Textile fiber breakage intelligent detection method based on ultrasonic imaging analysis

Abstract

The invention relates to the field of fiber detection, and discloses a textile fiber breakage intelligent detection method based on ultrasonic imaging analysis, which is used for solving the problem that fine or locally worn fiber breakage signals are ignored due to interference of ultrasonic signals when textile fiber breakage is analyzed through ultrasonic imaging. The method comprises the following steps: acquiring a running state image of textile fibers by using ultrasonic imaging equipment, analyzing the running state image of the textile fibers to obtain a fiber breakage index, carrying out first-time fiber breakage judgment according to the fiber breakage index, and if the first-time fiber breakage judgment is fiber breakage, directly sending out a breakage early warning prompt. If it is judged that the fibers are normal through the first-time fiber breakage, the current environment data are collected and analyzed to obtain the ultrasonic affected index, the actual breakage judgment threshold value is obtained according to the ultrasonic affected index, second-time fiber breakage judgment is carried out, and the situation that breakage signals are ignored due to tiny fiber breakage is effectively reduced.

Description

Technical Field

[0001] The invention relates to the field of fiber detection, and more specifically to an intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis. Background Art

[0002] Ultrasonic imaging analysis is a non-contact imaging technology based on ultrasonic signals, which is used to generate images by analyzing the propagation, reflection and attenuation characteristics of ultrasonic waves in the medium. Ultrasonic waves are emitted by the transmitter and reflected or scattered when encountering objects with different densities or elastic properties. The receiver captures the echo signal and reconstructs it into a two-dimensional or three-dimensional image through an algorithm. Ultrasonic imaging evaluates the structure and state of the object by analyzing the generated image features.

[0003] With the rapid development of the textile industry, fiber break detection has become an important part of ensuring production efficiency and textile quality. The existing intelligent detection method for textile fiber break based on ultrasonic imaging analysis uses ultrasonic sensors to obtain echo signals in the fiber running process in real time, and uses imaging algorithms to generate images of the fiber break area. The detection system combines image processing technology (such as edge detection, morphological analysis, etc.) to identify the break points and discontinuous areas of the fiber, and realizes automatic recognition and alarm of textile fiber breakage.

[0004] For example, the invention patent announcement with announcement number: CN113174667B discloses a multi-area visual inspection monitoring system and method for a spinning machine, including: judging the position and time of yarn breakage of a single spindle by visual recognition, judging the position and time of roller winding by visual recognition, judging the position and time of empty tube of roving by visual recognition, and interrupting the roving. It is also preferably included to identify spindles with large rotation speed deviation by using a stroboscope in combination with visual recognition. Advantages of the present invention: reasonable structural design, effective detection of yarn breakage in the winding area, sliver winding around rollers in the front roller area, and exhaustion of roving in the roving area, and automatic control of shutdown and notification of the operator to handle, high intelligence, and preferably with spindle speed detection function, which can timely repair the faulty single spindle, effectively ensure the continuity of production and improve production efficiency.

[0005] However, in the process of implementing the technical solution of the invention in the above application embodiment, the present application found that the above technology has at least the following technical problems: In practical applications, signal interference is inevitable due to the complex textile production environment. Signal interference may lead to a decrease in the quality of ultrasonic imaging. In this case, the fiber breakage signal of small or locally worn fibers may be ignored, and the discontinuity of the fiber edge in the image cannot be accurately reflected, which in turn causes the fiber breakage to be mistakenly detected as a normal state, resulting in missed detection. In addition, the fracture judgment criteria of existing detection methods are usually based on a fixed image feature threshold. In an environment with large interference, the threshold cannot be dynamically adjusted according to environmental changes, and the actual needs of high-precision textile detection cannot be met. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis to solve the problems existing in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: The intelligent detection method of textile fiber breakage based on ultrasonic imaging analysis comprises the following steps: Step 1: Use ultrasonic imaging equipment to obtain a running state image of the textile fiber, analyze the running state image of the textile fiber, obtain a fiber breakage index, set an initial breakage judgment threshold, and perform the first fiber breakage judgment based on the fiber breakage index and the initial breakage judgment threshold; Step 2: If the first fiber break is judged as a fiber break, a break warning reminder is directly issued; if the first fiber break is judged as a normal fiber, current environmental data is collected; Step 3: Analyze the current environmental data to obtain the ultrasonic impact index; Step 4: Dynamically adjust the initial disconnection judgment threshold according to the ultrasonic impact index to obtain the actual disconnection judgment threshold; Step 5: Perform a second fiber breakage judgment using the dynamically adjusted actual breakage judgment threshold; The steps of collecting current environment data are: A vibration sensor is provided at the ultrasonic imaging device to obtain vibration data in real time through the vibration sensor, the vibration data including vibration acceleration, vibration frequency and vibration displacement amplitude; A noise measuring device is arranged at the ultrasonic imaging device, and noise data is obtained in real time through the noise measuring device, wherein the noise data includes noise intensity and noise frequency; A particle mass sensor and an infrared gas sensor are arranged at the ultrasonic imaging device to collect the particle concentration and impurity volume fraction in the air through the particle mass sensor and the infrared gas sensor; The steps of analyzing the current environmental data to obtain the ultrasonic impact index are: The vibration influence coefficient is obtained according to the vibration acceleration, vibration frequency and vibration displacement amplitude; The noise influence coefficient is obtained according to the noise intensity and the noise frequency; The air impurity influence coefficient is obtained according to the particle concentration and impurity volume fraction in the air; The ultrasonic impact index is obtained by evaluating the vibration impact coefficient, noise impact coefficient and air impurity impact coefficient. The specific method of obtaining it is as follows: ; In the formula, Expressed as the ultrasound impact index, Expressed as the vibration influence coefficient, Expressed as the noise influence coefficient, Expressed as the air impurity influence coefficient, , , It is expressed as a weight coefficient of the vibration influence coefficient, the noise influence coefficient and the air impurity influence coefficient. Preferably, the fiber breakage index acquisition step is: Use the connected region marking algorithm to mark the continuous region of the fiber, mark all connected regions through a recursive method, and obtain the number of connected regions; Obtain the edge point coordinates of the connected area and calculate the minimum gap width between fiber connected areas; The morphological characteristics of the fibers are extracted by image recognition technology. The morphological characteristics include the fiber area and the fiber perimeter. The shape factor is obtained according to the fiber area and the fiber perimeter. The fiber breakage index is obtained by evaluating the number of connected areas, gap width and shape factor. The specific method is as follows: ; In the formula, Expressed as the fiber breakage index, It is expressed as the number of connected regions, Expressed as the minimum gap width, Preferably, the first fiber breakage judgment step based on the fiber breakage index and the initial breakage judgment threshold is: The fiber breakage index is compared with the initial breakage judgment threshold. If the fiber breakage index is greater than or equal to the initial breakage judgment threshold, it is determined that the current textile fiber is broken, the breakage position is located, and a breakage warning reminder is issued; if the fiber breakage index is less than the initial breakage judgment threshold, it is preliminarily determined that the current textile fiber is in normal condition.

[0008] Preferably, the step of obtaining the air impurity influence coefficient according to the particle concentration and impurity volume fraction in the air is: Divide the space from the ultrasonic imaging device to the detection textile fiber into n subspaces on average; The air impurity level of each subspace is calculated based on the particle concentration and impurity volume fraction in the air. The specific acquisition method is as follows: ; In the formula, Expressed as the degree of air impurities, Expressed as the particle concentration, It is expressed as the impurity volume fraction; the air impurity degree of each subspace is clustered, and the air impurity influence coefficient is obtained according to the clustering result.

[0009] Preferably, the step of clustering the air impurity level of each subspace is: Step 3.1: Take the air impurity level as the clustering feature, take the air impurity level of all subspaces as the data set, and each air impurity level in the data set as a data point; Step 3.2: Use distance statistics to determine the optimal number of clusters K for the data set; Step 3.3: Randomly select K data points in the data set as initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the initial cluster center with the closest Euclidean distance. Step 3.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 3.5: Repeat steps 3.3 and 3.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0010] Preferably, the step of obtaining the air impurity influence coefficient according to the clustering result is: The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set; The weight of each final cluster is obtained by weighted summing the weight of the final cluster center.

[0011] Preferably, the step of dynamically adjusting the initial disconnection judgment threshold according to the ultrasonic wave influence index to obtain the actual disconnection judgment threshold is: Set the affected threshold, compare the ultrasonic affected index with the affected threshold, if the ultrasonic affected index is less than the affected threshold, it is determined that the current environment has little impact on the ultrasonic wave, the current textile fiber state is determined to be normal, and continue to the next fiber detection; If the ultrasonic wave influence index is greater than or equal to the influence threshold, it is determined that the current environment has a great influence on the ultrasonic wave, and the adjustment factor is obtained by calculating the ratio of the influence threshold to the ultrasonic wave influence index; The actual disconnection judgment threshold is obtained by multiplying the adjustment factor by the initial disconnection judgment threshold.

[0012] Preferably, the step of performing a second fiber breakage judgment using the dynamically adjusted actual breakage judgment threshold is: The fiber breakage index is compared with the actual breakage judgment threshold. If the fiber breakage index is less than the actual breakage judgment threshold, the current textile fiber status is judged to be normal, no warning is issued, and the next fiber detection is continued; if the fiber breakage index is greater than or equal to the actual breakage judgment threshold, the current textile fiber is judged to be broken, the breakage position is located, and a breakage warning reminder is issued.

[0013] Technical effects and advantages of the present invention: An ultrasonic imaging device is used to obtain an image of the running status of the textile fibers, and the image of the running status of the textile fibers is analyzed to obtain a fiber breakage index. The first fiber breakage judgment is made based on the fiber breakage index. If the first fiber breakage is judged to be a fiber breakage, a breakage warning reminder is directly issued. If the first fiber breakage is judged to be normal, current environmental data is collected and analyzed to obtain an ultrasonic impact index. The actual breakage judgment threshold is obtained based on the ultrasonic impact index, and a second fiber breakage judgment is made, which effectively reduces the situation where the breakage signal due to small fiber breaks is ignored. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 A flow chart of an intelligent method for detecting textile fiber breakage based on ultrasonic imaging analysis provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] The technical solution of the present invention will be clearly and completely described below in conjunction with the drawings in the present invention. In addition, the forms of the various structures recorded in the following embodiments are merely illustrative. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis involved in the present invention is not limited to the various structures recorded in the following embodiments. All other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0016] The present invention provides a method for intelligently detecting broken textile fibers based on ultrasonic imaging analysis, comprising the following steps: Step 1: Use ultrasonic imaging equipment to obtain a running state image of the textile fiber, analyze the running state image of the textile fiber, obtain a fiber breakage index, set an initial breakage judgment threshold, and perform the first fiber breakage judgment based on the fiber breakage index and the initial breakage judgment threshold; In this embodiment, it should be specifically explained that the steps of using ultrasonic imaging equipment to obtain the running state image of the textile fiber are: The fiber image is obtained by ultrasonic imaging equipment, and the image contrast is enhanced by using the histogram equalization method to improve the distinction between the fiber and the background. Histogram equalization is an image processing technology used to enhance the image contrast. Its principle is to make the original grayscale histogram more uniform by redistributing the grayscale value of the image, thereby expanding the dynamic range of the grayscale value; Use filters to remove noise and improve the clarity of fiber edges. Filters are image processing tools used to reduce noise or interference in an image while retaining or enhancing important image features. The fiber boundaries are extracted and the fiber image is separated using an edge detection algorithm. The edge detection algorithm is an image processing method used to identify areas in an image where the brightness changes dramatically, thereby extracting the boundaries of objects. Through edge detection, the boundaries of the fibers can be accurately separated, highlighting their shape features, and providing a basis for further analysis (such as connectivity detection or gap calculation).

[0017] In this embodiment, it should be specifically explained that the steps for obtaining the fiber breakage index are: Define the connectivity of a pixel with its neighboring pixels, such as 4-connected or 8-connected, use the connected region labeling algorithm to label the continuous region of the fiber, label all connected regions through a recursive method, and obtain the number of connected regions, which indicates the number of regions where the fiber is segmented. The connected region labeling algorithm is an image processing method used to identify and label the connected regions formed by adjacent pixels in an image. It checks the connectivity between the pixel and its neighborhood, such as 4-connected or 8-connected, and assigns the same label to pixels belonging to the same connected region. The algorithm can be used to extract the continuous region of the fiber and help determine the integrity and fracture state of the fiber; 4-connectivity and 8-connectivity are connectivity concepts in image processing, which are used to define whether pixels are adjacent. 4-connectivity means that a pixel is adjacent to the four pixels above, below, left, and right of it, and is applicable to only considering the connectivity in the horizontal and vertical directions. 8-connectivity extends this concept, indicating that a pixel is adjacent to the pixels above, below, left, right, and four diagonal directions, covering connectivity in more directions.

[0018] Get the edge point coordinates of the connected area and calculate the minimum gap width between fiber connected areas. The specific acquisition method is as follows: ; In the formula, Expressed as the minimum gap width, and Represented as the edge point coordinates of two connected regions; The morphological characteristics of the fibers are extracted through image recognition technology. The morphological characteristics include the fiber area, that is, the number of pixels in the area, and the fiber perimeter, that is, the boundary length. The shape factor is obtained based on the fiber area and the fiber perimeter. The specific acquisition method is as follows: ; In the formula, Expressed as a shape factor, Expressed as the fiber perimeter, It is expressed as fiber area; The fiber breakage index is obtained by evaluating the number of connected areas, gap width and shape factor. The specific method is as follows: ; In the formula, Expressed as the fiber breakage index, Expressed as the number of connected regions, the number of connected regions indicates how many discontinuous regions the fiber is divided into, and is an important indicator for evaluating fiber breakage. Broken fibers are usually divided into multiple isolated regions, so the larger the number of connected regions, the higher the possibility and severity of fiber breakage. This direct correlation can reflect the overall integrity of the fiber. It is expressed as the minimum gap width, which is the core parameter to measure the degree of interruption of fiber continuity and represents the shortest distance between fiber connected areas. It directly reflects the spatial characteristics of the fracture: if the fiber breaks, there will be obvious gaps between the connected areas, and the larger the gap width, the more serious the fracture and the more unstable the overall structure of the fiber. The larger the gap, the higher the possibility and severity of the fracture. This relationship ensures that the break index can sensitively capture the degree of structural damage. Expressed as shape factor, shape factor is an indicator of the geometric regularity and integrity of the fiber connected area, reflecting whether the fiber maintains a coherent and regular shape. For unbroken fibers, the shape factor is usually high, close to the value of regular shape. In the case of breakage or wear, the edges of the fiber become irregular and the shape factor will be significantly reduced. As an overall feature, the shape factor makes up for minor anomalies that may be ignored by relying solely on gap width or connectivity, and plays a role in stabilizing judgments.

[0019] In this embodiment, it should be specifically explained that the first fiber breakage judgment step is performed according to the fiber breakage index and the initial breakage judgment threshold: The fiber breakage index is compared with the initial breakage judgment threshold. If the fiber breakage index is greater than or equal to the initial breakage judgment threshold, it is determined that the current textile fiber is broken, the breakage position is located, and a breakage warning reminder is issued; if the fiber breakage index is less than the initial breakage judgment threshold, it is preliminarily determined that the current textile fiber is in normal condition.

[0020] Step 2: If the first fiber break is judged as a fiber break, a break warning reminder is directly issued; if the first fiber break is judged as a normal fiber, current environmental data is collected; In this embodiment, it should be specifically explained that the steps of collecting current environment data are: A vibration sensor is provided at the ultrasonic imaging device to obtain vibration data in real time through the vibration sensor, the vibration data including vibration acceleration, vibration frequency and vibration displacement amplitude; A noise measuring device is arranged at the ultrasonic imaging device, and noise data is obtained in real time through the noise measuring device, wherein the noise data includes noise intensity and noise frequency; A particle mass sensor and an infrared gas sensor are arranged at the ultrasonic imaging device, and the particle concentration and impurity volume fraction in the air are collected through the particle mass sensor and the infrared gas sensor.

[0021] Step 3: Analyze the current environmental data to obtain the ultrasonic impact index; In this embodiment, it should be specifically explained that the steps of analyzing the current environmental data to obtain the ultrasonic wave impact index are: The vibration influence coefficient is obtained according to the vibration acceleration, vibration frequency and vibration displacement amplitude. The specific acquisition method is as follows: ; In the formula, Expressed as the vibration influence coefficient, Expressed as vibration acceleration, Expressed as the vibration frequency, Expressed as vibration displacement amplitude; The noise influence coefficient is obtained according to the noise intensity and noise frequency. The specific acquisition method is as follows: ; In the formula, Expressed as the noise influence coefficient, Expressed as the noise intensity, Expressed as the noise frequency; The air impurity influence coefficient is obtained according to the particle concentration and impurity volume fraction in the air; The ultrasonic impact index is obtained by evaluating the vibration impact coefficient, noise impact coefficient and air impurity impact coefficient. The specific method of obtaining it is as follows: ; In the formula, Expressed as the ultrasound impact index, Expressed as the vibration influence coefficient, when the vibration intensity increases, it may cause displacement or instability of the ultrasonic transmitter or receiver, resulting in signal offset, noise superposition or imaging fuzziness, thereby reducing the accuracy of fiber break detection. This proportional relationship shows that the intensity of vibration directly affects the quality of the ultrasonic signal and the reliability of the detection system. Expressed as the noise influence coefficient, environmental noise can come from mechanical operation, electromagnetic equipment or other acoustic interference, which may be superimposed on the ultrasonic echo signal in the receiver, forming indistinguishable pseudo signals or noise ripples. This interference will reduce image contrast, blur fiber boundaries, and even cover up small fracture features. In addition, noise may also cause misjudgment, such as causing the actual fracture area to be ignored. Expressed as the air impurity influence coefficient, impurities in the air can have a variety of adverse effects on the propagation path of ultrasound, such as scattering, absorption or reflection, which may cause signal energy attenuation, deviation from the propagation direction or loss of echo signals. In addition, the attachment of impurities to the sensor surface may further reduce the sensitivity of the sensor, increase the noise level of the detection system, interfere with the clarity and coherence of the image, and thus affect the accurate identification of fiber breakage characteristics. , , It is expressed as the weight coefficient of vibration influence coefficient, noise influence coefficient and air impurity influence coefficient, and , , , The specific value is determined by professionals according to the actual situation, for example, , , It can be 0.4, 0.4, 0.2. In this embodiment, it should be specifically explained that the steps for obtaining the air impurity influence coefficient according to the concentration of particulate matter in the air and the volume fraction of impurities are: Divide the space from the ultrasonic imaging device to the detection textile fiber into n subspaces on average; The air impurity level of each subspace is calculated based on the particle concentration and impurity volume fraction in the air. The specific acquisition method is as follows: ; In the formula, Expressed as the degree of air impurities, Expressed as the particle concentration, It is expressed as the impurity volume fraction. When the impurity volume fraction is small, the integral result is approximately linear, reflecting the direct contribution of low-concentration impurities to the air impurity level. When the impurity volume fraction is large, the integral result tends to logarithmic growth, controlling the excessive amplification of the result by high-concentration impurities. The air impurity level of each subspace is clustered, and the air impurity influence coefficient is obtained based on the clustering results.

[0022] In this embodiment, it should be specifically explained that the steps of clustering the air impurity level of each subspace are: Step 3.1: Take the air impurity level as the clustering feature, take the air impurity level of all subspaces as the data set, and each air impurity level in the data set as a data point; Step 3.2: Use distance statistics to determine the optimal number of clusters K for the data set; Distance statistics is a statistical method for evaluating clustering performance. It determines the optimal number of clusters K by comparing the intra-cluster dispersion of actual data with that of random reference data. Specifically, distance statistics calculates the intra-cluster dispersion of actual data (such as the average distance from the samples in the cluster to the cluster center) and the intra-cluster dispersion of randomly generated data under different K values, and evaluates the significance of clustering results by the logarithmic difference between the two. The optimal number of clusters K is usually the number of clusters that maximizes the logarithmic difference, or the point where the logarithmic difference increases significantly. This method reduces the random influence of the distribution characteristics of the data set on the clustering results by introducing a random data benchmark.

[0023] Step 3.3: Randomly select K data points in the data set as the initial cluster centers. For each data point, calculate the Euclidean distance from each initial cluster center. The specific method is as follows: ; In the formula, It is expressed as the Euclidean distance from the data point to the cluster center, where Represented as data points, It is represented as the initial cluster center. For each data point, K initial cluster centers are traversed and assigned to the cluster corresponding to the initial cluster center with the closest Euclidean distance. Euclidean distance is a commonly used distance measurement method, which is used to calculate the straight-line distance between two points in Euclidean space. It reflects the shortest path length between two points and is an intuitive distance measurement method with clear physical meaning. In cluster analysis, Euclidean distance is used to measure the similarity between data points and cluster centers. The smaller the distance, the closer the data point is to the cluster center.

[0024] Step 3.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 3.5: Repeat steps 3.3 and 3.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

[0025] In this embodiment, it should be specifically explained that the steps of obtaining the air impurity influence coefficient according to the clustering result are: The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set; The weight of each final cluster is weighted and summed with the final cluster center to obtain the air impurity influence coefficient, which is calculated as follows: ,in Expressed as the air impurity influence coefficient, Represented as the weight of the jth final cluster, It is represented as the jth final cluster center, and K is the optimal number of clusters of the data set.

[0026] By clustering the air impurity level in each subspace, the local characteristics of the air impurity distribution can be captured. Air impurities may be unevenly distributed in different subspaces. This method effectively incorporates these differences into the influence coefficient, making the calculation results more comprehensive and accurate; the weight of each cluster is based on the ratio of the number of data points to the total number, which directly reflects the representativeness of the cluster in the overall space. This weight distribution method can give priority to the influence of areas with denser distribution of impurity data, thereby avoiding the results being dominated by a few discrete points or noise points; the cluster center represents the typical characteristics of the air impurities in each cluster. The weighted sum of the cluster center and the weight can effectively summarize the overall air impurity influence. This method can reduce the influence of local abnormal data points (such as extremely high or low values), making the air impurity influence coefficient more stable and robust.

[0027] Step 4: Dynamically adjust the initial disconnection judgment threshold according to the ultrasonic impact index to obtain the actual disconnection judgment threshold; In this embodiment, it should be specifically explained that the initial disconnection judgment threshold is dynamically adjusted according to the ultrasonic wave influence index to obtain the actual disconnection judgment threshold in the following steps: Set the affected threshold, compare the ultrasonic affected index with the affected threshold, if the ultrasonic affected index is less than the affected threshold, it is determined that the current environment has little impact on the ultrasonic wave, the current textile fiber state is determined to be normal, and continue to the next fiber detection; If the ultrasonic wave affected index is greater than or equal to the affected threshold, it is determined that the current environment has a greater impact on the ultrasonic wave. Then the affected threshold and the ultrasonic wave affected index are calculated by ratio to obtain the adjustment factor. The specific acquisition method is as follows: ; In the formula, Expressed as the adjustment factor, Expressed as the ultrasound impact index, It is expressed as the affected threshold; The actual disconnection judgment threshold is obtained by multiplying the adjustment factor by the initial disconnection judgment threshold. The specific method of obtaining the threshold is as follows: ; In the formula, It represents the actual disconnection judgment threshold. Expressed as the adjustment factor, It is represented as the initial wire breakage judgment threshold. Step 5: Perform a second fiber breakage judgment using the dynamically adjusted actual wire breakage judgment threshold.

[0028] In this embodiment, it should be specifically explained that the steps of performing the second fiber breakage judgment using the dynamically adjusted actual breakage judgment threshold are: The fiber breakage index is compared with the actual breakage judgment threshold. If the fiber breakage index is less than the actual breakage judgment threshold, the current textile fiber status is judged to be normal, no warning is issued, and the next fiber detection is continued; if the fiber breakage index is greater than or equal to the actual breakage judgment threshold, the current textile fiber is judged to be broken, the breakage position is located, and a breakage warning reminder is issued to remind relevant staff to repair the broken textile fiber in time.

[0029] 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 protection scope of the present invention.

[0030] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent method for detecting textile fiber breakage based on ultrasonic imaging analysis, characterized in that: The following steps are involved: Step 1: Use ultrasonic imaging equipment to obtain a running state image of the textile fiber, analyze the running state image of the textile fiber, obtain a fiber breakage index, set an initial breakage judgment threshold, and perform the first fiber breakage judgment based on the fiber breakage index and the initial breakage judgment threshold; Step 2: If the first fiber break is judged as a fiber break, a break warning reminder is directly issued; if the first fiber break is judged as a normal fiber, current environmental data is collected; Step 3: Analyze the current environmental data to obtain the ultrasonic impact index; Step 4: Dynamically adjust the initial disconnection judgment threshold according to the ultrasonic impact index to obtain the actual disconnection judgment threshold; Step 5: Perform a second fiber breakage judgment using the dynamically adjusted actual breakage judgment threshold; The steps of collecting current environment data are: A vibration sensor is provided at the ultrasonic imaging device to obtain vibration data in real time through the vibration sensor, the vibration data including vibration acceleration, vibration frequency and vibration displacement amplitude; A noise measuring device is arranged at the ultrasonic imaging device, and noise data is obtained in real time through the noise measuring device, wherein the noise data includes noise intensity and noise frequency; A particle mass sensor and an infrared gas sensor are arranged at the ultrasonic imaging device to collect the particle concentration and impurity volume fraction in the air through the particle mass sensor and the infrared gas sensor; The steps of analyzing the current environmental data to obtain the ultrasonic impact index are: The vibration influence coefficient is obtained according to the vibration acceleration, vibration frequency and vibration displacement amplitude; The noise influence coefficient is obtained according to the noise intensity and the noise frequency; The air impurity influence coefficient is obtained according to the particle concentration and impurity volume fraction in the air; The ultrasonic impact index is obtained by evaluating the vibration impact coefficient, noise impact coefficient and air impurity impact coefficient. The specific method of obtaining it is as follows: ; In the formula, Expressed as the ultrasound impact index, Expressed as the vibration influence coefficient, Expressed as the noise influence coefficient, Expressed as the air impurity influence coefficient, , , It is expressed as the weight coefficient of vibration influence coefficient, noise influence coefficient and air impurity influence coefficient.

2. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 1 is characterized in that: The fiber breakage index acquisition steps are: Use the connected region marking algorithm to mark the continuous region of the fiber, mark all connected regions through a recursive method, and obtain the number of connected regions; Obtain the edge point coordinates of the connected area and calculate the minimum gap width between fiber connected areas; The morphological characteristics of the fibers are extracted by image recognition technology. The morphological characteristics include the fiber area and the fiber perimeter. The shape factor is obtained according to the fiber area and the fiber perimeter. The fiber breakage index is obtained by evaluating the number of connected areas, gap width and shape factor. The specific method is as follows: ; In the formula, Expressed as the fiber breakage index, It is expressed as the number of connected regions, Expressed as the minimum gap width, Expressed as a shape factor.

3. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 1 is characterized in that: The first fiber breakage judgment step according to the fiber breakage index and the initial breakage judgment threshold is as follows: The fiber breakage index is compared with the initial breakage judgment threshold. If the fiber breakage index is greater than or equal to the initial breakage judgment threshold, it is determined that the current textile fiber is broken, the breakage position is located, and a breakage warning reminder is issued; If the fiber breakage index is less than the initial breakage judgment threshold, it is preliminarily determined that the current textile fiber state is normal.

4. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 1 is characterized in that: The step of obtaining the air impurity influence coefficient according to the particle concentration and impurity volume fraction in the air is: Divide the space from the ultrasonic imaging device to the detection textile fiber into n subspaces on average; The air impurity level of each subspace is calculated based on the particle concentration and impurity volume fraction in the air. The specific acquisition method is as follows: ; In the formula, Expressed as the degree of air impurities, Expressed as the particle concentration, It is expressed as the impurity volume fraction; the air impurity degree of each subspace is clustered, and the air impurity influence coefficient is obtained according to the clustering result.

5. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 4 is characterized in that: The steps of clustering the air impurity level of each subspace are as follows: Step 3.1: Take the air impurity level as the clustering feature, take the air impurity level of all subspaces as the data set, and each air impurity level in the data set as a data point; Step 3.2: Use distance statistics to determine the optimal number of clusters K for the data set; Step 3.3: Randomly select K data points in the data set as initial cluster centers. For each data point, calculate its Euclidean distance to each initial cluster center. For each data point, traverse the K initial cluster centers and assign it to the cluster corresponding to the initial cluster center with the closest Euclidean distance. Step 3.4: After traversing all data points, the initial clusters are obtained. For each initial cluster, the mean of the data points in it is calculated to obtain a new cluster center. Step 3.5: Repeat steps 3.3 and 3.4 until the cluster center no longer changes, and obtain the final cluster and the final cluster center.

6. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 4 is characterized in that: The step of obtaining the air impurity influence coefficient according to the clustering result is: The weight of each final cluster is calculated by calculating the ratio of the number of data points in each final cluster to the total number of data points in the data set; The weight of each final cluster is obtained by weighted summing the weight of the final cluster center.

7. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 1 is characterized in that: The steps of dynamically adjusting the initial disconnection judgment threshold according to the ultrasonic wave influence index to obtain the actual disconnection judgment threshold are as follows: Set the affected threshold, compare the ultrasonic affected index with the affected threshold, if the ultrasonic affected index is less than the affected threshold, it is determined that the current environment has little impact on the ultrasonic wave, the current textile fiber state is determined to be normal, and continue to the next fiber detection; If the ultrasonic wave influence index is greater than or equal to the influence threshold, it is determined that the current environment has a great influence on the ultrasonic wave, and the adjustment factor is obtained by calculating the ratio of the influence threshold to the ultrasonic wave influence index; The actual disconnection judgment threshold is obtained by multiplying the adjustment factor by the initial disconnection judgment threshold.

8. The intelligent detection method for textile fiber breakage based on ultrasonic imaging analysis according to claim 1 is characterized in that: The steps of performing the second fiber breakage judgment using the dynamically adjusted actual breakage judgment threshold are as follows: The fiber breakage index is compared with the actual breakage judgment threshold. If the fiber breakage index is less than the actual breakage judgment threshold, the current textile fiber status is judged to be normal, no warning is issued, and the next fiber detection is continued; if the fiber breakage index is greater than or equal to the actual breakage judgment threshold, the current textile fiber is judged to be broken, the breakage position is located, and a breakage warning reminder is issued.

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