A precise control system and method for wool fiber spinning process

Abstract

The present invention discloses a precise control system and method for a wool-type fiber spinning process, relating to the field of fiber material technology. This method introduces a perturbation gas injection slot into the spinneret side structure, and combines it with MEMS micro-thermal film flowmeters, binocular infrared imagers, and other equipment to collect gas injection parameters in real time. The gas injection data is subjected to standardized preprocessing and feature extraction by a central control server, and the directional deflection amount Odev can be accurately calculated. Dynamic comparison and evaluation are performed based on the deflection interval threshold, thereby achieving the ability to actively control the initial deflection angle and curling direction of the fiber. Unlike traditional spinning processes in which fiber deflection is completely dependent on the mold, temperature, or subsequent drafting disturbances, this method can construct a stable perturbation-induced force field at the moment of fiber ejection, ensuring that the fiber's initial path is consistent, directional, and predictable, providing controllable guiding conditions for the subsequent wool-type structure construction.

Description

Technical Field

[0001] The present invention relates to the technical field of fiber materials, and in particular to a precise control system and method for a wool-type fiber spinning process. Background Art

[0002] The present invention relates to the technical field of manufacturing high-performance functional fiber materials, and in particular to wool-type fibers, such as self-curling fibers and self-supporting structural fibers, and the control sub-direction of the spinneret forming process. More specifically, it focuses on the structural control method of finely inducing the curling behavior of fibers in the outlet area of ​​the spinneret by active disturbance. Wool-type fibers are widely used in applications such as adsorption materials, thermal insulation enhancement of fabrics, flexible friction surfaces, and biological scaffolds due to their complex multi-layer curling and spatial self-entanglement. The difficulty in structural control of this type of fiber lies in the fact that the formation process is highly sensitive, and extremely high synchronization and precision are required for the disturbance environment, microscale force field, and physical properties of the fiber. Therefore, the development of a spinneret control technology that does not require an additional mechanical disturbance structure and is based on a micro-perturbation gas active induction mechanism is a key breakthrough direction for improving the quality of wool-type fiber preparation and the level of automated intelligence.

[0003] At present, the mainstream preparation methods of wool-type fibers mostly rely on mechanical curling mechanisms, external heat stretching treatments or electrostatic traction systems to induce curling direction and form structures. Although this type of method can form a curling effect on a macro scale, there are generally significant problems such as uncontrollable structural direction, poor molding consistency, and the need for multiple subsequent heat treatments to stabilize the curling structure. In addition, the spinning process is extremely sensitive to external interference, and often results in finished product defects such as asymmetric fiber curling, offset drift, and local disorder and instability. In addition, the traditional spinneret has a smooth outlet structure and lacks the ability to intervene in the primary path of the fiber. It is unable to guide the fiber direction early in the initial stage of spinning, making subsequent structural induction more dependent on secondary processing processes with high energy consumption and high complexity. This is not only inefficient and costly, but also seriously restricts consistency and intelligent control in large-scale application scenarios.

[0004] The fundamental reason for this phenomenon lies in the traditional spinning process's lack of a structural induction mechanism at the spinneret's force field level, failing to precisely introduce perturbation path control at the moment of fiber formation—the so-called "missing the initial guidance window." Methods such as external forced stretching and cooling air rings introduce perturbations later in the spinneret, making it difficult to establish an effective internal control path. When initial curl induction fails, the fibers fail to form the ideal wooly cluster structure, compromising their adsorption, compliance, and thermal insulation properties. This can also lead to serious consequences such as structural collapse, molding failure, and increased product scrap rates. Especially in intelligent manufacturing or high-speed continuous production scenarios, this unstable structure can lead to frequent interruptions, high debugging costs, and reduced output consistency, becoming a control bottleneck restricting the expanded application of wooly fiber materials. Therefore, a new methodological system is urgently needed to establish perturbation induction, automatic adjustment, and real-time feedback control capabilities at the initial stage of spinning, thereby achieving a closed-loop, integrated manufacturing control path from microscale force field design to macroscopic fiber structure. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present invention provides a precise control system and method for a wool fiber spinning process, which solves the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: comprising the following steps:

[0007] S1. Set up a gas injection slot on the side of the spinneret, adjust the relevant parameters of the gas injection slot, and set up a collection device around the spinneret to collect gas injection data in real time;

[0008] S2. The gas injection data is transmitted to the central control server, and the gas injection data is preprocessed in the central control server to obtain a standard gas injection data set;

[0009] S3. Calculate and output the directional deflection Odev of the spinneret based on the standard gas injection data set, set a deflection interval threshold, and perform a preliminary comparative evaluation of the directional deflection Odev and the deflection interval threshold;

[0010] S4. Triggering a hair pattern structure analysis mechanism based on the preliminary comparative evaluation results. The hair pattern structure analysis mechanism collects hair pattern structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs a hair pattern structure complexity function Mcomp;

[0011] S5. The directional deflection Odev is combined with the hair type structure complexity function Mcomp to calculate and output the fiber structure controllability comprehensive index Atotal. A secondary comparative evaluation is performed on the controllable interval threshold and the fiber structure controllability comprehensive index Atotal.

[0012] Preferably, said S1 includes S11 and S12;

[0013] S11, by setting an asymmetric gas injection slot on the side wall structure of the spinneret, a micro-perturbation airflow is injected into the molten spinneret flow at a set angle, forming a directional disturbance induction force field to control the initial deflection direction and curling tendency of the fiber, and the gas flow rate, injection angle and real-time frequency control of the gas injection slot are adjusted in real time by a micro-flow regulator of the gas injection slot;

[0014] S12. At the same time, a collection device is set around the spinneret to collect gas injection data in real time;

[0015] The collection equipment includes gas injection detection equipment and structure detection equipment;

[0016] The gas injection detection equipment includes a MEMS micro-thermal film flow meter, a binocular infrared imager, a micro-thermoelectric and a FLIR thermal imager;

[0017] The structural testing equipment includes an AFM atomic force microscope, a 5000fps visual tracking device, an air velocity sensor, and a torsional mechanics tester;

[0018] The gas injection data include gas density Pgas, gas injection velocity Ug, gas kinematic viscosity Vgas, gas injection angle Ag, gas injection flow rate per unit time Jg and gas temperature T.

[0019] Preferably, said S2 includes S21 and S22;

[0020] S21. Using the communication module built into the acquisition device, an industrial wireless network is set up to wirelessly connect the acquisition device to the central control server of the spinning device, and the real-time collected gas injection data is transmitted to the central control server;

[0021] S22. Preprocessing the gas injection data received in real time in the central control server to obtain a standard gas injection data set, wherein the preprocessing includes denoising, data alignment, standard unit conversion, and feature extraction;

[0022] The denoising may use Savgol filtering to remove noise data in the gas injection data;

[0023] The data alignment synchronizes the acquisition time of the de-noised gas injection data by using a time axis synchronization method;

[0024] The standard unit conversion is to perform dimensionless processing on all parameters in the gas injection data by using the standard deviation normalization method, thereby eliminating the dimension effects between all parameters in the gas injection data;

[0025] The feature extraction is performed based on dimensionless gas injection data to obtain a standard gas injection data set;

[0026] The standard gas injection data set includes the perturbation gas inertial response coefficient Pg, the gas injection angle Ag, the injection flow rate per unit time Jg, the injection flow rate fluctuation coefficient Eg and the viscosity gradient Nd in the spinning area.

[0027] Preferably, said S3 includes S31 and S32;

[0028] S31. Construct a response model for the deflection ability of the disturbed airflow on the initial path of the fiber, and input the standard air injection data set obtained in real time into the response model to calculate and output the directional deflection amount Odev to measure the offset angle of the fiber in the initial stage of spinning.

[0029] Preferably, in step S32, a deflection interval threshold is set according to the user's deflection requirement, wherein the deflection interval threshold includes a first deflection threshold F1 and a second deflection threshold F2. A preliminary comparison and evaluation is performed between the real-time acquired directional deflection amount Odev and the deflection interval threshold, and based on the preliminary comparison and evaluation results, the triggering status of the hair structure analysis mechanism is determined. The specific evaluation content is as follows;

[0030] When the directional deflection Odev> the first deflection threshold F1, it means that the disturbance has sufficient deflection capability, and at this time, the hair-shaped structure analysis mechanism is triggered;

[0031] When the second deflection threshold F2 ≤ directional deflection Odev ≤ the first deflection threshold F1, it indicates that the current deflection capability is in the transition zone. At this time, the current parameters are maintained, the directional deflection Odev is recalculated every 100ms, and the preliminary comparison and evaluation are continued.

[0032] When the directional deflection Odev is less than the second deflection threshold F2, it indicates insufficient deflection. In this case, the gas injection angle Ag of the gas injection slot is increased by 10%, and the injection flow rate per unit time Jg is increased by 50%. After the increase, steps S1 to S3 are repeated every 20 ms to re-judge.

[0033] The first deflection threshold F1 is set to 0.4, and the second deflection threshold F2 is set to 0.2.

[0034] Preferably, said S4 includes S41 and S42;

[0035] S41. After preliminary comparative evaluation of the mechanism of triggering the hair-shaped structure analysis, a nanoscale twist-induced texture array was designed in the 1-3mm forming window area at the edge of the spinneret outlet to perform passive physical coupling intervention on the ejected fiber.

[0036] The nanoscale distortion-induced texture array includes structure type, array form and functional surface;

[0037] The structural types include grooves, spiral patterns, micro-steps, lattice protrusions and corrugated surfaces;

[0038] The array form is a non-uniform random array;

[0039] The functional surface includes friction induction, fluid disturbance, surface energy gradient and micro-adhesion capabilities;

[0040] A structure detection device is set up on the nanoscale distortion-induced texture array to collect spinneret structure data in real time, and the spinneret structure data is preprocessed to eliminate noise in the spinneret structure data, align the spinneret structure acquisition time, and eliminate the dimensional influence of the spinneret structure data to obtain a standard spinneret structure data set;

[0041] The standard spinning structure data set includes texture induced strength Xw, fiber torsional tensor modulus Et, air shear tension factor Nv and free section disturbance zone length Δr.

[0042] Preferably, in S42, based on the standard spinneret structure data set and the current directional deflection Odev, a summary calculation is performed to output a hair type structure complexity function Mcomp to measure the effects of the formation of the fiber curling structure and the winding structure.

[0043] Preferably, said S5 includes S51 and S52;

[0044] S51. Use a 5000fps visual tracking device to record the path of the spinning fiber from the nozzle to the receiving room, and use OpenCV image analysis to extract the fiber contour coordinates of each frame. Calculate the standard deviation of these fiber contour coordinates to obtain the structural stability fluctuation amplitude Bd, and perform standard unit conversion to eliminate the unit dimension effect;

[0045] After comprehensively calculating the structural stability fluctuation amplitude Bd, the obtained directional deflection Odev and the hair structure complexity function Mcomp, the fiber structure controllability comprehensive index Atotal is output to comprehensively measure the directional control ability, hair structure curling complexity and structural stability consistency.

[0046] Preferably, S52, based on a large amount of historical spinning data, the historical spinning data including the structural stability fluctuation amplitude Bd, the obtained directional deflection amount Odev, and the hair type structure complexity function Mcomp, marking the hair type structure critical point and the lowest acceptable point that meet the user's expected spinning standards for each spinning, and setting a controllable interval threshold, wherein the controllable interval threshold includes a first controllable threshold Q1 and a second controllable threshold Q2, wherein the first controllable threshold Q1 is the hair type structure critical point, and the second controllable threshold Q2 is the lowest acceptable point;

[0047] The fiber structure controllability comprehensive index Atotal obtained in real time is compared and evaluated with the controllable range threshold to determine the overall compliance with the standards in terms of direction control ability, curl complexity of the hair structure, and consistency of structural stability. The specific evaluation contents are as follows;

[0048] When the fiber structure controllability comprehensive index Atotal ≥ the first controllable threshold Q1, it means that the spinneret structure is normal, and the spinning and heat setting process will be immediately started, and the sample will be saved;

[0049] When the second controllable threshold Q2 ≤ the fiber structure controllability comprehensive index Atotal < the first controllable threshold Q1, it indicates that there is a disturbance in the spinneret structure. At this time, it is prompted to replace a different spinneret texture sheet, and increase the gas injection angle Ag of the gas injection slot by 10% and the injection flow rate per unit time Jg by 50%. At the same time, after further adjustment, it is repeated to execute S1 to S5 for iterative adjustment until the fiber structure returns to normal and the iteration stops.

[0050] When the fiber structure controllability comprehensive index Atotal is less than the second controllable threshold Q2, it indicates that the spinneret structure is disordered and the forming is identified. At this time, the spinneret is stopped, and the abnormal data is recorded and fed back to the central control server for storage to form a training set. At the same time, an early warning message is generated to prompt the adjustment of the spinneret equipment until it resumes operation.

[0051] A precise control system for a wool-type fiber spinning process, comprising a disturbed airflow injection module, a data transmission and processing module, an active direction guidance module, a passive structure coupling module, and a fiber structure controllable analysis module;

[0052] The disturbed air flow injection module collects gas injection data in real time by setting a gas injection slot on the side of the spinneret, adjusting relevant parameters of the gas injection slot, and setting collection equipment around the spinneret.

[0053] The data transmission and processing module transmits the gas injection data to the central control server, pre-processes the gas injection data in the central control server, and obtains a standard gas injection data set;

[0054] The active direction guidance module calculates and outputs the directional deflection Odev of the spinneret based on the standard gas injection data set, sets a deflection interval threshold, and performs a preliminary comparison and evaluation between the directional deflection Odev and the deflection interval threshold;

[0055] The passive structure coupling module triggers a hair type structure analysis mechanism based on the preliminary comparative evaluation results. The hair type structure analysis mechanism collects hair type structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs a hair type structure complexity function Mcomp.

[0056] The fiber structure controllability analysis module calculates and outputs the fiber structure controllability comprehensive index Atotal by combining the directional deflection Odev with the hair structure complexity function Mcomp, and performs a secondary comparative evaluation on the controllable interval threshold and the fiber structure controllability comprehensive index Atotal.

[0057] The present invention provides a precise control system and method for a wool fiber spinning process, which has the following beneficial effects:

[0058] (1) This method introduces a perturbation gas injection slot into the spinneret side structure, and combines it with MEMS micro-thermal film flowmeters, binocular infrared imagers and other equipment to collect gas injection parameters in real time. The central control server performs standardized preprocessing and feature extraction on the gas injection data, accurately calculating the directional deflection amount Odev, and performing dynamic comparison and evaluation based on the deflection interval threshold, thereby achieving the ability to actively control the initial deflection angle and curling direction of the fiber. Unlike the traditional spinning process in which fiber deflection is completely dependent on the mold, temperature or subsequent drafting disturbance, this method can construct a stable perturbation-induced force field at the moment of fiber ejection, ensuring that the initial fiber path is consistent, directional and predictable, and providing controllable guiding conditions for the subsequent construction of the hair-shaped structure.

[0059] (2) This method designs a nanoscale twist-induced texture array within a 1-3 mm window at the spinneret outlet, including various non-uniform surface microstructures such as micro-steps, spiral grooves, and corrugated surfaces. Combined with structural detection equipment such as atomic force microscopes (AFMs) and visual tracking systems, the method collects and extracts multi-dimensional structural features such as texture-induced strength Xw, fiber torsional tensor modulus Et, air shear tension factor Nv, and free-segment disturbance zone length △r, thereby constructing a hair-shaped structural complexity function Mcomp. Through the synergy between the above-mentioned structural induction mechanism and the parameter model, non-contact structural guidance, path stability control, and quantifiable evaluation of structural complexity in the hair-shaped curling formation process are achieved, solving key problems such as random curling direction, insufficient winding layers, and difficulty in induction in the latter stage of traditional smooth spinnerets. This method significantly improves the self-rotation, self-winding, and self-supporting formation capabilities of hair-shaped fibers in space.

[0060] (3) This method comprehensively introduces the structural stability fluctuation amplitude Bd based on the directional deflection Odev and the hair type structure complexity function Mcomp. The fiber contour stability changes are extracted through OpenCV image processing and visual tracking, and the fiber structure controllability comprehensive index Atotal is constructed. The control logic of the controllable interval threshold is formed in combination with historical spinning data to achieve dynamic graded evaluation of the spinning process. When Atotal is higher than the controllable interval threshold, the spinning process can directly enter the spinning and heat setting process; when it is between the controllable interval threshold, the adjustment mechanism of increasing the disturbance angle by 10% and the injection flow rate by 50% is automatically triggered; if it is lower than the controllable interval threshold, the spinning is terminated and the abnormal data is automatically fed back to the central control system training module. Through this closed-loop control mechanism, the consistency of the spinning structure and the hair type forming stability can be significantly improved, structural disorder and failed spinning can be prevented, and the self-regulation, self-learning and process robustness of the system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 This is a schematic diagram of the steps of a method for accurately controlling a wool fiber spinning process according to the present invention;

[0062] Figure 2 This is a schematic diagram of a flow chart of a precise control system for a wool fiber spinning process according to the present invention;

[0063] Figure 3 Schematic diagram of asymmetric perturbation gas injection induction control of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] Example 1

[0066] See also Figure 1 The present invention provides a method for accurately controlling a wool fiber spinning process. To achieve the above object, the present invention is implemented through the following technical solutions: comprising the following steps:

[0067] S1. Set up a gas injection slot on the side of the spinneret, adjust the relevant parameters of the gas injection slot, and set up a collection device around the spinneret to collect gas injection data in real time;

[0068] S2. The gas injection data is transmitted to the central control server, and the gas injection data is preprocessed in the central control server to obtain a standard gas injection data set;

[0069] S3. Calculate and output the directional deflection Odev of the spinneret based on the standard gas injection data set, set a deflection interval threshold, and perform a preliminary comparative evaluation of the directional deflection Odev and the deflection interval threshold;

[0070] S4. Based on the preliminary comparative evaluation results, the hair type structure analysis mechanism is triggered. The hair type structure analysis mechanism collects hair type structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs a hair type structure complexity function Mcomp;

[0071] S5. The directional deflection Odev is combined with the hair type structure complexity function Mcomp to calculate and output the fiber structure controllability comprehensive index Atotal. A secondary comparative evaluation is performed on the controllable interval threshold and the fiber structure controllability comprehensive index Atotal.

[0072] In this embodiment, a method employs a micro-perturbation gas injection slot structure positioned on the spinneret side, combined with high-precision data acquisition equipment such as a MEMS micro-thermal film flowmeter and an infrared imager, to achieve real-time acquisition and control of the micro-perturbation gas flow during spinning. The injection data is standardized in a central control server to form a standard injection data set. The directional deflection Odev is then calculated and initially evaluated against a set deflection threshold, enabling active control of fiber deflection direction during the initial spinning phase. Subsequently, by triggering a hair pattern structure analysis mechanism, spinneret structure data is extracted using an atomic force microscope (AFM) and a high-speed visual tracking system. A hair pattern complexity function, Mcomp, is constructed to effectively quantify the fiber curl morphology and the formation trend of self-entanglement. Finally, the directional deflection Odev and the hair pattern complexity function Mcomp are coupled to form a comprehensive fiber structure controllability index, Atotal. This is then combined with the structural stability fluctuation amplitude, Bd, for a comprehensive secondary evaluation. By comparing the fiber structure controllability index Atotal with the controllable threshold, a process judgment mechanism for full-process closed-loop optimization is established. Through this implementation, the present invention achieves automated closed-loop control of the entire process, from micro-perturbation gas induction, structural response detection, deflection capability assessment, to structural complexity modeling and comprehensive quality assessment. This method not only significantly improves the structural consistency and spatial curl complexity of wool-type fibers, but also addresses key issues such as uncontrollable deflection direction, uneven curling, and difficulty adjusting structural levels in traditional processes. It significantly improves directional guidance, curl formation efficiency, and structural stability during the spinning process, ultimately achieving the goal of efficiently generating wool-type fibers in a single step. This provides a more precise and controllable production path for wool-type textiles.

[0073] Example 2

[0074] See also Figure 1 and Figure 3,Specifically: S1 includes S11 and S12;

[0075] S11. By setting an asymmetric gas injection slot on the side wall structure of the spinneret, a micro-perturbation airflow is injected into the molten spinneret flow at a set angle to form a directional perturbation induction force field to control the initial deflection direction and curling tendency of the fiber. The gas flow rate, injection angle and real-time frequency control of the gas injection slot are adjusted in real time through the micro-control flow regulator of the gas injection slot, thereby actively guiding the growth path of the fiber structure;

[0076] During the initial spin forming phase, an asymmetric gas injection slot is installed on the side of the spinneret to inject a low-pressure, slightly disturbed airflow into the molten spinneret at a specific angle, forming a directional disturbance-inducing force field to control the initial deflection direction and curling tendency of the fiber. Rather than simply applying airflow, the gas injection slot is designed as a quasi-micro-pneumatic controller, integrating adjustable angle, variable flow rate, and real-time frequency control capabilities, thereby actively guiding the growth path of the fiber structure.

[0077] S12. At the same time, a collection device is set around the spinneret to collect gas injection data in real time;

[0078] The acquisition equipment includes gas injection detection equipment and structural detection equipment;

[0079] Gas injection detection equipment includes MEMS micro-thermal film flow meter, binocular infrared imager and micro-thermoelectric and FLIR thermal imager;

[0080] Structural testing equipment includes an AFM atomic force microscope, a 5000fps visual tracking device, an air velocity sensor, and a torsional mechanics tester;

[0081] The gas injection data include gas density Pgas, gas injection velocity Ug, gas kinematic viscosity Vgas, gas injection angle Ag, gas injection flow rate per unit time Jg and gas temperature T;

[0082] Gas density Pgas, gas injection velocity Ug and gas dynamic viscosity Vgas are acquired through MEMS micro-thermal film flowmeter;

[0083] The gas injection angle Ag is acquired by binocular infrared imager;

[0084] The gas injection rate Jg and gas temperature T per unit time are acquired by micro-thermoelectric and FLIR thermal imager.

[0085] In this embodiment, the method forms a low-pressure, slightly disturbed airflow injected into the molten spinneret at a specific angle by providing an asymmetric gas injection slot in the sidewall structure of the spinneret. This airflow establishes a directional disturbance-inducing force field at the initial stage of spinning, effectively controlling the initial deflection direction and natural curling tendency of the fiber. The quasi-micro-pneumatic controller structure used has the characteristics of adjustable angle, variable flow rate, and controllable frequency, which can achieve precise regulation and closed-loop response to the airflow disturbance, thereby actively guiding the growth path of the fiber structure and avoiding the problem of uncontrollable directional deviation of traditional spinneret. In S12, a multimodal high-precision acquisition system is arranged around the spinneret, including gas injection detection equipment composed of MEMS micro-thermal film flowmeter, infrared imager, micro-thermoelectric element and FLIR thermal imager, as well as structural detection equipment such as AFM atomic force microscope, high-speed visual tracking equipment and torsion detector, to achieve full quantitative real-time acquisition of key parameters in the spinning process such as gas density Pgas, gas injection velocity Ug, gas kinematic viscosity Vgas, gas injection angle Ag, gas injection flow rate per unit time Jg and gas temperature T. The collected data laid a solid foundation for subsequent modeling analysis and precise control. In summary, this implementation method realizes a closed-loop control chain of disturbance application, data perception, and parameter feedback. It not only ensures the active control of fiber deflection direction and curling path, but also greatly improves structural consistency and process stability. It significantly optimizes the consistency, spatial curling accuracy, and spinning efficiency of wool-type fibers, providing key technical support for the one-step molding of wool-type fibers.

[0086] Example 3

[0087] See also Figure 1 , specifically: S2 includes S21 and S22;

[0088] S21. Using the communication module built into the acquisition device, an industrial wireless network is set up to wirelessly connect the acquisition device to the central control server of the spinning device, and the real-time collected gas injection data is transmitted to the central control server;

[0089] S22. Preprocessing the gas injection data received in real time in the central control server to obtain a standard gas injection data set. The preprocessing includes denoising, data alignment, standard unit conversion, and feature extraction.

[0090] Denoising is done by using Savgol filtering to remove noise from the gas injection data;

[0091] Data alignment uses the time axis synchronization method to synchronize the acquisition time of the denoised gas injection data;

[0092] Standard unit conversion is performed by using the standard deviation normalization method to make all parameters in the gas injection data dimensionless, eliminating the dimensional effects between all parameters in the gas injection data;

[0093] Feature extraction is performed based on dimensionless gas injection data to obtain a standard gas injection data set;

[0094] The standard gas injection data set includes the perturbation gas inertial response coefficient Pg, gas injection angle Ag, injection flow rate per unit time Jg, injection flow rate fluctuation coefficient Eg and spinneret area viscosity gradient Nd;

[0095] The inertial response coefficient Pg of the perturbation gas is calculated based on the ratio of the injection velocity Ug to the gas dynamic viscosity Vgas and then multiplied by the gas density Pgas for extraction. It measures the inertial response efficiency of the perturbation force exerted by the perturbation gas on the molten fiber in the spinneret outlet area. The physical meaning lies in the fact that it is affected by the gas density, dynamic viscosity and injection kinetic energy, and determines whether the gas disturbance is sufficient to impose effective deflection in the initial stage of the fiber.

[0096] The injection flow velocity fluctuation coefficient Eg is obtained by calculating the standard deviation and mean of the injection velocity Ug per unit time, and then calculating the ratio of the standard deviation to the mean of the injection velocity Ug. It is used to measure the instability of the air flow velocity during the injection process, that is, the amplitude of high-frequency disturbances, which affects the consistency of disturbances.

[0097] The viscosity gradient Nd in the spinning area is obtained by extracting the first-order derivative of the gas dynamic viscosity Vgas and the gas temperature T respectively and performing the absolute value product extraction, where the first-order derivative of the gas dynamic viscosity Vgas is: , the first-order numerical derivative of the gas temperature T is , where d represents the integral function, x represents the spatial position, and the viscosity of the fiber in the spinneret outlet area changes with the spatial position, which affects the disturbance propagation ability.

[0098] In this embodiment, the method uses the communication module embedded in the acquisition device to build an industrial wireless network in S21 to achieve real-time transmission of gas injection parameters, and uploads the data collected by various high-precision sensor devices at the spinning site to the central control server in a stable and high-speed manner, thereby building an instant feedback path between data and control. This method avoids the cable redundancy and lag problems in traditional data transmission, and greatly improves the system response efficiency and deployment flexibility. In S22, the server performs a systematic four-step preprocessing operation on the received raw gas injection data. First, Savgol filtering is used to remove high-frequency noise in the measurement to ensure smooth and reliable data; then, time alignment of multiple parameters is achieved through time axis synchronization to enhance the consistency of calculation inputs; then, standard deviation normalization is used for dimensionless processing to solve the interference of unit differences between multiple parameters on the algorithm weights; finally, feature extraction based on dimensionless data is completed, and a set of core standard gas injection data sets reflecting the effectiveness of spinning disturbance control is accurately output. Overall, this step builds a stable and reliable data-driven spinning control framework through system integration of data transmission and structured processing, which not only greatly improves the accuracy and timeliness of parameter perception, but also provides key guarantees for the precise and intelligent adjustment of fiber deflection control and hair structure induction, thereby significantly improving the overall controllability, flexibility and structural consistency of the spinning process.

[0099] Example 4

[0100] See also Figure 1 ,Specifically: S3 includes S31 and S32;

[0101] S31. Construct a response model for the deflection capability of disturbed airflow on the fiber's primary path. Input the standard air injection data set acquired in real time into the response model to calculate and output the directional deflection value Odev, which measures the fiber's deviation angle at the initial stage of spinning. The asymmetric micro-injection slot structure on the sidewall of the spinneret introduces a perturbed directional airflow at the moment of fiber ejection, applying an active disturbance-inducing force field to induce the fiber to naturally deviate from the main axis in space, thereby forming a directional primary curved path, laying the foundation for subsequent structural curling and hair shape construction.

[0102] The directional deflection Odev is calculated and output by the following algorithm formula:

[0103] ;

[0104] Where, sin represents the sine function;

[0105] in, It represents the disturbance energy output term. The higher it is, the more concentrated the gas injection energy is applied to the spindle deflection direction. The quadratic term reflects the nonlinear enhancement characteristics of gas disturbance and the projection efficiency of the sin(Ag) control force.

[0106] It represents the dissipation and resistance term. If the gas flow rate fluctuates greatly, Eg is high and the disturbance effect will be unstable. If the viscosity gradient is large, Nd, the effect of the disturbance transmission to the fiber path will be weakened. The larger the term, the stronger the disturbance absorption.

[0107] S32. Deflection interval thresholds are set based on user deflection requirements. The deflection interval thresholds include a first deflection threshold F1 and a second deflection threshold F2. A preliminary comparison and evaluation is performed between the real-time acquired directional deflection amount Odev and the deflection interval thresholds. Based on the preliminary comparison and evaluation results, the triggering status of the wool structure analysis mechanism is determined. The preliminary comparison and evaluation is a discrimination threshold in the control logic, a branch control trigger after the formula is executed, and an initiator of closed-loop tuning with the process parameters to determine whether the airflow disturbance has successfully deflected the fiber. The specific evaluation content is as follows;

[0108] When the directional deflection Odev> the first deflection threshold F1, it means that the disturbance has sufficient deflection capability, and at this time, the hair-shaped structure analysis mechanism is triggered;

[0109] When the second deflection threshold F2 ≤ directional deflection Odev ≤ the first deflection threshold F1, it indicates that the current deflection capability is in the transition zone. At this time, the current parameters are maintained, the directional deflection Odev is recalculated every 100ms, and the preliminary comparison and evaluation are continued.

[0110] When the directional deflection Odev is less than the second deflection threshold F2, it indicates insufficient deflection. In this case, the gas injection angle Ag of the gas injection slot is increased by 10%, and the injection flow rate per unit time Jg is increased by 50%. After the increase, steps S1 to S3 are repeated every 20 ms to re-judge.

[0111] The first deflection threshold F1 is set to 0.4, and the second deflection threshold F2 is set to 0.2.

[0112] In this embodiment, the method constructs a model of the fiber deflection response to the perturbed airflow based on a previously extracted standard gas injection dataset in S31. The directional deflection Odev is calculated using a formula, effectively reflecting the deflection angle caused by the perturbed gas on the nascent fiber. This quantitative indicator not only reflects the spatial concentration of the perturbed gas energy projection, as controlled by sin(Ag), but also systematically assesses the effectiveness of the perturbation control by integrating the nonlinear enhancement mechanism and dissipative attenuation term of the perturbation system. Furthermore, in S32, based on the pre-determined deflection performance requirements of the process, two deflection thresholds are set to establish a three-stage control logic: high deflection triggers structural analysis, intermediate transition parameter monitoring, and low deflection forced adjustment retry. This hierarchical evaluation system clearly distinguishes the effectiveness of the current perturbation effect, ensuring that each perturbation application operates within a controllable and traceable range. If the evaluation results do not meet the requirements, the injection angle and flow rate are adjusted, and the data processing and calculation process is quickly resumed, achieving a high-speed closed-loop feedback loop of 20ms, significantly improving the sensitivity and adjustment accuracy of the deflection control. In summary, this step not only realizes the quantitative evaluation and real-time optimization of the perturbation gas injection control effect, but also ensures the effectiveness, consistency and dynamic adaptability of the deflection guidance through a nested logical judgment mechanism, providing a solid initial path control foundation for the precise construction of the subsequent hair-type structure induction, and overall improving the stability, control accuracy and response efficiency of the spinning process.

[0113] Example 5

[0114] See also Figure 1 , specifically: S4 includes S41 and S42;

[0115] S41. After preliminary comparative evaluation of the mechanism of triggering the hair-shaped structure analysis, a nanoscale twist-induced texture array was designed in the 1-3mm forming window area at the edge of the spinneret outlet to perform passive physical coupling intervention on the ejected fiber.

[0116] Nanoscale distortion-induced texture arrays include structural types, array forms, and functional surfaces;

[0117] Structural types include grooves, spiral patterns, micro-steps, lattice protrusions, and corrugated surfaces;

[0118] The array form is a non-uniform random array;

[0119] Functional surfaces include those with friction induction, fluid disturbance, surface energy gradient and micro-adhesion capabilities;

[0120] Traditional spinnerets are often smooth and lack guiding features, and the ejected fiber path is quasi-linear. Heat treatment, drafting, physical disturbance, and other methods are required to induce curling in the later stage. However, this can easily lead to: uneven curling; difficulty in achieving natural clustering between fibers; uncontrollable curling direction; complex processing and high costs. A structured induction mechanism is directly embedded in the fiber formation window at the spinneret outlet area. Through nanoscale physical morphology, the fibers are guided to self-curl, self-rotate, and self-stabilize to form a spatial hair-like structure without contact, achieving one-step molding, fine structure, and adjustable parameters.

[0121] A structure detection device is set up on the nanoscale distortion-induced texture array to collect spinneret structure data in real time, and the spinneret structure data is preprocessed to eliminate noise in the spinneret structure data, align the spinneret structure acquisition time, and eliminate the dimensional influence of the spinneret structure data to obtain a standard spinneret structure data set;

[0122] The standard spinneret structure data set includes texture induced strength Xw, fiber torsional tensor modulus Et, air shear tension factor Nv and free section disturbance zone length △r;

[0123] The texture induction intensity Xw is obtained by using an atomic force microscope (AFM). Its physical significance lies in the intensity of the physical guiding effect exerted by the nanoscale surface structure in the spinneret outlet area on the fiber during contact. It is highly correlated with the depth, arrangement direction, and shape of the texture and is a quantitative parameter of the structure induction efficiency.

[0124] The length of the free-segment disturbance zone, △r, is acquired using a 5000fps visual tracking device. Its physical significance lies in the length of the free-flight bending interval between the fiber leaving the spinneret and contacting the receiving surface. It is the "spatial window" for the formation of the hairy structure and determines the degree of entanglement and bending freedom.

[0125] The air shear tension factor Nv is obtained by an air velocity sensor and a visual tracking device. The air velocity distribution is collected by the air velocity sensor, and the tension effect between the air and the fiber is collected by the visual tracking device. The fiber and air coupling model is established by CFD computational fluid dynamics software, and the shear stress on the fiber surface is inferred. Its physical significance lies in characterizing the tensile resistance and surrounding shear viscosity caused by the local air in the spinning area to the ejected fiber, which will be strongly affected by the air velocity distribution, temperature gradient, and turbulent conditions.

[0126] The fiber torsional tensor modulus Et is detected and collected by a torsional mechanics tester. Its physical meaning is to indicate the fiber's rigidity and elastic resistance to external disturbance curling and spatial torsion, and is closely related to the molecular chain structure, diameter, and elastic modulus of the material itself.

[0127] S42, based on the standard spinneret structure data set and the current directional deflection Odev, a summary calculation is performed to output a hair type structure complexity function Mcomp to measure the effects of the formation of the fiber curling structure and the winding structure;

[0128] The hair type structure complexity function Mcomp is calculated and output by the following algorithm formula;

[0129] ;

[0130] Where, log represents the logarithmic function;

[0131] The numerator represents the "cumulative curling promotion effect" of the disturbance guidance energy in the free segment. If the deflection angle is large, the texture is strong and deep, and the disturbance interval is long, the initial structure forming ability is stronger.

[0132] The larger the denominator air shear tension factor Nv is, the stronger the air damping is, the disturbance propagation is hindered, and the overall curling potential is weakened;

[0133] The summation term +Et indicates that when the fiber itself has strong torsion resistance, it may still limit the formation of hair shape, so the elastic resistance needs to be taken into account;

[0134] The square of the entire term is used to enhance the nonlinear mapping, causing the structural complexity to show an "explosive improvement" trend, which has an amplifying effect on small perturbation parameter differences and emphasizes the importance of precise control;

[0135] The overall log mapping is used to prevent the value from being too large, and the compression range is convenient for data normalization analysis. The larger the output value, the more complex the structural hierarchy and the stronger the curling and self-entanglement ability.

[0136] In this embodiment, S4 of the method constructs a wool-type fiber structure generation and evaluation mechanism based on nano-induction and structural complexity function calculation through S41 and S42 systems, realizing full process control from physical induction at the spinneret outlet to quantitative evaluation of structural level complexity. The S41 part arranges a nanoscale twist-induced texture array in the 1-3mm area at the edge of the spinneret outlet, so that the fiber is included in the non-contact, directional microscopic physical guidance at the moment of ejection, realizing the self-rotation, self-curling and self-winding of the fiber, avoiding the unevenness and hysteresis caused by traditional heat setting or physical intervention. At the same time, texture structure types, such as grooves, spiral patterns, corrugated surfaces and functional surfaces, such as friction induction and surface energy gradients, form a strongly coupled disturbance enhancement effect, which significantly improves the consistency and controllability of the wool-type structure formation. On this basis, by setting up multiple types of high-precision structural detection equipment, non-traditional structural parameters including texture induced strength Xw, air shear tension factor Nv, free segment disturbance zone length △r, and fiber torsional tensor modulus Et were obtained. A high-resolution, dynamically tracked structural data set was established. Complexity modeling was performed in S42 in combination with the directional deflection value Odev, and the final output was the wool-shaped structural complexity function Mcomp. This function, through the nonlinear enhancement and logarithmic compression mapping mechanism, not only can sensitively respond to tiny structural perturbation differences, but also has a high degree of normalization compatibility. It is used to quantitatively reflect the comprehensive performance of the fiber's structural hierarchy, winding efficiency, and curling ability within the molding window. In summary, this implementation method realizes the dual closed-loop linkage control of one-step molding induction + real-time structural complexity assessment of the wool-shaped structure, which not only improves the molding accuracy and self-stabilization ability of the structure, but also makes the structural quality judgment in the process more scientific and automated, providing key technical support for the realization of wool-shaped fibers with high consistency, high complexity, and high stability.

[0137] Example 6

[0138] See also Figure 1 ,Specifically: S5 includes S51 and S52;

[0139] S51. Use a 5000fps visual tracking device to record the path of the spinning fiber from the nozzle to the receiving room, and use OpenCV image analysis to extract the fiber contour coordinates of each frame. Calculate the standard deviation of these fiber contour coordinates to obtain the structural stability fluctuation amplitude Bd, and perform standard unit conversion to eliminate the unit dimension effect;

[0140] After comprehensively calculating the structural stability fluctuation amplitude Bd, the obtained directional deflection Odev and the hair type structure complexity function Mcomp, the fiber structure controllability comprehensive index Atotal is output to comprehensively measure the directional control ability, hair type structure curling complexity and structural stability consistency;

[0141] The fiber structure controllability comprehensive index Atotal is calculated and output by the following algorithm formula;

[0142] ;

[0143] Where Bdmax represents the maximum fluctuation threshold of the allowable structural stability fluctuation amplitude;

[0144] It represents the dual capability index of directional guidance and structural induction. The larger this part is, the more significant the curling potential of the current fiber is. The directional guidance is divided by 1.2 to map the directional influence to the same order of magnitude of structural complexity.

[0145] The consistency control weight coefficient that represents structural stability. If the fluctuation is severe, such as flying wires or dislocations, the ratio approaches 1 and the product approaches 0, reducing the total score.

[0146] S52. Based on a large amount of historical spinning data, including the structural stability fluctuation amplitude Bd, the obtained directional deflection amount Odev, and the hair pattern structure complexity function Mcomp, mark the hair pattern structure critical point and the lowest acceptable point that meet the user's expected spinning standards for each spinning, and set a controllable interval threshold. The controllable interval threshold includes a first controllable threshold Q1 and a second controllable threshold Q2, where the first controllable threshold Q1 is the hair pattern structure critical point, and the second controllable threshold Q2 is the lowest acceptable point.

[0147] The fiber structure controllability comprehensive index Atotal obtained in real time is compared and evaluated with the controllable range threshold to determine the overall compliance with the standards in terms of direction control ability, curl complexity of the hair structure, and consistency of structural stability. The specific evaluation contents are as follows;

[0148] When the fiber structure controllability comprehensive index Atotal ≥ the first controllable threshold Q1, it means that the spinneret structure is normal, and the spinning and heat setting process will be immediately started, and the sample will be saved;

[0149] When the second controllable threshold Q2 ≤ the fiber structure controllability comprehensive index Atotal < the first controllable threshold Q1, it indicates that there is a disturbance in the spinneret structure. At this time, it is prompted to replace a different spinneret texture sheet, and increase the gas injection angle Ag of the gas injection slot by 10% and the injection flow rate per unit time Jg by 50%. At the same time, after further adjustment, it is repeated to execute S1 to S5 for iterative adjustment until the fiber structure returns to normal and the iteration stops.

[0150] When the fiber structure controllability comprehensive index Atotal is less than the second controllable threshold Q2, it indicates that the spinneret structure is disordered and the forming is identified. At this time, the spinneret is stopped, and the abnormal data is recorded and fed back to the central control server for storage to form a training set. At the same time, an early warning message is generated to prompt the adjustment of the spinneret equipment until it resumes operation.

[0151] In this embodiment, the method, step S51, first uses a 5000fps high-frame-rate visual tracking device to capture the fiber's motion trajectory in real time along the spinneret path. Fiber profile data is extracted frame by frame using OpenCV image processing algorithms. The structural stability fluctuation amplitude Bd is then calculated through standard deviation calculation, eliminating unit dimension effects and obtaining a consistent quantitative stability indicator. Based on this, the directional deflection Odev, the structural complexity function Mcomp, and the structural stability fluctuation amplitude Bd are integrated to calculate and output a comprehensive fiber structure controllability index Atotal, thereby achieving a comprehensive assessment of the overall quality of the wool structure. S52 further introduces a historical data-driven dynamic threshold setting mechanism. By archiving data on the structural stability fluctuation amplitude Bd, the acquired directional deflection Odev, and the wool structure complexity function Mcomp from past spinning processes, and manually annotating the wool structure's "critical compliance point Q1" and "minimum acceptable point Q2," an evaluation baseline is established. Comparing the real-time assessment results with these thresholds enables a three-level response strategy. In summary, this implementation method effectively realizes the intelligent monitoring, abnormality warning and adaptive optimization of the wool-type fiber structure quality by integrating real-time image data, physical quantity calculation, empirical data threshold and feedback control strategy into an integrated process evaluation system, greatly improving the stability, control accuracy and structural consistency of the spinning process, and providing technical support for the preparation of industrial-grade high-performance wool-type fibers.

[0152] Example 7

[0153] See also Figure 1 and Figure 2 , a precise control system for wool-type fiber spinning process, including a disturbance airflow injection module, a data transmission and processing module, an active direction guidance module, a passive structure coupling module and a fiber structure controllable analysis module;

[0154] The disturbed air flow injection module sets a gas injection slot on the side of the spinneret, adjusts the relevant parameters of the gas injection slot, and sets a collection device around the spinneret to collect gas injection data in real time;

[0155] The data transmission and processing module transmits the gas injection data to the central control server, pre-processes the gas injection data in the central control server, and obtains a standard gas injection data set;

[0156] The active direction guidance module calculates and outputs the directional deflection Odev of the spinneret based on the standard gas injection data set, sets the deflection interval threshold, and performs a preliminary comparison and evaluation between the directional deflection Odev and the deflection interval threshold.

[0157] The passive structure coupling module triggers the hair structure analysis mechanism based on the preliminary comparative evaluation results. The hair structure analysis mechanism collects hair structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs the hair structure complexity function Mcomp.

[0158] The fiber structure controllability analysis module calculates and outputs the fiber structure controllability comprehensive index Atotal by combining the directional deflection Odev with the hair structure complexity function Mcomp, and performs a secondary comparative evaluation with the fiber structure controllability comprehensive index Atotal by setting the controllable interval threshold.

[0159] While the embodiments of the present invention have been shown and described, it will be apparent to those skilled in the art that various changes, modifications, substitutions, and alterations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. A method for accurately controlling a wool fiber spinning process, characterized in that: The following steps are involved: S1. Set up a gas injection slot on the side of the spinneret, adjust the relevant parameters of the gas injection slot, and set up a collection device around the spinneret to collect gas injection data in real time; S2. The gas injection data is transmitted to the central control server, and the gas injection data is preprocessed in the central control server to obtain a standard gas injection data set; S3. Calculate and output the directional deflection Odev of the spinneret based on the standard gas injection data set, set a deflection interval threshold, and perform a preliminary comparative evaluation of the directional deflection Odev and the deflection interval threshold; S4. Triggering a hair pattern structure analysis mechanism based on the preliminary comparative evaluation results. The hair pattern structure analysis mechanism collects hair pattern structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs a hair pattern structure complexity function Mcomp; S5. Combining the directional deflection Odev with the hair type structure complexity function Mcomp to calculate and output the fiber structure controllability comprehensive index Atotal, and performing a secondary comparative evaluation with the fiber structure controllability comprehensive index Atotal after setting the controllable interval threshold; Said S5 includes S51 and S52; S51. Use a 5000fps visual tracking device to record the path of the spinning fiber from the nozzle to the receiving room, and use OpenCV image analysis to extract the fiber contour coordinates of each frame. Calculate the standard deviation of these fiber contour coordinates to obtain the structural stability fluctuation amplitude Bd, and perform standard unit conversion to eliminate the unit dimension effect; After comprehensively calculating the structural stability fluctuation amplitude Bd, the obtained directional deflection Odev and the hair type structure complexity function Mcomp, the fiber structure controllability comprehensive index Atotal is output to comprehensively measure the directional control ability, hair type structure curling complexity and structural stability consistency; S52. Based on a large amount of historical spinning data, including the structural stability fluctuation amplitude Bd, the obtained directional deflection amount Odev, and the hair pattern structure complexity function Mcomp, mark the hair pattern structure critical point and the lowest acceptable point that meet the user's expected spinning standards for each spinning, and set controllable interval thresholds. The controllable interval thresholds include a first controllable threshold Q1 and a second controllable threshold Q2, where the first controllable threshold Q1 is the hair pattern structure critical point, and the second controllable threshold Q2 is the lowest acceptable point. The fiber structure controllability comprehensive index Atotal obtained in real time is compared and evaluated with the controllable range threshold to determine the overall compliance with the standards in terms of direction control ability, curl complexity of the hair structure, and consistency of structural stability. The specific evaluation contents are as follows; When the fiber structure controllability comprehensive index Atotal ≥ the first controllable threshold Q1, it means that the spinneret structure is normal, and the spinning and heat setting process will be immediately started, and the sample will be saved; When the second controllable threshold Q2 ≤ the fiber structure controllability comprehensive index Atotal < the first controllable threshold Q1, it indicates that there is a disturbance in the spinneret structure. At this time, it is prompted to replace a different spinneret texture sheet, and increase the gas injection angle Ag of the gas injection slot by 10% and the injection flow rate per unit time Jg by 50%. At the same time, after further adjustment, it is repeated to execute S1 to S5 for iterative adjustment until the fiber structure returns to normal and the iteration stops. When the fiber structure controllability comprehensive index Atotal is less than the second controllable threshold Q2, it indicates that the spinneret structure is disordered and the forming is identified. At this time, the spinneret is stopped, and the abnormal data is recorded and fed back to the central control server for storage to form a training set. At the same time, an early warning message is generated to prompt the adjustment of the spinneret equipment until it resumes operation.

2. The method for accurately controlling a wool fiber spinning process according to claim 1, wherein: Said S1 includes S11 and S12; S11, by setting an asymmetric gas injection slot on the side wall structure of the spinneret, a micro-perturbation airflow is injected into the molten spinneret flow at a set angle, forming a directional disturbance induction force field to control the initial deflection direction and curling tendency of the fiber, and the gas flow rate, injection angle and real-time frequency control of the gas injection slot are adjusted in real time by a micro-flow regulator of the gas injection slot; S12. At the same time, a collection device is set around the spinneret to collect gas injection data in real time; The collection equipment includes gas injection detection equipment and structure detection equipment; The gas injection detection equipment includes a MEMS micro-thermal film flow meter, a binocular infrared imager, a micro-thermoelectric and a FLIR thermal imager; The structural testing equipment includes an AFM atomic force microscope, a 5000fps visual tracking device, an air velocity sensor, and a torsional mechanics tester; The gas injection data include gas density Pgas, gas injection velocity Ug, gas kinematic viscosity Vgas, gas injection angle Ag, gas injection flow rate per unit time Jg and gas temperature T.

3. The method for accurately controlling a wool fiber spinning process according to claim 2, wherein: Said S2 includes S21 and S22; S21. Using the communication module built into the acquisition device, an industrial wireless network is set up to wirelessly connect the acquisition device to the central control server of the spinning device, and the real-time collected gas injection data is transmitted to the central control server; S22. Preprocessing the gas injection data received in real time in the central control server to obtain a standard gas injection data set, wherein the preprocessing includes denoising, data alignment, standard unit conversion, and feature extraction; The denoising may use Savgol filtering to remove noise data in the gas injection data; The data alignment synchronizes the acquisition time of the de-noised gas injection data by using a time axis synchronization method; The standard unit conversion is to perform dimensionless processing on all parameters in the gas injection data by using the standard deviation normalization method, thereby eliminating the dimension effects between all parameters in the gas injection data; The feature extraction is performed based on dimensionless gas injection data to obtain a standard gas injection data set; The standard gas injection data set includes the perturbation gas inertial response coefficient Pg, the gas injection angle Ag, the injection flow rate per unit time Jg, the injection flow rate fluctuation coefficient Eg and the viscosity gradient Nd in the spinning area.

4. The method for accurately controlling a wool fiber spinning process according to claim 3, wherein: Said S3 includes S31 and S32; S31. Construct a response model for the deflection ability of the disturbed airflow on the initial path of the fiber, and input the standard air injection data set obtained in real time into the response model to calculate and output the directional deflection amount Odev to measure the offset angle of the fiber in the initial stage of spinning.

5. The method for accurately controlling a wool fiber spinning process according to claim 4, characterized in that: S32. Deflection interval thresholds are set based on user deflection requirements. The deflection interval thresholds include a first deflection threshold F1 and a second deflection threshold F2. A preliminary comparison and evaluation is performed between the real-time acquired directional deflection amount Odev and the deflection interval thresholds. Based on the preliminary comparison and evaluation results, the triggering status of the hair structure analysis mechanism is determined. The specific evaluation contents are as follows; When the directional deflection Odev> the first deflection threshold F1, it means that the disturbance has sufficient deflection capability, and at this time, the hair-shaped structure analysis mechanism is triggered; When the second deflection threshold F2 ≤ directional deflection Odev ≤ the first deflection threshold F1, it indicates that the current deflection capability is in the transition zone. At this time, the current parameters are maintained, the directional deflection Odev is recalculated every 100ms, and the preliminary comparison and evaluation are continued. When the directional deflection Odev is less than the second deflection threshold F2, it indicates insufficient deflection. In this case, the gas injection angle Ag of the gas injection slot is increased by 10%, and the injection flow rate per unit time Jg is increased by 50%. After the increase, steps S1 to S3 are repeated every 20 ms to re-judge. The first deflection threshold F1 is set to 0.4, and the second deflection threshold F2 is set to 0.

2.

6. The method for accurately controlling a wool fiber spinning process according to claim 5, characterized in that: Said S4 includes S41 and S42; S41. After preliminary comparative evaluation of the mechanism of triggering the hair-shaped structure analysis, a nanoscale twist-induced texture array was designed in the 1-3mm forming window area at the edge of the spinneret outlet to perform passive physical coupling intervention on the ejected fiber. The nanoscale distortion-induced texture array includes structure type, array form and functional surface; The structural types include grooves, spiral patterns, micro-steps, lattice protrusions and corrugated surfaces; The array form is a non-uniform random array; The functional surface includes friction induction, fluid disturbance, surface energy gradient and micro-adhesion capabilities; A structure detection device is set up on the nanoscale distortion-induced texture array to collect spinneret structure data in real time, and the spinneret structure data is preprocessed to eliminate noise in the spinneret structure data, align the spinneret structure acquisition time, and eliminate the dimensional influence of the spinneret structure data to obtain a standard spinneret structure data set; The standard spinning structure data set includes texture induced strength Xw, fiber torsional tensor modulus Et, air shear tension factor Nv and free section disturbance zone length Δr.

7. The method for accurately controlling a wool fiber spinning process according to claim 6, characterized in that: S42. Based on the standard spinneret structure data set and the current directional deflection Odev, a summary calculation is performed to output the hair type structure complexity function Mcomp to measure the effects of the fiber curling structure and the winding structure formation.

8. A precise control system for a wool fiber spinning process, applied to a precise control method for a wool fiber spinning process according to any one of claims 1 to 7, characterized in that: It includes a disturbance airflow injection module, a data transmission and processing module, an active direction guidance module, a passive structure coupling module and a fiber structure controllable analysis module; The disturbed air flow injection module collects gas injection data in real time by setting a gas injection slot on the side of the spinneret, adjusting relevant parameters of the gas injection slot, and setting collection equipment around the spinneret. The data transmission and processing module transmits the gas injection data to the central control server, pre-processes the gas injection data in the central control server, and obtains a standard gas injection data set; The active direction guidance module calculates and outputs the directional deflection Odev of the spinneret based on the standard gas injection data set, sets a deflection interval threshold, and performs a preliminary comparison and evaluation between the directional deflection Odev and the deflection interval threshold; The passive structure coupling module triggers a hair type structure analysis mechanism based on the preliminary comparative evaluation results. The hair type structure analysis mechanism collects hair type structure data, pre-processes it into a standard spinneret structure data set, and then calculates and outputs a hair type structure complexity function Mcomp. The fiber structure controllability analysis module calculates and outputs the fiber structure controllability comprehensive index Atotal by combining the directional deflection Odev with the hair structure complexity function Mcomp, and performs a secondary comparative evaluation on the controllable interval threshold and the fiber structure controllability comprehensive index Atotal.

Images