A viscose staple fiber finished product whole-process intelligent processing system

Abstract

This invention discloses an intelligent processing system for the entire process of viscose staple fiber finished products, relating to the technical field of viscose staple fiber production equipment. It includes, in sequence: a centering module, located downstream of the packaging module's outlet, comprising symmetrically arranged photoelectric sensors, a lateral adjustment airbag, a positioning clamp, and an elastic buffer pad located inside the positioning clamp, used to adjust the cotton bales to the conveyor's central axis. By setting up the centering module, this invention utilizes photoelectric sensors to detect the edge position of the cotton bales in real time. The central control system controls the lateral adjustment airbag to push the positioning clamp, which, in conjunction with the elastic buffer pad, achieves automatic and precise centering of the cotton bales, avoiding labeling and welding errors caused by positional misalignment. Furthermore, by setting up a moisture regain detection module, using a microwave moisture regain sensor vertically arranged above the conveyor line, it achieves full-volume real-time non-contact detection. When the moisture regain rate is unqualified, an alarm is automatically triggered and the bales are diverted and rejected, preventing unqualified products from flowing into subsequent stages.

Description

Technical Field

[0001] This invention relates to the field of viscose staple fiber production equipment technology, specifically to an intelligent processing system for the entire process of viscose staple fiber finished products. Background Technology

[0002] The viscose staple fiber finished product processing system is a complete set of equipment used in the viscose fiber production process for post-processing of finished cotton bales, including packing, centering, conveying, moisture regain detection, labeling, welding, and stacking. It is widely used in the large-scale production of viscose staple fibers. In traditional processing systems, cotton bales after exiting the baling machine need to undergo centering adjustment, conveying, moisture regain detection, labeling, fabric welding, and bale stacking to ensure the product meets factory quality standards and facilitates warehousing and transportation. Specifically, the centering step adjusts the cotton bales to the center of the conveyor line to ensure accurate positioning in subsequent processes; moisture regain detection monitors the moisture content of the cotton; labeling records product information; welding seals the fabric to prevent contamination; and stacking neatly arranges the finished products.

[0003] However, in the actual production scenario of viscose staple fiber finished product processing, traditional processing systems rely on manual visual adjustment of the cotton bale position during the centering stage, which is inefficient and lacks sufficient centering accuracy. This leads to positional deviations in subsequent labeling and welding processes, affecting the labeling pass rate and welding quality. Furthermore, the moisture regain detection uses sampling inspection, which cannot achieve full real-time monitoring, easily resulting in substandard moisture regain products flowing into subsequent stages, creating potential quality hazards. In addition, the labeling process uses manual or semi-automatic methods, resulting in low labeling accuracy and frequent problems such as misaligned labels, missed labels, and damaged labels, affecting product information traceability. Welding of the fabric wrapping seams requires real-time manual adjustment of the welding position, resulting in delayed response, poor welding quality stability, and safety hazards due to high-temperature heat sealing operations. Fifth, the stacking process relies on forklift operations, consuming large amounts of fuel and electricity, resulting in poor stacking consistency and the risk of cotton bale collapse and injury. The aforementioned decentralized, primarily manual processing methods also suffer from low production efficiency, high labor costs, difficulty in quality control, and lack of product data traceability, making it difficult to meet the automation and intelligent requirements of large-scale viscose staple fiber production. Summary of the Invention

[0004] This invention provides an intelligent processing system for the entire process of viscose staple fiber finished products, in order to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows: A fully intelligent processing system for viscose staple fiber finished products includes, in sequence: a centering module, located downstream of the packaging module's outlet, comprising symmetrically arranged photoelectric sensors, lateral adjustment airbags, positioning clamps, and elastic buffer pads located inside the positioning clamps, used to adjust the cotton bales to the conveyor's central axis; a conveying module, running through all functional modules along the processing flow, including a chain conveyor line, drive motor, and tension adjustment device, used to convey cotton bales between modules; a moisture regain detection module, located downstream of the centering module, comprising a microwave moisture regain sensor vertically arranged above the conveyor line, a data acquisition unit, and an alarm device, used to detect the moisture regain rate of the cotton bales in real time; a cotton bale data acquisition and storage module, used to collect weighing data and moisture regain detection data, match cotton bale information, integrate and process the data, and store it in a database; and a labeling module. The system comprises the following modules: a label printer, a first robotic arm, and a positioning unit for attaching labels to the surface of cotton bales; an AI vision verification module, including an industrial camera, an image preprocessing unit, and an AI comparison algorithm for verifying the quality of the labels; an AI recognition module for fabric welding seams, including a high-definition vision camera, a light source supplementary lighting device, and a dedicated recognition algorithm for identifying the location of the fabric welding seams and generating position coordinates; a fabric welding module, including a high-frequency heat sealing welder, a second robotic arm, and a displacement adjustment unit for automatically completing the welding seams based on the weld seam position coordinates; a stacking module, including a lifting platform, a clamping device, a stacking unit, and a stacking counting unit for stacking cotton bales according to a preset number of layers; and a central control system, electrically connected to all the above modules, for receiving detection signals, sending control commands, and coordinating the working rhythm of each module.

[0006] A further improvement of the technical solution of the present invention is that: the positioning clamps of the centering module are two symmetrically arranged pieces, respectively set on both sides of the conveyor line; the lateral adjustment airbag is connected between the positioning clamps and the fixed bracket; and the photoelectric sensor is installed at the feed end of the positioning clamps to detect the edge position of the cotton bale.

[0007] A further improvement of the technical solution of the present invention is that: the chain conveyor line of the conveying module includes two conveying chains arranged in parallel, the chains are connected by crossbars, the drive motor is a three-phase asynchronous motor, the output shaft of which is connected to a drive sprocket, and the tension adjustment device includes a tension sprocket and an adjustment screw.

[0008] A further improvement of the technical solution of the present invention is that: the microwave moisture regain sensor of the moisture regain detection module is installed above the conveyor line through a lifting bracket, the lifting bracket including a column, a sliding seat and a locking handle, used to adjust the detection distance between the sensor and the surface of the cotton bale.

[0009] A further improvement of the technical solution of the present invention is that: the first robotic arm of the labeling module is a four-degree-of-freedom articulated robotic arm, and the end of which is equipped with the print head of a label printer; the position positioning unit is a laser displacement sensor or a visual positioning camera.

[0010] A further improvement of the technical solution of the present invention is that: the industrial camera of the AI ​​visual verification module is installed on the gantry downstream of the labeling module, the camera lens is perpendicular to the labeling surface of the cotton bale, and ring-shaped supplementary lights are provided on both sides of the camera.

[0011] A further improvement of the technical solution of the present invention is that: the high-definition vision camera and the light source supplementary lighting device of the AI ​​recognition module for the cloth-wrapped weld are installed above and to the side of the welding station, and the light source supplementary lighting device is a high-brightness LED matrix light source.

[0012] A further improvement of the technical solution of the present invention is that: the second robotic arm of the cloth-wrapping welding module is a six-degree-of-freedom industrial robotic arm, and the welding head of a high-frequency heat-sealing welding machine is fixed at its end; the displacement adjustment unit includes a ball screw slide driven by a servo motor, which is used to move the cotton bale or to move the robotic arm in translation.

[0013] A further improvement of the technical solution of the present invention is that: the lifting platform of the stacking module is driven by a scissor lift mechanism and a hydraulic cylinder, and the clamping device includes symmetrically arranged pneumatic grippers, with rubber anti-slip pads provided on the inner side of the grippers.

[0014] A further improvement of the technical solution of the present invention is that the central control system includes a PLC controller, a touch screen and a data storage unit, and the PLC controller communicates with the sensors and actuators of each module through an industrial Ethernet.

[0015] Due to the adoption of the above technical solution, the technical progress achieved by this invention compared to the prior art is as follows: This invention provides an intelligent processing system for viscose staple fiber finished products throughout the entire process. By incorporating a centering module, photoelectric sensors detect the edge position of the cotton bales in real time. A central control system controls lateral adjustment airbags to push positioning clamps, working in conjunction with elastic buffer pads to achieve automatic and precise centering of the cotton bales, avoiding labeling and welding errors caused by positional misalignment. Furthermore, a moisture regain detection module employs a microwave moisture regain sensor vertically positioned above the conveyor line for full-volume real-time non-contact detection. When the moisture regain rate is unqualified, an alarm is automatically triggered and the bales are diverted and rejected, preventing unqualified products from entering subsequent stages. The labeling module... In collaboration with the AI ​​vision verification module, the first robotic arm drives the label printer to automatically apply labels. The industrial camera and AI comparison algorithm verify the labeling position and quality in real time, ensuring accurate and qualified labeling and avoiding problems such as missed or crooked labeling. Through the linkage between the fabric wrapping weld AI recognition module and the fabric wrapping welding module, the high-definition vision camera and dedicated recognition algorithm accurately locate the weld seam, and the second robotic arm drives the high-frequency heat sealing welding machine to automatically complete the welding. The welding quality is stable and eliminates the safety risks of manual high-temperature operation. The stacking module automatically stacks the packaging through its lifting platform and clamping device, reducing the energy consumption and safety hazards of forklift operations. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the main structure of the present invention; Figure 2 This is a schematic diagram of the centering module and the conveying module of the present invention; Figure 3 This is a schematic diagram of the moisture regain detection module of the present invention; Figure 4 This is a schematic diagram of the labeling module and AI vision verification module of the present invention; Figure 5 This is a schematic diagram of the fabric-wrapping welding module structure of the present invention; Figure 6 This is a schematic diagram of the stacked module structure of the present invention.

[0017] In the diagram: 11. Photoelectric sensor; 12. Lateral adjustment airbag; 13. Positioning clamp; 14. Elastic buffer pad; 21. Chain conveyor line; 22. Drive motor; 23. Tension adjustment device; 31. Microwave moisture sensor; 32. Data acquisition unit; 33. Alarm device; 51. Label printer; 52. First robotic arm; 53. Positioning unit; 61. Industrial camera; 62. Image preprocessing unit; 71. High-definition vision camera; 72. Light source supplementary lighting device; 81. High-frequency heat sealing welding machine; 82. Second robotic arm; 83. Displacement adjustment unit; 91. Lifting platform; 92. Clamping device; 10. Central control system. Detailed Implementation

[0018] The present invention will be further described in detail below with reference to embodiments: Example 1, as Figures 1-6As shown, this invention provides an intelligent processing system for the entire process of viscose staple fiber finished products, comprising the following components connected in sequence: a centering module, located downstream of the outlet of the packaging module, including symmetrically arranged photoelectric sensors 11, lateral adjustment airbags 12, positioning clamps 13, and elastic buffer pads 14 located inside the positioning clamps 13, used to adjust the cotton bales to the conveying center axis; a conveying module, running through each functional module along the processing flow, including a chain conveyor line 21, a drive motor 22, and a tension adjustment device 23, used to convey cotton bales between modules; a moisture regain detection module, located downstream of the centering module 1, including a microwave moisture regain sensor 31 vertically arranged above the conveyor line, a data acquisition unit 32, and an alarm device 33, used to detect the moisture regain rate of the cotton bales in real time; and a cotton bale data acquisition and storage module, used to collect weighing data and moisture regain detection data, match cotton bale information, integrate and process the data, and store it in a database. The system comprises: a labeling module, including a label printer 51, a first robotic arm 52, and a positioning unit 53, for affixing labels to the surface of cotton bales; an AI vision verification module, including an industrial camera 61, an image preprocessing unit 62, and an AI comparison algorithm, for verifying the quality of the labels; an AI recognition module for fabric welding seams, including a high-definition vision camera 71, a light source supplementary lighting device 72, and a dedicated recognition algorithm, for identifying the position of the fabric welding seams and generating position coordinates; a fabric welding module, including a high-frequency heat sealing welder 81, a second robotic arm 82, and a displacement adjustment unit 83, for automatically completing the welding seams according to the weld seam position coordinates; a stacking module, including a lifting platform 91, a clamping device 92, a stacking unit, and a stacking counting unit, for stacking cotton bales according to a preset number of layers; and a central control system 10, electrically connected to the above modules, for receiving detection signals, sending control commands, and coordinating the working rhythm of each module.

[0019] It should be noted that: photoelectric sensor 11 is used to detect the edge position of the cotton bale on the conveyor line; lateral adjustment airbag 12 is used to inflate or deflate according to control commands to push positioning clamp 13; positioning clamp 13 is used to clamp and push the cotton bale from both sides to center it; elastic buffer pad 14 is used to prevent positioning clamp 13 from damaging the cotton bale covering fabric; chain conveyor 21 is used to carry and transport the cotton bale; drive motor 22 is used to provide power to chain conveyor 21; tension adjustment device 23 is used to adjust the tension of the conveyor chain; microwave moisture regain sensor 31 is used to detect the internal moisture regain of the cotton bale non-contactly; data acquisition unit 32 is used to collect sensor data and upload it to the central control system 10; alarm device 33 is used to issue an audible and visual alarm when the moisture regain exceeds the standard; label printer 51 is used to print labels containing product information; first robotic arm 52 is used to drive the print head of label printer 51. The system moves to the labeling position; the positioning unit 53 is used to obtain the reference coordinates of the cotton bale surface; the industrial camera 61 is used to acquire images of the cotton bales after labeling; the image preprocessing unit 62 is used to perform noise reduction and grayscale processing on the acquired images; the AI ​​comparison algorithm is used to compare the actual labeling image with the standard template to determine whether the labeling is qualified; the high-definition vision camera 71 is used to acquire high-definition images of the fabric welding area; the light source supplementary lighting device 72 is used to provide uniform illumination for the welding area; the high-frequency heat sealing welding machine 81 is used to weld the fabric welding seam through high-frequency heat sealing; the second robotic arm 82 is used to drive the welding head to move along the welding trajectory; the displacement adjustment unit 83 is used to adjust the relative position of the cotton bale or the welding head; the lifting platform 91 is used to carry the stacked cotton bales and adjust the height; the clamping device 92 is used to clamp the cotton bales and transport them to the stacking position; the central control system 10 is used to coordinate the actions of each module and process data.

[0020] The AI ​​comparison algorithm in the AI ​​visual verification module is used to determine whether the labeling position, angle, and label integrity are up to standard. The specific steps are as follows: S1: Image acquisition and preprocessing: Industrial camera 61 captures images of the side label of the cotton bag (resolution 1920×1080 pixels). Image preprocessing unit 62 sequentially performs grayscale conversion (weighted average method Gray=0.299R+0.587G+0.114B), Gaussian filtering (5×5 Gaussian kernel, σ=1.4) and histogram equalization to enhance the label edges.

[0021] S2: Label Region Localization: A lightweight SSD-MobileNet object detection model is used (1000 pre-labeled images under different lighting and angles are used as a training set). The rectangular region where the label is located is cropped from the image. The inference time is ≤50ms and the localization accuracy is ±5 pixels.

[0022] S3: Feature Extraction and Comparison ① Position Deviation Calculation: Extract the pixel coordinates of the four corner points of the label rectangle and calculate the Euclidean distance deviation with the theoretical coordinates in the standard template. For example, if the theoretical position of the label center point is (960, 540), and the actual detection center is (955, 545), the horizontal deviation is 5 pixels (approximately 0.5mm), the vertical deviation is 5 pixels (approximately 0.5mm), and a deviation ≤ 10 pixels (approximately 1mm) is considered acceptable.

[0023] ② Angle deviation calculation: Based on the angle between the straight line of the label edge and the horizontal line, compare it with the angle of the template (e.g., 0°). If it exceeds the preset threshold of 1.5°, it is judged as tilting and unqualified.

[0024] ③ Label integrity detection: After binarizing the label area image, calculate the area of ​​the connected components. If the area is less than 85% of the template area or the QR code cannot be decoded normally, it is judged as unqualified.

[0025] ④ Output results: The output results are based on three indicators: position deviation, angle deviation, and integrity. If any one of them fails to meet the requirements, the output will be "NG"; if it meets the requirements, the output will be "OK" and sent to the central control system 10.

[0026] The dedicated recognition algorithm of the AI ​​recognition module for fabric-covered welds is used to identify the start point, end point, and trajectory of the weld. The specific steps are as follows: S1: Image Acquisition and Enhancement: A high-definition vision camera 71 (resolution 3840×2160 pixels) acquires images of the weld area under the illumination of an LED matrix light source 72. The Retinex algorithm is used to enhance the image contrast and highlight the difference between the weld and the background.

[0027] S2: Weld seam region segmentation: The U-Net semantic segmentation network (the training set contains 2000 on-site images of labeled weld seams, which have been optimized through 16 iterations) is used to output a binary mask. A pixel value of 1 represents the weld seam region and 0 represents the background. The resulting continuous white strip-shaped region is the weld seam.

[0028] S3: Skeleton Extraction and Trajectory Fitting: Morphological refinement (Zhang-Suen algorithm) is performed on the binary mask of the weld seam to extract the skeleton line with a width of one pixel; the skeleton points are fitted into a cubic B-spline curve using the least squares method to obtain the mathematical expression of the weld seam trajectory C(t)=(x(t),y(t)), t∈[0,1]; the pixel coordinates of the curve start point C(0) and end point C(1) are calculated and converted into actual physical coordinates through camera calibration parameters (1 pixel = 0.1mm).

[0029] S4: Weld Position Coordinate Generation: Identify the starting point coordinates (e.g., (1500mm, 200mm)) and ending point coordinates (e.g., (1800mm, 200mm)) of the weld, discretize the weld trajectory into several interpolation points (5mm spacing), and generate a path point list for the second robotic arm 82 and displacement adjustment unit 83 of the fabric welding module to track. If the weld skeleton length is less than the preset minimum value (e.g., 100mm) or the curvature is too large (radius of curvature < 50mm), it is determined as a recognition failure and an alarm is triggered.

[0030] In this embodiment, the centering module enables automatic and precise centering of cotton bales, avoiding errors from manual adjustments; the moisture regain detection module enables real-time detection of the full amount of moisture regain, preventing unqualified products from entering subsequent processes; the labeling module, in conjunction with the AI ​​vision verification module, achieves accurate labeling and automatic verification, avoiding missed or misaligned labels; the AI ​​recognition module for fabric wrapping welds, linked with the fabric wrapping welding module, enables automatic weld recognition and precise welding, eliminating safety hazards associated with manual operation; the stacking module automatically stacks the bales, reducing forklift energy consumption and improving stacking consistency; and the central control system coordinates all modules to achieve fully automated processing, significantly improving production efficiency and product consistency.

[0031] Example 2, as Figures 1-6 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the positioning clamps 13 of the centering module are two symmetrically arranged pieces, respectively set on both sides of the conveyor line; the lateral adjustment airbag 12 is connected between the positioning clamps 13 and the fixed bracket; the photoelectric sensor 11 is installed at the feeding end of the positioning clamps 13 to detect the edge position of the cotton bale; the chain conveyor line 21 of the conveying module includes two parallel conveyor chains connected by crossbars; the drive motor 22 is a three-phase asynchronous motor, and its output shaft is connected to a drive sprocket; the tension adjustment device 23 includes a tension sprocket and a... The microwave moisture sensor 31 of the moisture detection module is installed above the conveyor line via a lifting bracket. The lifting bracket includes a column, a sliding seat, and a locking handle, which is used to adjust the detection distance between the sensor and the surface of the cotton bale. The first robotic arm 52 of the labeling module is a four-degree-of-freedom articulated robotic arm, and the print head of the label printer 51 is installed at its end. The position positioning unit 53 is a laser displacement sensor or a vision positioning camera. The industrial camera 61 of the AI ​​vision verification module is installed on the gantry downstream of the labeling module 5. The camera lens is perpendicular to the labeling surface of the cotton bale, and ring lights are set on both sides of the camera.

[0032] It should be noted that: the chain conveyor line 21, consisting of two parallel chains and crossbars, can stably carry heavy cotton bales; the three-phase asynchronous motor driving the sprocket provides stable and reliable conveying power; the tension sprocket and adjusting screw facilitate on-site adjustment of chain tension; the lifting support can adjust the sensor detection distance according to the height of the cotton bale to ensure detection accuracy; the four-degree-of-freedom articulated robotic arm can flexibly reach the labeling position on the side of the cotton bale; the laser displacement sensor can obtain the surface coordinates of the cotton bale non-contactly; and the gantry frame and ring supplementary light ensure clear and shadow-free images.

[0033] In this embodiment, the double-sided positioning clamps 13 and the lateral adjustment airbags 12 of the centering module enable rapid centering of the cotton bales with a centering accuracy of ≤±5mm. The crossbar structure of the chain conveyor line 21 prevents the cotton bales from slipping or tilting during transport. The lifting bracket adjusts the detection distance of the microwave moisture sensor 31 to accommodate cotton bales of different specifications with a detection accuracy of ≤±0.5%. The four-degree-of-freedom first robotic arm 52, in conjunction with a laser displacement sensor, enables precise label application with a labeling position deviation of ≤±10mm. The industrial camera 61 and ring light on the gantry ensure image acquisition quality, with an AI verification accuracy of ≥99.5%. These structural optimizations further improve the system's positioning accuracy, detection reliability, and automation level.

[0034] Example 3, as Figures 1-6 As shown, based on Embodiment 1, the present invention provides a technical solution: Preferably, the high-definition vision camera 71 and the light source supplementary lighting device 72 of the fabric welding seam AI recognition module are installed above and to the side of the welding station. The light source supplementary lighting device 72 is a high-brightness LED matrix light source. The second robotic arm 82 of the fabric welding module is a six-degree-of-freedom industrial robotic arm, and the welding head of the high-frequency heat sealing welding machine 81 is fixed at its end. The displacement adjustment unit 83 includes a ball screw slide driven by a servo motor, which is used to move the cotton bale or to move the robotic arm horizontally. The lifting platform 91 of the stacking module is driven by a scissor-type lifting mechanism and a hydraulic cylinder. The clamping device 92 includes symmetrically arranged pneumatic grippers, and the inner side of the grippers is provided with a rubber anti-slip pad. The central control system 10 includes a PLC controller, a touch screen and a data storage unit. The PLC controller communicates with the sensors and actuators of each module through an industrial Ethernet.

[0035] It should be noted that: the high-definition vision cameras 71 arranged on the top and sides can acquire weld seam images from multiple angles, avoiding blind spots; the high-brightness LED matrix light source ensures uniform lighting in the weld seam area, improving image quality; the six-degree-of-freedom industrial robotic arm can flexibly adjust the welding head posture to adapt to weld seams with different orientations; the servo motor-driven ball screw slide has high positioning accuracy and can precisely adjust the position of the cotton bale or welding head; the scissor lift mechanism has strong load-bearing capacity and smooth lifting; the pneumatic grippers, combined with rubber anti-slip pads, can firmly hold the cotton bale without damaging the fabric; and the PLC controller achieves high-speed data exchange and collaborative control of various modules through industrial Ethernet.

[0036] In this embodiment, through the cooperation of a multi-angle high-definition vision camera 71 and an LED matrix light source, the accuracy of identifying the wrapped weld seam is ≥99.99%, and the identification time is ≤0.2s / seam. Through the cooperation of a six-degree-of-freedom second robotic arm 82 and a ball screw slide, the repeatability of the welding head is ≤±1mm, and the welding quality is stable and reliable. Through the cooperation of a scissor lift platform 91 and a pneumatic gripper 92, the stacking tolerance is ≤±10mm, the number of stacking layers can be flexibly set from 1 to 4 layers, the stacking is neat and consistent, and the energy consumption of forklift operation is reduced by more than 30%. Through the PLC controller and industrial Ethernet, the central control system 10 can monitor the status of each module in real time, store ≥3 years of production data, and realize full-process data traceability.

[0037] The working principle of this intelligent processing system for viscose staple fiber finished products will be explained in detail below.

[0038] like Figures 1-6 As shown, the standardized cotton bales output by the packaging module fall onto the chain conveyor line 21 of the conveying module. The cotton bales first enter the centering module, where the photoelectric sensor 11 detects the edge position of the cotton bales on the conveyor line. The central control system 10 calculates the offset based on the detection signal and controls the lateral adjustment airbag 12 to inflate or deflate, pushing the positioning clamp 13 to adjust the cotton bales to the conveying center axis. The elastic buffer pad 14 protects the cotton bale wrapping fabric from damage, with a centering accuracy of ≤±5mm.

[0039] The chain conveyor 21 of the conveying module operates under the drive of the drive motor 22. The tension adjustment device 23 keeps the chain taut, smoothly conveying the cotton bales to the moisture regain detection module. The microwave moisture regain sensor 31 is vertically arranged above the conveyor line. The detection distance is adjusted (50-100mm) by the lifting bracket. It detects the moisture regain rate of the cotton bales in real time without contact, with a detection accuracy of ≤±0.5% and a detection speed of ≤5s / bales. The data acquisition unit 32 uploads the moisture regain rate data to the central control system 10. If the moisture regain rate exceeds the preset range (8-13%), the central control system 10 controls the alarm device 33 to issue an audible and visual alarm and controls the conveyor line to divert the unqualified cotton bales to the rejection area; qualified cotton bales continue to be conveyed.

[0040] Qualified cotton bales enter the labeling module. The positioning unit 53 (laser displacement sensor or visual positioning camera) acquires the reference coordinates of the cotton bale surface. The central control system 10 controls the four-degree-of-freedom first robotic arm 52 to move the print head of the label printer 51 to the preset position, accurately affixing a label containing information such as product name, specifications, batch number, production date, moisture regain, and QR code to the side of the cotton bale (at ±50mm from the center). The cotton bales then enter the AI ​​visual verification module. The industrial camera 61 captures the labeling image with the assistance of a ring light. The image preprocessing unit 62 performs noise reduction and grayscale processing. The AI ​​comparison algorithm compares the actual labeling image with a standard template to determine whether the labeling position and angle are qualified. The verification accuracy is ≥99.5%, and the verification time is ≤0.5s / bale. Unqualified products are rejected, while qualified products continue to be conveyed.

[0041] The labeled cotton bales enter the AI ​​recognition module for the fabric-covering weld seam. A high-definition vision camera 71, illuminated by an LED matrix light source, captures images of the weld seam area. A dedicated recognition algorithm uses image segmentation and feature extraction techniques to identify the relative position of the weld seam and the head of the cotton bale, generating weld seam position coordinates. The recognition accuracy is ≥99.99%, and the recognition time is ≤0.2s / bale. The central control system 10 sends the weld seam coordinates to the fabric-covering welding module. The displacement adjustment unit 83 (a ball screw slide driven by a servo motor) adjusts the position of the cotton bale or the second robotic arm 82 translates. The six-degree-of-freedom second robotic arm 82 drives the welding head of the high-frequency heat-sealing welding machine 81 to move along the weld seam trajectory. The output power is adjustable from 500-2000W, completing precise welding. The robotic arm's repeatability is ≤±1mm.

[0042] The welded cotton bales are conveyed to the stacking module via the conveyor module. The lifting platform 91, driven by a scissor lift mechanism and hydraulic cylinders, rises or falls to a predetermined height. The pneumatic grippers (with rubber anti-slip pads on the inside) of the clamping device 92 hold the cotton bales. The stacking unit stacks the cotton bales onto the lifting platform 91 according to a preset number of layers (set to 1-4 layers via a touchscreen). The stacking counting unit counts the layers using photoelectric sensors, with a stacking tolerance of ≤±10mm. After stacking, the lifting platform 91 descends to its lowest position, and a forklift or AGV transports the entire stack of cotton bales away.

[0043] Throughout the process, the PLC controller of the central control system 10 receives sensor signals from each module in real time via industrial Ethernet, sends control commands, coordinates the conveying speed with the processing rhythm of each module, displays system status, alarm information and production data on the touch screen, and records the weighing data, moisture regain, labeling information, welding parameters, number of stacked layers, etc. of each cotton bale. It supports USB export or network upload, realizing full-process automated control and full life-cycle data traceability of products.

[0044] The present invention has been described in detail above. However, modifications or improvements can be made to it, which will be obvious to those skilled in the art. Therefore, any modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A viscose staple fiber finished product full-process intelligent processing system, characterized in that: Including those connected sequentially: ​ The centering module is located downstream of the outlet of the packaging module and includes symmetrically arranged photoelectric sensors (11), lateral adjustment airbags (12), positioning clamps (13), and elastic buffer pads (14) located inside the positioning clamps (13) to adjust the cotton bale to the conveying center axis. The conveying module runs through each functional module along the processing flow, including a chain conveyor (21), a drive motor (22), and a tension adjustment device (23), and is used to convey cotton bales between modules; The moisture regain detection module is located downstream of the centering module (1) and includes a microwave moisture regain sensor (31), a data acquisition unit (32) and an alarm device (33) arranged vertically above the conveyor line, for real-time detection of the moisture regain rate of the cotton bale; The cotton bale data acquisition and storage module is used to collect weighing data and moisture regain detection data, match cotton bale information, integrate and process the data, and store it in the database. The labeling module includes a label printer (51), a first robotic arm (52), and a positioning unit (53) for attaching labels to the surface of cotton bales; The AI ​​visual verification module includes an industrial camera (61), an image preprocessing unit (62), and an AI comparison algorithm, used to verify whether the labeling is qualified; The AI ​​recognition module for fabric-covered weld seams includes a high-definition vision camera (71), a light source supplementary lighting device (72), and a dedicated recognition algorithm, which is used to identify the position of the fabric-covered weld seam and generate position coordinates; The fabric welding module includes a high-frequency heat sealing welder (81), a second robotic arm (82), and a displacement adjustment unit (83), which is used to automatically complete the welding of the weld according to the weld position coordinates; The stacking module includes a lifting platform (91), a clamping device (92), a stacking unit and a stacking counting unit, for stacking cotton bales according to a preset number of layers; The central control system (10) is electrically connected to the above modules and is used to receive detection signals, send control commands, and coordinate the working rhythm of each module.

2. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The positioning clamps (13) of the centering module are two symmetrically arranged pieces, respectively set on both sides of the conveyor line. The lateral adjustment airbag (12) is connected between the positioning clamps (13) and the fixed bracket. The photoelectric sensor (11) is installed at the feed end of the positioning clamps (13) to detect the edge position of the cotton bale.

3. The system according to claim 1, wherein the system is characterized in that: The chain conveyor line (21) of the conveying module includes two parallel conveying chains connected by crossbars. The drive motor (22) is a three-phase asynchronous motor with a drive sprocket connected to its output shaft. The tension adjustment device (23) includes a tension sprocket and an adjustment screw.

4. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The microwave moisture sensor (31) of the moisture detection module is installed above the conveyor line via a lifting bracket. The lifting bracket includes a column, a sliding seat, and a locking handle, which are used to adjust the detection distance between the sensor and the surface of the cotton bale.

5. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The first robotic arm (52) of the labeling module is a four-degree-of-freedom articulated robotic arm, with a print head of a label printer (51) installed at its end. The position positioning unit (53) is a laser displacement sensor or a visual positioning camera.

6. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The industrial camera (61) of the AI ​​visual verification module is installed on the gantry downstream of the labeling module (5). The camera lens is perpendicular to the labeling surface of the cotton bag, and ring lights are provided on both sides of the camera.

7. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The high-definition vision camera (71) and the light source supplementary lighting device (72) of the AI ​​recognition module for the fabric-covered weld are installed above and to the side of the welding station. The light source supplementary lighting device (72) is a high-brightness LED matrix light source.

8. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The second robotic arm (82) of the cloth welding module is a six-degree-of-freedom industrial robotic arm with a welding head of a high-frequency heat welding machine (81) fixed at its end. The displacement adjustment unit (83) includes a ball screw slide driven by a servo motor, which is used to move the cotton bale or to move the robotic arm.

9. The intelligent processing system for the whole process of viscose staple fiber finished product according to claim 1, characterized in that: The lifting platform (91) of the stacking module is driven by a scissor lift mechanism and a hydraulic cylinder. The clamping device (92) includes symmetrically arranged pneumatic grippers, and the inner side of the grippers is provided with a rubber anti-slip pad.

10. The intelligent processing system for the entire process of viscose staple fiber finished product according to claim 1, characterized in that: The central control system (10) includes a PLC controller, a touch screen and a data storage unit. The PLC controller communicates with the sensors and actuators of each module via an industrial Ethernet.

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