Busbar welding device

Abstract

The present application provides a kind of bus welding device, it is related to welding field, including workbench, welding mechanical arm, conveying device group, each conveying device includes two sliding table, protective box, smoke extraction device;Sensor group, controller, pre-stored in controller updateable smoke density-abstraction parameter mapping relationship, and integrated with pre-trained welding state identification model, is configured as: generating corresponding operator's assembly efficiency parameter and updating;Combining sliding table position data, the earliest weldable time of each sliding table is predicted, and the operation sequence of welding mechanical arm is scheduled;Based on the image data identification welding stage, output smoke density prediction value;Dynamically adjust the operating parameters of the smoke extraction device;Dynamically correct the smoke density-abstraction parameter mapping relationship, the present application is integrated with intelligent perception and adaptive control, to improve the welding efficiency of bus and reduce production energy consumption.

Description

Technical Field

[0001] This invention relates to the field of welding technology, and in particular to a busbar welding device. Background Technology

[0002] As a key connecting component in power transmission and distribution systems, busbars are often automated by forming welding workstations with welding robotic arms, conveyor tracks, and centralized fume purification systems. This process replaces manual labor to a certain extent and automates basic functions.

[0003] However, in low-volume, multi-variety busbar welding scenarios, it is often necessary to alternately weld multiple specifications on the same device. In this process, the assembly of busbar tooling is still difficult to eliminate from manual operation. The efficiency and quality of manual assembly vary greatly, which directly affects the automated production efficiency of the welding device. It is easy for the welding robot arm to be idle due to waiting for materials, and the conveying rhythm to be mismatched with the welding rhythm, resulting in the need to improve the utilization rate of the device. At the same time, the fume purification system often operates continuously at a fixed power or simply links to the arc ignition signal, which cannot detect the actual changes in fume concentration, which can easily lead to the inability to further reduce the energy consumption of the device. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a busbar welding device that improves the welding efficiency of busbars and reduces production energy consumption by integrating intelligent sensing and adaptive control.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0006] A busbar welding apparatus, comprising a workbench, including:

[0007] A welding robotic arm, mounted on the surface of the workbench, is used for welding busbars;

[0008] The conveying device group consists of several sets of conveying devices symmetrically distributed on both sides of the welding robot arm. Each conveying device includes two slides for conveying between the welding station and the assembly station and for carrying the busbar fixed by the tooling.

[0009] A protective enclosure is installed on the surface of the workbench to isolate high-temperature splashes, strong arc light, and smoke.

[0010] A smoke extraction device is installed on the surface of the protective box to extract and filter the smoke and dust generated inside the protective box.

[0011] The sensor array is used to collect image data of the welding station, position data of the slide table, and status data of the busbar.

[0012] The controller is electrically connected to the welding robotic arm, the driver of the conveying device, the fume extraction device, and the sensor group, respectively.

[0013] The controller has a pre-stored, updatable mapping relationship between fume concentration and extraction parameters, and integrates a pre-trained welding state recognition model, configured as follows:

[0014] Based on the operator's identity information and busbar status data, the assembly efficiency parameters of the corresponding operator are generated and updated; combined with the slide table position data, the earliest welding time of each slide table is predicted in order to schedule the movement sequence of the slide tables.

[0015] Based on the image data, the welding stage is identified, and the predicted value of smoke concentration is output based on the morphological features, brightness features and smoke occlusion features of the weld molten zone; the target extraction parameters are generated according to the smoke concentration-extraction parameter mapping relationship, and the operating parameters of the smoke extraction device are dynamically adjusted so that the operating parameters track the target extraction parameters in real time.

[0016] When the welding stage is identified as the end of welding and the slide meets the advance conditions, the fume extraction device is adjusted to maintain the operating parameters for extraction; and based on the model information of the next busbar to be welded and the preset welding path, the fume concentration-extraction parameter mapping relationship is dynamically corrected.

[0017] Furthermore, the controller is configured to: perform welding stage identification and fume concentration prediction based on image data.

[0018] The image sequence of the welding station is analyzed to extract the appearance features of the weld molten zone and the smoke and dust cover features. The extracted feature vectors are input into the pre-trained welding state recognition model, and the current welding stage, the predicted value of smoke and dust concentration and the corresponding confidence level are output.

[0019] When the predicted smoke and dust concentration is higher than the preset safe concentration threshold, the extraction parameters are increased to the upper limit of the target range of the smoke and dust concentration-extraction parameter mapping relationship.

[0020] When the predicted smoke and dust concentration is lower than the preset safe concentration threshold, the extraction parameters are gradually reduced to the target extraction parameters corresponding to the smoke and dust concentration-extraction parameter mapping relationship.

[0021] When the confidence level is lower than the confidence level threshold, the extraction parameters are controlled by a set range that is higher than the predicted dust concentration value, and the corresponding data is marked for subsequent online correction.

[0022] Furthermore, the controller is configured to:

[0023] Based on the operator's identity, the baseline efficiency parameter is generated by associating the historical data of the corresponding operator at the assembly station; based on the number of times the assembly position deviation exceeds the tolerance threshold within the current task cycle, the efficiency decay factor is calculated, and the baseline efficiency parameter is combined with the efficiency decay factor to generate dynamically updated assembly efficiency parameters.

[0024] Based on the real-time position of the slide table and the preset motion performance parameters of the conveying device, the shortest transmission time for the slide table to reach the welding station is calculated. The remaining assembly time is predicted by combining the assembly efficiency parameters. The larger value between the shortest transmission time and the remaining assembly time is taken as the earliest weldable time, and the earliest weldable time is used for the scheduling and control of the slide table.

[0025] Furthermore, the controller is configured to perform slide position scheduling as follows:

[0026] The welding advance window is determined based on the remaining time of the current operation of the welding robot arm and the time required for the target slide in the welding standby area to advance to the welding station.

[0027] The assembly advance window is determined based on the remaining assembly time determined by the operator's assembly efficiency parameters and busbar status data, and the estimated time required for the slide table located in the assembly standby area to advance to the assembly station.

[0028] When any carry window meets the preset threshold and the corresponding target station can accept it, the controller controls the corresponding slide to move to the target station within the corresponding carry window;

[0029] When both types of carry windows are satisfied and the corresponding target workstations can accept them, the controller drives the two slides to carry to the corresponding target workstations in parallel within the same control cycle.

[0030] When only one side is allowed to move within a control cycle, the controller selects the moving side based on the criterion of shortening the idle time of the welding robot arm, while the other side remains on standby until the next control cycle when the move window is re-evaluated.

[0031] Furthermore, the conveying device includes:

[0032] A mounting plate is fixedly connected to the surface of the workbench. A linear conveyor is symmetrically fixedly connected to the surface of the mounting plate. A C-shaped sliding arm is fixedly connected to the surface of the linear conveyor. A first lead screw is provided inside the C-shaped sliding arm. The first lead screw is rotatably connected to the C-shaped sliding arm through two first bearings. A connecting block is threadedly connected to the surface of the first lead screw. A plurality of constraint rods are provided on the surface of the connecting block. The constraint rods are slidably connected to the connecting block through a first circular groove on the surface of the connecting block. The constraint rods are fixedly connected to the C-shaped sliding arm. The constraint rods are arranged in a circular array around the central axis of the first lead screw. A slide is fixedly connected to the upper surface of the connecting block. A first motor is mounted on the surface of the C-shaped sliding arm. The output shaft of the first motor is fixedly connected to the first lead screw through a first through groove on the surface of the C-shaped sliding arm.

[0033] Furthermore, the controller is configured to:

[0034] When the welding stage is determined to be over based on the welding state recognition model and the target slide meets the advance condition, the fume extraction device is switched from the target extraction parameter during the welding period to the operating parameter for maintaining extraction. The operating parameter for maintaining extraction is set so that the collection effect in the protective box is not lower than the collection effect threshold in order to suppress the settling of smoke and dust.

[0035] Based on the next busbar model information to be welded and its associated preset welding path, the dust demand for the next welding cycle is predicted by combining the material and weld set characteristics of the corresponding busbar model. Accordingly, the dust concentration-extraction parameter mapping relationship is corrected online to generate target extraction parameters for the next welding cycle.

[0036] Furthermore, when the controller performs slide scheduling, it is also configured to: continuously update the earliest weldable time of each slide using a rolling time window, and set a freeze time and a minimum hold time to avoid frequent rescheduling; when both sides simultaneously meet the carry conditions and the target workstation can accept the slide, a stability weight is introduced to determine the priority based on the earliest weldable time sorting, and the stability weight is determined based on the fluctuation of the assembly efficiency parameter of the corresponding operator and the historical accuracy.

[0037] When the deviation between the predicted earliest welding time and the actual completion time exceeds the threshold within multiple consecutive control cycles, the assembly efficiency parameters of the corresponding operator are automatically converged and corrected, and a review prompt is triggered to ensure cycle stability while reducing idle waiting losses and improving scheduling robustness.

[0038] Furthermore, the controller is also configured to perform global energy efficiency optimization control:

[0039] Based on the historical correction records of the dust concentration-extraction parameter mapping relationship, the update records of the assembly efficiency parameters, and the actual working cycle of the welding robot, an energy efficiency model is constructed with the goal of minimizing energy consumption per unit of production capacity.

[0040] Within a preset statistical period, based on the welding stage duration distribution, extraction parameter energy consumption distribution, and slide table idle time ratio recorded within the corresponding period, the baseline parameters of the dust concentration-extraction parameter mapping relationship, the attenuation factor weight of the assembly efficiency parameter, and the preset threshold of the slide table carry window are optimized in reverse through the energy efficiency model.

[0041] The optimized parameter set is applied to the next production batch, and the weight coefficients of the energy efficiency model are learned and adjusted online based on the deviation between real-time operating data and the optimization target.

[0042] The above-described solution of the present invention has at least the following beneficial effects:

[0043] The above-mentioned solution of the present invention achieves seamless connection between welding and assembly processes by dynamically predicting the earliest welding time of each workstation and intelligently scheduling the operation sequence of the welding robotic arm, thereby reducing equipment idle time and improving production efficiency.

[0044] By using vision-based welding status recognition and fume concentration prediction, the system drives precise, on-demand fume extraction, ensuring effective fume capture while reducing energy consumption.

[0045] By implementing online optimization scheduling strategies based on the assembly efficiency of different operators and adaptively correcting control parameters according to different busbar models, production stability, energy efficiency, and production quality can be guaranteed. Attached Figure Description

[0046] Figure 1 This is a schematic diagram of the overall structure provided by the present invention.

[0047] Figure 2 This is a flowchart of the slide table scheduling and control process in this invention.

[0048] Figure 3 This is a schematic diagram of the slide table in this invention.

[0049] Figure 4 This is a schematic diagram of the C-shaped sliding arm in this invention.

[0050] Figure 5 This is a schematic diagram of the connecting block in this invention.

[0051] In the diagram: 101, workbench; 102, protective box; 103, smoke extraction device; 104, debugging table; 105, slide table;

[0052] 301. Mounting plate; 302. Linear conveyor; 303. C-shaped sliding arm; 304. First lead screw; 305. Constraint rod; 306. Connecting block; 307. First motor. Detailed Implementation

[0053] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0054] like Figures 1 to 5 As shown, an embodiment of the present invention provides a busbar welding device, including a workbench 101, comprising:

[0055] A welding robotic arm is mounted on the surface of workbench 101 for welding busbars;

[0056] The conveying device group consists of several sets of conveying devices symmetrically distributed on both sides of the welding robot arm. Each conveying device includes two slides 105, which are used for conveying between the welding station and the assembly station and to carry the busbar fixed by the tooling.

[0057] The protective box 102 is installed on the surface of the workbench 101 to isolate high-temperature splashes, strong arc light and smoke.

[0058] The smoke extraction device 103 is installed on the surface of the protective box 102 and is used to extract and filter the smoke and dust generated inside the protective box 102.

[0059] The sensor group is used to collect image data of the welding station, position data of the slide table 105, and status data of the busbar;

[0060] The controller is electrically connected to the welding robotic arm, the driver of the conveying device, the fume extraction device 103, and the sensor group, respectively.

[0061] The controller has a pre-stored, updatable mapping relationship between dust concentration and extraction parameters, and integrates a pre-trained welding condition recognition model, configured as follows:

[0062] Based on the operator's identity information and busbar status data, the assembly efficiency parameters of the corresponding operator are generated and updated; combined with the position data of slide table 105, the earliest welding time of each slide table 105 is predicted in order to schedule the movement sequence of the slide tables.

[0063] Based on image data, the welding stage is identified, and the predicted value of smoke and dust concentration is output based on the morphological characteristics, brightness characteristics and smoke and dust cover characteristics of the weld molten zone. The target extraction and discharge parameters are generated according to the mapping relationship between smoke and dust concentration and extraction parameters, and the operating parameters of the smoke extraction and discharge device 103 are dynamically adjusted so that the operating parameters track the target extraction and discharge parameters in real time.

[0064] When the welding stage is identified as the end of welding and the slide 105 meets the advance conditions, the fume extraction device 103 is adjusted to maintain the operating parameters for fume extraction; and based on the model information of the next busbar to be welded and the preset welding path, the fume concentration-extraction parameter mapping relationship is dynamically corrected.

[0065] Mounting plate 301 is fixedly connected to the surface of workbench 101. A linear conveyor 302 is symmetrically fixedly connected to the surface of mounting plate 301. A C-shaped sliding arm 303 is fixedly connected to the surface of the sliding block of linear conveyor 302. A first lead screw 304 is provided inside the C-shaped sliding arm 303. The first lead screw 304 is rotatably connected to the C-shaped sliding arm 303 through two first bearings. A connecting block 306 is threadedly connected to the surface of the first lead screw 304. A plurality of constraint rods 305 are provided on the surface of the connecting block 306. The constraint rods 305 are slidably connected to the connecting block 306 through a first circular groove on the surface of the connecting block 306. The constraint rods 305 are fixedly connected to the C-shaped sliding arm 303. The constraint rods 305 are arranged in a circular array around the central axis of the first lead screw 304. Slide table 105 is fixedly connected to the upper surface of connecting block 306. A first motor 307 is mounted on the surface of C-shaped sliding arm 303. The output shaft of the first motor 307 is fixedly connected to the first lead screw 304 through a first through groove on the surface of C-shaped sliding arm 303.

[0066] In this embodiment of the invention, the workbench 101 is integrally welded from Q235 steel plate and is used to fix the welding robotic arm, conveying device, and fume extraction device 103. The welding robotic arm is a six-axis articulated robotic arm, which is fixed to the central area of ​​the surface of the workbench 101 by a special mounting base. A molten metal arc welding torch is installed at the end of the welding robotic arm. The welding torch is connected to the end of the robotic arm by a quick-change clamp, which is known in the prior art and will not be described in detail here. The fume extraction device 103 is installed on the surface of the protective box 102 and includes pipes, filters, and fans. The working principle and usage process of the fume extraction device 103 are known in the prior art and will not be described in detail here. The suction port of the fume extraction device 103 extends into the interior of the protective box 102 through pipes and is located near the welding station. The speed of the fan is adjusted by the controller through a frequency converter.

[0067] The sensor group includes at least: an industrial camera, mounted above the welding station, with a lens equipped with a narrow-band filter and a controllable light source to suppress arc saturation and stabilize the imaging of the molten zone; and encoders, two of which are installed on the same conveying device, respectively mounted on the drive motor of the linear conveyor 302 and the output shaft of the first motor 307, to obtain the position and positioning status of the slide 105.

[0068] The controller is an industrial PLC, which is equipped with a pre-trained welding status recognition model and stores an updatable dust concentration-extraction parameter mapping table, as well as process and path data associated with the busbar model.

[0069] The two slides 105 on the conveying device are driven by the corresponding linear conveyor 302. Specifically, the linear conveyor 302 drives the corresponding sliding block to move the C-shaped sliding arm 303, and the movement of the C-shaped sliding arm 303 causes the slide 105 on the top of the connecting block 306 to move horizontally in sync.

[0070] like Figure 1 As shown, two conveying devices are set on each side of the welding robot arm, and an debugging table 104 is also set on one side of the welding robot arm. The debugging table 104 is fixed on the surface of the worktable 101; it is used for product prototyping and debugging of the welding robot arm; two slides 105 on the same conveying device are respectively set on two linear conveying tables 302. The slide 105 located inside the protective box 102 is the welding station, and the slide 105 located outside the protective box 102 is the assembly station. The upper surfaces of the slides 105 corresponding to the two stations are coplanar.

[0071] When the slide table 105 located at the welding station is driven by the first motor 307 on the surface of the corresponding C-shaped slide arm 303 to rotate the first lead screw 304, the connecting block 306 drives the slide table 105 to descend along the surface of the constraint rod 305 until the highest point of the busbar on the surface of the slide table 105 is below the bottom surface of the slide table 105 in the assembly station, the position of the slide table 105 at this time is the welding standby area.

[0072] When the slide table 105 located at the assembly station is driven by the first motor 307 on the surface of the corresponding C-shaped slide arm 303 to rotate the first lead screw 304, the connecting block 306 drives the slide table 105 to descend along the surface of the constraint rod 305 until the highest point of the busbar on the surface of the slide table 105 is below the bottom surface of the slide table 105 in the welding station, the position of the slide table 105 at this time is the assembly standby area.

[0073] When a slide table 105 exists in the assembly standby area, after the busbar of the assembly station is assembled, the corresponding slide table 105 directly moves to the welding station. When a slide table 105 exists in the welding standby area, the slide table 105 in the welding station directly moves to the assembly station. When neither standby area has a slide table 105, the slide table 105 located in the welding station first moves to the welding standby area, then moves to the assembly standby area, and the slide table 105 in the assembly station directly moves to the welding station.

[0074] When two slides 105 on the same conveying device move toward each other, one of the slides 105 needs to be lowered so that its upper surface is coplanar with the upper surface of the slide 105 in the standby area. During the movement toward each other, the lower slide 105 passes through the recess of the C-shaped slide arm 303 corresponding to the upper slide 105 to avoid motion interference.

[0075] The operator completes identity verification through an identification device (such as reading the operator's ID card through a card reader). The controller retrieves the operator's historical assembly records and generates baseline assembly efficiency parameters by combining them with the current busbar status data (model, number of pieces). During assembly, if the busbar status data reflects an increase in assembly deviations (such as the number of times the positioning offset exceeds the tolerance), the controller calculates an efficiency correction factor and updates the corresponding operator's assembly efficiency parameters online. Based on the real-time position of the slide table 105 and the preset motion performance parameters of the conveying device (such as the maximum speed of the slide table 105 being 0.8 m / s and the maximum acceleration being 3 m / s²), the controller calculates the estimated transmission time for the slide table 105 to reach the welding station from its current state and merges it with the remaining assembly time predicted based on the assembly efficiency parameters. The larger of the two values ​​is taken as the earliest weldable time for the slide table 105. The slide table 105 is sorted and scheduled according to the earliest weldable time, allowing the non-welding side slide table 105 to pre-advance to the predetermined standby position when safety conditions are met.

[0076] The controller preprocesses and extracts features from the image sequences captured by the industrial camera. A pre-trained welding state recognition model outputs the welding stage, fume concentration index, and confidence level. Based on this, the controller looks up the fume concentration-extraction parameter mapping table to generate target extraction parameters, which are then tracked in real-time via a frequency converter. When the fume concentration exceeds the safe concentration threshold, it is increased to the upper limit of the target range; when it falls below the safe concentration threshold, it gradually decreases back to the target value in the mapping table; when the confidence level is below the confidence threshold, a conservative extraction range higher than the set concentration is adopted, and the relevant data is marked for subsequent correction of the mapping relationship and model.

[0077] When the welding stage is identified as finished and the carry-over conditions are met (e.g., the target station is idle), the controller switches the extraction parameters from the current target parameters to the maintenance extraction operation parameters. The maintenance extraction operation parameters ensure that the collection effect is not lower than the collection effect threshold to suppress sedimentation. At the same time, based on the model information of the next busbar to be welded and the preset welding path, combined with its material and weld seam set characteristics, the dust demand for the next cycle is predicted, and the dust concentration-extraction parameter mapping relationship is corrected online (e.g., increasing the target fan speed in the response range). The corrected mapping relationship is used for the next welding cycle.

[0078] The safety concentration threshold and the capture effect threshold are determined according to the enterprise's production requirements, or obtained through on-site calibration based on the principle that the capture efficiency is not lower than the preset target; the safety concentration threshold and the capture effect threshold are distinguished and maintained according to the busbar model and welding stage.

[0079] In this embodiment, the pre-trained welding recognition model specifically includes:

[0080] The industrial camera continuously outputs a sequence of welding station images to the controller, which runs a pre-trained welding state recognition model and outputs three results:

[0081] Welding stages: one of four categories: arc initiation, molten pool stabilization, arc termination, and abnormality;

[0082] Predicted smoke and dust concentration: expressed in mg / m³;

[0083] Confidence level: 0-1

[0084] The welding identification model uses an improved MobileNetV2 backbone with dual-branch outputs, specifically including:

[0085] Input layer: Image size 224×224×3 (three-channel color image input);

[0086] Backbone: 12 inverted residual convolutional blocks (3×3 convolution, stride 1, dilation 1, activation function ReLU 6).

[0087] Feature fusion: Perform ROI pooling on the ROI of the weld molten zone and splice it with the global feature channels of the main trunk;

[0088] Classification branch: Fully connected layer with 128 nodes + Softmax, outputting the probability of four welding stages;

[0089] Regression branch: 64 nodes of fully connected layer + linear layer, outputting predicted dust concentration values ​​in the range of 0-20 mg / m³;

[0090] Confidence level calculation: The confidence level threshold of 0.85 is obtained by weighting the Softmax maximum probability with the regression residual statistic.

[0091] The training process for the welding recognition model includes:

[0092] Take 10,000 sets of images, including 2,000 for arc initiation, 4,000 for molten pool stability, 2,000 for arc termination, and 2,000 for abnormalities (abnormalities are further subdivided into 500 for arc breakage, 1,000 for weld deviation with an offset of 2-5mm, and 500 for excessive spatter).

[0093] Operating conditions covered: Busbar material: copper, aluminum; Typical cross-sectional dimensions: 40×4 to 100×10mm; Welding current: 150-300A; Smoke and dust masking coverage: 10%-60% of image area (three levels).

[0094] Stage labeling criteria:

[0095] Arc initiation: The molten zone has not yet formed stably, and the arc brightness fluctuates by more than 30%;

[0096] Arc termination: The area and brightness of the molten zone continue to decrease and tend to disappear;

[0097] Abnormalities: Broken welds, continuous weld deviation (2-5mm), excessive spatter.

[0098] Concentration label acquisition: Image-concentration pairs are established by aligning the smoke and dust monitor (sampling frequency greater than or equal to 1Hz, measurement error less than or equal to ±0.5mg / m³) with the image timestamp.

[0099] Data style: 70% training set, 15% validation set, 15% test set: Data augmentation uses random pruning, brightness scaling of 0.8-1.2, and Gaussian noise, with each augmentation ratio of 20%.

[0100] Loss function: Joint loss = cross-entropy + MSE (This example uses a cross-entropy weight of 0.6 and an MSE weight of 0.4. The weights can be determined by testing different combinations on the validation set and selecting the ratio that best combines classification and regression performance. This is an example value for this example).

[0101] Optimizer and learning rate: Adam, initial 1×10 -3 The learning rate is halved if the validation set loss does not decrease for 5 consecutive epochs; weight decay is 1×10. -4 ;

[0102] Batch Size and Number of Episodes: Batch Size 32, maximum 100 epochs;

[0103] Early stopping condition: Training is stopped when the training set loss is less than 0.05 and the validation set loss is less than 0.08.

[0104] Stage classification accuracy: When the classification accuracy of the welding recognition model on the independent test set reaches a preset performance threshold, the recognition model is deemed to have met the performance standard. In this embodiment, the performance threshold is set such that the classification accuracy of each welding stage is not lower than the preset standard, and the average classification accuracy is not lower than the overall threshold, thereby ensuring the reliability of welding stage recognition.

[0105] When performing welding stage identification and fume concentration prediction based on image data, the controller is configured to:

[0106] The image sequence of the welding station is analyzed to extract the appearance features of the weld molten zone and the smoke and dust cover features. The extracted feature vectors are input into the pre-trained welding state recognition model, and the current welding stage, the predicted value of smoke and dust concentration and the corresponding confidence level are output.

[0107] When the predicted smoke and dust concentration is higher than the preset safe concentration threshold, the extraction parameters are increased to the upper limit of the target range of the smoke and dust concentration-extraction parameter mapping relationship.

[0108] When the predicted dust concentration is lower than the preset safe concentration threshold, the extraction parameters are gradually reduced to the target extraction parameters corresponding to the dust concentration-extraction parameter mapping relationship.

[0109] When the confidence level is lower than the confidence level threshold, the extraction parameters are controlled by a set range that is higher than the predicted dust concentration value, and the corresponding data is marked for subsequent online correction.

[0110] In this embodiment of the invention, the controller continuously receives the image sequence output by the camera and processes each frame: removing edge occlusion areas, extracting an effective color image block centered on the weld seam with a size of 224×224 pixels, and performing brightness correction and noise reduction to stabilize the grayscale across frames. Subsequently, feature extraction is performed: for the preprocessed grayscale image, edge detection is performed using the Canny operator to obtain the outline of the molten area, and its pixel length, width, and area are calculated as morphological features; the mean and variance of the grayscale values ​​of pixels within the molten area are statistically analyzed as brightness features; and a threshold segmentation method is used to separate the smoke and dust area from the background, calculating the proportion of its pixel area to the total area of ​​the ROI as occlusion features.

[0111] The controller inputs the aforementioned engineering features along with a 224×224×3 ROI color image into a pre-stored welding state recognition model. A feature fusion layer is located at the end of the feature extraction network of the welding recognition model. After the feature fusion layer completes the splicing and fusion, it outputs the current welding stage (arc initiation, melting, solidification, arc termination), predicted fume concentration, and their corresponding confidence levels via classification and regression branches, respectively. The single inference latency is no greater than 120ms, and the inference frame rate is no less than 10fps to meet the timing requirements for control implementation. The controller queries the fume concentration-extraction parameter mapping relationship based on the output predicted values ​​and then issues control commands to the fume extraction device 103.

[0112] The safe concentration threshold is set differently according to the busbar model and welding stage, and stored in the threshold table of the controller; the confidence threshold is set to 0.85. The control logic is as follows:

[0113] When the dust concentration is higher than the safety concentration threshold for the corresponding stage, the controller will increase the extraction parameters (such as fan speed) to the upper limit of the target range between dust concentration and extraction parameters; when the predicted value is lower than the safety concentration threshold, the extraction parameters will be gradually reduced back to the target value corresponding to the dust concentration-extraction parameter mapping table.

[0114] When the confidence level is lower than the confidence threshold of 0.85, a conservative control is adopted by using extraction parameters higher than the set range corresponding to the predicted concentration. The image, predicted value, actual extraction parameters and other working condition information corresponding to this inference are marked as low confidence data and stored in the historical database for subsequent offline correction of the dust concentration-extraction parameter mapping relationship and welding recognition model.

[0115] To ensure the effectiveness of the mapping relationship between dust concentration and extraction parameters, as well as the confidence threshold and safe concentration threshold, a dust detector is used to compare typical operating conditions during the commissioning phase or periodic sampling inspections. Based on the comparison results, the mapping relationship, confidence threshold, and safe concentration threshold are corrected online and stored in a versioned manner.

[0116] When the controller performs the scheduling of slide 105, it is configured as follows:

[0117] Based on the operator's identity, the baseline efficiency parameter is generated by associating the historical data of the corresponding operator at the assembly station; based on the number of times the assembly position deviation exceeds the tolerance threshold within the current task cycle, the efficiency decay factor is calculated, and the baseline efficiency parameter is combined with the efficiency decay factor to generate dynamically updated assembly efficiency parameters.

[0118] Based on the real-time position of the slide table 105 and the preset motion performance parameters of the conveying device, the shortest transmission time for the slide table 105 to reach the welding station is calculated. The remaining assembly time is predicted by combining the assembly efficiency parameters. The larger value between the shortest transmission time and the remaining assembly time is taken as the earliest welding time, and the earliest welding time is used for the scheduling and control of the slide table 105.

[0119] In this embodiment of the invention, the assembly station is equipped with an operator identification device (such as an RFID reader). The operator completes identification authentication at the start of the operation. The controller retrieves the operator's recent historical data (such as records of the last 100 working days) based on the identification information, calculates the average single-piece assembly time of the same model of busbar, and uses it as the operator's benchmark efficiency parameter. If the operator has no historical data, a preset general benchmark value is used.

[0120] The assembly station uses sensors (such as a vision inspection system) to monitor assembly position deviations at fixed intervals. When the deviation exceeds a preset tolerance range, it is recorded as an out-of-tolerance event.

[0121] The controller applies a decay mechanism to the baseline efficiency parameter based on the frequency of out-of-tolerance events, generating an efficiency decay factor. A lower limit constraint is set in the factor calculation to prevent excessive decay of the efficiency value. The final dynamic assembly efficiency parameter is determined by combining the baseline efficiency parameter and the efficiency decay factor, and is continuously updated during the assembly process.

[0122] The controller determines the earliest weldable moment for the slide table 105 by combining the transmission time and the remaining assembly time. The transmission time is calculated based on the distance between the current position of the slide table 105 and the welding station, combined with the pre-stored maximum speed and acceleration / deceleration performance parameters of the conveyor device to obtain the shortest feasible transmission time. The remaining assembly time is estimated based on dynamic assembly efficiency parameters and the current assembly progress. The controller takes the larger of the two values ​​as the earliest weldable moment for the slide table 105, thereby ensuring that both the assembly operation and the transmission process are completed when the slide table 105 arrives at the welding station.

[0123] After the earliest weldable time is determined, the controller executes the scheduling decision for slide 105:

[0124] The controller compares the earliest welding times of the two slides 105 and selects the slide 105 with the earlier time as the priority scheduling object. Before scheduling, the controller verifies the acceptance conditions of the welding station, including the station's idle status and the workpiece in-place detection signal. If both are met, the controller drives the priority slide 105 to move to the welding station, while the non-priority slide 105 remains on standby until the next round of scheduling.

[0125] The controller also includes an anomaly handling mechanism. When a malfunction is detected in the slide 105, such as a sensor alarm or transmission timeout, the controller automatically removes the slide 105 from the current scheduling queue and triggers an alarm. At the same time, the updated scheduling sequence is displayed on the equipment's human-machine interface, supporting manual intervention by the operator.

[0126] The controller is configured to perform position scheduling of slide 105 as follows:

[0127] The welding advance window is determined based on the remaining time of the current operation of the welding robot arm and the time required for the target slide 105 located in the welding standby area to advance to the welding station.

[0128] The assembly advance window is determined based on the remaining assembly time determined by the operator's assembly efficiency parameters and busbar status data, and the estimated time required for the slide 105 located in the assembly standby area to advance to the assembly station.

[0129] When any carry window meets the preset threshold and the corresponding target station can accept it, the controller controls the corresponding slide 105 to carry to the target station within the corresponding carry window;

[0130] When both types of carry windows are satisfied and the corresponding target workstations can accept them, the controller drives the two slides 105 to carry to the corresponding target workstations in parallel within the same control cycle.

[0131] When only one side is allowed to move within a control cycle, the controller selects the moving side based on the criterion of shortening the idle time of the welding robot arm, while the other side remains on standby until the next control cycle when the move window is re-evaluated.

[0132] In this embodiment of the invention, the welding progress window includes:

[0133] The controller determines the time based on the remaining time of the welding robot arm's current operation and the shortest time required for the target slide 105, located in the welding standby area, to move to the welding station. The remaining time of the robot arm is calculated from the operation progress fed back in real time by its controller; the movement time of the slide 105 is calculated from its current position, target position, and preset motion performance parameters of the conveying device.

[0134] When the difference between the remaining time of the welding robot arm and the movement time of the slide table 105 is greater than or equal to the preset safety margin threshold, and the target welding station is in an acceptable state (station idle), the welding advance window is deemed to be satisfied.

[0135] The assembly carry window includes:

[0136] The controller estimates the remaining assembly time based on the operator's dynamic assembly efficiency parameters and the remaining assembly workload, and determines the minimum time required for the slide 105, located in the assembly standby area, to move to the assembly station. When the result of the remaining assembly time minus the slide 105 movement time is greater than or equal to the safety margin threshold, and the target assembly station is in an acceptable state, the assembly progress window is deemed to be satisfied.

[0137] The safety margin threshold is a configurable parameter (e.g., 0.3s) used to compensate for system response delay and positioning error.

[0138] The controller evaluates the two types of carry windows and station status in each control cycle (e.g., 50ms) and executes the following strategy:

[0139] Single-side window satisfaction: When only one type of carry window is satisfied and the corresponding target station can accept it, the controller immediately issues a carry command to the slide 105 on that side to the target station.

[0140] Simultaneous fulfillment of dual-window requirements: When both welding and assembly advance windows are simultaneously satisfied and both workstations on both sides can accommodate the movement, the controller has the capability to drive two slide tables 105 in parallel. To ensure safety, it is necessary to verify that there is no spatial interference risk between the two movement paths before parallel driving. If there is no interference, advance commands are issued to both slide tables 105 simultaneously; if there is an interference risk, the movement is switched to sequential driving according to time order.

[0141] When only one side of the slide 105 can be driven in the same cycle due to resource (such as power) limitations or safety policy constraints, the controller will select according to preset decision criteria.

[0142] The decision-making criteria include: predicting and comparing the possible waiting time of the welding robot arm after both slides 105 are ready, selecting the side with the longer waiting time to rest first to minimize the overall idle time of the welding robot arm, while the other slide 105 remains in standby and is re-evaluated in the next control cycle.

[0143] When a fault is detected in slide 105 (such as a drive motor alarm or encoder signal timeout failure), the controller automatically removes slide 105 from the scheduling queue and triggers an audible and visual alarm on the human-machine interface. The real-time updated scheduling queue, carry window status, and fault codes are clearly displayed on the human-machine interface, providing operators with an entry point for intervention and recovery. After troubleshooting, slide 105 must be confirmed through the interface before it can be reinstated into the automatic scheduling sequence.

[0144] When the welding stage is determined to be over based on the welding state recognition model and the target slide 105 meets the advance condition, the smoke extraction device 103 is switched from the target extraction parameter during the welding period to the operating parameter for maintaining extraction. The operating parameter for maintaining extraction is set so that the collection effect in the protective box 102 is not lower than the collection effect threshold in order to suppress the settling of smoke and dust.

[0145] Based on the next busbar model information to be welded and its associated preset welding path, the dust demand for the next welding cycle is predicted by combining the material and weld set characteristics of the corresponding busbar model. Accordingly, the dust concentration-extraction parameter mapping relationship is corrected online to generate target extraction parameters for the next welding cycle.

[0146] In this embodiment of the invention, the controller continuously receives the results output by the welding status recognition model. The switching of extraction parameters is triggered when both of the following conditions are simultaneously met:

[0147] The welding identification model determines the end of the welding stage (end characteristics include the disappearance of the molten pool area, the extinction of the electric arc, and the continuous lower-than-set thermal radiation brightness).

[0148] The controller confirms that the slide 105 meets the conditions for leaving the welding station (i.e., the target station / target standby area is free).

[0149] When both conditions are met, the controller sends a control signal to the smoke extraction device 103, driving the fan speed to smoothly transition from the target extraction parameters during the welding period to the preset maintenance extraction operation parameters.

[0150] The operating parameters are maintained through on-site calibration. The principle is that under this air volume, there is no obvious smoke or dust escaping from the opening of the protective box 102 (top opening, inlet and outlet of the sliding table 105), and the smoke and dust collection efficiency inside the protective box 102 is not lower than the set collection effect threshold, so as to suppress the settling of smoke and dust. The slope descent mode is switched to avoid sudden changes in fan pressure.

[0151] Upon triggering the switch, the controller immediately starts to retrieve the model information of the next busbar to be welded, and retrieves typical welding parameters for that model from the process database, including material properties, total weld length, welding current, and speed. Combining historical statistical data, the controller calculates a dust generation index to characterize the expected dust level under this operating condition.

[0152] The controller compares this predicted value with a baseline value for the existing dust concentration-fan speed mapping relationship:

[0153] If the predicted index is significantly lower than the benchmark value, the target fan speed for the corresponding concentration range will be reduced proportionally.

[0154] If the predicted index is higher than the benchmark value, the target fan speed will be increased proportionally to ensure that the capture effect is not lower than the capture effect threshold.

[0155] The correction range has an upper limit, such as no more than ±15% per adjustment, to avoid overcorrection. The corrected mapping version is saved in association with the busbar model information.

[0156] During subsequent welding, the controller records the correspondence between the fan operating parameters and the dust concentration predicted by the welding identification model. When a long-term deviation from the calibration value is detected, the controller can automatically initiate recalibration during equipment idle periods to gradually approach the optimal balance between energy efficiency and dust collection.

[0157] When the controller performs the scheduling of slide 105, it is also configured to: continuously update the earliest weldable time of each slide 105 using a rolling time window, and set a freeze time and a minimum hold time to avoid frequent rescheduling; when both sides meet the carry conditions and the target station can accept it, a stability weight is introduced to determine the priority based on the sorting by the earliest weldable time. The stability weight is determined based on the fluctuation of the assembly efficiency parameter of the corresponding operator and the historical accuracy.

[0158] When the deviation between the predicted earliest welding time and the actual completion time exceeds the threshold within multiple consecutive control cycles, the assembly efficiency parameters of the corresponding operator are automatically converged and corrected, and a review prompt is triggered to ensure cycle stability while reducing idle waiting losses and improving scheduling robustness.

[0159] In this embodiment of the invention, the controller sets a configurable rolling time window (e.g., 30 seconds). The data within the time window is used to calculate and update the earliest weldable time for each slide 105. To suppress frequent changes in scheduling instructions caused by minor data fluctuations, the controller is configured as follows:

[0160] For the earliest weldable moment of a single slide 105, if the change in its calculation result does not exceed the set threshold or reach the minimum update interval, the original value is maintained to avoid frequent changes.

[0161] Once the controller issues a scheduling instruction based on the sorting results, the scheduling instruction remains valid for a short freeze period and will not change due to minor fluctuations in the prediction results, unless a major event occurs (such as a fault alarm or emergency stop).

[0162] When both slides 105 simultaneously meet the carry-in conditions and the target workstations can accept the workstations, the controller introduces stability weights for priority arbitration based on comparing the earliest weldable time.

[0163] By statistically analyzing the fluctuation range of the operator's assembly efficiency parameters in the most recent few times and performing normalization, the stability of the corresponding operator's assembly efficiency can be characterized.

[0164] An accuracy index is obtained by statistically analyzing the proportion of operators whose predicted earliest welding time and actual completion time in recent assembly tasks fall within the allowable error range.

[0165] The stability weight is obtained by weighting the inverse ratio of efficiency fluctuation and historical prediction accuracy. The weighting coefficient is a configurable parameter (e.g., if the stability weight is more biased towards timeliness, the weight of the accuracy index can be higher than that of the stability index). When the earliest weldable times of the two slides 105 are small (e.g., 2s), the controller prioritizes the side with the higher stability weight to perform the carry.

[0166] The controller continuously monitors the predictive performance of each operator. When the deviation between the predicted earliest weldable time and the actual completion time exceeds a set threshold for multiple consecutive heavy tasks, it triggers automatic correction of the operator's assembly efficiency parameters.

[0167] The corrected assembly efficiency parameters are obtained by weighted averaging of historical parameters and parameters calculated based on actual completion status. The weighting ratio is set by a convergence coefficient to balance the correction speed and stability. After the correction is completed, the controller will trigger a prompt on the operation interface to notify the management personnel to review the status of the relevant workstations or equipment, but the controller will continue to operate directly using the corrected assembly efficiency parameters.

[0168] The controller is also configured to perform global energy efficiency optimization control:

[0169] Based on the historical correction records of the dust concentration-extraction parameter mapping relationship, the update records of the assembly efficiency parameters, and the actual working cycle of the welding robot, an energy efficiency model is constructed with the goal of minimizing energy consumption per unit of production capacity.

[0170] Within a preset statistical period, based on the welding stage duration distribution, extraction parameter energy consumption distribution, and slide table 105 idle time ratio recorded within the corresponding period, the baseline parameters of the dust concentration-extraction parameter mapping relationship, the attenuation factor weight of the assembly efficiency parameters, and the preset threshold of the slide table 105 carry window are optimized in reverse through the energy efficiency model.

[0171] The optimized parameter set is applied to the next production batch, and the weight coefficients of the energy efficiency model are learned and adjusted online based on the deviation between real-time operating data and the optimization target.

[0172] In this embodiment of the invention, the controller is configured to perform global energy efficiency optimization control. Based on historical and real-time data, the controller self-tunes and learns key control parameters with the goal of minimizing energy consumption per unit of production capacity. In this embodiment, energy consumption per unit of production capacity is defined as the total electrical energy consumed in producing one meter of weld or one product.

[0173] The energy efficiency model built by the controller is used to quantify the impact of different control parameters on total energy consumption, using multiple evaluation indicators as inputs:

[0174] The exhaust efficiency index is derived from the baseline parameters of the mapping relationship between smoke and dust concentration and exhaust parameters. It can be characterized by the weighted average of the baseline air volume for each concentration range.

[0175] Assembly efficiency index: Calculated from the assembly efficiency parameters of each operator. It is usually highly efficient. The index value at the end of the month can be the average level of the assembly efficiency parameters of all operators or its derivative.

[0176] Idle time loss index: It is calculated based on the proportion of idle time of slide 105. The larger the proportion, the higher the corresponding index value.

[0177] The energy efficiency model also includes a set of weighting coefficients to represent the relative impact of each indicator on the energy efficiency target. The initial values ​​can be set based on experience and then corrected through learning.

[0178] The data required to build the energy efficiency model is collected uniformly by the controller:

[0179] Retrieve different versions of the dust concentration-extraction parameter mapping table and their applicable time periods from the database; obtain extraction energy consumption data through the fan speed, power or energy consumption interface fed back by the frequency converter; retrieve assembly efficiency parameter update records from the database; obtain data through the communication interface with the welding robot arm controller and transmission device, and calculate the welding robot arm operation cycle and slide 105 idle time in the controller.

[0180] At the end of each preset statistical period (e.g., a shift), the controller performs parameter reverse optimization, specifically including:

[0181] The actual unit energy consumption for this period is statistically analyzed, and the values ​​of the extraction energy efficiency index, assembly efficiency index, and air loss index are calculated.

[0182] The controller uses data-driven methods (such as linear regression algorithms) to correct the weight coefficients of the energy efficiency model and calculates new control parameters accordingly, including the baseline parameters of the mapping relationship between dust concentration and extraction parameters, the weight of the attenuation factor of assembly efficiency, and the preset threshold of the 105-round window of the slide.

[0183] The optimized parameters must meet the following requirements: the dust collection effect should still be no less than the set threshold after the parameters are adjusted; the adjustment of the carry window threshold should not violate the safety interlock logic and path anti-conflict requirements.

[0184] A new version is created by combining the verified parameters and used in the next production batch.

[0185] During the operation of a new production batch, the controller continuously monitors the actual energy consumption per unit of production capacity and compares it with the predicted value from the energy efficiency model:

[0186] If the deviation is small and remains stable, keep the current parameters and weights unchanged.

[0187] If the deviation is large and persists, the controller initiates online learning and uses adaptive filtering or recursive update methods to fine-tune the weight coefficients of the energy efficiency model so that the prediction results gradually converge to the actual values.

[0188] If, during the learning and adjustment process, it is found that a change in a parameter leads to a decrease in capture performance or beat stability, the controller will immediately revert to the previous safe version and record the event for manual review.

[0189] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A busbar welding device, comprising a workbench, characterized in that, include: A welding robotic arm, mounted on the surface of the workbench, is used for welding busbars; The conveying device group consists of several sets of conveying devices symmetrically distributed on both sides of the welding robot arm. Each conveying device includes two slides for conveying between the welding station and the assembly station and for carrying the busbar fixed by the tooling. A protective enclosure is installed on the surface of the workbench to isolate high-temperature splashes, strong arc light, and smoke. A smoke extraction device is installed on the surface of the protective box to extract and filter the smoke and dust generated inside the protective box. The sensor array is used to collect image data of the welding station, position data of the slide table, and status data of the busbar. The controller is electrically connected to the welding robotic arm, the driver of the conveying device, the fume extraction device, and the sensor group, respectively. The controller has a pre-stored, updatable mapping relationship between dust concentration and extraction parameters, and integrates a pre-trained welding state recognition model, configured as follows: Based on the operator's identity information and busbar status data, the assembly efficiency parameters for the corresponding operator are generated and updated; combined with the slide position data, the earliest welding time for each slide is predicted in order to schedule the movement sequence of the slides. Based on the image data, the welding stage is identified, and the predicted value of smoke concentration is output based on the morphological features, brightness features and smoke occlusion features of the weld molten zone; the target extraction parameters are generated according to the smoke concentration-extraction parameter mapping relationship, and the operating parameters of the smoke extraction device are dynamically adjusted so that the operating parameters track the target extraction parameters in real time. When the welding stage is identified as the end of welding and the slide meets the advance condition, the fume extraction device is adjusted to maintain the operating parameters for extraction; and based on the model information of the next busbar to be welded and the preset welding path, the fume concentration-extraction parameter mapping relationship is dynamically corrected. When performing welding stage identification and fume concentration prediction based on image data, the controller is configured to: The image sequence of the welding station is analyzed to extract the appearance features of the weld molten zone and the smoke and dust cover features. The extracted feature vectors are input into the pre-trained welding state recognition model, and the current welding stage, the predicted value of smoke and dust concentration and the corresponding confidence level are output. When the predicted smoke and dust concentration is higher than the preset safe concentration threshold, the extraction parameters are increased to the upper limit of the target range of the smoke and dust concentration-extraction parameter mapping relationship. When the predicted smoke and dust concentration is lower than the preset safe concentration threshold, the extraction parameters are gradually reduced to the target extraction parameters corresponding to the smoke and dust concentration-extraction parameter mapping relationship. When the confidence level is lower than the confidence threshold, the extraction parameters are controlled by a set range that is higher than the predicted dust concentration value, and the corresponding data is marked for subsequent online correction. When performing slide table scheduling, the controller is configured as follows: Based on the operator's identity, the baseline efficiency parameter is generated by associating the historical data of the corresponding operator at the assembly station; based on the number of times the assembly position deviation exceeds the tolerance threshold within the current task cycle, the efficiency decay factor is calculated, and the baseline efficiency parameter is combined with the efficiency decay factor to generate dynamically updated assembly efficiency parameters. Based on the real-time position of the slide table and the preset motion performance parameters of the conveying device, the shortest transmission time for the slide table to reach the welding station is calculated. The remaining assembly time is predicted by combining the assembly efficiency parameters. The larger value between the shortest transmission time and the remaining assembly time is taken as the earliest weldable time, and the earliest weldable time is used for the scheduling and control of the slide table. The controller is configured to perform slide position scheduling as follows: The welding advance window is determined based on the remaining time of the current operation of the welding robot arm and the time required for the target slide in the welding standby area to advance to the welding station. The assembly advance window is determined based on the remaining assembly time determined by the operator's assembly efficiency parameters and busbar status data, and the estimated time required for the slide table located in the assembly standby area to advance to the assembly station. When any carry window meets the preset threshold and the corresponding target station can accept it, the controller controls the corresponding slide to carry to the target station within the corresponding carry window; When both types of carry windows are satisfied and the corresponding target workstations can accept them, the controller drives the two slides to carry to the corresponding target workstations in parallel within the same control cycle. When only one side is allowed to move within a control cycle, the controller selects the moving side based on the criterion of shortening the idle time of the welding robot arm, while the other side remains on standby until the next control cycle when the move window is re-evaluated. The controller is configured to: When the welding stage is determined to be over based on the welding state recognition model and the target slide meets the advance condition, the fume extraction device is switched from the target extraction parameter during the welding period to the operating parameter for maintaining extraction. The operating parameter for maintaining extraction is set so that the collection effect in the protective box is not lower than the collection effect threshold in order to suppress the settling of smoke and dust. Based on the next busbar model information to be welded and its associated preset welding path, the dust demand for the next welding cycle is predicted by combining the material and weld set characteristics of the corresponding busbar model. Accordingly, the dust concentration-extraction parameter mapping relationship is corrected online to generate target extraction parameters for the next welding cycle.

2. The busbar welding device according to claim 1, characterized in that, The conveying device includes: Mounting plate (301) is fixedly connected to the surface of workbench (101). Linear conveyor (302) is symmetrically fixedly connected to the surface of mounting plate (301). C-shaped sliding arm (303) is fixedly connected to the surface of linear conveyor (302). A first lead screw (304) is provided inside the C-shaped sliding arm (303). The first lead screw (304) is rotatably connected to the C-shaped sliding arm (303) through two first bearings. A connecting block (306) is threadedly connected to the surface of the first lead screw (304). A plurality of constraint rods (305) are provided on the surface of the connecting block (306). 305) The first circular groove on the surface of the connecting block (306) is slidably connected to the connecting block (306). The constraint rod (305) is fixedly connected to the C-shaped sliding arm (303). The constraint rod (305) is arranged in a ring array around the central axis of the first lead screw (304). The slide table (105) is fixedly connected to the upper surface of the connecting block (306). The surface of the C-shaped sliding arm (303) is equipped with a first motor (307). The output shaft of the first motor (307) is fixedly connected to the first lead screw (304) through the first through groove on the surface of the C-shaped sliding arm (303).

3. The busbar welding device according to claim 2, characterized in that, When the controller performs slide scheduling, it is also configured to: continuously update the earliest weldable time of each slide using a rolling time window, and set a freeze time and a minimum hold time to avoid frequent rescheduling; when both sides meet the carry conditions and the target station can accept the slide, a stability weight is introduced to determine the priority based on the earliest weldable time sorting, and the stability weight is determined based on the fluctuation of the assembly efficiency parameter of the corresponding operator and the historical accuracy. When the deviation between the predicted earliest welding time and the actual completion time exceeds the threshold within multiple consecutive control cycles, the assembly efficiency parameters of the corresponding operator are automatically converged and corrected, and a review prompt is triggered to ensure cycle stability while reducing idle waiting losses and improving scheduling robustness.

4. The busbar welding device according to claim 3, characterized in that, The controller is also configured to perform global energy efficiency optimization control: Based on the historical correction records of the dust concentration-extraction parameter mapping relationship, the update records of the assembly efficiency parameters, and the actual working cycle of the welding robot, an energy efficiency model is constructed with the goal of minimizing energy consumption per unit of production capacity. Within a preset statistical period, based on the welding stage duration distribution, extraction parameter energy consumption distribution, and slide table idle time ratio recorded within the corresponding period, the baseline parameters of the dust concentration-extraction parameter mapping relationship, the attenuation factor weight of the assembly efficiency parameter, and the preset threshold of the slide table carry window are optimized in reverse through the energy efficiency model. The optimized parameter set is applied to the next production batch, and the weight coefficients of the energy efficiency model are learned and adjusted online based on the deviation between real-time operating data and the optimization target.

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