Regulation and control method and device for automatic winding and unwinding machine driven by encoder and medium

Abstract

The invention provides an encoder-driven automatic take-up and pay-off machine regulation and control method, equipment and a medium, and relates to the technical field of automation control, an encoder is installed on a driving motor of a target take-up and pay-off machine, and the target take-up and pay-off length is determined according to production demand information and converted into target rotation displacement; a sensing sensor group is arranged to obtain a pay-off multi-dimensional sensing data stream, a pay-off pulse signal stream is read and obtained through an encoder, the pay-off multi-dimensional sensing data stream and the pay-off pulse signal stream are subjected to analysis processing and displacement prediction, a PID controller is adopted to carry out regulation and control analysis on rotation prediction displacement and target rotation displacement, and a displacement correction control quantity is obtained. And performing closed-loop feedback regulation and control on the target take-up and pay-off machine. The technical problems that in the prior art, regulation and control of a take-up and pay-off machine depend on manual or simple mechanical control, regulation and control precision is low, and efficiency is not high are solved. The technical effect that the winding and unwinding precision and the control efficiency are improved through cooperative work of the encoder and the sensor is achieved.

Description

Technical Field

[0001] The present invention relates to the field of automation control technology, and in particular to a control method, equipment and medium for an automatic winding and discharging wire machine driven by an encoder. Background Art

[0002] The wire-winding and cable-winding machine is a special equipment for winding and arranging wires in wire and cable factories. In the existing technology, the control process of the wire-winding and cable-winding machine mostly relies on manual operation or simple mechanical control. Although manual operation has a certain degree of flexibility, it is often difficult to ensure the accuracy and consistency of the control due to the experience and skill level of the operator. At the same time, manual operation is also easily affected by factors such as fatigue and distraction, resulting in errors in the control process. Although simple mechanical control can improve the stability and reliability of control to a certain extent, its control accuracy and efficiency are still limited. Faced with wires and cables of different specifications and materials, mechanical control is often difficult to achieve precise matching and flexible adjustment.

[0003] In the prior art, there is a technical problem that the regulation of the take-up and discharge wire machine relies on manual or simple mechanical control, resulting in low regulation accuracy and low efficiency. Summary of the invention

[0004] The present application provides an encoder-driven automatic winding and discharging wire machine control method, device and medium, which are used to solve the technical problem in the prior art that the winding and discharging wire machine control relies on manual or simple mechanical control, resulting in low control accuracy and low efficiency.

[0005] In view of the above problems, the present application provides an encoder-driven automatic winding and discharging wire machine control method, device and medium.

[0006] In the first aspect of the present application, the present application provides an encoder-driven automatic winding and discharge wire drawing machine control method, the method comprising: installing an encoder on a drive motor of a target winding and discharge wire drawing machine, the encoder being coaxially connected to the drive motor, and initializing the encoder; determining a target winding and discharge length of the target winding and discharge wire drawing machine according to production demand information of an automated production line, and converting the target winding and discharge length into a target rotational displacement of the drive motor; deploying a sensing sensor group on the automated production line, collecting and acquiring a wire-paying multi-dimensional sensing data stream through the sensing sensor group, and simultaneously acquiring a wire-paying pulse signal stream through real-time reading by the encoder; analyzing and processing the wire-paying multi-dimensional sensing data stream and the wire-paying pulse signal stream and predicting displacement to obtain a rotational predicted displacement; using a PID controller to control and analyze the rotational predicted displacement and the target rotational displacement to obtain a displacement correction control amount, and performing closed-loop feedback control on the target winding and discharge wire drawing machine based on the displacement correction control amount.

[0007] In a second aspect of the present application, the present application provides an electronic device, characterized in that the electronic device comprises: a processor; a memory for storing instructions executable by the processor; wherein the processor is used to execute the encoder-driven automatic winding and discharging wire machine control method provided in the present application According to a third aspect of the present application, a computer-readable storage medium is provided, wherein the storage medium stores a computer program, so that the computer program is used to execute the encoder-driven automatic winding and discharging wire machine control method provided in the present application.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided by the embodiment of the present application is to install an encoder on the drive motor of the target winding and discharging wire drawing machine, the encoder is coaxially connected to the drive motor, and the encoder is initialized. According to the production demand information of the automated production line, the target winding and discharging length of the target winding and discharging wire drawing machine is determined, and the target winding and discharging length is converted into the target rotational displacement of the drive motor. A sensing sensor group is arranged on the automated production line, and the multi-dimensional sensing data stream of the wire drawing is acquired through the sensing sensor group. At the same time, the wire drawing pulse signal stream is acquired through real-time reading by the encoder, and the multi-dimensional sensing data stream of the wire drawing and the wire drawing pulse signal stream are analyzed and processed, and displacement prediction is performed to obtain the rotational prediction displacement, and the rotational prediction displacement and the target rotational displacement are regulated and analyzed by a PID controller to obtain the displacement correction control amount, and the target winding and discharging wire drawing machine is closed-loop feedback regulated based on the displacement correction control amount. The technical effect of improving the winding and discharging wire drawing accuracy and control efficiency through the coordinated work of the encoder and the sensor is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for controlling an automatic winding and discharging wire machine driven by an encoder provided in the present application; Figure 2 A schematic diagram of the structure of an electronic device provided in this application.

[0011] Description of the reference numerals: processor 21 , memory 22 , input device 23 , output device 24 . DETAILED DESCRIPTION

[0012] The present application provides an encoder-driven automatic winding and discharge wire machine control method, device and medium, which are used to solve the technical problem in the prior art that the winding and discharge wire machine control relies on manual or simple mechanical control, resulting in low control accuracy and low efficiency. The technical effect of improving the winding and discharge wire accuracy and control efficiency through the coordinated work of the encoder and the sensor is achieved.

[0013] Below, the technical solutions in the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. 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. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all of them.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides an encoder-driven automatic winding and unwinding wire machine control method, the method comprising: An encoder is installed on a driving motor of a target winding and discharging wire machine, the encoder is coaxially connected to the driving motor, and the encoder is initialized.

[0015] Specifically, select an encoder that matches the drive motor of the winding and discharge wire machine. The encoder is a sensor device that is used to obtain information such as the position, angle or speed of mechanical rotation or displacement, and converts it into an electrical signal for output. When selecting the encoder, ensure that the specifications of the encoder and the drive motor, such as the shaft diameter and interface type, match, and ensure that the encoder and the drive motor of the target winding and discharge wire machine can be coaxially connected, that is, the rotating shaft of the encoder is tightly coupled with the output shaft of the drive motor and rotates together. Then, the encoder is fixedly installed on the drive motor of the target winding and discharge wire machine. During installation, ensure that the axis of the encoder and the drive motor are completely aligned to achieve coaxial connection. The coaxial connection is a precise alignment method on the mechanical structure, which can ensure that the encoder can accurately capture the rotation angle and position change of the drive motor without causing data distortion or error due to eccentricity. After the installation is completed, the encoder is initialized. The initialization setting includes calibrating the initial position, setting the zero reference point, and testing and verifying the pulse signal channel. The zero reference point is the starting position recorded by the encoder, which is used to determine the benchmark during the entire movement process to ensure that the encoder reading matches the actual state of the drive motor. The pulse signal is the core data form generated by the encoder, which reflects the rotation state by recording the number of electrical pulses each time the motor shaft rotates a certain angle. During the verification process, it is also necessary to confirm that the pulse signal stream generated by the encoder is free of noise interference and that the signal amplitude is stable to ensure that the signal output by the encoder can be reliably received and processed. By installing and initializing the encoder on the drive motor of the target take-up and discharge wire drawing machine, real-time monitoring and precise control of the drive motor's rotation state can be achieved, thereby improving the control accuracy and stability of the automatic take-up and discharge wire drawing machine.

[0016] According to the production demand information of the automated production line, the target take-up and discharge length of the target take-up and discharge wiring machine is determined, and the target take-up and discharge length is converted into the target rotational displacement of the drive motor.

[0017] Specifically, the production demand information of the automated production line is obtained through the production management system, including the size specifications and process flow requirements of the products to be produced, and the target take-up and discharge length of the target take-up and discharge wire machine is determined based on the production demand information. The target take-up and discharge length refers to the total cable length or take-up and discharge range that the take-up and discharge wire machine needs to operate during the entire production process, which determines the total rotation amount that the drive motor needs to complete. The linear conversion factor of the drive motor is obtained. The linear conversion factor is obtained through equipment calibration, which is the length of cable taken up per rotation of the drive motor shaft, recorded as a unit rotation length value. According to the formula: target rotational displacement = target take-up and discharge length / unit rotational length value, the target take-up and discharge length is converted into the target rotational displacement of the drive motor, thereby realizing precise quantitative control of the take-up and discharge wire machine, ensuring that the operation of the take-up and discharge wire machine can accurately meet production requirements and improve control efficiency and control accuracy.

[0018] A sensing sensor group is arranged on the automated production line, and a pay-off multi-dimensional sensing data stream is collected and acquired through the sensing sensor group, and a pay-off pulse signal stream is obtained through real-time reading by the encoder.

[0019] Specifically, a sensor group is arranged on the automated production line. The sensor group includes but is not limited to relevant production sensors such as material tension, speed, and temperature that affect the displacement of the winding and discharging lines, and is used to monitor and collect various production parameters that are closely related to the displacement of the winding and discharging lines. The positions of multiple sensor devices in the sensor group are set according to actual needs. For example, the tension sensor is usually installed on the cable stretching path to measure the tensile force of the cable, while the speed sensor is installed near the pay-off wheel to record the linear movement speed of the cable in real time. By acquiring the data collected in real time by the sensor group, a multi-dimensional pay-off perception data stream is formed. The multi-dimensional pay-off perception data stream contains information of multiple dimensions in the pay-off process, such as real-time data of tension changes, speed fluctuations, and temperature, which provides diversity and dynamism for the state analysis of the pay-off process. At the same time, the pay-off pulse signal stream is read in real time by the encoder installed on the drive motor. The pay-off pulse signal stream is a data stream that reflects the rotation state of the motor. It is composed of an electrical pulse signal output by the encoder every time it rotates a certain angle. The pay-off pulse signal is recorded by pulse counting and timestamps, which can accurately describe the rotation displacement and dynamic characteristics of the motor. By obtaining the multi-dimensional sensing data stream of the wire release collected by the sensing sensor group and combining it with the wire release pulse signal stream provided by the encoder, it is possible to accurately predict and monitor the motor displacement, thereby improving the intelligent management and control of the discharge line process of the automated production line.

[0020] The multi-dimensional sensing data stream of the pay-off and the pulse signal stream of the pay-off are analyzed, processed and displacement predicted to obtain the predicted rotation displacement.

[0021] Specifically, after obtaining the pay-off multi-dimensional perception data stream and the pay-off pulse signal stream, the two data sources are first preprocessed to ensure the quality and consistency of the data. After the pay-off multi-dimensional perception data stream is collected by the sensor, it may be affected by environmental noise. Data cleaning is performed to remove abnormal values ​​and invalid data. At the same time, the pay-off pulse signal stream is filtered and signal shaped. Then, deep learning technology is used to comprehensively analyze the current preprocessed pay-off multi-dimensional perception data stream and the pay-off pulse signal stream to predict the motor rotation displacement and obtain the predicted rotation displacement. The predicted rotation displacement is the predicted value of the motor rotation displacement after a period of time in the future or after the production line is completed. By performing rotation prediction displacement on the current automated production line situation, strong data support is provided for the intelligent control and optimization of the automated production line, thereby improving the accuracy and reliability of the control of the automatic winding and discharging wire drawing machine.

[0022] Furthermore, the method for obtaining the predicted rotational displacement includes: analyzing and processing the pay-off pulse signal stream to obtain a current motor motion parameter set; performing noise data identification and data preprocessing on the pay-off multidimensional perception data stream to obtain a standard pay-off multidimensional perception data stream; performing feature extraction and feature fusion based on the standard pay-off multidimensional perception data stream to obtain pay-off fusion perception feature data; establishing a displacement integrated prediction network, and performing displacement prediction on the current motor motion parameter set and the pay-off fusion perception feature data based on the displacement integrated prediction network to obtain the predicted rotational displacement.

[0023] Specifically, in the process of realizing the comprehensive processing and prediction of the wire-laying pulse signal stream and the wire-laying multi-dimensional perception data stream, the wire-laying pulse signal stream is first analyzed and processed, including filtering and signal shaping processing, and the wire-laying pulse signal stream is parsed to obtain the current motor motion parameter set, which includes information such as motor motion speed, rotational displacement and acceleration. At the same time, the wire-laying multi-dimensional perception data stream is subjected to noise data identification and data preprocessing. The multi-dimensional perception data stream includes a variety of dynamic parameters such as material tension, speed, and temperature, which are obtained through sensor collection and are easily affected by environmental interference and equipment errors. Through noise identification and preprocessing, the data outliers are cleaned to obtain a standard wire-laying multi-dimensional perception data stream. Then, through the correlation feature extraction technology, the standard wire-laying multi-dimensional perception data stream is subjected to correlation feature extraction and fusion to obtain wire-laying fusion perception feature data, such as the size, direction and stability of material tension, the size, direction of speed, temperature fluctuation, gradient, etc. Through the deep learning model, a displacement integrated prediction network is established in combination with the motor motion parameter set and the line-paying fusion perception feature data. The displacement integrated prediction network can automatically learn the potential laws and patterns in the data, so as to achieve accurate prediction of the motor rotation displacement. The displacement integrated prediction network is used to predict the displacement of the current motor motion parameter set and the line-paying fusion perception feature data to obtain accurate and reliable rotation prediction displacement, thereby improving the accuracy and reliability of the automatic winding and discharging wire drawing machine control.

[0024] Furthermore, the obtaining of the current motor motion parameter set includes: using a Kalman filter to perform filtering preprocessing on the pay-off pulse signal stream to obtain an available pay-off pulse signal stream; performing signal shaping processing on the available pay-off pulse signal stream according to the data application standard of the pay-off wire machine to obtain a standard pay-off pulse signal stream; performing pulse counting and pulse timestamp recording on the standard pay-off pulse signal stream to determine the total number of pay-off signal pulses and pulse timestamp information; performing motion parameter calculation processing based on the total number of pay-off signal pulses and pulse timestamp information to obtain the current motor motion parameter set.

[0025] Specifically, the wire-releasing pulse signal is filtered and preprocessed by a Kalman filter. Kalman filtering is a method for optimizing signal stability, which can effectively remove random noise in the pulse signal while retaining the real motion information. The signal is filtered by a Kalman filter to ensure the quality of the wire-releasing pulse signal stream, so that it can reflect the actual motion state of the drive motor and generate a highly reliable and available wire-releasing pulse signal stream. The data application standard of the wire-receiving and discharging machine is obtained by referring to industry standards and specifications or expert opinions. According to the data application standard of the wire-receiving and discharging machine, the available wire-releasing pulse signal stream is subjected to signal shaping processing. Signal shaping is a signal standardization technology, including steps such as amplification and shaping. Through signal shaping processing, a form that meets the data standard of the wire-receiving and discharging machine is obtained to generate a standard wire-releasing pulse signal stream. Next, the standard wire-releasing pulse signal stream is pulse counted and pulse timestamp recorded. The pulse count refers to the total number of pulses generated by the encoder within a certain period of time. The pulse timestamp record refers to the precise time point of marking each pulse. The pulse count and timestamp record can fully describe the rotation behavior of the motor in the time dimension, thereby obtaining the total number of wire-releasing signal pulses and pulse timestamp information. Based on the total number of release signal pulses and pulse timestamp information, motion parameter calculation processing is performed, that is, multiple motion parameters such as the current motor speed, acceleration and displacement are calculated. For example, the speed can be calculated by the ratio of the total number of pulses to the time interval, and the displacement is obtained by multiplying the total number of pulses by the unit rotation displacement. Through calculation, multiple calculation results are obtained, and the multiple calculation results are integrated to form the current motor motion parameter set, including information such as the current motor speed, displacement and dynamic change trend. By extracting the current motor motion parameter information from the encoder pulse signal, the accurate quantification of the motor motion is achieved, providing a solid foundation for the high precision and real-time performance of the entire control solution.

[0026] Furthermore, the method for obtaining the fused radiograph perception feature data includes: extracting associated features based on the standard radiograph multidimensional perception data stream to obtain an associated radiograph multidimensional perception feature data set; decentralizing the associated radiograph multidimensional perception feature data set to obtain a covariance matrix, and solving the covariance matrix to obtain eigenvalues ​​and eigenvectors; selecting principal component information according to the eigenvalues ​​and eigenvectors, and performing principal component dimensionality reduction on the standard radiograph multidimensional perception data stream based on the principal component information to obtain a key radiograph perception feature data set; and using a feature-level fusion algorithm to fuse the key radiograph perception feature data set to obtain the fused radiograph perception feature data.

[0027] Specifically, the multidimensional perception data stream is subjected to correlation feature extraction. Correlation feature extraction is a technology that extracts key information by analyzing the relationship between different perception data. Through correlation feature extraction, a multidimensional perception feature data set of correlation release is obtained, including the size, direction and stability of material tension, the size and direction of speed, temperature fluctuation, gradient, etc. Then, the correlation feature data set is decentralized. Decentralization is an important step in data standardization. By eliminating the influence of the average value of the data, the data distribution is more concentrated on the origin, and the characteristic covariance matrix is ​​calculated. The covariance matrix is ​​a matrix that represents the linear relationship between multiple variables. The eigenvalues ​​and eigenvectors are obtained by solving the covariance matrix. The eigenvalues ​​and eigenvectors represent the strength and direction of the correlation between the variables, respectively. Then, the principal component information is obtained by sorting and screening the eigenvalues ​​and eigenvectors of the covariance matrix. The principal component information is used to describe the most representative feature patterns in the multidimensional perception feature data set of correlation release, thereby realizing the dimensionality reduction processing of the multidimensional perception feature data set of correlation release. Optionally, principal component analysis dimensionality reduction technology is used to implement principal component dimensionality reduction on the standard pay-off multidimensional perception data stream, compressing the high-dimensional correlated pay-off multidimensional perception feature data set into a small number of principal components to form a key pay-off perception feature data set, reducing data redundancy while retaining key information related to the pay-off process. After obtaining the key pay-off perception feature data set, a feature-level fusion algorithm is used to fuse the data in the key pay-off perception feature data set. Feature-level fusion is a technology that synthesizes multi-source features into a unified data structure, which is achieved through weighted averaging, logical rules, and the like. Through fusion, the correlation between different features is fully utilized to generate more comprehensive pay-off fusion perception feature data, thereby providing more accurate basic data for establishing a displacement integrated prediction network, further improving the accuracy of the network output data rotation prediction displacement, and improving the control accuracy and reliability of the pay-off and pay-off machine.

[0028] Furthermore, the establishment of the displacement integrated prediction network includes: collecting and acquiring a displacement database of the winding and discharging wire machine, identifying and classifying the displacement database of the winding and discharging wire machine according to the motor motion parameter type and the wire-discharging perception feature type, and obtaining a displacement feature sample set; performing time series analysis and characteristic analysis on the displacement feature sample set to obtain data time series information and data characteristic information; performing deep learning network structure selection based on the data time series information and data characteristic information to determine a displacement feature deep learning network set; using the displacement feature deep learning network set to respectively train and tune the displacement feature sample set to obtain a displacement prediction branch network set; integrating and fusing the displacement prediction branch network set through a model integration strategy to establish the displacement integrated prediction network.

[0029] Specifically, the displacement database of the winding and discharging wire machine is collected and constructed. The displacement database of the winding and discharging wire machine contains multi-dimensional historical data, including the motion parameter types of the motor, such as speed, acceleration and displacement, and the wire release perception feature types, such as the tension, temperature and speed fluctuation of the discharge wire. The displacement database of the winding and discharging wire machine is identified and classified according to the motor motion parameter type and the wire release perception feature type. According to different parameters and feature types, each group of data is marked with a clear classification label to ensure that subsequent analysis can be processed in a targeted manner to form a displacement feature sample set. Then, the displacement feature sample set is subjected to time series analysis and characteristic analysis. Time series analysis is a method for studying the trend of data changes over time. By analyzing the time correlation, periodicity and randomness of the data, the time series information reflecting the movement law of the equipment is extracted. At the same time, characteristic analysis focuses on the data distribution characteristics, such as mean, variance and nonlinear characteristics. Through characteristic analysis, data indicators describing the data characteristics are generated to form data characteristic information. Based on the analysis result data time series information and data characteristic information, the deep learning network structure is selected, such as a neural network, a long short-term memory network, etc., to obtain a displacement feature deep learning network set. Next, the deep learning network set is used to train and tune the displacement feature sample set. The training process adjusts the network weights by inputting the displacement feature sample data so that the network model can learn the key patterns and features in the data. The tuning stage further improves the performance of the network model through hyperparameter optimization, such as learning rate and network depth. After training and tuning, multiple optimized displacement prediction branch network sets are formed. Finally, the displacement prediction branch network sets are fused through the model integration strategy. The model integration strategy is a method to improve the overall prediction accuracy by combining the results of multiple prediction models, such as weighted average, voting mechanism or meta-learning-based integration method. By fusing the prediction capabilities of different branch networks, a comprehensive displacement integrated prediction network is formed, which can provide accurate and efficient displacement prediction results. By constructing and optimizing the displacement integrated prediction network, accurate prediction of future motor rotation displacement is achieved, providing accurate and reliable data support for the intelligent control of the winder and discharger.

[0030] A PID controller is used to regulate and analyze the predicted rotation displacement and the target rotation displacement to obtain a displacement correction control amount, and closed-loop feedback regulation is performed on the target take-up and discharge wire machine based on the displacement correction control amount.

[0031] Specifically, the PID controller is a classic control algorithm that includes three main components of the controller: proportional (P), integral (I) and differential (D). These components work together to adjust the deviation of the controller in real time and generate a control quantity for correction. The PID controller first receives the predicted rotation displacement data and the target rotation displacement, and then compares the predicted rotation displacement with the target rotation displacement, calculates the difference between the two, and obtains the displacement correction control quantity. The displacement correction control quantity refers to a signal that adjusts the driving voltage or current of the motor, which directly acts on the control instruction on the motor to change the speed and torque of the motor, thereby achieving precise control of the motor rotation and making the motor approach the target rotation displacement. Based on the displacement correction control quantity, the target winding and discharge wire machine is closed-loop feedback controlled, and the actual rotation displacement obtained by sensor monitoring is compared with the target rotation displacement, and the control strategy is continuously corrected by the PID controller until the actual rotation displacement is very close to or even equal to the target rotation displacement. This process is dynamic and continuous. A PID controller is used to regulate and analyze the predicted rotation displacement and the target rotation displacement, and a closed-loop feedback regulation is performed on the target take-up and discharge wiring machine based on the displacement correction control quantity to ensure that the motor rotation state is consistent with the target displacement, thereby improving the reliability and accuracy of the automatic take-up and discharge wiring machine regulation, so that the take-up and discharge wiring machine can complete its work tasks efficiently and reliably according to predetermined requirements.

[0032] Furthermore, obtaining the displacement correction control amount includes: extracting control parameters of the PID controller to obtain controller parameters, wherein the controller parameters include proportional terms, integral terms and differential terms; determining an adaptive adjustment algorithm through a built-in self-adjustment mechanism of the PID controller; verifying the control response of the PID controller to obtain actual response parameters of the controller, and dynamically optimizing and adjusting the controller parameters based on the actual response parameters of the controller using the adaptive adjustment algorithm to obtain a PID update controller; taking the difference between the predicted rotation displacement and the target rotation displacement as a displacement error signal, performing displacement regulation analysis on the displacement error signal based on the PID update controller, and obtaining the displacement correction control amount.

[0033] Specifically, the controller parameters are first extracted, and the initial parameter values ​​of the core components, namely the proportional term (P), integral term (I) and differential term (D), are directly obtained from the PID controller. These parameters determine the response strength of the PID controller to the current error, historical error accumulation, and error change trend. The proportional term directly reflects the current value of the error, and generates an immediate correction signal by multiplying it with the proportional gain coefficient, which is used to quickly respond to the error. The integral term (I) accumulates historical errors and eliminates steady-state deviations by multiplying it with the integral gain coefficient. The differential term (D) predicts the change trend of the error and provides lead compensation by multiplying it with the differential gain coefficient, thereby reducing error overshoot and oscillation. The initial parameter values ​​are usually set according to empirical formulas or pre-calibration results. Then, the built-in self-adjustment mechanism of the PID controller is enabled, and the operating status of the drive motor is monitored in real time to determine the adaptive adjustment algorithm that adapts to the current working conditions. The adaptive adjustment algorithm can automatically adjust the PID parameters based on real-time feedback during the operation of the motor. For example, when the error changes greatly, the self-adjusting mechanism increases the proportional gain P to enhance the rapid response capability of the motor displacement, and when the motor displacement tends to be stable but there is still a deviation, the integral gain I is increased to gradually eliminate the residual error. Then the control response of the PID controller is verified, the controller parameters are applied to the actual operation process, and the response of the control signal is evaluated. For example, during the verification process, the convergence speed, overshoot amplitude and number of oscillations of the error are recorded to generate the actual response parameters of the controller, including key indicators such as response time, overshoot, and steady-state error. The actual response parameters reflect the performance of the current controller and provide a basis for further optimization. Then, based on the actual response parameters, the adaptive adjustment algorithm is applied to dynamically optimize and adjust the original parameters of the controller P, I, and D. Through continuous trial and error and learning, the PID parameter combination that best suits the current production line is obtained to form a PID update controller. Finally, the difference between the predicted rotation displacement and the target rotation displacement is input into the PID update controller as a displacement error signal for real-time displacement control analysis. The PID update controller generates a displacement correction control quantity based on the error signal. The displacement correction control quantity is directly used to adjust the drive signal (voltage or current) of the motor, thereby accurately correcting the rotational displacement of the motor to make it consistent with the target value, thereby improving the control accuracy of the automatic winding and discharge wire drawing machine.

[0034] Furthermore, the closed-loop feedback control of the target take-up and discharge wire-winding machine based on the displacement correction control amount includes: performing closed-loop feedback control and control effect evaluation on the target take-up and discharge wire-winding machine based on the displacement correction control amount to determine actual control effect parameters of the wire-winding machine; optimizing and analyzing the adaptive adjustment algorithm based on the actual control effect parameters of the wire-winding machine to obtain an adaptive adjustment optimization algorithm.

[0035] Specifically, the displacement correction control quantity is directly applied to the control signal adjustment of the drive motor, for example, by adjusting the voltage or current to change the rotation state of the motor. During the adjustment process, closed-loop feedback regulation and control effect evaluation are carried out, and the core indicators of the equipment operation status are collected and recorded in real time to form a set of actual control effect parameters of the pay-off machine, which include actual displacement, speed change curve, steady-state error size and response time, etc., which fully reflect the operation effect of the drive motor under the current control strategy. For example, the steady-state error can be used to evaluate the final accuracy of the rotation displacement of the drive motor, while the response time and overshoot amplitude indicate the adaptability of the drive motor to dynamic changes. Then, the actual control effect parameters of the pay-off machine are analyzed to verify whether the effect of the current closed-loop control meets expectations. If it is found that the control effect is insufficient, such as slow response speed or large steady-state error, it is necessary to further optimize the adaptive adjustment algorithm based on these parameters, dynamically modify the control parameter algorithm according to the real-time operation situation, and generate an adaptive adjustment optimization algorithm. The optimized adaptive adjustment algorithm will be reapplied to the PID controller to further improve the performance of closed-loop feedback regulation. Through real-time effect evaluation and algorithm optimization analysis, the performance of the controller can be continuously improved, the control accuracy of the drive motor can be improved, and the regulation accuracy and efficiency of the automatic wire-taking and arranging machine can be improved, so as to efficiently and accurately realize the task requirements of wire-taking and arranging in the automated production line.

[0036] Embodiment 2, based on the same inventive concept as the method for controlling an automatic winding and discharging wire machine driven by an encoder in the aforementioned embodiment, the present application provides an electronic device, Figure 2 The schematic structural diagram of the electronic device provided in the second embodiment of the present invention shows a block diagram of an exemplary electronic device suitable for implementing the implementation mode of the present invention. Figure 2 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present invention. Figure 2 As shown, the electronic device includes a processor 21, a memory 22, an input device 23 and an output device 24; the number of processors 21 in the electronic device can be one or more. Figure 2 Taking a processor 21 as an example, the processor 21, memory 22, input device 23 and output device 24 in the electronic device can be connected through a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0037] Embodiment 3, based on the same inventive concept as the encoder-driven automatic winding and discharging machine control method in the aforementioned embodiment, this embodiment provides a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as the program instructions / modules corresponding to the encoder-driven automatic winding and discharging machine control method in the embodiment of the present application. The processor executes various functional applications and data processing of the computer device by running the software programs, instructions and modules stored in the memory, that is, realizing the above-mentioned encoder-driven automatic winding and discharging machine control.

[0038] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0039] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for controlling an automatic winding and discharging wire machine driven by an encoder, characterized in that: The method comprises: Installing an encoder on a drive motor of a target take-up and discharge wire machine, wherein the encoder is coaxially connected to the drive motor, and initializing the encoder; Determine the target take-up and discharge length of the target take-up and discharge wire drawing machine according to the production demand information of the automated production line, and convert the target take-up and discharge length into the target rotational displacement of the drive motor; A sensing sensor group is arranged on the automated production line, and a multi-dimensional sensing data stream of the pay-off is collected and acquired through the sensing sensor group, and a pay-off pulse signal stream is obtained by real-time reading through the encoder; Analyzing and processing the multi-dimensional sensing data stream of the pay-off and the pulse signal stream of the pay-off and performing displacement prediction to obtain the predicted rotation displacement; A PID controller is used to regulate and analyze the predicted rotation displacement and the target rotation displacement to obtain a displacement correction control amount, and closed-loop feedback regulation is performed on the target take-up and discharge wire machine based on the displacement correction control amount.

2. The encoder-driven automatic winding and unwinding wire machine control method according to claim 1, characterized in that: The step of obtaining the predicted rotation displacement comprises: Analyzing and processing the pay-off pulse signal stream to obtain a current motor motion parameter set; Performing noise data identification and data preprocessing on the multi-dimensional sensing data stream of the line-laying to obtain a standard multi-dimensional sensing data stream of the line-laying; Perform feature extraction and feature fusion based on the standard line-laying multi-dimensional perception data stream to obtain line-laying fusion perception feature data; A displacement integrated prediction network is established, and displacement prediction is performed on the current motor motion parameter set and the line-release fusion perception feature data based on the displacement integrated prediction network to obtain the rotation predicted displacement.

3. The encoder-driven automatic winding and discharging wire machine control method according to claim 2, characterized in that: The current motor motion parameter set is obtained, including: A Kalman filter is used to perform filtering preprocessing on the line-release pulse signal stream to obtain an available line-release pulse signal stream; Performing signal shaping processing on the available pay-off pulse signal stream according to the data application standard of the pay-off machine to obtain a standard pay-off pulse signal stream; Counting pulses and recording pulse timestamps of the standard pay-off pulse signal stream to determine the total number of pay-off signal pulses and pulse timestamp information; The motion parameter calculation process is performed based on the total number of the pay-off signal pulses and the pulse timestamp information to obtain the current motor motion parameter set.

4. The encoder-driven automatic winding and unwinding wire machine control method according to claim 2, characterized in that: The obtaining of the line fusion perception feature data includes: Extracting associated features based on the standard line-laying multi-dimensional perception data stream to obtain an associated line-laying multi-dimensional perception feature data set; Decentralizing the associated radiometric multi-dimensional perception feature data set to obtain a covariance matrix, and solving the covariance matrix to obtain eigenvalues ​​and eigenvectors; Selecting principal component information according to the eigenvalues ​​and eigenvectors, and performing principal component dimensionality reduction on the standard line-setting multidimensional perception data stream based on the principal component information to obtain a key line-setting perception feature data set; The key strut perception feature data set is fused by using a feature-level fusion algorithm to obtain the strut fusion perception feature data.

5. The encoder-driven automatic winding and unwinding wire machine control method according to claim 2, characterized in that: The establishment of the displacement integrated prediction network comprises: Acquire a displacement database of a take-up and discharge wire machine, identify and classify the displacement database of the take-up and discharge wire machine according to motor motion parameter types and wire pay-out sensing feature types, and obtain a displacement feature sample set; Performing time series analysis and characteristic analysis on the displacement feature sample set to obtain data time series information and data characteristic information; Selecting a deep learning network structure based on the data time series information and the data characteristic information to determine a displacement feature deep learning network set; The displacement feature deep learning network set is used to train and optimize the displacement feature sample set to obtain a displacement prediction branch network set; The displacement prediction branch network set is integrated and fused through a model integration strategy to establish the displacement integrated prediction network.

6. The method for controlling an automatic winding and unwinding wire machine driven by an encoder as claimed in claim 1, characterized in that: The obtaining of the displacement correction control amount comprises: Extracting control parameters of the PID controller to obtain controller parameters, wherein the controller parameters include a proportional term, an integral term, and a differential term; Determining an adaptive tuning algorithm through a built-in self-tuning mechanism of the PID controller; Performing control response verification on the PID controller to obtain actual response parameters of the controller, and using the adaptive adjustment algorithm to dynamically optimize and adjust the controller parameters based on the actual response parameters of the controller to obtain a PID update controller; The difference between the predicted rotation displacement and the target rotation displacement is used as a displacement error signal, and a displacement control analysis is performed on the displacement error signal based on the PID update controller to obtain the displacement correction control amount.

7. The method for controlling an automatic winding and unwinding wire machine driven by an encoder as claimed in claim 6, characterized in that: The closed-loop feedback control of the target take-up and discharge wire machine based on the displacement correction control amount includes: Based on the displacement correction control amount, closed-loop feedback control and control effect evaluation are performed on the target pay-off and take-up wire machine to determine the actual control effect parameters of the pay-off and take-up wire machine; The adaptive adjustment algorithm is optimized and analyzed based on the actual control effect parameters of the pay-off machine to obtain an adaptive adjustment optimization algorithm.

8. An electronic device, characterized in that: The electronic device comprises: a processor; and a memory for storing instructions executable by the processor; wherein the processor is used to execute the encoder-driven automatic winding and discharging wire machine control method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, so the computer program is used to execute the encoder-driven automatic winding and discharging wire machine control method described in any one of claims 1 to 7.

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