Electric vehicle frame machining quality inspection analysis system based on data analysis

Abstract

The application discloses a data analysis-based electric vehicle frame machining quality inspection analysis system, which comprises a welding process data acquisition module, a welding data inspection module, a welding seam quality inspection analysis module and a quality inspection result output module. The welding process data acquisition module is used for monitoring and collecting welding equipment related data in a welding process. The welding process data acquisition module is in network connection with the welding data inspection module. The welding data inspection module is used for performing welding standard inspection on the welding data collected by the welding process data acquisition module. The welding seam quality inspection analysis module is used for identifying and analyzing defects in a welding seam to determine whether the welding seam is qualified. The quality inspection result output module is used for exporting and displaying the quality inspection analysis result of the welding seam. The welding process data acquisition module, the welding data inspection module and the quality inspection result output module are all in network connection with the welding seam quality inspection analysis module. The application has the characteristics of high efficiency, precision and intelligent control.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automation and data analysis processing, in particular to an electric vehicle frame machining quality inspection analysis system based on data analysis. BACKGROUND

[0002] In the process of manufacturing electric vehicle frames, quality control is a crucial link, directly related to the strength and safety of the frame. Traditional electric vehicle frame machining quality inspection mainly relies on manual visual inspection and simple physical testing. This method is not only inefficient, but also easily affected by human factors, resulting in inaccurate and unreliable quality inspection results. With the rapid development of the electric vehicle industry and the increasing demand for product quality from consumers, traditional quality inspection methods have been difficult to meet the needs of modern electric vehicle frame machining.

[0003] In particular, the presence of cracks, pores and other defects in the welding part of the frame can seriously affect the overall strength and durability of the frame. Traditional detection methods usually involve manual knocking, visual observation or the use of simple detection instruments to detect these defects, but these methods often fail to detect small or hidden defects and are inefficient.

[0004] In addition, the control of welding line energy is one of the key factors affecting welding quality. Too high or too low welding line energy can result in poor weld quality, such as excessively wide or narrow welds, insufficient depth, etc., which in turn affects the overall performance of the frame. Traditional welding line energy is usually estimated by human experience, making it difficult to achieve accurate quality control. Therefore, it is necessary to design an efficient, precise and intelligent control electric vehicle frame machining quality inspection analysis system based on data analysis. SUMMARY

[0005] The purpose of the present application is to provide an electric vehicle frame machining quality inspection analysis system based on data analysis to solve the problems raised in the background.

[0006] To solve the above technical problems, the present application provides the following technical solution: an electric vehicle frame machining quality inspection analysis system based on data analysis, comprising a welding process data acquisition module, a welding data verification module, a weld quality inspection analysis module and a quality inspection result output module, the welding process data acquisition module is used for monitoring and collecting welding equipment related data in the welding process, the welding process data acquisition module is connected with the welding data verification module, the welding data verification module is used for welding standard verification of the welding data collected by the welding process data acquisition module, the weld quality inspection analysis module is used for identifying and analyzing defects in the weld to determine whether the weld is qualified, the quality inspection result output module is used for exporting and displaying the quality inspection result of the weld, and the welding process data acquisition module, welding data verification module and quality inspection result output module are all connected with the weld quality inspection analysis module.

[0007] According to the technical scheme, the welding process data acquisition module comprises a welding current monitoring module, a welding voltage monitoring module and a welding speed monitoring module, the welding current monitoring module is used for monitoring the current size through the electrode in the welding process, the welding voltage monitoring module is used for monitoring the voltage difference between the electrode and the workpiece during welding, and the welding speed monitoring module is used for monitoring the speed of the electrode moving along the weld in the welding process.

[0008] According to the technical scheme, the welding data inspection module comprises a vehicle frame data input module, a welding parameter output module and a comparison and judgment module, the vehicle frame data input module is used for inputting the vehicle frame information of the current batch of welding, the welding parameter output module is electrically connected with the vehicle frame data input module, the welding output module is used for outputting the welding standard preset by the system according to the vehicle frame information, and the comparison and judgment module is used for comparing the welding process data with the welding standard and judging whether the welding process meets the requirements.

[0009] According to the technical scheme, the welding seam quality inspection analysis module comprises a welding seam image acquisition module, a crack feature recognition module, a pore feature recognition module and a welding seam completion degree recognition module, the welding seam image acquisition module is used for acquiring the welding seam image of the vehicle frame, the crack feature recognition module, the pore feature recognition module and the welding seam completion degree recognition module are all electrically connected with the welding seam image acquisition module, the crack feature recognition module is used for recognizing the crack feature of the acquired welding seam image, the pore feature recognition module is used for recognizing the pore feature of the acquired welding seam image, and the welding seam completion degree recognition module is used for recognizing the welding seam completion degree of the acquired welding seam image.

[0010] According to the technical scheme, the welding seam completion degree recognition module further comprises an image contour line fitting generation sub-module, a welding line energy calculation sub-module, a welding seam fitting generation sub-module and a comparison and output sub-module, the image contour line fitting generation sub-module is used for generating the contour line fitting of the acquired welding seam image, the welding line energy calculation sub-module is used for analyzing and calculating the welding line energy at each position in the welding process, the welding line energy calculation sub-module is electrically connected with the welding seam fitting generation sub-module, the welding seam fitting generation sub-module is used for fitting and generating the standard welding seam simulation image under the above data input condition in combination with the welding vehicle frame data and the welding line energy calculation result, and the comparison and output sub-module is used for comparing the fitting image contour line of the recognized image with the data fitting generation standard welding seam simulation image in terms of similarity, and calculating and outputting the similarity value.

[0011] According to the technical scheme, the operation steps of the electric vehicle frame processing quality inspection analysis system based on data analysis comprise:

[0012] Step S1: collect the current value I of the welding process through the electrode, the voltage value U between the electrode and the workpiece, and the speed value V of the electrode along the weld during the welding process;

[0013] Step S2: input the frame data of the current welding batch, wherein the frame data includes the frame material and the load requirement of each weld of the frame, and then output the welding standard of the corresponding weld according to the input frame data in the system preset welding parameter database, wherein the welding standard includes (I min ,I max ), (U min ,U max ), and (V min ,V max );

[0014] Step S3: take the collected data of a complete weld as a reference, and compare it with the welding standard of the weld, when the data set of the current, voltage, and speed of the weld collected all correspond to (I min ,I max ), (U min ,U max ), and (V min ,V max ), it is judged that the welding data is passed, otherwise it is judged that the weld is unqualified, and the quality inspection step of the weld is terminated;

[0015] Step S4: the weld that passes the welding data inspection is further collected for a weld image, and then a comprehensive quality inspection analysis is performed on the image level by using a weld quality inspection analysis module;

[0016] Step S5: finally, the quality inspection results of all frame welds are exported by a quality inspection result output module for the user to quickly view and filter the data.

[0017] According to the above technical solution, the step S4 further includes:

[0018] Step S41: collect the image after welding the weld;

[0019] Step S42: convert the color image into a grayscale image;

[0020] Step S43: use an edge detection algorithm to identify the edges in the image;

[0021] Step S44: evaluate the arrangement and repetition patterns of the edges in the image;

[0022] Step S45: the crack feature extraction module and the pore feature identification module respectively match the preset crack feature data package and pore feature data package with the identified image features;

[0023] Step S46: When the crack feature and the pore feature are not matched, it is judged that the weld does not have crack and pore defects, and then the welding completion degree recognition module is started to further recognize and analyze the welding completion degree of the weld.

[0024] According to the above technical solution, the method for recognizing and analyzing the welding completion degree of the weld in step S46 is specifically:

[0025] Step S461: The contour line of the image after welding of the weld is fitted, and then the fitted image contour line is evenly divided into n equal parts by using n calibration points;

[0026] Step S462: The welding line energy value E of the reference weld at each position in the welding process is calculated by formula (1);

[0027] Step S463: Then, according to the welding line energy value U at each position in the welding process and the material of the welded frame, the heat affected zone range F at each position in the welding process is calculated by formula (2), and finally the standard weld simulation diagram under the above data input condition is fitted and generated according to the heat affected zone range size at all positions of a complete weld;

[0028] Step S464: The generated weld simulation diagram is maximally overlapped with the contour line of the weld image fitted in step S461, and after the overlap, the shortest straight line of each calibration point to the edge of the weld simulation diagram is drawn, and the length values l1, l2,..., ln of each shortest straight line are calculated. n Then, the similarity value C of the fitted weld image and the generated weld simulation diagram is calculated by formula (3).

[0029] According to the above technical solution, the calculation formula of the welding line energy value E in step S462 is:

[0030]

[0031] In the formula, the current value I passing through the electrode during the welding process and the voltage value U between the electrode and the workpiece are both proportional to the welding line energy value U, and the speed value V of the electrode moving along the weld during the welding process is inversely proportional to the welding line energy value U. When the current and voltage values during welding are larger, or when the current and voltage values are constant, the welding speed is slower, and the welding energy value E at the corresponding welding position is larger.

[0032] In step S463, the calculation formula of the heat affected zone range F is:

[0033] F=k·E......(2)

[0034] In the formula, k is a frame material coefficient, is a constant value greater than 0, the heat affected zone range F is proportional to the welding line energy value E, and when the welding energy value E at the welding position is greater, the heat affected zone range of the welding position is wider;

[0035] In the step S464, the calculation formula of the similarity value C of the fitted weld image and the generated weld simulation image is:

[0036]

[0037] Wherein, α is a control parameter, is a constant value greater than 0, The shortest straight line length value l1, l2,..., l of the calibration point to the edge of the weld simulation image, n The average value of the length value; in the formula, the similarity value C is related to the fluctuation degree of the shortest straight line length of each calibration point to the edge of the weld simulation image and the average length of the shortest straight line of each calibration point to the edge of the weld simulation image, wherein the length value of l is in "millimeter".

[0038] According to the above technical scheme, the step S5 further comprises:

[0039] Setting the minimum threshold value C0 of the similarity judgment;

[0040] When C≥C0, the quality inspection result output module outputs the quality inspection qualified result;

[0041] When C<C0, the quality inspection result output module outputs the quality inspection unqualified result.

[0042] Compared with the prior art, the present application has the beneficial effects that: the present application generates a weld reference image by fitting the welding current, voltage, displacement speed, material and other parameters under the condition that the welding process output is generated, and compares the reference image with the actual recognition image to determine whether the weld is qualified, thereby avoiding the influence of the uneven technical level of welders or other external factors on the weld effect, and the welding data inspection module has already performed the first inspection on the welding standard from the data level, and finally comparing the reference image with the actual recognition image is the second inspection, which more comprehensively analyzes the completion degree of the weld, thereby greatly improving the accuracy and reliability of the quality inspection. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments thereof, and explain the present application, and do not constitute a limitation of the present application.

[0044] In the drawings:

[0045] Figure 1 It is a system module composition schematic diagram of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all the other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0047] Please refer to Figure 1 The present application provides a technical solution: an electric vehicle frame machining quality inspection analysis system based on data analysis, which comprises a welding process data acquisition module, a welding data inspection module, a welding seam quality inspection analysis module and a quality inspection result output module. The welding process data acquisition module is used for monitoring and collecting welding equipment related data in the welding process. The welding process data acquisition module is in network connection with the welding data inspection module. The welding data inspection module is used for welding standard inspection on the welding data collected by the welding process data acquisition module. The welding seam quality inspection analysis module is used for identifying and analyzing defects in the welding seam to determine whether the welding seam is qualified. The quality inspection result output module is used for exporting and displaying the quality inspection analysis result of the welding seam. The welding process data acquisition module, the welding data inspection module and the quality inspection result output module are all in network connection with the welding seam quality inspection analysis module.

[0048] The welding process data acquisition module comprises a welding current monitoring module, a welding voltage monitoring module and a welding speed monitoring module. The welding current monitoring module is used for monitoring the current size passing through the welding rod in the welding process. The welding voltage monitoring module is used for monitoring the voltage difference between the electrode and the workpiece during welding. The welding speed monitoring module is used for monitoring the speed of the welding rod moving along the welding seam in the welding process.

[0049] The welding data inspection module comprises a frame data input module, a welding parameter output module and a comparison and judgment module. The frame data input module is used for inputting the frame information of the current batch of welding. The welding parameter output module is in electrical connection with the frame data input module. The welding output module is used for outputting the welding standard preset by the system according to the frame information. The comparison and judgment module is used for comparing the welding process data with the welding standard and judging whether the welding process meets the requirements.

[0050] The weld quality inspection analysis module includes a weld image acquisition module, a crack feature recognition module, a porosity feature recognition module, and a weld completion degree recognition module. The weld image acquisition module is used to acquire the frame weld image. The crack feature recognition module, the porosity feature recognition module, and the weld completion degree recognition module are electrically connected with the weld image acquisition module. The crack feature recognition module is used to identify the crack features of the acquired weld image. The porosity feature recognition module is used to identify the porosity features of the acquired weld image. The weld completion degree recognition module is used to identify the weld completion degree of the acquired weld image.

[0051] The weld completion degree recognition module further includes an image contour line fitting generation submodule, a welding line energy calculation submodule, a weld fitting generation submodule, and a comparison output submodule. The image contour line fitting generation submodule is used to generate contour line fitting for the acquired weld image. The welding line energy calculation submodule is used to analyze and calculate the welding line energy at each position in the welding process. The welding line energy calculation submodule is electrically connected with the weld fitting generation submodule. The weld fitting generation submodule is used to combine the welding frame data and the welding line energy calculation result to generate a standard weld simulation image under the above data input condition. The comparison output submodule is used to compare the similarity of the fitting image contour line of the identified image with the data fitting standard weld simulation image and calculate the similarity value. Through the integration of multiple modules such as welding process data acquisition, welding data inspection, weld quality inspection analysis, and quality inspection result output, efficient, accurate, and intelligent control of the welding quality in the electric frame processing process is realized. The system not only effectively monitors the key parameters such as current, voltage, and speed in the welding process to ensure that the welding parameters meet the preset standards, but also accurately identifies the cracks, porosity, and completion degree of the weld through comprehensive quality inspection analysis at the image level, thereby greatly improving the accuracy and reliability of the quality inspection. Compared with traditional manual visual inspection and simple physical testing methods, the system significantly improves the detection efficiency and reduces the influence of human factors, providing more reliable quality assurance for electric frame processing and manufacturing. At the same time, the system further optimizes the control of welding quality by accurately calculating the welding line energy and the heat affected zone range, providing strong support for the strength and safety of the electric frame.

[0052] The operation steps of the electric frame processing quality inspection analysis system based on data analysis include:

[0053] Step S1: Acquire the current value I through the welding electrode, the voltage value U between the electrode and the workpiece, and the speed value V of the welding electrode along the weld during welding. Through the welding process data acquisition module, the welding equipment related data in the welding process is monitored and acquired in real time, including welding current, welding voltage, and welding speed, etc., providing a detailed data basis for subsequent welding data inspection and weld quality inspection analysis;

[0054] Step S2: input the frame data of the current welding batch, wherein the frame data includes the frame material and the load requirements of each weld of the frame, and then output the welding standards of the corresponding welds in the system preset welding parameter database according to the input frame data, wherein the welding standards include (I min ,I max ), (U min ,U max ), (V min ,V max );

[0055] Step S3: take the collection data of a complete weld as a reference, and compare it with the welding standards of the weld, when the data set of current, voltage and speed collected by the weld corresponds to (I min ,I max ), (U min ,U max ), (V min ,V max ), it is judged that the welding data inspection is passed, otherwise it is judged that the weld processing is unqualified, and the quality inspection step of the weld is terminated; the welding data inspection module can automatically output the system preset welding standards according to the input frame data, such as the frame material and the load requirements of the weld, and compare the data of the actual welding process with the above standards, so as to judge whether the welding process meets the requirements, greatly reducing the error of manual judgment, and improving the accuracy and efficiency of quality inspection;

[0056] Step S4: the welds that pass the welding data inspection are further collected for weld images, and then the welding quality inspection analysis module is used for comprehensive quality inspection analysis on the image level;

[0057] Step S5: finally, the quality inspection results of all frame welds are exported through the quality inspection result output module for users to quickly view and filter data.

[0058] Step S4 further includes:

[0059] Step S41: collect the image after welding the weld;

[0060] Step S42: convert the color image into a gray-scale image, simplify the image data, and at the same time retain enough information for subsequent processing;

[0061] Step S43: use an edge detection algorithm to identify the edges in the image;

[0062] Step S44: evaluate the arrangement and repetition mode of the edges in the image;

[0063] Step S45: The crack feature extraction module and the porosity feature recognition module match the preset crack feature data packet and the porosity feature data packet with the recognized image features respectively;

[0064] Step S46: When no crack feature and porosity feature are matched, it is judged that the weld does not have crack and porosity defects, and then the weld completion recognition module is started to further recognize and analyze the welding completion degree of the weld. The weld quality inspection analysis module can accurately recognize the crack, porosity and other defects in the weld, as well as the completion degree of the weld by collecting the weld image and comprehensively analyzing the image by using the crack feature recognition module, the porosity feature recognition module and the weld completion recognition module. Not only the accuracy of quality inspection is improved, but also the quality inspection process is more intuitive and easy to understand.

[0065] In step S46, the method for recognizing and analyzing the welding completion degree of the weld is specifically:

[0066] Step S461: The contour line of the image after welding of the weld is fitted, and then the fitted image contour line is evenly divided into n equal parts by using n calibration points;

[0067] Step S462: The welding line energy value E of each position of the reference weld in the welding process is calculated by formula (1);

[0068] Step S463: Then, according to the welding line energy value U of each position in the welding process and the material of the welded frame, the heat affected zone range F of each position in the welding process is calculated by formula (2), and finally the standard weld simulation diagram under the above data input condition is fitted and generated according to the heat affected zone range size of all positions of a complete weld;

[0069] Step S464: The generated weld simulation diagram is maximally overlapped with the contour line of the weld image fitted in step S461, and after the overlap, the shortest straight lines from each calibration point to the edge of the weld simulation diagram are drawn, and the length values l1, l2,..., ln of each shortest straight line are calculated. n Then, the similarity value C of the fitted weld image and the generated weld simulation diagram is calculated by formula (3); the welding line energy in the welding process is calculated, and the heat affected zone range of each position in the welding process is further calculated combined with the material of the frame, so that the standard weld simulation diagram under the above welding data input condition is fitted and generated, so that the system can more accurately evaluate the quality of the weld, and provide more specific quality inspection results for the user.

[0070] In step S462, the calculation formula of the welding line energy value E is:

[0071]

[0072] In the formula, the current value I passing through the electrode during the welding process and the voltage value U between the electrode and the workpiece are both proportional to the welding line energy value U, the speed value V of the electrode moving along the weld during the welding process is inversely proportional to the welding line energy value U, when the current and voltage values during the welding are larger, or under the condition of certain current and voltage values, the welding moving speed is slower, and the welding energy value E at the corresponding welding position is larger;

[0073] In step S463, the calculation formula of the heat affected zone range F is:

[0074] F=k·E......(2)

[0075] In the formula, k is a frame material coefficient, which is a constant value greater than 0, the heat affected zone range F is proportional to the welding line energy value E, and when the welding energy value E at the welding position is larger, the heat affected zone range of the welding position is wider;

[0076] In step S464, the calculation formula of the similarity value C of the fitted weld image and the generated weld simulation image is:

[0077]

[0078] Wherein, α is a control parameter, which is a constant value greater than 0, is the average value of the shortest straight line length values l1, l2,..., l n of the calibration points to the edge of the weld simulation image; in the formula, the similarity value C is related to the fluctuation degree of the shortest straight line length of each calibration point to the edge of the weld simulation image and the average length of the shortest straight line of each calibration point to the edge of the weld simulation image, wherein the length value of l is in "millimeters".

[0079] Step S5 further comprises:

[0080] Setting the minimum threshold value C0 of the similarity judgment;

[0081] When C≥C0, the quality inspection result output module outputs the qualified quality inspection result;

[0082] When C<C0, the quality inspection result output module outputs the unqualified quality inspection result.

[0083] The system can automatically identify defects such as cracks and pores in the weld through advanced image recognition technology and data analysis algorithm, and calculate key parameters such as welding line energy and heat affected zone of the weld. At the same time, the system can generate a reference diagram of the weld according to the welding current, voltage, displacement speed, material and other parameters, and compare the reference diagram with the actual recognition diagram to determine whether the weld is qualified, so as to avoid the influence of the uneven technical level of welders or other external factors on the welding effect. In addition, the welding data inspection module has already inspected the welding standard from the data level for the first time, and the comparison between the reference diagram and the actual recognition diagram is a second inspection, which can more comprehensively analyze the completion of the weld, thereby greatly improving the accuracy and reliability of the quality inspection.

[0084] In actual application, since the welding parts of the electric vehicle frame can be multiple, and the welding current, voltage and other parameters of each part can be different, the present application can perform independent parameter calculation and comparative analysis for each welding, to ensure that each welding meets the quality requirements. Through the dual judgment mechanism of parameter calculation and image comparison, the quality problems caused by the lack of technical skills of welders can be more effectively avoided, and the overall quality and safety of the electric vehicle frame can be improved.

[0085] It should be noted that, in this document, the terms such as first and second are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment.

[0086] Finally, it should be noted that: the above only describes the preferred embodiments of the present application, and does not limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, and those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part of the technical features. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A data analysis-based quality inspection and analysis system for electric vehicle frame processing, comprising a welding process data acquisition module, a welding data inspection module, a weld quality inspection and analysis module, and a quality inspection result output module, characterized in that: The welding process data acquisition module is used to monitor and collect relevant data of the welding equipment during the welding process. The welding process data acquisition module is connected to the welding data inspection module via a network. The welding data inspection module is used to perform welding standard inspection on the welding data collected by the welding process data acquisition module. The weld quality inspection and analysis module is used to identify and analyze defects in the weld to determine whether the weld is qualified. The quality inspection result output module is used to export and display the quality inspection analysis results of the weld. The welding process data acquisition module, the welding data inspection module, and the quality inspection result output module are all connected to the weld quality inspection and analysis module via a network. The weld quality inspection and analysis module includes a weld image acquisition module, a crack feature recognition module, a porosity feature recognition module, and a weld completion recognition module. The weld image acquisition module is used to acquire images of the chassis welds. The crack feature recognition module, porosity feature recognition module, and weld completion recognition module are all electrically connected to the weld image acquisition module. The crack feature recognition module is used to identify crack features in the acquired weld images. The porosity feature recognition module is used to identify porosity features in the acquired weld images. The weld completion recognition module is used to identify the weld completion level in the acquired weld images. The weld completion recognition module further includes an image contour line fitting and generation submodule, a welding line energy calculation submodule, a weld fitting and generation submodule, and a comparison and output submodule. The image contour line fitting and generation submodule is used to perform contour line fitting and generation on the acquired weld image. The welding line energy calculation submodule is used to analyze and calculate the welding line energy at each point in the welding process. The welding line energy calculation submodule is electrically connected to the weld fitting and generation submodule. The weld fitting and generation submodule is used to combine the welding frame data and the welding line energy calculation results to fit and generate a standard weld simulation diagram under the above data input conditions. The comparison and output submodule is used to compare the similarity between the fitted image contour line of the recognized image and the standard weld simulation diagram generated by the data fitting and calculate and output a similarity value. The operation steps of the data analysis-based electric vehicle frame processing quality inspection and analysis system include: Step S1: Collect the current value I passing through the welding rod, the voltage value U between the electrode and the workpiece, and the speed value V of the welding rod moving along the weld during the welding process; Step S2: Input the frame data for the current welding batch, including the frame material and the load requirements for each weld. Then, based on the input frame data, output the corresponding weld standards from the system's preset welding parameter database. The welding standards include ( ), ( ), ( ); Step S3: Using a complete weld as a reference, extract the collected data of the weld and compare it with the welding standard of the weld. When the data sets of current, voltage, and speed collected for the weld all correspond to ( ), ( ), ( If the weld passes the inspection, the weld is deemed to have passed the welding data test; otherwise, the weld is deemed to be substandard, and the quality inspection process for that weld is terminated. Step S4: The welds that pass the welding data inspection are further imaged, and then the weld quality inspection and analysis module is used to perform a comprehensive quality inspection and analysis at the image level. Step S5: Finally, export the quality inspection results of all frame welds through the quality inspection result output module for users to quickly view and filter data; Step S4 further includes: Step S41: Acquire images of the weld seam after welding; Step S42: Convert the color image to a grayscale image; Step S43: Use an edge detection algorithm to identify edges in the image; Step S44: Evaluate the arrangement and repetition patterns of edges in the image; Step S45: The crack feature extraction module and the pore feature recognition module match the preset crack feature data package and pore feature data package with the recognized image features, respectively; Step S46: When no crack feature or porosity feature is matched, it is determined that there are no crack or porosity defects in the weld. Then the weld completion recognition module is activated to further identify and analyze the welding completion of the weld. In step S46, the method for identifying and analyzing the welding completion degree of the weld is as follows: Step S461: Fit the contour line of the image after the weld is welded, and then use n calibration points to divide the fitted image contour line into n equal parts. Step S462: Calculate the welding line energy value E of the reference weld at each position during the welding process using formula (1); Step S463: Then, based on the welding line energy value U at each position during the welding process and the material of the welded frame, calculate the range F of the heat-affected zone at each position during the welding process using formula (2). Finally, based on the size of the heat-affected zone range at all positions of a complete weld, generate a standard weld simulation diagram under the above data input conditions. Step S464: Maximize the overlap between the generated weld simulation image and the contour line of the weld image fitted in step S461. After overlap, draw the shortest straight line from each calibration point to the edge of the weld simulation image, and calculate the length of each shortest straight line. Then, the similarity value C between the fitted weld image and the generated weld simulation image is calculated using formula (3).

2. The electric vehicle frame processing quality inspection and analysis system based on data analysis according to claim 1, characterized in that: The welding process data acquisition module includes a welding current monitoring module, a welding voltage monitoring module, and a welding speed monitoring module. The welding current monitoring module is used to monitor the current passing through the welding electrode during the welding process. The welding voltage monitoring module is used to monitor the voltage difference between the electrode and the workpiece during welding. The welding speed monitoring module is used to monitor the speed at which the welding electrode moves along the weld seam during the welding process.

3. The electric vehicle frame processing quality inspection and analysis system based on data analysis according to claim 2, characterized in that: The welding data inspection module includes a frame data input module, a welding parameter output module, and a comparison and judgment module. The frame data input module is used to input the frame information of the current batch of welding. The welding parameter output module is electrically connected to the frame data input module. The welding output module is used to output the welding standard preset by the system according to the frame information. The comparison and judgment module is used to compare the welding process data with the welding standard and judge whether the welding process meets the requirements.

4. The electric vehicle frame processing quality inspection and analysis system based on data analysis according to claim 1, characterized in that: In step S462, the formula for calculating the welding line energy value E is: ......（1） In the formula, the current value I passing through the welding rod and the voltage value U between the electrode and the workpiece during the welding process are both directly proportional to the welding line energy value U. The speed value V of the welding rod moving along the weld during the welding process is inversely proportional to the welding line energy value U. When the current and voltage values ​​are greater during welding, or when the current and voltage values ​​are constant, the welding moving speed is slower, and the welding energy value E at the corresponding welding position is greater. In step S463, the formula for calculating the range F of the heat-affected zone is: ......（2） In the formula, k is the frame material coefficient, which is a constant value greater than 0. The range of the heat-affected zone F is proportional to the welding line energy value E. The larger the welding energy value E at the welding position, the wider the range of the heat-affected zone at that welding position. In step S464, the formula for calculating the similarity value C between the fitted weld image and the generated weld simulation image is as follows: ......（3） in, This is a control parameter, and is a constant value greater than 0. The shortest straight line length from the calibration point to the edge of the weld simulation diagram. The average value of l is given by the formula. The similarity value C is related to the fluctuation of the shortest straight line length from each calibration point to the edge of the weld simulation diagram and the average length of the shortest straight line from each calibration point to the edge of the weld simulation diagram. The length value of l is in millimeters.

5. The electric vehicle frame processing quality inspection and analysis system based on data analysis according to claim 4, characterized in that: Step S5 further includes: Set a minimum threshold for similarity assessment. ; when At that time, the quality inspection result output module outputs the quality inspection qualified result; when At that time, the quality inspection result output module outputs a quality inspection failure result.

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