Intelligent management system for woven geotextile production equipment

Abstract

The invention belongs to the technical field of intelligent management of production equipment, and particularly relates to an intelligent management system for woven geotextile production equipment, which comprises a tension presetting module, a tension judging module, an abnormity identifying and positioning module and a parameter adjusting module, deviation grading is carried out through the tension relative deviation value and deviation duration, tension deviation is reduced by adjusting PID parameters and a guide roller wrap angle, the problem severity degree can be accurately positioned, system oscillation caused by excessive intervention is avoided, the system response speed and stability can be remarkably improved, yarn abrasion and equipment loss are reduced, and the yarn quality is improved. Different abnormal threshold values are set for different production links, high-frequency small-amplitude adjustment and low-frequency large-amplitude adjustment can be distinguished, single-index misjudgment is avoided, normal process adjustment can be filtered, abnormal grades can be quantified through risk coefficients, and accurate treatment from qualitative judgment to data grading is achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent management of production equipment, and specifically relates to an intelligent management system for woven geotextile production equipment. Background Art

[0002] With the expansion of the scale of related engineering construction and the improvement of quality requirements, higher requirements are placed on the output and quality of geotextiles. The degree of automation of traditional woven geotextile production equipment has gradually increased, but the coordination between various equipment and the overall management efficiency still need to be improved. Intelligent management systems are needed to optimize production processes and improve production efficiency and product quality.

[0003] However, the existing technology still has certain limitations, specifically: 1. The existing intelligent management of woven geotextile production equipment focuses more on the simple judgment of whether the instantaneous tension value exceeds the preset fixed threshold, or static classification of fixed levels. However, it seriously ignores the dynamics and continuity of tension fluctuations in actual production and the strong coupling with process parameters, resulting in slow recognition or inaccurate classification of sub-healthy tension in a critical state, and unable to provide early and refined warnings, so that potential quality risks cannot be discovered and intervened in time.

[0004] 2. Existing technologies focus more on static parameter matching and single fluctuation amplitude threshold monitoring based on preset process recipes, but seriously ignore the dynamic coupling mechanism of the guide roller system and the real-time coordination requirements of multiple variables, resulting in a lack of adaptive capabilities in the optimization process. The system is unable to dynamically generate the optimal parameter combination. At the same time, the optimization strategy lacks adaptability. Relying solely on a single indicator of fluctuation amplitude may cover up frequency domain hidden dangers or transient risks, ultimately causing hidden quality defects that are difficult to trace, making optimization a superficial maintenance of stability rather than a fundamental improvement. Summary of the Invention

[0005] In view of this, in order to solve the problems raised in the above background technology, an intelligent management system for woven geotextile production equipment is proposed.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An intelligent management system for woven geotextile production equipment, including: a tension pre-adjustment module, a tension judgment module, an abnormality identification and positioning module and a parameter adjustment module.

[0007] The tension pre-adjustment module is connected to the tension judgment module, the tension judgment module is connected to the abnormality identification and positioning module, the abnormality identification and positioning module is connected to the parameter adjustment module, and the parameter adjustment module is connected to the tension judgment module.

[0008] The tension pre-adjustment module obtains the geotextile tension value at the end of the current link and adjusts the initial tension value of the tension adjustment equipment in the next link based on the preset tension ratio relationship.

[0009] The tension judgment module monitors in real time whether there is any deviation in the actual tension value of the geotextile at the beginning of the next link. If there is a deviation, it determines whether the relative deviation of the tension exceeds the maximum allowable deviation threshold, and performs tension adjustment based on the judgment result.

[0010] The abnormality identification module collects the number of tension deviation adjustments and the tension deviation adjustment amplitude of each adjustment in the next production link to locate the abnormality, and outputs the optimized value of the mechanical parameters of the guide roller system based on the preset abnormal link identification model.

[0011] Parameter adjustment module: adjusts the parameters of the conveying device according to the optimized values ​​of the mechanical parameters of the guide roller system, and determines whether the adjusted parameters are effective.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention divides the deviation levels according to the relative tension deviation value and the deviation duration and reduces the tension deviation by adjusting the PID parameters and the guide roller wrap angle, which helps to accurately locate the severity of the problem, avoid excessive intervention leading to system oscillation, and significantly improve the system response speed and stability, thereby reducing yarn wear and equipment loss.

[0013] (2) The present invention locates abnormalities by counting the number of tension deviation adjustments and the amplitude of tension deviation adjustments for each adjustment, and sets different abnormality thresholds for different production links. It can distinguish between high-frequency small adjustments and low-frequency large adjustments, thus avoiding misjudgment of a single indicator. At the same time, it can filter normal process adjustments and quantify the abnormality level through risk factors, thus achieving accurate handling from qualitative judgment to data classification.

[0014] (3) The present invention optimizes the guide roller height and guide roller speed by constructing a model based on the number of tension deviation adjustments, the tension deviation adjustment amplitude, the initial value of the guide roller height and the initial value of the guide roller speed, and uses the three indicators of tension fluctuation amplitude, tension fluctuation frequency and tension overshoot to judge the effect after optimization. It can dynamically analyze the nonlinear coupling relationship between parameters and break through the limitations of traditional static parameter formulas. At the same time, it can accurately quantify the optimization effect and isolate the root cause of the fault, thereby forming an intelligent upgrade chain of "parameter optimization-effect feedback-closed-loop correction". BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of the system module connection of the present invention.

[0017] Figure 2 Schematic diagram of the tension determination process of the present invention.

[0018] Figure 3 This is a flow chart of the steps for constructing the preset abnormal link identification model of this embodiment. DETAILED DESCRIPTION

[0019] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0020] See also Figure 1 As shown, the present invention provides an intelligent management system for woven geotextile production equipment, including: a tension pre-adjustment module, a tension judgment module, an abnormality identification and positioning module and a parameter adjustment module.

[0021] The tension pre-adjustment module is connected to the tension judgment module, the tension judgment module is connected to the abnormality identification and positioning module, the abnormality identification and positioning module is connected to the parameter adjustment module, and the parameter adjustment module is connected to the tension judgment module.

[0022] The tension pre-adjustment module obtains the geotextile tension value at the end of the current link, and adjusts the initial tension value of the tension adjustment device of the next link based on the preset tension ratio relationship, that is, calculates the product of the geotextile tension value at the end of the current link and the preset tension ratio relationship, and uses it as the initial tension value of the tension adjustment device of the next link.

[0023] It should be added that the above-mentioned geotextile production links include raw material unwinding link, warping link, weaving warp feeding link and curling link, and their respective PID control devices are magnetic powder brake current controller, pneumatic servo valve group, warp feeding servo motor driver and hydraulic proportional valve. The preset tension ratio relationship is derived from the tension extreme value under the single-machine tension boundary test of each production link. The single-machine tension boundary test is to independently test the maximum tension tolerance limit of each production link, providing a boundary basis for the system-level tension ratio distribution.

[0024] See also Figure 2 As shown, the tension judgment module monitors in real time whether there is any deviation in the actual tension value of the geotextile at the beginning of the next link. If there is a deviation, it determines whether the relative tension deviation exceeds the maximum allowable deviation threshold, and performs tension adjustment based on the judgment result.

[0025] In a preferred embodiment of the present invention, the judgment of whether the relative tension deviation exceeds the maximum allowable deviation threshold refers to the following execution process: (a) using a tension sensor to monitor the actual tension value of the geotextile in real time and perform a preprocessing operation of noise removal, dividing the difference between the actual tension value of the geotextile and the initial tension value by the initial tension value to obtain a relative tension deviation value; if the relative tension deviation value is less than or equal to the maximum allowable deviation threshold, then continue to maintain operation, otherwise perform a graded judgment based on the relative tension deviation value to obtain the level of the relative tension deviation; wherein the preprocessing operation can be median filtering, Kalman filtering and wavelet transform denoising, etc., and the embodiment of the present invention does not impose too many restrictions on this.

[0026] It should be noted that the maximum allowable deviation thresholds vary across different links. For example, in the raw material unwinding process, the main function is to release the yarn smoothly. Tension fluctuations have a cumulative impact on subsequent processes, but the fabric structure has not yet been formed, allowing for a certain margin of error. The maximum allowable deviation threshold is usually set between 5% and 8%. In the warping process, hundreds of yarns need to be arranged in parallel according to the designed density. Uneven tension will directly lead to yarn displacement during weaving, affecting the strength distribution of the fabric. The maximum allowable deviation threshold is usually set between 3% and 5%. In the weaving warp let-off process, the warp density and thickness of the fabric are directly determined. Deviations exceeding the range will result in substandard mechanical properties. The maximum allowable deviation threshold is usually set between 2% and 4%. In the curling process, the package size directly affects the construction and laying efficiency. Uneven internal stress will cause wrinkles when the geotextile is unfolded. The maximum allowable deviation threshold is usually set between 4% and 8%. The range of the maximum allowable deviation threshold is derived from orthogonal experiments. That is, through orthogonal table design of multi-factor and multi-level experiments, the influence of each parameter on tension stability is analyzed, and the parameter combination that minimizes the tension deviation and meets product performance is screened out to form a threshold range.

[0027] (b) Obtain the deviation duration when the relative tension deviation value exceeds the maximum allowable deviation threshold If the relative deviation value of the tension is within the first level deviation tension range and , then the tension relative deviation corresponding level is marked as a first-level deviation, and the PID parameters of the tension regulating device of this link are adjusted; is the first level deviation time threshold.

[0028] If the relative deviation value of the tension is within the secondary deviation tension range and , then the tension deviation is marked as a secondary deviation, and the difference between the actual tension value and the preset tension value is taken as the absolute tension deviation. If the sign of the absolute tension deviation is positive, the guide roller wrap angle and the preset unit adjustment angle are subtracted to obtain the guide roller wrap angle to be adjusted. If the sign of the absolute tension deviation is negative, the guide roller wrap angle and the preset unit adjustment angle are added to obtain the guide roller wrap angle to be adjusted. The guide roller is adjusted according to the guide roller wrap angle to be adjusted. After the adjustment, re-check whether the tension deviation is less than the maximum allowable deviation threshold. If it is less than, maintain the current wrap angle. Otherwise, continue to adjust according to the above steps; is the secondary deviation time threshold.

[0029] If the relative deviation value of the tension is within the third level deviation tension range and , the tension deviation is marked as a level 3 deviation and a fault warning is immediately issued, and the relevant staff are notified to troubleshoot the sensor and the transmission device; is the third-level deviation time threshold.

[0030] It should be noted that the above-mentioned deviation level classification based on the relative tension deviation value and deviation duration and the reduction of tension deviation by adjusting PID parameters and guide roller wrap angle can help to accurately locate the severity of the problem, avoid system oscillation caused by excessive intervention, and significantly improve the system response speed and stability, thereby reducing yarn wear and equipment loss.

[0031] The PID parameters include a proportional coefficient, an integral time, and a differential time. The PID parameters of the tension regulating device in this link are adjusted by multiplying the integral time and the differential time with the corresponding preset integral time proportional relationship and the differential time proportional relationship respectively to obtain the integral time and differential time to be adjusted.

[0032] It should be noted that the proportional coefficient is the amplification coefficient of the deviation between the actual tension and the target tension in PID control, which determines the system's response speed and adjustment strength to the deviation; the integral time determines the system's cumulative effect on historical deviations, and is used to eliminate small deviations that exist for a long time; the differential time is used to predict the changing trend of the deviation, adjust the rate of change of the deviation, and suppress system oscillations.

[0033] If the sign of the absolute deviation of the tension is positive, the proportional coefficient is multiplied by the preset percentage relationship and then subtracted from the initial proportional coefficient to obtain the proportional coefficient that needs to be adjusted. If the sign of the absolute deviation of the tension is negative, the proportional coefficient is multiplied by the preset percentage relationship and then added to the initial proportional coefficient to obtain the proportional coefficient that needs to be adjusted. The initial proportional coefficient, integral time and differential time are updated to the proportional coefficient that needs to be adjusted, the integral time that needs to be adjusted and the differential time that needs to be adjusted. After adjustment, the relative deviation value of the tension is checked again according to the preset sampling frequency. If the relative deviation value of the tension is less than the maximum allowable deviation threshold value each time sampling, the current PID parameters are maintained, otherwise the auxiliary adjustment of the guide roller wrap angle is started.

[0034] The specific process of starting the auxiliary adjustment of the guide roller angle is as follows: marking the sampling times when the relative deviation value of the tension exceeds the maximum allowable deviation threshold as the number of deviations, comparing the number of deviations with the preset deviation threshold, if the number of deviations is greater than or equal to the preset deviation threshold, then when the absolute deviation of the tension is positive, subtracting the guide roller angle from the preset angle adjustment angle to obtain the guide roller angle to be adjusted; when the absolute deviation of the tension is negative, adding the guide roller angle to the preset angle adjustment angle to obtain the guide roller angle to be adjusted.

[0035] It should be noted that the preset wrap angle adjustment angle is derived from the standard PID formula of the PID control algorithm. The core of the PID control is a feedback loop, which continuously compares the target value, i.e. the preset tension value, and the actual measured value, i.e. the actual tension value, and calculates a control signal to reduce the difference between the two. This control signal is usually a physical quantity that can be directly used to adjust the system. It can be in any form as long as it can be accepted by the system and used for adjustment, such as the guide roller wrap angle adjustment angle.

[0036] It should be added that the preset integral time ratio ,in is the tension deviation duration, Is the first level deviation time threshold; the preset differential time ratio relationship ,in is the standard deviation of real-time tension fluctuation, is the standard deviation of normal tension fluctuations; the preset percentage relationship range is different in different production stages, which comes from the adaptive control algorithm, such as model reference adaptive control and recursive least squares method, and the embodiment of the present invention does not impose too many restrictions on this.

[0037] The abnormality identification module collects the number of tension deviation adjustments and the tension deviation adjustment amplitude of each adjustment in the next production link to locate the abnormality, and outputs the optimized value of the mechanical parameters of the guide roller system based on a preset abnormal link identification model.

[0038] The number of tension deviation adjustments of the next production link and the tension deviation adjustment amplitude of each adjustment are collected to locate abnormalities. The specific process is: according to the serial number of the tension deviation adjustment number of the next production link, the corresponding weight coefficient is obtained, and the tension deviation adjustment amplitude of each time is normalized and multiplied by the corresponding weight coefficient and then added to calculate the tension deviation adjustment index, and at the same time, the average tension deviation adjustment index is calculated, and the tension deviation adjustment index of the next production link is compared with the product of the average tension deviation adjustment index and the preset multiplication coefficient. If the tension deviation adjustment index of the production link is greater than or equal to the product, the production link is marked as an abnormal link.

[0039] It should be added that the weight coefficient corresponding to the sequence number of the tension deviation adjustment times is the ratio of the sequence number to the total sequence number. The normalization process is to divide the tension deviation adjustment amplitude of each time by the historical average tension deviation adjustment amplitude. For example, if there are four tension deviation adjustments, the sequence numbers are 1, 2, 3, and 4, and the weight coefficients corresponding to the sequence numbers are 0.1, 0.2, 0.3, and 0.4, respectively. If the tension deviation adjustment amplitudes are 5N, 7N, 6N, and 10N, the tension deviation adjustment index is: ; In addition, there are significant differences in the accuracy requirements, risk levels, and process sensitivity of tension control in different links of the geotextile production process, which requires targeted adjustment of the preset multiplier coefficient.

[0040] It should be noted that anomaly positioning is performed based on the number of tension deviation adjustments and the tension deviation adjustment amplitude of each adjustment, and different anomaly thresholds are set for different production links. This can distinguish between high-frequency small adjustments and low-frequency large adjustments, avoid misjudgment of a single indicator, and at the same time filter out normal process adjustments and quantify the anomaly level through risk factors, thereby achieving precise handling from qualitative judgment to data classification.

[0041] See also Figure 3 As shown, the output of the optimized value of the mechanical parameters of the guide roller system based on the preset abnormal link identification model is a specific process of obtaining the historical mechanical parameters of the guide roller system of the conveying device of the located abnormal link, wherein the historical mechanical parameters of the guide roller system include the initial value of the guide roller height and the initial value of the guide roller speed.

[0042] The tension deviation adjustment times, tension deviation adjustment amplitude, guide roller height initial value and guide roller speed initial value are combined into a quaternion data set, and the quaternion data set is divided into a quaternion training set and a quaternion validation set. For example, an instance of the data set may be: (tension deviation adjustment times 1, tension deviation adjustment amplitude 1, guide roller height initial value 1, guide roller speed initial value 1), (tension deviation adjustment times 2, tension deviation adjustment amplitude 2, guide roller height initial value 2, guide roller speed initial value 2). Model training is performed through multivariate data to improve prediction accuracy.

[0043] In an embodiment of the present invention, four parameters, namely, the number of tension deviation adjustments, the tension deviation adjustment amplitude, the initial value of the guide roller height, and the initial value of the guide roller speed, are processed by data cleaning, such as consistency checking or processing of invalid values ​​and missing values. The embodiment of the present invention does not impose excessive restrictions on this.

[0044] The four-element training set is input into the neural network model for training to obtain the abnormal link identification model to be verified.

[0045] The four-element verification set is input into the abnormal link identification model to be verified, and the model parameters are adjusted until the iteration is completed to obtain the trained abnormal link identification model.

[0046] In an embodiment of the present invention, it is first necessary to randomly divide the quaternion data set into a training set and a validation set ratio of 7:3 or 8:2 to ensure that the two groups of data are distributed consistently; then the quaternion data set is normalized to scale the features to the interval [0,1], and ensure that the sample ratios of the raw material unwinding link, warping link, weaving warp feeding link and curling link in the training set and the validation set are consistent to avoid model bias caused by category imbalance; then the model parameters of the abnormal link identification model are adjusted through the training set, and the abnormal link identification model is tested through the quaternion validation set. Specifically, when the model output result is inconsistent with the result of the quaternion validation set, the model coefficient is further adjusted to improve the accuracy of the output result. After adjusting the parameters according to the validation set result, the model is retrained, and the training-validation process is repeated until the validation set index no longer improves. The validation set index can be the accuracy rate or Scores, etc., are not subject to excessive restrictions in the embodiments of the present invention.

[0047] Regarding the type of abnormal link identification model, it can be other supervised neural network models, such as recursive neural networks, etc., or an unsupervised neural network model can be used to implement the functions of the embodiments of the present invention. It is only necessary to obtain data input to the model. The unsupervised neural network model can include a K-means algorithm model, a hierarchical clustering algorithm model, a PCA principal component analysis model, etc., and there are no excessive restrictions on this in the embodiments of the present invention.

[0048] The parameter adjustment module adjusts the parameters of the conveying device according to the optimized values ​​of the mechanical parameters of the guide roller system, and determines whether the adjusted parameters are valid.

[0049] The parameters of the conveying device are adjusted according to the optimized values ​​of the mechanical parameters of the guide roller system. The specific process is: obtaining the initial value of the guide roller height, fitting the geotextile tension timing sequence after the mechanical parameters of the guide roller system are adjusted to obtain the geotextile tension time curve, and obtaining the tension curve parameters according to the geotextile tension time curve. The tension curve parameters include tension fluctuation frequency and tension overshoot. If the tension fluctuation frequency is greater than the preset frequency threshold and the tension overshoot is greater than the preset overshoot threshold, or the tension fluctuation frequency is less than the preset frequency threshold and the tension overshoot is less than the preset overshoot threshold, then the optimized value of the guide roller height is added to the initial value to obtain the optimized guide roller height value, otherwise they are subtracted.

[0050] Regarding the data fitting method, the embodiment of the present invention can perform fitting through various methods such as linear regression, quadratic regression, nonlinear least squares fitting, spline interpolation, etc., and the embodiment of the present invention does not impose too many restrictions on this.

[0051] Obtain the initial value of the guide roller speed. If the tension fluctuation frequency is greater than the preset frequency threshold and the tension overshoot is greater than the preset overshoot threshold, subtract the guide roller speed optimization value from the initial value to obtain the optimized guide roller speed value. If the tension fluctuation frequency is less than the preset frequency threshold and the tension overshoot is less than the preset overshoot threshold, add the guide roller speed optimization value to the initial value to obtain the optimized guide roller speed value.

[0052] It should be noted that the tension fluctuation frequency refers to the number of times the tension value periodically deviates from the target value per unit time, reflecting the dynamic change rate of tension instability. The tension overshoot refers to the deviation amplitude of the instantaneous peak value of tension exceeding the steady-state target value, which characterizes the degree of overshoot of the system after disturbance.

[0053] The specific process of judging whether the adjusted parameters are effective is as follows: refitting the geotextile tension value according to the optimized guide roller height value and guide roller speed value to obtain the geotextile tension time curve, and reobtaining the tension fluctuation amplitude, tension fluctuation frequency and tension overshoot according to the geotextile tension time curve, wherein the tension fluctuation amplitude refers to the absolute value of the maximum deviation between the real-time tension value and the target value.

[0054] If the retrieved tension fluctuation amplitude, tension fluctuation frequency and tension overshoot are all smaller than the tension fluctuation amplitude threshold, tension fluctuation frequency threshold and tension overshoot threshold, no additional operation is performed; otherwise, the tension value is re-judged to determine whether there is a deviation.

[0055] It should be noted that the model constructed based on the number of tension deviation adjustments, the tension deviation adjustment amplitude, the initial value of the guide roller height and the initial value of the guide roller speed is used to optimize the guide roller height and guide roller speed, and the three indicators of tension fluctuation amplitude, tension fluctuation frequency and tension overshoot are used to judge the optimized effect. It can dynamically analyze the nonlinear coupling relationship between parameters and break through the limitations of traditional static parameter formulas. At the same time, it can not only accurately quantify the optimization effect, but also isolate the root cause of the fault, thus forming an intelligent upgrade chain of parameter optimization-effect feedback-closed-loop correction.

[0056] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0057] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0058] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0059] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0060] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0061] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An intelligent management system for woven geotextile production equipment, characterized in that: include: Tension pre-adjustment module: obtains the geotextile tension value at the end of the current link, and adjusts the initial tension value of the tension adjustment equipment in the next link based on the preset tension ratio relationship; Tension judgment module: At the beginning of the next stage, the actual tension value of the geotextile is monitored in real time to see if there is a deviation. If there is a deviation, it is determined whether the relative deviation of the tension exceeds the maximum allowable deviation threshold, and the tension is adjusted according to the judgment result; Abnormal identification module: collects the number of tension deviation adjustments and the tension deviation adjustment amplitude of each adjustment in the next production link to locate abnormalities, and outputs the optimized values ​​of the mechanical parameters of the guide roller system based on the preset abnormal link identification model; Parameter adjustment module: adjusts the parameters of the conveying device according to the optimized values ​​of the mechanical parameters of the guide roller system, and determines whether the adjusted parameters are effective.

2. The intelligent management system for woven geotextile production equipment according to claim 1, characterized in that: The method of regulating the initial tension value of the tension regulating device of the next link based on the preset tension ratio relationship includes: Calculate the product of the geotextile tension value at the end of the current link and the preset tension ratio, and use it as the initial tension value of the tension adjustment device in the next link.

3. The intelligent management system for woven geotextile production equipment according to claim 1, characterized in that: The determining whether the relative tension deviation exceeds a maximum allowable deviation threshold includes: The actual tension value of the geotextile is monitored in real time using a tension sensor, and the difference between the actual tension value of the geotextile and the initial tension value is divided by the initial tension value to obtain the relative tension deviation value; if the relative tension deviation value is less than or equal to the maximum allowable deviation threshold, the operation is continued; otherwise, a graded judgment is made based on the relative tension deviation value to obtain the level of the relative tension deviation.

4. The intelligent management system for woven geotextile production equipment according to claim 3, characterized in that: The level of the relative tension deviation is determined as follows: Get the deviation duration of the relative tension deviation value exceeding the maximum allowable deviation threshold If the relative deviation value of the tension is within the first level deviation tension range and , then the tension relative deviation corresponding level is marked as a first-level deviation, and the PID parameters of the tension regulating device of this link are adjusted; is the first-level deviation time threshold; If the relative deviation value of the tension is within the secondary deviation tension range and , then the tension deviation is marked as a secondary deviation, and the difference between the actual tension value and the preset tension value is used as the absolute tension deviation. If the sign of the absolute tension deviation is positive, the guide roller wrap angle and the preset adjustment angle are subtracted to obtain the guide roller wrap angle to be adjusted. If the sign of the absolute tension deviation is negative, the guide roller wrap angle and the preset adjustment angle are added to obtain the guide roller wrap angle to be adjusted. The guide roller is adjusted according to the guide roller wrap angle to be adjusted. After the adjustment, re-check whether the tension deviation is less than the maximum allowable deviation threshold. If it is less than, maintain the current wrap angle. Otherwise, continue to adjust according to the above steps; is the secondary deviation time threshold; If the relative deviation value of the tension is within the third level deviation tension range and , the tension deviation is marked as a level 3 deviation and a fault warning is immediately issued, and the relevant staff are notified to troubleshoot the sensor and the transmission device; is the third-level deviation time threshold.

5. The intelligent management system for woven geotextile production equipment according to claim 4, characterized in that: The PID parameters include proportional coefficient, integral time and differential time. The method of adjusting the PID parameters of the tension regulating device in this link is: Multiplying the integral time and differential time by the corresponding preset integral time ratio and differential time ratio respectively to obtain the integral time and differential time to be adjusted; If the sign of the absolute deviation of the tension is positive, the proportional coefficient is multiplied by the preset percentage relationship and then subtracted from the initial proportional coefficient to obtain the proportional coefficient that needs to be adjusted. If the sign of the absolute deviation of the tension is negative, the proportional coefficient is multiplied by the preset percentage relationship and then added to the initial proportional coefficient to obtain the proportional coefficient that needs to be adjusted. The initial proportional coefficient, integral time and differential time are updated to the proportional coefficient that needs to be adjusted, the integral time that needs to be adjusted and the differential time that needs to be adjusted. After adjustment, the relative deviation value of the tension is checked again according to the preset sampling frequency. If the relative deviation value of the tension is less than the maximum allowable deviation threshold value each time sampling, the current PID parameters are maintained, otherwise the auxiliary adjustment of the guide roller wrap angle is started.

6. The intelligent management system for woven geotextile production equipment according to claim 5, characterized in that: The auxiliary adjustment of the guide roller wrap angle is started, including: The sampling times when the relative deviation value of tension exceeds the maximum allowable deviation threshold are marked as the number of deviations, and the number of deviations is compared with the preset deviation threshold. If the number of deviations is greater than or equal to the preset deviation threshold, then when the absolute deviation of tension is positive, the guide roller wrap angle and the preset wrap angle adjustment angle are subtracted to obtain the guide roller wrap angle that needs to be adjusted; when the absolute deviation of tension is negative, the guide roller wrap angle and the preset wrap angle adjustment angle are added to obtain the guide roller wrap angle that needs to be adjusted.

7. The intelligent management system for woven geotextile production equipment according to claim 1, characterized in that: The collecting of the number of tension deviation adjustments and the tension deviation adjustment amplitude of each adjustment in the next production link for abnormality location includes: The corresponding weight coefficient is obtained according to the serial number of the tension deviation adjustment times of the next production link. The tension deviation adjustment amplitude of each time is normalized and multiplied by the corresponding weight coefficient and then added to calculate the tension deviation adjustment index. At the same time, the average tension deviation adjustment index is calculated, and the tension deviation adjustment index of the next production link is compared with the product of the average tension deviation adjustment index and the preset multiplication coefficient. If the tension deviation adjustment index of the production link is greater than or equal to the product, the production link is marked as an abnormal link.

8. The intelligent management system for woven geotextile production equipment according to claim 1, characterized in that: The preset abnormal link identification model includes: Acquire multiple sets of historical guide roller system mechanical parameters of the conveying device of the located abnormal link, wherein the historical guide roller system mechanical parameters include an initial value of the guide roller height and an initial value of the guide roller speed; The tension deviation adjustment times, the tension deviation adjustment amplitude, the guide roller height initial value and the guide roller speed initial value are combined into a quaternion data set, and the quaternion data set is divided into a quaternion training set and a quaternion validation set; Inputting the four-element training set into the screened set neural network model for training to obtain an abnormal link identification model to be verified; The four-element verification set is input into the abnormal link identification model to be verified, and the model parameters are adjusted until the iteration is completed to obtain the trained abnormal link identification model.

9. The intelligent management system for woven geotextile production equipment according to claim 8, characterized in that: The parameter adjustment of the transmission device includes: The geotextile tension time curve after the mechanical parameters of the guide roller system are adjusted is fitted, and the tension curve parameters are obtained according to the geotextile tension time curve, wherein the tension curve parameters include tension fluctuation frequency and tension overshoot. If the tension fluctuation frequency is greater than a preset frequency threshold and the tension overshoot is greater than a preset overshoot threshold, or the tension fluctuation frequency is less than a preset frequency threshold and the tension overshoot is less than a preset overshoot threshold, then the guide roller height optimization value is added to the initial value to obtain the optimized guide roller height value; otherwise, the two values ​​are subtracted; If the tension fluctuation frequency is greater than the preset frequency threshold and the tension overshoot is greater than the preset overshoot threshold, the guide roller speed optimization value is subtracted from the initial value to obtain the optimized guide roller speed value; if the tension fluctuation frequency is less than the preset frequency threshold and the tension overshoot is less than the preset overshoot threshold, the guide roller speed optimization value is added to the initial value to obtain the optimized guide roller speed value.

10. The intelligent management system for woven geotextile production equipment according to claim 1, characterized in that: The determining whether the adjusted parameters are valid includes: The geotextile tension value is refitted according to the optimized guide roller height value and guide roller speed value to obtain a geotextile tension time curve, and the tension fluctuation amplitude, tension fluctuation frequency and tension overshoot are reobtained according to the geotextile tension time curve; If the retrieved tension fluctuation amplitude, tension fluctuation frequency and tension overshoot are all smaller than the tension fluctuation amplitude threshold, tension fluctuation frequency threshold and tension overshoot threshold, no additional operation is performed; otherwise, the tension value is re-judged to determine whether there is a deviation.

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