Automatic control method and system for textile production line

Abstract

The invention provides an automatic control method and system for a textile production line, and relates to the technical field of textile production, and the method comprises the steps: applying a controlled dynamic excitation signal to a yarn, so as to obtain the instant response data of the yarn; according to the instant response data, physical characteristic information of the yarn is calculated; determining a running parameter range of the yarn according to the physical characteristic information; continuously acquiring running state information of the loom and the yarn, and comparing the running state information with the running parameter range to judge whether the production state is close to or beyond the boundary of the running parameter range; when it is judged that the production state approaches or exceeds the boundary of the operation parameter range, control parameters in the production process are adjusted according to the operation state information, so that the production state is maintained within the operation parameter range. The system can effectively avoid production efficiency reduction and product quality problems caused by material characteristic changes, and overcomes the limitation that a traditional system cannot adapt to new materials.

Description

Technical Field

[0001] This application relates to the field of textile production technology, and in particular to an automated control method and system for textile production lines. Background Technology

[0002] In modern textile production, to meet market demand for high-performance, high-value-added textiles, production lines often need to be adjusted to process new materials. However, traditional production lines rely on manual intervention for equipment adjustment and process management, leading to fluctuations in production efficiency and difficulty in ensuring consistent product quality. To overcome these challenges, automated control methods and systems for intelligent textile production lines have emerged, aiming to achieve intelligent management of the production process through sophisticated sensors and control logic.

[0003] In modern textile factories, traditional automated systems perform exceptionally well in the stable production of known materials (such as pure cotton). They monitor operating parameters in real time and automatically adjust to maintain stable production, achieving high efficiency and high quality. However, when switching to new blended yarns with different physical properties, the system's limitations become glaringly apparent. Because the control logic and parameter benchmarks are still based on pure cotton, the system cannot recognize that "normal" tension is nearing the breaking point for the new material, leading to frequent yarn breaks. Faced with this fault, the system, based on a preset program, determines that the speed is too high and adopts a passive countermeasure of globally slowing down. While this reduces yarn breaks, it introduces new problems such as decreased output, frequent start-stop cycles, and an increase in fabric defects at "stopped" points, resulting in a double loss of efficiency and quality. Summary of the Invention

[0004] This application discloses an automated control method and system for textile production lines, aiming to solve the technical problem that when traditional textile production lines encounter new materials, the automated control system cannot effectively identify changes in material properties, resulting in decreased production efficiency, damaged product quality, and an inability to proactively adapt to changes in production conditions.

[0005] In a first aspect, this application discloses an automated control method for a textile production line, comprising:

[0006] A controlled dynamic excitation signal is applied to the yarn to obtain real-time response data of the yarn;

[0007] Based on the real-time response data, the physical properties of the yarn are deduced;

[0008] Based on the physical property information, determine the operating parameter range of the yarn;

[0009] The system continuously acquires the operating status information of the loom and yarn, and compares the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0010] When it is determined that the production status is close to or exceeds the boundary of the operating parameter range, the control parameters in the production process are adjusted according to the operating status information so that the production status is maintained within the operating parameter range.

[0011] As an optional approach, the step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes:

[0012] A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected;

[0013] Based on the preliminary response data, determine the viscoelastic characteristics of the yarn;

[0014] Based on the viscoelastic characteristics, a detection waveform is generated;

[0015] The probe waveform is applied to the yarn, and the yarn's real-time response data to the probe waveform is collected.

[0016] As an optional approach, the step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes:

[0017] A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected;

[0018] Based on the preliminary response data, determine the viscoelastic characteristics of the yarn;

[0019] If the viscoelasticity of the yarn is lower than a first preset threshold, the preliminary response data will be used as the real-time response data.

[0020] If the viscoelastic characteristics of the yarn are higher than or equal to a first preset threshold, a detection waveform is generated based on the viscoelastic characteristics; the detection waveform is applied to the yarn, and the real-time response data of the yarn to the detection waveform is collected.

[0021] Further, in one embodiment, the step of generating the probe waveform based on the viscoelastic characteristics includes:

[0022] If the viscoelastic characteristics of the yarn are higher than or equal to the second preset threshold, a low-frequency periodic oscillation waveform is generated as the detection waveform;

[0023] If the viscoelastic characteristics of the yarn are lower than the second preset threshold, a mid-frequency step hold waveform is generated as the detection waveform.

[0024] The second preset threshold is higher than the first preset threshold.

[0025] More specifically, in some implementations, the step of generating the probe waveform based on the viscoelastic characteristics includes:

[0026] Based on the viscoelastic characteristics, the viscoelastic fluctuations of the yarn along its length direction are identified;

[0027] Based on the viscoelastic fluctuations, a spatially adaptive detection waveform is generated, wherein the action parameters of the detection waveform are enhanced at spatial locations where the viscoelastic fluctuations are increased, and weakened at spatial locations where the viscoelastic fluctuations are decreased.

[0028] More specifically, in some implementations, in the step of determining the operating parameter range of the yarn based on the physical property information:

[0029] The physical property information includes the breaking strength of the yarn;

[0030] The range of operating parameters includes the maximum permissible operating speed and the upper limit of permissible tension that match the fracture strength.

[0031] More specifically, in some implementations, the step of continuously acquiring the operating status information of the loom and yarn, and comparing the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range includes:

[0032] Continuously acquire operating status information of the loom and yarn;

[0033] Based on the operating status information and the operating parameter range, the safety margin index of the yarn is calculated; the safety margin index is compared with a preset warning threshold to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0034] Based on the above, in a further embodiment of this application, in the step of continuously acquiring the operating status information of the loom and yarn:

[0035] The operating status information includes a first type of status information used to characterize the operating conditions of the loom and a second type of status information used to characterize the physical state of the yarn.

[0036] Based on the above, in a further embodiment of this application, when it is determined that the production status is close to or exceeds the boundary of the operating parameter range, the step of adjusting the control parameters in the production process according to the operating status information is as follows:

[0037] If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the first type of status information, then the first type of adjustment signal that directly adjusts the loom's operating condition is used as the control parameter.

[0038] If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the second type of status information, then the second type of adjustment signal that directly adjusts the yarn tension or the amount of warp feed is used as the control parameter.

[0039] Secondly, this application also discloses an automated control system for a textile production line, comprising:

[0040] The action and response module is used to apply a controlled dynamic excitation signal to the yarn in order to obtain real-time response data of the yarn;

[0041] The characteristic estimation module is used to estimate the physical characteristic information of the yarn based on the real-time response data;

[0042] The parameter determination module is used to determine the operating parameter range of the yarn based on the physical characteristic information.

[0043] The status comparison module is used to continuously acquire the operating status information of the loom and the yarn, and compare the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0044] The parameter adjustment module is used to adjust the control parameters in the production process according to the operating status information when it is determined that the production status is close to or exceeds the boundary of the operating parameter range, so as to keep the production status within the operating parameter range.

[0045] According to the technical solution of the embodiments of this application, it has at least the following beneficial effects: The automated control method for textile production lines disclosed in this application can actively sense material characteristics and dynamically adjust production strategies, thereby realizing the leap from "fault response" to "preventive adaptive control", effectively avoiding the decline in production efficiency and product quality problems caused by changes in material characteristics, and overcoming the limitations of traditional systems in adapting to new materials.

[0046] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0047] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0048] Figure 1 A flowchart illustrating an automated control method for a textile production line provided in this application embodiment;

[0049] Figure 2 This is a schematic diagram of the architecture of an automated control system for a textile production line, provided as an embodiment of this application. Detailed Implementation

[0050] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0051] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0052] In modern textile production, to meet market demand for high-performance, high-value-added textiles, production lines often need to be adjusted to process new materials. However, traditional production lines rely on manual intervention for equipment adjustment and process management, leading to fluctuations in production efficiency and difficulty in ensuring consistent product quality. For example, when a factory switches to producing a new type of blended yarn, the traditional automated control system still uses the control logic and parameter benchmarks established for pure cotton yarn, failing to recognize the differences in the physical properties of the new material. This results in a sharp increase in yarn breakage frequency, leading to reduced production speed, decreased output, and lower product quality. The system cannot proactively adapt to changes in production conditions, falling into a passive and inefficient cycle.

[0053] For this, see Figure 1 This application proposes an automated control method for a textile production line, comprising:

[0054] S110. Apply a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn;

[0055] S120. Based on the real-time response data, calculate the physical properties of the yarn;

[0056] S130. Determine the operating parameter range of the yarn based on the physical property information;

[0057] S140. Continuously acquire the operating status information of the loom and yarn, and compare the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0058] When it is determined that the production status is close to or exceeds the boundary of the operating parameter range, the control parameters in the production process are adjusted according to the operating status information so that the production status is maintained within the operating parameter range.

[0059] It should be noted that, in this application, "yarn" refers to the linear material used for weaving or braiding in the textile production process, whose physical properties (such as breaking strength, viscoelasticity, etc.) directly affect production efficiency and product quality. "Controlled dynamic excitation signal" refers to an adjustable, non-constant physical action, such as mechanical vibration, sound waves, or electromagnetic waves, acting on the yarn to elicit a measurable response. "Instant response data" refers to real-time measurement data showing the change of the yarn's physical state (such as amplitude, frequency, deformation, etc.) over time when subjected to a dynamic excitation signal. "Physical characteristic information" refers to the inherent properties of the yarn obtained by analyzing instant response data, such as breaking strength, elastic modulus, and viscosity coefficient. "Operating parameter range" refers to the allowable value range of key production parameters such as loom speed and yarn tension, provided that the yarn is not damaged and the product quality is qualified. "Operating status information" refers to real-time monitoring data of the loom (such as operating speed, warp feed, take-up) and yarn (such as real-time tension, vibration frequency) during the production process. "Control parameters" refer to variables that can be directly adjusted in the production process, such as loom speed, warp feed, take-up, or yarn tension setpoints. This method is typically implemented in an automated textile production line environment equipped with sensors, actuators, and a control system capable of data acquisition, processing, decision-making, and the issuance of control commands.

[0060] The core of the automated control method for textile production lines proposed in this application lies in achieving adaptive control of the production process through real-time sensing and dynamic adjustment of the physical properties of yarn.

[0061] Specifically, the step of "applying a controlled dynamic excitation signal to the yarn to obtain the yarn's instantaneous response data" can be implemented in various ways. For example, a mechanical vibrator can be used to apply periodic vibrations to the running yarn at a preset frequency and amplitude, and the displacement or stress response data of the yarn to the vibration can be collected in real time by a laser displacement sensor or piezoelectric sensor installed near the yarn. Another method is to use a non-contact acoustic generator to emit sound waves of a specific frequency to the yarn, and capture the vibration mode or reflected waveform of the yarn after being affected by the sound waves using a microphone array or optical sensor. Alternatively, a changing electromagnetic field can be applied to the yarn containing conductive fibers using an electromagnetic induction coil, and the changes in induced current or magnetic field in the yarn can be measured to obtain its instantaneous response data. These methods can all effectively obtain the instantaneous response data of the yarn under dynamic excitation, providing a basis for subsequent characteristic calculations. In a preferred embodiment of this application, the production line control system can send instructions to the warp feeding device on the loom. The warp feed device, typically consisting of a roller driven by a high-precision servo motor, applies a series of minute and safe instantaneous tension changes or speed fluctuations to the yarn being processed according to a preset program. For example, while maintaining a constant average warp feed speed, the servo motor can slightly accelerate the warp feed speed by 0.5% within a very short time (e.g., 50 milliseconds) and then immediately decelerate it by 0.5%. By adjusting the fluctuations in the warp feed speed according to the desired fluctuations within a preset time, a controllable dynamic excitation signal is formed. Simultaneously, a series of high-precision sensors can capture the yarn's response in real time. High-precision tension sensors (e.g., piezoelectric or strain gauge sensors with sampling frequencies up to 2000Hz) accurately measure the instantaneous tension changes of the yarn under these minute perturbations. Elongation sensors (e.g., non-contact laser rangefinders that calculate elongation by measuring the distance change of the yarn between two fixed points) record the instantaneous elongation response of the yarn. Furthermore, micro-vibration sensors (e.g., MEMS accelerometers mounted on the yarn guide) capture the minute vibration patterns as the yarn passes through.

[0062] In the step of "inferring the physical properties of the yarn based on real-time response data," different data processing and model-based estimation methods can be used. For example, the collected real-time response data can be input into a pre-trained machine learning model. This model, by learning the relationship between response data from a large number of different yarn materials and known physical properties, can infer physical properties such as the yarn's breaking strength, elastic modulus, and viscosity coefficient based on new response data. Another approach is to perform estimations based on physical models. For instance, by analyzing the yarn's vibration frequency and attenuation characteristics under excitation, and combining this with materials mechanics and vibration theory, the Young's modulus and damping coefficient of the yarn can be calculated, thereby inferring its viscoelastic characteristics. Alternatively, the natural frequencies of the yarn can be identified through spectral analysis of the response data, and combined with its geometric dimensions, information such as its linear density and strength can be inferred.

[0063] In the step of "determining the operating parameter range of the yarn based on its physical properties," production parameters can be dynamically set based on the calculated physical properties of the yarn. For example, if the calculated breaking strength of the yarn is low, the maximum allowable operating speed of the loom and the upper limit of the allowable tension of the yarn can be reduced accordingly to avoid breakage. If the elastic modulus of the yarn is high, the allowable tension fluctuation range can be appropriately increased to adapt to instantaneous tension changes during production. Specifically, a parameter mapping table or a rule-based expert system can be established to associate different ranges of physical property information with corresponding safe operating parameter ranges (such as loom speed, tension, warp feed, etc.). For example, for a yarn with a breaking strength of X, its maximum allowable operating speed is set to Vmax, and the upper limit of the allowable tension is Tmax.

[0064] In the step of "continuously acquiring the operating status information of the loom and yarn, and comparing the operating status information with the operating parameter range to determine whether the production status is approaching or exceeding the boundary of the operating parameter range," real-time monitoring of the production process is required. For example, operating status information such as the loom's operating speed, yarn tension, and warp feed can be continuously collected using speed sensors, tension sensors, and warp feed sensors installed on the loom. Simultaneously, vibration, swaying, or breakage of the yarn can be monitored using a vision system or infrared sensors. This real-time acquired operating status information is then compared with the previously determined yarn operating parameter range. For example, if the real-time monitored yarn tension continuously approaches or exceeds the upper limit of the allowable tension, or if the loom's operating speed approaches or exceeds the maximum allowable operating speed, then the production status is determined to be approaching or exceeding the boundary of the operating parameter range.

[0065] In the step of "adjusting control parameters in the production process to maintain the production state within the operating parameter range based on the operating status information when the production state is judged to be close to or beyond the boundary of the operating parameter range," the system will immediately take corrective measures once a deviation in the production state is detected. For example, if it is judged that the yarn tension is too high, the control system can send a command to the warp feed device to increase the warp feed, thereby reducing the yarn tension. If it is judged that the loom speed is too fast, leading to an increased risk of yarn breakage, the loom speed can be appropriately reduced. The adjustment of control parameters can be linear, proportional-integral-derivative (PID) control, or adaptive control based on fuzzy logic or neural networks, to ensure that the production state can smoothly return to the preset operating parameter range.

[0066] The overall working principle of this application is as follows: First, by applying a controlled dynamic excitation signal to the yarn and acquiring its instantaneous response data, the system can capture the physical performance of the yarn under dynamic conditions in real time. This instantaneous response data is then used to calculate the physical properties of the yarn, thus overcoming the limitation of traditional systems that rely on preset parameters and cannot identify the properties of new materials. Based on this calculated physical property information, the system can dynamically determine the range of operating parameters that matches the current yarn characteristics, rather than rigidly adhering to old standards. This directly solves the problem of a surge in yarn breakage rate due to parameter mismatch when replacing new blended yarns in traditional systems. During production, the system continuously acquires the operating status information of the loom and the yarn and compares it with the dynamically determined range of operating parameters. This real-time comparison mechanism allows the system to promptly determine whether the production status is approaching or has exceeded the safety boundary, thus avoiding the misjudgment of traditional systems that consider the tension to be "normal" when the yarn is about to break. Once a deviation in the production status is detected, the system will intelligently adjust the control parameters in the production process according to the current operating status information. This dynamic adjustment mechanism ensures that the production status is always maintained within the parameter range for safe yarn operation, effectively reducing the frequency of yarn breakage and avoiding the decline in production efficiency and product quality defects (such as stoppage) caused by frequent shutdowns.

[0067] Compared with existing technologies, this application no longer passively responds to fault phenomena such as yarn breakage, but actively senses material characteristics and dynamically adjusts production strategies, thereby achieving a leap from "fault response" to "preventive adaptive control", effectively avoiding the decline in production efficiency and product quality problems caused by changes in material characteristics, and overcoming the limitations of traditional systems in adapting to new materials.

[0068] In some embodiments of this application, the step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes:

[0069] A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected;

[0070] Based on the preliminary response data, determine the viscoelastic characteristics of the yarn;

[0071] Based on the viscoelastic characteristics, a detection waveform is generated;

[0072] The probe waveform is applied to the yarn, and the yarn's real-time response data to the probe waveform is collected.

[0073] First, a preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected. This refers to a preliminary, non-destructive test of the yarn before formally collecting real-time response data. The test excitation signal can be a low-amplitude, short-duration mechanical vibration, sound wave, or electromagnetic wave. Its purpose is to obtain the yarn's preliminary response to external excitation without affecting its normal operation. Preliminary response data can include information such as the yarn's vibration frequency, amplitude attenuation, and phase hysteresis.

[0074] Furthermore, judging the viscoelastic characteristics of the yarn based on preliminary response data refers to evaluating the viscous and elastic behavior of the yarn material by analyzing the yarn's preliminary response data to the test excitation signal. Viscoelasticity is an important physical property of textile materials, reflecting the ratio of energy dissipation (viscosity) to energy storage (elasticity) when the material deforms under stress. For example, the viscoelastic characteristics of the yarn can be characterized by calculating parameters such as loss factor, storage modulus, and loss modulus.

[0075] Based on this, generating a probe waveform according to the viscoelastic characteristics of the yarn refers to dynamically designing and generating an excitation waveform more suitable for the current yarn state based on the preliminary assessment of the yarn's viscoelastic characteristics. Different viscoelastic characteristics may require excitation signals of different frequencies, amplitudes, or waveforms to more effectively stimulate the yarn's response and obtain more accurate real-time response data. For example, for highly viscous yarns, a more slowly changing waveform may be needed; for highly elastic yarns, a higher frequency waveform may be needed.

[0076] Finally, applying the probe waveform to the yarn and collecting the yarn's real-time response data to the probe waveform means applying the more targeted probe waveform generated above to the yarn and collecting the yarn's response data under the action of the probe waveform in real time and with high precision. This real-time response data is the basis for subsequent calculations of the yarn's physical properties, and its accuracy directly affects the effectiveness of the entire control method.

[0077] The proposed solution first applies a preset test excitation signal and collects preliminary response data to determine the viscoelastic characteristics of the yarn. Based on this, a customized probe waveform is generated, and finally, this probe waveform is applied to the yarn to collect real-time response data. This step-by-step and adaptive excitation application method overcomes the limitations of traditional single excitation signals when dealing with different yarn characteristics or changes in the characteristics of the same yarn at different production stages. By initially assessing the viscoelastic characteristics of the yarn, it avoids using inappropriate excitation signals that could lead to unclear responses or data distortion, thus ensuring that the collected real-time response data more accurately and comprehensively reflects the true physical state of the yarn. Dynamically adjusting the waveform of the excitation signal according to the actual viscoelastic characteristics of the yarn significantly improves the accuracy and effectiveness of real-time response data acquisition. This allows the system to better adapt to the characteristic changes that may occur in different types of yarn or the same yarn during production, providing a more reliable and accurate data foundation for subsequent physical characteristic calculations, operating parameter determination, and production state adjustments.

[0078] In some embodiments of this application, the step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes:

[0079] A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected;

[0080] Based on the preliminary response data, determine the viscoelastic characteristics of the yarn;

[0081] If the viscoelasticity of the yarn is lower than a first preset threshold, the preliminary response data will be used as the real-time response data.

[0082] If the viscoelastic characteristics of the yarn are higher than or equal to a first preset threshold, a detection waveform is generated based on the viscoelastic characteristics; the detection waveform is applied to the yarn, and the real-time response data of the yarn to the detection waveform is collected.

[0083] Applying a preset probing excitation signal to the yarn refers to applying a pre-set excitation signal, typically with low intensity or short duration, to the yarn for preliminary detection to obtain basic response information. Further, based on the preliminary response data...

[0084] When the viscoelastic characteristics of the yarn are determined to be below a first preset threshold, it indicates that the viscoelastic effect of the yarn is not significant, and its response behavior is relatively simple and predictable. In this case, the preliminary response data is sufficient to reflect the immediate state of the yarn, and therefore can be directly used as the immediate response data without the need for more complex detection. The first preset threshold is a pre-set critical value used to distinguish between yarn types with significant and insignificant viscoelastic characteristics.

[0085] However, if the viscoelastic characteristics of the yarn are higher than or equal to a first preset threshold, it means that the yarn has more complex viscoelastic behavior, and its response may contain more nonlinear or time-dependent components. In this case, to obtain its real-time response data more comprehensively and accurately, a specialized probe waveform needs to be generated based on the determined viscoelastic characteristics. This probe waveform is designed for the characteristics of highly viscoelastic yarns; for example, it may have a specific frequency, amplitude, or waveform structure to better excite the yarn's response and capture its complex viscoelastic behavior. Subsequently, the probe waveform is applied to the yarn, and the yarn's real-time response data to the probe waveform is collected. This data will be used as more accurate real-time response data for subsequent processing.

[0086] Through the above technical solution, this application can intelligently select the most suitable excitation signal application strategy based on the actual viscoelastic characteristics of the yarn, thereby significantly improving the accuracy and efficiency of real-time response data acquisition. Compared with the solution using a fixed excitation signal, this application avoids the waste of resources caused by over-probing simple yarns.

[0087] The following is a specific example to illustrate this.

[0088] For example, in a textile production line, two different types of yarn need to be automatically controlled: one is pure cotton yarn, which has low viscoelasticity; the other is blended yarn containing spandex, which has high viscoelasticity.

[0089] First, for pure cotton yarn, the system applies a preset probing excitation signal, such as a short-duration, low-amplitude pulse signal, and collects preliminary response data of the pure cotton yarn to this pulse signal. Based on the analysis of this preliminary response data, it is determined that the viscoelastic characteristics of the pure cotton yarn are below a first preset threshold. Since the viscoelasticity of pure cotton yarn is low, its response behavior is relatively simple, and the preliminary response data is sufficient to reflect its immediate state. Therefore, the system directly uses this preliminary response data as immediate response data for subsequent physical property calculations.

[0090] Secondly, for blended yarns containing spandex, the system applies the same preset excitation signal and collects preliminary response data. However, analysis of this preliminary response data determines that the viscoelastic characteristics of the blended yarn are higher than or equal to a first preset threshold. Given the high viscoelasticity of blended yarns and their more complex dynamic response, the preliminary response data may not be sufficient to fully capture their characteristics. Therefore, the system generates a specialized probe waveform based on the determined viscoelastic characteristics of the blended yarn, such as a sinusoidal oscillation waveform with a specific frequency and amplitude, which can better excite the viscoelastic response of the blended yarn. Subsequently, this probe waveform is applied to the blended yarn, and the instantaneous response data of the blended yarn to the probe waveform is collected. This more detailed and accurate response data will be used as instantaneous response data for subsequent precise analysis and control.

[0091] In a further embodiment of this application, the step of generating the probe waveform based on the viscoelastic characteristics is proposed to include:

[0092] If the viscoelastic characteristics of the yarn are higher than or equal to the second preset threshold, a low-frequency periodic oscillation waveform is generated as the detection waveform;

[0093] If the viscoelastic characteristics of the yarn are lower than the second preset threshold, a mid-frequency step hold waveform is generated as the detection waveform.

[0094] The second preset threshold is higher than the first preset threshold. In fact, the second preset threshold is used to distinguish between yarns with high viscoelasticity and yarns with low viscoelasticity.

[0095] When the viscoelastic characteristics of the yarn are higher than or equal to a second preset threshold, it indicates that the yarn has high viscoelasticity. At this time, a low-frequency periodic oscillation waveform is generated as the detection waveform. For yarns with high viscoelasticity, they may exhibit significant hysteresis and energy dissipation in response to rapidly changing excitation signals. Using high-frequency or transient excitation may make it difficult to accurately capture their intrinsic characteristics and could even lead to localized overload of the yarn. Therefore, by generating a low-frequency periodic oscillation waveform, the yarn can be subjected to a relatively small strain rate for a longer period, thereby fully stimulating its viscoelastic response without causing excessive deformation or damage, allowing sufficient time for response and thus obtaining more stable and representative real-time response data. The low frequency typically refers to a frequency between 0.1Hz and 10Hz, and the periodic oscillation waveform can be a sine wave, triangular wave, or square wave, etc.

[0096] When the viscoelastic characteristics of the yarn are below a second preset threshold, it indicates that the yarn's viscoelasticity is relatively low. At this time, a mid-frequency step hold waveform is generated as the probe waveform. For yarns with relatively low viscoelasticity, the response speed is faster, and the response to transient excitation is more sensitive. The mid-frequency step hold waveform can rapidly apply a constant strain or stress and hold it for a period of time to capture the yarn's response in transient and steady-state conditions. This is more effective for analyzing the creep or stress relaxation behavior of materials with low viscoelasticity. Here, the mid-frequency typically refers to a frequency between 10Hz and 100Hz, and the step hold waveform refers to a waveform that reaches a set value and maintains that value for a short period of time.

[0097] This hierarchical, adaptive waveform generation strategy ensures that the most suitable excitation method can be used for detection regardless of the viscoelasticity of the yarn, thereby improving the quality and accuracy of real-time response data.

[0098] The following will illustrate this with specific examples.

[0099] In one example, on a textile production line, a preset probing excitation signal is applied to the yarn, and preliminary response data is collected to determine the current viscoelastic characteristics of the yarn. If the viscoelastic characteristic value of the yarn is measured to be 80 units, and the first preset threshold is 50 units and the second preset threshold is 70 units, then since 80 units is higher than the first preset threshold of 50 units, a probe waveform needs to be generated. Furthermore, since 80 units is also higher than the second preset threshold of 70 units, the system will generate a low-frequency periodic oscillation waveform as the probe waveform, such as a 2Hz sine wave, and apply it to the yarn to collect immediate response data.

[0100] In another example, if the measured viscoelastic characteristic value of the yarn is 60 units, a probe waveform still needs to be generated because 60 units is higher than the first preset threshold of 50 units. However, because 60 units is lower than the second preset threshold of 70 units, the system will generate a mid-frequency step-hold waveform as the probe waveform, for example, a step waveform that reaches and maintains a tension of 5N within 0.1 seconds, and apply it to the yarn to collect immediate response data. In this way, the system can intelligently select the probe waveform that most effectively excites its response without causing damage, based on the actual viscoelastic characteristics of the yarn, thereby ensuring the accuracy and reliability of data acquisition.

[0101] It is worth mentioning that, in a further embodiment of this application, the above-mentioned step of generating the probe waveform based on viscoelastic characteristics includes:

[0102] Based on the viscoelastic characteristics, the viscoelastic fluctuations of the yarn along its length direction are identified;

[0103] Based on the viscoelastic fluctuations, a spatially adaptive detection waveform is generated, wherein the action parameters of the detection waveform are enhanced at spatial locations where the viscoelastic fluctuations are increased, and weakened at spatial locations where the viscoelastic fluctuations are decreased.

[0104] Specifically, identifying the viscoelastic fluctuations of the yarn along its length refers to continuously or discretely measuring the viscoelastic characteristics of the yarn at different spatial locations using a sensor array or scanning sensor, and analyzing these measurement data to determine the trend and amplitude of the viscoelastic characteristics along the yarn's length. For example, ultrasonic sensors, laser Doppler vibrometers, or miniature strain sensors can be used to scan the yarn in real-time or near real-time as it passes through, collecting viscoelastic-related data at different locations, and quantifying the viscoelastic fluctuations using signal processing algorithms (such as wavelet analysis, Fourier transform, or statistical analysis). The aim is to obtain detailed viscoelastic distribution information of the yarn in the spatial dimension, providing a basis for subsequent adaptive adjustment of the detection waveform.

[0105] The generation of spatially adaptive probe waveforms can be understood as dynamically adjusting the parameters of the probe waveform at different spatial locations based on the identified viscoelastic fluctuations along the length of the yarn. For example, when the viscoelastic fluctuations in a certain section of the yarn are large, it means that the physical characteristics of that section may be more complex or sensitive. In this case, the parameters of the probe waveform can be enhanced, such as increasing the amplitude of the excitation signal, extending the action time, or adjusting the frequency range, to ensure that the response of that section is fully excited and clear, real-time response data is obtained. Conversely, when the viscoelastic fluctuations in a certain section of the yarn are small, the parameters of the probe waveform can be appropriately weakened to avoid over-excitation or save energy, while still obtaining effective response data. The enhancement or weakening of the parameters can be adjusted according to a preset mapping relationship or adaptive control algorithm. The purpose is to enable the probe waveform to better match the local characteristics of the yarn, improving the efficiency and accuracy of data acquisition.

[0106] The proposed solution first identifies the viscoelastic fluctuations of the yarn along its length, thereby obtaining detailed physical property information of the yarn at different spatial locations. It is precisely because of this spatial distribution information that a spatially adaptive probe waveform can be generated and applied based on these fluctuations. This adaptability is manifested in that, in regions with large viscoelastic fluctuations, the action parameters of the probe waveform are enhanced to ensure sufficient and accurate response data even in regions with complex or unstable characteristics; while in regions with small viscoelastic fluctuations, the action parameters are weakened, avoiding unnecessary energy consumption and preventing excessive interference to the yarn. This targeted excitation method overcomes the limitations of traditional single probe waveforms when processing non-uniform yarns, ensuring that the collected real-time response data more realistically and comprehensively reflects the dynamic behavior of the yarn at various locations.

[0107] Through the above technical solution, this application can dynamically adjust the operating parameters of the detection waveform according to the viscoelastic fluctuations of the yarn along its length, thereby achieving precise excitation of different regions of the yarn. Compared with the basic scheme using a non-adaptive detection waveform, this application can significantly improve the acquisition accuracy and reliability of real-time response data, especially when dealing with yarns with uneven physical properties, and can more accurately capture the local dynamic response of the yarn.

[0108] The following will illustrate this with specific examples.

[0109] On a textile production line, yarn passes at a constant speed through an array of miniature sensors. These sensors are evenly spaced along the yarn's length and can measure viscoelastic parameters in localized areas of the yarn in real time, such as the damping coefficient or relaxation time under minute vibrations. When the system detects a sudden increase in the viscoelastic characteristics (e.g., damping coefficient) of a section of yarn, it indicates increased viscoelastic fluctuations in that area, potentially indicating localized defects or structural inhomogeneities. In this case, the control system immediately adjusts the probe waveform applied to that area, for example, increasing the amplitude of the excitation signal by 10% or adjusting the excitation frequency to a specific frequency that better reflects the characteristics of that region. Conversely, if the viscoelastic characteristics of a section of yarn are detected to be very stable with minimal fluctuations, the amplitude of the probe waveform can be appropriately reduced or the application time shortened to optimize energy consumption. In this way, the probe waveform can "intelligently" adapt to the localized characteristics of the yarn, ensuring high-quality, real-time response data is acquired along the entire yarn length.

[0110] In some embodiments of this application, in the step of determining the operating parameter range of the yarn based on the physical property information: the physical property information includes the breaking strength of the yarn; the operating parameter range includes the maximum permissible operating speed and the upper limit of permissible tension that match the breaking strength.

[0111] Among them, breaking strength is a key indicator characterizing the mechanical properties of yarn. It refers to the maximum force a yarn can withstand when it breaks under tensile force, directly reflecting its ability to resist external damage. The determination of the operating parameter range is based on consideration of the yarn's breaking strength to ensure that operating conditions during production will not lead to yarn breakage. Specifically, the operating parameter range is set to include the maximum permissible operating speed and the upper limit of permissible tension that match the breaking strength. The maximum permissible operating speed refers to the maximum operating speed that the loom can reach without breaking the yarn; the upper limit of permissible tension refers to the maximum tension that the yarn can withstand during production. Since the operating parameter range is directly determined based on the yarn's breaking strength, the set maximum permissible operating speed and upper limit of permissible tension can more accurately reflect the actual load-bearing capacity of the current yarn. The setting of these parameters aims to ensure that the yarn is always within its safe load-bearing range during production, effectively avoiding yarn breakage caused by improper operating parameter settings, thereby ensuring the continuity and stability of production.

[0112] In some embodiments of this application, the step of continuously acquiring the operating status information of the loom and yarn, and comparing the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range includes:

[0113] Continuously acquire operating status information of the loom and yarn;

[0114] Based on the operating status information and the operating parameter range, the safety margin index of the yarn is calculated;

[0115] The safety margin index is compared with a preset warning threshold to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0116] Specifically, the safety margin index can be understood as a quantitative indicator used to characterize the distance or deviation between the current production state and the boundary of the operating parameter range. The calculation of the safety margin index can comprehensively consider various operating state information, such as yarn tension, speed, temperature, humidity, and the loom's operating frequency, combined with the operating parameter range of the yarn, and calculated using a specific mathematical model or algorithm. For example, the safety margin index can be defined as the relative distance between the current operating parameters and the boundary of the allowable operating parameter range, or it can be an integration of the deviation of multiple parameters through weighted averaging or other methods. Its purpose is to provide a unified, quantifiable indicator for a more intuitive and accurate assessment of production risks. The preset warning threshold refers to one or more pre-set values ​​used to define different risk levels of the safety margin index. When the safety margin index is compared with the warning threshold, it can be determined whether the production state is in a safe zone, a warning zone, or a danger zone. For example, a higher warning threshold can be set as the basis for judging "approaching the boundary," and a lower warning threshold can be set as the basis for judging "exceeding the boundary." In practical applications, the warning threshold can be calibrated and optimized based on historical data, expert experience, or the specific requirements of the production line to ensure that it can effectively reflect actual production risks.

[0117] When continuously acquired information on the operating status of looms and yarns is used to calculate the safety margin index, the index comprehensively reflects the degree to which the current production status matches the preset operating parameter range across multiple dimensions. By comparing this safety margin index with a preset warning threshold, potential risks in the production status can be identified earlier and more accurately. For example, when the safety margin index begins to decline and approaches the warning threshold, even if the production status has not yet completely exceeded the operating parameter range, the system can issue an early warning, thus gaining valuable time for subsequent control parameter adjustments and achieving a shift from passive response to proactive prevention. By calculating and comparing the safety margin index with the warning threshold, judgment lags or misjudgments caused by simple comparisons can be effectively avoided, significantly improving the sensitivity and accuracy of production status monitoring.

[0118] In some preferred embodiments, the operating parameters of the yarn may include a maximum permissible operating speed and a maximum permissible tension. The safety margin index may be defined as:

[0119] Safety margin index = Min { (maximum permissible operating speed - current operating speed) / (maximum permissible operating speed - minimum safe speed), (maximum permissible tension - current tension) / (maximum permissible tension - minimum safe tension)}.

[0120] The minimum safe speed and minimum safe tension are safety lower limits determined based on experience or experiments. When the calculated safety margin index is higher than 0.8, the production status is considered to be in the safe zone; when the index is between 0.5 and 0.8, it is considered to be in the warning zone; when the index is lower than 0.5, it is considered to be in the danger zone. The warning thresholds can be set to 0.8 and 0.5. When the safety margin index drops below 0.8, the system issues a warning signal, prompting operators or the automatic control system to prepare for adjustments; when the index further drops below 0.5, it indicates that the production status has approached or exceeded the danger boundary, requiring immediate and forceful adjustment measures. In this way, refined hierarchical management and early warning of the production status can be achieved.

[0121] In a further embodiment of this application, in the step of continuously acquiring the operating status information of the loom and yarn, the operating status information includes a first type of status information characterizing the operating condition of the loom and a second type of status information characterizing the physical state of the yarn. The first type of status information can be understood as data directly reflecting the current working condition of the loom, such as the loom's operating speed, tension setpoint, warp feed, take-up amount, stop frequency, and yarn breakage frequency. This information is directly related to the mechanical motion and control parameters of the loom itself. The second type of status information can be understood as data directly reflecting the actual physical properties and stress conditions of the yarn during the production process, such as the yarn's real-time tension, elongation, vibration frequency, diameter change, and surface defects. This information is directly related to the characteristics of the yarn itself and its dynamic performance on the production line.

[0122] This application, by subdividing operational status information into a first category and a second category, enables more refined monitoring of the overall operation of the textile production line. The first category provides macroscopic control and operational data at the loom level, allowing the system to understand whether the loom is operating stably according to preset parameters. The second category provides microscopic physical state data at the yarn level, enabling the system to perceive the actual stress, deformation, and potential damage of the yarn during production in real time. This classification helps the system more accurately identify the root cause of problems during subsequent judgment and adjustment, such as whether the problem is caused by improper loom parameter settings or fluctuations in the yarn's own physical properties, providing a more precise basis for subsequent control parameter adjustments. For example, when the production status is detected to be approaching or exceeding the boundary of the operational parameter range, the system can determine whether to adjust the loom operating parameters, yarn tension, or warp feed based on whether the anomaly is in the first or second category of status information. This achieves more targeted and efficient automated control, effectively avoiding misjudgments or delayed adjustments due to insufficient information from a single dimension, further improving the stability of the textile production line and product quality.

[0123] In a further embodiment of this application, in the step of adjusting the control parameters in the production process based on the operating status information when it is determined that the production status is close to or exceeds the boundary of the operating parameter range:

[0124] If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the first type of status information, then the first type of adjustment signal that directly adjusts the loom's operating condition is used as the control parameter.

[0125] If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the second type of status information, then the second type of adjustment signal that directly adjusts the yarn tension or the amount of warp feed is used as the control parameter.

[0126] When the first type of status information indicates that the production status is approaching or exceeding the boundary of the operating parameter range, it indicates that the problem mainly lies in the working state of the loom itself. At this time, the first type of adjustment signal used is a control parameter that directly acts on the loom's operating condition, such as adjusting the loom's operating speed and frequency, or making fine adjustments to certain mechanical components inside the loom, so that the loom can be restored to a normal or optimized operating state.

[0127] When the second type of status information indicates that the production status is approaching or exceeding the boundary of the operating parameter range, it suggests that the problem is mainly related to the physical characteristics of the yarn or its stress state on the production line. In this case, the second type of adjustment signal used is a control parameter that directly acts on the yarn tension or warp feed. For example, the warp feed can be changed by adjusting the rotational speed of the warp feed mechanism, or the yarn tension can be changed by adjusting the tension control device, thereby maintaining the yarn in a suitable physical state during production.

[0128] Compared to general adjustment methods that do not distinguish the source of the problem, this application can adopt targeted control parameter adjustment strategies based on the specific cause of the abnormal production status, namely, the first type of status information from the loom's operating conditions or the second type of status information from the yarn's physical state. This avoids blind adjustments, improves the accuracy and effectiveness of control, and can more effectively maintain the production status within the operating parameter range, reduce yarn breakage rate, and improve product quality and production efficiency.

[0129] See Figure 2 The specific embodiments of this application also disclose an automated control system 200 for a textile production line, comprising:

[0130] The action and response module 210 is used to apply a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn;

[0131] The characteristic calculation module 220 is used to calculate the physical characteristic information of the yarn based on the real-time response data;

[0132] The parameter determination module 230 is used to determine the operating parameter range of the yarn based on the physical characteristic information.

[0133] The status comparison module 240 is used to continuously acquire the operating status information of the loom and the yarn, and compare the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

[0134] The parameter adjustment module 250 is used to adjust the control parameters in the production process according to the operating status information when it is determined that the production status is close to or exceeds the boundary of the operating parameter range, so as to keep the production status within the operating parameter range.

[0135] The action and response module 210 may include a mechanical vibrator configured to apply mechanical vibration of a preset frequency and amplitude to the yarn. Simultaneously, the module also includes a laser displacement sensor configured to monitor the displacement changes of the yarn under vibration in real time and output these displacement data as instantaneous response data. Alternatively, the action and response module may consist of an electromagnetic coil and a Hall sensor. The electromagnetic coil applies a changing magnetic field to the yarn containing conductive fibers, while the Hall sensor senses changes in induced current or magnetic field generated in the yarn, thereby acquiring instantaneous response data. In a preferred embodiment of this application, the production line control system can send instructions to the warp feeding device on the loom. The warp feeding device, typically composed of a roller driven by a high-precision servo motor, applies a series of small and safe instantaneous tension changes or speed fluctuations to the yarn being processed according to a preset program, forming a controllable dynamic excitation signal. Simultaneously, a series of high-precision sensors (such as high-precision tension sensors, elongation sensors, and micro-vibration sensors) can be used to capture the yarn's response in real time.

[0136] The characteristic estimation module 220 can be a computational module integrating a data processing unit and a pre-trained model. For example, this module can be configured to receive real-time response data output from the action-response module and input it into a neural network-based analysis model. This model calculates physical properties of the yarn, such as breaking strength, elastic modulus, and viscosity coefficient, by analyzing features such as vibration decay curves and frequency response.

[0137] The parameter determination module 230 can be a rule engine or a lookup table, which internally stores the mapping relationship between different yarn physical properties and corresponding safe operating parameter ranges. For example, this module can be configured to calculate the yarn breaking strength and elastic modulus output by the characteristic calculation module, retrieve the highest allowable operating speed and allowable tension limit that best match the current yarn characteristics from a preset database, and output them as the operating parameter range of the yarn.

[0138] The status comparison module 240 can be configured to receive real-time operating status information from devices such as the loom speed sensor and yarn tension sensor, and compare it with the operating parameter range provided by the parameter determination module in real time. When any monitored operating status parameter (such as real-time tension) continuously approaches or exceeds its allowable upper limit, the module will generate a warning or over-limit signal.

[0139] The parameter adjustment module 250 can be configured to calculate the required adjustment range of the warp feed based on the current tension value and the target tension range, and send corresponding control commands to the driver of the warp feed device, thereby reducing the yarn tension and restoring the production state to the safe operating parameter range.

[0140] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0141] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An automated control method for a textile production line, characterized in that, include: A controlled dynamic excitation signal is applied to the yarn to obtain real-time response data of the yarn; Based on the real-time response data, the physical properties of the yarn are deduced; Based on the physical property information, determine the operating parameter range of the yarn; The system continuously acquires the operating status information of the loom and yarn, and compares the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range. When it is determined that the production status is close to or exceeds the boundary of the operating parameter range, the control parameters in the production process are adjusted according to the operating status information so that the production status is maintained within the operating parameter range.

2. The automated control method for a textile production line according to claim 1, characterized in that, The step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes: A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected; Based on the preliminary response data, determine the viscoelastic characteristics of the yarn; Based on the viscoelastic characteristics, a detection waveform is generated; The probe waveform is applied to the yarn, and the yarn's real-time response data to the probe waveform is collected.

3. The automated control method for a textile production line according to claim 1, characterized in that, The step of applying a controlled dynamic excitation signal to the yarn to obtain real-time response data of the yarn includes: A preset test excitation signal is applied to the yarn, and preliminary response data of the yarn to the test excitation signal is collected; Based on the preliminary response data, determine the viscoelastic characteristics of the yarn; If the viscoelasticity of the yarn is lower than a first preset threshold, the preliminary response data will be used as the real-time response data. If the viscoelastic characteristics of the yarn are higher than or equal to a first preset threshold, a detection waveform is generated based on the viscoelastic characteristics; the detection waveform is applied to the yarn, and the real-time response data of the yarn to the detection waveform is collected.

4. The automated control method for a textile production line according to claim 3, characterized in that, The step of generating the probe waveform based on the viscoelastic characteristics includes: If the viscoelastic characteristics of the yarn are higher than or equal to the second preset threshold, a low-frequency periodic oscillation waveform is generated as the detection waveform; If the viscoelastic characteristics of the yarn are lower than the second preset threshold, a mid-frequency step hold waveform is generated as the detection waveform. The second preset threshold is higher than the first preset threshold.

5. The automated control method for a textile production line according to claim 3, characterized in that, The step of generating the probe waveform based on the viscoelastic characteristics includes: Based on the viscoelastic characteristics, the viscoelastic fluctuations of the yarn along its length direction are identified; Based on the viscoelastic fluctuations, a spatially adaptive detection waveform is generated, wherein the action parameters of the detection waveform are enhanced at spatial locations where the viscoelastic fluctuations are increased, and weakened at spatial locations where the viscoelastic fluctuations are decreased.

6. The automated control method for a textile production line according to claim 1, characterized in that, In the step of determining the operating parameter range of the yarn based on the physical property information: The physical property information includes the breaking strength of the yarn; The range of operating parameters includes the maximum permissible operating speed and the upper limit of permissible tension that match the fracture strength.

7. The automated control method for a textile production line according to claim 1, characterized in that, The step of continuously acquiring the operating status information of the loom and yarn, and comparing the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range includes: Continuously acquire operating status information of the loom and yarn; Based on the operating status information and the operating parameter range, the safety margin index of the yarn is calculated; the safety margin index is compared with a preset warning threshold to determine whether the production status is close to or exceeds the boundary of the operating parameter range.

8. The automated control method for a textile production line according to claim 7, characterized in that, In the step of continuously acquiring the operating status information of the loom and yarn: The operating status information includes a first type of status information used to characterize the operating conditions of the loom and a second type of status information used to characterize the physical state of the yarn.

9. The automated control method for a textile production line according to claim 8, characterized in that, In the step of adjusting the control parameters during production based on the operating status information when the production status is determined to be close to or beyond the boundary of the operating parameter range: If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the first type of status information, then the first type of adjustment signal that directly adjusts the loom's operating condition is used as the control parameter. If the basis for determining whether the production status is close to or exceeds the boundary of the operating parameter range comes from the second type of status information, then the second type of adjustment signal that directly adjusts the yarn tension or the amount of warp feed is used as the control parameter.

10. An automated control system for a textile production line, characterized in that, include: The action and response module is used to apply a controlled dynamic excitation signal to the yarn in order to obtain real-time response data of the yarn; The characteristic estimation module is used to estimate the physical characteristic information of the yarn based on the real-time response data; The parameter determination module is used to determine the operating parameter range of the yarn based on the physical characteristic information. The status comparison module is used to continuously acquire the operating status information of the loom and the yarn, and compare the operating status information with the operating parameter range to determine whether the production status is close to or exceeds the boundary of the operating parameter range. The parameter adjustment module is used to adjust the control parameters in the production process according to the operating status information when it is determined that the production status is close to or exceeds the boundary of the operating parameter range, so as to keep the production status within the operating parameter range.

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