Fiber damage avoiding system for air vortex spinning device

Abstract

The invention belongs to the technical field of spinning equipment, and particularly discloses a fiber damage avoiding system for a gas vortex spinning device, which integrates multiple functions of fiber pretreatment, airflow control, fiber conveying, real-time monitoring, intelligent control and the like, and aims to comprehensively improve the fiber protection capability in the spinning process, and improve the spinning quality by optimizing the pretreatment process of fibers. By means of the intelligent humidity control and mechanical softening technology, the fibers reach the optimal processing state before entering a spinning area, initial damage to the fibers is reduced, stress uniformity of the fibers in the spinning process is guaranteed, fiber damage caused by airflow fluctuation is avoided, data are fed back to the damage prevention control unit, and the damage prevention control unit is used for preventing the fibers from being damaged. Spinning parameters are automatically adjusted, intelligent and dynamic optimization of the spinning process is achieved, and the possibility of fiber damage is further reduced.

Description

Technical Field

[0001] The invention relates to the technical field of spinning equipment, in particular to a fiber damage avoiding system for a gas vortex spinning device. Background Art

[0002] Gas vortex spinning technology is a modern spinning technology that is efficient and fast and has been widely used in the textile industry. Compared with traditional ring spinning, gas vortex spinning uses high-speed rotating airflow to wind fiber bundles into yarn, eliminating the process of mechanical twist formation, thereby greatly improving production efficiency. However, during the gas vortex spinning process, the fibers are affected by the high-speed airflow and are prone to various forms of damage, such as fiber breakage, wear, and surface structure damage, which not only affects the quality of the yarn, but may also lead to frequent shutdown and maintenance of the spinning equipment, increasing production costs.

[0003] In the gas vortex spinning device, the mechanical control means often cannot achieve the desired effect because the force of the airflow on the fiber is complex and difficult to control accurately. The high-speed rotating airflow not only exerts uneven pressure on the fiber, but also causes the fiber to shake and become unstable during the transportation process. Since the motion trajectory of the fiber in the airflow is difficult to accurately predict, the traditional monitoring means cannot detect the state of the fiber in real time and make corresponding adjustments, which leads to the uncontrollability of fiber damage during the spinning process. With the introduction of advanced monitoring and control technologies, the above problems are gradually solved. For example, by introducing high-precision sensors and high-speed imaging equipment, the tension, temperature, motion trajectory and surface state of the fiber can be monitored in real time, and the spinning parameters can be automatically adjusted based on these data to reduce fiber damage. However, these technologies still have certain limitations: the accuracy of sensors and imaging equipment is limited, and it is difficult to capture the slight changes of the fiber in the high-speed motion state; at the same time, the existing automatic control system often only adjusts a single or a small number of parameters, and cannot achieve full optimization of the spinning process, resulting in the fiber damage problem has not been fundamentally solved.

[0004] In addition, with the advancement of artificial intelligence and machine learning technologies, intelligent control systems based on self-learning algorithms have gradually become a research hotspot for solving the problem of fiber damage in the spinning process. By continuously collecting and analyzing the state data of fibers under different production conditions, such systems can establish a set of multidimensional feature space models, which can accurately predict the risk of fiber damage under different conditions and dynamically optimize spinning parameters. This intelligent control method not only improves the stability and quality of spinning, but also effectively reduces the frequency of fiber damage. Although self-learning algorithms have significant advantages in theory, they still face some challenges in practical applications. For example, the complexity of the algorithm and the large amount of calculation may lead to a slow system response speed; in addition, the accuracy and completeness of the data also directly affect the prediction effect of the model.

[0005] In view of this, the present invention provides a fiber damage avoidance system for a gas vortex spinning device. Summary of the invention

[0006] The present invention provides a fiber damage avoidance system for a gas vortex spinning device, which reduces fiber damage during the gas vortex spinning process by integrating precision control and monitoring technology, improves yarn quality and reduces production losses, and promotes the intelligent and automated development of the spinning process.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a fiber damage avoidance system for a gas vortex spinning device, comprising a fiber pretreatment unit, an airflow control system, a fiber delivery unit, a fiber monitoring unit and a damage prevention control unit, wherein the modules are connected by wire or wirelessly;

[0009] The fiber pretreatment unit integrates a humidification device and a softening device. By controlling the humidity and temperature parameters, it performs preliminary treatment on the input fiber, optimizes the fiber's molecular structure, and adjusts the fiber's tensile strength.

[0010] Airflow control system, used to control the airflow speed, pressure and direction acting on the fiber;

[0011] The fiber delivery unit is used to deliver the pretreated fibers evenly and stably to the spinning area;

[0012] Fiber monitoring unit, including high-precision sensors and high-speed imaging equipment, is used to monitor the tension, temperature, movement trajectory and damage of fibers in real time;

[0013] The damage prevention control unit automatically optimizes the spinning parameter settings based on historical monitoring data and real-time monitoring data, and can predict potential fiber damage risks in advance. Real-time data includes but is not limited to airflow intensity, fiber tension and temperature to prevent fiber damage.

[0014] As a preferred embodiment of the present invention, the fiber pretreatment unit includes a humidifying device and a softening device, wherein:

[0015] The humidification device accurately controls the humidity level within the range of 5% to 20% through formula (1), so that the moisture content of the fiber reaches the ideal state. The formula is:

[0016] H(t)=H 0 ×e - λ t (1)

[0017] Where H(t) is the humidity of the fiber at time t, H 0 is the initial humidity, λ is the humidity control parameter;

[0018] The softening device uses intelligent temperature control and mechanical softening. The fiber flexibility E during the softening process can be controlled by the following formula:

[0019]

[0020] Where σ is the stress applied, ε is the strain of the fiber, α is the thermal expansion coefficient of the fiber material, and T(t) is the softening temperature at time t;

[0021] The softening process follows Newton's law of cooling:

[0022] T(t)=T 0 +(T f -T 0 )·e -kt (3)

[0023] Where T(t) is the temperature of the fiber at time t, T 0 is the initial temperature, T f is the target softening temperature, and k is the thermal conductivity of the softening device.

[0024] As a preferred embodiment of the present invention, the airflow control system comprises a plurality of adjustable nozzles and a dynamic adjustment module, wherein the adjustable nozzles are vortex tube jet holes.

[0025] The dynamic adjustment module automatically adjusts the airflow angle and pressure of each nozzle based on the feedback data of real-time monitoring of fiber status. The dynamic adjustment formula for nozzle pressure and angle is:

[0026]

[0027] Where P(t) is the airflow pressure of the nozzle at time t, θ(t) is the airflow angle, and P 0 and θ 0are the initial pressure and angle of the nozzle, ΔF is the deviation of the real-time tension of the fiber, and F m is the average tension of the fiber, ΔL is the deviation of the fiber displacement, L m is the average displacement of the fiber, and k is the adjustment coefficient.

[0028] As a preferred embodiment of the present invention, the fiber conveying unit includes a variable speed conveyor belt and a tension control device, which automatically and dynamically adjusts the conveying speed and tension of the fiber based on the real-time tension data provided by the fiber monitoring unit;

[0029] The conveyor belt speed v(t) is determined by the real-time tension J(t) of the fiber and the target tension setting value J 0 Make adjustments, and the adjustment formula is as follows:

[0030]

[0031] Among them, v 0 is the initial conveying speed, k 1 is the adjustment coefficient, J(t) is the fiber tension at time t, J 0 is the set target tension; the tension balance formula is:

[0032] J(t)=J 0 +ΔJ(t) (6)

[0033] Among them, ΔJ(t) is the tension adjustment value calculated by the system based on real-time monitoring data;

[0034] The fiber conveying unit also integrates an abnormality monitoring mechanism. When the tension adjustment value exceeds the set deviation threshold, it is detected as an abnormal tension change, automatically triggering an early warning and making rapid corrections to the speed and tension.

[0035] As a preferred embodiment of the present invention, the fiber monitoring unit includes a surface detection system, which uses multi-spectral imaging technology to capture reflected light signals of the fiber surface at different wavelength spectra to generate high-resolution spectral images, thereby being able to identify surface defects that are invisible to the naked eye;

[0036] The control formula of the spectral reflectance R(λ) during the detection process is:

[0037]

[0038] Among them, I r (λ) is the intensity of reflected light of a specific wavelength λ from the fiber surface, I 0 (λ) is the incident light intensity;

[0039] The recognition results are obtained by analyzing the reflectivity changes at multiple wavelengths and classified by the following image processing algorithms:

[0040]

[0041] Where D(x,y) is the damage characteristic value at point (x,y), w r is the weight coefficient of different wavelengths, I r (λ,x,y) is the reflected light intensity at the detection point, When D(x,y) exceeds the set threshold, the system determines that there is damage at that location and transmits the detection result to the damage prevention control unit in real time.

[0042] As a preferred embodiment of the present invention, the damage prevention control unit includes a self-learning algorithm module, which automatically optimizes the spinning parameter settings and predicts potential fiber damage risks based on historical monitoring data and real-time monitoring data.

[0043] The self-learning algorithm module continuously collects and analyzes fibers under different production conditions to build a multi-dimensional feature space model, which avoids overfitting by minimizing the loss function.

[0044]

[0045] Among them, f(x i ; θ) is the prediction function, x i is the feature vector of the i-th sample, y i is the actual observation value, θ is the model parameter, and λ is the regularization coefficient;

[0046] Through time series analysis and deep learning, the risk trend that may cause fiber damage in the current spinning process is identified. The formula is as follows:

[0047] P(Damage|x)=σ(w x+b) (10)

[0048] Where P(Damage|x) represents the fiber damage probability under a given feature vector x, σ is the sigmoid activation function, and w and b are the model weights and biases.

[0049] As a preferred embodiment of the present invention, the system also includes a data recording and analysis module, which adopts a distributed database structure to store and manage real-time data, including fiber tension, speed, temperature, surface state and airflow pressure.

[0050] Correlate the spinning parameters in different time periods with the corresponding fiber damage conditions, use regression analysis, cluster analysis and machine learning techniques to identify the key factors that cause fiber damage, and quantify the impact of each factor on fiber damage;

[0051] Based on the real-time data collected and analyzed, a comprehensive report on the spinning process is automatically generated. The report content includes but is not limited to: the frequency of fiber damage, the changing trend of key parameters, parameter adjustment records and damage risk prediction results.

[0052] As a preferred embodiment of the present invention, the system further comprises a remote monitoring and operation module, which efficiently transmits the real-time data of the spinning process to a remote server, so that the remote operator can monitor and analyze the spinning process in real time and make precise adjustments to the spinning process;

[0053] On the remote server side, the system integrates a visual monitoring platform to display real-time data in the form of charts and trend curves. Based on historical data and real-time feedback, the system can automatically adjust the control strategy to adapt to different production conditions and environmental changes.

[0054] As a preferred embodiment of the present invention, the system further comprises an emergency stop device, which can execute a stop command within a millisecond response time by maintaining a real-time connection with a high-speed data link of the fiber monitoring unit;

[0055] When the fiber monitoring unit detects fiber abnormalities exceeding a set threshold, the monitoring data is immediately transmitted to the damage prevention control unit, which quickly evaluates the detected risk level through the set safety algorithm and judgment logic;

[0056] If the damage risk exceeds the preset safety threshold, a shutdown signal is sent to the emergency shutdown device; and a shutdown reason and equipment status report is sent to the operator to locate the problem and take corresponding measures.

[0057] As a preferred embodiment of the present invention, the system further comprises a user interface module for displaying real-time monitoring data, fiber damage conditions, adjustment records and warning information, and operators can interact through the user interface module.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] (1) Fiber pretreatment and intelligent control: Through the fiber pretreatment unit with integrated humidification and softening devices, the fiber is optimized before spinning, so that the fiber enters the spinning process in the optimal state. This intelligent humidity and temperature control not only improves the flexibility of the fiber, but also effectively reduces the risk of fiber breakage and wear during the spinning process.

[0060] (2) Real-time monitoring and dynamic adjustment: The fiber monitoring unit combines high-precision sensors and high-speed imaging equipment to achieve real-time monitoring of fiber tension, temperature, motion trajectory and damage. By automatically adjusting spinning parameters such as airflow pressure, tension and temperature, the system can quickly respond to changes in fiber status and avoid further expansion of fiber damage.

[0061] (3) Self-learning algorithm and prediction capability: The self-learning algorithm module integrated in the system uses machine learning technology to automatically optimize spinning parameter settings based on historical and real-time data, and can predict potential fiber damage risks in advance. This adaptive adjustment and prediction capability greatly improves the stability of the spinning process and the quality of the yarn.

[0062] (4) Multispectral imaging and micro-damage detection: Through multispectral imaging technology, the system can detect micro-cracks or wear on the fiber surface. These micro-damages that are imperceptible to the naked eye will be identified and fed back in real time, further strengthening the prevention and control of fiber damage.

[0063] (5) Remote monitoring and intelligent interface: The system has remote monitoring and operation functions and supports multiple communication protocols to ensure stable data transmission in different environments. At the same time, the intelligent user interface provides an intuitive display and interactive function of real-time monitoring data, allowing operators to easily adjust parameters and review historical data, effectively optimizing the spinning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A logic diagram of a fiber damage avoidance system for a gas vortex spinning device; DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0066] In the description of the present invention, it should be noted that the terms "vertical", "up", "down", "horizontal", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0067] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0068] Example 1

[0069] like Figure 1 As shown, this embodiment provides a fiber damage avoidance system for a gas vortex spinning device, including a fiber pretreatment unit, an airflow control system, a fiber delivery unit, a fiber monitoring unit and a damage prevention control unit, and each module is connected by wire or wirelessly;

[0070] The fiber pretreatment unit integrates a humidification device and a softening device. By controlling the humidity and temperature parameters, it performs preliminary treatment on the input fiber, optimizes the fiber's molecular structure, and adjusts the fiber's tensile strength.

[0071] Specifically, the fiber pretreatment unit includes a module that integrates a humidification device and a softening device. The humidification device uses micro-atomization technology to disperse water molecules to the nanometer level, and accurately adjusts the ambient humidity through an intelligent humidity control system to maintain the moisture content of the fiber within the range of 5% to 20%, so as to ensure the flexibility of the fiber during the spinning process and reduce the brittle cracking caused by uneven humidity. The softening device applies moderate heat through an intelligent temperature control system, uses the principle of elastic deformation of the material, optimizes the molecular structure of the fiber, and improves the tensile strength of the fiber. It is used to perform preliminary treatment on the input fiber to reduce damage to the fiber during the spinning process.

[0072] More specifically, the humidification device controls the humidity level RH within the range of 5% to 20% to achieve an ideal moisture content in the fiber. The control formula is as follows:

[0073] H(t)=H 0 ×e - λ t (1)

[0074] Where H(t) is the humidity of the fiber at time t, H 0 is the initial humidity, and λ is the humidity control parameter.

[0075] The softening device uses intelligent temperature control and mechanical softening. The fiber flexibility E during the softening process can be controlled by the following formula:

[0076]

[0077] Where σ is the stress applied, ε is the strain of the fiber, α is the thermal expansion coefficient of the fiber material, and T(t) is the softening temperature at time t. The softening process follows Newton's law of cooling:

[0078] T(t)=T 0 +(T f -T 0 )·e -kt (3)

[0079] Where T(t) is the temperature of the fiber at time t, T 0 is the initial temperature, T f is the target softening temperature, and k is the thermal conductivity of the softening device.

[0080] Airflow control system, used to control the airflow speed, pressure and direction acting on the fiber;

[0081] Specifically, the airflow control system includes a plurality of adjustable nozzles and a dynamic adjustment module. The adjustable nozzle is a vortex tube jet hole with adjustable angle and pressure. The dynamic adjustment module automatically adjusts the airflow angle and pressure of each nozzle based on the feedback data of real-time monitoring of the fiber state to ensure the force uniformity and stability of the fiber during the spinning process. The airflow angle of the nozzle can be accurately adjusted within the range of 25° to 45° by an electric servo mechanism, and the airflow pressure is maintained between 0.2MPa and 0.8MPa through a closed-loop control system. The specific adjustment is based on the real-time tension, speed and displacement data of the fiber. The airflow control system also integrates a pressure sensor and a flow rate sensor in the vortex tube to monitor and feedback the actual working state of the nozzle. Based on this information, the dynamic adjustment module can adjust the working parameters of the nozzle in real time during the spinning process, so that the deviation of the uniformity of the force on the fiber is controlled within ±10%, thereby ensuring uniform twisting of the fiber and high-quality yarn formation.

[0082] The dynamic adjustment formula of the airflow control system for the nozzle pressure and angle is:

[0083]

[0084] Where P(t) is the airflow pressure of the nozzle at time t, θ(t) is the airflow angle, and P 0 and θ 0 are the initial pressure and angle of the nozzle, ΔF is the deviation of the real-time tension of the fiber, and F m is the average tension of the fiber, ΔL is the deviation of the fiber displacement, L m is the average displacement of the fiber, and k is the adjustment coefficient.

[0085] The fiber delivery unit is used to deliver the pretreated fibers evenly and stably to the spinning area;

[0086] Specifically, the fiber conveying unit includes a variable-speed conveyor belt and a high-precision tension control device. The conveying unit can automatically adjust the conveyor belt speed and fiber tension based on the real-time tension data provided by the fiber monitoring unit to ensure the continuous stability and uniformity of the fiber during the conveying process. The variable-speed conveyor belt is driven by a continuously variable speed motor and directly coupled with the real-time feedback fiber tension data to ensure that the tension fluctuation is controlled within ±5%, effectively reducing the damage to the fiber caused by unstable tension.

[0087] More specifically, the conveyor belt speed v(t) is determined by the real-time tension J(t) of the fiber and the target tension setting value J 0 Make adjustments, and the adjustment formula is as follows:

[0088]

[0089] Among them, v 0 is the initial conveying speed, k 1 is the adjustment coefficient, J(t) is the fiber tension at time t, J 0 is the target tension to be set.

[0090] The tension control device monitors the fiber tension in real time through a precision force sensor and uses servo control technology to apply appropriate tension adjustment to the fiber to maintain the balance of the fiber during transportation. The control process can be described by the following tension balance formula:

[0091] J(t)=J 0 +ΔJ(t) (6)

[0092] Among them, ΔJ(t) is the tension adjustment value calculated by the system based on real-time monitoring data, ensuring that the tension fluctuation of the fiber during the entire transportation process is controlled within ±5%, thereby effectively reducing the breakage, stretching or deformation of the fiber caused by unstable tension.

[0093] The fiber conveying unit also integrates an abnormality monitoring mechanism. When an abnormal change in tension is detected (such as exceeding 20% ​​of the set deviation threshold), it automatically triggers an early warning and makes rapid corrections to the speed and tension, further enhancing the safety of the fiber during the conveying process and the stability of the yarn quality.

[0094] Fiber monitoring unit, including high-precision sensors and high-speed imaging equipment, is used to monitor the tension, temperature, movement trajectory and damage of fibers in real time;

[0095] Specifically, the fiber monitoring unit includes high-precision sensors and multi-spectral imaging equipment to monitor the tension, temperature, movement trajectory and surface state of the fiber in real time. Multi-spectral imaging technology can detect tiny cracks or wear on the fiber surface, generate high-resolution spectral images by capturing reflected light signals under different wavelength spectra, and identify potential surface damage. The monitoring data is transmitted to the damage prevention control unit in real time to adjust the spinning parameters to prevent further expansion of fiber damage.

[0096] More specifically, the surface inspection system integrates near infrared (NIR), visible light (VIS) and ultraviolet (UV) spectral imaging modules, each of which adjusts the lighting and imaging parameters to achieve optimal detection of different types of fibers. The control formula of the spectral reflectance R(λ) during the inspection process is:

[0097]

[0098] Among them, I r (λ) is the intensity of reflected light of a specific wavelength λ from the fiber surface, I 0 (λ) is the incident light intensity. By analyzing the reflectivity changes at multiple wavelengths, abnormal spectral features due to cracks or wear can be identified.

[0099] The detection system uses a highly sensitive multispectral imaging sensor and intelligent image processing algorithm. By comparing the multispectral image data with the pre-established fiber surface defect database, it can automatically identify and classify tiny cracks, wear and tear or other potential damage on the fiber surface. The identification results are classified using the following image processing algorithms:

[0100]

[0101] Where D(x,y) is the damage characteristic value at point (x,y), w r is the weight coefficient of different wavelengths, I r (λ,x,y) is the reflected light intensity at the detection point, When D(x,y) exceeds the set threshold, the system determines that there is damage at that location and transmits the detection result to the damage prevention control unit in real time.

[0102] After receiving the detection data, the damage prevention control unit automatically adjusts the spinning parameters such as air flow intensity, fiber tension and delivery speed based on the detection results to avoid the extension of damage and further reduce the risk of fiber breakage.

[0103] The damage prevention control unit automatically optimizes the spinning parameter settings based on historical monitoring data and real-time monitoring data, and can predict potential fiber damage risks in advance. Real-time data includes but is not limited to airflow intensity, fiber tension and temperature to prevent fiber damage.

[0104] Specifically, the damage prevention control unit uses a self-learning algorithm module to automatically optimize the spinning parameter settings based on historical and real-time monitoring data. Through machine learning technology, the system can identify parameter combinations that are prone to fiber damage and dynamically adjust these parameters to reduce the probability of fiber damage. The system also has a prediction function, through time series analysis and deep learning models, to identify potential damage risks in the spinning process in advance, ensuring fiber safety and yarn quality.

[0105] More specifically, the self-learning algorithm module continuously collects and analyzes key parameters such as tension, speed, temperature, surface condition, etc. of the fiber under different production conditions to establish and update a set of multi-dimensional feature space models. Each model corresponds to specific spinning conditions and fiber states. The system will compare the real-time monitoring data with the historical data to find the optimal combination of spinning parameters. The model training process can be achieved by minimizing the following loss function:

[0106]

[0107] Among them, f(x i ; θ) is the prediction function, x i is the feature vector of the i-th sample, y i is the actual observation value, θ is the model parameter, and λ is the regularization coefficient used to avoid overfitting.

[0108] In this way, the self-learning algorithm can identify the parameter combination that is most likely to cause fiber damage under specific spinning conditions, such as excessive airflow pressure, excessive fiber tension, etc., and dynamically adjust these parameters to reduce the probability of fiber damage. Based on Bayesian optimization or genetic algorithms, the system can quickly search for the optimal parameter combination in a huge parameter space and achieve adaptive parameter adjustment.

[0109] In addition, the module also has a prediction function. Through time series analysis and deep learning models, the module can identify the risk trend that may cause fiber damage in the current spinning process. The formula is as follows:

[0110] P(Damage|x)=σ(w x+b) (10)

[0111] Where P(Damage|x) represents the fiber damage probability under a given feature vector x, σ is the sigmoid activation function, and w and b are the model weights and biases.

[0112] The data recording and analysis module can comprehensively record all monitoring data, parameter adjustment records and fiber damage conditions, and generate detailed reports based on these data for subsequent analysis, equipment optimization and production process improvement. The data recording and analysis module adopts a distributed database structure, which can efficiently store and manage a large amount of real-time data, including fiber tension, speed, temperature, surface state and airflow pressure. Through data aggregation and grouping technology, the module can associate the spinning parameters in different time periods with the corresponding fiber damage conditions, thereby providing valuable data support for the optimization of the spinning process.

[0113] The distributed database structure and advanced data analysis algorithms enable in-depth mining of historical data. Using regression analysis, cluster analysis, and machine learning techniques, the module can identify the key factors that lead to fiber damage and quantify the degree of influence of each factor on fiber damage. For example, using a linear regression model, the influence of a spinning parameter xi on the probability of fiber damage y can be quantified:

[0114]

[0115] Among them, β 0 is the intercept term, β i is the parameter x i is the regression coefficient and ε is the error term.

[0116] The data recording and analysis module also has an automatic report generation function. Based on the collected and analyzed data, a comprehensive report of the spinning process is automatically generated. The report content includes but is not limited to: the frequency of fiber damage, the change trend of key parameters, parameter adjustment records, damage risk prediction results, etc. The report also provides optimization suggestions, such as adjusting the range of a certain parameter, improving the monitoring accuracy of a certain process link, etc., so as to provide specific guidance for operators and equipment maintenance personnel.

[0117] The remote monitoring and operation module can efficiently transmit the real-time data of the spinning process to the remote server through an advanced wireless communication module, so that the remote operator can monitor and analyze the spinning process in real time and make precise adjustments to the spinning process. The wireless communication module supports multiple communication protocols, such as Wi-Fi, 4G / 5G, LoRa, etc., to ensure stable and high-speed data transmission in different environments. The module can not only transmit key monitoring data such as fiber tension, speed, temperature, surface state, etc., but also upload all operation records and parameter adjustment history of the system in real time, thereby providing all-round data support for remote operators. On the remote server side, the system integrates a visual monitoring platform, which intuitively displays real-time data in the form of charts, trend curves, etc. through an advanced graphical user interface (GUI). Operators can remotely access the platform to monitor the operating status of the spinning equipment in real time and make precise operational decisions based on the monitoring data. The platform also supports parallel monitoring of multiple devices and multi-user collaboration, allowing operators to manage and adjust multiple devices at different locations at the same time. In addition, the remote operation function of the system also includes an automatic alarm and remote emergency handling module. When fiber damage risk or equipment abnormality is detected, the system automatically sends real-time alarm information to the remote operator through the wireless communication module and provides a detailed risk analysis report. The operator can immediately make necessary adjustments to the spinning equipment through the remote platform, such as changing the airflow pressure, adjusting the fiber tension, or even remotely shutting down the equipment to prevent further fiber damage. The system also has an adaptive remote control function. Based on historical data and real-time feedback, the system can automatically adjust the control strategy to adapt to different production conditions and environmental changes. For example, in the case of unstable communication signals or large delays, the system will automatically switch to local control mode to ensure the continuity and safety of the spinning process.

[0118] Highly responsive emergency stop device: When the fiber monitoring unit detects a serious risk of fiber damage, the damage prevention control unit will immediately trigger the emergency stop device, instantly cut off the power source of the spinning device, and stop its operation, thereby effectively avoiding excessive fiber damage and potential damage to the equipment. The emergency stop device maintains a real-time connection with the fiber monitoring unit through a high-speed data link, and can execute a stop command within a millisecond response time.

[0119] Specifically, the fiber monitoring unit uses multispectral imaging, ultrasonic detection and strain sensor technology to detect the tension, surface condition and internal structure of the fiber in real time. When the fiber is detected to show signs of breakage, wear or severe deformation exceeding the set threshold, the monitoring data will be transmitted to the damage prevention control unit in real time. The damage prevention control unit quickly evaluates the detected risk level through the set safety algorithm and judgment logic. If the damage risk exceeds the preset safety threshold, the unit will immediately send a shutdown signal to the emergency stop device. In order to ensure the high reliability and safety of the system, the emergency stop device adopts a dual redundant design, that is, it has both mechanical power-off and software shutdown modes. At the hardware level, the physical movement of the equipment is directly stopped by cutting off the power supply or activating the mechanical brake system; at the software level, the operation of the spinning equipment is stopped by turning off the control signal. In addition, the emergency stop device also integrates a status feedback system that can send the operator a shutdown reason and equipment status report immediately after the shutdown, helping to quickly locate the problem and take corresponding measures. For example, the report may include the specific fiber damage type that caused the shutdown, the abnormal key parameters monitored, and the possible fault location. Operators can perform timely troubleshooting and equipment maintenance based on the reported information to ensure that the equipment is in optimal condition before restarting.

[0120] A highly intelligent user interface module that displays real-time monitoring data, fiber damage, adjustment records and warning messages. Operators can interact directly with the system through this interface to make manual adjustments or view details of automatic adjustments, thereby accurately controlling and optimizing the spinning process.

[0121] The user interface module uses a multi-level information display method to present key data in the spinning process in an intuitive and easy-to-understand form. For example, real-time monitoring data is displayed in the form of dynamic charts, trend curves, heat maps, etc., and information such as fiber tension, speed, temperature, and surface condition can be seen at a glance. Fiber damage is displayed through a color-coded warning system, and fibers with different damage levels will be marked with different colors, which is convenient for operators to quickly identify and respond. The module integrates a variety of interactive functions, and operators can operate the system through a variety of input methods such as touch screen, keyboard or voice command. In manual adjustment mode, users can directly select and adjust specific spinning parameters, such as airflow intensity, fiber tension or conveying speed, and view in real time how the adjustment results affect the state and damage of the fiber. In automatic adjustment mode, the user interface module will display the system's automatic adjustment record, including detailed information such as the time of adjustment, adjustment content, and adjusted fiber status, ensuring that the operator is fully aware of every step of the system operation. In addition, the user interface module is equipped with a historical data review function. Operators can call up historical monitoring data, adjustment records and warning information at any time, and conduct in-depth analysis of the spinning process in different time periods through data comparison and analysis functions. For example, by comparing damage data from different batches of fibers, operators can identify potential process problems or equipment failures, and make targeted adjustments and maintenance before the next batch of production.

[0122] Although embodiments of the present invention have been described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A fiber damage avoidance system for a gas vortex spinning device, characterized in that: It includes a fiber pretreatment unit, an airflow control system, a fiber delivery unit, a fiber monitoring unit and a damage prevention control unit, and each module is connected by wire or wirelessly; The fiber pretreatment unit integrates a humidification device and a softening device. By controlling the humidity and temperature parameters, it performs preliminary treatment on the input fiber, optimizes the fiber's molecular structure, and adjusts the fiber's tensile strength. Airflow control system, used to control the airflow speed, pressure and direction acting on the fiber; The fiber delivery unit is used to deliver the pretreated fibers evenly and stably to the spinning area; Fiber monitoring unit, including high-precision sensors and high-speed imaging equipment, is used to monitor the tension, temperature, movement trajectory and damage of fibers in real time; The damage prevention control unit automatically optimizes the spinning parameter settings based on historical monitoring data and real-time monitoring data, and can predict potential fiber damage risks in advance. Real-time data includes but is not limited to airflow intensity, fiber tension and temperature to prevent fiber damage.

2. A fiber damage avoidance system for a gas vortex spinning device according to claim 1, characterized in that: The fiber pretreatment unit comprises a humidifying device and a softening device, wherein: The humidification device accurately controls the humidity level within the range of 5% to 20% through formula (1) so that the moisture content of the fiber reaches the ideal state. Formula (1) is: H(t)=H0×e -λt (1) Among them, H(t) is the humidity of the fiber at time t, H0 is the initial humidity, and λ is the humidity control parameter; The softening device uses intelligent temperature control and mechanical softening. The fiber flexibility E during the softening process can be controlled by the following formula: Where σ is the stress applied, ε is the strain of the fiber, α is the thermal expansion coefficient of the fiber material, and T(t) is the softening temperature at time t; The softening process follows Newton's law of cooling: T(t)=T0+(T f -T0)·e -kt (3) Where T(t) is the temperature of the fiber at time t, T0 is the initial temperature, and T f is the target softening temperature, and k is the thermal conductivity of the softening device.

3. A fiber damage avoidance system for a gas vortex spinning device according to claim 2, characterized in that: The airflow control system includes a plurality of adjustable nozzles and a dynamic adjustment module, wherein the adjustable nozzles are vortex tube jet holes. The dynamic adjustment module automatically adjusts the airflow angle and pressure of each nozzle based on the feedback data of real-time monitoring of fiber status. The dynamic adjustment formula for nozzle pressure and angle is: Where P(t) is the airflow pressure of the nozzle at time t, θ(t) is the airflow angle, P0 and θ0 are the initial pressure and angle of the nozzle, respectively, ΔF is the deviation of the real-time tension of the fiber, and F m is the average tension of the fiber, ΔL is the deviation of the fiber displacement, L m is the average displacement of the fiber, and k is the adjustment coefficient.

4. A fiber damage avoidance system for a gas vortex spinning device according to claim 3, characterized in that: The fiber conveying unit includes a variable speed conveyor belt and a tension control device, which automatically and dynamically adjusts the conveying speed and tension of the fiber based on the real-time tension data provided by the fiber monitoring unit; The conveyor belt speed v(t) is adjusted according to the real-time tension J(t) of the fiber and the target tension setting value J0. The adjustment formula is as follows: Where v0 is the initial conveying speed, k1 is the adjustment coefficient, J(t) is the fiber tension at time t, and J0 is the set target tension; the tension balance formula is: J(t)=J0+ΔJ(t) (6) Among them, ΔJ(t) is the tension adjustment value calculated by the system based on real-time monitoring data; The fiber conveying unit also integrates an abnormality monitoring mechanism. When the tension adjustment value exceeds the set deviation threshold, it is detected as an abnormal tension change, automatically triggering an early warning and making rapid corrections to the speed and tension.

5. A fiber damage avoidance system for a gas vortex spinning device according to claim 4, characterized in that: The fiber monitoring unit includes a surface detection system, which uses multi-spectral imaging technology to capture reflected light signals of the fiber surface at different wavelength spectra to generate high-resolution spectral images, thereby being able to identify surface defects that are invisible to the naked eye; The control formula of the spectral reflectance R(λ) during the detection process is: Among them, I r (λ) is the intensity of reflected light of a specific wavelength λ from the fiber surface, and I0(λ) is the intensity of the incident light; The recognition results are obtained by analyzing the reflectivity changes at multiple wavelengths and classified by the following image processing algorithms: Where D(x,y) is the damage characteristic value at point (x,y), w r is the weight coefficient of different wavelengths, I r (λ,x,y) is the reflected light intensity at the detection point, When D(x,y) exceeds the set threshold, the system determines that there is damage at that location and transmits the detection result to the damage prevention control unit in real time.

6. A fiber damage avoidance system for a gas vortex spinning device according to claim 5, characterized in that: The damage prevention control unit includes a self-learning algorithm module, which automatically optimizes the spinning parameter settings and predicts potential fiber damage risks based on historical monitoring data and real-time monitoring data. The self-learning algorithm module continuously collects and analyzes fibers under different production conditions to build a multi-dimensional feature space model, which avoids overfitting by minimizing the loss function. Among them, f(x i ; θ) is the prediction function, x i is the feature vector of the i-th sample, y i is the actual observation value, θ is the model parameter, and λ is the regularization coefficient; Through time series analysis and deep learning, the risk trend that may cause fiber damage in the current spinning process is identified. The formula is as follows: P(Damagex)=σ(w x+b) (10) Where P(Damagex) represents the fiber damage probability under a given feature vector x, σ is the sigmoid activation function, and w and b are the model weights and biases.

7. A fiber damage avoidance system for a gas vortex spinning device according to claim 6, characterized in that: The system also includes a data recording and analysis module, which uses a distributed database structure to store and manage real-time data, including fiber tension, speed, temperature, surface state and airflow pressure. Correlate the spinning parameters in different time periods with the corresponding fiber damage conditions, use regression analysis, cluster analysis and machine learning techniques to identify the key factors that cause fiber damage, and quantify the impact of each factor on fiber damage; Based on the real-time data collected and analyzed, a comprehensive report on the spinning process is automatically generated. The report content includes but is not limited to: the frequency of fiber damage, the changing trend of key parameters, parameter adjustment records and damage risk prediction results.

8. A fiber damage avoidance system for a gas vortex spinning device according to claim 6, characterized in that: The system also includes a remote monitoring and operation module, which efficiently transmits real-time data of the spinning process to a remote server for remote operators to monitor and analyze in real time and make precise adjustments to the spinning process; On the remote server side, the system integrates a visual monitoring platform to display real-time data in the form of charts and trend curves. Based on historical data and real-time feedback, the system can automatically adjust the control strategy to adapt to different production conditions and environmental changes.

9. A fiber damage avoidance system for a gas vortex spinning device according to claim 6, characterized in that: The system also includes an emergency shutdown device that maintains a real-time connection with a high-speed data link with the fiber monitoring unit and is capable of executing a shutdown command within a millisecond response time; When the fiber monitoring unit detects fiber abnormalities exceeding a set threshold, the monitoring data is immediately transmitted to the damage prevention control unit, which quickly evaluates the detected risk level through the set safety algorithm and judgment logic; If the damage risk exceeds the preset safety threshold, a shutdown signal is sent to the emergency shutdown device; and a shutdown reason and equipment status report is sent to the operator to locate the problem and take corresponding measures.

10. A fiber damage avoidance system for a gas vortex spinning device according to claim 6, characterized in that: The system also includes a user interface module for displaying real-time monitoring data, fiber damage conditions, adjustment records and warning information, and operators can interact through the user interface module.

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