Digital textile equipment monitoring method and system based on data analysis

Abstract

The invention relates to the technical field of textile, in particular to a digital textile equipment monitoring method and system based on data analysis, and the method comprises the steps: collecting a yarn image in the operation process of textile equipment; yarn evenness pixel information in the yarn image is extracted, and the yarn bending degree and the yarn vibration characteristic are evaluated according to the yarn evenness pixel information; based on the yarn evenness pixel information, yarn spiral texture pixel information is further extracted, and the yarn structure uniformity is evaluated by analyzing the yarn spiral texture distribution condition; and evaluating a yarn stretching state based on a yarn structure uniformity evaluation result in combination with a yarn bending degree and a yarn vibration characteristic, and performing yarn breakage early warning according to the yarn stretching state. By quantitatively evaluating the yarn bending degree, the yarn vibration characteristic and the yarn structure uniformity, the accuracy and the reliability of yarn breakage early warning are improved.

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 of fiber or slender material through textile equipment. By twisting short fiber raw materials such as cotton, linen, silk and wool, the drafted strands are twisted to form continuous yarn with a certain fineness and strength. In the textile process, the continuity of the yarn is crucial to the quality of textile products and textile production efficiency. However, due to the uneven quality of yarn, when the tension of the textile equipment on the yarn is greater than the maximum tension that the yarn can withstand, the yarn will break, which is usually called "yarn breakage".

[0003] When yarn breakage occurs, it will not only affect 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, the uniformity of yarn structure, the degree of yarn bending and the vibration characteristics of yarn are of great significance for yarn break detection and early warning. The uniformity of yarn structure reflects the tightness and consistency of the fiber arrangement along the length direction of the yarn. Yarns with poor uniformity are more likely to experience stress concentration when subjected to tension, thereby increasing the risk of yarn breakage. The degree of yarn bending and the vibration characteristics of yarn can indirectly evaluate the stress state of the yarn. However, the existing digital textile equipment monitoring method ignores the influence of yarn structure uniformity, yarn bending and yarn vibration characteristics on yarn break early warning when conducting early warning of yarn breakage, resulting in low accuracy of early warning of yarn breakage. Summary of the invention

[0005] In order to overcome the defects and shortcomings of the prior art, 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 the yarn straightness pixel information in the yarn image and evaluating the yarn bending degree and yarn vibration characteristics according to the yarn straightness pixel information;

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

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

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

[0013] Acquire the yarn image and preprocess the yarn image, wherein the preprocessing includes image denoising and image enhancement;

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

[0015] The yarn bending index and yarn vibration index are calculated by the yarn dryness 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 length, y s represents the ordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn length, y s+1 represents the ordinate of the s+1th pixel in the yarn evenness, x S represents 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 1st 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 1st 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 evenness vibration period T through the peak point information:

[0021]

[0022] Where t crepresents the acquisition time of the yarn image at the cth 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 pixel information of the yarn straightness in the yarn image, and further extract the pixel information of the yarn spiral texture 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 continuous pixel Euclidean distance threshold, 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 )’s 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 ordinate 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 Represents the number of pixels of 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, and 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 providing a yarn breakage warning according to the yarn stretching state include:

[0045] Obtaining yarn bending index, yarn vibration index and yarn structural 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 structural uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the weight of yarn structural uniformity, 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 taken 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 in the yarn image and evaluate the yarn bending degree and yarn vibration characteristics according to 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 thread breakage warning module 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.

[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, enable 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 dryness 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, the yarn bending degree and the yarn vibration characteristics, and a yarn breakage warning is issued according to the yarn stretching state, thereby effectively improving 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 It 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 It is a structural schematic diagram of the spinning triangle area provided in an embodiment of the present application;

[0063] Figure 3 It is a structural schematic 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 schematic diagram of the structure 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. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other.

[0066] See also 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 in 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 straightness pixel information in the yarn image and evaluating the yarn bending degree and yarn vibration characteristics according to the yarn straightness pixel information;

[0069] The degree of yarn bending is closely related to the tension state of the yarn. Generally, the greater the degree of yarn bending, the looser the stress state of the yarn. The smaller the tension, the smaller the risk of yarn breakage. During the textile process, when the yarn is subjected to a large tension, the yarn usually maintains a relatively straight shape. Therefore, monitoring the degree of yarn bending can reflect the tension state of the yarn, 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 preprocess the yarn images. The preprocessing includes image denoising and image enhancement. Image denoising can reduce the measurement fluctuation caused by random noise and help to highlight the edge features of the yarn more clearly. Image enhancement can restore the texture details and contrast lost by denoising.

[0071] Extracting the yarn straightness pixel information in the preprocessed yarn image based on the threshold segmentation algorithm, where the threshold segmentation algorithm is any one of the Otsu threshold segmentation algorithm and the maximum entropy threshold segmentation algorithm;

[0072] The yarn bending index and yarn vibration index are calculated by the yarn dryness 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 length, y s represents the ordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn length, 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 represents 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 starting pixel 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 ending pixel 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 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 pixels of the yarn. 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 bending degree of the yarn.

[0075] When textile equipment is running at high speed, as the tension of the yarn increases, its inherent elasticity and inertia will cause stronger vibration, which in turn leads to an increase in the vibration frequency of the yarn. When the yarn is in a state of large tension, the increase in vibration frequency may mean that the force on the yarn is close to or exceeds its bearing limit, which is easy 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 the vibration frequency of the yarn is often a precursor to yarn fatigue or breakage. The calculation formula of the yarn vibration index is:

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

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

[0078]

[0079] Where t c represents the acquisition time of the yarn image at the cth 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 uniformity pixel information and evaluating the yarn structure uniformity by analyzing the yarn spiral texture distribution;

[0084] The uniformity of yarn structure reflects the tightness and consistency of the 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 excessive tension in local areas and increasing the risk of yarn breakage. For example, when the yarn is subjected to high tension during the weaving process, stress concentration will occur in yarns with uneven structures. These local high stress points are likely to become the starting point of fracture. When subjected to force, yarns with uniform structures can distribute stress more evenly in each part, thereby enhancing the overall tensile strength of the yarn and reducing the possibility of yarn breakage. The specific steps for evaluating the uniformity of yarn structure include:

[0085] Obtain the pixel information of the yarn straightness in the yarn image, and further extract the pixel information of the yarn spiral texture 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. 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 continuous pixel Euclidean distance threshold, 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 also Figure 2 , Figure 2 : is a structural schematic diagram of the spinning triangle provided in an embodiment of the present application. In the spinning process, the area after the fiber strip comes out of the drafting device and before it reaches 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 )’s 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 ordinate 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 Represents the number of pixels of 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, and 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 result combined with the yarn bending degree and the yarn vibration characteristics, and issue a yarn breakage warning according to the yarn stretching state;

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

[0104] Obtaining yarn bending index, yarn vibration index and yarn structural 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 structural uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the weight of yarn structural uniformity, 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, and when the yarn stretch index is less than or equal to the preset yarn stretch threshold, no yarn breakage warning is issued.

[0109] See also Figure 3 , Figure 3 : 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, for extracting yarn straightness pixel information in the yarn image and evaluating the yarn bending degree and the yarn vibration characteristics according to the yarn straightness pixel information;

[0112] A 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 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 the embodiment of the present application, the first evaluation module 220 is used to extract the yarn line pixel information in the yarn image and evaluate the yarn bending degree and the yarn vibration characteristics according to the yarn line pixel information. The specific steps of evaluating the yarn bending degree and the yarn vibration characteristics include:

[0115] Acquire the yarn image and preprocess the yarn image, wherein the preprocessing includes image denoising and image enhancement;

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

[0117] The yarn bending index and yarn vibration index are calculated by the yarn dryness 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 length, y s represents the ordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn length, y s+1 represents the ordinate of the s+1th pixel in the yarn evenness, x S represents 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 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, 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 evenness vibration period T through the peak point information:

[0123]

[0124] Where t c represents the acquisition time of the yarn image at the cth 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 the embodiment of the present application, the second evaluation module 230 is used to further extract the yarn spiral texture pixel information based on the yarn linearity 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 pixel information of the yarn straightness in the yarn image, and further extract the pixel information of the yarn spiral texture 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 continuous pixel Euclidean distance threshold, 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 )’s 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 ordinate 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 Represents the number of pixels of 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, and 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 wire 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 perform a yarn breakage warning according to the yarn stretching state. The specific steps of evaluating the yarn stretching state and performing a yarn breakage warning according to the yarn stretching state include:

[0147] Obtaining yarn bending index, yarn vibration index and yarn structural 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 structural uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the weight of yarn structural uniformity, 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, and 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 parameters and steps for each unit module to realize the corresponding functions in a digital textile equipment monitoring system based on data analysis of the present application can refer to the 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, including 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, wherein the program storage area can store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing a digital textile equipment monitoring method based on data analysis provided in the above embodiment, etc.; the data storage area can store data involved in a digital textile equipment monitoring method based on data analysis provided in the above embodiment, etc.

[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 may 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 embodiment of the present application is not specifically limited.

[0156] The communication bus 330 may include a path to transmit 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 only 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 execute 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 podium random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, an optical disk, a magnetic disk, a mechanical encoding device, and 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 includes a list of elements includes not only those elements but also other elements not expressly listed, or also includes elements inherent to such process, method, article, or apparatus.

[0160] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of application involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned application concept. For example, the above features are replaced with (but not limited to) technical features with similar functions applied in the present 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 the yarn straightness pixel information in the yarn image and evaluating the yarn bending degree and yarn vibration characteristics according to the yarn straightness pixel information; The yarn spiral texture pixel information is further extracted based on the yarn uniformity pixel information, and the yarn structure uniformity is evaluated by analyzing the yarn spiral texture distribution; The yarn stretching state is evaluated based on the yarn structure uniformity evaluation results combined with the yarn bending degree and yarn vibration characteristics, and the yarn breakage warning is given according to the yarn stretching state.

2. A digital textile equipment monitoring method based on data analysis according to claim 1, characterized in that: The specific steps of evaluating the yarn bending degree and the yarn vibration characteristics include: Acquire the yarn image and preprocess the yarn image, wherein the preprocessing includes image denoising and image enhancement; Extracting the pixel information of yarn evenness in the preprocessed yarn image based on the threshold segmentation algorithm; The yarn bending index and yarn vibration index are calculated by the yarn dryness pixel information. The yarn bending index is used to evaluate the degree of yarn bending. The calculation formula of the yarn bending index is: Where x s Indicates the horizontal coordinate of the sth pixel in the yarn length, y s represents the ordinate of the sth pixel in the yarn evenness, x s+1 Indicates the horizontal coordinate of the s+1th pixel in the yarn length, y s+1 represents the ordinate of the s+1th pixel in the yarn evenness, x S represents 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 1st 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 1st 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.

3. A digital textile equipment monitoring method based on data analysis according to claim 2, characterized in that: 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 T through the peak point information: Where t c represents the acquisition time of the yarn image at the cth 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; The yarn vibration index is calculated by the yarn linear vibration period T. The calculation formula of the yarn vibration index is: Where T′ represents the time interval for collecting yarn images, and VI represents the yarn vibration index.

4. 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 pixel information of the yarn straightness in the yarn image, and further extract the pixel information of the yarn spiral texture 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: 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 continuous pixel Euclidean distance threshold, 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; 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: QI=ω TCC ×TCC+ω SCC ×SCC; 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.

5. A digital textile equipment monitoring method based on data analysis according to claim 4, characterized in that: The specific steps of calculating the texture spacing uniformity coefficient include: 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 )’s Euclidean distance d i (p,q): 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 ordinate 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; 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 : Where N i Represents the number of pixels of 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, and min(·) represents the minimum function; Based on the spiral texture distance D i 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, M represents the number of spiral textures in the yarn image, and SCC represents the texture spacing uniformity coefficient.

6. A digital textile equipment monitoring method based on data analysis according to claim 1, characterized in that: The specific steps of evaluating the yarn stretching state and providing a yarn breakage warning according to the yarn stretching state include: Obtaining yarn bending index, yarn vibration index and yarn structural 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: Where BI represents the yarn bending index, VI represents the yarn vibration index, QI represents the yarn structural uniformity index, ω B represents the yarn bending weight, ω V represents the yarn vibration weight, ω Q represents the weight of yarn structural uniformity, SI represents the yarn tensile index; When the yarn stretch index is greater than the preset yarn stretch threshold, a yarn breakage warning is issued.

7. A digital textile equipment monitoring system based on data analysis, applied to a digital textile equipment monitoring method based on data analysis as claimed in any one of claims 1 to 6, 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 in the yarn image and evaluate the yarn bending degree and yarn vibration characteristics according to 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 thread breakage warning module 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.

8. 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 6 by calling the computer program stored in the memory.

9. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed 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 6.

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