Real-time monitoring method and system for yarn conveying operation state

Abstract

The invention relates to the technical field of yarn operation data processing, in particular to a yarn conveying operation state real-time monitoring method and system. The method comprises the following steps: acquiring a speed value and a tension value corresponding to each data point in a yarn operation data time sequence, and presetting a local range of the data points; the noise index of the data point is obtained through the ratio of the absolute value of the difference value between the speed value of the data point and the speed mean value in the local range to the speed standard deviation in the local range; recording a normalized value of a ratio of the noise index of the data point to the mean value of the vertical distance between each data point in the local range and the regression curve as the noise degree of the data point; the speed values of the data points form a speed data time sequence, and the speed data time sequence is de-noised by using the target mean value of each data point in the MVI algorithm to obtain a yarn operation monitoring result, so that the accuracy of the yarn conveying operation state monitoring result is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of yarn running data processing, and in particular to a method and system for real-time monitoring of yarn conveying running status. Background Art

[0002] Yarn is the basic material of the textile industry. In the textile process, textile machinery weaves the input yarn into various fabrics with complex structures through interweaving and weaving. The delivery and running status of the yarn directly affects the quality and production efficiency of the product. Therefore, it is necessary to monitor and adjust the running status of the yarn in the textile process.

[0003] In the prior art, the adjustment of the yarn running state is mostly achieved by collecting yarn running data. For example, the patent application document with publication number CN118348274A discloses a charge-based yarn intelligent detection system and method. The application obtains the time series of the yarn charge signal to obtain the yarn movement speed value. In this way, a deep learning algorithm is used to analyze and process the potential time series correlation characteristics of the yarn movement state contained in the time series of the yarn charge, and a priori information is introduced as a reasoning clue to comprehensively characterize the yarn movement speed. The yarn movement speed value is estimated by decoding regression, thereby realizing the monitoring of the yarn running state.

[0004] The above-mentioned prior art determines the monitoring result through the movement speed value obtained by the yarn charge amount. However, there may be noise interference when collecting the yarn movement speed during the operation of the textile machinery, resulting in low accuracy of the obtained speed data, and the accuracy of the yarn operation status monitoring result obtained based on this is also low.

[0005] Based on this, how to accurately obtain the monitoring results of the yarn conveying operation status is a problem that needs to be solved urgently by technical personnel in this field. Summary of the invention

[0006] In order to solve the technical problem of how to accurately obtain the monitoring result of the yarn conveying operation status, the present invention provides a real-time monitoring method and system for the yarn conveying operation status.

[0007] In a first aspect, the present invention provides a method for real-time monitoring of the running status of a yarn conveying, which adopts the following technical solution: A method for real-time monitoring of yarn conveying operation status, comprising the steps of: Obtain the speed value and tension value corresponding to each data point in the yarn running data time series, and preset the local range of the data point; obtain the absolute value of the difference between the speed value of the data point and the speed mean in its local range, and record the ratio of the absolute value of the difference to the speed standard deviation in the local range as the noise index of the data point; obtain the regression curve of the local range of the data point, and record the normalized value of the ratio of the noise index of the data point to the mean of the vertical distances of each data point in its local range to the regression curve as the noise degree of the data point; ; is the target mean of the ith data point, is the mean velocity of the left and right adjacent data points of the i-th data point, is the velocity value of the ith data point, is the noise level of the ith data point, is an exponential function with e as the base; the speed values ​​of the data points are combined into a speed data time series sequence, and the target mean value of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn operation monitoring result.

[0008] When determining the yarn operation monitoring results, the present invention takes into account the possible existence of noise data in the yarn speed data. Therefore, the yarn speed data is denoised by the MVI algorithm before abnormal monitoring is performed, which can effectively reduce the influence of noise data on the monitoring results and improve the accuracy of the operation monitoring results. In this process, the present invention takes into account that when the noise data is directly identified based on the speed mean of the adjacent data points on the left and right of the data point in the MVI algorithm, the abnormal data generated by the yarn itself during operation may be identified as noise data. Based on this, the present invention obtains the data performance of the speed data in its local range and the tension data that is correlated with the speed data, obtains the possibility that the speed data is noise data, and adjusts the speed mean based on the possibility that the speed data is noise data, thereby reducing the possibility of misidentifying the abnormal data as noise data, thereby effectively improving the accuracy of the yarn conveying operation status monitoring results.

[0009] According to a real-time monitoring method for the yarn conveying operation status provided by the present invention, the speed value and tension value corresponding to each data point in the yarn operation data time series sequence are obtained, and before that, the speed value and tension value of the yarn obtained at the same collection time are combined into one point as a data point, and the yarn operation data time series sequence is obtained after preprocessing.

[0010] The present invention takes into account the problem that there may be data missing in the originally collected speed data and tension data, and therefore improves the overall quality of the data through preprocessing.

[0011] According to a real-time monitoring method for the yarn conveying operation status provided by the present invention, the method for obtaining the local range of the data point includes: presetting the length of the local range, taking the data point as the center, and obtaining other data points in equal amounts on both sides of the data point to construct the local range of the data point; wherein the length of the local range is an odd number.

[0012] The present invention constructs the local range of the data point by acquiring other data points on both sides of the data point, fully considering the discrete degree of the current data point compared with its surrounding data, so as to accurately obtain the noise index of the data point based on this.

[0013] According to a real-time monitoring method for the yarn conveying operation status provided by the present invention, the regression curve of the local range of data points is obtained, including: taking the speed value of each data point in the local range of data points as the horizontal coordinate and the tension value as the vertical coordinate to obtain a scatter plot of the operation data, and using the least squares method to obtain the regression curve of the scatter plot of the operation data.

[0014] The present invention constructs a regression curve of speed data and tension data, obtains the correlation between the speed value and the tension value in the data point through the vertical distance from each data point to the regression curve, and verifies the noise index of the data point through the correlation.

[0015] According to a real-time monitoring method for the yarn conveying operation status provided by the present invention, the target mean of each data point is used in the MVI algorithm to denoise the speed data time series, including: obtaining the absolute value of the difference between the target mean of each data point and the speed value, eliminating the data points corresponding to the absolute value of the difference greater than a preset threshold as noise points, and then using the target mean corresponding to the data point for interpolation.

[0016] According to a real-time monitoring method for the yarn conveying operation status provided by the present invention, the target mean value of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn operation monitoring result, including: if the speed value in the denoised speed data time series sequence is greater than a preset abnormal threshold, the yarn operation monitoring result is abnormal; otherwise, the yarn operation monitoring result is normal.

[0017] According to a real-time monitoring method for yarn conveying operation status provided by the present invention, when the yarn operation monitoring result is abnormal, the method further includes: issuing an early warning of the abnormal yarn operation monitoring result.

[0018] The present invention takes into account that abnormal data in the yarn running speed data may affect product quality, and therefore sends out a prompt when an abnormal state is detected, so that the staff can deal with it in time.

[0019] In a second aspect, the present invention provides a real-time monitoring system for the yarn conveying operation status, which adopts the following technical solution: A yarn conveying operation status real-time monitoring system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned yarn conveying operation status real-time monitoring method is implemented.

[0020] By adopting the above technical solution, the above-mentioned method for real-time monitoring of the yarn conveying operation status is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0021] The present invention has the following technical effects: Based on the above technical solution, when determining the yarn running status monitoring results, the present invention uses the MVI algorithm to denoise the yarn speed data and then conduct abnormal monitoring, which can effectively reduce the impact of noise data on the monitoring results and improve the accuracy of the running monitoring results. In this process, the present invention obtains the data performance of the speed data itself and the tension data that is correlated with the speed data to obtain the possibility that the speed data is noise data, and adjusts the speed mean based on the possibility that the speed data is noise data, thereby reducing the possibility of misidentifying abnormal data as noise data, thereby effectively improving the accuracy of the yarn conveying running status monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0023] Figure 1 A schematic flow chart of a method for real-time monitoring of yarn conveying operation status provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0026] It should be noted that yarn is the basic material of the textile industry. In the textile process, textile machinery weaves the input yarn into various fabrics with complex structures through interweaving and weaving. The delivery and running status of the yarn directly affects the quality and production efficiency of the product, so it is necessary to monitor and adjust the running status of the yarn in the textile process.

[0027] Yarn speed is a key parameter that affects the textile process. If the yarn speed is abnormal, it may cause uneven thickness and density of textiles. By monitoring the yarn speed data in real time, early warning prompts can be issued in time in the event of an abnormality to ensure the stability and quality of production. However, when collecting the yarn speed during the operation of textile machinery, there may be noise interference, resulting in low accuracy of the speed data, and it is impossible to accurately obtain the yarn operation status monitoring results based on this.

[0028] Based on this, an embodiment of the present invention discloses a real-time monitoring method for the yarn conveying operation status. The method can effectively improve the accuracy of the obtained abnormal monitoring results by denoising the yarn operation data and then performing abnormal monitoring.

[0029] Please refer to Figure 1 As shown, Figure 1 A flow chart of a method for real-time monitoring of the yarn conveying operation status provided in an embodiment of the present invention, the method specifically comprises the following steps.

[0030] S1: Obtain the speed value and tension value corresponding to each data point in the yarn running data time series, and preset the local range of the data point.

[0031] It should be noted that the mean-value iteration filter (MVI) is a filtering and smoothing method based on small window movement. It obtains the mean of the adjacent data points on the left and right sides of the data point, and identifies the noise data through the difference between the value of the data point and the data mean. Therefore, it can be used for denoising of yarn speed data.

[0032] However, during the operation of textile machinery, the speed data may fluctuate unstably due to problems such as equipment aging. If the current data point is judged as noise data based on the mean of the adjacent data points on the left and right sides of the data point, normal data fluctuations may be identified as noise data and smoothed, resulting in the loss of data details and affecting the accuracy of subsequent data anomaly monitoring.

[0033] It should be further explained that the normal speed data generated during yarn operation is relatively smooth and concentrated in the local range, while the randomly generated noise data is relatively discrete in the local range.

[0034] Based on this, the embodiment of the present invention obtains the data discreteness of the speed value of the data point in the local range to obtain the possibility that it is noise data, and adjusts the speed mean on the left and right sides of the data point according to the noise possibility of the data point, thereby achieving accurate denoising of the speed data.

[0035] For example, in an embodiment of the present invention, the speed value and tension value corresponding to each data point in the yarn running data time series sequence are obtained, which also includes: the speed value and tension value of the yarn obtained at the same collection time are combined into one point as a data point, and the yarn running data time series sequence is obtained after preprocessing.

[0036] Among them, preprocessing can be missing data interpolation, data format conversion, etc., which can be set according to actual needs.

[0037] Specifically, a collection frequency is set for the speed sensor and the tension sensor, and a speed value and a tension value are collected at each collection moment.

[0038] The acquisition frequency may be set to 1 Hz; the acquisition frequency may be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0039] For example, in the embodiment of the present invention, the method of obtaining the local range of data points includes the following two possible implementation methods: In one embodiment, the length of the local range can be preset, and the local range of the data point can be constructed by taking the data point as the center and acquiring other data points equally on both sides of the data point; wherein the length of the local range is an odd number.

[0040] It can be understood that the length of the local range is the number of data points contained in the local range, and the local range of the data point finally obtained includes the data point itself.

[0041] Among them, the length of the local range can be set to 7, which can be set according to actual needs.

[0042] Take the local range as an example: if the length of the local range of a data point is 7, then we can take the data point as the center and obtain 3 other data points on both sides of it to finally get the local range of the data point.

[0043] In another embodiment, the number k of the nearest data points may be preset, the distances between the current data point and other data points on both sides of the current data point are obtained, and the k data points closest to the current data point are used as data points in the local range of the current data point.

[0044] The number of the nearest data points can be set to 6, which can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this. The distance can be a Euclidean distance, which can be set according to actual needs.

[0045] After the local range of each data point is obtained based on the above steps, the possibility of the data point being noise data can be obtained based on the degree of discreteness between the data point and its local range, that is, continue to perform the following steps.

[0046] S2: Obtain the absolute value of the difference between the speed value of the data point and the speed mean in its local range, and record the ratio of the absolute value of the difference to the speed standard deviation in the local range as the noise index of the data point.

[0047] It should be noted that the local range of each data point can be obtained based on the above steps. The discreteness of the data point can be obtained through the discreteness of the speed value of the data point relative to the speed data of its local range. The higher the discreteness of the data point, the higher the possibility that it is noise data.

[0048] For example, in the embodiment of the present invention, the noise index of the data point is determined, and the specific relationship can be as follows: ; In the formula, is the noise index of the ith data point, is the velocity value of the ith data point, is the mean velocity in the local range of the i-th data point, is the standard deviation of velocity in the local range of the ith data point, is the absolute value symbol.

[0049] In the above formula, It represents the absolute value of the difference between the speed value of the ith data point and the speed mean in its local range. The larger the value, the greater the deviation of the current data point, the greater the possibility of being noise data, and the larger the corresponding noise index.

[0050] The greater the deviation of the data point speed from the standard deviation of the speed in the local range, the greater the deviation between the data point and the overall local range, the more prominent its performance, and the greater the possibility that it is noise data.

[0051] Based on the above steps, the speed change of the data point and its local range is analyzed to obtain the noise index of each data point, and then continue to perform the following steps.

[0052] S3: Obtain the regression curve of the local range of the data point, and record the normalized value of the ratio of the noise index of the data point to the average of the vertical distances of each data point in its local range to the regression curve as the noise level of the data point.

[0053] It should be noted that the noise index of the data points obtained based on the above steps only considers the speed change of the data points in a local range. However, during the yarn conveying process, the conveying speed may be affected by factors such as yarn quality and equipment failure, resulting in fluctuations in the yarn conveying speed data. Directly calculating the possibility that such speed data is noise data based on the above steps may identify normally changing data points as noise data.

[0054] It should be further explained that the yarn will be subject to various frictions and resistances during transportation, resulting in an increase in speed. The increase in speed will cause the yarn tension to increase. The change in yarn speed will cause the tension data to change. Therefore, in general, the yarn transportation speed data and the tension data show a good positive correlation.

[0055] Based on this, the embodiment of the present invention can obtain yarn tension data that is correlated with the yarn speed data, and verify the noise index of the data point obtained in the above steps through the correlation between the two. If the correlation between the speed data and the tension data of the data point is high, it means that the current data point is likely to be generated during the working process of the textile machinery, and at this time, it is necessary to reduce the noise index of the data point; conversely, if the correlation between the speed data and the tension data of the data point is low, it means that the current data point is likely to be noise data, and at this time, it is necessary to increase the noise index of the data point.

[0056] By way of example, in an embodiment of the present invention, obtaining a regression curve of a local range of data points includes: using the speed value of each data point in the local range of data points as the horizontal coordinate and the tension value as the vertical coordinate to obtain a scatter plot of the operating data, and using the least squares method to obtain a regression curve of the scatter plot of the operating data.

[0057] Among them, the specific steps of obtaining the regression curve of the operating data scatter plot by the least square method can be obtained by the existing algorithm, and the embodiment of the present invention will not be described in detail here.

[0058] For example, in another embodiment of the present invention, when obtaining the regression curve of a local range of data points, the tension value of each data point in the local range of data points can be used as the horizontal coordinate and the speed value as the vertical coordinate to obtain a scatter plot of the operating data, and the regression curve of the scatter plot of the operating data can be obtained using the least squares method.

[0059] It can be understood that the regression curve can reveal the overall trend between two variables and show the expected relationship. After obtaining the regression curve of the local range of the data point based on the above embodiment, the vertical distance from each data point to the regression curve can be obtained as the correlation between the speed data and the tension data in the data point, and the noise index of the data point can be corrected based on the correlation between the speed data and the tension data of the data point to obtain the noise level of the data point.

[0060] For example, in the embodiment of the present invention, the noise level of a data point is determined, and specifically, the following relationship can be referred to: ; In the formula, is the noise level of the ith data point, is the noise index of the ith data point, is the mean vertical distance from each data point to the regression curve in the local range of the i-th data point, is the hyperbolic tangent function.

[0061] In the above formula, the hyperbolic tangent function is used to Perform normalization.

[0062] The larger the mean of the vertical distances from each data point to the regression curve in the local range of the i-th data point, the farther the data points in its local range are from the regression curve, that is, the weaker the positive correlation between the speed data and the tension data of each data point, the greater the possibility that the i-th data point is noise data, and the greater the corresponding noise level.

[0063] Based on the above steps, the noise index of each data point is corrected to accurately obtain the noise level of each data point. Based on the noise level of the data point, accurate denoising of the data point can be achieved, that is, continue to perform the following steps.

[0064] S4: The speed values ​​of the data points are combined into a speed data time series sequence, and the target mean value of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn operation monitoring result.

[0065] It should be noted that the MVI algorithm identifies noise data through the difference between the speed value of a data point and the speed mean of its left and right adjacent data points. After the embodiment of the present invention obtains the possibility that each data point is noise data based on the above steps, the speed mean of the left and right adjacent data points of the data point can be adjusted based on the possibility that the data point is noise data. The adjusted target mean of the data point with a greater degree of noise is closer to the initial mean of the data point, so that it can be accurately identified as noise data; the adjusted target mean of the data point with a smaller degree of noise has a smaller difference with the speed value of the data point, reducing the possibility of the data point being identified as noise data.

[0066] It should be further explained that the relationship between the speed value of the current data point and the average speed of the adjacent data points on its left and right sides specifically includes the following two possible situations: In one possible case, the speed value of the current data point is greater than or equal to the average speed of the left and right adjacent data points; in this case, a noise level adjustment portion can be added based on the average speed of the left and right adjacent data points.

[0067] In another possible case, the speed value of the current data point is less than the average speed of the left and right adjacent data points; in this case, the noise level adjustment portion can be subtracted from the average speed of the left and right adjacent data points.

[0068] For example, in the embodiment of the present invention, the target mean of the data points is determined, and the specific relationship can be as follows: ; is the target mean of the ith data point, is the mean velocity of the left and right adjacent data points of the i-th data point, is the velocity value of the ith data point, is the noise level of the ith data point, is an exponential function with base e, is the absolute value symbol.

[0069] In the above formula, Represents the adjustment factor. The higher the noise level of the i-th data point, the greater the possibility that the data point is noise data, and the smaller the corresponding adjustment factor.

[0070] After adjusting the speed mean of the left and right adjacent data points of each data point based on the above steps, accurate denoising of the speed data can be achieved based on the target mean obtained after the adjustment.

[0071] By way of example, in an embodiment of the present invention, the target mean of each data point is used in the MVI algorithm to denoise the speed data time series, including: obtaining the absolute value of the difference between the target mean of each data point and the speed value, removing the data points corresponding to the absolute value of the difference greater than a preset threshold as noise points, and then interpolating using the target mean corresponding to the data point.

[0072] The preset threshold may be set to 0.63; the preset threshold may be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0073] It can be understood that if the absolute value of the difference between the target mean value of a data point and its speed value is not greater than the preset threshold, it means that the data point is speed data generated during the yarn conveying process.

[0074] After the yarn speed data time series sequence is denoised based on the above steps, the yarn operation monitoring result can be accurately obtained based on the denoised speed data time series sequence.

[0075] For example, in an embodiment of the present invention, the target mean of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn operation monitoring result, including: if the speed value in the denoised speed data time series sequence is greater than a preset abnormal threshold, the yarn operation monitoring result is abnormal; otherwise, the yarn operation monitoring result is normal.

[0076] Among them, the preset abnormal threshold can be set to Thousand revolutions per minute; the preset abnormal threshold value can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0077] After determining the yarn operation monitoring result in the above manner, the embodiment of the present invention also considers that if the yarn operation monitoring result is abnormal, there may be safety hazards or affect the quality of the product. Based on this, after obtaining the abnormal monitoring result, the embodiment of the present invention can also issue a prompt to the outside so that the staff can handle it in time.

[0078] For example, in the embodiment of the present invention, if the yarn running monitoring result is abnormal, the method further includes: issuing an early warning of the abnormal yarn running monitoring result.

[0079] Among them, the warning method can be a sound warning, a signal light warning, etc., which can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions here.

[0080] It can be seen that in the embodiment of the present invention, when determining the monitoring result of the yarn conveying operation status, the speed value and tension value corresponding to each data point in the yarn operation data time series sequence can be obtained, and the local range of the data point can be preset; the absolute value of the difference between the speed value of the data point and the speed mean in its local range is obtained, and the ratio of the absolute value of the difference to the speed standard deviation in the local range is recorded as the noise index of the data point; the regression curve of the local range of the data point is obtained, and the normalized value of the ratio of the noise index of the data point to the mean of the vertical distances of each data point in its local range to the regression curve is recorded as the noise degree of the data point; the speed values ​​of the data points are combined into a speed data time series sequence, and the target mean of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn operation monitoring result.

[0081] In this way, the embodiment of the present invention uses the MVI algorithm to denoise the speed data of the yarn and then conduct abnormal monitoring, which can effectively reduce the impact of noise data on the monitoring results and improve the accuracy of the operation monitoring results. In this process, the embodiment of the present invention takes into account that when the noise data is directly identified based on the speed mean of the adjacent data points on the left and right of the data point in the MVI algorithm, the abnormal data generated by the yarn itself may be identified as noise data. Based on this, the embodiment of the present invention obtains the possibility that the speed data is noise data by obtaining the data performance of the speed data itself and the tension data that is correlated with the speed data, and adjusts the speed mean based on the possibility that the speed data is noise data, thereby reducing the possibility of misidentifying abnormal data as noise data, thereby effectively improving the accuracy of the monitoring results of the yarn conveying operation status.

[0082] An embodiment of the present invention further discloses a real-time monitoring system for the operation status of yarn conveying, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a real-time monitoring method for the operation status of yarn conveying provided by the present invention is implemented.

[0083] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0084] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM, a dynamic random access memory DRAM, a static random access memory SRAM, an enhanced dynamic random access memory EDRAM, a high bandwidth memory HBM, a hybrid memory cube HMC, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0085] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0086] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for real-time monitoring of yarn conveying operation status, characterized in that: include: Obtain the speed value and tension value corresponding to each data point in the yarn running data time series, and preset the local range of the data point; Obtain the absolute value of the difference between the speed value of the data point and the speed mean in its local range, and record the ratio of the absolute value of the difference to the speed standard deviation in the local range as the noise index of the data point; Obtain the regression curve of the local range of the data point, and record the normalized value of the ratio of the noise index of the data point to the average of the vertical distances of each data point in its local range to the regression curve as the noise level of the data point; ； is the target mean of the ith data point, is the mean velocity of the left and right adjacent data points of the i-th data point, is the velocity value of the ith data point, is the noise level of the ith data point, is an exponential function with base e, is the absolute value symbol; The speed values ​​of the data points are combined into a speed data time series sequence. The target mean of each data point is used in the MVI algorithm to denoise the speed data time series sequence to obtain the yarn running monitoring results.

2. A method for real-time monitoring of yarn conveying operation status according to claim 1, characterized in that: The method of obtaining the speed value and tension value corresponding to each data point in the yarn running data time series also includes: The speed value and tension value of the yarn obtained at the same acquisition time are combined into one point as a data point, and the yarn running data time series sequence is obtained after preprocessing.

3. A method for real-time monitoring of yarn conveying operation status according to claim 1, characterized in that: The method for obtaining the local range of the data point includes: The length of the local range is preset, and other data points are equally acquired on both sides of the data point with the data point as the center to construct the local range of the data point; the length of the local range is an odd number.

4. A method for real-time monitoring of yarn conveying operation status according to claim 1, characterized in that: The step of obtaining a regression curve of a local range of data points includes: The speed value of each data point in the local range of the data point is used as the horizontal coordinate and the tension value is used as the vertical coordinate to obtain the operation data scatter plot, and the regression curve of the operation data scatter plot is obtained by using the least squares method.

5. A method for real-time monitoring of yarn conveying operation status according to claim 1, characterized in that: The method of using the target mean value of each data point in the MVI algorithm to denoise the speed data time series includes: The absolute value of the difference between the target mean and the speed value of each data point is obtained, and the data points corresponding to the absolute value of the difference greater than the preset threshold are removed as noise points, and the target mean corresponding to the data point is used for interpolation.

6. A method for real-time monitoring of yarn conveying operation status according to claim 1, characterized in that: The method of using the target mean value of each data point in the MVI algorithm to denoise the speed data time series to obtain the yarn running monitoring result includes: If the speed value in the speed data time series after denoising is greater than the preset abnormal threshold, the yarn running monitoring result is abnormal; otherwise, the yarn running monitoring result is normal.

7. A method for real-time monitoring of yarn conveying operation status according to claim 6, characterized in that: The yarn operation monitoring result is abnormal, and then further includes: Abnormal yarn operation monitoring results will be issued as an early warning.

8. A real-time monitoring system for yarn conveying operation status, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for real-time monitoring of the yarn conveying operation status according to any one of claims 1 to 7 is implemented.

Images