Control system for automatically wrapping insulating tape on coil

Abstract

The invention discloses a control system for automatically wrapping a coil with an insulating tape, which relates to the field of power systems and comprises an image acquisition and processing module, a tape wrapping mechanism sensing module, an operation control module, a safety monitoring module, a data interaction module and a memory module. A multi-sensor network is combined to monitor the state of a taping mechanism in real time, track optimization and active-disturbance-rejection closed-loop control are utilized to ensure that taping is accurate and efficient, big data analysis is introduced to rapidly recognize and call control instructions of similar historical events, the control efficiency and accuracy are improved, and the taping accuracy is improved. The system also has visual monitoring and remote parameter adjustment functions and a cloud database historical data storage function; the limitation problem that a traditional automatic packaging system is only suitable for coils in regular shapes is solved, the efficiency, precision and safety of automatic insulating tape wrapping of the coils are improved, and intelligentization and automation of the production process are achieved.

Description

Technical Field

[0001] The invention relates to the field of automatic control, and in particular to a control system for automatically wrapping a coil with an insulating tape. Background Art

[0002] Coils are very common components in electrical equipment, such as transformers and motors. These coils usually need to be insulated to prevent electric shock, discharge or other faults. When producing and manufacturing coils, it is usually necessary to wrap a layer of insulating tape on the coil to improve its voltage resistance and service life. Traditional manual wrapping has problems such as low packaging quality and efficiency, high labor intensity, and cannot meet the needs of large-scale production. Therefore, the control system for automatic coil insulation tape wrapping came into being, which automatically controls the process of automatic coil insulation tape wrapping, greatly improving production efficiency, ensuring product quality, and reducing labor costs.

[0003] However, the traditional control system for automatic wrapping of coils with insulating tape is often only suitable for coils or wire rods of regular shapes. For some special parts of the coils, such as the nose end of the coil, extremely complex actions need to be performed when wrapping the insulating tape, which often leads to problems such as loose wrapping and uneven winding. Manual intervention is required, making it difficult to achieve automatic wrapping, which greatly reduces the wrapping quality and efficiency.

[0004] Therefore, a control system for automatically wrapping the coil with insulating tape is needed to solve the above problems. Summary of the invention

[0005] In view of the deficiencies in the prior art, the present invention discloses a control system for automatically wrapping coils with insulating tape, which realizes real-time monitoring and intelligent control of the process of automatically wrapping coils with insulating tape, thereby effectively solving the limitation problem that traditional automatic packaging systems are only suitable for coils of regular shapes, and has a high degree of automation and intelligence.

[0006] The present invention adopts the following technical solutions:

[0007] A control system for automatically wrapping a coil with an insulating tape, the control system comprising:

[0008] An image acquisition and processing module, which is used to acquire binocular images of the coil, and to process and analyze the images to obtain geometric dimensions, position, and appearance information of the coil, so as to realize path planning for automatically wrapping the coil with insulating tape;

[0009] A taping mechanism sensing module, wherein the taping mechanism sensing module uses a sensor network to provide real-time working status feedback of the taping mechanism used in the process of automatically taping the coil, and transmits the feedback information to the next layer for real-time adjustment and operation control, wherein the sensor network includes at least a flexible sensor, a pressure sensor, a photoelectric sensor, and a temperature sensor;

[0010] An operation control module, wherein the operation control module generates motion control instructions for automatic coil insulation tape wrapping according to image processing results and real-time sensor feedback information, and controls and adjusts the automatic coil insulation tape wrapping process based on a trajectory optimization self-disturbance rejection closed-loop control model. The operation control module compares and analyzes the similarity between the current image processing results and historical events through big data. If the similarity is greater than a threshold, the control instructions of the historical events are directly called for operation control. The trajectory optimization self-disturbance rejection closed-loop control model controls and executes a predetermined tape wrapping path and parameters by combining a trajectory optimization TO method and an ADRC self-disturbance rejection controller, and adaptively adjusts the control process according to changes in coil shape and external environment.

[0011] A safety monitoring module, which monitors abnormal conditions during the automatic coil wrapping process based on real-time feedback information from sensors. When abnormal conditions are detected during the automatic coil wrapping process, an emergency stop mechanism is triggered and an alarm message is sent out through an alarm indicator light and a display screen, and the user performs troubleshooting and recovery operations based on the alarm message;

[0012] A data interaction module, which reports the real-time status and progress of the coil automatic insulation tape wrapping process to the management personnel through a visual dashboard, and allows the user to manually set and change the control parameters of the coil automatic insulation tape wrapping through a human-computer interaction interface;

[0013] A memory module, which uses a cloud database to recover abnormal alarm information during the coil automatic insulation tape wrapping process and to store and regularly clear generated historical event control instructions for a long time;

[0014] The output ends of the image acquisition and processing module and the tape wrapping mechanism sensing module are connected to the input end of the operation control module, the tape wrapping mechanism sensing module is bidirectionally connected to the safety monitoring module, the operation control module is bidirectionally connected to the safety monitoring module, the output end of the safety monitoring module is connected to the input end of the data interaction module, the output end of the data interaction module is connected to the input end of the operation control module, and the output ends of the safety monitoring module and the data interaction module are connected to the input end of the memory module.

[0015] Furthermore, the working method of the image acquisition and processing module includes the following steps:

[0016] Step 1: Use a binocular camera to simultaneously capture two target images of the same coil at different angles, and use a digital signal processor to filter and enhance the target images based on different coil shapes and ambient lighting conditions;

[0017] Step 2: Use binocular camera calibration to measure and calculate the internal and external parameters of the binocular camera, and perform dedistortion and offset correction processing on the target image according to the internal parameter matrix and distortion coefficient;

[0018] Step 3: Perform stereo matching on the corrected binocular image to obtain the depth map or point cloud data of the coil and the surrounding environment, and calculate the three-dimensional coordinates of the pixel points in the image scene according to the stereo matching results and the calibration parameters of the binocular camera;

[0019] Step 4: Calculate the distance and position information of the target object on the coil image according to the parallax principle to obtain the stereoscopic vision information of the coil image, and perform coil detection and shape fitting based on the stereoscopic vision information to obtain the geometric size, position and appearance posture of the coil, including at least the coil outline, radius and center position;

[0020] Step 5: Perform path planning for automatic wrapping of the coil with insulating tape based on the geometric size, position and appearance information of the coil, and determine the starting point, end point, path and coverage parameters of the wrapping tape.

[0021] Furthermore, the working method of the trajectory optimization auto-disturbance rejection closed-loop control model comprises the following steps:

[0022] S1, data input, the geometric size, position, appearance posture information of the coil, the path planning result of the automatic insulation tape wrapping of the coil and the generated control instructions are formatted and input into the trajectory optimization self-disturbance rejection closed-loop control model;

[0023] S2. Obtain control parameters and constraints from the input data, and initialize the execution parameters of the ADRC according to the trajectory optimization results and the real-time feedback information of the sensor. The control parameters and constraints include control objectives, execution functions, constraints and variable ranges.

[0024] S3, dynamically adjusting control parameters, the self-disturbance rejection controller performs operation control on the coil automatic insulation tape wrapping process based on the initialization control target and control parameters, the self-disturbance rejection controller includes a tracking differentiator, a nonlinear state feedback controller and an extended state observer, the tracking differentiator is used to track real-time state information, the nonlinear state feedback controller is used to perform closed-loop control according to real-time control parameters, the extended state observer is used to estimate the control state and the disturbance state in real time, and compensate for external disturbances as extended states, the self-disturbance rejection controller dynamically optimizes the tape wrapping operation control strategy according to the feedback information in the actual tape wrapping process and the trajectory optimization TO algorithm, the trajectory optimization TO algorithm adjusts the control parameters and the predetermined tape wrapping path by comparing the deviation between the actual path and the reference path, as well as the total disturbance of internal and external interference;

[0025] S4, inputting the optimized control command into the anti-disturbance control controller, and monitoring and providing feedback on the automatic insulation tape wrapping process of the coil through real-time feedback information from the sensor to achieve closed-loop control;

[0026] S5. Iterative calculation, parameter correction, and comparison between calculation results and true values ​​are performed on the trajectory optimization self-disturbance rejection closed-loop control model, and the threshold and number of iterations are set by adaptive parameter selection. Then, a neighbor list of the computing node is obtained according to the objective function and the distribution of the computing nodes, and the calculation speed is optimized by maintaining the neighbor list of the computing node. The trajectory optimization self-disturbance rejection closed-loop control model uses parallel computing to distribute computing tasks to multiple processors or computing nodes.

[0027] Furthermore, the operation control module uses a difference mining algorithm to mine the similarity between the current image processing result and the historical event. The historical event feature data set is C = {c1, c2, c3}, where c1 represents the starting feature set of the coil automatic insulation tape wrapping path, c1 = c 11 ,...,c 1i ,...,c 1n}, c2 represents the curvature feature set of the coil automatic insulation tape wrapping path, c2 = {c 21 ,...,c 2i ,...,c 2n}, c3 represents the end point feature set of the coil automatic insulation tape wrapping path, c3 = {c 31 ,...,c 3i ,...,c 3n}, n represents the total number of historical events, 1≤i≤n, and the output function formula of the similarity evaluation of the starting point feature, curvature feature and end point feature of the coil automatic insulation tape path is:

[0028]

[0029] In formula (1), W1 is the similarity evaluation result of the starting point feature, W2 is the similarity evaluation result of the curvature feature, W3 is the similarity evaluation result of the end point feature, σ is the weighted value of the starting point feature similarity, ω is the weighted value of the curvature feature similarity, μ is the weighted value of the end point feature similarity, v represents the damping attenuation factor, which is used to adjust the similarity influence of each event in the historical event feature data set on the current image processing result, max(c1) is the maximum value of the starting point feature set, min(c1) is the minimum value of the starting point feature set, max(c2) is the maximum value of the curvature feature set, min(c2) is the minimum value of the curvature feature set, max(c3) is the maximum value of the end point feature set, min(c3) is the minimum value of the end point feature set, c 1i is the eigenvalue of the i-th starting point, c 2i is the i-th curvature eigenvalue, c 3iThe eigenvalue of the i-th end point is, and the comprehensive similarity output function formula is:

[0030]

[0031] In formula (2), S is the comprehensive similarity between the current image processing result and the historical event, p represents the discount factor, which is used to weigh the previous comprehensive similarity evaluation result, and r is the immediate reward, which is used to adjust the final similarity evaluation result.

[0032] Furthermore, the tape wrapping mechanism includes a winding mobile base, a clamping mobile base, a mechanical arm control base, a mechanical arm, a wrapping component, a transverse clamping assembly and a longitudinal clamping assembly. A control base guide rail is fixedly installed on the top of the winding mobile base. The mechanical arm control base is connected to the control base guide rail via a slider. A wrapping component is arranged on the top of the mechanical arm control base. A rotating ring is rotatably connected to the inner wall of the wrapping component. A length-direction linear guide rail and a rack are installed on the upper part of the clamping mobile base. A length slider is provided on the length-direction linear guide rail. A width slider is fixedly installed on the top of the length slider. A linear guide in the degree direction and a forward and reverse bidirectional lead screw, the forward and reverse bidirectional lead screw is connected with a transverse clamping assembly via a nut, a width slider is provided on the width direction linear guide, the number of the width direction linear guides is multiple, two in a group, the two ends of the bottom of the transverse clamping assembly are connected to the width direction linear guide through width sliders, vertical clamping assemblies are installed at both ends of the first base, the transverse clamping assembly includes an angle adjustment seat and a clamping claw, the bottom end of the angle adjustment seat is fixedly installed on the upper end of the forward and reverse bidirectional lead screw, and the upper end of the angle adjustment seat is hinged to the clamping claw.

[0033] Furthermore, the clamping mechanism adjusts the height and angle according to the position information of the coil, and realizes automatic positioning and automatic release operations according to the geometric size and appearance posture of the coil. Each clamping mechanism is driven by a single servo motor. The trajectory optimization self-anti-disturbance closed-loop control model controls the servo motor in linkage, and positions each clamping mechanism according to the reference point and the constructed coordinate system. When the clamping mechanism clamps the coil area to automatically wrap the coil with insulating tape, the clamp automatically releases the coil and adjusts the height downward. The area operation ends, the height is adjusted upward and restored to the original height, and the clamp automatically clamps the coil to fix it.

[0034] Furthermore, the wrapping mechanism realizes automatic shifting and automatic wrapping operations according to the wrapping path and coverage range, and realizes the control of the tension of the wrapping components and the start and stop of the rotation of the rotating ring according to different coil sizes, insulating tape widths and winding speed parameters. The robotic arm control seat drives the rotating ring to rotate by driving the motor to drive the insulating tape to wrap around the coil, and adjusts the winding angle by the robotic arm.

[0035] Further, the cloud database records the number of times the historical event control instructions and abnormal alarm information of the coil package insulating tape are called through an access counter, and uses a timer to trigger a clearing operation. The timer periodically clears the historical event control instructions and abnormal alarm information data of the coil package insulating tape based on the LRU cache policy. The LRU cache policy sorts the cache information items in descending order of weight according to the number of times the cache information items are called and the call time, and clears the cache information items in reverse order. The historical event control instructions and abnormal alarm information are saved to a local storage device or uploaded to a storage platform for long-term storage.

[0036] The beneficial effects of the present invention are as follows:

[0037] 1. The present invention uses binocular images for processing and analysis to obtain the geometric dimensions, positions, and appearance postures of the coils, improving the accuracy and stability of the automatic insulating tape wrapping of the coils. At the same time, a sensor network is used to real-time feedback the working state of the tape wrapping mechanism, so as to realize the real-time adjustment and control of the automatic insulating tape wrapping process of the coils.

[0038] 2. The present invention combines big data analysis and a trajectory optimization active disturbance rejection closed-loop control model to realize the intelligent control of the automatic insulating tape wrapping process of the coils. By comparing and analyzing big data, the control instructions of similar historical events are quickly identified and called, improving the control efficiency and accuracy. At the same time, the combination of the trajectory optimization TO method and the ADRC active disturbance rejection controller enables the system to automatically adapt to the changes in the coil shape and external environment, so that the control system has stronger adaptability and execution ability for the tape wrapping process of complex coils.

[0039] 3. The present invention monitors abnormal situations in the automatic insulating tape wrapping process of the coils through real-time feedback information and triggers an emergency stop mechanism to ensure the safety of workers. At the same time, the data interaction module provides a visual dashboard and a human-machine interaction interface, facilitating users to manually set and change the control parameters of the automatic insulating tape wrapping of the coils, enhancing the controllability and operability of the automatic insulating tape wrapping of the coils. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of the overall system architecture of the present invention;

[0041] Figure 2 is a schematic diagram of the working process of the image acquisition and processing module in the present invention;

[0042] Figure 3 is a schematic diagram of the working process of the trajectory optimization active disturbance rejection closed-loop control model in the present invention;

[0043] Figure 4 is a three-dimensional architecture diagram of the tape wrapping mechanism in the present invention;

[0044] In the figure: 11, winding movable base; 12, robot arm control base; 13, robot arm; 14, wrapping component; 15, rotating ring; 21, clamping movable base; 23, angle adjustment base; 24, clamping claw; 25, longitudinal clamping assembly; 26, transverse clamping assembly. DETAILED DESCRIPTION

[0045] The following will be combined with the attached embodiment of the present invention Figure 1 To Attachment Figure 4 , the technical solutions in the embodiments of the present invention are clearly and completely described. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The embodiment of the present invention discloses a control system for automatically wrapping a coil with an insulating tape, the control system comprising:

[0047] Image acquisition and processing module: Use binocular image acquisition equipment to obtain the image data of the coil. Process and analyze the acquired images to extract the geometric size, position and appearance information of the coil. Perform path planning based on the processing results to determine the packaging path of the insulating tape.

[0048] Tape wrapping mechanism sensing module: deploys a sensor network, including flexible sensors, pressure sensors, photoelectric sensors, and temperature sensors. It monitors the working status of the tape wrapping mechanism in real time and transmits the feedback information to the operation control module. It provides real-time adjustment and control support to ensure the stable operation of the tape wrapping mechanism.

[0049] Operation control module: Combine image processing results and sensor feedback information to generate coil wrapping motion control instructions. Use trajectory optimization self-disturbance rejection closed-loop control model to accurately control and adjust the coil wrapping process. Use big data to analyze the similarity between the current image processing results and historical events. If the similarity is high, directly call the control instructions of the historical events to optimize operation efficiency and accuracy. Adaptive adjustments are made according to changes in coil shape and external environment to ensure the stability and reliability of the wrapping process.

[0050] Safety monitoring module: monitors abnormal conditions during the coil wrapping process based on real-time feedback from sensors. When an abnormality is detected, the emergency stop mechanism is triggered immediately, and an alarm message is issued through the alarm indicator and display. Provides guidance for users to troubleshoot and restore operations to ensure the safety of operators and equipment.

[0051] Data interaction module: Display the status and progress of the coil wrapping process in real time through a visual dashboard and report to management personnel. Provide a human-computer interaction interface to allow users to set and change the control parameters of the coil wrapping.

[0052] Memory module, which uses a cloud database to recover abnormal alarm information during the coil automatic insulation tape wrapping process and to store and regularly clear the generated historical event control instructions for a long time

[0053] Connection configuration: The output of the image acquisition and processing module and the tape wrapping mechanism perception module are connected to the input of the operation control module. The tape wrapping mechanism perception module is bidirectionally connected to the safety monitoring module to ensure real-time status monitoring and safety response. The operation control module is bidirectionally connected to the safety monitoring module for timely exception handling and safety control. The output of the tape wrapping mechanism perception module is connected to the input of the safety monitoring module to ensure the transmission and response of the safety status. The output of the safety monitoring module is connected to the input of the data interaction module to realize the transmission and management of abnormal information. The output of the data interaction module is connected to the input of the operation control module to support parameter setting and the transmission of control instructions. The output of the safety monitoring module and the data interaction module is connected to the input of the memory module.

[0054] In summary, the control system of the automatic coil insulating tape wrapping system has significantly improved the packaging quality and efficiency of the automatic coil insulating tape wrapping system through innovative designs such as high-precision path planning, real-time state feedback and adaptive adjustment, intelligent control and trajectory optimization, safety assurance and exception handling, data visualization and interactive convenience, and reduced the cost of manual intervention, providing strong support for the automated production of related industries.

[0055] The working method of the image acquisition and processing module includes the following detailed steps to realize the path planning of the coil automatic insulation tape wrapping:

[0056] Step 1: Image acquisition and preprocessing. Make sure the binocular camera is properly installed and calibrated, and the lens is clean and unobstructed. Start the binocular camera and simultaneously capture two target images of the same coil from two different angles (usually two viewing angles slightly offset in the horizontal direction). The acquired images are transferred to the digital signal processor (DSP), and appropriate filtering algorithms (such as Gaussian filtering, median filtering, etc.) are used to remove image noise, and enhancement algorithms (such as histogram equalization, contrast stretching, etc.) are used to improve image quality, especially for different coil shapes and ambient lighting conditions.

[0057] Step 2: Binocular camera calibration and image correction. Use professional calibration tools (such as calibration plates) to calibrate the binocular camera, measure and calculate the internal parameters (focal length, principal point coordinates, etc.) and external parameters (rotation matrix, translation vector, etc.) of the binocular camera. According to the internal parameter matrix and distortion coefficient (radial distortion, tangential distortion, etc.), dedistort the target image to eliminate the image deformation caused by lens distortion. Further perform offset correction on the image to ensure that the binocular images are aligned on the same horizontal line, providing a basis for subsequent stereo matching.

[0058] Step 3: Stereo matching and 3D reconstruction. Use appropriate stereo matching algorithms (such as block matching, feature matching, graph cut method, etc.) to match the corrected binocular images at the pixel level to obtain the disparity map. According to the disparity map and the calibration parameters of the binocular camera, calculate the 3D coordinates of each pixel in the image scene and generate a depth map or point cloud data of the coil and the surrounding environment.

[0059] Step 4: Coil detection and shape fitting. Using the parallax principle, combined with the depth map or point cloud data, calculate the distance and position information of the target object in the coil image. Based on the calculated distance and position information, further process the stereoscopic visual information of the coil image, such as texture, edge and other features. Use image segmentation, edge detection and other technologies to separate the coil area from the background. Use mathematical methods (such as least squares method, Hough transform, etc.) to fit the coil contour to obtain the geometric size (such as diameter, length, etc.), position and appearance posture (such as tilt angle, rotation direction, etc.) of the coil, including at least the coil contour, radius and center position.

[0060] Step 5: Path planning. According to the geometric size, position and appearance information of the coil, determine the key parameters such as the starting point, end point, path and coverage of the tape wrapping. Considering the shape characteristics of the coil and the physical properties of the insulating tape material, a path optimization algorithm (such as genetic algorithm, ant colony algorithm, adaptive AI path planning model, etc.) is used to optimize the tape wrapping path to ensure that the tape wrapping process is both efficient and material-saving. The optimized path parameters are converted into control instructions and transmitted to the operation control module to guide the tape wrapping mechanism to perform automatic tape wrapping operations.

[0061] Through the specific implementation of the above steps, the image acquisition and processing module can accurately obtain the three-dimensional information of the coil and provide a reliable path planning basis for the subsequent automatic insulation tape wrapping process.

[0062] The detailed implementation steps of the trajectory optimization self-disturbance rejection closed-loop control model working method are as follows:

[0063] S1. Data input: The geometric size, position, appearance and posture information of the coil, the path planning results of the coil automatic insulation tape wrapping and the generated control instructions are converted into a format and input into the trajectory optimization self-disturbance rejection closed-loop control model. These data will be collected and transmitted in real time through sensors and other devices, and processed and managed by the control system.

[0064] S2. Obtain control parameters and constraints: Obtain control parameters and constraints from input data, including control objectives, execution functions, constraints, and variable ranges, and initialize the execution parameters of the ADRC based on trajectory optimization results and real-time sensor feedback information.

[0065] S3. Dynamically adjust control parameters: The ADRC dynamically optimizes the taping operation control strategy based on the feedback information during the actual taping process and the trajectory optimization TO algorithm. The optimization objectives include control accuracy, control convergence time, and volatility indicators. The trajectory optimization TO algorithm adjusts the control parameters and the predetermined taping path by comparing the deviation between the actual path and the reference path, as well as the total disturbance of internal and external interference. The ADRC includes a tracking differentiator, a nonlinear state feedback controller, and an extended state observer, which are used to track real-time state information, perform closed-loop control according to real-time control parameters, estimate the control state and disturbance state in real time, and compensate for external disturbances as extended states.

[0066] S4, closed-loop control: The optimized control command is input into the anti-disturbance control controller, and the automatic insulation tape wrapping process of the coil is monitored and fed back through the real-time feedback information of the sensor to realize closed-loop control.

[0067] S5, iterative calculation and optimization: Iterative calculation, parameter correction, and comparison of calculation results and true values ​​are performed on the trajectory optimization self-disturbance rejection closed-loop control model. The threshold and number of iterations are set by adaptive parameter selection, and then the neighbor list of the computing node is obtained according to the objective function and the distribution of computing nodes, and the calculation speed is optimized by maintaining the neighbor list of the computing node. The trajectory optimization self-disturbance rejection closed-loop control model uses parallel computing to distribute computing tasks to multiple processors or computing nodes to improve computing efficiency.

[0068] Through the implementation of the above steps, the trajectory optimization self-disturbance rejection closed-loop control model can adjust the control strategy in real time, effectively deal with uncertain factors such as coil shape changes and environmental interference, and ensure the accuracy and stability of the coil automatic insulation tape wrapping process. The hardware working environment of the trajectory optimization self-disturbance rejection closed-loop control model usually includes the following components:

[0069] Sensors: Various sensors are required, such as inertial measurement units (IMUs), encoders, and visual sensors, to collect the position, speed, attitude, and other status information required by the system in real time.

[0070] Actuator: Actuators, such as motors and hydraulic cylinders, are required to perform corresponding motion control operations according to the control instructions generated by the control algorithm.

[0071] Controller: It needs to have powerful control computing capabilities and real-time performance. Usually, an embedded controller or industrial-grade computer is used as the main control device, responsible for running trajectory optimization and self-disturbance rejection closed-loop control algorithms.

[0072] Communication module: It needs to communicate data and transmit commands with the host system or other devices to receive external commands or send system status data.

[0073] Power supply: A stable and reliable power supply is required to ensure the normal operation of the entire system and provide sufficient power to devices such as sensors, actuators and controllers.

[0074] Mechanical structure: Appropriate mechanical structure design is required to support the installation and movement of sensors, actuators and other equipment, and to ensure the stability and accuracy of the system during movement.

[0075] In summary, the hardware working environment of the trajectory optimization self-disturbance rejection closed-loop control model needs to integrate various hardware devices and ensure that they can work together to achieve optimal adjustment of the system's motion trajectory and anti-interference capabilities.

[0076] The laboratory configuration uses a Core i964+128G memory computer, and the simulation environment is established using simulation software. The on-site experimental environment is set up, the simulation data accuracy is 95%, and the algorithm operation error does not exceed 2.5%. The trajectory optimization self-disturbance rejection closed-loop control model (Group A) and the PID control algorithm model (Group B) are used for comparative experiments. Three coils of different shapes are selected, namely, coils with irregular noses, regular circular coils, and regular square coils. The control model is input with artificial paths, and simulation work is performed in the experimental environment. The control effect and accuracy of the two algorithms are compared. The experiment is repeated 5 times to calculate the average value, and the record is shown in Table 1.

[0077] Table 1 Results Statistics

[0078]

[0079] It can be seen from Table 1 that the trajectory optimization self-disturbance rejection closed-loop control model has higher accuracy and better control effect in experiments with coils of different shapes. Its average error is lower than that of the PID control algorithm model, and its control accuracy is also higher than that of the PID control algorithm model. Therefore, in the automation control system, the application prospect of the trajectory optimization self-disturbance rejection closed-loop control model is broader.

[0080] The laboratory configuration uses a Core i964+128G memory computer, and the simulation environment is established using simulation software. The on-site experimental environment is set up, the simulation data accuracy is 95%, and the algorithm operation error does not exceed 2.5%. The trajectory optimization self-disturbance rejection closed-loop control model (Group A) and the PID control algorithm model (Group B) are used for comparative experiments. Three different operating environments are set up artificially, namely, the interference-free environment, the Du Guangzhai environment, and the shadow-blocking environment. The control model is input with artificial paths, and simulation work is performed in the experimental environment. The control effect and accuracy of the two algorithms are compared, and the experiment is repeated 5 times to calculate the average value, which is recorded in Table 2.

[0081] Table 2 Results Statistics

[0082]

[0083] It can be seen from Table 1 that in the non-interference operating environment, the average errors of the trajectory optimization self-disturbance rejection closed-loop control model and the PID control algorithm model are both small; in the interference operating environment, the advantage of the trajectory optimization self-disturbance rejection closed-loop control model is more obvious, and its average error is lower than that of the PID control algorithm model. In particular, in the environment with Du Guangzhai and the environment with shadow occlusion, the average error and maximum error of the trajectory optimization self-disturbance rejection closed-loop control model are significantly smaller than the PID control algorithm model.

[0084] The operation control module uses a difference mining algorithm to mine the similarity between the current image processing result and the historical event. The historical event feature data set is C = {c1, c2, c3}, where c1 represents the starting feature set of the coil automatic insulation tape wrapping path, c1 = {c 11 ,...,c 1i ,...,c 1n}, c2 represents the curvature feature set of the coil automatic insulation tape wrapping path, c2 = {c 21 ,...,c 2i ,...,c 2n}, c3 represents the end point feature set of the coil automatic insulation tape wrapping path, c3 = {c 31 ,...,c 3i ,...,c 3n}, n represents the total number of historical events, 1≤i≤n, and the output function formula of the similarity evaluation of the starting point feature, curvature feature and end point feature of the coil automatic insulation tape path is:

[0085]

[0086] In formula (1), W1 is the similarity evaluation result of the starting point feature, W2 is the similarity evaluation result of the curvature feature, W3 is the similarity evaluation result of the end point feature, σ is the weighted value of the starting point feature similarity, ω is the weighted value of the curvature feature similarity, μ is the weighted value of the end point feature similarity, v represents the damping attenuation factor, which is used to adjust the similarity influence of each event in the historical event feature data set on the current image processing result, max(c1) is the maximum value of the starting point feature set, min(c1) is the minimum value of the starting point feature set, max(c2) is the maximum value of the curvature feature set, min(c2) is the minimum value of the curvature feature set, max(c3) is the maximum value of the end point feature set, min(c3) is the minimum value of the end point feature set, c 1i is the eigenvalue of the i-th starting point, c 2i is the i-th curvature eigenvalue, c 3i is the eigenvalue of the i-th endpoint, and the comprehensive similarity output function formula is:

[0087]

[0088] In formula (2), S is the comprehensive similarity between the current image processing result and the historical event, p represents the discount factor, which is used to weigh the previous comprehensive similarity evaluation result, and r is the immediate reward, which is used to adjust the final similarity evaluation result.

[0089] Extract the starting point feature, curvature feature and end point feature of the current image processing result. Extract the starting point feature, curvature feature and end point feature of each historical event from the historical event feature data set. Calculate the Euclidean distance between the current image processing result and the starting point feature, curvature feature and end point feature of each historical event. According to formula (1), calculate the similarity evaluation results between the current image processing result and the starting point feature, curvature feature and end point feature of each historical event respectively. According to formula (2), calculate the comprehensive similarity evaluation results between the current image processing result and the historical event. Adjust the starting point feature similarity weighted value, curvature feature similarity weighted value and end point feature similarity weighted value, as well as the damping attenuation factor according to user needs and actual conditions. Continuously optimize the model according to experimental data to improve the accuracy and efficiency of similarity evaluation. Output the final similarity evaluation result to the control module for the next decision and control operation. The hardware working environment of the difference mining algorithm usually involves the following hardware equipment and environment:

[0090] Computing device: usually refers to the computer or server that runs the algorithm. Its performance directly affects the speed and efficiency of the algorithm. Hardware includes processor (CPU), graphics processing unit (GPU) and corresponding memory (RAM). For complex difference mining algorithms, high-performance multi-core CPU or GPU that supports parallel computing may be required to accelerate the operation of the algorithm.

[0091] Storage devices: The difference mining algorithm may need to store a large amount of historical event feature data sets and current image processing results, so sufficient storage capacity is required to store and manage this data. The hardware includes hard disk drives (HDD) or solid-state drives (SSD), and possible external storage devices.

[0092] Data transmission equipment: used for data input and output, including network interfaces, data transmission lines and related equipment. Especially when it comes to real-time data analysis and feedback, high-speed and stable data transmission equipment is crucial to the performance of the algorithm.

[0093] Sensors and acquisition devices: If the algorithm needs to process real-time data, such as sensor data or image data, then the relevant sensors and acquisition devices are also part of the hardware working environment. These devices are responsible for collecting and transmitting raw data to the computing device for subsequent algorithm analysis and processing.

[0094] Operating environment stability equipment: In order to ensure the stable operation of the algorithm, a certain degree of environmental control equipment may be required, such as air conditioning systems to maintain appropriate temperature and humidity, and power stabilizers or backup power systems to prevent system interruptions due to power problems.

[0095] In summary, the hardware working environment of the difference discovery algorithm requires suitable computing equipment, storage equipment, data transmission equipment, and may also involve sensors and acquisition equipment. At the same time, appropriate environmental stability equipment is required to ensure the stable operation of the algorithm and accurate processing of data.

[0096] The average error refers to the average value of the difference between the actual measurement value and the standard value, and is often used to evaluate the accuracy of an algorithm or model. The maximum error refers to the maximum value of the difference between the actual measurement value and the standard value, and is often used to evaluate the degree of deviation of an algorithm or model.

[0097] The laboratory configuration uses a Core i964+128G memory computer, and the simulation environment is established using simulation software. The on-site experimental environment is set up, the simulation data accuracy is 95%, and the algorithm operation error does not exceed 2.5%. The difference mining algorithm (Group A) and the traditional similarity algorithm (Group B) are used for comparative experiments. A coil with an irregular nose is selected, and the difference mining algorithm (Group A) and the traditional similarity algorithm (Group B) are used to find similar control instructions from the control instructions of historical events for simulated control execution. The control effects and accuracy of the two algorithms are compared. The experiment is repeated 5 times, and the records are as shown in Table 3;

[0098] Table 3 Results comparison statistics

[0099]

[0100]

[0101] It can be seen from Table 3 that the difference mining algorithm has higher control accuracy and smaller control error than the traditional similarity algorithm. In the five experiments, the average error of the difference mining algorithm model is smaller than that of the traditional similarity algorithm model, and the maximum error is also smaller. Therefore, in coil control, using the difference mining algorithm to search for control instructions can obtain better control effects and higher control accuracy.

[0102] 1. Winding mobile base: used to support the entire winding mechanism, with the control seat guide rail and rack fixed on the top, connected to the robot arm control seat through a slider.

[0103] 2. Clamping mobile base: used to clamp the coil, with length-direction linear guides and racks installed on the upper part, a length slider on the length-direction linear guide, and a width-direction linear guide and a forward and reverse bidirectional lead screw fixedly installed on the top.

[0104] 3. Robotic arm control seat: It is connected to the winding moving base through the control seat guide rail and slider. A winding component is set on the top, and a rotating ring is rotatably connected to the inner wall.

[0105] 4. Robotic arm: used to control the position and direction of the wrapped parts.

[0106] 5. Wrapping parts: Wrap the insulating tape around the coil by turning the ring.

[0107] 6. Horizontal clamping assembly: It can adapt to the clamping of various coil sizes through the forward and reverse bidirectional lead screw, connected to the length slider, equipped with an angle adjustment seat and clamping claws to clamp the middle of the coil. The bottom is connected to the linear guide in the width direction through the width slider.

[0108] 7. Longitudinal clamping assembly: installed at both ends of the clamping base, used to clamp the coil ends.

[0109] The clamping movable base can control its position in the length direction through the linear guide and rack in the length direction, and clamp the coil in the width direction through the forward and reverse bidirectional screw and the clamping claw. The angle adjustment seat can be connected to the forward and reverse bidirectional screw to control the angle and direction of the clamping claw in the lateral direction. At the same time, the robot arm can realize the position change and angle adjustment during the winding process by controlling the position and moving direction of the wrapping component. Finally, the longitudinal clamping assembly can clamp the two ends of the coil to ensure that the coil is firmly clamped.

[0110] In short, the taping mechanism can control the position and direction of the insulating tape during the taping process, achieving an accurate, stable and efficient taping process. In practical applications, operators can select appropriate clamping components and winding mechanisms according to the size and shape of the coil to ensure the quality and efficiency of the taping. At the same time, the motion trajectory of the robot arm and the taping component can be optimized through the trajectory planning algorithm to make the taping process more stable and efficient.

[0111] The clamping mechanism adjusts the height and angle according to the position information of the coil to ensure the stability and accuracy of the coil during the wrapping process, and realizes automatic positioning and automatic release operations according to the geometric size and appearance of the coil. Each clamping mechanism is driven by a single servo motor, and the position and posture of the clamping mechanism can be adjusted by controlling the angle and height.

[0112] The trajectory optimization self-disturbance rejection closed-loop control model is used to control the servo motor in a linked manner to make the positioning of the clamping mechanism more accurate and efficient. At the same time, the positioning algorithm based on the reference point and the construction coordinate system can accurately control the position and posture of each clamping mechanism.

[0113] When the clamping mechanism clamps the coil area for automatic insulation tape wrapping, the clamping claw can automatically release the coil so that the wrapping mechanism can wrap the tape smoothly. After the regional operation is completed, when the clamping mechanism needs to clamp the coil again, adjust the height upward and restore it to the original height. The clamping claw automatically clamps the coil and fixes it, completing the tape wrapping operation.

[0114] Through the above control and algorithm, the automatic taping operation of the coil can be realized, the production efficiency and product quality can be improved, and human errors and labor intensity can be reduced.

[0115] The wrapping mechanism realizes automatic shifting and automatic wrapping operations according to the wrapping path and coverage range, and controls the tension of the wrapping components and starts and stops the rotation of the rotating ring according to different coil sizes, insulating tape widths and winding speed parameters. The robotic arm control seat drives the rotating ring to rotate through the driving motor to drive the insulating tape to wrap around the coil, and adjusts the winding angle through the robotic arm.

[0116] The cloud database records the number of times the coil wrap insulation tape historical event control instructions and abnormal alarm information are called through access counters, so as to monitor and manage the operation of the coil wrap insulation tape in real time. At the same time, a timer is used to trigger the clearing operation to avoid excessive use of storage space and computing resources by the database.

[0117] The timer periodically clears the historical event control instructions and abnormal alarm information of the coil package insulation tape based on the LRU (Least Recently Used) cache strategy. The LRU cache strategy sorts the cache information items by weight from large to small according to the number of times the cache information items are called and the call time, and clears the cache information items in reverse order to ensure that the high-frequency and earliest used operation instructions are stored for a long time.

[0118] Historical event control instructions and abnormal alarm information can be saved to a local storage device or uploaded to a storage platform for long-term storage. These historical records can be used for post-analysis and optimization of the operation process of coil wrapping insulation tape, and can be used as a reference for quality management.

[0119] Through the above measures, the historical event control instructions and abnormal alarm information of the coil wrap insulation tape can be effectively managed and saved to improve production efficiency and quality management level.

[0120] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

Claims

1. A control system for automatically wrapping a coil with insulating tape, characterized in that: The control system comprises: An image acquisition and processing module, which is used to acquire binocular images of the coil, and to process and analyze the images to obtain geometric dimensions, position, and appearance information of the coil, so as to realize path planning for automatically wrapping the coil with insulating tape; A taping mechanism sensing module, wherein the taping mechanism sensing module uses a sensor network to provide real-time working status feedback of the taping mechanism used in the process of automatically taping the coil, and transmits the feedback information to the next layer for real-time adjustment and operation control, wherein the sensor network includes at least a flexible sensor, a pressure sensor, a photoelectric sensor, and a temperature sensor; An operation control module, wherein the operation control module generates motion control instructions for automatic coil insulation tape wrapping according to image processing results and real-time sensor feedback information, and controls and adjusts the automatic coil insulation tape wrapping process based on a trajectory optimization self-disturbance rejection closed-loop control model. The operation control module compares and analyzes the similarity between the current image processing results and historical events through big data. If the similarity is greater than a threshold, the control instructions of the historical events are directly called for operation control. The trajectory optimization self-disturbance rejection closed-loop control model controls and executes a predetermined tape wrapping path and parameters by combining a trajectory optimization TO method and an ADRC self-disturbance rejection controller, and adaptively adjusts the control process according to changes in coil shape and external environment. A safety monitoring module, which monitors abnormal conditions during the automatic coil wrapping process based on real-time feedback information from sensors. When abnormal conditions are detected during the automatic coil wrapping process, an emergency stop mechanism is triggered and an alarm message is sent out through an alarm indicator light and a display screen, and the user performs troubleshooting and recovery operations based on the alarm message; A data interaction module, which reports the real-time status and progress of the coil automatic insulation tape wrapping process to the management personnel through a visual dashboard, and allows the user to manually set and change the control parameters of the coil automatic insulation tape wrapping through a human-computer interaction interface; A memory module, which uses a cloud database to recover abnormal alarm information during the coil automatic insulation tape wrapping process and to store and regularly clear generated historical event control instructions; The output ends of the image acquisition and processing module and the tape wrapping mechanism sensing module are connected to the input end of the operation control module, the tape wrapping mechanism sensing module is bidirectionally connected to the safety monitoring module, the operation control module is bidirectionally connected to the safety monitoring module, the output end of the safety monitoring module is connected to the input end of the data interaction module, the output end of the data interaction module is connected to the input end of the operation control module, and the output ends of the safety monitoring module and the data interaction module are connected to the input end of the memory module.

2. A control system for automatically wrapping a coil with insulating tape according to claim 1, characterized in that: The working method of the image acquisition and processing module comprises the following steps: Step 1: Use a binocular camera to simultaneously capture two target images of the same coil at different angles, and use a digital signal processor to filter and enhance the target images based on different coil shapes and ambient lighting conditions; Step 2: Use binocular camera calibration to measure and calculate the internal and external parameters of the binocular camera, and perform dedistortion and offset correction processing on the target image according to the internal parameter matrix and distortion coefficient; Step 3: Perform stereo matching on the corrected binocular image to obtain the depth map or point cloud data of the coil and the surrounding environment, and calculate the three-dimensional coordinates of the pixel points in the image scene according to the stereo matching results and the calibration parameters of the binocular camera; Step 4: Calculate the distance and position information of the target object on the coil image according to the parallax principle to obtain the stereoscopic vision information of the coil image, and perform coil detection and shape fitting based on the stereoscopic vision information to obtain the geometric size, position and appearance posture of the coil, including at least the coil outline, radius and center position; Step 5: Perform path planning for automatic wrapping of the coil with insulating tape based on the geometric size, position and appearance information of the coil, and determine the starting point, end point, path and coverage parameters of the wrapping tape.

3. A control system for automatically wrapping a coil with insulating tape according to claim 1, characterized in that: The working method of the trajectory optimization self-disturbance rejection closed-loop control model comprises the following steps: S1, data input, the geometric size, position, appearance posture information of the coil, the path planning result of the automatic insulation tape wrapping of the coil and the generated control instructions are formatted and input into the trajectory optimization self-disturbance rejection closed-loop control model; S2. Obtain control parameters and constraints from the input data, and initialize the execution parameters of the ADRC according to the trajectory optimization results and the real-time feedback information of the sensor. The control parameters and constraints include control objectives, execution functions, constraints and variable ranges. S3, dynamically adjusting control parameters, the self-disturbance rejection controller performs operation control on the coil automatic insulation tape wrapping process based on the initialization control target and control parameters, the self-disturbance rejection controller includes a tracking differentiator, a nonlinear state feedback controller and an extended state observer, the tracking differentiator is used to track real-time state information, the nonlinear state feedback controller is used to perform closed-loop control according to real-time control parameters, the extended state observer is used to estimate the control state and the disturbance state in real time, and compensate for external disturbances as extended states, the self-disturbance rejection controller dynamically optimizes the tape wrapping operation control strategy according to the feedback information in the actual tape wrapping process and the trajectory optimization TO algorithm, the trajectory optimization TO algorithm adjusts the control parameters and the predetermined tape wrapping path by comparing the deviation between the actual path and the reference path, as well as the total disturbance of internal and external interference; S4, inputting the optimized control command into the anti-disturbance control controller, and monitoring and providing feedback on the automatic insulation tape wrapping process of the coil through real-time feedback information from the sensor to achieve closed-loop control; S5. Iterative calculation, parameter correction, and comparison between calculation results and true values ​​are performed on the trajectory optimization self-disturbance rejection closed-loop control model, and the threshold and number of iterations are set by adaptive parameter selection. Then, a neighbor list of the computing node is obtained according to the objective function and the distribution of the computing nodes, and the calculation speed is optimized by maintaining the neighbor list of the computing node. The trajectory optimization self-disturbance rejection closed-loop control model uses parallel computing to distribute computing tasks to multiple processors or computing nodes.

4. A control system for automatically wrapping a coil with insulating tape according to claim 1, characterized in that: The operation control module uses a difference mining algorithm to mine the similarity between the current image processing result and the historical event. The historical event feature data set is C = {c1, c2, c3}, where c1 represents the starting point feature set of the coil automatic insulation tape wrapping path, c1 = {c 11 ,...,c 1i ,...,c 1n }, c2 represents the curvature feature set of the coil automatic insulation tape wrapping path, c2 = {c 21 ,...,c 2i ,...,c 2n }, c3 represents the end point feature set of the coil automatic insulation tape path, c3 = {c 31 ,...,c 3i ,...,c 3n }, n represents the total number of historical events, 1≤i≤n, and the output function formula of the similarity evaluation of the starting point feature, curvature feature and end point feature of the coil automatic insulation tape path is: In formula (1), W1 is the similarity evaluation result of the starting point feature, W2 is the similarity evaluation result of the curvature feature, W3 is the similarity evaluation result of the end point feature, σ is the weighted value of the starting point feature similarity, ω is the weighted value of the curvature feature similarity, μ is the weighted value of the end point feature similarity, v represents the damping attenuation factor, which is used to adjust the similarity influence of each event in the historical event feature data set on the current image processing result, max(c1) is the maximum value of the starting point feature set, min(c1) is the minimum value of the starting point feature set, max(c2) is the maximum value of the curvature feature set, min(c2) is the minimum value of the curvature feature set, max(c3) is the maximum value of the end point feature set, min(c3) is the minimum value of the end point feature set, c 1i is the eigenvalue of the i-th starting point, c 2i is the i-th curvature eigenvalue, c 3i is the eigenvalue of the i-th endpoint, and the comprehensive similarity output function formula is: In formula (2), S is the comprehensive similarity between the current image processing result and the historical event, p represents the discount factor, which is used to weigh the previous comprehensive similarity evaluation result, and r is the immediate reward, which is used to adjust the final similarity evaluation result.

5. The control system for automatically wrapping the coil with insulating tape according to claim 1, characterized in that: The tape wrapping mechanism includes a winding mobile base, a clamping mobile base, a mechanical arm control base, a mechanical arm, a wrapping component, a transverse clamping assembly and a longitudinal clamping assembly. A control seat guide rail is fixedly installed on the top of the winding mobile base. The mechanical arm control base is connected to the control seat guide rail through a slider. A wrapping component is arranged on the top of the mechanical arm control base. A rotating ring is rotatably connected to the inner wall of the wrapping component. A length-direction linear guide rail and a rack are installed on the upper part of the clamping mobile base. A length slider is provided on the length-direction linear guide rail. A width-direction slider is fixedly installed on the top of the length slider. A linear guide and a forward and reverse bidirectional lead screw, a transverse clamping assembly being connected to the forward and reverse bidirectional lead screw via a nut, a width slider being provided on the width-direction linear guide, a plurality of width-direction linear guides being provided, two forming a group, both ends of the bottom of the transverse clamping assembly being connected to the width-direction linear guide via width sliders, vertical clamping assemblies being installed at both ends of the first base, the transverse clamping assembly comprising an angle adjustment seat and a clamping claw, the bottom end of the angle adjustment seat being fixedly installed on the upper end of the forward and reverse bidirectional lead screw, and the upper end of the angle adjustment seat being hinged to the clamping claw.

6. A control system for automatically wrapping a coil with insulating tape according to claim 5, characterized in that: The clamping mechanism adjusts the height and angle according to the position information of the coil, and realizes automatic positioning and automatic release operations according to the geometric size and appearance posture of the coil. Each clamping mechanism is driven by a single servo motor. The trajectory optimization self-anti-disturbance closed-loop control model controls the servo motor in linkage, and positions each clamping mechanism according to the reference point and the construction coordinate system. When the clamping mechanism clamps the coil area to automatically wrap the coil with insulating tape, the clamp automatically releases the coil and adjusts the height downward. The area operation ends, the height is adjusted upward and restored to the original height, and the clamp automatically clamps the coil to fix it.

7. A control system for automatically wrapping a coil with insulating tape according to claim 5, characterized in that: The wrapping mechanism realizes automatic shifting and automatic wrapping operations according to the wrapping path and coverage range, and realizes the control of the tension of the wrapping components and the start and stop of the rotation of the rotating ring according to different coil sizes, insulating tape widths and winding speed parameters. The robotic arm control seat drives the rotating ring to rotate by driving the motor to drive the insulating tape to wrap around the coil, and adjusts the winding angle by the robotic arm.

8. The control system for automatically wrapping a coil with insulating tape according to claim 1, characterized in that: The cloud database records the number of times the historical event control instructions and abnormal alarm information of the coil wrap insulation tape are called through an access counter, and uses a timer to trigger a clearing operation. The timer periodically clears the historical event control instructions and abnormal alarm information data of the coil wrap insulation tape based on an LRU cache strategy. The LRU cache strategy sorts the cache information items by weight from large to small according to the number of times the cache information items are called and the calling time, and clears the cache information items in reverse order. The historical event control instructions and abnormal alarm information are saved in a local storage device or uploaded to a storage platform for long-term storage.

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