Spinning frame suspended spindle roving monitoring and early warning control method and system

Abstract

The invention belongs to the technical field of spinning frame bobbin hanger roving monitoring intelligent control systems, and particularly relates to a spinning frame bobbin hanger roving monitoring and early warning control method and system, and the method comprises the steps: collecting order information in management ERP software of a sales department, forming the shape, size and number of target glass needed by an order, and forming a first data set; the shape, the size and the number of raw glass sheets stored in a glass frame in storage MES software are obtained, and a second data set is formed; constructing a typesetting model; the system has the beneficial effects that the sheet taking machine is controlled by adjusting control parameters in real time to place raw glass sheets on the cutting machine, and glass cutting is carried out according to a production information data set; mutual feedback control of speed reduction and vibration of an end executor of the sheet taking machine is achieved, and the technical problem that the sheet taking machine needs to grasp glass to move for a certain distance, and the risk that raw glass slides off in the acceleration, speed reduction and parking positioning process exists is solved.

Description

Technical Field

[0001] This invention belongs to the technical field of intelligent control system for monitoring and controlling roving on spinning frame spindles, and particularly relates to a method and system for monitoring, early warning and control of roving on spinning frame spindles. Background Technology

[0002] In the textile industry, especially in the production of ring spinning machines, monitoring the consumption of yarn on roving rollers and detecting yarn breakage are crucial for ensuring continuous production and stable quality. Traditional methods for monitoring the consumption of yarn on roving rollers mainly rely on manual inspection. However, each worker needs to oversee multiple ring spinning machines simultaneously. This means that when a certain number of roving rollers are short of yarn, workers cannot detect it in time, affecting the continuity of spinning production and the stability of quality. Summary of the Invention

[0003] This application provides a method and system for monitoring and early warning control of roving on spinning machine spindles. It addresses the technical problem that existing methods for monitoring the consumption of yarn on roving rollers mainly rely on manual inspection. Since each worker needs to oversee multiple spinning machines simultaneously, workers cannot promptly detect when a certain number of roving rollers are short of yarn, which affects the continuity and quality stability of spinning production.

[0004] In view of the above problems, this application provides a method for monitoring and early warning control of roving suspension on a spinning frame, the control method comprising: Collect production information on roving rolls from the production management ERP software to form the first dataset, which includes the shape, dimensions, rated speed, and number of target roving rolls required for production. The shape, size, and number of roving rollers in the spindle rack of each spinning machine, as well as the actual rotation speed of the corresponding spindle, are obtained to form a second dataset; The rotation and pressure information of the guide wheel are obtained to form a third dataset; Construct an early warning analysis model; The second and third datasets are input into the early warning analysis model to obtain the real-time change parameters of the roving roller in the spindle. The real-time change parameters of the roving roller include the real-time shape, real-time external dimensions, and real-time rotation speed. A one-to-one correspondence is established between the real-time change parameters of the roving roller and the rotation information and pressure information of the guide wheel in the third dataset to form a real-time production dataset.

[0005] The real-time production dataset is normalized to form an analysis dataset. The data in the analysis dataset with the one-to-one correspondence are differentiated. The roving consumption is calculated by integrating the speed difference of the roving roller. The remaining amount of roving is estimated by combining the initial volume of the roving. A yarn breakage accident is determined when at least one of the parameters in the remaining quantity or the third dataset remains unchanged for two consecutive three-second intervals. Based on the remaining amount of roving and the result of the yarn breakage accident determination, the corresponding spinning machine is controlled to issue an alarm, and the position of the roving roller corresponding to the alarm is given.

[0006] Furthermore, the construction of the early warning analysis model includes: Analyzing the first dataset, the shape, dimensions, rated speed, and number of roving rolls of the target spindle are divided into shape subset, dimension subset, rated speed subset, and number of roving rolls subset; Analyze the second dataset; classify the shape, size, and number of roving rollers in the spindle rack of each spinning machine, as well as the actual rotation speed of the corresponding spindle, into real-time shape subset, real-time external size subset, real-time rotation speed subset, and online production roving roller quantity subset; Analyze the third dataset; divide the rotation information and pressure information of the guide wheel into a rotation information subset and a pressure information subset, respectively. Using the shape subset, external dimension subset, rated speed subset, and roving roll number subset as initial references, a one-to-one correspondence is established between the guide wheel rotation information subset and the guide wheel pressure information subset and the corresponding real-time shape subset, real-time external dimension subset, real-time speed subset, and online production roving roll number subset, forming a mapping analysis dataset. After normalizing the data with mapping relationships in the mapping analysis dataset, the data within each mapping relationship is differentiated using timestamps as markers. The difference between data corresponding to adjacent timestamps is calculated. When the difference is greater than zero, it indicates that yarn consumption has increased. When the difference is less than zero, it indicates that yarn consumption has decreased. When the difference is equal to zero, it indicates that yarn consumption is normal. By analyzing the frequency of change of the difference using Fourier transform, it can be determined whether the guide wheel is slipping. The roving consumption is calculated by integrating the speed difference of the roving rolls, and the final output is an estimate of the remaining amount of roving by combining the shape subset, the external size subset, the rated speed subset, and the number of roving rolls subset.

[0007] Furthermore, the step of determining whether the guide wheel is slipping by analyzing the frequency of change of the difference using Fourier transform includes: When the frequency of the difference changes repeatedly oscillates around a fixed value, it is determined that the guide wheel is slipping and a yarn breakage alarm is issued. Furthermore, the step of calculating roving consumption by integrating the speed difference of the roving rollers, and finally outputting an estimate of the remaining roving quantity by combining the shape subset, the external size subset, the rated speed subset, and the roving roller number subset, includes: The trend of real-time rotational speed of the yarn roller is obtained by calculating the integral speed difference of the roving roller using the trapezoidal method. By observing the real-time rotational speed of the yarn rollers and comparing it with the changes in the yarn roller's external dimensions, the team leader pre-calculated the remaining amount of roving corresponding to the completed database of yarn roller size changes, and estimated the actual remaining amount of yarn.

[0008] Furthermore, by analyzing the real-time rotational speed of the yarn rollers and comparing it with changes in the roller's external dimensions, the team leader pre-calculated the remaining roving quantity corresponding to the completed database of yarn roller size changes, and estimated the actual remaining yarn quantity, including: When the real-time rotation speed of the yarn roller continues to increase, it is determined that the roving is about to run out, and a yarn shortage warning is issued. The priority of the yarn shortage is determined based on the continuous increasing trend. The greater the continuous increasing trend, the more it is determined that the yarn is about to run out, and a warning for priority handling is issued.

[0009] Furthermore, this invention also discloses a ring spinning frame spindle roving monitoring and early warning control system using a ring spinning frame spindle roving monitoring and early warning control method. The monitoring and early warning control system is used to implement the ring spinning frame spindle roving monitoring and early warning control method according to any one of claims 1 to 5. The monitoring and early warning control system includes: The parameter acquisition module is used to collect production information of roving rollers from the production management ERP software, forming the first dataset by acquiring the shape, dimensions, rated speed, and number of roving rollers of the target spindle required for production; it also acquires the shape, dimensions, and number of roving rollers in the spindle frame of each spinning frame, as well as the actual speed of the corresponding spindle, forming the second dataset; and it acquires the rotation information and pressure information of the guide wheel, forming the third dataset. A warning analysis model module is constructed to input the second and third datasets into the warning analysis model to obtain the real-time change parameters of the roving roller in the spindle. The real-time change parameters of the roving roller include the real-time shape, real-time external dimensions, and real-time rotation speed. A one-to-one correspondence is established between the real-time change parameters of the roving roller and the rotation information and pressure information of the guide wheel in the third dataset to form a real-time production dataset. The real-time production dataset is normalized to form an analysis dataset. The data in the analysis dataset with the one-to-one correspondence are differentiated, and the roving consumption is calculated by integrating the speed difference of the roving roller. The remaining amount of roving is estimated by combining the initial roving volume. The data processing module will determine the warning signal corresponding to each analysis result and the priority of the warning signal based on the analysis results of the constructed warning analysis model. The warning signal display module is used to display warning signals, including an LCD display at the head of the spinning machine and a wristband worn on the wrist of the on-duty personnel; the warning signal display module sends the judgment results of the data processing module to the LCD display at the head of the spinning machine and the wristband worn on the wrist of the on-duty personnel.

[0010] The historical database module is used to store the information collected by the parameter acquisition module, as well as the analysis results of the corresponding early warning analysis model and the judgment results of the data processing module.

[0011] Beneficial effects: One or more technical solutions provided in this application have at least the following technical effects or advantages: The method provided in this application collects production information of roving rollers from the production management ERP software to form a first dataset, which includes the shape, dimensions, rated speed, and number of roving rollers required for production. A second dataset is formed by acquiring the shape, dimensions, and number of roving rollers in the spindle frame of each spinning machine, as well as the actual speed of the corresponding spindle. A third dataset is formed by acquiring the rotation and pressure information of the guide rollers. An early warning analysis model is then used to analyze the second and third datasets. By integrating the speed difference and comparing it with the first dataset, real-time correction of the analyzed data is achieved. This method can accurately determine and warn of roving yarn shortages, providing timely operational prompts for workers. It solves the technical problem in existing methods for monitoring roving roller yarn consumption, which mainly relies on manual inspection. Since each worker needs to oversee multiple spinning machines simultaneously, workers cannot promptly detect yarn shortages on a certain number of roving rollers, affecting the continuity and quality stability of spinning production.

[0012] The monitoring and early warning control system provided by this invention utilizes a parameter acquisition module to collect data from multiple sources. This data is then analyzed through an early warning analysis model module. A data processing module determines the corresponding early warning signal and its priority based on the analysis results of the early warning analysis model. The early warning signal display module sends the results from the data processing module to an LCD display at the spinning machine head and a wristband worn by the operator. This enables real-time monitoring and timely early warning of the roving roller yarn, and directly sends the yarn shortage status and location information of the roving roller to the worker, facilitating timely detection and handling. Attached Figure Description

[0013] Figure 1 A flowchart illustrating the control method in the roving monitoring and early warning control method and system of a spinning frame provided in this application; Figure 2A flowchart illustrating the construction of an early warning analysis model for a spinning frame spindle roving monitoring and early warning control method and system provided in this application; Figure 3 The diagram below illustrates the framework of a ring spinning machine roving monitoring and early warning control system that utilizes the ring spinning machine roving monitoring and early warning control method provided in this application. Detailed Implementation

[0014] This application provides a method and system for monitoring and early warning control of roving on spinning machine spindles. It addresses the technical problem that existing methods for monitoring the consumption of yarn on roving rollers mainly rely on manual inspection. Since each worker needs to oversee multiple spinning machines simultaneously, workers cannot promptly detect when a certain number of roving rollers are short of yarn, which affects the continuity and quality stability of spinning production. This solution provides a method that collects production information of roving rollers from production management ERP software to form a first dataset, which includes the shape, dimensions, rated speed, and number of roving rollers required for production. A second dataset is formed by acquiring the shape, dimensions, and number of roving rollers in the spindle frames of each spinning frame, as well as the actual speed of the corresponding spindles. A third dataset is formed by acquiring the rotation and pressure information of the guide rollers. An early warning analysis model is then used to analyze the second and third datasets. By integrating the speed difference and comparing it with the first dataset, real-time correction of the analyzed data is achieved. This method can accurately determine and warn of roving yarn shortages, providing timely operational prompts for workers. It solves the technical problem that existing methods for monitoring roving roller yarn consumption mainly rely on manual inspection, where each worker needs to oversee multiple spinning frames simultaneously. This results in workers not being able to detect yarn shortages on a certain number of roving rollers in a timely manner, affecting the continuity and quality stability of spinning production.

[0015] The acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of national laws and regulations.

[0016] The technical solutions of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. It should be understood that the present invention is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. It should also be noted that, for ease of description, only the parts related to the present invention are shown in the accompanying drawings, not all of them.

[0017] Example 1: A method for monitoring and early warning control of roving on a spinning frame, the control method comprising: S100: Collects production information of roving rolls from the production management ERP software, and forms the shape, dimensions, rated speed and number of target roving rolls required for production, thus forming the first dataset; The production information for roving rolls in the production management ERP software includes the shape, dimensions, rated speed, and number of roving rolls of the target spindle. This data is obtained by directly inputting the actual roving roll information into the production management ERP software.

[0018] S200: Obtain the shape, size, and number of roving rollers in the spindle frame of each spinning machine, as well as the actual rotation speed of the corresponding spindle, to form a second dataset; The acquisition of the shape, size, and number of roving rollers in the spindle rack of each spinning frame, as well as the actual rotation speed of the corresponding spindle, mainly relies on corresponding sensors or vision cameras. The shape and size of the roving rollers depend on the images captured by the vision camera, and the system analyzes these images to obtain the shape and size of the roving rollers. The number of rovings can be obtained through the vision camera and photoelectric sensors, while the actual rotation speed of the corresponding spindle is obtained through photoelectric sensors or angle sensors. The installation positions of the sensors need to be carefully selected to ensure the stability of the data acquisition required by this invention.

[0019] S300: Acquires the rotation information and pressure information of the guide wheel to form a third dataset; The rotation information of the guide wheel is obtained by an angle sensor or a photoelectric sensor installed on the axis of the guide wheel. An angle sensor can accurately determine how many revolutions the guide wheel has made and the angle it has rotated at a certain moment in one revolution. A photoelectric sensor can only obtain the number of revolutions that have been completed. The pressure information of the guide wheel is obtained from a pressure sensor installed on the guide wheel shaft.

[0020] S400: Construct an early warning analysis model; The construction of the early warning analysis model includes: S410: Analyze the first dataset and divide the data on the shape, dimensions, rated speed and number of roving rolls of the target spindle into a shape subset, a dimension subset, a rated speed subset and a number of roving rolls subset; S420: Analyze the second dataset; classify the shape, size, and number of roving rollers in the spindle rack of each spinning machine, as well as the actual rotation speed of the corresponding spindle, into real-time shape subset, real-time external size subset, real-time rotation speed subset, and online production roving roller quantity subset; S430: Analyze the third dataset; divide the rotation information and pressure information of the guide wheel into a rotation information subset and a pressure information subset, respectively. S440: Using the shape subset, external dimension subset, rated speed subset, and roving roll number subset as initial references; establishing a one-to-one correspondence between the guide wheel rotation information subset and guide wheel pressure information subset and the corresponding real-time shape subset, real-time external dimension subset, real-time speed subset, and online production roving roll number subset, forming a mapping analysis dataset. S450: After normalizing the data with mapping relationships in the mapping analysis dataset, differentiate the data within each mapping relationship using timestamps as markers, and calculate the difference between data corresponding to adjacent timestamps. When the difference is greater than zero, it indicates that yarn consumption has increased; when the difference is less than zero, it indicates that yarn consumption has decreased; and when the difference is equal to zero, it indicates that yarn consumption is normal. Normalization can be achieved using the Min-Max algorithm or the Z-Score algorithm. For parameters with different dimensions, taking pressure and speed as examples, the dimensions are eliminated by comparing the difference with the minimum value and then making a ratio. The speed is given a greater weight than the pressure, so that the system can make selections and analyses faster and more accurately.

[0021] S460: Determines whether the guide wheel is slipping by analyzing the frequency of the difference through Fourier transform; The step of determining whether the guide wheel is slipping by analyzing the frequency of change of the difference using Fourier transform includes: When the frequency of the difference changes repeatedly oscillates around a fixed value, it is determined that the guide wheel is slipping and a yarn breakage alarm is issued. Furthermore, Fourier transform analysis of frequency changes requires a sampling rate of over 500 times per minute, and slippage and normal yarn vibration may overlap in frequency bands. Since the yarn tension disappears instantly during slippage, the amplitude of the pressure signal drops sharply. Therefore, combining time-domain analysis and frequency-domain analysis improves the accuracy of slippage identification. Here, time-domain analysis refers to capturing sudden changes in the pressure signal, while frequency-domain analysis refers to identifying periodic vibration characteristics in the pressure signal. By analyzing the continuous pressure signal frame by frame, the variation pattern and periodic vibration characteristics of the pressure signal are obtained. By using the variation pattern and periodic vibration characteristics of the pressure signal, slippage and normal yarn vibration are determined, thereby improving the accuracy of determining whether the guide roller is slipping.

[0022] S470: Calculates roving consumption by integrating the speed difference of the roving rolls, and finally outputs an estimate of the remaining roving quantity by combining the shape subset, the external size subset, the rated speed subset, and the number of roving rolls subset.

[0023] In the step of calculating roving consumption by integrating the speed difference of the roving rollers and finally outputting an estimate of the remaining amount of roving by combining the shape subset, the external size subset, the rated speed subset, and the number subset of roving rollers, an influence factor coefficient needs to be added to this estimate due to the actual influence of yarn density fluctuations and differences in winding tightness. That is, the remaining amount is multiplied by the influence factor coefficient. The value range of the influence factor coefficient is 0.75 to 1.5, and the commonly used influence factor coefficients are 0.8, 0.9, 1.0, 1.1, 1.2, 1.3, and 1.4.

[0024] The step of calculating roving consumption by integrating the speed difference of the roving rollers, and finally outputting an estimate of the remaining roving quantity by combining a shape subset, a size subset, a rated speed subset, and a roving roller number subset, includes: S471: The trend of real-time rotational speed of the yarn roller is obtained by calculating the integral speed difference of the roving roller using the trapezoidal method; S472: By comparing the real-time rotational speed of the yarn roller with the changes in the outer dimensions of the yarn roller, the team leader pre-calculates the remaining amount of roving corresponding to the completed yarn roller size change database, and estimates the actual remaining amount of yarn.

[0025] Specifically, by analyzing the real-time rotational speed of the yarn rollers and comparing it with changes in the roller's external dimensions, the team leader pre-calculated the remaining roving quantity corresponding to the completed database of yarn roller size changes, and estimated the actual remaining yarn quantity, including: When the real-time rotational speed of the yarn roller continues to increase, it is determined that the roving is about to run out, and a yarn shortage warning is issued. The priority of the yarn shortage is determined based on the continuous increasing trend. The greater the continuous increasing trend, the more determined that the yarn is about to run out, and a warning is issued for priority handling. For example, a rotational speed change rate >10% is defined as a first-level warning and is issued with priority. A rotational speed change rate >5% is a second-level warning and is issued after the first-level warning, and so on.

[0026] S500: Input the second and third datasets into the early warning analysis model to obtain the real-time change parameters of the roving roller in the spindle; the real-time change parameters of the roving roller include the real-time shape, real-time external dimensions, and real-time rotation speed; establish a one-to-one correspondence between the real-time change parameters of the roving roller and the rotation information and pressure information of the guide wheel in the third dataset to form a real-time production dataset.

[0027] S600: Normalize the real-time production dataset to form an analysis dataset. Differentiate the data in the analysis dataset that have the one-to-one correspondence mentioned above. Calculate the roving consumption by integrating the speed difference of the roving roller. Estimate the remaining amount of roving by combining the initial roving volume. S700: When at least one of the parameters in the remaining quantity or the third dataset remains unchanged for two consecutive three-second intervals, it is determined that a yarn breakage accident has occurred. S800: Based on the remaining amount of roving and the determination result of the yarn breakage accident, control the corresponding spinning machine to issue an alarm and give the position of the roving roller corresponding to the alarm.

[0028] Example 2: A ring spinning frame spindle roving monitoring and early warning control system using a ring spinning frame spindle roving monitoring and early warning control method, wherein the monitoring and early warning control system is used to implement the ring spinning frame spindle roving monitoring and early warning control method according to any one of claims 1 to 5, and the monitoring and early warning control system includes: The parameter acquisition module is used to collect production information of roving rollers from the production management ERP software, forming the first dataset by acquiring the shape, dimensions, rated speed, and number of roving rollers of the target spindle required for production; it also acquires the shape, dimensions, and number of roving rollers in the spindle frame of each spinning frame, as well as the actual speed of the corresponding spindle, forming the second dataset; and it acquires the rotation information and pressure information of the guide wheel, forming the third dataset. A pre-warning analysis model module is constructed to input the second and third datasets into the model and obtain the real-time change parameters of the roving roller in the spindle. These parameters include the real-time shape, dimensions, and rotational speed. A one-to-one correspondence is established between the real-time roving roller change parameters and the corresponding guide wheel rotation and pressure information in the third dataset, forming a real-time production dataset. This dataset is then normalized to form an analysis dataset. Data within the analysis dataset with the established one-to-one correspondence is differentiated, and the roving consumption is calculated by integrating the roving roller rotational speed difference. The remaining roving quantity is estimated by combining this with the initial roving volume. The system response time is set to <500ms from yarn breakage to alarm. An edge computing architecture is employed to perform real-time analysis on the device side, avoiding cloud transmission delays.

[0029] The data processing module will determine the warning signal corresponding to each analysis result and the priority of the warning signal based on the analysis results of the constructed warning analysis model. The warning signal display module is used to display warning signals, including an LCD display at the head of the spinning machine and a wristband worn on the wrist of the on-duty personnel; the warning signal display module sends the judgment result of the data processing module to the LCD display at the head of the spinning machine and the wristband worn on the wrist of the on-duty personnel, and the wristband communication protocol is Bluetooth or WIFI.

[0030] The historical database module is used to store the information collected by the parameter acquisition module, as well as the analysis results of the corresponding early warning analysis model and the judgment results of the data processing module.

[0031] In summary, any of the methods or steps described above can be stored as computer instructions or programs in various types of computer memory, and the computer instructions or programs can be recognized by various types of computer processors to implement any of the above methods or steps.

[0032] Based on the above specific embodiments of the present invention, any improvements and modifications made to the present invention by those skilled in the art without departing from the principle of the present invention shall fall within the patent protection scope of the present invention.

Claims

1. A method for monitoring and early warning control of roving on a spinning frame, characterized in that, The control method includes: Collect production information on roving rolls from the production management ERP software to form the first dataset, which includes the shape, dimensions, rated speed, and number of target roving rolls required for production. The shape, size, and number of roving rollers in the spindle rack of each spinning machine, as well as the actual rotation speed of the corresponding spindle, are obtained to form a second dataset; The rotation and pressure information of the guide wheel are obtained to form a third dataset; Construct an early warning analysis model; Input the second and third datasets into the early warning analysis model to obtain the real-time change parameters of the roving roller in the spindle; the real-time change parameters of the roving roller include the real-time shape, real-time external dimensions, and real-time rotation speed; establish a one-to-one correspondence between the real-time change parameters of the roving roller and the rotation information and pressure information of the guide wheel in the third dataset to form a real-time production dataset; The real-time production dataset is normalized to form an analysis dataset. The data in the analysis dataset with the one-to-one correspondence are differentiated. The roving consumption is calculated by integrating the speed difference of the roving roller. The remaining amount of roving is estimated by combining the initial volume of the roving. A yarn breakage accident is determined when at least one of the parameters in the remaining quantity or the third dataset remains unchanged for two consecutive three-second intervals. Based on the remaining amount of roving and the result of the yarn breakage accident determination, the corresponding spinning machine is controlled to issue an alarm, and the position of the roving roller corresponding to the alarm is given.

2. The method for monitoring and early warning control of roving hanging on a spinning frame according to claim 1, characterized in that, The construction of the early warning analysis model includes: Analyzing the first dataset, the shape, dimensions, rated speed, and number of roving rolls of the target spindle are divided into shape subset, dimension subset, rated speed subset, and number of roving rolls subset; Analyze the second dataset; classify the shape, size, and number of roving rollers in the spindle rack of each spinning machine, as well as the actual rotation speed of the corresponding spindle, into real-time shape subset, real-time external size subset, real-time rotation speed subset, and online production roving roller quantity subset; Analyze the third dataset; divide the rotation information and pressure information of the guide wheel into a rotation information subset and a pressure information subset, respectively. Using the shape subset, external dimension subset, rated speed subset, and roving roll number subset as initial references, a one-to-one correspondence is established between the guide wheel rotation information subset and the guide wheel pressure information subset and the corresponding real-time shape subset, real-time external dimension subset, real-time speed subset, and online production roving roll number subset, forming a mapping analysis dataset. After normalizing the data with mapping relationships in the mapping analysis dataset, the data within each mapping relationship is differentiated using timestamps as markers. The difference between data corresponding to adjacent timestamps is calculated. When the difference is greater than zero, it indicates that yarn consumption has increased. When the difference is less than zero, it indicates that yarn consumption has decreased. When the difference is equal to zero, it indicates that yarn consumption is normal. By analyzing the frequency of change of the difference using Fourier transform, it can be determined whether the guide wheel is slipping. The roving consumption is calculated by integrating the speed difference of the roving rolls, and the final output is an estimate of the remaining amount of roving by combining the shape subset, the external size subset, the rated speed subset, and the number of roving rolls subset.

3. The method for monitoring and early warning control of roving on a spinning frame according to claim 2, characterized in that, The step of determining whether the guide wheel is slipping by analyzing the frequency of change of the difference using Fourier transform includes: When the frequency of the difference changes repeatedly oscillates around a fixed value, it is determined that the guide wheel is slipping and a yarn breakage alarm is issued.

4. The method for monitoring and early warning control of roving hanging on a spinning frame according to claim 3, characterized in that, The method of calculating roving consumption by integrating the speed difference of the roving rollers, and finally outputting an estimate of the remaining roving quantity by combining a shape subset, an external size subset, a rated speed subset, and a roving roller number subset, includes: The trend of real-time rotational speed of the yarn roller is obtained by calculating the integral speed difference of the roving roller using the trapezoidal method. By observing the real-time rotational speed of the yarn rollers and comparing it with the changes in the yarn roller's external dimensions, the team leader pre-calculated the remaining amount of roving corresponding to the completed database of yarn roller size changes, and estimated the actual remaining amount of yarn.

5. The method for monitoring and early warning control of roving hanging on a spinning frame according to claim 4, characterized in that, By analyzing the real-time rotational speed of the yarn rollers and comparing it with changes in the roller's external dimensions, the team leader pre-calculated the remaining roving quantity corresponding to the completed database of yarn roller size changes, and estimated the actual remaining yarn quantity, including: When the real-time rotation speed of the yarn roller continues to increase, it is determined that the roving is about to run out, and a yarn shortage warning is issued. The priority of the yarn shortage is determined based on the continuous increasing trend. The greater the continuous increasing trend, the more it is determined that the yarn is about to run out, and a warning for priority handling is issued.

6. A ring spinning frame spindle roving monitoring and early warning control system using a ring spinning frame spindle roving monitoring and early warning control method, wherein the monitoring and early warning control system is used to implement the ring spinning frame spindle roving monitoring and early warning control method according to any one of claims 1 to 5, characterized in that, The monitoring and early warning control system includes: The parameter acquisition module is used to collect production information of roving rollers from the production management ERP software, forming the first dataset by acquiring the shape, dimensions, rated speed, and number of roving rollers of the target spindle required for production; it also acquires the shape, dimensions, and number of roving rollers in the spindle frame of each spinning frame, as well as the actual speed of the corresponding spindle, forming the second dataset; and it acquires the rotation information and pressure information of the guide wheel, forming the third dataset. A warning analysis model module is constructed to input the second and third datasets into the warning analysis model to obtain the real-time change parameters of the roving roller in the spindle. The real-time change parameters of the roving roller include the real-time shape, real-time external dimensions, and real-time rotation speed. A one-to-one correspondence is established between the real-time change parameters of the roving roller and the rotation information and pressure information of the guide wheel in the third dataset to form a real-time production dataset. The real-time production dataset is normalized to form an analysis dataset. The data in the analysis dataset with the one-to-one correspondence are differentiated, and the roving consumption is calculated by integrating the speed difference of the roving roller. The remaining amount of roving is estimated by combining the initial roving volume. The data processing module will determine the warning signal corresponding to each analysis result and the priority of the warning signal based on the analysis results of the constructed warning analysis model. The warning signal display module is used to display warning signals, including an LCD display at the head of the spinning machine and a wristband worn on the wrist of the on-duty personnel; the warning signal display module sends the judgment results of the data processing module to the LCD display at the head of the spinning machine and the wristband worn on the wrist of the on-duty personnel; The historical database module is used to store the information collected by the parameter acquisition module, as well as the analysis results of the corresponding early warning analysis model and the judgment results of the data processing module.

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