Winding forming loss management and control method and equipment in fiber yarn production

Abstract

The invention provides a winding forming loss management and control method and device in fiber yarn production, and relates to the technical field of fiber yarn production, and the method comprises the steps: traversing the whole production cycle of fiber yarns, carrying out control division, and determining a plurality of control units; cooperative control is carried out based on a plurality of control units, and a control prediction model is constructed; performing winding control prediction on the plurality of control units through the control prediction model, and generating a plurality of prediction results of the fibers; introducing a production scene parameter set to carry out integrated learning on the plurality of prediction results to obtain an integrated prediction data set; performing winding forming loss calculation on the plurality of control units according to the integrated prediction data set to obtain a plurality of loss coefficients; and performing linkage optimization on the plurality of control units according to the plurality of loss coefficients, and formulating an optimization control strategy. The technical problem that the quality of finished fiber yarn products is affected due to the fact that an existing process lacks in-depth analysis on winding forming loss is solved, and the stability of the production process and the product quality are improved.

Description

Technical Field

[0001] The present application relates to the technical field of fiber filament production, and specifically to a method and device for controlling winding forming losses in fiber filament production. Background Art

[0002] In fiber filament production, winding is a key process for winding continuous fiber filaments into rolls in an orderly manner from the production line. In this process, multiple equipment are operated in coordination to accurately control multiple parameters such as temperature, pressure, winding speed, etc. to ensure the quality and performance of the finished product.

[0003] Current winding control methods generally adopt control strategies based on fixed parameters and simple feedback mechanisms to monitor and adjust key parameters such as temperature, pressure, and speed during the winding process. These methods mainly focus on the control of the production process and lack in-depth analysis and preventive control of winding losses. This rigid control method limits the production system's ability to respond and adjust to molding losses in a timely manner. When faced with slight tension changes, equipment wear or environmental fluctuations, they often react slowly, resulting in uneven tension, unstable speed, position deviation, and insufficient cooling of the fiber during the winding process, which in turn causes winding losses, resulting in reduced fiber performance, reduced production efficiency, and increased equipment maintenance costs. Summary of the invention

[0004] The present application provides a method and equipment for controlling winding and forming losses in fiber filament production, which solves the technical problem that the current winding and forming process in fiber filament production lacks in-depth analysis of forming losses, cannot predict and solve potential winding and forming losses in advance, and causes fiber filaments to experience performance degradation and increased losses during the winding process, thereby affecting the quality of the finished product. The application achieves the technical effect of significantly reducing losses in the winding and forming process, improving product quality and production efficiency, and enhancing the stability of the production process and the operating life of the equipment.

[0005] In view of the above problems, on the one hand, the present application provides a method for controlling winding forming losses in fiber filament production, the method comprising: traversing the entire production cycle of the fiber filament to perform control division and determine multiple control units; performing collaborative control based on the multiple control units, and constructing a control prediction model according to the collaborative control results; performing winding control predictions on the multiple control units through the control prediction model to generate multiple prediction results for the fiber filament; introducing a production scenario parameter set to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set; calculating the winding forming losses of the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients; performing linkage optimization on the multiple control units according to the multiple loss coefficients, and formulating an optimized control strategy to control the winding losses of the fiber filament.

[0006] On the other hand, the present application also provides a winding forming loss control device in fiber filament production, and the device includes: a control division module, the control division module is used to traverse the entire production cycle of the fiber filament to perform control division and determine multiple control units; a collaborative control module, the collaborative control module is used to perform collaborative control based on the multiple control units, and build a control prediction model according to the collaborative control results; a winding control prediction module, the winding control prediction module is used to perform winding control prediction on the multiple control units through the control prediction model to generate multiple prediction results for the fiber filament; an integrated learning module, the integrated learning module is used to introduce a production scenario parameter set to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set; a loss calculation module, the loss calculation module is used to perform winding forming loss calculation on the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients; a linkage optimization module, the linkage optimization module is used to perform linkage optimization on the multiple control units according to the multiple loss coefficients, and formulate an optimized control strategy to control the winding loss of the fiber filament.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: The whole production cycle of the fiber yarn is traversed to perform control division and determine multiple control units. Through detailed division, the comprehensiveness and pertinence of subsequent control are ensured. Based on the multiple control units, collaborative control is performed, and a control prediction model is constructed according to the collaborative control results. The winding control prediction of the multiple control units is performed through the control prediction model to generate multiple prediction results of the fiber yarn. The production scenario parameter set is introduced to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set. According to different production scenarios, the future production situation is predicted to provide accurate basic data for subsequent loss calculation. According to the integrated prediction data set, the winding molding loss of the multiple control units is calculated to obtain multiple loss coefficients. Through quantitative analysis, the direction and degree of control optimization are clarified. According to the multiple loss coefficients, the multiple control units are linked and optimized, and an optimized control strategy is formulated to control the winding loss of the fiber yarn to minimize the loss in the winding molding process and improve the accuracy and production efficiency of the winding molding.

[0008] In summary, this application improves the accuracy and response speed of prediction by systematically dividing the control units and realizing collaborative control, combining real-time data with integrated learning; through multi-dimensional loss analysis and targeted optimization strategies, it effectively reduces the losses in the winding forming process, and ultimately achieves the technical effect of enhancing the stability of the production process and improving product quality.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of a process for controlling winding loss in fiber filament production provided in an embodiment of the present application; Figure 2 A schematic diagram of a process for constructing a production scenario parameter set in a method for controlling winding forming losses in fiber filament production provided in an embodiment of the present application; Figure 3 A schematic diagram of a process for linking and optimizing multiple control units in a method for controlling winding forming losses in fiber filament production provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a winding forming loss control device in fiber filament production provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: control division module 10 , collaborative control module 20 , winding control prediction module 30 , integrated learning module 40 , loss calculation module 50 , linkage optimization module 60 . DETAILED DESCRIPTION

[0012] The embodiments of the present application provide a method and equipment for controlling winding and forming losses in fiber filament production, thereby solving the technical problem that the winding and forming process in the current fiber filament production lacks in-depth analysis of forming losses, and is unable to predict and solve potential winding and forming losses in advance, resulting in performance degradation and increased losses of the fiber filaments during the winding process, thereby affecting the quality of the finished product. The technical effect of significantly reducing losses in the winding and forming process, improving product quality, and enhancing the stability of the production process is achieved.

[0013] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a method for controlling winding forming losses in fiber filament production, the method comprising: The whole production cycle of the fiber yarn is traversed to perform control division and determine multiple control units.

[0014] Specifically, the fiber production cycle includes the entire process from raw material preparation to the final product, including multiple stages such as spinning, stretching, and winding. The control unit is an independent link or subsystem in the production process. In fiber production, the control unit can be a drafting machine, cooling zone, or crimping machine, etc.

[0015] First, the production process of the fiber yarn is analyzed in detail, including key steps such as fiber preparation, drawing, cooling, and winding. Based on the results of the production process analysis, the key control points that have a direct impact on the performance of the fiber yarn are identified. For example, the melting temperature, drawing speed, cooling efficiency, and winding tension, etc. These control points will be subdivided into multiple control units, each of which is responsible for monitoring and controlling specific production parameters. For example, the control unit in the heating zone focuses on temperature control, while the control unit in the winding zone focuses on tension and winding speed.

[0016] By finely dividing the fiber filament production and winding molding processes and determining multiple control units, it is convenient to conduct specific loss analysis on each control unit later, thereby improving the accuracy of loss management.

[0017] Cooperative control is performed based on the multiple control units, and a control prediction model is constructed according to the cooperative control results.

[0018] Specifically, the control prediction model is a mathematical model built based on historical data and current collaborative control results, which is used to predict changes in key parameters in future production processes.

[0019] Control the information exchange and coordination between the various control units, obtain the collaborative control results, and provide real-time data and dynamic response information for building the control prediction model. These results reflect the interaction between the various control units and the real-time status of the entire production system. By integrating the collaborative control data and combining the historical control data of fiber production, a prediction model is built through machine learning algorithms. This model will integrate the inputs of various control units, such as tension, speed, temperature, etc., and use time series analysis, regression analysis or neural network algorithms to analyze these input data to predict future winding status.

[0020] The control prediction model is used to perform winding control prediction on the plurality of control units, thereby generating a plurality of prediction results of the fiber filaments.

[0021] Specifically, the constructed control prediction model is used to receive real-time data from each control unit, including but not limited to temperature, humidity, tension, speed and other parameters. The control prediction model analyzes these data, predicts key indicators in the winding process, and generates expected performance results of each control unit, that is, multiple prediction results, such as tension level, winding speed, etc. Through winding control prediction, the future winding production situation is obtained, thereby reducing the uncertainty in the production process and providing data reference for loss analysis.

[0022] A production scenario parameter set is introduced to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set.

[0023] Specifically, the production scenario parameter set includes parameters such as various environments, equipment states, and raw material properties that may be encountered during the production process, such as temperature, humidity, raw material composition, equipment wear, etc. The multiple prediction results generated by the control prediction model are combined with the production scenario parameters, and the multiple prediction results are integrated through ensemble learning technology to form an integrated prediction data set. Among them, the integrated prediction data set is the comprehensive prediction results under different scenario parameters obtained through ensemble learning. Common ensemble learning methods include weighted average, random forest, gradient boosting, and voting methods to integrate the predictions of various models to improve the robustness and accuracy of the prediction. For example, using the random forest algorithm, different prediction results are used as input features, and combined with the production scenario parameters to obtain the winding control results of the fiber filaments under different production scenarios.

[0024] By introducing production scenario parameter sets and integrated learning, a more comprehensive perspective can be provided, providing more effective data support for the calculation of winding forming losses in complex production environments.

[0025] The winding forming losses of the plurality of control units are calculated according to the integrated prediction data set to obtain a plurality of loss coefficients.

[0026] Specifically, the information of the prediction results of different control units in the integrated prediction data set under a specific production environment is extracted. Through data analysis, the difference between the integrated prediction data and the ideal operation data is compared, and the loss calculation is performed for different control units to obtain the corresponding loss coefficient of each control unit. Loss calculation can use statistical analysis (such as regression analysis) or machine learning algorithms (such as decision trees, neural networks) to analyze the integrated prediction data set. Among them, the loss coefficient is an indicator used to quantify the loss, usually expressed in numerical form, which can reflect the degree of loss that may occur under different control conditions. By calculating the winding forming loss, the degree to which each control unit deviates from the ideal winding shape in actual production can be quantified, indicating the specific direction of winding loss control, and providing data basis for the formulation of optimized control strategies.

[0027] The multiple control units are optimized in a coordinated manner according to the multiple loss coefficients, and an optimized control strategy is formulated to control the winding loss of the fiber filaments.

[0028] Specifically, multiple control units are optimized in linkage using multiple loss coefficients calculated previously. First, the loss coefficients of each control unit are analyzed to identify the control unit or parameter that has the greatest impact on the winding loss and determine the optimization priority. Then, the interaction between the control units is analyzed to determine the impact that changes in each control unit may have on other units. For example, the temperature setting of the heating zone may directly affect the tension of the subsequent winding zone. Based on the loss coefficient and linkage relationship, the parameters of the relevant control units are adjusted, an optimization control strategy is formulated, and the optimization control strategy is executed through a fiber production control system (such as a PLC system). Exemplarily, it is found through analysis that a slight fluctuation in the temperature of the heating zone can cause a significant change in the tension of the winding zone, thereby increasing the winding loss. Therefore, the formulation of an optimization control strategy includes: improving the accuracy of the temperature control of the heating zone and reducing temperature fluctuations. When a temperature change in the heating zone is detected, the tension of the winding zone is automatically adjusted to compensate for the impact of the temperature change. After the optimization control strategy is implemented, the production process is monitored and feedback data is collected to evaluate the effectiveness of the strategy. The strategy is adjusted according to the feedback and optimized iteratively until a satisfactory winding effect is achieved.

[0029] Through linkage optimization and the formulation of optimal control strategies, refined control compensation can be carried out for the various losses that may occur in the production process to ensure the stability of the production process and improve product quality.

[0030] Furthermore, in the embodiment of the present application, the whole production cycle of the fiber yarn is traversed to perform control division and determine multiple control units, and further includes: Traverse the entire production cycle and extract according to the winding production nodes to determine the winding production cycle; perform tension control analysis on the fiber filament winding molding based on the winding production cycle to generate a first control unit; perform speed control analysis on the fiber filament winding molding based on the winding production cycle to generate a second control unit; perform position control analysis on the fiber filament winding molding based on the winding production cycle to generate a third control unit; perform cooling control analysis on the fiber filament winding molding based on the winding production cycle to generate a fourth control unit; add the first control unit, the second control unit, the third control unit, and the fourth control unit to the multiple control units.

[0031] Specifically, the entire production cycle of the fiber filament is traversed, the start and end points of the winding process are analyzed, and the winding production cycle is determined. The winding production cycle is the entire time period from the start of winding to the completion of winding of the fiber filament.

[0032] Analyze the changing pattern of fiber filament tension during the winding production cycle, identify the key tension control devices that affect the fiber filament winding forming, such as tension sensors and tension regulators, and mark these devices as the first control unit, which is used to monitor and adjust the tension to ensure the stability and structural integrity of the fiber filament during the winding process.

[0033] Analyze the control devices that affect the fiber filament winding speed in the winding production cycle, such as frequency converters, servo motors, etc., and mark these devices as the second control unit, which can accurately control the winding speed and achieve efficient winding while maintaining quality.

[0034] Similarly, the key equipment for controlling the position and cooling temperature of the fiber filaments in the winding production cycle is analyzed and marked as the third control unit and the fourth control unit respectively. The third control unit is responsible for the precise positioning of the fiber filaments during the winding process to avoid offset or stacking during the winding process, such as encoders, limit switches, etc.; the fourth control unit is used to control the cooling speed and temperature of the fiber filaments to ensure the stability of the physical and chemical properties of the fiber filaments, such as cooling fans, cooling water circulation systems, etc.

[0035] The first control unit (tension control), the second control unit (speed control), the third control unit (position control), and the fourth control unit (cooling control) are integrated into multiple control units. Each control unit works independently, but cooperates with other units to form a complete winding control system. Through the meticulous division and optimization of the control units, it is convenient to conduct targeted loss analysis in the future and determine the source of winding forming losses.

[0036] Furthermore, in the embodiment of the present application, after performing winding control prediction on the multiple control units through the control prediction model, the method further includes: The first control unit, the second control unit, the third control unit and the fourth control unit are connected by communication to share information and generate multiple real-time shared data; based on the control prediction model combined with the multiple real-time shared data, the winding tension of the first control unit is predicted to obtain a first prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding speed of the second control unit is predicted to obtain a second prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding position of the third control unit is predicted to obtain a third prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding cooling of the fourth control unit is predicted to obtain a fourth prediction result; the first prediction result, the second prediction result, the third prediction result and the fourth prediction result are iteratively verified and fed back to generate multi-level prediction feedback information; data is accumulated according to the multi-level prediction feedback information to improve and optimize the control prediction model.

[0037] Specifically, communication connections are established between the first control unit, the second control unit, the third control unit, and the fourth control unit. The data generated by each control unit during operation, such as tension changes, speed fluctuations, position offsets, cooling temperature, etc., are collected in real time and transmitted to the shared platform to share information among the control units, ensuring that each unit can obtain status information of other units in real time to adjust its own operation, thereby optimizing the performance of the overall winding forming system.

[0038] The real-time operation data shared by each control unit through a communication connection is the real-time shared data. The real-time shared data is input into the control prediction model. The control prediction model performs control prediction on each control unit respectively to generate corresponding control prediction results, specifically including: the control prediction model predicts the winding tension of the first control unit in combination with the tension data in the real-time shared data to obtain a first prediction result, which is a possible tension parameter under the control of the first control unit; the control prediction model predicts the winding speed of the second control unit in combination with the speed data in the real-time shared data to obtain a second prediction result, which is a possible winding speed parameter under the control of the second control unit; the control prediction model predicts the winding position of the third control unit based on the position data in the real-time shared data to obtain a third prediction result, which is a possible winding position parameter under the control of the third control unit; the control prediction model predicts the winding cooling of the fourth control unit in combination with the cooling data in the real-time shared data to obtain a fourth prediction result, which is a possible cooling parameter under the control of the fourth control unit.

[0039] Collect and organize historical control data, including the actual values ​​of parameters such as tension, speed, position and cooling in previous production cycles. Compare the first prediction result, the second prediction result, the third prediction result and the fourth prediction result with the corresponding control parameters in the historical control data, perform deviation verification in turn, calculate the deviation between the prediction result and the historical data, and record it as multi-level prediction feedback information. Based on the multi-level prediction feedback information, accumulate more production data, including prediction deviation, control effect, adjustment parameters, etc. Through continuous data accumulation and analysis, identify the error source in the model prediction, adjust the model parameters, and improve the accuracy and stability of the control prediction model. Specifically, it includes: comparing the deviation calculated in the accumulated data with the preset expected error value. If the deviation exceeds the expected value, trace back to the model prediction step, adjust the model parameters or algorithm, and re-predict. Repeat the prediction, verification and backtracking until the prediction deviation is reduced to an acceptable range. After each backtracking, adjust the model parameters based on the deviation analysis, optimize the prediction algorithm, and improve the adaptability and prediction accuracy of the model.

[0040] The above steps ensure that the control prediction model can continuously improve itself as the production environment changes, thereby improving the accuracy of prediction and the sophistication of control.

[0041] Further, such as Figure 2 As shown, in the embodiment of the present application, the production scenario parameter set is constructed, and further includes: Determine a target winding device for the fiber filament, perform winding analysis based on the target winding device, and determine winding process parameters; perform winding simulation control on the target winding device according to the winding process parameters, perform winding environment sensing according to the simulation control parameters, and obtain multiple winding environment parameters; perform multi-dimensional analysis on the winding process parameters in combination with the multiple winding environment parameters to generate multiple winding labels; add timestamps to the multiple winding labels according to the winding production cycle, arrange the multiple winding labels according to the production sequence, and construct the production scenario parameter set.

[0042] Specifically, determine the target winding equipment for fiber filament winding, which must meet the specific needs and specifications of fiber filament winding. Based on the characteristics of the target winding equipment, analyze and determine the key process parameters in the winding process, including but not limited to tension, speed, position, cooling conditions, etc. For example, for a specific type of fiber filament, the optimal tension should be set to 50N and the winding speed to 10m / s. Using the determined winding process parameters, use professional simulation software such as ANSYS, MATLAB, etc. to digitally simulate the winding process of the target winding equipment, simulate the winding process, and predict the winding effect under different parameter combinations. During the simulation control process, sensors are used to collect winding environment parameters in real time, including temperature, humidity, pressure, etc.

[0043] Define a multidimensional data structure, which can be a database table or a data frame. Each dimension corresponds to a data type, including environmental parameters (temperature, humidity, air pressure, etc.), equipment parameters (equipment model, equipment wear, etc.), material properties (raw material batch, raw material composition, etc.) and process parameters (winding speed, winding tension, cooling speed, etc.). Then, the winding process parameters are matched with the winding environment parameters and stored in the multidimensional data structure. Each record corresponds to the scene data of a production cycle, and the corresponding winding label is set. The winding label is used to describe the winding process characteristics of the corresponding record, such as "high tension and low speed", "stable cooling", etc. According to the winding production cycle, a timestamp is added to each winding label to record its specific time sequence position in the winding process. According to the production sequence, multiple winding labels with timestamps are sorted to construct a production scene parameter set. Through the above steps, the constructed production scene parameter set can describe the key parameters and environmental conditions in the fiber winding process and the dynamic changes of the winding process in detail, providing data support for subsequent loss control.

[0044] Further, in the embodiment of the present application, the winding forming loss calculation is performed on the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients, and further includes: Based on the integrated prediction data set, the tension deviation of the first control unit is calculated to obtain a tension loss coefficient; based on the integrated prediction data set, the speed deviation of the second control unit is calculated to obtain a speed loss coefficient; based on the fourth control unit, a cooling analysis is performed on the target winding equipment, and the life of the target winding equipment is evaluated according to the cooling analysis result to obtain an equipment life evaluation value; based on the integrated prediction data set combined with the equipment life evaluation value, the wear position deviation of the third control unit is calculated to obtain a wear position loss coefficient; based on the first control unit, the second control unit, the third control unit, and the fourth control unit, an impact analysis is performed on the winding forming, and a plurality of weight coefficients are set according to the impact analysis results, and the plurality of weight coefficients correspond to the first control unit, the second control unit, the third control unit, and the fourth control unit; based on the tension loss coefficient, the speed loss coefficient, the wear position loss coefficient, and the plurality of weight coefficients, a winding forming loss analysis is performed to obtain the plurality of loss coefficients.

[0045] Specifically, the tension prediction data in the integrated prediction data set is extracted, and the prediction data represents the optimal tension value under ideal conditions. The real-time tension of the first control unit is obtained through the sensor monitoring system of the target winding equipment, and the difference between the tension prediction data and the real-time tension is calculated to determine the tension deviation. The tension loss coefficient is obtained according to the tension deviation, where the tension loss coefficient is the ratio of the absolute value of the tension deviation to the tension prediction data. The specific calculation formula is: tension deviation = real-time tension - tension prediction data; tension loss coefficient = (|tension deviation|) / tension prediction data.

[0046] Similarly, the speed prediction data in the integrated prediction data set is extracted. The prediction data represents the optimal winding speed value under ideal conditions. The real-time winding speed of the second control unit is obtained through the sensor monitoring system of the target winding equipment. The difference between the speed prediction data and the real-time tension is calculated, the speed deviation is determined, and the speed loss coefficient is obtained according to the speed deviation. The specific calculation process is similar to the tension loss coefficient calculation process, and the above formula can be used as a reference.

[0047] According to the fourth control unit, a cooling analysis is performed on the target winding equipment, and the historical control data of the fourth control unit of the target winding equipment is collected, including the flow rate, temperature, pressure, etc. of the cooling medium. Using the collected data, the temperature distribution and thermal stress of the target winding equipment during operation are analyzed through thermodynamic simulation and calculation. Through temperature distribution analysis, the hot spots in the target winding equipment, that is, the parts with abnormally high temperatures, are identified. At the same time, it is evaluated whether the temperature distribution of the entire target winding equipment is uniform, and whether there is local overheating or overcooling. Based on the results of temperature distribution and thermal stress analysis, fatigue analysis is performed on the equipment material, the life of the equipment is predicted, and the equipment life assessment value is determined. The equipment life assessment value is usually a numerical value or indicator that represents the remaining service life of the equipment.

[0048] Extract the position prediction data from the integrated prediction data set, combine it with the equipment life assessment value, analyze the wear pattern, wear speed and wear position of the key components of the equipment, and compare it with the position prediction data of the target winding equipment. Calculate the fiber position deviation of the target winding equipment due to wear, and calculate the wear position loss coefficient based on the position deviation. The calculation process is similar to the tension loss coefficient. You can refer to the aforementioned tension loss coefficient calculation formula.

[0049] Next, data related to each control unit is collected, including historical production data, equipment performance parameters, product quality indicators, etc. Based on these data, the influence of the control parameters of the first control unit, the second control unit, the third control unit and the fourth control unit on the winding forming results is analyzed using data analysis software, and a corresponding weight coefficient is configured for each control unit according to the impact analysis results. Each loss coefficient is multiplied by the corresponding weight coefficient to obtain the corresponding loss coefficient of each control unit. These multiple loss coefficients reflect the influence of each control unit on the forming loss during the winding forming process.

[0050] Through the above analysis process, we can fully understand the impact of each control unit on the winding process, and provide direction and reference for the subsequent formulation of optimization strategies to improve product quality.

[0051] Further, such as Figure 3 As shown, in the embodiment of the present application, the multiple control units are optimized in a linked manner according to the multiple loss coefficients, and further include: According to the tension loss coefficient, the first control unit is matched with the second control unit in linkage, and the loss is optimized according to the first matching result to generate a first loss optimization parameter; according to the speed loss coefficient, the second control unit is matched with the fourth control unit in linkage, and the loss is optimized according to the second matching result to generate a second loss optimization parameter; according to the wear position loss coefficient, the first control unit, the second control unit, the third control unit and the fourth control unit are matched in linkage, and the loss is optimized according to the third matching result to generate a third loss optimization parameter; loss analysis is performed based on the first loss optimization parameter, the second loss optimization parameter and the third loss optimization parameter, and the optimization control strategy is formulated according to the analysis results.

[0052] Specifically, according to the tension loss coefficient, the first control unit is linked and matched with the second control unit. When tension fluctuation is detected, the correlation between the winding speed and the tension is analyzed, and the winding speed is adjusted based on the tension loss coefficient to achieve dynamic linkage between the two units to keep the tension stable near the preset value, thereby reducing the loss caused by tension fluctuation. Through the above linkage, the tension loss is optimized and the first loss optimization parameter is generated. The parameter includes the tension value and speed value expected to be achieved in the subsequent production process to reduce tension fluctuations.

[0053] According to the speed loss coefficient, the second control unit is linked and matched with the fourth control unit. By analyzing the relationship between speed and cooling, the speed control parameters are adjusted to avoid uneven fiber crystallinity caused by uneven winding speed, thereby reducing the occurrence of quality problems. Through the above linkage, the speed loss is optimized and the second loss optimization parameter is generated. The parameter reflects the speed parameter value and cooling parameter value expected to be achieved in the subsequent production process, such as the winding speed setting value, speed fluctuation range, cooling temperature, cooling efficiency, etc.

[0054] Equipment wear affects the accuracy of each link in the production process, so the first control unit, the second control unit, the third control unit, and the fourth control unit are linked and matched. By analyzing the interaction between the control units, the parameters of each control unit are dynamically adjusted according to the wear position loss coefficient to compensate for the impact of equipment wear on the overall production accuracy. For example, when the equipment is worn, the overall winding speed is reduced to reduce the loss. Through the above linkage, the wear position loss is optimized to generate the third loss optimization parameter, which reflects the tension parameters, speed parameters, cooling parameters, and position parameters expected to be achieved by all control units in the subsequent production process.

[0055] Based on the first loss optimization parameter, the second loss optimization parameter, and the third loss optimization parameter, loss analysis is performed to evaluate the impact of each control unit adjustment on the overall winding process. According to the loss analysis results, the final optimization control strategy is formulated, which includes adjusting parameters such as tension, speed, and cooling to minimize losses.

[0056] Furthermore, in the embodiment of the present application, the optimization control strategy is formulated to control the winding loss of the fiber filament, and further includes: Execute the optimization control strategy to perform backtracking control on the fiber filament winding forming, and determine the loss backtracking control parameters; calculate the first control unit based on the loss backtracking control parameters to determine the tension loss compensation amount; calculate the fourth control unit based on the loss backtracking control parameters to determine the cooling loss compensation amount; calculate the first control unit, the second control unit, the third control unit, and the fourth control unit based on the loss backtracking control parameters to determine the equipment loss compensation amount; integrate the tension loss compensation amount, the cooling loss compensation amount, and the equipment loss compensation amount to generate a comprehensive compensation amount; trigger a compensation mechanism based on the comprehensive compensation amount to control the fiber filament winding loss.

[0057] Specifically, an optimization control strategy is executed to determine the loss backtracking control parameters, that is, the quantitative indicators of the loss and the direction that needs to be compensated, including the tension, speed, wear position, cooling and other parameter values ​​that need to be adjusted, based on the first loss optimization parameter, the second loss optimization parameter, and the third loss optimization parameter.

[0058] According to the information about the tension fluctuation in the loss backtracking control parameter, that is, the tension value that needs to be increased or decreased, combined with the equipment characteristics of the first control unit, the tension loss compensation amount is calculated. Among them, the tension loss compensation amount refers to the specific value output by the first control unit that needs to be adjusted in order to compensate for the impact of the tension fluctuation on the winding process. For example, if the tension fluctuation is detected to exceed the preset threshold, the tension value that needs to be increased or decreased is calculated, and then the output force of the tension motor that needs to be increased or decreased is determined according to the equipment parameters of the tension power to reduce the tension loss.

[0059] The cooling loss compensation refers to the specific value of the cooling unit parameters that need to be adjusted in order to compensate for the impact of the cooling efficiency reduction on the winding process. For example, the cooling water flow rate and cooling water temperature that need to be increased or decreased. The parameters that need to be adjusted in the cooling unit are determined based on the loss retrospective control parameters. For example, if the cooling rate is found to be lower than expected, the cooling flow rate that needs to be increased or the cooling water temperature that needs to be adjusted is calculated as the cooling loss compensation.

[0060] Equipment loss compensation refers to the adjustment strategy that needs to be made to all control units in order to compensate for the impact of equipment wear or aging on the winding process. Comprehensively analyze the impact of factors such as wear position deviation and equipment aging on the winding process to determine the adjustment strategy that needs to be made to all control units. For example, if it is found that equipment wear affects the accuracy of tension control, the parameters that need to be adjusted simultaneously for tension, speed, and cooling strategies are calculated as equipment loss compensation.

[0061] The tension loss compensation, cooling loss compensation, and equipment loss compensation are integrated to form a comprehensive compensation strategy, namely the comprehensive compensation. According to the comprehensive compensation, the parameters of each control unit are dynamically adjusted, such as the output force of the tension motor, the cooling water flow, the winding speed, etc., to ensure that the losses in the winding process are compensated in time and the loss is minimized.

[0062] Through the above compensation steps, it is possible to achieve refined control of the fiber filament winding process, ensure that the losses are compensated in a timely and effective manner, and improve the production stability and product quality of the winding molding.

[0063] In summary, the winding forming loss control method in fiber filament production provided in the embodiment of the present application has the following technical effects: The whole production cycle of the fiber yarn is traversed to perform control division and determine multiple control units, ensuring that each stage in the entire production process can be finely managed and controlled. Based on the multiple control units, collaborative control is performed, and a control prediction model is constructed according to the collaborative control results. The winding control prediction of the multiple control units is performed through the control prediction model to generate multiple prediction results of the fiber yarn, and the future production situation is predicted and optimized, which enhances the foresight of production control. Subsequently, through real-time data sharing and multi-level prediction feedback, the control prediction model is continuously optimized to improve the accuracy of model prediction and provide more accurate data support for subsequent loss analysis. The production scenario parameter set is introduced to integrate the multiple prediction results and obtain an integrated prediction data set, which further improves the adaptability of the method to different production environments. According to the integrated prediction data set, the winding forming loss of the multiple control units is calculated. By calculating the tension loss compensation, cooling loss compensation and equipment loss compensation, the direction and degree of control optimization are clarified, so as to accurately compensate for different types of losses, thereby reducing overall losses. The multiple control units are optimized in linkage according to the multiple loss coefficients, and an optimized control strategy is formulated to control the winding loss of the fiber filaments, so as to minimize the loss in the winding forming process, thereby significantly improving the accuracy and production efficiency of the winding forming.

[0064] Overall, the embodiments of the present application divide the control units for collaborative control, construct a control prediction model, introduce a production scenario parameter set for integrated learning, calculate the loss coefficient for linkage optimization, and formulate multiple steps of optimization control strategies, thereby achieving precise control and efficient management of fiber filament winding and molding losses, significantly reducing losses in the winding and molding process, improving product quality, and at the same time enhancing the stability of the production process and the operating life of the equipment.

[0065] Embodiment 2, as Figure 4 As shown, the embodiment of the present application provides a winding forming loss control device in fiber filament production, the device comprising: The control division module 10 is used to traverse the entire production cycle of the fiber yarn to perform control division and determine multiple control units.

[0066] The collaborative control module 20 is used to perform collaborative control based on the multiple control units and to build a control prediction model according to the collaborative control results.

[0067] The winding control prediction module 30 is used to perform winding control prediction on the multiple control units through the control prediction model to generate multiple prediction results of the fiber filaments.

[0068] The integrated learning module 40 is used to introduce a production scenario parameter set to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set.

[0069] The loss calculation module 50 is used to perform winding forming loss calculation on the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients.

[0070] The linkage optimization module 60 is used to perform linkage optimization on the multiple control units according to the multiple loss coefficients, and formulate an optimization control strategy to control the winding loss of the fiber filament.

[0071] Furthermore, the control division module 10 of the embodiment of the present application is also used to perform the following steps: Traverse the entire production cycle and extract according to the winding production nodes to determine the winding production cycle; perform tension control analysis on the fiber filament winding molding based on the winding production cycle to generate a first control unit; perform speed control analysis on the fiber filament winding molding based on the winding production cycle to generate a second control unit; perform position control analysis on the fiber filament winding molding based on the winding production cycle to generate a third control unit; perform cooling control analysis on the fiber filament winding molding based on the winding production cycle to generate a fourth control unit; add the first control unit, the second control unit, the third control unit, and the fourth control unit to the multiple control units.

[0072] Furthermore, the winding control prediction module 30 of the embodiment of the present application is also used to perform the following steps: The first control unit, the second control unit, the third control unit and the fourth control unit are connected by communication to share information and generate multiple real-time shared data; based on the control prediction model combined with the multiple real-time shared data, the winding tension of the first control unit is predicted to obtain a first prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding speed of the second control unit is predicted to obtain a second prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding position of the third control unit is predicted to obtain a third prediction result; based on the control prediction model combined with the multiple real-time shared data, the winding cooling of the fourth control unit is predicted to obtain a fourth prediction result; the first prediction result, the second prediction result, the third prediction result and the fourth prediction result are iteratively verified and fed back to generate multi-level prediction feedback information; data is accumulated according to the multi-level prediction feedback information to improve and optimize the control prediction model.

[0073] Furthermore, the integrated learning module 40 of the embodiment of the present application is also used to perform the following steps: Determine a target winding device for the fiber filament, perform winding analysis based on the target winding device, and determine winding process parameters; perform winding simulation control on the target winding device according to the winding process parameters, perform winding environment sensing according to the simulation control parameters, and obtain multiple winding environment parameters; perform multi-dimensional analysis on the winding process parameters in combination with the multiple winding environment parameters to generate multiple winding labels; add timestamps to the multiple winding labels according to the winding production cycle, arrange the multiple winding labels according to the production sequence, and construct the production scenario parameter set.

[0074] Furthermore, the loss calculation module 50 of the embodiment of the present application is also used to perform the following steps: Based on the integrated prediction data set, the tension deviation of the first control unit is calculated to obtain a tension loss coefficient; based on the integrated prediction data set, the speed deviation of the second control unit is calculated to obtain a speed loss coefficient; based on the fourth control unit, a cooling analysis is performed on the target winding equipment, and the life of the target winding equipment is evaluated according to the cooling analysis result to obtain an equipment life evaluation value; based on the integrated prediction data set combined with the equipment life evaluation value, the wear position deviation of the third control unit is calculated to obtain a wear position loss coefficient; based on the first control unit, the second control unit, the third control unit, and the fourth control unit, an impact analysis is performed on the winding forming, and a plurality of weight coefficients are set according to the impact analysis results, and the plurality of weight coefficients correspond to the first control unit, the second control unit, the third control unit, and the fourth control unit; based on the tension loss coefficient, the speed loss coefficient, the wear position loss coefficient, and the plurality of weight coefficients, a winding forming loss analysis is performed to obtain the plurality of loss coefficients.

[0075] Furthermore, the linkage optimization module 60 of the embodiment of the present application is also used to perform the following steps: According to the tension loss coefficient, the first control unit is matched with the second control unit in linkage, and the loss is optimized according to the first matching result to generate a first loss optimization parameter; according to the speed loss coefficient, the second control unit is matched with the fourth control unit in linkage, and the loss is optimized according to the second matching result to generate a second loss optimization parameter; according to the wear position loss coefficient, the first control unit, the second control unit, the third control unit and the fourth control unit are matched in linkage, and the loss is optimized according to the third matching result to generate a third loss optimization parameter; loss analysis is performed based on the first loss optimization parameter, the second loss optimization parameter and the third loss optimization parameter, and the optimization control strategy is formulated according to the analysis results.

[0076] Furthermore, the linkage optimization module 60 of the embodiment of the present application is also used to perform the following steps: Execute the optimization control strategy to perform backtracking control on the fiber filament winding forming, and determine the loss backtracking control parameters; calculate the first control unit based on the loss backtracking control parameters to determine the tension loss compensation amount; calculate the fourth control unit based on the loss backtracking control parameters to determine the cooling loss compensation amount; calculate the first control unit, the second control unit, the third control unit, and the fourth control unit based on the loss backtracking control parameters to determine the equipment loss compensation amount; integrate the tension loss compensation amount, the cooling loss compensation amount, and the equipment loss compensation amount to generate a comprehensive compensation amount; trigger a compensation mechanism based on the comprehensive compensation amount to control the fiber filament winding loss.

[0077] Through the above-mentioned detailed description of the winding and forming loss control method in the fiber filament production in this specification, those skilled in the art can clearly know the winding and forming loss control equipment in the fiber filament production in this embodiment. For the equipment disclosed in Example 2, since it corresponds to the method disclosed in Example 1, it has corresponding functional modules and beneficial effects. For relevant matters, please refer to the method part description.

[0078] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling winding loss in fiber filament production, characterized in that: The method comprises: Traverse the entire production cycle of fiber yarns to divide the control and determine multiple control units; Performing coordinated control based on the multiple control units, and building a control prediction model according to the coordinated control results; Performing winding control prediction on the plurality of control units through the control prediction model to generate a plurality of prediction results of the fiber filaments; Introducing a production scenario parameter set to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set; Calculating the winding forming losses of the plurality of control units according to the integrated prediction data set to obtain a plurality of loss coefficients; The multiple control units are optimized in a coordinated manner according to the multiple loss coefficients, and an optimized control strategy is formulated to control the winding loss of the fiber filaments.

2. The method for controlling winding loss in fiber filament production according to claim 1, characterized in that: The whole production cycle of the fiber yarn is traversed to perform control division and determine multiple control units, and the method includes: Traversing the entire production cycle, extracting according to the winding production nodes, and determining the winding production cycle; Performing tension control analysis on fiber filament winding molding based on the winding production cycle to generate a first control unit; Performing speed control analysis on the fiber filament winding molding based on the winding production cycle to generate a second control unit; Performing position control analysis on the fiber filament winding molding based on the winding production cycle to generate a third control unit; Perform cooling control analysis on the fiber filament winding molding based on the winding production cycle to generate a fourth control unit; The first control unit, the second control unit, the third control unit, and the fourth control unit are added to the plurality of control units.

3. The method for controlling winding loss in fiber filament production according to claim 2, characterized in that: After performing winding control prediction on the multiple control units through the control prediction model, the method includes: The first control unit, the second control unit, the third control unit, and the fourth control unit are connected in communication to share information and generate a plurality of real-time shared data; Based on the control prediction model and the plurality of real-time shared data, the first control unit is predicted to perform winding tension prediction to obtain a first prediction result; Based on the control prediction model and the plurality of real-time shared data, the second control unit is predicted to perform winding speed prediction to obtain a second prediction result; Based on the control prediction model and the plurality of real-time shared data, the third control unit is predicted to have a winding position, so as to obtain a third prediction result; Based on the control prediction model and the plurality of real-time shared data, the fourth control unit performs winding cooling prediction to obtain a fourth prediction result; Iteratively verify and feedback the first prediction result, the second prediction result, the third prediction result, and the fourth prediction result to generate multi-level prediction feedback information; The control prediction model is improved and optimized by accumulating data based on the multi-level prediction feedback information.

4. The method for controlling winding loss in fiber filament production according to claim 2, characterized in that: The production scenario parameter set method includes: Determining a target winding device for the fiber filaments, performing a winding analysis based on the target winding device, and determining winding process parameters; Performing winding simulation control on the target winding equipment according to the winding process parameters, performing winding environment sensing according to the simulation control parameters, and obtaining a plurality of winding environment parameters; Performing multi-dimensional analysis on the winding process parameters in combination with the plurality of winding environment parameters to generate a plurality of winding labels; The multiple winding labels are timestamped according to the winding production cycle, the multiple winding labels are arranged according to the production sequence, and the production scenario parameter set is constructed.

5. The method for controlling winding loss in fiber filament production according to claim 4, characterized in that: The winding forming loss calculation is performed on the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients, and the method includes: Calculating the tension deviation of the first control unit based on the integrated prediction data set to obtain a tension loss coefficient; Calculating the speed deviation of the second control unit based on the integrated prediction data set to obtain a speed loss coefficient; Performing a cooling analysis on the target winding device based on the fourth control unit, and performing a life assessment on the target winding device according to the cooling analysis result to obtain a device life assessment value; Based on the integrated prediction data set and the equipment life assessment value, the wear position deviation of the third control unit is calculated to obtain a wear position loss coefficient; Based on the first control unit, the second control unit, the third control unit, and the fourth control unit, an impact analysis is performed on the winding molding, and a plurality of weight coefficients are set according to the impact analysis results, and the plurality of weight coefficients correspond to the first control unit, the second control unit, the third control unit, and the fourth control unit; The plurality of loss coefficients are obtained by performing a winding loss analysis based on the tension loss coefficient, the speed loss coefficient, the wear position loss coefficient and the plurality of weight coefficients.

6. The method for controlling winding loss in fiber filament production according to claim 5, characterized in that: The plurality of control units are optimized in a coordinated manner according to the plurality of loss coefficients, the method comprising: According to the tension loss coefficient, the first control unit is linked and matched with the second control unit, and loss optimization is performed according to a first matching result to generate a first loss optimization parameter; According to the speed loss coefficient, the second control unit is matched with the fourth control unit in a linkage manner, and loss optimization is performed according to a second matching result to generate a second loss optimization parameter; According to the wear position loss coefficient, the first control unit, the second control unit, the third control unit, and the fourth control unit are matched in a linkage manner, and loss optimization is performed according to a third matching result to generate a third loss optimization parameter; A loss analysis is performed based on the first loss optimization parameter, the second loss optimization parameter, and the third loss optimization parameter, and the optimization control strategy is formulated according to the analysis results.

7. The method for controlling winding loss in fiber filament production according to claim 6, characterized in that: Formulating the optimization control strategy to control the winding loss of the fiber filaments includes: Executing the optimization control strategy to perform retrospective control on the fiber filament winding molding, and determining the loss retrospective control parameters; Calculating the first control unit based on the loss backtracking control parameter to determine a tension loss compensation amount; Calculating the fourth control unit based on the loss backtracking control parameter to determine a cooling loss compensation amount; Calculate the first control unit, the second control unit, the third control unit, and the fourth control unit based on the loss backtracking control parameter to determine the equipment loss compensation amount; Integrate the tension loss compensation amount, the cooling loss compensation amount, and the equipment loss compensation amount to generate a comprehensive compensation amount; A compensation mechanism is triggered based on the comprehensive compensation amount to control the winding loss of the fiber filament.

8. Winding loss control equipment in fiber filament production, characterized in that: The device is used to implement the winding forming loss control method in fiber filament production according to any one of claims 1 to 7, and the device comprises: A control division module, the control division module is used to traverse the entire production cycle of the fiber filaments to perform control division and determine multiple control units; A collaborative control module, the collaborative control module is used to perform collaborative control based on the multiple control units and to build a control prediction model according to the collaborative control result; A winding control prediction module, the winding control prediction module is used to perform winding control prediction on the multiple control units through the control prediction model to generate multiple prediction results of the fiber filaments; An integrated learning module, wherein the integrated learning module is used to introduce a production scenario parameter set to perform integrated learning on the multiple prediction results to obtain an integrated prediction data set; A loss calculation module, the loss calculation module is used to perform winding forming loss calculation on the multiple control units according to the integrated prediction data set to obtain multiple loss coefficients; A linkage optimization module is used to perform linkage optimization on the multiple control units according to the multiple loss coefficients, and formulate an optimization control strategy to control the winding loss of the fiber filament.

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