A method and system for controlling the production of high-elasticity antibacterial yarn

Abstract

This invention discloses a production control method and system for high-elasticity antibacterial yarn, relating to the field of textile manufacturing technology. The method includes: collecting multi-dimensional state information such as tension, temperature, and antibacterial agent distribution; generating a comprehensive performance evaluation index based on a feature projection and gated residual fusion model; comparing this index with the target performance to identify key influencing parameters such as fiber arrangement unevenness and antibacterial agent application distribution density; calling a process response model to generate parameter adjustment strategies; driving the adjustment device to adjust process variables such as tension and temperature through control commands; dynamically collecting feedback and correcting the strategy; and finally, periodically updating the adjustment rules based on a stochastic gradient descent algorithm combined with an expert knowledge base to achieve adaptive performance control. This invention improves the intelligence and robustness of yarn elasticity and antibacterial performance regulation, and is suitable for the industrial production of high-end textiles such as medical yarns and smart wearable fabrics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of textile manufacturing, and particularly relates to a high-elasticity antibacterial yarn production control method and system. BACKGROUND

[0002] With the rapid development of the textile industry towards functionalization and intelligentization, yarns with high elasticity and antibacterial properties are widely used in medical protection, high-performance sportswear, military materials and other fields, and higher requirements are put forward for the mechanical properties and functional properties of yarns. In order to realize large-scale production of high-performance yarns, more and more enterprises try to combine automatic production lines with performance monitoring systems to improve quality consistency and production efficiency.

[0003] However, in the existing yarn manufacturing process, the elasticity and antibacterial properties are coupled and affected by multiple process parameters such as raw materials, temperature, tension, and antibacterial agent application, and the control process has the following main technical problems: the yarn performance evaluation method is lagging behind: at present, off-line detection is generally used to carry out sampling inspection on finished yarns, it is difficult to obtain the elasticity and antibacterial properties of yarns in the production process in a timely manner, and real-time feedback cannot be provided for process adjustment. The control strategy is single and the feedback mechanism is missing: the existing system mainly depends on static control parameters, lacks the ability to adjust according to real-time production state, and cannot cope with the performance deviation caused by raw material fluctuation and environmental change. Lack of intelligent control mechanism for multiple performance targets: the performance of yarns is multi-dimensional, and the existing method is difficult to coordinate the mutual influence and control optimization between multiple performance indicators (such as elasticity and antibacterial property). The strategy updating mechanism relies on manual experience and lacks data-driven support: in terms of control strategy optimization, it mainly relies on manual experience accumulation at present, and has not formed a strategy updating mechanism based on the fusion of historical production data and knowledge rules, and lacks continuous adaptive ability.

[0004] Therefore, the existing technology urgently needs a high-performance yarn production control method and system which can obtain state information in real time, evaluate performance by fusing multi-dimensional features, identify key parameters and dynamically close-loop adjust process variables, and continuously optimize the control strategy by combining data-driven and knowledge-guided. SUMMARY

[0005] The present application aims to provide a high-elasticity antibacterial yarn production control method and system to solve the problems of lack of real-time performance evaluation, process adjustment lag, single control strategy and difficulty in adapting to batch differences and environmental fluctuations in the existing yarn production process, realize online perception of yarn elasticity and antibacterial properties, accurate identification of key influencing parameters, dynamic optimization and adaptive updating of process control strategy, and significantly improve the consistency of yarn products, intelligent control level and production stability.

[0006] The present application realizes the above-mentioned purpose through the following technical solutions:

[0007] In one aspect, the application provides a high-elasticity antibacterial yarn production control method, comprising the following steps:

[0008] Step 1: Collecting state information in the yarn production process, the state information including multi-dimensional parameter data representing the elasticity performance and antibacterial performance of the yarn;

[0009] Step 2: Performing feature fusion processing on the state information, and generating a comprehensive performance evaluation index using a multi-dimensional feature fusion model, the performance evaluation index reflecting the elasticity performance and antibacterial performance of the yarn at the same time;

[0010] Step 3: Comparing the performance evaluation index with a preset target performance value in terms of component level, and identifying key influence parameters causing performance deviation based on deviation determination results, the key influence parameters including fiber arrangement unevenness and antibacterial agent distribution density;

[0011] Step 4: Generating a control parameter adjustment strategy according to the identified key influence parameters, the control parameter adjustment strategy including process variables associated with the key influence parameters;

[0012] Step 5: Inputting the control parameter adjustment strategy into a control instruction generation module, generating and issuing an execution control instruction for driving a process adjustment action of a production process adjustment device, the production process adjustment action including set value adjustment of process variables associated with the key influence parameters;

[0013] Step 6: After completing the process adjustment, continuously collecting updated state information, and regenerating a performance evaluation index, dynamically modifying the control parameter adjustment strategy based on the performance feedback results, until the performance evaluation index meets the preset tolerance requirement;

[0014] Step 7: Taking performance data in multiple production batches as input samples, periodically updating adjustment rules for strategy generation using a stochastic gradient descent algorithm, and combining an expert knowledge base or external experience rules for auxiliary modification.

[0015] A further improvement of the application is that the multi-dimensional parameter data is obtained by a tension sensor, a displacement sensor, a temperature sensor, an antibacterial agent application rate monitoring device and an image acquisition device arranged on a yarn production line, and the multi-dimensional parameter data includes tension, deformation, temperature, antibacterial agent distribution density and yarn surface image information.

[0016] Further improvement of the present application is that the performance evaluation index is generated by a gating weighted fusion model that fuses yarn surface image features and production process state features, the gating weighted fusion model includes an image feature extraction module and a process state modeling module, which are respectively used for extracting a yarn surface image feature vector and a process state feature vector composed of tension, temperature and antibacterial agent distribution parameters, and includes the following steps:

[0017] A feature projection module is used for linearly mapping the process state feature vector to a feature space with the same dimension as the image feature vector to form a projected feature vector:

[0018] V' dyn = W surf ×V gate +B perf ;

[0019] A fusion gating module is used for generating a fusion adjustment coefficient Θ gate based on the spliced features [V' dyn , V gate ] and calculating a comprehensive performance evaluation vector through the following gating weighted formula:

[0020] V surf = Θ dyn ×V' surf +(1-Θ proj )×V proj ;

[0021] Wherein, V dyn is a dynamic process state feature vector in the yarn production process, containing real-time tension, temperature and antibacterial agent concentration changes; V gate is a yarn surface image feature vector, containing arrangement density, antibacterial particle distribution and surface texture image features; W perf and B perf are a projection weight matrix and a bias vector, used for projecting dynamic features to the same dimension as image features; V' target is a projected process state standard feature vector; Θ perf is a fusion gating coefficient vector, generated by the spliced image and dynamic features through an activation function (such as Sigmoid), ranging from 0 to 1; V perf is a weighted fused comprehensive performance feature vector, which serves as the basis for subsequent generation of yarn elasticity performance and antibacterial performance evaluation indexes.

[0022] Further improvement of the present application is that the step of identifying key influence parameters causing performance deviation based on the deviation determination result includes:

[0023] The comprehensive performance feature vector V target is compared with a target performance vector V impactCalculate the component level difference to obtain the performance deviation vector:

[0024] ΔV perf =V perf -V target ;

[0025] Based on the absolute values ​​of each dimension in the deviation vector and the performance sensitivity of the input parameters, the key influencing parameters that cause performance deviation are identified, and the calculation formula is as follows:

[0026]

[0027] in: Let S be the component of the performance deviation vector in the i-th dimension, representing the difference between the actual and target performance in that term; impact (i) represents the sensitivity coefficient generated during the prior modeling stage, indicating the sensitivity of the i-th performance dimension to the input feature x. i The response intensity; θ imp The minimum response threshold is set, and input features exceeding this value are identified as key impact parameters; KeyParams is the final set of key impact parameters identified, used to guide the dynamic adjustment of subsequent control strategies.

[0028] A further improvement of the present invention is that the step of calling the process parameter influence response model to generate the control parameter adjustment strategy includes:

[0029] Based on the identified set of key influencing parameters, the performance category associated with each parameter is determined, and the associated process variables are identified, including tension, temperature, and antimicrobial agent application rate.

[0030] Obtain the current deviation value of the key influencing parameters, and call the process parameter influence response model to calculate the target adjustment amount for each process variable. The process parameter influence response model is used to characterize the correspondence between the key influencing parameters and the process variables.

[0031] Based on the target adjustment amount, a control parameter adjustment strategy is constructed. The adjustment strategy defines the direction and magnitude of the change of the target set value of each process variable, which is used to adjust the elasticity or antibacterial properties of the yarn.

[0032] A further improvement of the present invention is that the step of generating and issuing execution control commands for driving the process adjustment actions of the production process adjustment device includes:

[0033] The target adjustment amount of each process variable included in the control parameter adjustment strategy is input to the control instruction generation module. The control instruction generation module generates execution control instructions corresponding to each process variable according to the preset control instruction mapping rules.

[0034] The execution control commands are respectively sent to the control units of the tension control system, temperature control system and antibacterial agent application system for real-time adjustment of the corresponding process execution devices;

[0035] The adjustment process includes adjusting the temperature control curve of the heating unit, the tension setting value of the stretching mechanism, and the spraying rate or application frequency of the antibacterial agent application device.

[0036] A further improvement of the present invention is that the step of dynamically correcting the control parameter adjustment strategy based on the performance feedback result includes:

[0037] After the production process adjustment is performed, the yarn status information after the adjustment is obtained in real time, and the performance evaluation index is recalculated.

[0038] The current performance evaluation index is compared with the preset target performance value to obtain the performance correction error vector;

[0039] Determine whether the error vector continuously exceeds the preset tolerance threshold. If so, dynamically adjust the adjustment gain factor in the process parameter influence response model based on the historical response data of the control adjustment.

[0040] Based on the updated adjustment gain factor, a new control parameter adjustment strategy is generated and used for the next round of control command generation and process adjustment to achieve convergence of performance errors.

[0041] A further improvement of the present invention is that the step of periodically updating the adjustment rules used for policy generation using a machine learning algorithm includes:

[0042] Continuously record the mapping relationship between each control parameter adjustment strategy and the corresponding performance evaluation index over multiple production cycles to build a historical database of control strategies and performance responses;

[0043] Periodic analysis is performed on the historical database to extract the statistical correlation between parameter changes and performance improvements in key adjustment paths;

[0044] Based on the statistical correlation results, the stochastic gradient descent method is used to update the adjustment rules in the control strategy generation module used to derive the process variable adjustment amount.

[0045] The updated adjustment rules include new adjustment gain factors, parameter adjustment thresholds, and feature selection priorities, designed to adapt to long-term changes in production conditions or performance requirements.

[0046] A further improvement of the present invention is that the updating process of the adjustment rule includes:

[0047] An expert knowledge base is constructed, which includes industry standard parameter ranges, empirical adjustment suggestions, and typical process deviation response schemes to provide auxiliary adjustment suggestions. When updating adjustment rules, statistical analysis results from historical production performance data are compared with empirical rules in the expert knowledge base. If significant deviations or rule conflicts exist, the adjustment parameters are weighted and corrected according to the priority labeling rules in the expert knowledge base. A composite adjustment rule that integrates data-driven results and expert experience suggestions is formed to improve the stability and adaptability of the control strategy.

[0048] On the other hand, the present invention provides a high-elasticity antibacterial yarn production control system, which applies the high-elasticity antibacterial yarn production control method described above, the system comprising:

[0049] The status acquisition module is used to collect status information during the yarn production process. The status information includes multi-dimensional parameter data characterizing the elasticity and antibacterial properties of the yarn.

[0050] The fusion evaluation module is used to perform feature fusion processing on the state information and generate performance evaluation indicators using a multi-dimensional feature fusion model. The performance evaluation indicators simultaneously reflect the elasticity and antibacterial properties of the yarn.

[0051] The deviation identification module is used to compare the performance evaluation index with the preset target performance value by component level, and identify the key influencing parameters that cause performance deviation based on the deviation judgment result. The key influencing parameters include fiber arrangement unevenness and antibacterial agent distribution density.

[0052] The strategy generation module is used to generate control parameter adjustment strategies based on the identified key influencing parameters by calling the process parameter influence response model. The control parameter adjustment strategies include process variables associated with the key influencing parameters.

[0053] The instruction generation module is used to convert the control parameter adjustment strategy into execution control instructions and send them to the production process adjustment device. The execution control instructions are used to drive process adjustment actions and adjust the set values ​​of process variables associated with key influencing parameters.

[0054] The strategy correction module is used to continuously collect updated status information after the process adjustment is completed, regenerate performance evaluation indicators, and dynamically correct the control parameter adjustment strategy based on the performance feedback results until the performance evaluation indicators meet the preset tolerance requirements.

[0055] The strategy update module uses performance data from multiple production batches as input samples and employs machine learning algorithms to periodically update the adjustment rules used to generate control strategies, and combines expert knowledge bases or external empirical rules for auxiliary correction.

[0056] The beneficial effects of this invention are as follows: By constructing a closed-loop control mechanism covering the entire process—including state perception, performance fusion evaluation, parameter identification, process control, and strategy updates—this invention solves key problems in existing technologies, such as the inability to obtain yarn performance status in real time, lag in process adjustment response, and lack of adaptability in control strategies. This invention first collects multi-dimensional state information during yarn production, comprehensively covering parameters highly related to elasticity and antibacterial performance, such as tension, temperature, and antibacterial agent application characteristics. Based on a fusion model, feature extraction and performance evaluation are performed on the above data, significantly improving the real-time identification accuracy of comprehensive yarn performance. Through component-level deviation analysis of performance indicators and preset targets, key factors affecting performance fluctuations can be quickly located, such as fiber unevenness and antibacterial agent distribution density, thereby achieving precise control. The process adjustment process automatically generates control strategies based on a response model and drives the production device to adjust process variables in real time through control commands, effectively improving the response speed and targeting of control. Simultaneously, a self-learning mechanism based on stochastic gradient descent is introduced, combined with an expert knowledge base to continuously optimize adjustment rules. This enables the control strategy to dynamically evolve with batch changes, significantly enhancing the system's adaptability and robustness to raw material differences and operating condition fluctuations. Overall, this invention possesses advantages such as comprehensive performance perception, intelligent control paths, and adaptive strategy updates. It can significantly improve the product consistency, production efficiency, and quality control level of high-elasticity antibacterial yarn, making it suitable for intelligent manufacturing scenarios of high-performance yarn under complex process conditions. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the 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.

[0058] in:

[0059] Figure 1 This is a flowchart of the method of the present invention;

[0060] Figure 2 This is a modular diagram of the system of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0062] like Figure 1 As shown, this is an embodiment of the present invention, which provides a method for controlling the production of high-elasticity antibacterial yarn, comprising the following steps:

[0063] Step 1: Collect status information during the yarn production process, including multi-dimensional parameter data characterizing the yarn's elasticity and antibacterial properties;

[0064] The multidimensional parameter data is acquired by tension sensors, displacement sensors, temperature sensors, antibacterial agent application rate monitoring devices, and image acquisition devices installed on the yarn production line. The multidimensional parameter data includes tension, deformation, temperature, antibacterial agent distribution density, and yarn surface image information.

[0065] In this embodiment, the status information acquisition link is deployed on the continuous production line of high elasticity antibacterial yarn. The acquisition objects include multi-dimensional parameter data related to the elasticity and antibacterial properties of the yarn, mainly including tension, deformation, temperature, antibacterial agent application rate, antibacterial agent distribution density, and yarn surface image information.

[0066] Specifically:

[0067] Tension data is collected in real time by a miniature tension sensor on the yarn guide rail, and the output unit is N (Newtons), which is used to reflect the stress state of the yarn during the drafting process.

[0068] Deformation is measured by displacement sensors to obtain changes in the cross-section or unit length of the yarn, which are used to calculate the stretch ratio and elastic recovery capacity.

[0069] Temperature information is collected by an infrared temperature sensor installed on the inner wall of the drawing channel to monitor the temperature control environment when heat setting or antibacterial agents are applied.

[0070] The application rate of the antibacterial agent is obtained by the dosing monitoring device of the antibacterial agent spraying module, and the amount applied per unit time is obtained by combining the time-flow rate integral.

[0071] The distribution density of the antibacterial agent was obtained by the combined use of the antibacterial agent component fluorescence response system and the yarn scanning acquisition device, and the resulting distribution image was converted into a density matrix.

[0072] The yarn surface image information is acquired by an industrial vision camera array, with a frame rate of no less than 30 FPS. The images are used to further extract texture features, coating uniformity, and surface defects.

[0073] Data from all sensors and image acquisition devices is aggregated through an edge processor, and after noise reduction, standardization, and time synchronization by a preprocessing module, it is input into the subsequent fusion evaluation module.

[0074] This implementation method enables comprehensive perception of key state quantities in the production process, ensuring the accuracy and real-time nature of subsequent performance evaluation and control strategy generation.

[0075] Step 2: Perform feature fusion processing on the state information and generate a comprehensive performance evaluation index using a multi-dimensional feature fusion model. The performance evaluation index reflects both the elasticity and antibacterial properties of the yarn.

[0076] In one embodiment, the performance evaluation index is generated by a gated weighted fusion model that integrates yarn surface image features and production process state features. The gated weighted fusion model includes an image feature extraction module and a process state modeling module, which are respectively used to extract the yarn surface image feature vector and the process state feature vector composed of tension, temperature, and antibacterial agent distribution parameters, including the following steps:

[0077] The feature projection module is used to linearly map the process state feature vector to a feature space of the same dimension as the image feature vector, forming a projected feature vector:

[0078] V′ dyn =W proj ×V dyn +B proj ;

[0079] Fusion gating module, used for splicing features [V′ dyn V surf Generate fusion adjustment coefficient Θ gate The overall performance evaluation vector is calculated using the following gating weighting formula:

[0080] V perf =Θ gate ×V′ dyn +(1-Θ gate )×V surf ;

[0081] Where: V dyn This is a dynamic process state feature vector during yarn production, including real-time changes in tension, temperature, and antibacterial agent concentration; V surf W is a feature vector of the yarn surface image, containing arrangement density, antibacterial particle distribution, and surface texture image features. proj B proj V′ is the projection weight matrix and bias vector, used to project dynamic features to the same dimension as image features; dyn The projected standard feature vector of the process state; Θ gate To fuse the gate coefficient vector, it is generated from the concatenated image and dynamic features using an activation function (such as Sigmoid), with a range of [0,1]; V perfThe weighted and fused comprehensive performance feature vector serves as the basis for subsequently generating evaluation indicators for yarn elasticity and antibacterial properties.

[0082] In this embodiment, in order to achieve effective fusion of multi-source heterogeneous features in the state information, the fusion module includes a nonlinear gated residual structure. This structure combines the complementarity of static image features and dynamic process state features, and achieves flexible weighting by controlling the fusion intensity coefficient to ensure the dynamic adaptability of the fusion result to features from different sources.

[0083] The aforementioned fusion method avoids feature conflicts caused by direct splicing, improving the robustness and distinguishability of performance metrics. In practical deployments, gating parameters can be adaptively trained using a shallow neural network, employing cross-entropy or minimum mean square error as the loss function to guide the network in learning the optimal fusion ratio under different production conditions.

[0084] Furthermore, to ensure the reconfigurability and deployment efficiency of this fusion process, the fusion module can be implemented as a lightweight structure based on matrix multiplication and activation functions, adapted for deployment on edge computing devices, enabling it to meet real-time inference requirements in high-speed yarn production lines. The fused performance evaluation vector is then fed into the subsequent deviation determination module, serving as a core reference indicator for assessing whether the current yarn elasticity and antibacterial properties meet the standards.

[0085] The gated weighted fusion mechanism in this embodiment enables semantic mapping and collaborative expression of image information and process parameters in a unified space, effectively improving the accuracy and stability of performance evaluation indicators and the model's ability to perceive changes in production conditions.

[0086] Step 3: Compare the performance evaluation index with the preset target performance value by component level, and identify the key influencing parameters that cause performance deviation based on the deviation judgment result. The key influencing parameters include fiber arrangement unevenness and antibacterial agent distribution density.

[0087] In this embodiment, to identify the core influencing factors causing the yarn's overall performance to fail to meet the standards, the overall performance feature vector V generated in step 2 is first... perf With the preset target performance vector V target Calculate the component level difference to obtain the performance deviation vector:

[0088] ΔV perf =V perf -V target ;

[0089] in: This represents the degree of deviation of the i-th performance component from the target value in the current production batch. To further evaluate the impact of each input variable on performance deviation, the system incorporates a sensitivity coefficient library pre-built during the experimental modeling phase. For each input parameter x... i Its normalized sensitivity to the i-th performance term is determined by the coefficient S. impact (i) represents the strength of the response of the input disturbance to fluctuations in the performance index.

[0090] Combining the absolute value of the deviation components with the sensitivity of the input parameters, the system identifies the set of key influencing parameters using the following formula:

[0091]

[0092] Where: θ imp The minimum impact threshold is set. When the performance deviation multiplied by the impact coefficient exceeds this threshold, the input variable x is considered to be... i Under current operating conditions, the KeyParams play a dominant role in performance deviation and should be the target for subsequent process control. The system inputs the aforementioned KeyParams as key influencing parameters to the control strategy generation module to dynamically construct corresponding control variable adjustment schemes, ensuring that the control strategy has the specificity and adaptability to respond to deviations.

[0093] Step 4: Based on the identified key influencing parameters, call the process parameter influence response model to generate a control parameter adjustment strategy. The control parameter adjustment strategy includes process variables associated with the key influencing parameters.

[0094] In one embodiment, the step of invoking the process parameter influence response model to generate a control parameter adjustment strategy includes:

[0095] Based on the identified set of key influencing parameters, the performance category associated with each parameter is determined, and the associated process variables are identified, including tension, temperature, and antimicrobial agent application rate.

[0096] Obtain the current deviation value of the key influencing parameters, and call the process parameter influence response model to calculate the target adjustment amount for each process variable. The process parameter influence response model is used to characterize the correspondence between the key influencing parameters and the process variables.

[0097] Based on the target adjustment amount, a control parameter adjustment strategy is constructed. The adjustment strategy defines the direction and magnitude of the change of the target set value of each process variable, which is used to adjust the elasticity or antibacterial properties of the yarn.

[0098] Step 5: Input the control parameter adjustment strategy into the control command generation module to generate and issue execution control commands for driving the process adjustment action of the production process adjustment device. The production process adjustment action includes adjusting the set value of the process variable associated with the key influencing parameter.

[0099] In one specific embodiment, the control instruction generation module generates execution control instructions corresponding to each process variable according to a preset control instruction mapping rule;

[0100] The execution control commands are respectively sent to the control units of the tension control system, temperature control system and antibacterial agent application system for real-time adjustment of the corresponding process execution devices;

[0101] The adjustment process includes adjusting the temperature control curve of the heating unit, the tension setting value of the stretching mechanism, and the spraying rate or application frequency of the antibacterial agent application device.

[0102] Step 6: After completing the process adjustment, continuously collect updated status information and regenerate performance evaluation indicators. Based on the performance feedback results, dynamically correct the control parameter adjustment strategy until the performance evaluation indicators meet the preset tolerance requirements.

[0103] In a specific embodiment, the step of dynamically correcting the control parameter adjustment strategy based on the performance feedback result includes:

[0104] After the production process adjustment is performed, the yarn status information after the adjustment is obtained in real time, and the performance evaluation index is recalculated.

[0105] The current performance evaluation index is compared with the preset target performance value to obtain the performance correction error vector;

[0106] Determine whether the error vector continuously exceeds the preset tolerance threshold. If so, dynamically adjust the adjustment gain factor in the process parameter influence response model based on the historical response data of the control adjustment.

[0107] Based on the updated adjustment gain factor, a new control parameter adjustment strategy is generated and used for the next round of control command generation and process adjustment to achieve convergence of performance errors.

[0108] Step 7: Using performance data from multiple production batches as input samples, periodically update the adjustment rules used for strategy generation using the stochastic gradient descent algorithm, and combine them with expert knowledge bases or external empirical rules for auxiliary correction.

[0109] In one embodiment, the step of periodically updating the adjustment rules used for policy generation using the stochastic gradient descent algorithm includes:

[0110] Continuously record the mapping relationship between each control parameter adjustment strategy and the corresponding performance evaluation index over multiple production cycles to build a historical database of control strategies and performance responses;

[0111] Periodic analysis is performed on the historical database to extract the statistical correlation between parameter changes and performance improvements in key adjustment paths;

[0112] Based on the statistical correlation results, the stochastic gradient descent method is used to update the adjustment rules in the control strategy generation module used to derive the process variable adjustment amount.

[0113] The updated adjustment rules include new adjustment gain factors, parameter adjustment thresholds, and feature selection priorities, designed to adapt to long-term changes in production conditions or performance requirements.

[0114] In one specific embodiment, the update process of the adjustment rule includes:

[0115] An expert knowledge base is constructed, which includes industry standard parameter ranges, empirical adjustment suggestions, and typical process deviation response schemes to provide auxiliary adjustment suggestions. When updating adjustment rules, statistical analysis results from historical production performance data are compared with empirical rules in the expert knowledge base. If significant deviations or rule conflicts exist, the adjustment parameters are weighted and corrected according to the priority labeling rules in the expert knowledge base. A composite adjustment rule that integrates data-driven results and expert experience suggestions is formed to improve the stability and adaptability of the control strategy.

[0116] like Figure 2 As shown, another embodiment of the present invention provides a high-elasticity antibacterial yarn production control system, which applies the high-elasticity antibacterial yarn production control method described above. The system comprises:

[0117] The status acquisition module is used to collect status information during the yarn production process. The status information includes multi-dimensional parameter data characterizing the elasticity and antibacterial properties of the yarn.

[0118] The fusion evaluation module is used to perform feature fusion processing on the state information and generate performance evaluation indicators using a multi-dimensional feature fusion model. The performance evaluation indicators simultaneously reflect the elasticity and antibacterial properties of the yarn.

[0119] The deviation identification module is used to compare the performance evaluation index with the preset target performance value by component level, and identify the key influencing parameters that cause performance deviation based on the deviation judgment result. The key influencing parameters include fiber arrangement unevenness and antibacterial agent distribution density.

[0120] The strategy generation module is used to generate control parameter adjustment strategies based on the identified key influencing parameters by calling the process parameter influence response model. The control parameter adjustment strategies include process variables associated with the key influencing parameters.

[0121] The instruction generation module is used to convert the control parameter adjustment strategy into execution control instructions and send them to the production process adjustment device. The execution control instructions are used to drive process adjustment actions and adjust the set values ​​of process variables associated with key influencing parameters.

[0122] The strategy correction module is used to continuously collect updated status information after the process adjustment is completed, regenerate performance evaluation indicators, and dynamically correct the control parameter adjustment strategy based on the performance feedback results until the performance evaluation indicators meet the preset tolerance requirements.

[0123] The strategy update module uses performance data from multiple production batches as input samples and employs machine learning algorithms to periodically update the adjustment rules used to generate control strategies, and combines expert knowledge bases or external empirical rules for auxiliary correction.

[0124] In summary, this invention significantly improves the accuracy and response efficiency of yarn elasticity and antibacterial performance control by constructing a closed-loop control process that integrates state information acquisition, heterogeneous feature fusion, performance deviation analysis, key parameter identification, and dynamic updating of control strategies. Compared to existing traditional methods that rely on manual experience or static rule control, this invention introduces machine learning models and process parameter response models to achieve online adjustment and adaptive optimization of multiple process variables. It is particularly suitable for scenarios with high performance stability requirements, such as high-performance medical yarns, functional fabrics, and smart fiber materials. This technology has the advantages of strong scalability, high control accuracy, and good industrial adaptability, and has broad application prospects and significant industrial promotion value.

[0125] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0126] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0127] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A high-elasticity antibacterial yarn production control method characterized by, The method comprises the following steps: Step 1: collecting state information in the yarn production process, the state information comprising multi-dimensional parameter data representing the elasticity and antibacterial properties of the yarn; Step 2: performing feature fusion processing on the state information, and generating a comprehensive performance evaluation index by using a multi-dimensional feature fusion model, the performance evaluation index reflecting the elasticity and antibacterial properties of the yarn; Step 3: comparing the performance evaluation index with a preset target performance value in terms of component level, and identifying key influencing parameters causing performance deviation based on a deviation determination result, the key influencing parameters comprising fiber arrangement unevenness and antibacterial agent distribution density; Step 4: calling a process parameter influence response model to generate a control parameter adjustment strategy according to the identified key influencing parameters, the control parameter adjustment strategy comprising process variables associated with the key influencing parameters; Step 5: inputting the control parameter adjustment strategy into a control instruction generation module to generate and issue an execution control instruction for driving a process adjustment action of a production process adjustment device, the process adjustment action comprising setting value adjustment on process variables associated with the key influencing parameters; Step 6: continuously collecting updated state information after the process adjustment, and re-generating a performance evaluation index, and dynamically modifying the control parameter adjustment strategy based on the performance feedback result until the performance evaluation index meets a preset tolerance requirement; Step 7: periodically updating an adjustment rule used for strategy generation by using a stochastic gradient descent algorithm with performance data in multiple production batches as input samples, and combining an expert knowledge base or external experience rules for auxiliary modification.

2. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The multi-dimensional parameter data is collected by a tension sensor, a displacement sensor, a temperature sensor, an antibacterial agent application rate monitoring device and an image acquisition device arranged on a yarn production line, and comprises tension, deformation, temperature, antibacterial agent distribution density and yarn surface image information.

3. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The performance evaluation index is generated by a gating weighted fusion model fusing yarn surface image features and production process state features, the gating weighted fusion model comprising an image feature extraction module and a process state modeling module, which are respectively used for extracting a yarn surface image feature vector and a process state feature vector composed of tension, temperature and antibacterial agent distribution parameters, and the gating weighted fusion model comprises the following steps: A feature projection module is configured to linearly map the process state feature vector to a feature space of the same dimension as the image feature vector to form a projected feature vector: V' dyn = W proj x V dyn + B proj ; Fusion gating module, used for splicing features [V′ dyn V surf Generate fusion adjustment coefficient Θ gate The overall performance evaluation vector is calculated using the following gating weighting formula: V perf = Θ gate × V' dyn + (1 - Θ gate ) × V surf ; Wherein: V dyn is the dynamic process state feature vector in the yarn production process; V surf is the yarn surface image feature vector; W proj , B proj is the projection weight matrix and the bias vector, used to project the dynamic features to the same dimension as the image features; V' dyn is the projected process state standard feature vector; Θ gate is the fusion gating coefficient vector, generated by the spliced image and dynamic features through the activation function, ranging from [0, 1]; V perf is the weighted fused comprehensive performance feature vector, which serves as the basis for subsequent generation of yarn elasticity performance and antibacterial performance evaluation indexes.

4. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The step of identifying key influencing parameters causing performance deviation based on a deviation determination result comprises: The comprehensive performance feature vector V perf is compared with the target performance vector V target The component level difference is calculated to obtain the performance deviation vector: ΔV perf = V perf - V target ; According to the absolute value of each dimension in the deviation vector and the performance sensitivity of the input parameters, the key influencing parameters causing performance deviation are identified, and the calculation formula is: wherein: is the component of the performance bias vector in the i-th dimension, representing the difference between actual and target performance in this dimension; S impact (i) is the sensitivity coefficient generated in the prior modeling stage, representing the response strength of the i-th performance dimension to the input feature x i ; θ imp is the set minimum response threshold value, and input features exceeding this value are determined to be key impact parameters; KeyParams is the final identified set of key impact parameters.

5. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The step of calling a process parameter influence response model to generate a control parameter adjustment strategy comprises: According to the identified key influencing parameter set, the performance category associated with each parameter is determined, and the associated process variables are determined, the process variables comprising tension, temperature and antibacterial agent application rate; obtaining a current deviation value of the key influencing parameter, and calling a process parameter influence response model to calculate a target adjustment amount of each process variable, the process parameter influence response model being used to represent a corresponding relationship between the key influencing parameter and the process variable; according to the target adjustment amount, constructing a control parameter adjustment strategy, the adjustment strategy defining a target setting value change direction and a change amplitude of each process variable, and being used to adjust the elastic performance or the antibacterial performance of the yarn.

6. The high-elasticity antibacterial yarn production control method according to claim 5, characterized by, The step of generating and issuing an execution control instruction for driving a production process adjustment device to perform a process adjustment action includes: inputting the target adjustment amount of each process variable included in the control parameter adjustment strategy into a control instruction generation module, the control instruction generation module generating an execution control instruction corresponding to each process variable according to a preset control instruction mapping rule; and issuing the execution control instruction to a control unit in a tension control system, a temperature control system, and an antibacterial agent application system, respectively, for real-time adjustment of a corresponding process execution device; The adjustment process includes adjusting a temperature control curve of a heating unit, a tension setting value of a drafting mechanism, and a spraying rate or application frequency of an antibacterial agent application device.

7. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The step of dynamically correcting the control parameter adjustment strategy based on the performance feedback result includes: after performing the production process adjustment action, obtaining real-time yarn state information after adjustment, and recalculating the performance evaluation index; comparing the current performance evaluation index with a preset target performance value to obtain a performance correction error vector; determining whether the error vector continuously exceeds a preset tolerance threshold, and if so, dynamically adjusting an adjustment gain factor in the process parameter influence response model according to historical response data of control adjustment; based on the updated adjustment gain factor, re-generating the control parameter adjustment strategy, and using it for the next round of control instruction generation and process adjustment to realize convergence of the performance error.

8. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The step of periodically updating the adjustment rule used for strategy generation using a machine learning algorithm includes: continuously recording a mapping relationship between each control parameter adjustment strategy and a corresponding performance evaluation index in multiple production cycles to construct a control strategy and performance response historical database; periodically analyzing the historical database to extract statistical correlations between parameter changes and performance improvements in key adjustment paths; based on the statistical correlation results, using a stochastic gradient descent method to update the adjustment rule used to derive the process variable adjustment amount in the control strategy generation module; The updated adjustment rule includes a new adjustment gain factor, a parameter adjustment threshold, and a feature selection priority, and is used to adapt to long-term changes in production conditions or performance requirements.

9. The high-elasticity antibacterial yarn production control method according to claim 1, characterized by, The updating process of the adjustment rule includes: An expert knowledge base containing industry standard parameter ranges, experience adjustment suggestions and typical process bias response schemes is constructed for providing auxiliary adjustment suggestions; when updating the adjustment rules, statistical analysis results from production performance history data are compared with experience rules in the expert knowledge base; if there is a significant deviation or rule conflict, the adjustment parameters are weighted and corrected according to the priority marking rules in the expert knowledge base; a composite adjustment rule is formed by fusing the data-driven results and expert experience suggestions, for improving the stability and adaptability of the control strategy.

10. A high-elasticity antibacterial yarn production control system applying a high-elasticity antibacterial yarn production control method according to any one of claims 1 to 9, characterized by, The system comprises: A state acquisition module for acquiring state information in the yarn production process, the state information including multi-dimensional parameter data representing the elasticity performance and antibacterial performance of the yarn; A fusion evaluation module for performing feature fusion processing on the state information, and generating a performance evaluation index using a multi-dimensional feature fusion model, the performance evaluation index reflecting the elasticity performance and antibacterial performance of the yarn at the same time; A bias identification module for comparing the performance evaluation index with a preset target performance value in component level, and identifying key influence parameters causing performance deviation based on bias determination results, the key influence parameters including fiber arrangement unevenness and antibacterial agent distribution density; A strategy generation module for generating a control parameter adjustment strategy according to the identified key influence parameters, the control parameter adjustment strategy including process variables associated with the key influence parameters; An instruction generation module for converting the control parameter adjustment strategy into an execution control instruction, and issuing the execution control instruction to a production process adjustment device, the execution control instruction being used to drive process adjustment actions and adjust the set values of process variables associated with the key influence parameters; A strategy correction module for continuously acquiring updated state information after completing process adjustment, regenerating a performance evaluation index, and dynamically correcting the control parameter adjustment strategy based on performance feedback results until the performance evaluation index meets a preset tolerance requirement; and a strategy updating module for periodically updating adjustment rules used for control strategy generation using machine learning algorithms with performance data in multiple production batches as input samples, and combining expert knowledge bases or external experience rules for auxiliary correction.

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