Welding strategy determination method and device, nonvolatile storage medium and electronic equipment

Abstract

The invention discloses a welding strategy determination method and device, a nonvolatile storage medium and electronic equipment. The method comprises the steps that a plurality of target welding parameters are determined according to first associated data, and the first associated data represent the association degrees of different candidate welding parameters and welding results; based on the historical welding result and historical parameter values corresponding to the multiple target welding parameters, target parameter values corresponding to the multiple target welding parameters are determined; and according to the target parameter values corresponding to the multiple target welding parameters, a target control strategy is obtained. The technical problem that the welding yield is reduced due to the fact that control strategy setting is not ideal in the prior art is solved.

Description

Technical Field

[0001] The present application relates to the field of battery manufacturing, and in particular, to a welding strategy determination method, device, non-volatile storage medium and electronic device. Background Art

[0002] In the production process of lithium batteries, the welding process is an extremely critical link. The welding quality directly determines the performance, safety and reliability of the lithium battery as a whole. A tiny welding defect may lead to a decline in battery performance, increase safety hazards, and even affect the service life of the entire product. In the related art, the quality control method often adopts a post-detection method in the lithium battery welding process, that is, after the battery welding is completed, the good and defective products are identified, and only batteries with good quality are retained. Although the related technology can ensure the quality of battery products entering the market, it does not fundamentally solve the problem of improving the welding yield rate, and defective products will still cause unnecessary waste of manpower and resources. Therefore, the related technology has the problem of being unable to analyze relevant data, predict the future, lack of real-time closed-loop control, and unable to control the production of defective products.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a welding strategy determination method, device, non-volatile storage medium and electronic device to at least solve the technical problem in the related art that the control strategy setting is not ideal, resulting in a decrease in welding yield.

[0005] According to one aspect of an embodiment of the present application, a welding strategy determination method is provided, including: determining multiple target welding parameters according to first associated data, wherein the first associated data indicates the degree of association between different candidate welding parameters and welding results; determining target parameter values ​​corresponding to multiple target welding parameters based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters; obtaining a target control strategy based on the target parameter values ​​corresponding to multiple target welding parameters. Through the above processing, while completing quality inspection, it is possible to provide real-time feedback to the production line based on the inspection results, thereby automatically adjusting and optimizing production parameters, effectively preventing potential quality problems, and reducing the defective rate. It not only enhances the accuracy of product quality control, but also forms a virtuous circle in improving the yield rate. Through continuous data analysis and feedback, the production line can continuously optimize itself and achieve continuous improvement in product quality.

[0006] Optionally, based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters, target parameter values ​​corresponding to multiple target welding parameters are determined, including: determining the fitting relationship between historical welding results and historical parameter values ​​corresponding to multiple target welding parameters; determining prediction results corresponding to multiple candidate parameter combinations according to the fitting relationship, wherein the multiple candidate parameter combinations are obtained by setting multiple target welding parameters to different candidate parameter values; determining welding strength scores corresponding to the multiple candidate parameter combinations based on the prediction results corresponding to the multiple candidate parameter combinations; determining the parameter combination with the highest welding strength score among the multiple candidate parameter combinations as the target parameter combination; and obtaining target parameter values ​​corresponding to the target welding parameters according to the target parameter combination. Based on historical data and real-time feedback, the standards of welding process parameters are continuously optimized to continuously improve the yield rate of products.

[0007] Optionally, the method further includes: performing image acquisition on the historical welding area to obtain historical acquisition images; processing the historical acquisition images using corresponding image processing methods according to predetermined defect extraction requirements to obtain two-dimensional welding information; obtaining welding defect characteristics based on the two-dimensional welding information; and labeling the welding defect characteristics with defect types to obtain historical welding results. After the model is trained using historical welding results, the data of the current welding process is input into the model in real time, so that defect type predictions and process parameter optimization suggestions can be quickly obtained. Continuously collecting and analyzing historical welding data can not only optimize current production in real time, but also provide data support for future process improvements.

[0008] Optionally, based on the two-dimensional welding information, the welding defect characteristics are obtained, including: laser scanning the historical welding area to obtain three-dimensional point cloud data; based on the three-dimensional point cloud data, the three-dimensional surface characteristics of the historical welding area are determined; based on the two-dimensional welding information and the three-dimensional surface characteristics, feature splicing is performed to determine the welding defect characteristics. Through the fusion of two-dimensional and three-dimensional features, the condition of the welding area can be more comprehensively understood, and the recognition accuracy and classification accuracy of welding defects can be improved.

[0009] Optionally, according to the first correlation data, determining multiple target welding parameters includes: determining second correlation data indicating the degree of correlation between multiple candidate welding parameters; based on the first correlation data and the second correlation data, screening multiple candidate welding parameters to determine multiple target welding parameters. Through the above processing, multiple candidate welding parameters are screened to achieve the best balanced combination. By using a data-driven decision-making approach, production interruptions and time waste caused by improper parameter adjustment are reduced, thereby improving the efficiency of the entire production process.

[0010] Optionally, based on the first associated data and the second associated data, multiple candidate welding parameters are screened to determine multiple target welding parameters, including: determining third associated data indicating the degree of association between multiple candidate welding parameters and timing characteristics, wherein the timing characteristics at least include the operating time of the welding equipment and seasonal characteristics; determining fourth associated data indicating the degree of association between multiple candidate welding parameters and welding stability based on the data distribution corresponding to the multiple candidate welding parameters; based on the first associated data, the second associated data, the third associated data, and the fourth associated data, multiple candidate welding parameters are screened to determine multiple target welding parameters. Through the above processing, through the analysis and association of multi-source data, the key factors in the welding parameters can be more accurately identified, and the accuracy of welding quality control can be improved. Taking into account the timing characteristics such as equipment operating time and seasonal changes helps to enhance the stability of the welding process and reduce welding quality fluctuations caused by environmental changes.

[0011] Optionally, the method further includes: determining a strategy update frequency based on the device type of the welding device; and updating the target control strategy to the welding device according to the strategy update frequency. The strategy update frequency is set taking into account the response time of the device, the data processing speed, and the production rhythm to ensure that the update frequency is neither too frequent to avoid unnecessary consumption of computing resources nor too sparse to ensure timely response to production changes.

[0012] According to another aspect of an embodiment of the present application, a welding strategy determination device is provided, including: a parameter screening module, used to determine multiple target welding parameters according to first associated data, wherein the first associated data represents the degree of association between different candidate welding parameters and welding results; a parameter determination module, used to determine target parameter values ​​corresponding to multiple target welding parameters based on historical welding results and historical parameter values ​​corresponding to the multiple target welding parameters; a strategy generation module, used to obtain a target control strategy based on the target parameter values ​​corresponding to the multiple target welding parameters.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is provided, wherein the non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing any one of the welding strategy determination methods.

[0014] According to another aspect of an embodiment of the present application, an electronic device is provided, comprising: one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors implement any one of the welding strategy determination methods.

[0015] In the embodiment of the present application, a closed-loop control method is adopted to determine multiple target welding parameters according to the first associated data, wherein the first associated data indicates the degree of association between different candidate welding parameters and welding results; based on the historical welding results and the historical parameter values ​​corresponding to the multiple target welding parameters, the target parameter values ​​corresponding to the multiple target welding parameters are determined; and the target control strategy is obtained according to the target parameter values ​​corresponding to the multiple target welding parameters. The purpose of optimizing the welding control strategy according to the welding results is achieved, the accuracy of the welding control strategy is improved, and the technical effect of improving the welding yield is achieved, thereby solving the technical problem in the related technology that the control strategy setting is not ideal, resulting in a decrease in the welding yield. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1 is a flow chart of an optional welding strategy determination method provided according to an embodiment of the present application;

[0018] Figure 2 is a first schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application;

[0019] Figure 3 is a second schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application;

[0020] Figure 4 is a third schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application;

[0021] Figure 5 It is a schematic diagram of an optional welding strategy determination device provided according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] According to an embodiment of the present application, a method embodiment for determining a welding strategy is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0025] Figure 1 is a flow chart of an optional method for determining a welding strategy according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0026] Step S102, determining a plurality of target welding parameters according to first correlation data, wherein the first correlation data indicates the correlation degree between different candidate welding parameters and welding results;

[0027] It can be understood that the correlation between all candidate welding parameters and welding results in the welding process is analyzed. The candidate welding parameters may include welding speed, defocus, weld width, head pressure, etc., while the welding results involve characteristics such as weld length, penetration depth, and weld width. Through statistical analysis or machine learning algorithms (such as Pearson correlation coefficient, multivariate regression analysis, etc.), the degree of correlation between candidate welding parameters and welding results, that is, the first correlation data, can be effectively determined. By quantifying the correlation between parameters and results, it is ensured that parameter optimization decisions are based on objective data rather than subjective experience, which improves the accuracy of control decisions.

[0028] Optionally, the Pearson correlation coefficient is used to calculate the correlation between the feature (candidate welding parameter) and the target variable (welding result) as the first associated data, and the candidate welding parameters are preliminarily screened.

[0029] Optionally, multiple candidate welding parameters can be selected from key welding process parameters and key characteristic parameters, including, for example, welding speed, defocus, weld width, ram pressure, weld length, penetration depth, weld width, etc.

[0030] Optionally, the image data of the welding area captured by the industrial camera and the three-dimensional point cloud data scanned by the laser radar collected by the big data system are preprocessed, including data cleaning, feature selection, time series data preparation, classification variable encoding, etc., to form training sets and test sets for subsequent algorithm use. At the same time, the system forms a work-in-progress welding archive to display the production process traceability data and key process analysis results in an intuitive way. Table 1 is a schematic diagram of the initial data, and the following specific values ​​are only examples and are not limited.

[0031] Table 1

[0032]

[0033] After identifying and correcting errors, removing duplications, filling missing values, converting data types and formatting to obtain an accurate, consistent and easy-to-analyze data set, Table 2 shows the corrected data, in which the missing part of the welding speed is filled with 600, and the obviously deviated welding speed of 1800 is corrected to 800.

[0034] Table 2

[0035]

[0036]

[0037] The dataset is divided into a training set and a test set according to (70%:30%). The training set is used to train the model, and the test set is used to evaluate the performance of the model.

[0038] In an optional embodiment, multiple target welding parameters are determined according to first association data, including: determining second association data representing the degree of association between multiple candidate welding parameters; based on the first association data and the second association data, screening multiple candidate welding parameters to determine multiple target welding parameters.

[0039] It can be understood that the second associated data refers to the statistical relationship established between multiple candidate welding parameters, which is used to quantify the mutual influence between these parameters. For example, the correlation between welding speed and welding power, defocus and weld width. By calculating statistical indicators such as the Pearson correlation coefficient and the Spearman rank correlation coefficient, the second associated data can be obtained to understand the strength and direction of the relationship between the parameters. The first associated data refers to the relationship between the welding parameters and the quality indicators of the welding results, such as the direct correlation between the welding speed and the weld quality. Combined with the second associated data (the relationship between the parameters), the comprehensive impact of the welding parameters on the welding quality can be more comprehensively analyzed. For example, if the weld quality decreases when the welding speed increases, but the welding power also increases, the negative impact of the welding speed on the weld quality can be partially offset. Then, when selecting the target welding parameters, it is necessary to consider the balance and optimization between the two. Through the above processing, multiple candidate welding parameters are screened to find a combination that can optimize the welding quality and achieve the best balance between the parameters. Using the data-driven decision-making method, according to the role of the parameters in the welding process control, the decision-making based on the associated data is realized, and the objectivity of the welding control is improved.

[0040] Optionally, an algorithm model (such as a multi-objective optimization algorithm) can be used to determine an appropriate screening method, and then determine the target welding parameters. After data analysis and model screening, the parameter combination that can most effectively improve the welding quality is found, providing data support for the subsequent determination of parameter assignments. By finding the optimal welding parameter combination, the welding quality is significantly improved, and the occurrence of welding defects such as cold welds and cracks is reduced. Once the target welding parameters are determined, the welding process can be more stable and efficient, reducing production interruptions and time waste caused by improper parameter adjustment, thereby improving the efficiency of the entire production process.

[0041] Optionally, assuming that the correlation between welding speed (marked as V), defocus (marked as F), weld length (marked as L), weld width (marked as W) and penetration (marked as D) is evaluated, the following is the Pearson correlation coefficient between these feature pairs (i.e., paired candidate welding parameters), i.e., the second correlation data. The specific values ​​in Table 3 are for illustration only and are not specifically limited.

[0042] Table 3

[0043] Feature pairs Second related data V and F -0.25 V and L 0.72 V and W 0.51 V and D 0.68 F and L -0.18 F and W -0.05 F and D -0.12 L and W 0.83 L and D 0.75 W and D 0.55

[0044] According to the analysis of the second correlation data in Table 3, it can be seen that there is a high positive correlation (0.72 and 0.68) between welding speed (V) and weld length (L) and penetration (D), indicating that welding speed may directly affect the length and penetration of the weld. There is a weak negative correlation (-0.25) between welding speed (V) and defocus (F), indicating that welding speed may have an opposite trend with defocus. There is a high positive correlation (0.83) between weld length (L) and weld width (W), which may mean that longer welds tend to be wider. The correlations between other feature pairs are low, indicating that their associations may be weak or there is no obvious linear relationship.

[0045] In an optional embodiment, based on the first association data and the second association data, multiple candidate welding parameters are screened to determine multiple target welding parameters, including: determining third association data representing the degree of association between the multiple candidate welding parameters and the timing characteristics, wherein the timing characteristics include at least the operating time of the welding equipment and the seasonal characteristics; based on the data distribution corresponding to the multiple candidate welding parameters, determining fourth association data representing the degree of association between the multiple candidate welding parameters and welding stability; based on the first association data, the second association data, the third association data, and the fourth association data, multiple candidate welding parameters are screened to determine multiple target welding parameters.

[0046] It can be understood that by analyzing the mutual influence of parameters such as welding speed, defocus, weld width, and pressure head pressure through data analysis, such as using statistical methods (such as Pearson correlation coefficient) or machine learning models, it is helpful to identify which parameters have a more significant impact on welding quality when combined. Time series features such as the operating time of welding equipment and seasonal characteristics are introduced. For example, long-term operation of equipment may cause temperature changes, which in turn affect welding parameters. Seasonal changes may affect ambient humidity and have potential effects on welding quality. The data distribution of each candidate welding parameter is analyzed to understand its volatility or stability, and then evaluate its impact on welding stability. Based on the data distribution of the parameters, the fourth correlation data is calculated, which reflects the relationship between parameter stability and welding quality. Combined with the first, second, third and fourth correlation data, a multi-dimensional analysis is performed to identify the parameter combination that has the greatest impact on welding quality and the highest stability. Based on the comprehensive evaluation results, the parameters that have the greatest impact on improving welding quality, namely the target welding parameters, are screened out.

[0047] Optionally, the third associated data can be determined by analyzing the association between the time series characteristics and the candidate welding parameters. The ARIMA (Autoregressive Integrated Moving Average) model can be used, which is a statistical model for time series analysis and prediction, or the simple moving average (SMA) can be used to complete the time series analysis. The ARIMA model combines the three methods of autoregression, difference and moving average, and can be used to describe the trend, seasonality and randomness of time series data. The regularity, trend and seasonal characteristics of the simple moving average over time can be used for time series analysis, which is used to analyze and monitor and predict the patterns and trends of key quality parameters, so as to adjust the process parameters in time and ensure product quality.

[0048] For ease of understanding, the SMA method is used to give a specific example of the weld length (L). Figure 2 is a first schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application, such as Figure 2 As shown, if the data of weld length begins to show a gradual downward trend over a period of time, even if this decline may not be obvious in a short period of time, SMA can reveal this trend in advance, thereby ensuring the stability of the entire production process and product quality.

[0049] The formula for calculating the simple moving average (SMA) is:

[0050] Among them, MA t Y represents the simple moving average at time t, n represents the window size of the moving average, that is, how many observations at past time points are considered to calculate the average value. The choice of window size depends on the length of the period that needs to be smoothed. For example, if you want to capture the trend within a week, n ​​may be set to 7. t-i Represents the observation value at time ti, where i is the time mark before and after time t.

[0051] For each time point t, collect the observations of the past n time points, that is, all the observations from t-n+1 to t. Repeat the window interception and calculate the corresponding simple moving average for each time point t to form a smooth data series. The resulting simple moving average sequence identifies long-term trends, seasonal patterns or cyclical changes, and adjusts process parameters or takes other quality control measures accordingly. Simple moving average can effectively filter out random fluctuations and provide clear insights into data trends. For quality control in the lithium battery production process, especially the welding process, this method can help achieve more accurate and timely process parameter adjustments.

[0052] Optionally, the data distribution can be a normal distribution. For example, for the weld length, the W statistic is 0.987 and the p-value is 0.234. The W value and the p-value are related to the normality test. The W value is a statistic in the Shapiro-Wilk test, a statistical method used to test whether the data conforms to the normal distribution, and is used to measure the degree of fit between the sample data and the theoretical normal distribution. The calculation of the W value is based on the linear relationship between the sample data and the normal distribution, and the range of the W value is between 0 and 1. When the W value is equal to 1, it means that the sample data is completely consistent with the normal distribution. The closer the W value is to 1, the higher the degree of fit between the sample data and the normal distribution, and vice versa. In the Shapiro-Wilk test, the W value is used to determine whether the data conforms to the normal distribution assumption. The p-value is used in hypothesis testing to measure the inconsistency between the result and the null hypothesis (the data conforms to the normal distribution). It is the probability of obtaining the currently observed or more extreme results, assuming that the null hypothesis is true. When the p-value is less than the preset significance level (usually 0.05), we reject the null hypothesis and believe that the data is unlikely to be normally distributed; if the p-value is greater than the significance level, there is not enough evidence to reject the null hypothesis and the data may come from a normal distribution. In the Shapiro-Wilk test, if the p-value is greater than the significance level (for example, 0.05), the hypothesis that the data conforms to a normal distribution is accepted. Since the p-value is greater than 0.05, it can be considered that the weld length data conforms to a normal distribution. Based on the weld length (L) data, the mean (μ), standard deviation (σ), and variance (σ2) of the data set are calculated.

[0053] The calculation formula for the mean (μ) is: Among them, x1 to x m They represent m welding parameters respectively, such as weld length, penetration, welding speed, etc.

[0054] The calculation formula of standard deviation (σ) (for a given normal distribution, the sample standard deviation formula can be used) is used to estimate the degree of fluctuation of the data set, x j is the data set x1 to x m The jth data point in :

[0055]

[0056] The formula for calculating the variance (σ2) (for a given normal distribution, the variance is directly equal to the parameter σ2), which is also used to measure the volatility of the data set:

[0057]

[0058] Using the calculated normal distribution related data and the upper specification limit (USL) and lower specification limit (LSL) of the weld length maintained by the system, the statistical data Cp and Cpk are calculated to evaluate the process capability. Cp and Cpk can be used as two statistics to evaluate the process capability. Cp (Process Capability Index) is a capability index that evaluates the relationship between the process center (mean) and the specification limit. It measures the relationship between the process fluctuation and the specification limit, without considering the process offset (that is, the relationship between the mean and the specification center). The calculation formula is: Among them, δ is the correlation coefficient.

[0059] Cpk (Process Capability Index with K factor) is a capability index that takes into account process deviation. It takes into account both the center and dispersion of the process, and the calculation formula is:

[0060] Based on the values ​​of Cp and Cpk, the process capability of the welding process can be evaluated. If the value of Cp or Cpk is greater than 1.33, it means that the process capability is sufficient and most of the products produced by the welding process will fall within the specification limits. If the value is between 1.0 and 1.33, the process capability may not be sufficient to ensure that all products are within the specification limits, but it is acceptable. If the value is less than 1.0, the process capability is seriously insufficient, the welding stability is not ideal, and measures need to be taken to improve it to ensure product quality.

[0061] Step S104, determining target parameter values ​​corresponding to the plurality of target welding parameters based on historical welding results and the historical parameter values ​​corresponding to the plurality of target welding parameters;

[0062] It can be understood that based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters. Based on historical performance, find the target parameter values ​​that best match high-quality welding results in historical data, determine the target parameter values ​​corresponding to multiple target welding parameters, and predict possible welding results under different parameter combinations, thereby determining the optimal parameters. Through the analysis of historical data, it is possible to identify the parameter combination that is most likely to produce high-quality welding results, thereby achieving parameter optimization.

[0063] In an optional embodiment, based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters, target parameter values ​​corresponding to multiple target welding parameters are determined, including: determining a fitting relationship between historical welding results and historical parameter values ​​corresponding to multiple target welding parameters; determining prediction results corresponding to multiple candidate parameter combinations according to the fitting relationship, wherein the multiple candidate parameter combinations are obtained by setting multiple target welding parameters to different candidate parameter values; determining welding strength scores corresponding to the multiple candidate parameter combinations based on the prediction results corresponding to the multiple candidate parameter combinations; determining the parameter combination with the highest welding strength score among the multiple candidate parameter combinations as the target parameter combination; and obtaining the target parameter values ​​corresponding to the target welding parameters according to the target parameter combination.

[0064] It can be understood that the fitting relationship is determined by analyzing a large amount of historical welding data as input (i.e., the historical parameter values ​​corresponding to the target welding parameters) and the quality of the welding results as output. The mathematical relationship between these input parameters and the output results, i.e., the fitting relationship, is determined. The above fitting relationship can be linear, nonlinear, or in the form of a function. Each candidate parameter combination is input into the established model to predict the welding strength score of the welding process. Using the relationship between welding parameters and welding results, the output score reflects the expected quality level of the welding result under a given parameter combination. From multiple candidate parameter combinations, the combination with the highest predicted welding strength score is selected as the target parameter combination. The target parameter combination represents the optimal parameter combination that can theoretically produce the best welding quality under the current model and historical data. By predicting and selecting the optimal parameter combination, it is ensured that the welding process operates under the optimal parameters, thereby significantly improving the quality of welded products.

[0065] Optionally, the above historical welding data may include welding speed, defocus, weld width, pressure head pressure, etc.

[0066] Optionally, the welding results include, for example: weld penetration depth, weld width, whether there are welding defects, etc.

[0067] Optionally, statistical analysis or machine learning techniques, such as linear regression, support vector machine, neural network, etc., are used to determine the fitting relationship.

[0068] Optionally, the values ​​of each parameter in the target parameter combination are determined as the target parameter values ​​corresponding to the target welding parameters. The parameter value updates of these key parameters will be used in the actual welding production process to obtain the best welding results and product yield.

[0069] Optionally, based on the determined fitting relationship, multiple candidate parameter combinations are generated, each candidate parameter combination represents a set of specific parameter values ​​that may be used in the welding process, such as different welding speeds, defocus amounts, etc. These combinations can be set by exhaustive enumeration, random generation, or based on historical experience knowledge. Each set of parameter values ​​is selected within the given upper and lower limits of the parameters, with a certain step size or randomly, to form a set of candidate parameter combinations. Through continuous learning and optimization, production efficiency and product yield can be improved in the long term.

[0070] In an optional embodiment, the method further includes: performing image acquisition on the historical welding area to obtain a historical acquired image; processing the historical acquired image using a corresponding image processing method according to a predetermined defect extraction requirement to obtain two-dimensional welding information; obtaining welding defect characteristics based on the two-dimensional welding information; and labeling the welding defect characteristics with defect types to obtain historical welding results.

[0071] It can be understood that the image acquisition of the historical welding area is carried out, and the acquired historical welding images use specific image processing techniques, such as edge detection, grayscale transformation, filtering, etc., to extract two-dimensional welding information. The two-dimensional welding information includes the shape, size, surface quality, etc. of the weld, which is the basis for subsequent feature extraction and defect type judgment. Based on the extracted two-dimensional welding information, the image features are further analyzed to identify and quantify the specific manifestations of welding defects. These features will be used to train machine learning models to identify different types of welding defects. The identified welding defect features are classified and labeled, and the defect type of each historical welding area is clearly marked. These annotation results, together with the defect features and corresponding process parameters, constitute the historical welding result data set, which is the key data source for subsequent model training and optimization.

[0072] By collecting and annotating a large amount of historical welding data, a rich learning material is provided for the intelligent diagnosis algorithm model, helping the model to better understand the pattern of welding defects under different process parameter settings, thereby improving the accuracy and generalization ability of the model. With the continuous accumulation of data, the performance of the model will be further improved, achieving long-term stability and continuous improvement of welding quality.

[0073] Optionally, in a welding production line, images are collected from product areas that have been welded in the past period of time. These images are from products that have been welded and recorded. The purpose of collecting these images is to establish a historical database containing various welding defects for subsequent analysis and model training.

[0074] Optionally, 2D welding can be characterized by, for example, the size of the weld, the continuity of the weld, the smoothness of the surface, the presence of cracks or pores, etc. An industrial camera is used to capture an image of the welding area, and feature extraction technology is applied to filter out key information from the image, such as basic weld location, shape, texture, etc. More detailed features can also be extracted for specific defect types, such as: extracting features such as edge irregularities and color changes; for pores, dark or light spots in the image can be detected.

[0075] Optionally, a convolutional neural network (CNN) can be used to build a machine learning or deep learning model to identify defects based on welding defect characteristics. During the training process, a loss function (cross entropy loss) and an optimization algorithm (gradient descent algorithm) are used to optimize the model parameters. For the trained model, evaluation indicators including accuracy, precision, recall, and F1 score are introduced, and the model is iteratively optimized according to the evaluation results until satisfactory performance is achieved.

[0076]

[0077] Among them, TP (True Positive) is a true positive example, which indicates the number of positive samples predicted by the model as positive samples. FP (False Positive) is a false positive example, which indicates the number of negative samples incorrectly predicted by the model as positive samples. FN (False Negative) is a false negative example, which indicates the number of positive samples incorrectly predicted by the model as negative samples. TN (True Negative) is a true negative example, which indicates the number of negative samples predicted by the model as negative samples.

[0078] Figure 3 is a second schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application, such as Figure 3 As shown in the figure, as the recall rate increases, the accuracy rate decreases. Relying on the mature data model, data is collected and features are extracted in real time during the welding process, and the extracted features are input into the corresponding model in the model library for diagnosis. The welding quality is judged and marked according to the output results of the model to form a data result set, and the predicted data and actual results are cyclically written into the model training set to achieve the machine's autonomous learning ability.

[0079] In an optional embodiment, welding defect characteristics are obtained based on two-dimensional welding information, including: laser scanning the historical welding area to obtain three-dimensional point cloud data; determining the three-dimensional surface characteristics of the historical welding area based on the three-dimensional point cloud data; and determining the welding defect characteristics by feature stitching based on the two-dimensional welding information and the three-dimensional surface characteristics.

[0080] It can be understood that a 3D (three-dimensional) laser scanning device can be used to scan the same historical welding area to obtain its three-dimensional point cloud data. Point cloud data can accurately reflect the three-dimensional surface features of the welding area. Based on the three-dimensional point cloud data, the three-dimensional surface features of the historical welding area are determined through the point cloud processing algorithm. The three-dimensional surface features can supplement the depth and spatial information that cannot be reflected in the two-dimensional image information, and provide more comprehensive data support for the evaluation of welding quality. The features extracted based on the two-dimensional welding information are fused with the data obtained based on the three-dimensional surface features, and a comprehensive welding defect feature is formed through feature stitching technology, which can be represented in the form of a feature vector. Based on the analysis of the comprehensive welding defect features, the welding process parameters, such as welding speed, defocus, welding gun pressure, etc., can be adjusted in reverse to achieve the optimal welding effect and reduce the occurrence of welding defects.

[0081] Through real-time data collection and feature analysis, the system can automatically adjust the parameters of the welding equipment and continuously optimize the control model based on the feedback of welding quality, forming an intelligent closed-loop control process, effectively improving the yield rate of products and reducing the defective rate. The application of automated feature extraction and defect recognition technology reduces the reliance on manual visual inspection, improves production efficiency and stability, and also reduces production costs.

[0082] Optionally, the three-dimensional surface features may include: the protrusion degree of the welding point, surface smoothness, spatial distribution of holes or irregular shapes, etc.

[0083] Optionally, the above point cloud processing algorithm can be multiple, such as calculating normal vector, curvature, surface area, volume, etc. The laser radar is used to obtain three-dimensional point cloud data by scanning the welding area, and the data is processed to extract geometric features (such as normal vector, curvature, etc.) and descriptor features (such as normal histogram, surface area, etc.) to obtain the three-dimensional morphology, smoothness, concave-convex changes and other information of the welding surface.

[0084] Optionally, the above-mentioned welding defect features include geometric information, texture information and spatial morphological information of the welding area, which can more accurately identify and classify welding defects such as hot spots, cold welds, leaking welds, weld penetrations, pores, etc.

[0085] Optionally, the feature splicing technology can be multiple, for example, position-based alignment, feature weighted fusion, etc. The two feature vectors are spliced ​​and fused by weighted algorithm in combination with the vector threshold maintained by the system. The fused feature vector will contain comprehensive information of the welding area, including both two-dimensional image information (i.e., two-dimensional welding information) and three-dimensional morphological information (three-dimensional surface features), which is conducive to the establishment of a labeled data set containing samples of various defect types in the subsequent data process.

[0086] Step S106, obtaining a target control strategy according to target parameter values ​​corresponding to the plurality of target welding parameters.

[0087] It can be understood that the target control strategy allows real-time adjustment of key parameters during the welding process to cope with changes in production and ensure the stability of welding quality. It realizes closed-loop control from parameter analysis to optimization and real-time adjustment, ensuring continuous improvement and optimization of the welding process. Through the automated control strategy, the need for continuous manual monitoring and adjustment is reduced, and production efficiency is improved.

[0088] In an optional embodiment, the method further includes: determining a strategy update frequency based on a device type of the welding device; and updating the target control strategy to the welding device according to the strategy update frequency.

[0089] It can be understood that the type of welding equipment can be identified through the equipment data collected by the IoT platform, including the manufacturer, model, power, degree of automation, etc. Based on the equipment type, the range of the strategy update frequency can be pre-set or determined through historical data analysis. For example, for equipment with a higher degree of automation and stronger data processing capabilities, a shorter strategy update cycle can be set to achieve faster response speed and more refined control.

[0090] Optionally, a reasonable strategy update frequency can be determined according to the specific situation of the welding equipment, such as a fixed time interval (such as every hour, every day or every batch), or a dynamic frequency based on the equipment status. Generate a target control strategy based on the predicted optimal process parameter combination. The target control strategy needs to match the actual status and capabilities of the welding equipment to ensure its feasibility and effectiveness. Through the continuous collection of 3D inspection data and the use of machine learning algorithms, the target control strategy is continuously optimized and adjusted to adapt to changes in the production environment and improvements in equipment performance.

[0091] According to the determined strategy update frequency, the optimized target control strategy is updated to the welding equipment in real time to achieve automatic adjustment and optimization of equipment parameters, such as welding speed, defocusing amount, pressure head, etc., to ensure the continuity of the welding process and the stability of quality. Through the real-time data feedback mechanism, the equipment can dynamically respond to the changes in the optimal parameters, further improving the controllability of welding quality and production efficiency.

[0092] Through the above step S102, multiple target welding parameters are determined according to the first associated data, wherein the first associated data indicates the degree of association between different candidate welding parameters and welding results; step S104, based on the historical welding results and the historical parameter values ​​corresponding to the multiple target welding parameters, the target parameter values ​​corresponding to the multiple target welding parameters are determined; step S106, according to the target parameter values ​​corresponding to the multiple target welding parameters, the target control strategy is obtained. The purpose of optimizing the welding control strategy according to the welding results can be achieved, and the accuracy of the welding control strategy can be improved, thereby improving the technical effect of the welding yield rate, thereby solving the technical problem that the control strategy setting in the related technology is not ideal, resulting in a decrease in the welding yield rate.

[0093] Based on the above embodiments and optional embodiments, this application proposes an optional implementation method, which designs an intelligent diagnosis algorithm model for the quality closed-loop control of the lithium battery welding production process, and for the diversified defect classification and model result prediction of the current welding production. This model quantizes the features of industrial cameras and 3D laser detection, predicts the trend of welding results, and reversely optimizes the combination of key welding process parameters to form a quality control closed loop, thereby improving the quality of welding production.

[0094] Step S1, confirming key welding process parameters, such as welding speed, defocusing amount, pressing head pressure, etc.

[0095] Step S2, confirming key characteristic parameters of welding, such as weld length, weld width, penetration depth, weld width, etc.

[0096] Step S3, using the IoT platform to create device types and corresponding data models, and configure collection parameters and collection frequency. Use the IoT platform to create device types and corresponding data models, and configure collection parameters and collection frequency according to the device type and data model. The parameters include the data type, data format, data unit, etc. that need to be collected. To ensure the real-time nature of the data, the collection frequency adopts the change-and-collect mode.

[0097] Step S4, using TCP / IP (Transmission Control Protocol / Internet Protocol), MQTT (Message Queuing Telemetry Transport), OPCUA (Open Platform Communications Unified Architecture) and other communication methods to achieve automatic data collection, and use high-performance storage platforms for big data storage. Configure TCP / IP, MQTT, OPCUA and other communication methods to achieve automatic data collection, send device data to a central server or cloud platform, and use high-performance storage platforms such as distributed file systems or databases to store big data.

[0098] Step S5, configuring process standards and setting upper and lower thresholds of parameters. The process standards can be configured by the manufacturing system according to the welding production process file, and the upper and lower thresholds of parameters can be set.

[0099] Step S6, preprocessing the data collected by the big data system, including data cleaning, feature selection, time series data preparation, classification variable encoding, etc., to form training sets and test sets for subsequent algorithm use. At the same time, the system forms a work-in-progress welding file to display the production process traceability data and key process analysis results in an intuitive way.

[0100] Step S7, based on multi-source feature fusion, a model library containing diagnostic algorithms is established to accurately classify, efficiently label and intelligently index welding features, so as to realize online real-time diagnosis and judgment of welding quality.

[0101] Step S8, constructing a quality inspection and process parameter feature algorithm model, and predicting and reversely analyzing the optimal process parameter combination by analyzing the intrinsic relationship between the features and the process parameters.

[0102] According to the feature set of the training set and the test set in step S6 and the result data set generated in step S7, a quality inspection and process parameter feature algorithm model is constructed, and the optimal process parameter combination is predicted and reversely analyzed by analyzing the intrinsic relationship between the features and the process parameters.

[0103] A linear regression algorithm is used to predict welding strength. A linear regression model is trained based on the stored historical data, including key coating quality parameters (inner ring welding power, outer ring welding power, welding height, welding speed, cooling water flow) and one or more target variables (such as welding strength score).

[0104] The optional form of the linear regression model is: y = β0 + β1X1 + β2X2 + β3X3 + β4X4 + β5X5. Among them, y is the target variable (i.e. welding strength), X1, X2, X3, X4, X5 are the characteristic values ​​of different welding parameters, such as welding power (inner loop), welding power (outer loop), welding height, welding speed, and cooling water flow. β0, β1, β2, β3, β4, β5 are the parameters of the model (intercept and slope). Figure 4 is a third schematic diagram of an optional welding strategy determination method provided according to an embodiment of the present application, such as Figure 4 As shown, a fitting trend can be fitted from multiple data points, with the horizontal axis being the welding parameters and the vertical axis being the welding strength. The welding strength score is predicted based on the new data points, and the optimal process parameter combination is searched in reverse according to the prediction results.

[0105] Use the training set and the selected result feature set to train the linear regression model, set the model learning rate and the number of iterations. During the training process, the model will continuously adjust the intercept and slope terms, compare the model's prediction results with the true values, and display them through scatter plots, draw the best fit line, and display the model's prediction trend.

[0106] At the same time, the mean square error (MSE) loss function is introduced to measure the prediction performance of the model. The calculation formula of the mean square error (MSE) is:

[0107] Where q represents the number of samples. i represents the true value, y pred Represents the predicted value. The accuracy of the prediction model is measured by calculating the mean of the square of the difference between the predicted value and the true value. The smaller the MSE value, the better the prediction effect of the model, that is, the model can more accurately predict the true value.

[0108] Step S9, based on the predicted optimal parameters, dynamically adjust the key process parameters of the welding equipment, and continuously optimize the control model through continuous data feedback to achieve closed-loop management and improvement of welding quality.

[0109] The above optional implementation method can at least achieve the following effects: receiving various data in the welding production process in real time, extracting local features of the image and point cloud data features, and using advanced algorithm models for rapid analysis to accurately identify potential quality problem points, such as explosion points, cold welding, leaking welding, welding penetration, pores, etc. The system automatically summarizes production data, and through the intelligent diagnosis algorithm model of the present invention, combined with automatic analysis of normal distribution, it can accurately evaluate the manufacturing capacity of welding production, ensure that product quality conforms to the law of normal distribution, and further improve production stability and product quality. The algorithm model has the ability of continuous learning, can continuously adapt to changes in the welding production process, and optimize algorithms and models through real-time data regression feedback analysis, further improving the accuracy and efficiency of welding quality detection. The intelligent diagnosis algorithm model can realize linkage control with welding equipment, adjust equipment parameters in real time according to the optimal parameter combination of reverse analysis of the model, realize automated and intelligent management of the welding production process, and ensure efficient, stable and high-quality welding production.

[0110] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0111] In this embodiment, a welding strategy determination device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the terms "module" and "device" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0112] According to an embodiment of the present application, a device embodiment for implementing a welding strategy determination method is also provided. Figure 5 is a schematic diagram of a welding strategy determination device according to an embodiment of the present application, such as Figure 5 As shown, the welding strategy determination device includes: a parameter screening module 502, a parameter determination module 504, and a strategy generation module 506. The device is described below.

[0113] A parameter screening module 502 is used to determine a plurality of target welding parameters according to first correlation data, wherein the first correlation data indicates the correlation degree between different candidate welding parameters and the welding result;

[0114] A parameter determination module 504, connected to the parameter screening module 502, is used to determine target parameter values ​​corresponding to the plurality of target welding parameters based on historical welding results and the historical parameter values ​​corresponding to the plurality of target welding parameters;

[0115] The strategy generation module 506 is connected to the parameter determination module 504 and is used to obtain a target control strategy according to target parameter values ​​corresponding to a plurality of target welding parameters.

[0116] In a welding strategy determination device provided by an embodiment of the present application, a parameter screening module 502 is set to determine multiple target welding parameters according to first associated data, wherein the first associated data indicates the degree of association between different candidate welding parameters and welding results; a parameter determination module 504 is connected to the parameter screening module 502, and is used to determine target parameter values ​​corresponding to multiple target welding parameters based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters; a strategy generation module 506 is connected to the parameter determination module 504, and is used to obtain a target control strategy based on target parameter values ​​corresponding to multiple target welding parameters. The purpose of optimizing the welding control strategy according to the welding results is achieved, and the accuracy of the welding control strategy is improved, thereby improving the technical effect of the welding yield rate, thereby solving the technical problem that the control strategy setting in the related art is not ideal, resulting in a reduction in the welding yield rate.

[0117] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0118] It should be noted that the parameter screening module 502, parameter determination module 504, and strategy generation module 506 correspond to steps S102 to S106 in the embodiment, and the examples and application scenarios implemented by the modules and the corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment. It should be noted that the modules as part of the device can be run in a computer terminal.

[0119] It should be noted that the optional or preferred implementation of this embodiment can refer to the relevant description in the embodiment, which will not be repeated here.

[0120] The welding strategy determination device may also include a processor and a memory. The parameter screening module 502, the parameter determination module 504, the strategy generation module 506, etc. are all stored in the memory as program units, and the processor executes the program units stored in the memory to implement corresponding functions.

[0121] The processor includes a kernel, which retrieves the corresponding program unit from the memory. There can be one or more kernels. The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one storage chip.

[0122] An embodiment of the present application provides a non-volatile storage medium on which a program is stored. When the program is executed by a processor, a welding strategy determination method is implemented.

[0123] The embodiment of the present application provides an electronic device, which includes a processor, a memory, and a program stored in the memory and can be run on the processor. When the processor executes the program, the following steps are implemented: according to the first association data, multiple target welding parameters are determined, wherein the first association data indicates the degree of association between different candidate welding parameters and welding results; based on the historical welding results and the historical parameter values ​​corresponding to the multiple target welding parameters, the target parameter values ​​corresponding to the multiple target welding parameters are determined; according to the target parameter values ​​corresponding to the multiple target welding parameters, the target control strategy is obtained. The device in this article can be a server, a PC, etc.

[0124] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program that is initialized with the following method steps: determining multiple target welding parameters according to first associated data, wherein the first associated data represents the degree of association between different candidate welding parameters and welding results; determining target parameter values ​​corresponding to multiple target welding parameters based on historical welding results and historical parameter values ​​corresponding to multiple target welding parameters; and obtaining a target control strategy based on the target parameter values ​​corresponding to multiple target welding parameters.

[0125] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0126] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0127] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0128] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0129] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0130] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0131] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0132] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0133] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0134] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A method for determining a welding strategy, characterized in that: include: Determining a plurality of target welding parameters according to first association data, wherein the first association data indicates the degree of association between different candidate welding parameters and welding results; Determining target parameter values ​​corresponding to the plurality of target welding parameters based on historical welding results and historical parameter values ​​corresponding to the plurality of target welding parameters; A target control strategy is obtained according to the target parameter values ​​respectively corresponding to the multiple target welding parameters.

2. The method according to claim 1, characterized in that The determining, based on the historical welding results and the historical parameter values ​​corresponding to the multiple target welding parameters, the target parameter values ​​corresponding to the multiple target welding parameters respectively include: Determine a fitting relationship between the historical welding result and the historical parameter values ​​respectively corresponding to the plurality of target welding parameters; According to the fitting relationship, determining prediction results corresponding to a plurality of candidate parameter combinations, respectively, wherein the plurality of candidate parameter combinations are obtained by setting the plurality of target welding parameters to different candidate parameter values; Determining welding strength scores corresponding to the plurality of candidate parameter combinations respectively based on the prediction results corresponding to the plurality of candidate parameter combinations respectively; Determine a parameter combination with the highest welding strength score among the multiple candidate parameter combinations as a target parameter combination; According to the target parameter combination, the target parameter values ​​corresponding to the target welding parameters are obtained.

3. The method according to claim 1, characterized in that The method further comprises: Capture images of historical welding areas to obtain historical acquisition images; According to the predetermined defect extraction requirements, the historically collected images are processed by using a corresponding image processing method to obtain two-dimensional welding information; Based on the two-dimensional welding information, a welding defect feature is obtained; The welding defect features are annotated with defect types to obtain the historical welding results.

4. The method according to claim 3, characterized in that The obtaining of welding defect characteristics based on the two-dimensional welding information includes: Performing laser scanning on the historical welding area to obtain three-dimensional point cloud data; Determining three-dimensional surface features of the historical welding area based on the three-dimensional point cloud data; Based on the two-dimensional welding information and the three-dimensional surface features, feature stitching is performed to determine the welding defect features.

5. The method according to claim 1, characterized in that Determining a plurality of target welding parameters according to the first associated data includes: determining second correlation data representing a degree of correlation between the plurality of candidate welding parameters; Based on the first correlation data and the second correlation data, the plurality of candidate welding parameters are screened to determine the plurality of target welding parameters.

6. The method according to claim 5, characterized in that The step of screening the plurality of candidate welding parameters based on the first associated data and the second associated data to determine the plurality of target welding parameters includes: Determining third correlation data indicating the correlation degree between the plurality of candidate welding parameters and the time series characteristics, wherein the time series characteristics at least include the operation time of the welding equipment and seasonal characteristics; Based on the data distributions respectively corresponding to the plurality of candidate welding parameters, determining fourth correlation data indicating the correlation degree between the plurality of candidate welding parameters and welding stability; Based on the first correlation data, the second correlation data, the third correlation data, and the fourth correlation data, the plurality of candidate welding parameters are screened to determine the plurality of target welding parameters.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: Determine the frequency of policy updates based on the equipment type of welding equipment; The target control strategy is updated to the welding equipment according to the strategy update frequency.

8. A welding strategy determination device, characterized in that: include: A parameter screening module, configured to determine a plurality of target welding parameters according to first association data, wherein the first association data indicates the degree of association between different candidate welding parameters and the welding result; A parameter determination module, configured to determine target parameter values ​​corresponding to the plurality of target welding parameters based on historical welding results and historical parameter values ​​corresponding to the plurality of target welding parameters; The strategy generation module is used to obtain the target control strategy according to the target parameter values ​​respectively corresponding to the multiple target welding parameters.

9. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the welding strategy determination method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: include: One or more processors and a memory, wherein the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the welding strategy determination method described in any one of claims 1 to 7.

Images