Automatic control method and system for underwear production equipment

Abstract

The invention relates to the technical field of equipment control, in particular to an automatic control method and system for underwear production equipment. The method comprises the steps of collecting running state data; based on the operation state data, adaptive adjustment is carried out on the proportion parameter of speed closed-loop control of the main driving motor, and the adjustment value, the adjustment direction, the adjustment frequency and the accumulated adjustment amount of the proportion parameter of the current rotating speed after adjustment are obtained; based on the adjusted adjustment value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportion parameter of the current rotating speed, adjustment track data of the proportion parameter changing along with time is determined; performing performance analysis of the main driving motor on the adjustment track data, and determining a performance degradation coefficient of the main driving motor; and obtaining an early warning signal based on the performance degradation coefficient of the main driving motor. The invention aims to solve the problem that the quality of produced seamless underwear is low due to the fact that the abnormal condition of slight drifting is difficult to diagnose by the existing control method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of device control, in particular to an underwear production device automatic control method and system. BACKGROUND

[0002] In the production and manufacturing of seamless underwear, the production equipment is controlled by an automatic program to produce underwear. During long-term operation of the production equipment, the heat dissipation system of the main drive motor itself will decrease in efficiency due to the accumulation of dust and fibers and other impurities, resulting in a continuous increase in the internal working temperature of the motor. The increase in motor winding resistance and the change in magnetic permeability of the magnetic material cause the motor to slightly drift in response speed and torque output characteristics when it receives a control signal from the automatic program, resulting in a mismatch between the speed closed-loop proportional parameters based on the initial standard working condition fixed in the automatic program and the actual dynamic response of the motor.

[0003] However, the threshold setting of the existing automatic program fault diagnosis system is based on more relaxed quality standards and traditional monitoring indicators, which cannot identify minor or extremely subtle non-periodic tension changes, making it difficult to diagnose abnormal situations that have slightly drifted, resulting in defects in the product quality of the produced seamless underwear. SUMMARY

[0004] The purpose of the present application is to provide an underwear production device automatic control method and system to solve the problem that the existing control method is difficult to diagnose abnormal situations that have slightly drifted, resulting in low product quality of the produced seamless underwear, in the long-term operation of the existing seamless underwear production equipment.

[0005] To achieve the above purpose, the technical scheme adopted by the present application is as follows: an underwear production device automatic control method, comprising the following steps: Collecting the running state data of the current speed and speed fluctuation information of the main drive motor spindle in the underwear production equipment; Based on the running state data, the proportional parameters of the main drive motor speed closed-loop control are adaptively adjusted to obtain the adjustment value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameters of the current speed after adjustment; Based on the adjustment value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameters of the current speed after adjustment, the adjustment trajectory data of the proportional parameters changing with time are determined; Performing performance analysis of the main drive motor on the adjustment trajectory data to determine the performance degradation coefficient of the main drive motor; Based on the performance degradation coefficient of the main drive motor, a warning signal for potential performance degradation of the main drive motor is obtained.

[0006] Preferably, the step of adaptively adjusting the proportional parameter of the main drive motor speed closed-loop control based on the operating status data to obtain the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current speed includes: Based on the aforementioned operating status data, determine the proportional parameters for the closed-loop speed control of the main drive motor; Data cleaning is performed on the proportional parameters of the closed-loop speed control of the main drive motor to obtain the processed proportional parameters; The processed proportional parameter is adaptively adjusted to obtain the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed.

[0007] Preferably, the step of adaptively adjusting the processed proportional parameter to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed includes: An error analysis is performed between the processed proportional parameter and the preset proportional parameter to obtain proportional parameter error data; Multidimensional feature analysis is performed on the proportional parameter error data to determine the type of disturbance component; Based on the type of disturbance component and the preset adaptive module, an adaptive adjustment scheme for the proportional parameters of the slow trend compensation layer and the fast instantaneous compensation layer is determined. Using the aforementioned adaptive adjustment scheme for proportional parameters, the processed proportional parameters are adaptively adjusted to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameters for the current rotational speed.

[0008] Preferably, the step of determining the adjustment trajectory data of the proportional parameter changing over time based on the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter to the current rotational speed includes: The adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter to the current rotational speed are aggregated to obtain aggregated adjustment data; By using aggregated historical data, trajectory analysis is performed on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter changing over time; The original data of the adjustment trajectory of the proportional parameter changing over time is verified to obtain the adjustment trajectory data of the proportional parameter changing over time.

[0009] Preferably, the step of using aggregated historical data to perform trajectory analysis processing on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter changing over time includes: Based on the aggregated adjustment data, production task information including the current type, material, and pattern of underwear being produced is determined; By using aggregated historical data, baseline data matching is performed on the production task information to obtain trajectory baseline data; Using the baseline trajectory data, trajectory analysis is performed on the aggregated adjustment data to determine the original adjustment trajectory data in which the proportional parameter changes over time.

[0010] Preferably, the step of performing performance analysis on the adjusted trajectory data to determine the performance degradation coefficient of the main drive motor includes: The adjustment trajectory data is compared and analyzed with the preset adjustment trajectory benchmark data to determine the trend deviation of the current adjustment trajectory; Based on the trend deviation state, feature extraction is performed on the adjusted trajectory data to obtain trajectory feature data including trajectory fluctuation frequency, amplitude, periodicity, nonlinearity and deviation state type; The trajectory feature data is matched with a preset degradation feature library to determine the performance degradation coefficient of the main drive motor.

[0011] Preferably, the step of extracting features from the adjusted trajectory data based on the trend deviation state to obtain trajectory feature data including the trajectory fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type includes: Based on the trend deviation state, the adjustment trajectory data is decomposed into multiple scales to obtain all adjustment trajectory components with different time scales. Feature extraction is performed on all adjustment trajectory components to obtain the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each adjustment trajectory component; Analyze the interrelationships and change patterns among each adjustment trajectory component to determine the coupling characteristics of different decay states; Based on the coupling characteristics of the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each adjusted trajectory component and different decay states, trajectory feature data including the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of the trajectory are obtained.

[0012] Preferably, after the step of obtaining a warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor, the method further includes: Based on the performance degradation coefficient of the main drive motor, the risk weighting factor is determined; Based on risk weighting factors and early warning signals, a comprehensive risk index is determined for the current production task. By using a pre-defined maintenance suggestion library and a comprehensive risk index, the optimal maintenance suggestion is obtained by matching the suggested maintenance solutions.

[0013] Preferably, the step of determining the comprehensive risk index under the current production task based on risk weighting factors and early warning signals includes: Using the aforementioned warning signal, determine the degree coefficient of the impact of the main drive motor's degradation type on production quality and equipment safety; The risk weighting factor is adjusted using the influence coefficient to determine the comprehensive risk index under the current production task.

[0014] The present invention also provides an automated control system for underwear production equipment, the system comprising: The data acquisition module is used to collect operating status data of the underwear production equipment, including the current speed and speed fluctuation information of the main drive motor spindle. The parameter adjustment module is used to adaptively adjust the proportional parameter of the main drive motor speed closed-loop control based on the operating status data, and obtain the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter for the current speed after adjustment; The trajectory determination module is used to determine the adjustment trajectory data of the proportional parameter changing over time based on the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter to the current rotational speed after the adjustment. The performance analysis module is used to perform performance analysis on the main drive motor of the adjusted trajectory data and determine the performance degradation coefficient of the main drive motor. The early warning signal module is used to obtain an early warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor.

[0015] Compared with the prior art, the automated control method and system for underwear production equipment of the present invention has the following advantages: This invention acquires detailed real-time information on the motor's operation by collecting operational status data, including the current speed and speed fluctuations of the main drive motor's spindle in an underwear production equipment. Based on this operational status data, the proportional parameters of the main drive motor's speed closed-loop control are adaptively adjusted, yielding the adjusted value, direction, frequency, and cumulative adjustment amount for the current speed. This adaptive adjustment dynamically corrects the control parameters, ensuring they match the actual motor response and preventing mismatches caused by motor performance drift. Then, based on the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameters, the adjustment trajectory data of the proportional parameters over time is determined, transforming discrete adjustment information into a continuous performance evolution trajectory. By analyzing the main drive motor's performance using this trajectory data, the performance degradation coefficient of the main drive motor can be determined, enabling a quantitative assessment of potential performance degradation. Finally, based on the main drive motor's performance degradation coefficient, an early warning signal for controlling potential performance degradation of the main drive motor can be obtained. This invention, through adaptive adjustment of control parameters and quantitative analysis of performance degradation, can detect subtle drifts in motor performance in advance, avoiding quality problems such as uneven light and shadow texture on the fabric surface caused by minute deviations. This significantly improves the quality control level and equipment reliability in seamless underwear production, thereby enhancing production management and quality control of seamless underwear. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention, the accompanying drawings used in the specific embodiments will be briefly described below. In all the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart of an automated control method for underwear production equipment according to the present invention.

[0018] Figure 2 This is a structural block diagram of an automated control system for underwear production equipment according to the present invention.

[0019] In the diagram: 210, data acquisition module; 220, parameter adjustment module; 230, trajectory determination module; 240, performance analysis module; 250, early warning signal module.

[0020] The implementation and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] The following drawings disclose several embodiments of the present invention. For clarity, many practical details will be described in the following description. However, it should be understood that these practical details are not intended to limit the invention. That is, in some embodiments of the invention, these practical details are not essential. Furthermore, for the sake of simplicity, some conventional structures and components will be shown in the drawings in a simple schematic manner.

[0022] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0023] Furthermore, in this invention, the use of terms such as "first" and "second" is for descriptive purposes only and does not specifically refer to any order or sequence, nor is it intended to limit the invention. They are merely used to distinguish components or operations described using the same technical terms, and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but only if they are feasible for those skilled in the art. If a combination of technical solutions is contradictory or impossible to implement, such a combination should be considered nonexistent and not within the scope of protection claimed by this invention.

[0024] In the production of seamless underwear, production equipment is controlled by automated programs. During long-term continuous operation, the core components of the equipment, such as the main drive motor, will undergo subtle changes in physical characteristics due to environmental factors and wear. When producing high-precision and complex-patterned seamless underwear, minute deviations can lead to difficult-to-identify quality problems on the fabric surface. Existing technologies struggle to detect these hidden performance drifts, resulting in a mismatch between the original parameter settings of the control system and the actual response of the motor, which in turn affects the final quality of the product.

[0025] To further understand the content, features, and effects of this invention, the following embodiments are provided, and detailed descriptions are given below in conjunction with the accompanying drawings: Please see Figure 1 This invention provides an automated control method for lingerie production equipment, applicable to automated lingerie production lines equipped with advanced sensors and data processing units. The automated control method for lingerie production equipment includes the following steps: S100. Collect operational status data from the lingerie production equipment, including the current speed and speed fluctuation information of the main drive motor spindle. The main drive motor is the core power component in the lingerie production equipment responsible for driving the spindle rotation; its speed and stability directly affect the precision of the weaving process and the quality of the product. The operational status data, including the current speed and speed fluctuation information of the main drive motor spindle, is the basis for evaluating the motor's operating status and adjusting control parameters. This step can be achieved by installing a high-precision encoder or speed sensor on the main drive motor spindle to acquire the current spindle speed in real time. Simultaneously, to capture minute speed fluctuations, a high sampling rate sensor can be used, combined with digital signal processing technology, to filter and analyze the acquired raw speed signal, thereby extracting the speed fluctuation information.

[0026] S200. Based on the aforementioned operating status data, the proportional parameter of the main drive motor speed closed-loop control is adaptively adjusted to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameter for the current speed. Speed ​​closed-loop control is an existing motor control strategy that monitors the motor speed in real time and compares it with a set value to form a feedback loop, thereby adjusting the control parameters to maintain stable motor speed. The proportional parameter is a crucial parameter in speed closed-loop control, determining the controller's response strength to speed errors. This step can employ a rule-based adaptive adjustment method; when the speed fluctuation of the main drive motor spindle exceeds a preset threshold, the proportional parameter is adjusted slightly according to preset adjustment rules. The adjustment value can be a fixed increment or decrement, the adjustment direction can be increasing or decreasing the proportional parameter, the adjustment frequency can be set to adjust at regular intervals or when specific conditions are met, and the cumulative adjustment amount records the total adjustment magnitude of the proportional parameter. Alternatively, this step can employ a fuzzy logic-based adaptive adjustment method. By defining fuzzy rules, speed fluctuation information is mapped to the adjustment value and direction of the proportional parameter, thereby achieving smoother and more intelligent adaptive adjustment.

[0027] S300. Based on the adjusted value, direction, frequency, and cumulative adjustment of the proportional parameter for the current rotational speed, determine the adjustment trajectory data of the proportional parameter over time. This step records the proportional parameter value, adjustment time, direction, and cumulative adjustment amount after each adjustment and stores them in chronological order. Connecting the discrete data points forms a curve reflecting the dynamic changes of the proportional parameter, i.e., the adjustment trajectory data. The adjustment trajectory data can intuitively display the changing trend and pattern of the proportional parameter under different operating conditions.

[0028] S400. Perform performance analysis on the adjusted trajectory data for the main drive motor to determine the performance degradation coefficient of the main drive motor. The performance degradation coefficient is an indicator that quantifies the degree of performance degradation of the main drive motor; analysis of the coefficient can predict potential motor failure risks. This step compares the adjusted trajectory data with a preset normal operating trajectory model. If the adjusted trajectory data exhibits a continuous unidirectional deviation or abnormal fluctuation patterns, it may indicate performance degradation of the main drive motor. The performance degradation coefficient can be determined by quantifying the degree of deviation or abnormal pattern. For example, the mean square error between the adjusted trajectory data and the normal trajectory model can be calculated, or the spectral characteristics of the trajectory data can be analyzed to identify specific frequency components related to performance degradation.

[0029] S500: Based on the performance degradation coefficient of the main drive motor, a warning signal for controlling the potential performance degradation of the main drive motor is obtained. The warning signal is a prompt message issued based on the performance degradation coefficient, used to remind operators or maintenance systems to take appropriate intervention measures. This step can set one or more threshold values ​​for the performance degradation coefficient. When the obtained performance degradation coefficient exceeds the preset threshold, a warning signal is generated. The warning signal can be issued in various forms, such as through audible and visual alarms, sending SMS or email notifications to operators, or displaying warning information on the control interface. The level of the warning signal can also be graded according to the magnitude of the performance degradation coefficient, so that operators can take appropriate measures according to the severity of the warning.

[0030] This invention captures real-time, detailed micro-changes in motor operation by collecting operational status data, including the current speed and speed fluctuations of the main drive motor's spindle. This provides a foundation for subsequent analysis. Next, based on the operational status data, the proportional parameters of the main drive motor's speed closed-loop control are adaptively adjusted. This adaptive adjustment mechanism dynamically corrects the control parameters according to the motor's actual operating conditions, compensating for control mismatches caused by changes in the motor's physical characteristics and effectively suppressing minor spindle speed fluctuations. By recording the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameters, an adjustment trajectory data of the proportional parameters over time can be constructed. This not only reflects the dynamic changes in control parameters but also indirectly reveals the inherent laws governing performance drift of the main drive motor during long-term operation. Subsequently, performance analysis of the main drive motor is performed on the adjustment trajectory data to determine the performance degradation coefficient. The adaptive adjustment behavior of the control parameters is correlated with the motor's performance degradation, and the motor's health is assessed by analyzing the adjustment patterns of the control parameters. For example, if the proportional parameters need to be continuously adjusted in a certain direction, or if the frequency and magnitude of adjustment increase significantly, it indicates that the motor may be experiencing performance degradation. Ultimately, based on the performance degradation coefficient of the main drive motor, an early warning signal for potential performance degradation of the main drive motor can be obtained. This early warning signal can promptly alert operators or maintenance systems, allowing for preventative maintenance measures to be taken before motor performance severely impacts product quality or causes equipment failure. This avoids hidden problems that traditional fault diagnosis systems cannot identify. This invention achieves early and accurate early warning of performance degradation in the main drive motor of lingerie production equipment, significantly improving the robustness of automated control and the stability of production quality.

[0031] In some embodiments of this application described above, the step of adaptively adjusting the proportional parameter of the main drive motor speed closed-loop control based on the operating state data to obtain the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current speed includes: Based on the aforementioned operating status data, the proportional parameters for the closed-loop speed control of the main drive motor are determined. This step involves analyzing the operating status data collected from the lingerie production equipment, which includes information on the current speed and speed fluctuations of the main drive motor's spindle. For example, using real-time data analysis algorithms or preset models, the proportional parameters required for the closed-loop speed control of the main drive motor at the current moment are calculated and extracted. The proportional parameters are key control variables affecting the motor's response speed and stability.

[0032] The proportional parameters of the main drive motor speed closed-loop control are cleaned to obtain processed proportional parameters. This step involves preprocessing the determined proportional parameters. Due to factors such as sensor noise, environmental interference, or data transmission errors, the original proportional parameter data may contain outliers or noise. Therefore, data cleaning techniques, such as filtering, smoothing, outlier detection and removal, are needed to remove redundant, erroneous, or inconsistent information from the data, thereby obtaining more accurate and reliable processed proportional parameters to ensure the effectiveness of subsequent adaptive adjustments.

[0033] The processed proportional parameter is adaptively adjusted to obtain the adjusted value, direction, frequency, and cumulative adjustment amount for the current speed. This step refers to dynamically adjusting the proportional parameter based on the deviation between the processed proportional parameter and the desired performance using a specific adaptive control algorithm. The adaptive adjustment process outputs specific adjustment values ​​to indicate the amount by which the proportional parameter should be increased or decreased; the adjustment direction indicates whether the proportional parameter is increased or decreased; the adjustment frequency indicates the interval between adjustments; and the cumulative adjustment amount is recorded to facilitate tracking and evaluation of the adjustment effect.

[0034] This embodiment refines the adaptive adjustment process of the proportional parameter for the main drive motor speed closed-loop control into three steps: determination, data cleaning, and adaptive adjustment. This ensures the quality and accuracy of the proportional parameter used for adaptive adjustment. First, the initial proportional parameter is determined from the original operating state data, providing a foundation for subsequent adjustments. Second, the data cleaning step effectively filters out noise and outliers, avoiding erroneous adjustments or system instability caused by data quality issues, thereby improving the reliability and accuracy of adaptive adjustment. Because the cleaned proportional parameter data more accurately reflects the system's operating state, subsequent adaptive adjustments can more accurately respond to system requirements, thus achieving precise, stable, and efficient adaptive adjustment of the proportional parameter for the main drive motor speed closed-loop control.

[0035] In some embodiments of this application described above, the step of adaptively adjusting the processed proportional parameter to obtain the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed includes: Error analysis is performed between the processed proportional parameter and the preset proportional parameter to obtain proportional parameter error data. Specifically, error analysis between the processed proportional parameter and the preset proportional parameter involves comparing the data-cleaned proportional parameter with the preset proportional parameter determined under ideal stable operating conditions to quantify the deviation between the current control parameter and the desired parameter. Therefore, proportional parameter error data can be obtained to reflect the real-time deviation of the proportional parameter in the closed-loop control of the main drive motor speed, with the aim of providing a quantitative basis for subsequent adaptive adjustments.

[0036] Multidimensional feature analysis is performed on the proportional parameter error data to determine the type of disturbance component. This step involves in-depth mining of the error data to identify various dynamic characteristics and potential patterns contained within it. For example, techniques such as Fourier transform, wavelet analysis, or principal component analysis can be used to extract features such as frequency, amplitude, phase, trend, and periodicity of the error data, thereby determining the type of disturbance component. The type of disturbance component refers to the nature and source of various disturbance factors affecting the closed-loop speed control of the main drive motor, such as slow drift, periodic oscillation, random noise, or instantaneous impact. Its purpose is to provide a classification basis for targeted compensation strategies.

[0037] Based on the disturbance component type and the preset adaptive module, a proportional parameter adaptive adjustment scheme is determined, including a slow trend compensation layer and a fast instantaneous compensation layer. The preset adaptive module can be a pre-designed software or hardware module containing multiple adaptive algorithms and strategies. The slow trend compensation layer aims to handle long-term, slow drift of the proportional parameter caused by equipment aging or slow changes in ambient temperature, for example, by using integral control or low-pass filtering. The fast instantaneous compensation layer is used to cope with short-term, rapid changes in the proportional parameter caused by sudden load changes and power fluctuations, for example, by using proportional-derivative control or high-pass filtering. Through this layered compensation mechanism, disturbances of different natures can be handled more precisely and effectively, ensuring accurate adjustment of the proportional parameter.

[0038] Using the aforementioned adaptive adjustment scheme for proportional parameters, the processed proportional parameters are adaptively adjusted to obtain the adjusted value, direction, frequency, and cumulative adjustment amount for the current speed. This step refers to real-time correction of the proportional parameters according to a determined hierarchical compensation strategy, enabling them to dynamically adapt to changes in the operating state of the main drive motor, ultimately yielding the adjusted value, direction, frequency, and cumulative adjustment amount for the current speed. The adjustment value represents the magnitude of each adjustment, the adjustment direction indicates whether the parameter increases or decreases, the adjustment frequency represents the rate of adjustment, and the cumulative adjustment amount records the total change in the parameter from its initial value.

[0039] Specifically, after prolonged operation, the main drive motor of the lingerie production equipment experiences a slow downward drift in its speed closed-loop control proportional parameter due to bearing wear. Simultaneously, during production, occasional instantaneous load shocks occur due to differences in material hardness between batches, causing brief and drastic fluctuations in speed. This embodiment first detects a persistent negative error between the processed proportional parameter and the preset proportional parameter through error analysis. Subsequently, multi-dimensional feature analysis identifies a slow trend component and a fast instantaneous fluctuation component within the error data. The preset adaptive module activates a slow trend compensation layer, for example, using a low-gain integral controller, to slowly increase the proportional parameter to offset the drift caused by bearing wear. Simultaneously, when an instantaneous load shock occurs, a fast instantaneous compensation layer is activated, for example, using a high-gain proportional-derivative controller, to rapidly adjust the proportional parameter to suppress instantaneous speed fluctuations. Through this layered collaborative approach, the proportional parameter can be effectively adjusted to its optimal state, ensuring that the main drive motor maintains high-precision speed control even under long-term wear and instantaneous load shocks, thereby guaranteeing the stability of lingerie production and product quality.

[0040] This embodiment introduces error analysis between the processed proportional parameter and the preset proportional parameter, enabling precise quantification of the deviation of the current control parameter. Furthermore, by performing multi-dimensional feature analysis on the proportional parameter error data, various disturbance components affecting the main drive motor speed closed-loop control can be identified, such as distinguishing between slow drift and rapid instantaneous impact. Through fine-grained disturbance classification, adaptive adjustment schemes for the proportional parameter, including a slow trend compensation layer and a rapid instantaneous compensation layer, can be determined based on different disturbance types. The slow trend compensation layer focuses on eliminating long-term, slow parameter drift, while the rapid instantaneous compensation layer quickly responds to sudden, drastic parameter fluctuations. This layered and refined adaptive adjustment mechanism ensures that the adjustment of the proportional parameter is no longer a simple, coarse process, but rather can precisely compensate for disturbances of different natures, thereby significantly improving the robustness and accuracy of the main drive motor speed closed-loop control.

[0041] In some embodiments of this application described above, the step of determining the adjustment trajectory data of the proportional parameter changing over time based on the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed includes: The adjusted values, directions, frequencies, and cumulative adjustments of the proportional parameter to the current rotational speed are aggregated to obtain aggregated adjustment data. This step involves integrating discrete adjustment information that may arise at different times or under different operating conditions. For example, all adjustment values, directions, frequencies, and cumulative adjustments within a specific time window can be statistically analyzed, averaged, or weighted to form more representative and comprehensive aggregated adjustment data. Aggregation helps eliminate instantaneous noise or abnormal fluctuations and extracts the overall trend and pattern of proportional parameter adjustments.

[0042] By utilizing aggregated historical data, trajectory analysis is performed on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter over time. The aggregated historical data can include the adjustment trajectory data of the proportional parameter under similar production tasks, equipment states, or environmental conditions over a past period. By comparing the current aggregated adjustment data with this historical data, performing pattern recognition, or trend prediction, the original trajectory of the proportional parameter changing over time can be preliminarily outlined. Trajectory analysis can employ methods such as time series analysis, machine learning models (e.g., recurrent neural networks (RNNs) or long short-term memory networks (LSTMs), or statistical regression to identify the dynamic characteristics and evolution patterns of the proportional parameter adjustment.

[0043] The original data of the adjusted trajectory showing the ratio parameter changing over time is validated to obtain adjusted trajectory data showing the ratio parameter changing over time. Specifically, the validation process aims to ensure the accuracy, reliability, and reasonableness of the determined original trajectory data. For example, validation rules can be set, such as the smoothness of the trajectory, the reasonable range of the rate of change, or compliance with physical constraints. If the original trajectory data does not meet these validation criteria, it may be necessary to perform corrections, smoothing, or outlier removal to obtain the final adjusted trajectory data showing the ratio parameter changing over time that can be used for subsequent performance analysis.

[0044] This embodiment effectively reduces data noise and extracts more stable adjustment patterns by aggregating discrete proportional parameter adjustment information. Subsequently, trajectory analysis with aggregated historical data allows for more accurate capture of the dynamic changing trends of the proportional parameters, forming a preliminary adjustment trajectory. Finally, rigorous verification of the original trajectory data ensures its reliability and validity, providing high-quality input for subsequent main drive motor performance analysis. This step-by-step, refined processing approach enables precise modeling and understanding of the proportional parameter adjustment trajectory, laying a solid foundation for equipment condition assessment and early warning.

[0045] In some embodiments of this application described above, the step of using aggregated historical data to perform trajectory analysis processing on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter changing over time includes: Based on the aggregated adjustment data, production task information, including the current type, material, and pattern of underwear being produced, is determined. This production task information refers to specific parameters related to the current underwear production task, such as the type of underwear being produced, the materials used, and the pattern. This is crucial for understanding the operating characteristics of the main drive motor under specific working conditions. Specifically, production task information can be obtained from the production management system or equipment control system, for example, by reading the detailed content of the current work order.

[0046] By utilizing aggregated historical data, baseline data matching is performed on the production task information to obtain trajectory baseline data. This trajectory baseline data serves as a reference standard or expected behavior pattern for the adjustment trajectory of the main drive motor's proportional parameters under specific production task information. The baseline data is obtained by analyzing aggregated historical data and matching it with current production task information. For example, when producing underwear of a specific type, material, and pattern, the adjustment of the main drive motor's proportional parameters should follow specific trajectory patterns, which are pre-stored in the aggregated historical data and obtained through matching.

[0047] Using the baseline trajectory data, trajectory analysis is performed on the aggregated adjustment data to determine the original data of the adjustment trajectory showing the change of the proportional parameter over time. In this step, when analyzing the adjustment trajectory of the main drive motor's proportional parameter, it no longer relies solely on general historical data, but compares and analyzes it with the expected baseline under a specific production task. Various statistical or machine learning methods can be used for this comparison, such as calculating the deviation, similarity, or correlation between the current trajectory and the baseline trajectory.

[0048] Specifically, the lingerie production equipment is producing lace bras made of silk with French embroidery patterns. When analyzing the proportional parameter adjustment trajectory, the current production task information is first extracted from the aggregated adjustment data: lingerie type: lace bra, material: silk, pattern: French embroidery. Then, historical trajectory data matching this production task information is searched in the aggregated historical data; for example, typical adjustment trajectories of the main drive motor's proportional parameters when producing the same type, material, and pattern of lingerie in the past. This matched historical trajectory data is used as the trajectory baseline data. Next, the currently collected aggregated adjustment data is compared and analyzed with this trajectory baseline data. For example, if the current adjustment trajectory continuously deviates from the baseline data within a specific time period, and the deviation exceeds a preset threshold, an anomaly can be preliminarily identified, and this can be included as part of the original data for the proportional parameter adjustment trajectory over time. This effectively distinguishes between normal adjustments caused by changes in production tasks and abnormal adjustments caused by motor performance degradation, thereby improving the accuracy of trajectory analysis.

[0049] This embodiment first determines the production task information of the current underwear type, material, and pattern based on aggregated adjustment data, thereby identifying the specific working scenario of the main drive motor. Subsequently, baseline data matching of the production task information using aggregated historical data yields trajectory baseline data highly correlated with the current production task. The introduction of trajectory baseline data related to specific production tasks allows for a more accurate assessment of proportional parameter adjustments during subsequent trajectory analysis of the aggregated adjustment data. Baseline comparison based on specific production tasks effectively filters out normal fluctuations caused by changes in production tasks, thus more accurately identifying the true performance degradation trend of the main drive motor and avoiding misjudgments or omissions.

[0050] In some embodiments of this application described above, the step of performing performance analysis on the main drive motor based on the adjusted trajectory data to determine the performance degradation coefficient of the main drive motor includes: The adjusted trajectory data is compared and analyzed with preset adjusted trajectory benchmark data to determine the trend deviation of the current adjusted trajectory. The purpose of this step is to identify the difference between the overall trend of the proportional parameter adjustment trajectory under the current main drive motor operating state and the normal or ideal operating mode. The preset adjusted trajectory benchmark data can be obtained by training with a large amount of historical data, representing proportional parameter adjustment trajectory models under different health states or normal operating modes. By comparison, the degree and direction of deviation of the current trajectory can be quantified, such as continuous increase, decrease, increased fluctuation, or the appearance of abnormal peaks.

[0051] Based on the trend deviation, feature extraction is performed on the adjustment trajectory data to obtain trajectory feature data including trajectory fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type. This step aims to identify key indicators closely related to the performance degradation of the main drive motor from the complex adjustment trajectory. The trajectory fluctuation frequency reflects the stability of the motor response; the amplitude indicates the severity of the adjustment; periodicity may reveal a repetitive mechanical or electrical fault mode; nonlinearity characterizes the complexity of the system response and potential nonlinear faults; and the deviation state type further refines the specific manifestation of the trend deviation. These feature data collectively constitute a comprehensive quantitative description of the main drive motor's operating state.

[0052] The trajectory feature data is matched with a preset degradation feature library to determine the performance degradation coefficient of the main drive motor. The purpose of this step is to associate the current operating characteristics of the motor with various known performance degradation modes through pattern recognition. The preset degradation feature library can contain typical feature patterns exhibited by different types of faults (such as bearing wear, winding aging, and controller parameter drift) on the proportional parameter adjustment trajectory. Through matching, the possible types and severity of performance degradation in the main drive motor can be quickly and accurately identified, thereby determining the performance degradation coefficient of the main drive motor.

[0053] Specifically, in the underwear production process, under normal operating conditions, the proportional parameter adjustment trajectory of the main drive motor's speed closed-loop control should fluctuate within a preset stable range. When the motor bearings begin to wear, it causes slight periodic fluctuations in the spindle speed, which are reflected in the proportional parameter adjustment trajectory as an increase in fluctuation frequency and a slight increase in amplitude, while possibly exhibiting a specific change in nonlinearity. This invention compares the currently collected proportional parameter adjustment trajectory data with the baseline trajectory under normal operating conditions and finds a continuous small-amplitude periodic deviation. Based on the trend deviation, the fluctuation frequency, amplitude, periodicity (e.g., consistent with bearing wear frequency), nonlinearity, and deviation type (e.g., periodic oscillation) of this trajectory are extracted. Subsequently, the extracted trajectory feature data is input into a preset degradation feature library for matching. If the feature library contains a feature pattern corresponding to the initial bearing wear and highly matches the current trajectory features, the performance degradation coefficient of the main drive motor is determined, for example, it is marked as slight bearing wear with a degradation coefficient of 0.2, thus providing accurate input for subsequent early warning signal generation and maintenance recommendations.

[0054] This embodiment effectively identifies abnormal trends in the current operating state of the main drive motor by comparing and analyzing the adjusted trajectory data with preset adjusted trajectory benchmark data. Determining the trend deviation provides direction for subsequent refined analysis. Furthermore, based on the determined trend deviation, multi-dimensional feature extraction is performed on the adjusted trajectory data, including fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation type, resulting in a more comprehensive and in-depth description of the main drive motor's operating characteristics. The trajectory feature data can capture subtle changes exhibited by the main drive motor at different degradation stages. Finally, by matching the rich trajectory feature data with a preset degradation feature library, accurate identification of the main drive motor's performance degradation type and quantification of the degradation degree can be achieved, overcoming the limitation of simply relying on analysis to accurately determine motor performance degradation.

[0055] In some embodiments of this application described above, the step of extracting features from the adjusted trajectory data based on the trend deviation state to obtain trajectory feature data including the trajectory's fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type includes: Based on the trend deviation state, the adjustment trajectory data is decomposed into multiple scales to obtain all adjustment trajectory components with different time scales. This step can employ signal processing techniques such as wavelet decomposition, empirical mode decomposition (EMD), or variational mode decomposition (VMD). Through multi-scale decomposition, the original complex signal can be separated into simpler components reflecting different physical processes or decay mechanisms, thus facilitating independent analysis of each component. The aim is to reveal the decay patterns hidden at different time scales.

[0056] Feature extraction is performed on all adjustment trajectory components to obtain the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each component. This step involves calculating the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each decomposed adjustment trajectory component. For example, for each component, its dominant frequency, energy, peak amplitude, autocorrelation coefficient, and degree of deviation from the baseline state can be calculated. This allows for characterizing the dynamic behavior and decay performance of each scale component from different dimensions.

[0057] Analyzing the interrelationships and change patterns among each adjustment trajectory component helps determine the coupling characteristics of different decay states. This step involves using methods such as cross-correlation analysis, Granger causality analysis, coherence analysis, or mutual information to investigate whether there are temporal lags, causal relationships, synchronicities, or energy transfers between components at different scales. For example, a change in a slow decay component may lead to an increase in the amplitude of a fast vibration component. The aim is to identify the interactions and influences between different decay mechanisms, thereby gaining a more comprehensive understanding of the overall decay process of the main drive motor.

[0058] Based on the coupling characteristics of the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each adjusted trajectory component with different decay states, trajectory feature data including the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of the trajectory is obtained. Specifically, the trajectory feature data not only contains decay information at a single scale, but also incorporates complex patterns of multi-scale interactions, providing richer and more accurate input for subsequent determination of performance degradation coefficients.

[0059] Specifically, during the operation of the main drive motor of the underwear production equipment, its speed adjustment trajectory data exhibits two main decay modes: one is due to slight bearing wear caused by long-term operation, manifested as a slowly rising nonlinear trend and low-frequency micro-fluctuations in the adjustment trajectory; the other is due to intermittent high-frequency oscillations caused by power fluctuations or local winding faults. If only traditional single-scale feature extraction methods are used, the decay features at different scales may mask or confuse each other, making it impossible to accurately identify the true state of each decay. This invention performs wavelet decomposition on the collected adjustment trajectory data. Through wavelet decomposition, the original signal is decomposed into multiple wavelet coefficients in different frequency ranges, which correspond to adjustment trajectory components at different time scales. For example, low-frequency components reflecting long-term trends (corresponding to bearing wear) and multiple high-frequency components reflecting instantaneous fluctuations (corresponding to power fluctuations or winding faults) can be obtained. Then, feature extraction is performed on each decomposed wavelet component. For example, for low-frequency components, their nonlinearity, average amplitude, and trend deviation rate can be extracted; for high-frequency components, their dominant frequency, peak amplitude, and periodicity can be extracted. Furthermore, the interrelationships between different components are analyzed. For example, it can be observed that when the low-frequency component (bearing wear) reaches a certain level, the amplitude of the high-frequency component (oscillation) increases significantly, indicating that bearing wear exacerbates motor vibration, forming a degradation coupling. This allows it to be determined that there are not just two independent faults, but a mutually reinforcing relationship, thus indicating a more serious potential risk. Ultimately, based on the characteristics of multi-scale components and their coupling characteristics, trajectory feature data containing richer information can be generated. For example, this data may include information such as low-frequency trend deviation: moderate, high-frequency oscillation amplitude: high, and a positive correlation between the two. Detailed feature data allows the performance analysis module to more accurately determine the performance degradation coefficient of the main drive motor and issue more targeted early warning signals, such as bearing wear and vibration coupling, suggesting immediate inspection of bearing condition and investigation of power supply stability.

[0060] This embodiment introduces multi-scale decomposition technology to decompose the adjustment trajectory data of the main drive motor performance degradation into multiple components with different time scales. This allows the features caused by different degradation mechanisms to be separated from what was originally mixed together. For example, slow mechanical wear may manifest as a low-frequency component, while rapid electrical faults or vibrations may manifest as a high-frequency component. By extracting features from each component separately, these degradation features at different scales can be captured independently. Furthermore, by analyzing the interrelationships and change patterns between these components, the coupling effects between different degradation states can be revealed. For example, slight wear on a bearing may cause the motor to resonate at a specific speed. The coupling relationship is crucial for accurately judging the severity of degradation and predicting future faults. Because it can comprehensively capture multi-scale features and their coupling relationships, this embodiment overcomes the limitations of single-scale analysis, thus providing a deeper and more accurate data foundation for subsequent main drive motor performance analysis.

[0061] In some embodiments of this application described above, after the step of obtaining a warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor, the method further includes: Based on the performance degradation coefficient of the main drive motor, a risk weighting factor is determined. Specifically, the risk weighting factor is a parameter used to quantify the impact of main drive motor performance degradation on the production process. The risk weighting factor can be assessed and set according to multiple dimensions such as the type and severity of performance degradation, its potential impact on product quality, and the economic losses caused by equipment downtime. For example, severe bearing wear may correspond to a higher risk weighting factor, while minor vibration abnormalities may correspond to a lower factor. The aim is to transform abstract performance degradation information into comparable and quantifiable risk indicators.

[0062] Based on risk weighting factors and early warning signals, a comprehensive risk index is determined for the current production task. This comprehensive risk index can be understood as a quantitative representation of the overall risk level resulting from the potential performance degradation of the main drive motor under the current production task. Calculated by combining risk weighting factors and early warning signals, this index comprehensively reflects the probability of equipment failure and its potential impact. For example, when the early warning signal strength is high and the risk weighting factor is large, the comprehensive risk index will increase accordingly, indicating that immediate intervention is required. Its purpose is to provide a unified and comprehensive risk assessment basis for maintenance decisions.

[0063] By utilizing a pre-defined maintenance suggestion library and a comprehensive risk index, the optimal maintenance suggestion is obtained by matching suggested solutions. Specifically, the pre-defined maintenance suggestion library is a database containing various maintenance strategies, procedures, required tools, and spare parts information for common faults and performance degradation of main drive motors. This library can be categorized and stored according to different equipment models, fault types, and severity. For example, for bearing wear, it might include solutions such as replacing or lubricating the bearing, with detailed operating procedures and required materials. Its purpose is to provide a standardized and selectable set of maintenance options. The optimal maintenance suggestion refers to the maintenance strategy most suitable for the current equipment condition and production needs, selected from the pre-defined maintenance suggestion library based on the currently calculated comprehensive risk index using an intelligent matching algorithm. This solution not only considers the severity of the fault but may also take into account factors such as maintenance cost, downtime, and spare parts availability. Its purpose is to ensure the timeliness, effectiveness, and economy of maintenance measures.

[0064] Specifically, during the operation of the main drive motor of the lingerie production equipment, its performance degradation coefficient is determined to be 0.8 (indicating moderate wear), and a high-intensity warning signal is generated. At this point, based on the performance degradation coefficient of 0.8 and combined with preset rules (e.g., moderate wear has a significant impact on production quality), a higher risk weighting factor, such as 0.7, is determined. Subsequently, the risk weighting factor of 0.7 is combined with the high-intensity warning signal (e.g., a quantified value of 0.9), and through a preset calculation model (e.g., the comprehensive risk index is the product of the risk weighting factor and the warning signal intensity), a comprehensive risk index of 0.63 is calculated for the current production task. This comprehensive risk index of 0.63 is then used for matching within a preset maintenance suggestion library. For example, if the library contains two options: replacing the main drive motor bearing and lubricating the main drive motor, and the risk index threshold range for replacing the main drive motor bearing is 0.6-0.8, while the risk index threshold range for lubrication is 0.3-0.5, then replacing the main drive motor bearing will be matched and recommended as the optimal maintenance suggestion. This plan will detail the specific bearing type, tools, operating procedures, and estimated downtime required for bearing replacement, thereby guiding maintenance personnel to make precise and efficient interventions.

[0065] This embodiment transforms a single warning signal into a multi-dimensional risk assessment by further incorporating risk weighting factors and a comprehensive risk index after receiving the warning signal. Specifically, the performance degradation coefficient of the main drive motor is used to determine the risk weighting factor, allowing different types of degradation to be assigned different risk priorities. Subsequently, the risk weighting factor is combined with the warning signal to jointly calculate the comprehensive risk index under the current production task, thereby comprehensively quantifying potential failure risks. This enables the system to intelligently match the optimal maintenance suggestion plan based on the specific risk index using a preset maintenance suggestion plan library. This effectively compensates for the shortcomings of simply providing warning signals, providing a more complete closed-loop management for the automated control of underwear production equipment.

[0066] In some embodiments of this application, the step of determining the comprehensive risk index under the current production task based on risk weighting factors and early warning signals includes: The warning signal is used to determine the impact coefficient of the main drive motor's degradation type on production quality and equipment safety. The warning signal is based on the main drive motor's performance degradation coefficient, indicating not only potential performance degradation but also implying the type of degradation. For example, the warning signal might indicate specific degradation types such as bearing wear, winding insulation aging, or rotor imbalance. The impact coefficient of the main drive motor's degradation type on production quality and equipment safety is a quantitative indicator of the potential impact of different degradation types on product quality (e.g., sewing precision or material damage) and equipment operational safety (e.g., sudden shutdown or overheating risk) in the underwear production process. It can be determined based on historical data, expert experience, or a pre-set risk assessment model. For example, slight bearing wear may have a low impact coefficient on production quality, while severe winding insulation aging may have a very high impact coefficient on equipment safety.

[0067] The risk weighting factor is adjusted exponentially using the aforementioned impact factor to determine the comprehensive risk index under the current production task. The risk weighting factor is determined based on the performance degradation coefficient of the main drive motor, representing the overall risk level of performance degradation. Exponential adjustment refers to applying the impact factor exponentially to the risk weighting factor, thereby enabling the comprehensive risk index to more sensitively and accurately reflect the risks brought about by specific types of degradation. For example, when the impact factor is high, exponential adjustment can significantly amplify the risk weighting factor, resulting in a higher final comprehensive risk index, thus prompting the system to take more urgent maintenance measures.

[0068] Specifically, the main drive motor exhibits two different potential degradation scenarios. The first scenario involves minor wear of the main drive motor bearings, indicated by a warning signal, with an impact factor of 1.2 on production quality and equipment safety. The second scenario involves aging of the main drive motor winding insulation, indicated by a warning signal, with an impact factor of 2.5 on production quality and equipment safety. In both scenarios, the initial risk weighting factor might be the same, for example, 0.6. Using simple linear superposition or multiplication, the difference in the overall risk index between the two scenarios might not be significant. However, through index adjustment, such as multiplying the risk weighting factor by an index (e.g., the square of the impact factor), the overall risk index might be 0.864 for minor bearing wear, while it might be 3.75 for aging winding insulation. Therefore, the overall risk index from aging winding insulation is significantly higher than that from minor bearing wear, which is more consistent with actual risk assessment and encourages the system to prioritize maintenance of main drive motors with aging winding insulation, thereby effectively avoiding potential serious production interruptions or safety accidents.

[0069] This embodiment utilizes the degradation type information contained in the early warning signal to further determine the specific impact coefficient of the degradation type on production quality and equipment safety. Subsequently, this impact coefficient is applied exponentially to the risk weighting factor, thereby fine-tuning the comprehensive risk index. This allows for the differentiation of risks arising from different degradation types. For example, degradation types that could lead to serious safety accidents or major quality defects have higher impact coefficients; after exponential adjustment, the comprehensive risk index will increase significantly, prompting the system to prioritize handling such high-risk issues. Conversely, degradation types with less impact have lower impact coefficients, and the increase in the comprehensive risk index is relatively smaller, avoiding over-maintenance.

[0070] Based on any of the above embodiments, please refer to the automated control method for underwear production equipment. Figure 2 The present invention also provides an automated control system for underwear production equipment, which includes a data acquisition module 210, a parameter adjustment module 220, a trajectory determination module 230, a performance analysis module 240, and an early warning signal module 250.

[0071] The data acquisition module 210 is used to collect operating status data of the underwear production equipment, including the current speed and speed fluctuation information of the main drive motor spindle.

[0072] The parameter adjustment module 220 is used to adaptively adjust the proportional parameter of the main drive motor speed closed-loop control based on the operating status data, and obtain the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter for the current speed.

[0073] The trajectory determination module 230 is used to determine the adjustment trajectory data of the proportional parameter changing over time based on the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter of the current rotation speed after adjustment.

[0074] The performance analysis module 240 is used to perform performance analysis on the main drive motor of the adjusted trajectory data and determine the performance degradation coefficient of the main drive motor.

[0075] The early warning signal module 250 is used to obtain an early warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor.

[0076] In this embodiment, the data acquisition module 210 acquires the real-time operating status data of the main drive motor, the parameter adjustment module 220 adaptively adjusts the proportional parameter of the speed closed-loop control, the trajectory determination module 230 constructs the adjustment trajectory data of the proportional parameter, the performance analysis module 240 evaluates the performance degradation coefficient of the main drive motor, and finally, the early warning signal module 250 issues an early warning signal for potential performance degradation. Therefore, this invention can construct a complete closed-loop system from data acquisition to early warning signal generation, effectively identifying and warning of potential performance degradation of the main drive motor, thereby avoiding production quality problems caused by motor performance drift and improving the accuracy and reliability of automated control.

[0077] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention specification.

Claims

1. An automated control method for underwear production equipment, characterized in that, Includes the following steps: Collect operating status data from the underwear production equipment, including the current speed and speed fluctuation information of the main drive motor spindle. Based on the operating status data, the proportional parameter of the main drive motor speed closed-loop control is adaptively adjusted to obtain the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter for the current speed. Based on the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameter to the current rotational speed after the adjustment, determine the adjustment trajectory data of the proportional parameter changing over time; The performance of the main drive motor is analyzed based on the adjusted trajectory data to determine the performance degradation coefficient of the main drive motor. Based on the performance degradation coefficient of the main drive motor, an early warning signal for controlling the potential performance degradation of the main drive motor is obtained.

2. The automated control method for underwear production equipment according to claim 1, characterized in that, Based on the aforementioned operating status data, the steps for adaptively adjusting the proportional parameters of the main drive motor speed closed-loop control to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameters for the current speed include: Based on the aforementioned operating status data, determine the proportional parameters for the closed-loop speed control of the main drive motor; Data cleaning is performed on the proportional parameters of the closed-loop speed control of the main drive motor to obtain the processed proportional parameters; The processed proportional parameter is adaptively adjusted to obtain the adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed.

3. The automated control method for underwear production equipment according to claim 2, characterized in that, The steps of adaptively adjusting the processed proportional parameter to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameter for the current rotational speed include: An error analysis is performed between the processed proportional parameter and the preset proportional parameter to obtain proportional parameter error data; Multidimensional feature analysis is performed on the proportional parameter error data to determine the type of disturbance component; Based on the type of disturbance component and the preset adaptive module, an adaptive adjustment scheme for the proportional parameters of the slow trend compensation layer and the fast instantaneous compensation layer is determined. Using the aforementioned adaptive adjustment scheme for proportional parameters, the processed proportional parameters are adaptively adjusted to obtain the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameters for the current rotational speed.

4. The automated control method for underwear production equipment according to claim 1, characterized in that, The steps for determining the adjustment trajectory data of the proportional parameter over time based on the adjusted value, direction, frequency, and cumulative adjustment amount of the proportional parameter to the current rotational speed include: The adjusted value, adjustment direction, adjustment frequency, and cumulative adjustment amount of the proportional parameter to the current rotational speed are aggregated to obtain aggregated adjustment data; By using aggregated historical data, trajectory analysis is performed on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter changing over time; The original data of the adjustment trajectory of the proportional parameter changing over time is verified to obtain the adjustment trajectory data of the proportional parameter changing over time.

5. The automated control method for underwear production equipment according to claim 4, characterized in that, The step of using aggregated historical data to perform trajectory analysis processing on the aggregated adjustment data to determine the original data of the adjustment trajectory of the proportional parameter changing over time includes: Based on the aggregated adjustment data, production task information including the current type, material, and pattern of underwear being produced is determined; By using aggregated historical data, baseline data matching is performed on the production task information to obtain trajectory baseline data; Using the baseline trajectory data, trajectory analysis is performed on the aggregated adjustment data to determine the original adjustment trajectory data in which the proportional parameter changes over time.

6. The automated control method for underwear production equipment according to claim 1, characterized in that, The steps for performing performance analysis on the main drive motor based on the adjusted trajectory data and determining the performance degradation coefficient of the main drive motor include: The adjustment trajectory data is compared and analyzed with the preset adjustment trajectory benchmark data to determine the trend deviation of the current adjustment trajectory; Based on the trend deviation state, feature extraction is performed on the adjusted trajectory data to obtain trajectory feature data including trajectory fluctuation frequency, amplitude, periodicity, nonlinearity and deviation state type; The trajectory feature data is matched with a preset degradation feature library to determine the performance degradation coefficient of the main drive motor.

7. The automated control method for underwear production equipment according to claim 6, characterized in that, The step of extracting features from the adjusted trajectory data based on the trend deviation state to obtain trajectory feature data including the trajectory fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type includes: Based on the trend deviation state, the adjustment trajectory data is decomposed into multiple scales to obtain all adjustment trajectory components with different time scales. Feature extraction is performed on all adjustment trajectory components to obtain the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each adjustment trajectory component; Analyze the interrelationships and change patterns among each adjustment trajectory component to determine the coupling characteristics of different decay states; Based on the coupling characteristics of the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of each adjusted trajectory component and different decay states, trajectory feature data including the fluctuation frequency, amplitude, periodicity, nonlinearity, and deviation state type of the trajectory are obtained.

8. The automated control method for underwear production equipment according to claim 1, characterized in that, After obtaining the early warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor, the method further includes: Based on the performance degradation coefficient of the main drive motor, the risk weighting factor is determined; Based on risk weighting factors and early warning signals, a comprehensive risk index is determined for the current production task. By using a pre-defined maintenance suggestion library and a comprehensive risk index, the optimal maintenance suggestion is obtained by matching the suggested maintenance solutions.

9. The automated control method for underwear production equipment according to claim 8, characterized in that, The steps for determining the comprehensive risk index under the current production task based on risk weighting factors and early warning signals include: Using the aforementioned warning signal, determine the degree coefficient of the impact of the main drive motor's degradation type on production quality and equipment safety; The risk weighting factor is adjusted using the influence coefficient to determine the comprehensive risk index under the current production task.

10. An automated control system for underwear production equipment, characterized in that, The system includes: The data acquisition module is used to collect operating status data of the underwear production equipment, including the current speed and speed fluctuation information of the main drive motor spindle. The parameter adjustment module is used to adaptively adjust the proportional parameter of the main drive motor speed closed-loop control based on the operating status data, and obtain the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter for the current speed after adjustment; The trajectory determination module is used to determine the adjustment trajectory data of the proportional parameter changing over time based on the adjusted value, adjustment direction, adjustment frequency and cumulative adjustment amount of the proportional parameter to the current rotational speed after the adjustment. The performance analysis module is used to perform performance analysis on the main drive motor of the adjusted trajectory data and determine the performance degradation coefficient of the main drive motor. The early warning signal module is used to obtain an early warning signal for controlling the potential performance degradation of the main drive motor based on the performance degradation coefficient of the main drive motor.

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