and a plurality of welding tail end weldability coefficients are obtained; the plurality of welding tail end weldability coefficients are identified for distribution in combination with the positions of the plurality of welding tail ends, and the welding tail end weldability distribution is obtained.

[0060] Further, the weldability identification module 12 is configured to perform the following method:

[0061] The preset tail end indicators include tail length, position, included angle, and warping degree.

[0062] Further, the process scheme obtaining module 13 is configured to perform the following method:

[0063] The welding peak temperature distribution and the welding tail end weldability distribution are subjected to two-project superposition process connection authentication, if the authentication passes, a secondary reflow heat curve set is obtained according to the welding peak temperature distribution through secondary reflow heat curve linkage matching, positioning compensation is performed based on the welding tail end weldability distribution to obtain a positioning compensation distribution, and a preset back surface stringing process is optimized by combining the matched secondary reflow heat curve set and the positioning compensation distribution to obtain the back surface stringing process optimization scheme; if the authentication fails, a warning information is obtained.

[0064] Further, the process scheme obtaining module 13 is configured to perform the following method:

[0065] Based on the material information of the battery piece to be welded, a material allowable heat input threshold is determined, and a standard reflow heat curve of back surface stringing is obtained, wherein the standard reflow heat curve includes predicted zone temperature and time, peak temperature and time of the heating zone, and cooling zone temperature; heat input margin analysis is performed based on the welding peak temperature distribution and the material allowable heat input threshold to obtain a heat input margin distribution; and the standard reflow heat curve is mapped based on the heat input margin distribution to obtain a matched secondary reflow heat curve set.

[0066] Further, the process scheme obtaining module 13 is configured to perform the following method:

[0067] A process optimization analyzer is obtained, wherein the process optimization analyzer is obtained after training a framework based on a feedforward neural network; and the preset back surface stringing process is analyzed by using the process optimization analyzer on the matched secondary reflow heat curve set and the positioning compensation distribution to obtain the back surface stringing process optimization scheme.

[0068] The above is only a preferred embodiment of the present application, and does not limit the present application in any form. Although the present application has been disclosed as above with a preferred embodiment, it is not intended to limit the present application. Any person skilled in the art can make some changes or modifications to the above disclosed technical content to obtain equivalent embodiments with equivalent changes, without departing from the technical solution of the present application. Any modification, equivalent change and modification of the above embodiments based on the technical essence of the present application are still within the scope of the technical solution of the present application.

Claims

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2. 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Performing weld peak temperature analysis based on the weld position set sequence and the calibrated thermal image frame sequence to obtain the weld peak temperature distribution.

3. The method for production process optimization of photovoltaic modules according to claim 2, characterized in that, Performing weld peak temperature analysis based on the weld position set sequence and the calibrated thermal image frame sequence to obtain the weld peak temperature distribution, comprising: Based on the weld position set sequence, the effective peak temperature extraction area set sequence is obtained by identifying the effective peak temperature extraction area of the calibrated thermal image frame sequence; Combined with the effective peak temperature extraction area set sequence and the calibrated thermal image frame sequence, the highest temperature value set sequence is determined by searching the mapping highest temperature value. Combined with the welding head position encoding information, the region highest temperature value set sequence is classified with the welding head, and the maximum temperature value in the classification result is extracted to obtain the weld peak temperature distribution.

4. The method for production process optimization of photovoltaic modules according to claim 1, characterized in that, The preset tail end index includes tail length, position, angle and warping degree.

5. The method for production process optimization of photovoltaic modules according to claim 1, characterized in that, According to the weld peak temperature distribution, the secondary reflow thermal curve set is obtained by matching, comprising: Based on the material information of the to-be-welded battery piece, the material allowable heat input threshold is determined; Obtain the standard reflow thermal curve of back stringing, wherein the standard reflow thermal curve includes predicted area temperature and time, peak temperature and time of warming-up area, and cooling area temperature; Based on the weld peak temperature distribution and the material allowable heat input threshold, the heat input margin distribution is obtained by heat input margin analysis; Based on the heat input margin distribution, the standard reflow thermal curve is mapped to obtain the matching secondary reflow thermal curve set.

6. The method for production process optimization of photovoltaic modules according to claim 1, characterized in that, Combined with the matching secondary reflow thermal curve set and the positioning compensation distribution, the preset back stringing process is optimized to obtain the back stringing process optimization scheme, comprising: Obtain the process optimization analyzer, wherein the process optimization analyzer is obtained by training the framework based on the feedforward neural network; Using the process optimization analyzer, the matching secondary reflow thermal curve set and the positioning compensation distribution are analyzed to obtain the back stringing process optimization scheme.

7. A production process optimization system for photovoltaic modules, characterized by, The system for implementing the production process optimization method for photovoltaic modules according to any one of claims 1-6, comprising: Temperature monitoring module: front welding of target photovoltaic module, and monitoring the heat distribution of welding head and weld area in the welding area by infrared thermal imaging, and obtaining the weld peak temperature distribution after analysis; Weldability identification module: weldability identification of multiple welding tail ends according to preset tail end index after front welding, to obtain the weldability distribution of welding tail end; Process scheme obtaining module: based on the weld peak temperature distribution and the weldability distribution of welding tail end, positioning compensation and secondary reflow thermal curve set matching are performed on the back stringing of target photovoltaic module to obtain the back stringing process optimization scheme; Back stringing module: back stringing of target photovoltaic module according to the back stringing process optimization scheme.

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