A digital textile equipment monitoring method and system based on data analysis

Abstract

The present application relates to the field of textile technology, and more particularly to a digital textile equipment monitoring method and system based on data analysis. The method comprises the following steps: collecting yarn images during the operation of the textile equipment; extracting yarn strand pixel information from the yarn image and evaluating the degree of yarn bending and yarn vibration characteristics based on the yarn strand pixel information; further extracting yarn spiral texture pixel information based on the yarn strand pixel information and evaluating the yarn structural uniformity by analyzing the distribution of the yarn spiral texture; evaluating the yarn stretching state based on the yarn structural uniformity evaluation results in combination with the yarn bending degree and yarn vibration characteristics, and providing a yarn breakage warning based on the yarn stretching state. The present application improves the accuracy and reliability of yarn breakage warnings by quantitatively evaluating the degree of yarn bending, yarn vibration characteristics, and yarn structural uniformity.

Description

Technical Field

[0001] The present application relates to the field of textile technology, and in particular to a digital textile equipment monitoring method and system based on data analysis. Background Art

[0002] Yarn is a continuous, linear product made from fibers or elongated materials through textile machinery. By twisting short fibers like cotton, linen, silk, and wool, the drafted strands are twisted to form a continuous yarn with a certain fineness and strength. In the textile process, yarn continuity is crucial to both product quality and production efficiency. However, due to the varying quality of yarn, when the tension applied by the textile machinery exceeds the maximum tension the yarn can withstand, the yarn will break, commonly known as "yarn breakage."

[0003] When yarn breakage occurs, it not only affects product quality, but may also cause damage to textile equipment and production interruption. Therefore, introducing yarn breakage detection technology in digital textile equipment monitoring can effectively improve the stability of the production process and the reliability of product quality.

[0004] In the textile process, yarn structural uniformity, yarn curvature, and yarn vibration characteristics are crucial for yarn break detection and early warning. Yarn structural uniformity reflects the tightness and consistency of fiber arrangement along the yarn's length. Yarns with poor uniformity are more susceptible to stress concentration when subjected to tension, increasing the risk of yarn breakage. Yarn curvature and yarn vibration characteristics can indirectly assess the stress state of the yarn. However, existing digital textile equipment monitoring methods ignore the impact of yarn structural uniformity, curvature, and yarn vibration characteristics on yarn breakage warnings when providing early warnings, resulting in low accuracy. Summary of the Invention

[0005] In order to overcome the defects and shortcomings of the existing technology, the present application provides a digital textile equipment monitoring method and system based on data analysis, which improves the accuracy and reliability of yarn break warning by quantitatively evaluating the degree of yarn bending, yarn vibration characteristics and yarn structure uniformity.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a digital textile equipment monitoring method based on data analysis, comprising the following steps:

[0008] Collect yarn images during the operation of textile equipment;

[0009] Extracting yarn evenness pixel information from the yarn image and evaluating the yarn bending degree and yarn vibration characteristics based on the yarn evenness pixel information;

[0010] Based on the yarn uniformity pixel information, the yarn spiral texture pixel information is further extracted and the yarn structure uniformity is evaluated by analyzing the yarn spiral texture distribution;

[0011] The yarn tensile state is evaluated based on the yarn structure uniformity evaluation results combined with the yarn bending degree and yarn vibration characteristics, and a yarn breakage warning is issued according to the yarn tensile state.

[0012] Preferably, the specific steps of evaluating the yarn bending degree and yarn vibration characteristics include:

[0013] Acquire yarn images and preprocess the yarn images, including image denoising and image enhancement;

[0014] The pixel information of yarn evenness in the preprocessed yarn image is extracted based on the threshold segmentation algorithm;

[0015] The yarn bending index and yarn vibration index are calculated based on the yarn straightness pixel information. The yarn bending index is used to evaluate the degree of yarn bending. The calculation formula of the yarn bending index is:

[0016]

[0017] Where x s Indicates the horizontal coordinate of the sth pixel in the yarn evenness, y s Indicates the vertical coordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn evenness, y s+1 Indicates the vertical coordinate of the s+1th pixel in the yarn evenness, x S The horizontal coordinate of the Sth pixel in the yarn line, that is, the horizontal coordinate of the starting pixel of the yarn line in the yarn image, y S represents the ordinate of the Sth pixel in the yarn line, that is, the ordinate of the pixel at the starting point of the yarn line in the yarn image, x1 represents the abscissa of the first pixel in the yarn line, that is, the abscissa of the pixel at the end point of the yarn line in the yarn image, y1 represents the ordinate of the first pixel in the yarn line, that is, the ordinate of the pixel at the end point of the yarn line in the yarn image, S represents the number of pixels in the yarn line, and BI represents the yarn bending index.

[0018] Preferably, the calculation formula of the yarn vibration index is:

[0019] Obtaining yarn straightness pixel information and extracting peak point information of yarn straightness pixels;

[0020] Calculate the yarn straightness vibration period T through the peak point information:

[0021]

[0022] Where t cIndicates the acquisition time of the yarn image at the c-th peak point, t c+1 represents the acquisition time of the yarn image where the c+1th peak point is located, and C represents the number of peak points;

[0023] The yarn vibration index is calculated by the yarn linear vibration period T. The calculation formula of the yarn vibration index is:

[0024]

[0025] Where T′ represents the time interval for collecting yarn images, and VI represents the yarn vibration index.

[0026] Preferably, the specific steps of evaluating the uniformity of the yarn structure include:

[0027] Obtain the yarn straightness pixel information in the yarn image, and further extract the yarn spiral texture pixel information through the Hough transform algorithm;

[0028] The texture consistency coefficient and texture spacing uniformity coefficient are calculated based on the yarn spiral texture pixel information. The calculation formula of the texture consistency coefficient is:

[0029]

[0030] Where L i represents the length of the spiral texture of the i-th yarn, represents the mean length of the yarn spiral texture, M represents the number of yarn spiral textures in the yarn image, and d i,k represents the Euclidean distance between the kth pair of consecutive pixels in the spiral texture of the i-th yarn, d th Indicates the preset Euclidean distance threshold of continuous pixels, X(d i,k ≤d th ) represents the indicator function, when d i,k ≤d th When X(d i,k ≤d th ) is 1, otherwise it is 0, N i represents the number of pixels of the spiral texture of the i-th yarn, ω LC represents the texture length uniformity weight, ω TC represents the texture continuity weight, TCC represents the texture consistency coefficient;

[0031] The yarn structure uniformity index is calculated based on the texture consistency coefficient and the texture spacing uniformity coefficient. The yarn structure uniformity index is used to evaluate the yarn structure uniformity. The calculation formula of the yarn structure uniformity index is:

[0032] QI=ω TCC ×TCC+ω SCC ×SCC;

[0033] Where TCC represents the texture consistency coefficient, SCC represents the texture spacing uniformity coefficient, ω TCC represents the texture consistency weight, ω SCC represents the texture spacing uniformity weight, and QI represents the yarn structure uniformity index.

[0034] Preferably, the specific steps of calculating the texture spacing uniformity coefficient include:

[0035] For the pth pixel (x i,p ,y i,p ), calculate its distance to the qth pixel (x i+1,q ,y i+1,q ) of the Euclidean distance d i (p,q):

[0036]

[0037] Where x i,p represents the horizontal coordinate of the pth pixel in the spiral texture of the i-th yarn, y i,p represents the vertical coordinate of the pth pixel in the spiral texture of the i-th yarn, x i+1,q represents the horizontal coordinate of the qth pixel in the spiral texture of the i+1th yarn, y i+1,q represents the ordinate of the qth pixel in the spiral texture of the i+1th yarn;

[0038] Calculate the spiral texture distance D from the spiral texture of the i-th yarn to the spiral texture of the i+1-th yarn i :

[0039]

[0040] Where N i The number of pixels representing the spiral texture of the i-th yarn, N i+1 represents the number of pixels of the spiral texture of the i+1th yarn, min(·) represents the minimum function;

[0041] Based on the spiral texture distance D i Calculate the texture spacing uniformity coefficient. The calculation formula of the texture spacing uniformity coefficient is:

[0042]

[0043] In the formula represents the mean spiral texture distance, M represents the number of spiral textures in the yarn image, and SCC represents the texture spacing uniformity coefficient.

[0044] Preferably, the specific steps of evaluating the yarn stretching state and issuing a yarn breakage warning according to the yarn stretching state include:

[0045] Obtain yarn bending index, yarn vibration index and yarn structure uniformity index;

[0046] The yarn tensile index is calculated by the yarn bending index, yarn vibration index and yarn structural uniformity index. The yarn tensile index is used to evaluate the yarn tensile state. The calculation formula of the yarn tensile index is:

[0047]

[0048] Where BI represents the yarn bending index, VI represents the yarn vibration index, QI represents the yarn structure uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the yarn structural uniformity weight, SI represents the yarn tensile index;

[0049] When the yarn stretch index is greater than the preset yarn stretch threshold, a yarn breakage warning is issued.

[0050] It should be noted here that the texture length uniformity weight, texture continuity weight, preset continuous pixel Euclidean distance threshold, texture consistency weight, texture spacing uniformity weight, yarn bending weight, yarn vibration weight and preset yarn stretching threshold are obtained as follows: 10,000 yarn images are collected to distinguish whether the yarn breakage warning effect meets the production requirements, the yarn image is substituted into the yarn stretching index calculation formula for calculation, the calculated yarn stretching index and the distinction result are simultaneously imported into the fitting software, and the optimal texture length uniformity weight, texture continuity weight, preset continuous pixel Euclidean distance threshold, texture consistency weight, texture spacing uniformity weight, yarn bending weight, yarn vibration weight and preset yarn stretching threshold that meet the distinction accuracy of the distinction result are output.

[0051] In a second aspect, the present application provides a digital textile equipment monitoring system based on data analysis, comprising:

[0052] An image acquisition module is used to acquire yarn images during the operation of the textile equipment;

[0053] A first evaluation module is used to extract yarn straightness pixel information from the yarn image and evaluate the yarn bending degree and yarn vibration characteristics based on the yarn straightness pixel information;

[0054] The second evaluation module is used to further extract the yarn spiral texture pixel information based on the yarn uniformity pixel information and evaluate the yarn structure uniformity by analyzing the yarn spiral texture distribution;

[0055] The yarn breakage warning module is used to evaluate the yarn stretching state based on the yarn structure uniformity evaluation results combined with the yarn bending degree and yarn vibration characteristics, and to issue a yarn breakage warning according to the yarn stretching state.

[0056] In a third aspect, the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a digital textile equipment monitoring method based on data analysis by calling the computer program stored in the memory.

[0057] In a fourth aspect, the present application provides a computer-readable storage medium storing instructions, which, when executed on a computer, enables the computer to execute a digital textile equipment monitoring method based on data analysis.

[0058] Compared with the prior art, this application has the following advantages and beneficial effects:

[0059] This application first evaluates the bending degree and vibration characteristics of the yarn by extracting the pixel information of the yarn line in the yarn image, and then further extracts the pixel information of the yarn spiral texture and evaluates the structural uniformity of the yarn by analyzing the distribution of the spiral texture. Finally, the yarn stretching state is evaluated by comprehensively considering the yarn structural uniformity, yarn bending degree and yarn vibration characteristics, and a yarn breakage warning is issued according to the yarn stretching state, which effectively improves the accuracy and reliability of the yarn breakage warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0061] Figure 1 This is a schematic diagram of the overall process of a digital textile equipment monitoring method based on data analysis provided in an embodiment of the present application;

[0062] Figure 2 This is a schematic structural diagram of the spinning triangle provided in an embodiment of the present application;

[0063] Figure 3 This is a structural diagram of a digital textile equipment monitoring system based on data analysis provided in an embodiment of the present application;

[0064] Figure 4 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0065] The technical solution of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. Unless there is a conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0066] See Figure 1 , Figure 1 This is a schematic diagram of the overall process of a digital textile equipment monitoring method based on data analysis provided by an embodiment of the present application, which specifically includes the following steps:

[0067] S110: Collecting yarn images during the operation of the textile equipment. The yarn images are collected using a CCD industrial camera.

[0068] S120: extracting yarn evenness pixel information from the yarn image and evaluating the yarn bending degree and yarn vibration characteristics based on the yarn evenness pixel information;

[0069] The degree of yarn bending is closely related to the yarn tension state. Generally, the greater the degree of yarn bending, the looser the yarn stress state is. The smaller the tension is, the lower the risk of yarn breakage is. During the textile process, when the yarn is subjected to high tension, the yarn usually maintains a relatively straight shape. Therefore, monitoring the degree of yarn bending can reflect the yarn tension state, and thus provide an important reference for judging the risk of yarn breakage. The specific steps for evaluating the degree of yarn bending and yarn vibration characteristics include:

[0070] Acquire yarn images and perform preprocessing on them. The preprocessing includes image denoising and image enhancement. Image denoising can reduce measurement fluctuations caused by random noise and help highlight the edge features of the yarn more clearly. Image enhancement can restore texture details and contrast lost by denoising.

[0071] Extracting yarn evenness pixel information from the preprocessed yarn image based on a threshold segmentation algorithm, where the threshold segmentation algorithm is either an Otsu threshold segmentation algorithm or a maximum entropy threshold segmentation algorithm;

[0072] The yarn bending index and yarn vibration index are calculated based on the yarn straightness pixel information. The yarn bending index is used to evaluate the degree of yarn bending. The calculation formula of the yarn bending index is:

[0073]

[0074] Where x s Indicates the horizontal coordinate of the sth pixel in the yarn evenness, y s Indicates the vertical coordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn evenness, ys+1 represents the ordinate of the s+1th pixel in the yarn evenness, Indicates the actual length of the yarn in the yarn image, x S The horizontal coordinate of the Sth pixel in the yarn line, that is, the horizontal coordinate of the starting pixel of the yarn line in the yarn image, y S represents the ordinate of the Sth pixel in the yarn line, i.e. the ordinate of the starting pixel of the yarn line in the yarn image, x1 represents the abscissa of the first pixel in the yarn line, i.e. the abscissa of the ending pixel of the yarn line in the yarn image, y1 represents the ordinate of the first pixel in the yarn line, i.e. the ordinate of the ending pixel of the yarn line in the yarn image, It represents the Euclidean distance between the starting pixel and the ending pixel of the yarn in the yarn image, that is, the straight-line distance from the starting point to the end point of the yarn. S represents the number of yarn pixels. BI represents the yarn bending index. BI is the ratio of the actual length of the yarn in the yarn image to the straight-line distance, which is used to measure the degree of yarn bending.

[0075] When textile equipment is running at high speed, as the tension on the yarn increases, its inherent elasticity and inertia will cause stronger vibrations, which in turn leads to an increase in the vibration frequency of the yarn. When the yarn is in a large tensile state, the increase in vibration frequency may mean that the force on the yarn is close to or exceeds its bearing limit, which is prone to cause local fatigue or breakage. By monitoring and analyzing the changes in the vibration frequency of the yarn, the stress state of the yarn can be evaluated in real time, and the excessive tension or abnormal vibration that may exist in the yarn during operation can be identified, thereby predicting the risk of yarn breakage. The abnormal increase in yarn vibration frequency is often a precursor to yarn fatigue or breakage. The calculation formula of the yarn vibration index is:

[0076] Obtaining yarn straightness pixel information and extracting the peak point information of the yarn straightness pixel. The peak point is a feature point extracted by performing a sliding window operation on the yarn straightness pixel position, representing the "peak" or vibration "apex" of the yarn straightness;

[0077] Calculate the yarn straightness vibration period T through the peak point information:

[0078]

[0079] Where t c Indicates the acquisition time of the yarn image at the c-th peak point, t c+1 represents the acquisition time of the yarn image where the c+1th peak point is located, and C represents the number of peak points;

[0080] The yarn vibration index is calculated by the yarn linear vibration period T. The calculation formula of the yarn vibration index is:

[0081]

[0082] Where T′ represents the time interval for collecting yarn images, and VI represents the yarn vibration index.

[0083] S130: further extracting yarn spiral texture pixel information based on the yarn evenness pixel information and evaluating the yarn structure uniformity by analyzing the yarn spiral texture distribution;

[0084] Yarn structural uniformity reflects the tightness and consistency of fiber arrangement along the length of the yarn. Yarns with poor uniformity usually have uneven fiber distribution. These uneven areas are more likely to experience stress concentration when subjected to external forces, causing local areas to bear excessive tension and increasing the risk of yarn breakage. For example, when the yarn is subjected to high tension during the weaving process, yarns with uneven structures will experience stress concentration. These local high stress points are likely to become the starting point of fracture. Yarns with uniform structures can distribute stress more evenly when subjected to force, thereby enhancing the overall tensile strength of the yarn and reducing the possibility of yarn breakage. The specific steps for evaluating yarn structural uniformity include:

[0085] Obtain the yarn straightness pixel information in the yarn image, and further extract the yarn spiral texture pixel information through the Hough transform algorithm;

[0086] The texture consistency coefficient and texture spacing uniformity coefficient are calculated based on the yarn spiral texture pixel information. The calculation formula of the texture consistency coefficient is:

[0087]

[0088] Where L i represents the length of the spiral texture of the i-th yarn, represents the mean length of the yarn spiral texture, is the standard deviation of the yarn spiral texture length, which is used to evaluate the degree of dispersion of each yarn spiral texture length relative to the mean length. M represents the number of yarn spiral textures in the yarn image. d i,k represents the Euclidean distance between the kth pair of consecutive pixels in the spiral texture of the i-th yarn, d th Indicates the preset Euclidean distance threshold of continuous pixels, X(d i,k ≤d th ) represents the indicator function, when d i,k ≤d th When X(d i,k ≤d th ) is 1, otherwise it is 0, N i represents the number of pixels of the spiral texture of the i-th yarn, It is used to evaluate the continuity of the yarn spiral texture by calculating the ratio of continuous pixel pairs, that is, to determine whether the spiral texture is broken, ωLC represents the texture length uniformity weight, ω TC represents the texture continuity weight, TCC represents the texture consistency coefficient;

[0089] The yarn structure uniformity index is calculated based on the texture consistency coefficient and the texture spacing uniformity coefficient. The yarn structure uniformity index is used to evaluate the yarn structure uniformity. The calculation formula of the yarn structure uniformity index is:

[0090] QI=ω TCC ×TCC+ω SCC ×SCC;

[0091] Where TCC represents the texture consistency coefficient, SCC represents the texture spacing uniformity coefficient, ω TCC represents the texture consistency weight, ω SCC represents the texture spacing uniformity weight, QI represents the yarn structure uniformity index;

[0092] See Figure 2 , Figure 2 : This is a structural schematic diagram of the spinning triangle provided by an embodiment of the present application. During the spinning process, the area between the fiber strip coming out of the drafting device and reaching the twisting point is called the spinning triangle. The fiber strip is drafted, twisted and wound in the spinning triangle to form a yarn with a certain strength and structure. The fibers are arranged in a spiral shape during the twisting process. Therefore, a yarn spiral texture is formed on the surface of the yarn. The spiral texture of the yarn reflects the torsion state of the yarn during the spinning process. The uniformity of the spiral texture directly affects the strength, elasticity and force distribution of the yarn. The uniformity of the spiral texture spacing indicates that the torsion degree of the yarn is consistent. The uniform spiral texture can make the tensile force distributed along the entire length of the yarn more uniform, thereby improving its tensile strength and durability. The specific steps for calculating the texture spacing uniformity coefficient include:

[0093] For the pth pixel (x i,p ,y i,p ), calculate its distance to the qth pixel (x i+1,q ,y i+1,q ) of the Euclidean distance d i (p,q):

[0094]

[0095] Where x i,p represents the horizontal coordinate of the pth pixel in the spiral texture of the i-th yarn, y i,p represents the vertical coordinate of the pth pixel in the spiral texture of the i-th yarn, x i+1,q represents the horizontal coordinate of the qth pixel in the spiral texture of the i+1th yarn, y i+1,qrepresents the ordinate of the qth pixel in the spiral texture of the i+1th yarn;

[0096] Calculate the spiral texture distance D from the spiral texture of the i-th yarn to the spiral texture of the i+1-th yarn i :

[0097]

[0098] Where N i The number of pixels representing the spiral texture of the i-th yarn, N i+1 represents the number of pixels of the spiral texture of the i+1th yarn, min(·) represents the minimum function;

[0099] Based on the spiral texture distance D i Calculate the texture spacing uniformity coefficient. The calculation formula of the texture spacing uniformity coefficient is:

[0100]

[0101] In the formula represents the mean spiral texture distance, M represents the number of spiral textures in the yarn image, and SCC represents the texture spacing uniformity coefficient.

[0102] S140: Evaluate the yarn stretching state based on the yarn structure uniformity evaluation results combined with the yarn bending degree and yarn vibration characteristics, and issue a yarn breakage warning based on the yarn stretching state;

[0103] The specific steps of evaluating the yarn stretching state and issuing a yarn breakage warning based on the yarn stretching state include:

[0104] Obtain yarn bending index, yarn vibration index and yarn structure uniformity index;

[0105] The yarn tensile index is calculated by the yarn bending index, yarn vibration index and yarn structural uniformity index. The yarn tensile index is used to evaluate the yarn tensile state. The calculation formula of the yarn tensile index is:

[0106]

[0107] Where BI represents the yarn bending index, VI represents the yarn vibration index, QI represents the yarn structure uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the yarn structural uniformity weight, SI represents the yarn tensile index;

[0108] When the yarn stretch index is greater than the preset yarn stretch threshold, a yarn breakage warning is issued; when the yarn stretch index is less than or equal to the preset yarn stretch threshold, no yarn breakage warning is issued.

[0109] See Figure 3 , Figure 3 This is a structural diagram of a digital textile equipment monitoring system based on data analysis provided by an embodiment of the present application, including:

[0110] An image acquisition module 210 is used to acquire yarn images during the operation of the textile equipment;

[0111] A first evaluation module 220 is configured to extract yarn straightness pixel information from the yarn image and evaluate the yarn bending degree and yarn vibration characteristics based on the yarn straightness pixel information;

[0112] A second evaluation module 230 is configured to further extract yarn spiral texture pixel information based on the yarn evenness pixel information and evaluate the yarn structure uniformity by analyzing the yarn spiral texture distribution;

[0113] The thread breakage warning module 240 is used to evaluate the yarn stretching state based on the yarn structure uniformity evaluation result combined with the yarn bending degree and the yarn vibration characteristics, and to issue a yarn breakage warning according to the yarn stretching state.

[0114] In this embodiment of the present application, the first evaluation module 220 is used to extract yarn straightness pixel information from the yarn image and evaluate the yarn bending degree and yarn vibration characteristics based on the yarn straightness pixel information. The specific steps of evaluating the yarn bending degree and yarn vibration characteristics include:

[0115] Acquire yarn images and preprocess the yarn images, including image denoising and image enhancement;

[0116] The pixel information of yarn evenness in the preprocessed yarn image is extracted based on the threshold segmentation algorithm;

[0117] The yarn bending index and yarn vibration index are calculated based on the yarn straightness pixel information. The yarn bending index is used to evaluate the degree of yarn bending. The calculation formula of the yarn bending index is:

[0118]

[0119] Where x s Indicates the horizontal coordinate of the sth pixel in the yarn evenness, y s Indicates the vertical coordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn evenness, y s+1 Indicates the vertical coordinate of the s+1th pixel in the yarn evenness, x S The horizontal coordinate of the Sth pixel in the yarn line, that is, the horizontal coordinate of the starting pixel of the yarn line in the yarn image, y Srepresents the ordinate of the Sth pixel in the yarn line, i.e. the ordinate of the pixel at the starting point of the yarn line in the yarn image, x1 represents the abscissa of the first pixel in the yarn line, i.e. the abscissa of the pixel at the ending point of the yarn line in the yarn image, y1 represents the ordinate of the first pixel in the yarn line, i.e. the ordinate of the pixel at the ending point of the yarn line in the yarn image, S represents the number of pixels in the yarn line, and BI represents the yarn bending index;

[0120] The calculation formula of yarn vibration index is:

[0121] Obtaining yarn straightness pixel information and extracting peak point information of yarn straightness pixels;

[0122] Calculate the yarn straightness vibration period T through the peak point information:

[0123]

[0124] Where t c Indicates the acquisition time of the yarn image at the c-th peak point, t c+1 represents the acquisition time of the yarn image where the c+1th peak point is located, and C represents the number of peak points;

[0125] The yarn vibration index is calculated by the yarn linear vibration period T. The calculation formula of the yarn vibration index is:

[0126]

[0127] Where T′ represents the time interval for collecting yarn images, and VI represents the yarn vibration index.

[0128] In this embodiment of the present application, the second evaluation module 230 is used to further extract yarn spiral texture pixel information based on the yarn uniformity pixel information and evaluate the yarn structural uniformity by analyzing the yarn spiral texture distribution. The specific steps of evaluating the yarn structural uniformity include:

[0129] Obtain the yarn straightness pixel information in the yarn image, and further extract the yarn spiral texture pixel information through the Hough transform algorithm;

[0130] The texture consistency coefficient and texture spacing uniformity coefficient are calculated based on the yarn spiral texture pixel information. The calculation formula of the texture consistency coefficient is:

[0131]

[0132] Where L i represents the length of the spiral texture of the i-th yarn, represents the mean length of the yarn spiral texture, M represents the number of yarn spiral textures in the yarn image, and d i,krepresents the Euclidean distance between the kth pair of consecutive pixels in the spiral texture of the i-th yarn, d th Indicates the preset Euclidean distance threshold of continuous pixels, X(d i,k ≤d th ) represents the indicator function, when d i,k ≤d th When X(d i,k ≤d th ) is 1, otherwise it is 0, N i represents the number of pixels of the spiral texture of the i-th yarn, ω LC represents the texture length uniformity weight, ω TC represents the texture continuity weight, TCC represents the texture consistency coefficient;

[0133] The yarn structure uniformity index is calculated based on the texture consistency coefficient and the texture spacing uniformity coefficient. The yarn structure uniformity index is used to evaluate the yarn structure uniformity. The calculation formula of the yarn structure uniformity index is:

[0134] QI=ω TCC ×TCC+ω SCC ×SCC;

[0135] Where TCC represents the texture consistency coefficient, SCC represents the texture spacing uniformity coefficient, ω TCC represents the texture consistency weight, ω SCC represents the texture spacing uniformity weight, QI represents the yarn structure uniformity index;

[0136] The specific steps for calculating the texture spacing uniformity coefficient include:

[0137] For the pth pixel (x i,p ,y i,p ), calculate its distance to the qth pixel (x i+1,q ,y i+1,q ) of the Euclidean distance d i (p,q):

[0138]

[0139] Where x i,p represents the horizontal coordinate of the pth pixel in the spiral texture of the i-th yarn, y i,p represents the vertical coordinate of the pth pixel in the spiral texture of the i-th yarn, x i+1,q represents the horizontal coordinate of the qth pixel in the spiral texture of the i+1th yarn, y i+1,q represents the ordinate of the qth pixel in the spiral texture of the i+1th yarn;

[0140] Calculate the spiral texture distance D from the spiral texture of the i-th yarn to the spiral texture of the i+1-th yarn i :

[0141]

[0142] Where N i The number of pixels representing the spiral texture of the i-th yarn, N i+1 represents the number of pixels of the spiral texture of the i+1th yarn, min(·) represents the minimum function;

[0143] Based on the spiral texture distance D i Calculate the texture spacing uniformity coefficient. The calculation formula of the texture spacing uniformity coefficient is:

[0144]

[0145] In the formula represents the mean spiral texture distance, M represents the number of spiral textures in the yarn image, and SCC represents the texture spacing uniformity coefficient.

[0146] In the embodiment of the present application, the thread breakage warning module 240 is used to evaluate the yarn stretching state based on the yarn structure uniformity evaluation result in combination with the yarn bending degree and the yarn vibration characteristics, and to issue a yarn breakage warning according to the yarn stretching state. The specific steps of evaluating the yarn stretching state and issuing a yarn breakage warning according to the yarn stretching state include:

[0147] Obtain yarn bending index, yarn vibration index and yarn structure uniformity index;

[0148] The yarn tensile index is calculated by the yarn bending index, yarn vibration index and yarn structural uniformity index. The yarn tensile index is used to evaluate the yarn tensile state. The calculation formula of the yarn tensile index is:

[0149]

[0150] Where BI represents the yarn bending index, VI represents the yarn vibration index, QI represents the yarn structure uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the yarn structural uniformity weight, SI represents the yarn tensile index;

[0151] When the yarn stretch index is greater than the preset yarn stretch threshold, a yarn breakage warning is issued; when the yarn stretch index is less than or equal to the preset yarn stretch threshold, no yarn breakage warning is issued.

[0152] The above-mentioned steps for realizing corresponding functions of various parameters and unit modules in a digital textile equipment monitoring system based on data analysis of the present application can refer to the various parameters and steps in the embodiment of a digital textile equipment monitoring method based on data analysis above, and will not be repeated here.

[0153] like Figure 4 As shown, an embodiment of the present invention further provides an electronic device 300, comprising a memory 310, a processor 320, and a communication bus 330; the memory 310 and the processor 320 are connected via the communication bus 330. The memory 310 stores a digital textile equipment monitoring method based on data analysis as provided in the above embodiment, which can be loaded and executed by the processor 320.

[0154] The memory 310 can be used to store instructions, programs, codes, code sets, or instruction sets. The memory 310 can include a program storage area and a data storage area. The program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the digital textile equipment monitoring method based on data analysis provided in the above embodiment. The data storage area can store data involved in the digital textile equipment monitoring method based on data analysis provided in the above embodiment.

[0155] The processor 320 may include one or more processing cores. The processor 320 calls the data stored in the memory 310 by running or executing the instructions, programs, code sets or instruction sets stored in the memory 310, and performs various functions and processes data of the present application. The processor 320 can be at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a central processing unit (CPU), a controller, a microcontroller and a microprocessor. It is understandable that for different devices, the electronic device used to implement the above-mentioned processor 320 function can also be other, and the embodiments of the present application are not specifically limited.

[0156] The communication bus 330 may include a path for transmitting information between the above components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The communication bus 330 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one double arrow is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0157] An embodiment of the present application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed by a digital textile equipment monitoring method based on data analysis as provided in the above embodiment.

[0158] In this embodiment, the computer-readable storage medium may be a tangible device that holds and stores instructions used by the instruction execution device. The computer-readable storage medium may be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium may be a portable computer disk, a hard disk, a USB flash drive, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a lectern random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disc (DVD), a memory stick, a floppy disk, an optical disc, a magnetic disk, a mechanical encoding device, or any combination thereof.

[0159] The terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, method, article, or apparatus.

[0160] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of application involved in this application is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the aforementioned application concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions applied for in this application.

Claims

1. A digital textile equipment monitoring method based on data analysis, characterized in that: The steps include: Collect yarn images during the operation of textile equipment; Extracting yarn evenness pixel information from the yarn image and evaluating the yarn bending degree and yarn vibration characteristics based on the yarn evenness pixel information; Based on the yarn uniformity pixel information, the yarn spiral texture pixel information is further extracted and the yarn structure uniformity is evaluated by analyzing the yarn spiral texture distribution; Based on the evaluation results of yarn structure uniformity, combined with the yarn bending degree and yarn vibration characteristics, the yarn stretching state is evaluated and yarn breakage warning is issued according to the yarn stretching state; The specific steps of evaluating the yarn bending degree and yarn vibration characteristics include: Acquire yarn images and preprocess the yarn images, including image denoising and image enhancement; The pixel information of yarn evenness in the preprocessed yarn image is extracted based on the threshold segmentation algorithm; The yarn bending index and yarn vibration index are calculated based on the yarn straightness pixel information. The yarn bending index is used to evaluate the degree of yarn bending. The calculation formula of the yarn bending index is: ； In the formula Indicates the yarn evenness The horizontal coordinate of the pixel, Indicates the yarn evenness The vertical coordinate of the pixel, Indicates the yarn evenness The horizontal coordinate of the pixel, Indicates the yarn evenness The vertical coordinate of the pixel, Indicates the yarn evenness The horizontal coordinate of the pixel is the horizontal coordinate of the starting pixel of the yarn in the yarn image. Indicates the yarn evenness The vertical coordinate of the pixel is the vertical coordinate of the starting pixel of the yarn in the yarn image. Indicates the abscissa of the first pixel in the yarn line, that is, the abscissa of the pixel at the end of the yarn line in the yarn image. Indicates the ordinate of the first pixel in the yarn line, that is, the ordinate of the pixel at the end of the yarn line in the yarn image. Indicates the number of pixels of yarn evenness, represents the yarn bending index; the calculation formula of the yarn vibration index is: Obtaining yarn straightness pixel information and extracting peak point information of yarn straightness pixels; Calculate the yarn evenness vibration period based on peak point information : ； In the formula Indicates the The acquisition time of the yarn image where the peak point is located, Indicates the The acquisition time of the yarn image where the peak point is located, Indicates the number of peak points; Through the yarn dryness vibration cycle Calculate the yarn vibration index. The formula for calculating the yarn vibration index is: ； In the formula Indicates the time interval for collecting yarn images, Indicates the yarn vibration index.

2. A digital textile equipment monitoring method based on data analysis according to claim 1, characterized in that: The specific steps of evaluating the uniformity of the yarn structure include: Obtain the yarn straightness pixel information in the yarn image, and further extract the yarn spiral texture pixel information through the Hough transform algorithm; The texture consistency coefficient and texture spacing uniformity coefficient are calculated based on the yarn spiral texture pixel information. The calculation formula of the texture consistency coefficient is: ； In the formula Indicates the The length of the spiral texture of the yarn, represents the mean length of the yarn spiral texture, Indicates the amount of yarn spiral texture in the yarn image, Indicates the The spiral texture of the yarn For the Euclidean distance between consecutive pixels, Indicates the preset continuous pixel Euclidean distance threshold, represents the indicator function, when hour The value of is 1, otherwise it is 0. Indicates the The number of pixels of the yarn spiral texture, represents the texture length uniformity weight, represents the texture continuity weight, represents the texture consistency coefficient; The yarn structure uniformity index is calculated based on the texture consistency coefficient and the texture spacing uniformity coefficient. The yarn structure uniformity index is used to evaluate the yarn structure uniformity. The calculation formula of the yarn structure uniformity index is: ； In the formula represents the texture consistency coefficient, represents the texture spacing uniformity coefficient, represents the texture consistency weight, represents the texture spacing uniformity weight, Indicates the yarn structure uniformity index.

3. A digital textile equipment monitoring method based on data analysis according to claim 2, characterized in that: The specific steps of calculating the texture spacing uniformity coefficient include: For the The spiral texture of the yarn pixels , calculate its The spiral texture of the yarn pixels Euclidean distance : ； In the formula Indicates the The spiral texture of the yarn The horizontal coordinate of the pixel, Indicates the The spiral texture of the yarn The vertical coordinate of the pixel, Indicates the The spiral texture of the yarn The horizontal coordinate of the pixel, Indicates the The spiral texture of the yarn The vertical coordinate of the pixel; Calculate the Yarn spiral texture to the Spiral texture distance of a yarn spiral texture : ； In the formula Indicates the The number of pixels of the yarn spiral texture, Indicates the The number of pixels of the yarn spiral texture, represents the minimum function; Spiral texture distance Calculate the texture spacing uniformity coefficient. The calculation formula of the texture spacing uniformity coefficient is: ； In the formula represents the mean spiral texture distance, Indicates the amount of yarn spiral texture in the yarn image, Represents the texture spacing uniformity coefficient.

4. The digital textile equipment monitoring method based on data analysis according to claim 1 is characterized in that: The specific steps of evaluating the yarn stretching state and issuing a yarn breakage warning according to the yarn stretching state include: Obtain yarn bending index, yarn vibration index and yarn structure uniformity index; The yarn tensile index is calculated by the yarn bending index, yarn vibration index and yarn structural uniformity index. The yarn tensile index is used to evaluate the yarn tensile state. The calculation formula of the yarn tensile index is: ； In the formula represents the yarn bending index, represents the yarn vibration index, represents the yarn structure uniformity index, represents the yarn bending weight, represents the yarn vibration weight, represents the weight of yarn structure uniformity, Indicates the yarn tensile index; When the yarn stretch index is greater than the preset yarn stretch threshold, a yarn breakage warning is issued.

5. A digital textile equipment monitoring system based on data analysis, applied to a digital textile equipment monitoring method based on data analysis according to any one of claims 1 to 4, characterized in that: The system comprises: An image acquisition module is used to acquire yarn images during the operation of the textile equipment; A first evaluation module is used to extract yarn straightness pixel information from the yarn image and evaluate the yarn bending degree and yarn vibration characteristics based on the yarn straightness pixel information; The second evaluation module is used to further extract the yarn spiral texture pixel information based on the yarn uniformity pixel information and evaluate the yarn structure uniformity by analyzing the yarn spiral texture distribution; The yarn breakage warning module is used to evaluate the yarn stretching state based on the yarn structure uniformity evaluation results combined with the yarn bending degree and yarn vibration characteristics, and to issue a yarn breakage warning according to the yarn stretching state.

6. An electronic device comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes a digital textile equipment monitoring method based on data analysis as described in any one of claims 1 to 4 by calling the computer program stored in the memory.

7. A computer-readable storage medium, characterized in that Instructions are stored, and when the instructions are run on a computer, the computer is caused to execute a digital textile equipment monitoring method based on data analysis as described in any one of claims 1 to 4.

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