Encoder-driven automatic take-up and pay-off machine control method, apparatus and medium

Abstract

The application provides an encoder-driven automatic take-up and pay-off machine regulation method, equipment and medium, relates to the technical field of automation control, installs an encoder on a driving motor of a target take-up and pay-off machine, determines a target take-up length according to production demand information, converts the target take-up length into a target rotational displacement, arranges a perception sensor group to obtain a pay-off multi-dimensional perception data stream, obtains a pay-off pulse signal stream through encoder reading, analyzes and processes the pay-off multi-dimensional perception data stream and the pay-off pulse signal stream and predicts a displacement, adopts a PID controller to regulate and analyze the rotational predicted displacement and the target rotational displacement, obtains a displacement correction control quantity, and performs closed-loop feedback regulation on the target take-up and pay-off machine. The technical problems that the regulation of the take-up and pay-off machine in the prior art relies on manual or simple mechanical control, resulting in low regulation accuracy and low efficiency are solved. The technical effects that the encoder and the sensor work cooperatively, the take-up and pay-off accuracy and the control efficiency are improved are achieved.

Description

Technical Field

[0001] This invention relates to the field of automation control technology, specifically to an encoder-driven automatic take-up and unload machine control method, equipment, and medium. Background Technology

[0002] Cable take-up and unwinding machines are specialized equipment used in wire and cable factories. Currently, the control process of these machines largely relies on manual operation or simple mechanical control. While manual operation offers some flexibility, it is limited by the operator's experience and skill level, often making it difficult to guarantee accuracy and consistency. Furthermore, manual operation is susceptible to fatigue and distraction, leading to errors during control. While simple mechanical control can improve stability and reliability to some extent, its precision and efficiency remain limited. When dealing with wires and cables of different specifications and materials, mechanical control often struggles to achieve precise matching and flexible adjustment.

[0003] Existing technologies suffer from the problem that the control of the feed line machine relies on manual or simple mechanical control, resulting in low control accuracy and low efficiency. Summary of the Invention

[0004] This application provides an encoder-driven automatic take-up and unload machine control method, equipment, and medium to solve the technical problem in the prior art where the control of the take-up and unload machine relies on manual or simple mechanical control, resulting in low control accuracy and low efficiency.

[0005] In view of the above problems, this application provides an encoder-driven automatic take-up and unload machine control method, device and medium.

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

[0007] In a second aspect, this application provides an electronic device, characterized in that the electronic device comprises: a processor; and a memory for storing processor-executable instructions; wherein the processor is used to execute the encoder-driven automatic take-up and unload machine control method provided in this application.

[0008] In a third aspect, this application provides a computer-readable storage medium, characterized in that the storage medium stores a computer program, so that the computer program is used to execute the encoder-driven automatic take-up and unload machine control method provided in this application.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The method provided in this application embodiment involves installing an encoder on the drive motor of a target take-up and unload machine. The encoder is coaxially connected to the drive motor and initialized. Based on the production requirements of the automated production line, the target take-up and unload length of the target take-up and unload machine is determined. This target take-up and unload length is converted into the target rotational displacement of the drive motor. A group of sensing sensors is deployed on the automated production line to collect multi-dimensional sensing data streams of the unloaded wire. Simultaneously, the encoder reads the unloaded wire pulse signal stream in real time. The multi-dimensional sensing data streams and the unloaded wire pulse signal streams are analyzed and processed for displacement prediction to obtain a predicted rotational displacement. A PID controller is used to regulate and analyze the predicted rotational displacement and the target rotational displacement to obtain a displacement correction control quantity. Based on the displacement correction control quantity, closed-loop feedback regulation of the target take-up and unload machine is performed. This achieves the technical effect of improving the accuracy and control efficiency of take-up and unload through the collaborative work of the encoder and sensors. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 A flowchart illustrating the encoder-driven automatic take-up and unload machine control method provided in this application;

[0013] Figure 2 This is a schematic diagram of the structure of an electronic device provided in this application.

[0014] Explanation of reference numerals in the attached drawings: Processor 21, Memory 22, Input device 23, Output device 24. Detailed Implementation

[0015] This application provides an encoder-driven automatic take-up and unload machine control method, device, and medium to solve the technical problems in the prior art where the control of take-up and unload machines relies on manual or simple mechanical control, resulting in low control accuracy and low efficiency. It achieves the technical effect of improving take-up and unload accuracy and control efficiency through the collaborative work of encoders and sensors.

[0016] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0017] Example 1, as Figure 1 As shown, this application provides an encoder-driven automatic take-up and unload machine control method, the method comprising:

[0018] An encoder is installed on the drive motor of the target take-up and discharge machine, the encoder is coaxially connected to the drive motor, and the encoder is initialized.

[0019] Specifically, an encoder matching the drive motor of the take-up and unwinding machine is selected. An encoder is a sensor device used to input information such as position, angle, or speed of mechanical rotation or displacement, converting it into an electrical signal for output. When selecting an encoder, ensure that its specifications match the drive motor's, such as shaft diameter and interface type. Ensure that the encoder and the target take-up and unwinding machine's drive motor can be coaxially connected, meaning the encoder's rotation axis is tightly coupled to the drive motor's output shaft, rotating together. Then, fix the encoder onto the target take-up and unwinding machine's drive motor. During installation, ensure the encoder and drive motor axes are perfectly aligned to achieve a coaxial connection. This coaxial connection is a precise alignment method in mechanical structure, ensuring the encoder accurately captures changes in the drive motor's rotation angle and position without data distortion or error due to misalignment. After installation, initialize the encoder. Initialization includes calibrating the initial position, setting the zero-point reference point, and testing and verifying the pulse signal channel. The zero-point reference point is the starting position recorded by the encoder, used to determine the reference throughout the entire movement process, ensuring the encoder reading matches the actual state of the drive motor. Pulse signals are the core data form generated by the encoder. They reflect the rotational state by recording the number of electrical pulses that occur when the motor shaft rotates a certain angle each time. During the verification process, it is also necessary to confirm that the pulse signal stream generated by the encoder is free from noise interference and that the signal amplitude is stable, ensuring that the encoder's output signal can be reliably received and processed. By installing and initializing the encoder on the drive motor of the target take-up and undo machine, real-time monitoring and precise control of the drive motor's rotational state can be achieved, thereby improving the control accuracy and stability of the automatic take-up and undo machine.

[0020] Based on the production demand information of the automated production line, the target take-up and take-up length of the target take-up and take-up machine is determined, and the target take-up and take-up length is converted into the target rotational displacement of the drive motor.

[0021] Specifically, the production management system acquires production demand information from the automated production line, including product dimensions and process requirements. Based on this information, the target take-up and unwinding length of the take-up and unwinding machine is determined. This target length refers to the total cable length or range the take-up and unwinding machine needs to operate throughout the entire production process, which determines the total rotational length the drive motor needs to complete. A linear conversion factor for the drive motor is obtained, derived through equipment calibration, representing the cable length taken up and unwinding per revolution of the drive motor shaft, recorded as a unit rotation length value. Using the formula: Target rotational displacement = Target take-up and unwinding length / Unit rotational length value, the target take-up and unwinding length is converted into the target rotational displacement of the drive motor. This achieves precise quantitative control of the take-up and unwinding machine, ensuring its operation accurately meets production requirements and improving control efficiency and precision.

[0022] A sensor array is deployed on the automated production line to collect multi-dimensional sensing data streams of the wire feeding process, while the encoder reads the wire feeding pulse signal streams in real time.

[0023] Specifically, a sensor array is deployed on the automated production line. This sensor array includes, but is not limited to, production sensors that monitor and collect various production parameters closely related to the cable take-up and release displacement, such as material tension, speed, and temperature. The positions of multiple sensors within the sensor array are configured according to actual needs. For example, tension sensors are typically installed on the cable stretching path to measure the cable's tensile force, while speed sensors are installed near the cable reel to record the cable's linear movement speed in real time. By acquiring the real-time data collected by the sensor array, a multi-dimensional cable release data stream is formed. This multi-dimensional data stream contains information from multiple dimensions during the cable release process, such as real-time data on tension changes, speed fluctuations, and temperature, providing diversity and dynamism for the state analysis of the cable release process. Simultaneously, an encoder installed on the drive motor reads the cable release pulse signal stream in real time. This cable release pulse signal stream is a data stream reflecting the motor's rotational state, composed of electrical pulse signals output by the encoder each time it rotates a certain angle. The cable release pulse signals are recorded through pulse counting and timestamps, accurately describing the motor's rotational displacement and dynamic characteristics. By combining the multi-dimensional sensing data stream of the wire feeding process collected by the sensing sensor group with the wire feeding 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 wire feeding process in automated production lines.

[0024] The multidimensional sensing data stream and the wire laying pulse signal stream are analyzed, processed, and displacement predicted to obtain the rotational predicted displacement.

[0025] Specifically, after obtaining the multi-dimensional sensing data stream and the pay-off pulse signal stream, the two data sources are first preprocessed to ensure data quality and consistency. The multi-dimensional sensing data stream, acquired by sensors, may be affected by environmental noise; therefore, it undergoes data cleaning to remove outliers and invalid data. Simultaneously, the pay-off pulse signal stream is filtered and shaped. Then, deep learning technology is used to comprehensively analyze the preprocessed multi-dimensional sensing data stream and the pay-off pulse signal stream to predict the motor rotation displacement, obtaining a predicted rotation displacement. This predicted rotation displacement is the estimated value of the motor rotation displacement after a certain period in the future or after the production line ends. By predicting the rotation displacement based on the current automated production line status, strong data support is provided for the intelligent control and optimization of the automated production line, improving the accuracy and reliability of the automatic take-up and pay-off machine control.

[0026] Furthermore, obtaining the rotational predicted displacement includes: analyzing and processing the wire-laying pulse signal stream to obtain a current motor motion parameter set; performing noise data identification and data preprocessing on the wire-laying multi-dimensional sensing data stream to obtain a standard wire-laying multi-dimensional sensing data stream; performing feature extraction and feature fusion based on the standard wire-laying multi-dimensional sensing data stream to obtain wire-laying fused sensing feature data; establishing a displacement integrated prediction network, and performing displacement prediction on the current motor motion parameter set and the wire-laying fused sensing feature data based on the displacement integrated prediction network to obtain the rotational predicted displacement.

[0027] Specifically, in the process of comprehensively processing and predicting the pay-off pulse signal stream and the pay-off multi-dimensional sensing data stream, the pay-off pulse signal stream is first analyzed and processed, including filtering and signal shaping. The pay-off pulse signal stream is parsed to obtain the current motor motion parameter set, which includes information such as motor speed, rotational displacement, and acceleration. Simultaneously, noise data identification and data preprocessing are performed on the pay-off multi-dimensional sensing data stream. The multi-dimensional sensing data stream includes various dynamic parameters such as material tension, velocity, and temperature, acquired through sensors, and is easily affected by environmental interference and equipment errors. Through noise identification and preprocessing, outliers are cleaned to obtain a standard pay-off multi-dimensional sensing data stream. Then, through correlation feature extraction technology, correlation feature extraction and fusion are performed on the standard pay-off multi-dimensional sensing data stream to obtain fused sensing feature data, such as the magnitude, direction, and stability of material tension, the magnitude and direction of velocity, and temperature fluctuations and gradients. By using a deep learning model, a displacement integrated prediction network is established by combining the motor motion parameter set and the wire feeding fusion sensing feature data. The displacement integrated prediction network can automatically learn the potential rules and patterns in the data, thereby achieving accurate prediction of the motor rotation displacement. By using the displacement integrated prediction network to perform displacement prediction on the current motor motion parameter set and the wire feeding fusion sensing feature data, accurate and reliable rotation prediction displacement can be obtained, thereby improving the accuracy and reliability of automatic take-up and release machine control.

[0028] Furthermore, obtaining the current motor motion parameter set includes: pre-processing the wire-feeding pulse signal stream using a Kalman filter to obtain an available wire-feeding pulse signal stream; performing signal shaping processing on the available wire-feeding pulse signal stream according to the data application standard for take-up and release machines to obtain a standard wire-feeding pulse signal stream; counting pulses and recording pulse timestamps on the standard wire-feeding pulse signal stream to determine the total number of wire-feeding signal pulses and pulse timestamp information; and performing motion parameter calculation processing based on the total number of wire-feeding signal pulses and pulse timestamp information to obtain the current motor motion parameter set.

[0029] Specifically, the pay-off pulse signal is preprocessed using a Kalman filter. Kalman filtering is a method to optimize signal stability, effectively removing random noise from the pulse signal while preserving true motion information. Signal filtering using a Kalman filter ensures the quality of the pay-off pulse signal stream, enabling it to reflect the actual motion state of the drive motor and generating a highly reliable, usable pay-off pulse signal stream. The data application standard for take-up and pay-off machines is obtained by referring to industry standards, specifications, or expert opinions. According to the take-up and pay-off machine data application standard, the usable pay-off pulse signal stream undergoes signal shaping processing. Signal shaping is a signal standardization technique that includes amplification and shaping steps. Through signal shaping, a form conforming to the take-up and pay-off machine data standard is obtained, generating a standard pay-off pulse signal stream. Next, pulse counting and pulse timestamp recording are performed on the standard pay-off pulse signal stream. Pulse counting refers to counting the total number of pulses generated by the encoder within a certain time period, and pulse timestamp recording refers to marking the precise time point of each pulse. Through pulse counting and timestamp recording, the rotational behavior of the motor in the time dimension can be completely described, thereby obtaining the total number of pay-off signal pulses and pulse timestamp information. Based on the total number of pulses and pulse timestamp information of the wire-laying signal, motion parameters are calculated, including the current motor speed, acceleration, and displacement. For example, speed can be calculated as the ratio of the total number of pulses to the time interval, while displacement is obtained by multiplying the total number of pulses by the unit rotational displacement. Multiple calculation results are obtained and integrated to form a set of current motor motion parameters, including current motor speed, displacement, and dynamic change trends. By extracting the current motor motion parameters from the encoder pulse signal, precise quantification of motor motion is achieved, providing a solid foundation for the high precision and real-time performance of the entire control scheme.

[0030] Furthermore, obtaining the fused sensing feature data for cable laying includes: extracting associated features based on the standard cable laying multidimensional sensing data stream to obtain an associated cable laying multidimensional sensing feature dataset; decentralizing the associated cable laying multidimensional sensing feature dataset to obtain a covariance matrix, and solving the covariance matrix to obtain eigenvalues ​​and eigenvectors; selecting principal component information based on the eigenvalues ​​and eigenvectors, and performing principal component dimensionality reduction on the standard cable laying multidimensional sensing data stream based on the principal component information to obtain a key cable laying sensing feature dataset; and using a feature-level fusion algorithm to fuse the key cable laying sensing feature dataset to obtain the fused sensing feature data for cable laying.

[0031] Specifically, correlation feature extraction is performed on the multidimensional sensing data stream. Correlation feature extraction is a technique that extracts key information by analyzing the relationships between different sensing data. Through correlation feature extraction, a multidimensional sensing feature dataset for associated laying is obtained, including the magnitude, direction, and stability of material tension, the magnitude and direction of velocity, temperature fluctuations, gradients, etc. Then, the associated feature dataset 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 made more concentrated at the origin, and the feature covariance matrix is ​​calculated. The covariance matrix is ​​a matrix that represents the linear relationship between multiple variables. By solving the covariance matrix, eigenvalues ​​and eigenvectors are obtained. The eigenvalues ​​and eigenvectors represent the correlation strength and direction between the variables, respectively. Furthermore, by sorting and filtering the eigenvalues ​​and eigenvectors of the covariance matrix, principal component information is obtained. The principal component information is used to describe the most representative feature patterns in the multidimensional sensing feature dataset for associated laying, thereby achieving dimensionality reduction of the multidimensional sensing feature dataset for associated laying. Optionally, principal component analysis (PCA) can be used to reduce the dimensionality of the standard wire-laying multidimensional sensing data stream. This compresses the high-dimensional correlated wire-laying multidimensional sensing feature dataset into a small number of principal components, forming a key wire-laying sensing feature dataset. This reduces data redundancy while retaining key information related to the wire-laying process. After obtaining the key wire-laying sensing feature dataset, a feature-level fusion algorithm is used to fuse the data in the dataset. Feature-level fusion is a technique that synthesizes multi-source features into a unified data structure, achieved through weighted averaging, logical rules, and other methods. Through fusion, the interrelationships between different features are fully utilized, generating more comprehensive wire-laying fusion sensing feature data. This provides more accurate basic data for establishing a displacement integrated prediction network, further improving the accuracy of the network output data's rotational displacement prediction and enhancing the control precision and reliability of the wire-laying machine.

[0032] Furthermore, the establishment of the displacement integrated prediction network includes: acquiring a displacement database of a take-up and release machine; classifying the displacement database according to the type of motor motion parameters and the type of wire release sensing features to 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 data characteristic information to determine a displacement feature deep learning network set; training and optimizing the displacement feature sample set using the displacement feature deep learning network set to obtain a displacement prediction branch network set; and integrating and fusing the displacement prediction branch network set through a model fusion strategy to establish the displacement integrated prediction network.

[0033] Specifically, a displacement database for take-up and release machines is collected and constructed. This database contains multi-dimensional historical data, including motor motion parameter types such as speed, acceleration, and displacement, and release sensing characteristic types such as release wire tension, temperature, and speed fluctuations. The displacement database is categorized according to motor motion parameter types and release sensing characteristic types. Each data set is clearly labeled based on different parameters and characteristic types to ensure targeted processing for subsequent analysis, forming a displacement characteristic sample set. Then, time series analysis and characteristic analysis are performed on the displacement characteristic sample set. Time series analysis studies the trend of data changes over time, extracting time series information reflecting the motion law of the equipment by analyzing the time correlation, periodicity, and randomness of the data. Simultaneously, characteristic analysis focuses on data distribution characteristics, such as mean, variance, and nonlinearity, generating data indicators describing data characteristics, forming data characteristic information. Based on the analysis results, the time series information and data characteristic information are used to select a deep learning network structure, such as neural networks or long short-term memory networks, to obtain a deep learning network set for displacement characteristics. Next, a deep learning network set was used to train and optimize the displacement feature sample set. During training, the network weights were adjusted by inputting displacement feature sample data, enabling the network model to learn key patterns and features from the data. The optimization phase further improved the network model performance through hyperparameter optimization, such as learning rate and network depth. After training and optimization, multiple optimized displacement prediction branch network sets were formed. Finally, the displacement prediction branch network sets were fused using a model ensemble strategy. Model ensemble strategy is a method to improve overall prediction accuracy by combining the results of multiple prediction models, such as weighted averaging, voting mechanisms, or meta-learning-based ensemble methods. By fusing the prediction capabilities of different branch networks, a comprehensive displacement ensemble prediction network was formed, which can provide accurate and efficient displacement prediction results. By constructing and optimizing the displacement ensemble prediction network, accurate prediction of future motor rotation displacement was achieved, providing accurate and reliable data support for the intelligent control of the take-up and discharge machine.

[0034] A PID controller is used to regulate and analyze the predicted rotational displacement and the target rotational displacement to obtain the displacement correction control quantity, and the target take-up and discharge line machine is controlled by closed-loop feedback based on the displacement correction control quantity.

[0035] Specifically, the PID controller is a classic control algorithm comprising three main components: proportional (P), integral (I), and derivative (D). These components work synergistically to adjust the controller's deviation in real time, generating corrective control inputs. The PID controller first receives the predicted rotational displacement data and the target rotational displacement. Then, by comparing the predicted and target displacements, it calculates the difference to obtain the displacement correction control input. This input is a signal that adjusts the motor's drive voltage or current, directly acting as a control command to change the motor's speed and torque, thereby achieving precise control of the motor's rotation and bringing it closer to the target rotational displacement. Based on this displacement correction control input, closed-loop feedback control is applied to the target take-up and discharge machine. The actual rotational displacement monitored by sensors is compared with the target displacement, and the PID controller continuously refines the control strategy until the actual rotational displacement is very close to or even equal to the target displacement. This process is dynamic and continuous. The process involves using a PID controller to analyze and regulate the predicted and target rotational displacements, and then implementing closed-loop feedback control of the target take-up and unwind machine based on the displacement correction control quantity. This ensures that the motor rotation state remains consistent with the target displacement, improving the reliability and accuracy of the automatic take-up and unwind machine's regulation. As a result, the take-up and unwind machine can efficiently and reliably complete its work tasks according to predetermined requirements.

[0036] Furthermore, obtaining the displacement correction control quantity includes: extracting control parameters from the PID controller to obtain controller parameters, which include proportional, integral, and derivative terms; determining an adaptive adjustment algorithm through the built-in self-adjustment mechanism of the PID controller; verifying the control response of the PID controller to obtain the actual response parameters; using the adaptive adjustment algorithm to dynamically optimize and adjust the controller parameters based on the actual response parameters to obtain a PID update controller; using the difference between the predicted rotational displacement and the target rotational displacement as a displacement error signal; and performing displacement regulation analysis on the displacement error signal based on the PID update controller to obtain the displacement correction control quantity.

[0037] Specifically, the controller parameters are first extracted directly from the PID controller, specifically the initial parameter values ​​of its core components: the proportional term (P), integral term (I), and derivative term (D). These parameters determine the PID controller's response strength to current error, historical error accumulation, and error change trends. The proportional term directly reflects the current error value and generates an immediate correction signal by multiplying it with the proportional gain coefficient, enabling rapid error response. The integral term (I) accumulates historical errors and eliminates steady-state deviations by multiplying it with the integral gain coefficient. The derivative term (D) predicts error change trends and provides lead compensation by multiplying it with the derivative gain coefficient, thereby reducing error overshoot and oscillation. The initial parameter values ​​are typically set based on empirical formulas or pre-calibration results. Then, the PID controller's built-in self-tuning mechanism is activated. By monitoring the driving motor's operating status in real time, an adaptive adjustment algorithm is determined to suit the current operating conditions. This adaptive adjustment algorithm automatically adjusts the PID parameters based on real-time feedback during motor operation. For example, when the error varies significantly, the self-adjusting mechanism increases the proportional gain P to enhance the rapid response capability of the motor displacement. Conversely, when the motor displacement tends to stabilize but deviations still exist, the integral gain I is increased to gradually eliminate residual errors. Then, the PID controller's control response is verified by applying the controller parameters to the actual operation process and evaluating the response of the control signal. For instance, during verification, the convergence speed, overshoot amplitude, and number of oscillations are recorded to generate actual controller response parameters, including key indicators such as response time, overshoot, and steady-state error. These actual response parameters reflect the current controller performance and provide a basis for further optimization. Furthermore, based on the actual response parameters, an adaptive adjustment algorithm is applied to dynamically optimize and adjust the original P, I, and D parameters of the controller. Through continuous trial and error and learning, the most suitable PID parameter combination for the current production line is obtained, forming a PID update controller. Finally, the difference between the predicted rotational displacement and the target rotational displacement is used as a displacement error signal input to the PID update controller for real-time displacement control analysis. The PID update controller generates a displacement correction control quantity based on the error signal. This displacement correction control quantity is directly used to adjust the motor's drive signal (voltage or current), thereby accurately correcting the motor's rotational displacement to match the target value and improving the control accuracy of the automatic take-up and unload machine.

[0038] Furthermore, the closed-loop feedback control of the target take-up and release machine based on the displacement correction control quantity includes: performing closed-loop feedback control and control effect evaluation of the target take-up and release machine based on the displacement correction control quantity to determine the actual control effect parameters of the release machine; and optimizing the adaptive adjustment algorithm based on the actual control effect parameters of the release machine to obtain an adaptive adjustment optimization algorithm.

[0039] Specifically, the displacement correction control quantity is directly applied to adjust the control signal of the drive motor, for example, by adjusting the voltage or current to change the motor's rotation state. During the adjustment process, closed-loop feedback control and control effect evaluation are performed. Core indicators of the equipment's operating status are collected and recorded in real time, forming a set of actual control effect parameters for the pay-off machine. These parameters include actual displacement, speed change curves, steady-state error magnitude, and response time, comprehensively reflecting the driving motor's operating effect under the current control strategy. For example, steady-state error can be used to evaluate the final accuracy of the drive motor's rotational displacement, while response time and overshoot amplitude indicate the drive motor's adaptability 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 insufficient control effect is found, such as slow response speed or large steady-state error, the adaptive adjustment algorithm needs to be further optimized based on these parameters. The algorithm dynamically modifies the control parameters according to the real-time operating conditions to generate an adaptive adjustment optimization algorithm. Finally, the optimized adaptive adjustment algorithm is reapplied to the PID controller to further improve the performance of closed-loop feedback control. Through real-time performance evaluation and algorithm optimization analysis, the performance of the controller can be continuously improved, the control accuracy of the drive motor can be enhanced, and the control precision and efficiency of the automatic take-up and unload machine can be improved, so as to efficiently and accurately achieve the take-up and unload task requirements in the automated production line.

[0040] Example 2: Based on the same inventive concept as the encoder-driven automatic take-up and unload machine control method in the foregoing examples, this application provides an electronic device. Figure 2 This is a schematic diagram of the structure of an electronic device provided in Embodiment 2 of the present invention, showing a block diagram of an exemplary electronic device suitable for implementing the embodiments of the present invention. Figure 2 The electronic device shown is merely an example and should not be construed as limiting the functionality or scope 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 an electronic device can be connected via a bus or other means. Figure 2 Taking the example of a connection between China and Israel via a bus.

[0041] Example 3: Based on the same inventive concept as the encoder-driven automatic take-up and undo machine control method in the foregoing examples, this example provides a computer-readable storage medium for storing software programs, computer-executable programs, and modules, such as the program instructions / modules corresponding to the encoder-driven automatic take-up and undo machine control method in this application. The processor executes the software programs, instructions, and modules stored in the memory to perform various functional applications and data processing of the computer device, thereby realizing the encoder-driven automatic take-up and undo machine control described above.

[0042] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0043] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An encoder-driven automatic wire coiling and uncoiling machine control method, characterized by, The method comprises: installing an encoder on a drive motor of a target take-up and pay-off machine, the encoder being coaxially connected with the drive motor, and initializing the encoder; determining a target take-up and pay-off length of the target take-up and pay-off machine according to production demand information of an automatic production line, and converting the target take-up and pay-off length into a target rotational displacement of the drive motor; laying out a perception sensor group on the automatic production line, acquiring a pay-off multi-dimensional perception data stream through the perception sensor group, and simultaneously reading and obtaining a pay-off pulse signal stream in real time through the encoder; analyzing and processing the pay-off multi-dimensional perception data stream and the pay-off pulse signal stream, and predicting a rotational predicted displacement; adopting a PID controller to analyze and control the rotational predicted displacement and the target rotational displacement, obtaining a displacement correction control quantity, and performing closed-loop feedback control on the target take-up and pay-off machine based on the displacement correction control quantity; the obtaining of the displacement correction control quantity comprises: extracting control parameters of the PID controller to obtain controller parameters, the controller parameters comprising a proportional term, an integral term and a differential term; determining an adaptive adjustment algorithm through a built-in self-adjusting mechanism of the PID controller; verifying a control response of the PID controller to obtain actual response parameters of the controller, dynamically optimizing and adjusting the controller parameters based on the actual response parameters of the controller by using the adaptive adjustment algorithm, and obtaining a PID updated controller; taking a difference between the rotational predicted displacement and the target rotational displacement as a displacement error signal, performing displacement control analysis on the displacement error signal based on the PID updated controller, and obtaining the displacement correction control quantity; the closed-loop feedback control on the target take-up and pay-off machine based on the displacement correction control quantity comprises: performing closed-loop feedback control on the target take-up and pay-off machine based on the displacement correction control quantity and evaluating a control effect, and determining actual control effect parameters of the pay-off machine; optimizing and analyzing the adaptive adjustment algorithm based on the actual control effect parameters of the pay-off machine, and obtaining an adaptive adjustment optimized algorithm.

2. The encoder-driven automatic take-up and pay-out machine regulation method of claim 1, wherein, the obtaining of the rotational predicted displacement comprises: analyzing and processing the pay-off pulse signal stream to obtain a current motor motion parameter set; identifying noise data and pre-processing data of the pay-off multi-dimensional perception data stream to obtain a standard pay-off multi-dimensional perception data stream; extracting and fusing features based on the standard pay-off multi-dimensional 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 rotational predicted displacement.

3. The encoder-driven automatic take-up and pay-out machine regulation method of claim 2, wherein, the obtaining of the current motor motion parameter set comprises: filtering and pre-processing the pay-off pulse signal stream by using a Kalman filter to obtain a usable pay-off pulse signal stream; performing signal shaping processing on the usable pay-off pulse signal stream according to a data application standard of a take-up and pay-off machine to obtain a standard pay-off pulse signal stream; The standard unwinding pulse signal stream is subjected to pulse counting and pulse time stamp recording to determine the total number of unwinding signal pulses and pulse time stamp information; Based on the total number of unwinding signal pulses and pulse time stamp information, motion parameter calculation processing is performed to obtain the current motor motion parameter set.

4. The encoder-driven automatic take-up and pay-out machine regulation method of claim 2, wherein, The obtained unwinding fusion perception feature data includes: Based on the standard unwinding multi-dimensional perception data stream, associated feature extraction is performed to obtain an associated unwinding multi-dimensional perception feature data set; The associated unwinding multi-dimensional perception feature data set is subjected to decentralized processing to obtain a covariance matrix, and the covariance matrix is solved to obtain eigenvalues and eigenvectors; According to the eigenvalues and eigenvectors, principal component information is selected, and based on the principal component information, principal component dimension reduction is performed on the standard unwinding multi-dimensional perception data stream to obtain a key unwinding perception feature data set; A feature-level fusion algorithm is used to perform data fusion on the key unwinding perception feature data set to obtain the unwinding fusion perception feature data.

5. The encoder-driven automatic take-up and pay-out machine regulation method of claim 2, wherein, The establishment of the displacement integrated prediction network includes: A displacement database of the winding and unwinding machine is collected, and the displacement database of the winding and unwinding machine is identified and classified according to motor motion parameter types and unwinding perception feature types to obtain a displacement feature sample set; The displacement feature sample set is subjected to time series analysis and characteristic analysis to obtain data time series information and data characteristic information; Based on the data time series information and data characteristic information, a displacement feature deep learning network set is selected to determine a displacement prediction branch network set; The displacement feature deep learning network set is used to train and optimize the displacement feature sample set respectively to obtain the displacement prediction branch network set; Through a model integration strategy, the displacement prediction branch network set is integrated and fused to establish the displacement integrated prediction network.

6. An electronic device, comprising: The electronic device includes a processor, a memory for storing instructions executable by the processor, and the processor is configured to execute the encoder-driven automatic winding and unwinding machine control method of any one of claims 1 to 5.

7. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, and the computer program is configured to execute the encoder-driven automatic winding and unwinding machine control method of any one of claims 1 to 5.

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