Digital intelligent welding method for controller production

Abstract

The invention discloses a digital intelligent welding method for controller production, and relates to the related field of welding control, and the method comprises the following steps: carrying out real-time welding sensing on a controller of an electric tricycle to obtain a plurality of real-time welding data, and carrying out data fusion according to multi-dimensional production environment information, and an obtained welding fusion result is synchronized to a quality detection module to be recognized, a welding defect data set is obtained to conduct dynamic feedback correction on the multiple pieces of real-time welding data, and a welding control strategy is formulated to conduct digital intelligent welding on a controller of the electric tricycle. The technical problems of unstable welding quality and inaccurate defect prediction caused by untimely adjustment of traditional welding parameters are solved, and the technical effects of real-time data acquisition, dynamic performance feedback correction and welding prediction trend analysis, improvement of welding quality and reduction of production defects are achieved.

Description

Technical Field

[0001] The present application relates to the field of welding control, and in particular to a digitalized welding method for controller production. Background Art

[0002] As an important tool for urban transportation and logistics, the electric control system of electric tricycles has a vital impact on the overall performance and safety. In the production process of electric tricycles, welding is one of the key manufacturing processes, especially in the welding process of the frame and battery pack. The welding quality is directly related to the safety, durability and stability of the whole vehicle. However, traditional welding technology still faces many challenges in the manufacturing of electric tricycles, especially in the scenarios of high-precision welding and mass production, it is often difficult to maintain consistent quality. Traditional welding methods rely on manually set welding parameters (such as current, voltage and welding speed), but these parameters often lack real-time adjustment and feedback mechanisms, which leads to defects such as pores, cracks, and lack of fusion during the welding process, which in turn affects the frame strength of the electric tricycle and the safety of the battery pack, causing product instability. Summary of the invention

[0003] This application solves the technical problem of unstable welding quality and inaccurate defect prediction caused by untimely adjustment of traditional welding parameters by providing a digital welding method for controller production. It achieves the technical effect of real-time data collection, dynamic performance feedback correction and welding prediction trend analysis, improving welding quality and reducing the occurrence of production defects.

[0004] The present application provides a digital welding method for controller production, including: performing real-time welding sensing on the controller of an electric tricycle through a sensing device group to obtain multiple real-time welding data; introducing multi-dimensional production environment information of the controller, and fusing the multiple real-time welding data according to the multi-dimensional production environment information to obtain a welding fusion result; synchronizing the welding fusion result to a quality inspection module for identification to obtain a welding defect data set; performing dynamic feedback correction on the multiple real-time welding data based on the welding defect data set to formulate a welding control strategy; and executing the welding control strategy to perform digital intelligent welding on the controller of the electric tricycle.

[0005] In a possible implementation, the method for constructing the multi-dimensional production environment information performs the following processing: calling the historical production data record log of the controller, and extracting the production process information of the controller based on the historical production data record log; dividing the production stages according to the production process information to obtain multiple production nodes of the controller; performing production operation analysis on the controller based on the multiple production nodes to determine multiple production operation status information; performing multi-dimensional analysis on the controller according to the multiple production nodes with the multiple production operation status information to obtain the multi-dimensional production environment information.

[0006] In a possible implementation, the multiple real-time welding data are data-fused according to the multi-dimensional production environment information to obtain a welding fusion result, and the following processing is performed: the multiple real-time welding data are data-grouped according to the multi-dimensional production environment information to obtain multiple welding data groups; feature analysis is performed based on the multiple welding data groups to obtain multiple welding features; weights are assigned to the multiple real-time welding data according to the multiple welding features based on the multi-dimensional production environment information to obtain multiple weight coefficients; the multiple real-time welding data are matched and fused with the multi-dimensional production environment information according to the multiple weight coefficients to obtain multiple welding-environment data pairs; the multiple welding-environment data pairs are added to the welding fusion result.

[0007] In a possible implementation, the welding fusion result is synchronized to a quality inspection module for identification, and a welding defect data set is obtained, and the following processing is performed: vector analysis is performed on the multiple welding-environment data pairs according to the multiple welding features to determine multiple feature vectors; based on the multiple feature vectors, the welding fusion result is synchronized to the quality inspection module, and the multiple welding-environment data pairs are traversed by the quality inspection module for deep learning to obtain a multidimensional inspection result; the multidimensional inspection result is dynamically monitored according to a time series to obtain multiple quality numerical features; the multidimensional inspection result is defect-classified according to the multiple quality numerical features to generate multiple defect labels; the multiple defect labels are annotated to the multidimensional inspection result to obtain the welding defect data set.

[0008] In a possible implementation, the multidimensional detection results are defect-classified according to the multiple quality value features, multiple defect labels are generated, and the following processing is performed: reverse backtracking is performed based on the multiple quality value features to obtain multiple data acquisition channels, and the multiple data acquisition channels contain multiple welding point information; multiple defect types are determined according to the multiple welding point information combined with the multiple quality value features; defect impact calculations are performed on the multiple data acquisition channels according to the multiple defect types combined with the multiple welding point information to obtain multiple defect impact coefficients; the multiple quality value features are sorted in descending order according to the multiple defect impact coefficients to generate a quality value sequence, and the quality value sequence is graded to generate the multiple defect labels.

[0009] In a possible implementation, the multiple real-time welding data are dynamically feedback corrected based on the welding defect data set, a welding control strategy is formulated, and the following processing is performed: multiple defect impact levels are extracted based on the multiple defect labels; a defect prediction model is constructed according to the multiple defect impact levels, and predictions are made through the defect prediction model combined with the multiple defect impact levels to generate a welding prediction trend data set; a performance evaluation of the controller is performed based on the welding prediction trend data set to generate a dynamic performance score; feedback response is performed based on the dynamic performance score combined with the welding prediction trend data set to generate dynamic welding correction parameters; the multiple real-time welding data are synchronously adjusted according to the dynamic welding correction parameters to formulate the welding control strategy.

[0010] In a possible implementation, a defect prediction model is constructed according to the multiple defect impact levels, predictions are made through the defect prediction model in combination with the multiple defect impact levels, a welding prediction trend data set is generated, and the following processing is performed: training is performed based on the historical production data record log according to the time series, and the defect prediction model is constructed according to the training results in combination with the multiple defect impact levels; the multiple real-time welding data are synchronized to the defect prediction model for capture, and welding defect prediction associated parameters are determined; the welding defect prediction associated parameters are mapped and matched with the multiple defect impact levels, and calculations are performed according to the matching results to obtain defect occurrence probability; defect analysis is performed according to the defect occurrence probability in combination with the time series to obtain defect distribution data, the defect distribution data is synchronized to the defect prediction model for updating, and the welding prediction trend data set is drawn.

[0011] In a possible implementation, feedback response is performed based on the dynamic performance score in combination with the welding prediction trend data set to generate dynamic welding correction parameters, and the following processing is performed: deviation analysis is performed based on the welding prediction trend data set, and feedback rules are constructed according to the predicted deviation values, wherein the feedback rules include a feedback cycle; dynamic analysis is performed based on the dynamic performance score to generate correction factors; feedback is performed on the welding prediction trend data set according to the feedback cycle to generate a set of parameters to be responded to; and dynamic correction is performed on the set of parameters to be responded to based on the correction factors to generate the dynamic welding correction parameters.

[0012] One or more technical solutions provided in this application have at least the following technical effects or advantages: The digital welding method for controller production provided in this application solves the technical problems of traditional welding parameters not being adjusted in time, resulting in unstable welding quality and inaccurate defect prediction, and achieves the technical effects of real-time data collection, dynamic performance feedback correction and welding prediction trend analysis, improving welding quality and reducing the occurrence of production defects. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the method according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0014] Figure 1 A schematic flow chart of a digital welding method for controller production provided in an embodiment of the present application.

[0015] Figure 2 A schematic diagram of a flow chart for formulating a welding control strategy for a digital welding method for controller production provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0017] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0018] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0019] The present application embodiment provides a digital welding method for controller production, such as Figure 1 As shown, the method includes: Step A100, performing real-time welding sensing on the controller of the electric tricycle through a sensor device group to obtain a plurality of real-time welding data; In order to monitor the welding process of the electric three-wheel controller in real time, it is necessary to select a suitable sensor device group, which is used to provide accurate physical quantities and environmental data. Temperature, current, voltage, pressure and other sensors can be installed near the welding gun, welding workbench, and welding materials to capture various important data of the controller during the production process. Through the installed sensor device group, data in the welding process, including welding current, voltage, temperature, pressure, etc., can be collected in real time. The weld can also be photographed by a high-resolution camera or laser scanning sensor to obtain image data of the welding surface for subsequent defect detection and quality assessment. Multiple real-time welding data are acquired and stored in a local database or cloud for subsequent query and analysis, providing strong data support for the high-quality production of electric three-wheelers and ensuring welding quality and production efficiency.

[0020] Execute step A200, introduce the multi-dimensional production environment information of the controller, perform data fusion on the multiple real-time welding data according to the multi-dimensional production environment information, and obtain the welding fusion result; in a possible implementation method, step A200 further includes step A210, call the historical production data record log of the controller, and extract the production process information of the controller based on the historical production data record log; execute step A220, divide the production stages according to the production process information, and obtain multiple production nodes of the controller; execute step A230, perform production operation analysis on the controller based on the multiple production nodes, and determine multiple production operation status information; execute step A240, perform multi-dimensional analysis on the controller according to the multiple production nodes with the multiple production operation status information, and obtain the multi-dimensional production environment information.

[0021] First, retrieve the controller's historical production data log through the enterprise production management system (such as the MES system). The data record contains the controller's status, parameter settings, and related events at each stage of the production process. The historical production data log is usually in a structured data format, such as CSV, JSON, or a database table, and contains information such as production time, stage, production parameters, operators, equipment status, and quality inspection results.

[0022] Further, key production link information is extracted from the historical production data. Usually, this information includes production stages, operating parameters and production results. By analyzing identification information such as timestamps and operation events, the data is divided into different production stages (for example, raw material preparation, assembly, debugging, testing, etc.). Based on the stage division, each key node in the production process is further identified, such as equipment startup, product assembly, testing, final quality inspection, etc., so as to obtain production process information. Further, according to the time record of controller production, the production process is divided into multiple stages. For example, it can be divided into "startup stage", "stable production stage" and "debugging stage" according to the operation cycle, and then different production stages are identified by event identifiers in the historical production data record log (such as "equipment startup" or "quality inspection passed"), and the key nodes in each stage are determined according to the production flow chart and the events of each stage. For example, "assembly starts", "welding is completed", "quality inspection", etc. are used as key nodes in the production process, so as to determine multiple production nodes, each of which corresponds to a set of status information, recording equipment parameters, personnel information, material flow and other contents.

[0023] Further based on the historical production data record log and multiple production nodes, the production status is classified into categories such as "normal", "abnormal", "to be adjusted", etc. The production status of each stage or node is determined by analyzing the equipment operation parameters, quality data, etc., and then the status data of each production node in multiple production nodes is recorded, including equipment operation status (such as power on, standby, fault), personnel operation status (such as operating, standby), etc.

[0024] Finally, multiple production operation status information is used to perform multi-dimensional analysis on the controller according to multiple production nodes. This means analyzing the environmental impact of the controller at each production node and stage based on multiple production operation status information, such as the impact of excessive temperature on equipment operation, the impact of humidity on product quality, etc., thereby determining multi-dimensional production environment information, which can effectively improve the automation level of the production line and achieve refined management in a complex production environment.

[0025] In a possible implementation, step A200 further includes step A250, grouping the multiple real-time welding data according to the multi-dimensional production environment information to obtain multiple welding data groups; executing step A260, performing feature analysis based on the multiple welding data groups to obtain multiple welding features; executing step A270, weighting the multiple real-time welding data according to the multiple welding features based on the multi-dimensional production environment information to obtain multiple weight coefficients; executing step A280, matching and fusing the multiple real-time welding data with the multi-dimensional production environment information according to the multiple weight coefficients to obtain multiple welding-environment data pairs; executing step A290, adding the multiple welding-environment data pairs to the welding fusion result.

[0026] First, the real-time welding data is grouped according to different production environment conditions (such as temperature, humidity, air pressure, etc.), which means grouping the data according to parameter types, such as environmental data group (temperature, humidity, etc.), welding data group, i.e. current, voltage, welding speed, etc., and each data group represents the welding process under specific environmental conditions. Furthermore, feature analysis is performed based on multiple welding data groups, which means that each welding data group can be analyzed using statistical methods (such as mean, standard deviation, skewness, kurtosis, etc.), or electrical data (current, voltage) can be analyzed in the time domain or frequency domain to extract characteristics such as fluctuation amplitude and frequency, and then key welding characteristics can be extracted, such as welding current fluctuation range, welding speed, weld quality indicators, etc.

[0027] Furthermore, the weighted average method can be used to assign weights to the multiple real-time welding data according to the multiple welding characteristics using the multi-dimensional production environment information, which means that each characteristic (such as welding current, temperature, humidity, etc.) is assigned a weight value according to its impact on welding quality and process stability. The weight coefficient can be optimized through a machine learning model to more accurately reflect the impact of environmental factors on the welding process, thereby obtaining multiple weight coefficients. Multiple weight coefficients are further used to match multiple welding data groups and multi-dimensional production environment data. Each welding data group will be weighted and combined according to its corresponding environmental data (such as temperature, current, voltage, etc.), and the welding data and environmental information will be multi-dimensionally fused by using data fusion algorithms (such as Kalman filtering, weighted average, principal component analysis, etc.) to generate welding-environment data pairs, which can effectively reflect the impact of environmental factors on the welding process, and each welding-environment data pair can include: real-time data of the welding process (such as current, voltage, welding speed, etc.), corresponding environmental data (such as temperature, humidity, equipment status, etc.), and fused weighted data for further analysis and prediction. Finally, multiple welding-environment data pairs are added to the welding fusion results, which means that the welding-environment data pairs are stored in a database (such as a relational database or a time series database) to facilitate subsequent analysis and query, and provide strong data support for efficient and stable welding production processes.

[0028] Execute step A300, synchronize the welding fusion result to the quality inspection module for identification, and obtain a welding defect data set; in a possible implementation, step A300 further includes step A310, perform vector analysis on the multiple welding-environment data pairs according to the multiple welding features, and determine multiple feature vectors; execute step A320, synchronize the welding fusion result to the quality inspection module based on the multiple feature vectors, and traverse the multiple welding-environment data pairs through the quality inspection module to perform deep learning to obtain a multi-dimensional detection result; execute step A330, dynamically monitor the multi-dimensional detection result according to the time series, and obtain multiple quality numerical features; Performing vector analysis on multiple welding-environment data pairs according to multiple welding features means that each welding-environment data pair corresponds to a feature vector, which contains all key feature values. For example, welding current, voltage, and temperature, etc., and converting multiple welding-environment data corresponding to multiple welding features into feature vectors, and then standardizing the values ​​of each feature vector to ensure the comparability of data in each dimension, thereby obtaining multiple feature vectors, and further synchronizing the welding fusion results to the quality detection module based on multiple feature vectors, and traversing the multiple welding-environment data pairs through the quality detection module for deep learning, which may include weld surface defect detection, weld geometric characteristic analysis, and welding process parameter abnormality detection. The weld surface defect detection may include detecting defects such as pores, cracks, and unfused welds. Weld geometric characteristic analysis can be used to evaluate whether the width, depth, uniformity, etc. of the weld meet the standards. Weld process parameter abnormality detection can identify abnormalities in parameters such as current, voltage, and temperature during welding, thereby obtaining multi-dimensional detection results.

[0029] Finally, dynamically monitoring the multidimensional detection results according to the time series means that the detection results are monitored in time series according to the real-time welding data stream and the output of the deep learning model, and in the production process, the welding parameters and quality detection results are monitored in real time, and the welding control parameters (such as current, voltage, speed, etc.) are dynamically adjusted to avoid defects, and then the quality numerical features of each welding process are extracted, such as the frequency of welding defects, the probability of occurrence of defect types, etc. At the same time, the number of various defects is counted, and their occurrence frequency is calculated to evaluate the welding quality, and a quality score is generated for each welding according to the type and impact of the defect (for example, the impact coefficient is evaluated according to the location and size of the defect), so as to obtain multiple quality numerical features, which can timely discover potential problems, optimize the welding process, and improve product quality and production efficiency.

[0030] Execute step A340, classify the multidimensional detection results into defects according to the multiple quality value features, and generate multiple defect labels; in a possible implementation, step A340 further includes step A341, reverse backtracking based on the multiple quality value features to obtain multiple data acquisition channels, and the multiple data acquisition channels contain multiple welding point information; execute step A342, analyze according to the multiple welding point information in combination with the multiple quality value features, and determine multiple defect types; execute step A343, calculate the defect impact of the multiple data acquisition channels according to the multiple defect types in combination with the multiple welding point information, and obtain multiple defect impact coefficients; execute step A344, sort the multiple quality value features in descending order according to the multiple defect impact coefficients to generate a quality value sequence, perform grade classification according to the quality value sequence, and generate the multiple defect labels.

[0031] First, based on the obtained multiple quality numerical features (such as welding quality score, current fluctuation, temperature change, etc.), reverse backtracking analysis is performed to obtain the welding data channel and welding point information associated with each quality feature. Starting from the current quality numerical features, trace back to the specific welding process and related data acquisition channels, and match the welding data and related environmental data at the corresponding time point through timestamps or production logs to ensure data consistency, and determine multiple data acquisition channels. The multiple data acquisition channels are the information of the original acquisition process for initial data acquisition of the control. At the same time, according to the results of the backtracking, identify and obtain all multiple data acquisition channels related to the quality numerical features. These channels may involve data such as current, voltage, welding position, temperature, pressure, etc., and the welding point information corresponding to each data acquisition channel (such as welding position, operator, equipment number, etc.) provides necessary positioning data for subsequent analysis. Further integration of multiple welding point information with corresponding quality numerical features means analyzing the welding points in the welding process based on quality numerical features (such as current fluctuation, temperature anomaly, etc.), finding the features and positions related to the occurrence of defects, and determining the defect type associated with each welding point through data analysis, and obtaining multiple defect types.

[0032] Further, according to the defect type of each welding point and its impact on the overall welding quality, the defect influence coefficient is calculated. The influence coefficient reflects the severity of the defect, and the influence coefficient calculation formula can be: Idefect = f (defect type, defect size, defect location), where Idefect is the defect influence coefficient, and f() is a function based on the defect type, size and location, outputting the influence coefficient.

[0033] Finally, the calculated multiple defect influence coefficients are arranged in descending order to reflect which defects have the greatest impact on welding quality. Sorting algorithms (such as quick sort, heap sort, etc.) can be used to sort multiple defect influence coefficients to clarify the degree of influence of each defect on welding quality. Based on the defect influence coefficients after descending sorting, a grade standard is formulated. The grade standard can include the first grade, that is, extremely serious defects (influence coefficient ≥ 90%), the second grade medium defects (influence coefficient between 70%-90%), and the third grade minor defects (influence coefficient <70%). The defect influence coefficients are divided using the threshold method or the segmentation method to generate defect labels. For each welding data point, a corresponding defect grade label is generated, thereby providing basic data support and improving production efficiency and product quality.

[0034] Execute step A350 to annotate the multiple defect labels to the multi-dimensional detection results to obtain the welding defect data set.

[0035] The generated defect labels and corresponding welding-environment data pairs are integrated into a complete data set. The welding defect data set contains the feature vector of each welding data pair and the corresponding defect type, location, influence coefficient and other information. The welding defect data set can be used as the basic data for subsequent welding quality assessment, defect prediction, control strategy optimization and other applications.

[0036] Execute step A400, perform dynamic feedback correction on the multiple real-time welding data based on the welding defect data set, and formulate a welding control strategy; in a possible implementation, Figure 2 As shown, step A400 further includes step A410, extracting multiple defect impact levels based on the multiple defect labels; First, the impact level of each defect is calculated based on its type, size, location and welding quality score. The impact level can be divided into multiple levels (e.g., high, medium, low) to indicate the degree of impact of the defect on the welding quality. By learning historical welding data and defect impact levels, the defect types and impact levels that may occur in different production environments are predicted.

[0037] Execute step A420, construct a defect prediction model according to the multiple defect impact levels, perform predictions through the defect prediction model in combination with the multiple defect impact levels, and generate a welding prediction trend data set; in a possible implementation, step A420 further includes step A421, perform training according to the time series based on the historical production data record log, and construct the defect prediction model according to the training results in combination with the multiple defect impact levels; execute step A422, synchronize the multiple real-time welding data to the defect prediction model for capture, and determine welding defect prediction associated parameters; execute step A423, map and match the welding defect prediction associated parameters with the multiple defect impact levels, calculate according to the matching results, and obtain the defect occurrence probability; execute step A424, perform defect analysis according to the defect occurrence probability in combination with the time series, obtain defect distribution data, synchronize the defect distribution data to the defect prediction model for updating, and draw the welding prediction trend data set.

[0038] Training based on the historical production data logs in time series means using machine learning algorithms, such as random forests, support vector machines (SVMs), neural networks, etc., to train defect prediction models. The model input includes the characteristics of historical welding data (such as welding current, voltage, temperature, etc.), and the output is the defect impact level or defect type. Based on the historical defect data set, the defect type and impact level are defined for each sample according to multiple defect impact levels. The model is trained using historical data, and the optimal model and hyperparameters are selected through cross-validation to obtain a defect prediction model.

[0039] Further synchronizing the multiple real-time welding data to the defect prediction model for capture means predicting welding defects based on the real-time data and the defect prediction model, and outputting welding defect prediction parameters. Data in the welding process, including welding current, voltage, temperature, welding speed, etc., and real-time quality feedback are collected in real time through sensors, and welding defects are predicted based on the correlation between defects and welding to obtain welding defect prediction correlation parameters.

[0040] Further mapping and matching the welding defect prediction associated parameters with the multiple defect impact levels refers to mapping and matching the predicted welding defect prediction associated parameters with the multiple defect impact levels obtained in the historical data. By calculating the matching results, the probability of defect occurrence in each welding process is determined. The prediction results of welding defects can be mapped and matched with the defect impact level through a regression model or a classification model to calculate the probability of defect occurrence. The specific formula is as follows: P defect occurrence = f (welding data features, defect impact level), where P defect occurrence is the probability of defect occurrence, and f() is a mapping function obtained through training. According to real-time welding data, the probability of defect occurrence is predicted. For example, when the welding current is too high and the temperature is too low, the probability of defect occurrence may be high. And the matching results of all defect impact levels and welding data are calculated to obtain the probability of defect occurrence in each welding process.

[0041] Finally, through time series analysis methods (such as sliding windows, time series regression models, long short-term memory networks (LSTM), etc.), combined with real-time data and defect occurrence probability in the welding process, the trend of defects changing over time is analyzed, and the combination of defect occurrence probability and time series data is used to predict the trend of defects that may occur in the future, identify potential risk points in the welding process, and generate defect distribution data based on defect occurrence probability and time series data. The defect distribution data may include the distribution of various types of defects in time, the influence coefficient of each defect, and the probability of occurrence of different defect types in each welding cycle. The defect distribution data is synchronized to the defect prediction model for update to generate a welding prediction trend data set. The welding prediction trend data set may include the predicted trend of defect occurrence, the optimization direction in the welding process, and possible future production adjustment suggestions, improve welding quality, reduce the occurrence of defects, optimize production efficiency, and make real-time adjustments to the production process.

[0042] Executing step A430, performing performance evaluation on the controller based on the welding prediction trend data set to generate a dynamic performance score; According to the welding prediction trend data set, combined with key parameters in the welding process (such as current, voltage, temperature, welding speed, etc.), the overall performance of the welding process is evaluated, and the dynamic performance score of each welding process is calculated based on the welding prediction trend data set. The score takes into account factors such as welding quality, production efficiency, and defect incidence. At the same time, during the welding process, the working state of the controller and welding parameters are dynamically adjusted according to the real-time feedback welding data and prediction trends. For example, if the dynamic performance score is low, it may be necessary to adjust parameters such as current or temperature to avoid defects. Execute step A440, perform feedback response based on the dynamic performance score in combination with the welding prediction trend data set, and generate dynamic welding correction parameters; in one possible implementation, step A440 further includes step A441, perform deviation analysis based on the welding prediction trend data set, and construct feedback rules based on the predicted deviation values, wherein the feedback rules include a feedback cycle; execute step A442, perform dynamic analysis based on the dynamic performance score, and generate a correction factor; execute step A443, perform feedback on the welding prediction trend data set according to the feedback cycle, and generate a parameter set to be responded; execute step A444, dynamically correct the parameter set to be responded based on the correction factor, and generate the dynamic welding correction parameters.

[0043] Deviation analysis is performed by comparing the welding prediction trend data set with the welding quality data (such as welding temperature, current, voltage, etc.) in the actual welding process. The difference between the predicted trend data and the actual value is the deviation. Among them, the larger the deviation value, the greater the difference between the welding process and the expected target, which may cause quality problems. According to the predetermined quality standard, an acceptable deviation range is set. For example, when the deviation exceeds 5%, it may indicate that the welding parameters need to be adjusted. And a feedback rule is constructed based on the predicted deviation value, and the feedback rule includes a feedback cycle, which refers to the update frequency of the feedback control mechanism during the welding process. According to the timing and deviation analysis of the welding process, a reasonable feedback cycle is determined. The feedback cycle can be time-based (such as updated every minute) or based on key events in the welding process (such as updated after each batch of welding is completed). Different deviation value ranges and corresponding feedback responses are set. The feedback rule determines what adjustments the system needs to make (such as adjusting the welding current, voltage, speed, etc.) when the deviation between the prediction and the actual exceeds a certain threshold.

[0044] The performance of the welding process is further evaluated based on real-time welding data and predicted trend data. The dynamic performance score may include multiple aspects such as welding quality, production efficiency, defect incidence, etc. Based on deviation analysis and dynamic performance score, the correction factor is calculated as the basis for adjusting welding parameters. The correction factor is a dynamic adjustment in the welding process, which can be used to optimize various parameters in the welding process (such as current, voltage, temperature, etc.). The correction factor is a function optimized based on historical data and real-time performance scores, which represents the impact of deviation values ​​and performance scores on the adjustment of welding control parameters.

[0045] Further, according to the set feedback cycle, the welding data is collected in real time and the deviation value is calculated to determine whether the control strategy needs to be updated. For example, when the deviation exceeds the preset range, the welding controller immediately performs adjustments. If the system detects a large welding deviation, the feedback cycle will be shortened, and data collection, analysis and adjustment will be performed frequently; conversely, if the deviation is small or within the normal range, the feedback cycle can be extended. At the same time, based on the feedback results of the deviation analysis and welding prediction trend data set, the welding parameters to be responded to (such as welding current, voltage, speed, etc.) are determined. These parameters will be used as the target of adjustment and optimization to obtain the parameter set to be responded to.

[0046] Dynamically correcting the parameter set to be responded to based on the correction factor means using the generated dynamic welding correction parameters to dynamically adjust the parameter set to be responded to according to the deviation analysis and performance scoring results. Based on the dynamically corrected welding parameters, the parameter set to be responded to is updated to ensure that each welding task can be performed within the optimal parameter range. This may include adjusting welding parameters such as current, voltage, speed, and temperature. Through multiple feedback cycles, the parameter set to be responded to is gradually optimized to ensure that the welding process remains stable and defects are reduced under various production conditions. Efficient and accurate operation support is provided for each welding task in the production process.

[0047] Execute step A450 to synchronously adjust the multiple real-time welding data according to the dynamic welding correction parameters and formulate the welding control strategy.

[0048] Synchronously adjusting multiple real-time welding data according to dynamic welding correction parameters means formulating new welding control strategies based on dynamic welding correction parameters to ensure the quality and efficiency of each welding process. Welding control strategies can include adjusting welding paths, welding currents and temperatures, etc., automatically adjusting welding parameters according to real-time monitoring data and predicted trends to maintain the stability and optimize performance of the welding process, and optimizing the next welding task according to feedback response data, reducing defects in the production process, and improving product quality and production efficiency.

[0049] Next, step A500 is executed to execute the welding control strategy to perform digital intelligent welding on the controller of the electric tricycle.

[0050] The welding control strategy for the controller of the electric three-wheeler refers to the welding controller feeding back the real-time collected welding data and environmental data to the control system. The control system automatically adjusts the welding parameters based on the feedback information and strategy model, and the welding process is a closed-loop control system. The welding conditions are continuously adjusted according to the real-time data to ensure that the quality of each welding point reaches the expected target. At the same time, the welding process of the electric three-wheeler controller is monitored in real time to ensure that all operations are within the range set by the control strategy, and possible welding defects are predicted in real time. The welding parameters are adjusted in advance according to the prediction results to avoid the occurrence of defects. According to the real-time monitoring data, the controller intelligently adjusts the welding current, voltage, speed and other parameters to meet the requirements of welding quality. By executing the welding control strategy and combining digital intelligent control technology, the welding parameters can be monitored and dynamically adjusted in real time during the production process of the electric three-wheeler controller to ensure the quality and stability of each welding point. Through intelligent feedback and continuous optimization, the stability and quality of the welding process can be significantly improved, reducing manual intervention, improving production efficiency, and effectively reducing the occurrence of welding defects.

[0051] The embodiments of the present application solve the technical problem that traditional welding parameters are not adjusted in time, resulting in unstable welding quality and inaccurate defect prediction, and achieve the technical effects of real-time data collection, dynamic performance feedback correction and welding prediction trend analysis, improving welding quality and reducing the occurrence of production defects.

[0052] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A digital welding method for controller production, characterized in that: The method comprises: The controller of the electric tricycle is subjected to real-time welding sensing by a sensor device group to obtain a plurality of real-time welding data; Introducing multi-dimensional production environment information of the controller, fusing the plurality of real-time welding data according to the multi-dimensional production environment information, and obtaining a welding fusion result; Synchronizing the welding fusion result to the quality inspection module for identification to obtain a welding defect data set; Performing dynamic feedback correction on the plurality of real-time welding data based on the welding defect data set, and formulating a welding control strategy; The welding control strategy is executed to perform digital intelligent welding on the controller of the electric tricycle.

2. The digital intelligent welding method for controller production according to claim 1, characterized in that: The method for constructing the multi-dimensional production environment information includes: Retrieving a historical production data record log of the controller, and extracting production process information of the controller based on the historical production data record log; Divide the production stages according to the production process information to obtain multiple production nodes of the controller; Performing production operation analysis on the controller based on the multiple production nodes to determine multiple production operation status information; The controller performs multi-dimensional analysis on the multiple production operation status information according to the multiple production nodes to obtain the multi-dimensional production environment information.

3. The digital intelligent welding method for controller production according to claim 2, characterized in that: The plurality of real-time welding data are subjected to data fusion according to the multi-dimensional production environment information to obtain a welding fusion result, the method comprising: Grouping the plurality of real-time welding data according to the multi-dimensional production environment information to obtain a plurality of welding data groups; Perform feature analysis based on the multiple welding data groups to obtain multiple welding features; Based on the multi-dimensional production environment information, weights are assigned to the multiple real-time welding data according to the multiple welding features to obtain multiple weight coefficients; Matching and fusing the multiple real-time welding data with the multi-dimensional production environment information according to the multiple weight coefficients to obtain multiple welding-environment data pairs; The plurality of welding-environment data pairs are added to the welding fusion result.

4. The digital intelligent welding method for controller production according to claim 3, characterized in that: The welding fusion result is synchronized to the quality inspection module for identification to obtain a welding defect data set, the method comprising: Performing vector analysis on the plurality of welding-environment data pairs according to the plurality of welding features to determine a plurality of feature vectors; Synchronizing the welding fusion result to the quality detection module based on the multiple feature vectors, and performing deep learning through the multiple welding-environment data pairs by the quality detection module to obtain a multi-dimensional detection result; Dynamically monitor the multidimensional detection results according to a time series to obtain multiple quality numerical features; Classifying the multidimensional detection results into defects according to the multiple quality numerical features to generate multiple defect labels; The multiple defect labels are annotated to the multidimensional detection results to obtain the welding defect data set.

5. The digitalized welding method for controller production according to claim 4, characterized in that: The method includes: classifying the multidimensional detection results into defects according to the multiple quality numerical features to generate multiple defect labels. Performing reverse tracing based on the multiple quality numerical features to obtain multiple data acquisition channels, wherein the multiple data acquisition channels contain multiple welding point information; Analyze the multiple welding point information in combination with the multiple quality numerical features to determine multiple defect types; According to the multiple defect types and the multiple welding point information, defect influence calculation is performed on the multiple data acquisition channels to obtain multiple defect influence coefficients; The multiple quality value features are sorted in descending order according to the multiple defect influence coefficients to generate a quality value sequence, and the quality value sequence is graded to generate the multiple defect labels.

6. The digitalized welding method for controller production according to claim 5, characterized in that: Based on the welding defect data set, dynamic feedback correction is performed on the multiple real-time welding data to formulate a welding control strategy, the method comprising: extracting a plurality of defect impact levels based on the plurality of defect labels; Building a defect prediction model according to the multiple defect impact levels, and performing prediction by combining the defect prediction model with the multiple defect impact levels to generate a welding prediction trend data set; Performing performance evaluation on the controller based on the welding prediction trend data set to generate a dynamic performance score; Performing feedback response based on the dynamic performance score combined with the welding prediction trend data set to generate dynamic welding correction parameters; The multiple real-time welding data are synchronously adjusted according to the dynamic welding correction parameters to formulate the welding control strategy.

7. The digitalized welding method for controller production according to claim 6, characterized in that: A defect prediction model is constructed according to the multiple defect impact levels, and prediction is performed by combining the defect prediction model with the multiple defect impact levels to generate a welding prediction trend data set. The method includes: Based on the historical production data record log, training is performed according to the time series, and the defect prediction model is constructed according to the training result and the multiple defect impact levels; Synchronizing the plurality of real-time welding data to the defect prediction model for capturing, and determining welding defect prediction associated parameters; Mapping and matching the welding defect prediction associated parameters with the multiple defect impact levels, and performing calculations based on the matching results to obtain the defect occurrence probability; Defect analysis is performed according to the defect occurrence probability in combination with the time series to obtain defect distribution data, the defect distribution data is synchronized to the defect prediction model for updating, and the welding prediction trend data set is drawn.

8. The digitalized welding method for controller production according to claim 6, characterized in that: The method comprises: performing feedback response according to the dynamic performance score combined with the welding prediction trend data set to generate dynamic welding correction parameters. Performing deviation analysis based on the welding prediction trend data set, and constructing a feedback rule according to the prediction deviation value, wherein the feedback rule includes a feedback cycle; Performing dynamic analysis based on the dynamic performance score to generate a correction factor; Feedback is performed on the welding prediction trend data set according to the feedback cycle to generate a parameter set to be responded; The set of parameters to be responded to is dynamically corrected based on the correction factor to generate the dynamic welding correction parameter.

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