Method, apparatus, electronic device and computer-readable storage medium for predicting dyeing performance of yarn

Abstract

To provide a method and apparatus for predetermining yarn dyeing performance, an electronic device and a computer-readable storage medium.SOLUTION: A method includes: defining a yarn normally dyed for a yarn to be determined; performing spectral detection on the yarn normally dyed to obtain first spectral information; calculating a covariance by performing simulation through a Gaussian process kernel; obtaining a plurality of pieces of continuous second spectrum information for the yarn to be determined; and establishing a Gaussian process regression model; obtaining third spectrum information for a yarn to be detected; subtracting a matrix of the third spectral information and a matrix of the second spectral information; and determining yarn dyeing performance according to numerals of the matrix value obtained by the subtraction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to the field of intelligent manufacturing, and in particular to intelligent detection technology for yarn dyeing performance. [Background technology]

[0002] In the production of yarns (e.g., polyester filament yarns) used in spinning and printing, their dyeability is typically assessed by randomly inspecting the yarn after it has been wound onto a yarn package and dropped. The inspection method is typically manual, using a hosiery dyeing method. This method suffers from delays and large manual inspection errors, making it difficult to timely identify products with unacceptable dyeability during production line operation, preventing manufacturers from adjusting production conditions in a timely manner. This leads to an increase in defective products, lowering product quality and affecting the grade of the product sold. Furthermore, this manual sampling inspection method is prone to oversights, resulting in economic losses for downstream manufacturers.

[0003] Therefore, there is a need in the industry for a detection method that can accurately, timely and completely detect the dyeing performance of yarn online. Summary of the Invention [Means for solving the problem]

[0004] The present disclosure provides a method and apparatus for pre-determining the dye performance of a yarn, an electronic device, and a computer-readable storage medium for determining the dye performance of a yarn in a simple, reliable, and timely manner.

[0005] According to one aspect of the present disclosure, there is provided a method for pre-determining dye performance of a yarn, the method comprising: Identifying a normally dyed yarn sample from the same lot for the yarn sample to be judged; A spectrum detection is performed on the normally dyed yarn sample in the same lot, and the first spectrum information [x i ,y i ], but x i is the wavenumber sampling value, and y i is the spectral information intensity, and i is a natural number from 1 to 400; performing a simulation using a Gaussian process kernel RBF kernel of the following calculation formula (I) based on the first spectral information, and calculating a covariance;

number

[0006] According to a second aspect of the present disclosure, there is provided an apparatus for carrying out the method and pre-determining yarn dyeing performance, the apparatus including a spectrum detection unit, a data pre-processing unit, a pre-determination unit, and a control unit. Here, the data pre-processing unit calculates covariance according to the continuous spectral information data acquired from the spectrum detection unit using the Gaussian process kernel RBF Kernel formula. The pre-determination unit receives the spectral information from the spectral detection unit and constructs a Gaussian process regression model based on the covariance.

[0007] According to a third aspect of the present disclosure, there is provided an electronic device, comprising: at least one processing unit; and a storage unit communicatively connected to the at least one processing unit. wherein the storage unit stores instructions executable by the at least one processing unit to enable the at least one processing unit to perform the method.

[0008] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having computer instructions stored thereon, wherein the computer instructions are non-transitory and are used to cause the computer to perform the above method. [Effects of the Invention]

[0009] The beneficial effects of the invention provided by this disclosure include at least the following: Only manual weaving and dyeing judgment is performed at the beginning of production, and based on this manual color judgment, spectrum detection and data simulation processing can be used to realize dynamic complete detection of the yarn production process. This detection method has simple and convenient steps and can be used for online detection on the production line. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a schematic flow chart of a method for pre-determining dye performance of a yarn according to one embodiment of the present disclosure. [Figure 2] FIG. 2 is a flowchart of a color determination method for dyeing a sock according to one embodiment of the present disclosure. [Figure 3] FIG. 3 is a Raman spectrum of a polyester filament yarn according to one embodiment of the present disclosure. [Figure 4] FIG. 4 is a structural schematic diagram of an apparatus for determining the dyeing performance of a yarn according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0011] It should be understood that the contents described in the Summary of the Invention section do not limit the key points or important features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will be easily understood from the following description.

[0012] In the drawings, unless otherwise specified, the same reference numerals in several views indicate the same or similar parts or elements. The drawings are not necessarily drawn to scale. It should be understood that the drawings merely illustrate some embodiments according to the present disclosure and are not to be considered as limiting the scope of the present disclosure.

[0013] The present disclosure will now be described in more detail with reference to the drawings, in which like reference numerals indicate functionally identical or similar elements, and in which various aspects of the embodiments are shown, but which are not necessarily drawn to scale unless otherwise noted.

[0014] Furthermore, in order to better explain the present disclosure, numerous specific details are described in the following specific embodiments. Those skilled in the art should understand that the present disclosure can be similarly practiced without specific details. In some instances, methods, means, components, agents, etc. that are well known to those skilled in the art are not described in detail, so as to emphasize the gist of the present disclosure.

[0015] In the prior art, a hosiery dyeing assessment method is generally adopted to measure the dyeing performance of polyester filament yarn, and the typical hosiery dyeing assessment method includes steps such as knitting a hosiery, preparing a dye solution, dyeing, rinsing, drying, and manual color assessment, etc. This method involves many steps, takes a long time to use, is inefficient, and has a severe delay in color assessment results. The number of samples sampled during the production process is small, making it easy for inspection to be missed. Moreover, the manual color assessment process is influenced by the inspector's experience, the test environment, etc., which can cause variations in color assessment results.

[0016] To at least partially solve one or more of the above problems, an embodiment of the present disclosure provides a method for predicting dyeing effect. By using the technical solution of the embodiment of the present disclosure, the dyeing level of wound yarn packages in each lot can be objectively predicted without dyeing, thereby improving detection accuracy and detection efficiency.

[0017] The main types of yarns according to the technical solutions of the embodiments of the present disclosure may include one or more of partially oriented yarns (POY), fully drawn yarns (FDY), draw textured yarns (DTY) (also referred to as low stretch yarns), etc. For example, the types of yarns may specifically include polyester partially oriented yarns, polyester fully drawn yarns, polyester drawn yarns, polyester low stretch yarns (Polyester Draw Textured Yarns), polyester staple fiber, etc.

[0018] 1 shows a method for pre-determining the dyeing performance of a yarn according to one embodiment of the present disclosure. As shown in FIG. 1, the method includes at least the following steps:

[0019] S101: For the yarn sample to be judged, a normally dyed yarn sample in the same lot is determined.

[0020] According to one specific embodiment, determining the properly dyed yarn samples in the same lot is performed by manually judging the yarn samples to be judged using a sock dyeing method, which includes the steps of knitting the yarn samples into socks, dyeing them, and judging the color.

[0021] Specifically, referring to Figure 2, the knitting method for the sock can be performed in accordance with the GB / T 6508-2001 standard. The dyeing step involves preparing a dye solution, and for example, blue dye, purple dye, or red dye can be used. The amount of dye used can be selected depending on the fineness specification of the yarn filament, and may be, for example, 0.6% to 1.5% of the weight of the sock, preferably 0.6% to 0.8%, or 0.8% to 1.2%, or 1.2% to 1.5% of the weight of the sock.

[0022] According to one specific embodiment, the dyeing step further includes placing the dyed socks in a dye solution at 60°C to 90°C and maintaining the solution for a predetermined period of time, for example, 10 to 20 minutes. Preferably, before placing the socks, the pH of the dye solution is adjusted to an acidic value, for example, a pH value of less than 7, e.g., a pH of 5. The pH of the dye solution can be adjusted using a buffer system of an organic acid and its salt, for example, an acetic acid-sodium acetate buffer solution or a citric acid-sodium citrate buffer solution. The temperature of the dye solution is then raised to the dyeing temperature and maintained at that temperature for a certain period of time. The dyeing temperature may be 100°C. The dyeing step can be performed using a dyeing device selected from a jet dyeing tank, a rope dyeing tank, and a high-temperature, high-pressure dyeing tank.

[0023] After the dyed socks are removed from the dyeing device, they are preferably washed with water and dehydrated before being used in the subsequent color evaluation step, in which the socks are placed over a color evaluation board (e.g., a black and white two-color board), and the appropriate observation conditions are selected to compare the color with a standard color chart to determine the dye level of the yarn.

[0024] The observation conditions may include, for example, lighting conditions, the angle of incidence of light on the surface of the sock-like article, the observation angle, and the observation distance. Regarding the lighting conditions, for example, referring to the method of FZ / T 01047-1997, the light source used may be a D65 standard light source with an illuminance of 600 to 1000 lx. Regarding the angle of incidence of light on the surface of the sock-like article, for example, the angle between the incident light and the middle part of the surface of the sock-like article may be 45°. Regarding the observation angle, it may be approximately perpendicular to the surface of the sock-like article. Regarding the observation distance, it may be approximately 30 to 40 cm. If clear observation is not possible, the angle of incidence of light may be adjusted to 70°, and the observation angle between the observation direction and the surface of the sock-like article may be adjusted to 30°.

[0025] The level of staining may be determined as normal, dark, or light based on a standard color chart.

[0026] S102: Perform spectrum detection on the normally dyed yarn sample in the same lot, and obtain first spectrum information [x i ,y i ], where x i is the wavenumber sampling value, and y i is the spectral information intensity, and i is a natural number from 1 to 400.

[0027] The spectrum may be a Raman spectrum. Among these, Raman spectroscopy utilizes the Raman scattering effect of the filament fiber structure to analyze the scattering spectrum of frequencies different from the frequency of the incident light, thereby obtaining spectral information related to the structure, vibration, and rotation of fiber molecules and performing various qualitative and quantitative analyses. In Raman spectroscopy, a high-quality, high-intensity monochromatic laser beam is irradiated onto the surface of the fiber to be analyzed, and the scattered light forms different scattering spectra (i.e., Raman spectra) depending on the microstructure of the fiber to be analyzed, which can be used for high-precision qualitative and quantitative analysis of fiber properties.

[0028] In comparison, infrared spectroscopy uses an infrared radiation source to excite a sample to be measured, and measures the intensity of the infrared radiation absorbed, scattered, and transmitted by the material to identify chemical bonds and vibrational modes in the molecular structure, obtain an absorption spectrum (i.e., an infrared spectrum), and determine the molecular structure.

[0029] In comparison, the Raman effect is more widespread and the excitation source used is a high-purity, high-intensity laser, so the signal of Raman spectrum analysis is purer, the signal-to-noise ratio is higher, the system is simpler, the configuration is more flexible, and there is less environmental interference.

[0030] In this disclosure, the filament to be measured may be a polyester fiber product with a crystalline structure or an amorphous polyester fiber product. The polyester fiber product may be a polyester filament yarn obtained through spinning and post-treatment. A crystalline structure refers to a state in which polymers are regularly arranged in the fiber. In a crystalline structure, polymer chains are densely arranged with each other, forming a regular lattice structure. Fibers with a crystalline structure generally have a high degree of crystallinity. Furthermore, due to the regular arrangement of molecules, fibers with a crystalline structure generally are relatively stable and have high strength and hardness. An amorphous structure refers to a state in which polymers are in an irregular state in the fiber and do not form a regular lattice structure. In an amorphous structure, polymer chains are relatively relaxed and do not have a significant periodic arrangement. Lattice structures generally cannot be colored during dyeing. Differences in the dyeing index of fibers of the same type are mainly due to differences in the crystallinity (or vitrification degree), crystalline distribution, degree of orientation, and end group concentration of the fibers. During the dyeing process, the dye molecules can only bond with the end groups of the amorphous parts of the fiber, and the more dye molecules bond, the darker the dyed color will be; conversely, the lighter the dyed color will be.

[0031] Raman spectroscopy has a sensitive response to the crystallinity, orientation, and component concentrations in the fiber sample being measured. In one specific example, a 500 cm -1 ~3500cm-1 A Raman spectrum may be formed by selecting a plurality of spectral information measured at a plurality of wavenumbers in the wavenumber range.

[0032] In the embodiment of the present disclosure, step S102 performs spectrum detection on the wound yarn package determined to be a normally dyed yarn in step S101, obtains a spectrum chart, and calculates continuous spectrum information [x i ,y i Preferably, the spectral information includes wavenumber sampling values ​​x i (corresponding to the horizontal axis of the Raman spectrum chart in Figure 3) and the spectral information intensity y i (corresponding to the vertical axis of the Raman spectrum chart in FIG. 3), where i is a natural number from 1 to 400, and may preferably be a natural number from 100 to 300, or may be a natural number from 200 to 400.

[0033] According to one specific embodiment, the step size of the wave number sampling in step S102 is 1 to 10 cm -1 , for example, 3 to 7 cm -1 may be, preferably 5 cm -1 may be.

[0034] Specifically, in the method of the present disclosure, the spectral detection is preferably performed downstream of the winding process of the wound yarn package, i.e., the spectrometer is installed on the winding machine side or downstream of the yarn production line, for example, by installing the spectrometer on a conveying device of the wound yarn package, so that the spectral detection can be completed while the wound yarn package is being wound or conveyed. Preferably, the measurement positions can be distributed on the annular side of the wound yarn package, for example, by rotating the wound yarn package during measurement, so that the measurement positions can be distributed as differently as possible, thereby improving the accuracy of the measurement.

[0035] Meanwhile, in this step, the spectral information obtained includes spectral intensity values ​​rather than any specific structural or performance features.

[0036] In the method of the present disclosure, the acquired spectral information includes wavenumber sampling values ​​and spectral intensity values, rather than the numerical information of a single physicochemical property. This is because the acquired spectral chart is a comprehensive representation of multiple factors, such as the chemical structure, crystalline state, degree of orientation, glass transition temperature, and oil contained in the fiber, and can more accurately reflect the dyeing characteristics of the fiber. Furthermore, the wavenumber sampling values ​​and spectral intensity values ​​can be obtained directly from the spectral graph, avoiding methods and steps related to complex parameter conversion and processing.

[0037] S103: A simulation is performed using the Gaussian process kernel RBF Kernel of the following calculation formula (I) to calculate the covariance.

number

[0038] where σ is 0.5, l is 1.0, and t m and t n are the notations for the dimensions, corresponding to the mth feature and the nth feature, respectively. m is y when i=m i corresponds to the value, t n is y when i=n i In addition, m and n are natural numbers from 1 to 400.

[0039] According to the Gaussian process kernel RBF kernel, the closer two features are in a dimension, the higher the covariance coefficient between them, meaning the two features are more correlated, while the farther apart the two features are, the smaller the covariance coefficient, meaning the two features are less correlated.

[0040] S104: With a predetermined wavenumber sampling step size, a plurality of consecutive second spectral information [x j ,y j ] and obtain second spectral information [xj ,y j ] to construct a Gaussian process regression model.

[0041] The plurality of consecutive second spectral information [x j ,y j ] is a 2×N1 matrix, where x j is the wavenumber value of the jth sampling point in the second spectrum chart, and y j is the spectral intensity of the j-th sampling point on the second spectral chart, where j=1, 2, ..., N1, and N1 is the number of yarn samples in the lot to be evaluated, and is a natural number equal to or greater than 200.

[0042] In order to improve the fitting accuracy of the Gaussian process regression, it is preferable to adopt a high sampling rate, i.e., to select a large value of N1. According to one embodiment, the wavenumber step size used for sampling is 10 cm -1 , the sampling number N is 200. According to another embodiment, the wavenumber step size used for sampling is 5 cm -1 In this case, the sampling number N is 400. Alternatively, other wave number step sizes may be selected according to actual needs, and the corresponding sampling numbers may be selected accordingly.

[0043] The sampling process may be performed on a single wound yarn package or on multiple wound yarn packages in the same lot. According to one embodiment, the spectral scanning may be performed while rotating a single wound yarn package so that the sampling points are evenly distributed on the circumferential side surface of the wound yarn package. According to another embodiment, the spectral scanning may be performed on multiple wound yarn packages wound in parallel on a winding machine so that the sampling points are evenly distributed.

[0044] S105: The third spectral information [a j , b j] is acquired, where the third spectrum information [a j , b j ] is a 2×N2 matrix, where N2 is the number of samples of the wound yarn package of yarn to be detected.

[0045] However, a j is the wavenumber value of the jth sampling point in the third spectrum chart, and b j is the spectral intensity of the j-th sampling point on the third spectral chart. Specifically, j=1, 2, ..., N1, and N2 is the number of yarn samples in the lot to be evaluated, and is a natural number equal to or greater than 200. Preferably, N2 is equal to N1.

[0046] In steps S104 and S105, the same size of spectral information matrix is ​​obtained, that is, the sampling method for the wound yarn package to be detected is preferably the same as the sampling method of the Gaussian process regression model, so as to facilitate the data processing in the subsequent steps.

[0047] S106: Third spectrum information [a j ,b j ], and the second spectral information [x j ,y i ] and the 2×N1 matrix [u j ,v j ] where N3 is equal to N1 and N2.

[0048] The measured spectral information of the yarn sample to be detected (e.g., the wound yarn package to be detected) is compared with the spectral information processed by the Gaussian process regression model to obtain the variation range of the spectral characteristics of the yarn sample to be detected, which is further used to evaluate the dyeing performance of the yarn sample.

[0049] S107:v j Based on the numerical value, the dyeing performance of the yarn sample to be detected is judged.

[0050] Based on the characteristics of the adopted spectrum detection method and the detected yarn characteristics, if the variation range of the spectrum intensity is within 0.01, the dyeing performance of the yarn is determined to be within the normal range.

[0051] Referring to FIG. 3, according to one specific embodiment, j If the value of is within the interval [-0.01, 0.01], it is determined that the yarn sample to be detected has been dyed normally, that is, as shown in the S area in FIG.

[0052] According to another specific embodiment, v j If the value of v is greater than 0.01, the dyeing of the yarn sample to be detected is determined to be dark, i.e., as shown in area D in FIG. 3. Preferably, the value of v is greater than 0.01. j If the number of the detected colors exceeds 3, it is determined that the dyeing of the yarn sample to be detected is deep.

[0053] According to yet another specific embodiment, v j If the value of v is less than -0.01, the dyeing performance of the yarn sample to be detected is determined to be weak, that is, as shown in the L area in Figure 3. Preferably, the value of v is less than -0.01. j If the number of the above exceeds 3, it is determined that the dyeing of the yarn sample to be detected is light.

[0054] As can be seen from the above, compared to conventional infrared spectrum detection methods, which generally have a sampling number of 5 to 10, the method disclosed herein has a significantly larger sampling number (200 or more), which results in more accurate detection results and helps prevent missed detections. Furthermore, by processing the detection data using a Gaussian process regression model, the spectral intensity values ​​directly correspond to the yarn dyeing characteristics, saving the data conversion process and avoiding the problem of deviations that tend to occur when evaluating yarn dyeing performance using data from a single performance feature. This results in more accurate judgment results, simplifies the detection process, and improves detection efficiency, making it more suitable for online real-time detection on production lines.

[0055] Referring to FIG. 4 , according to a second aspect of the present disclosure, there is provided an apparatus 300 for pre-determining yarn dyeing performance, including a spectrum detection unit 310, a data pre-processing unit 320, a pre-determination unit 330, and a control unit 340.

[0056] The spectral detection unit 310 is used to perform spectral scanning on the yarn samples to be detected in the same lot that have been judged to be color-acceptable based on set scanning parameters (e.g., wavenumber step size, number and distribution of scanning positions, etc.), obtain spectral charts, and transmit continuous spectral information data to the data pre-processing unit 320.

[0057] The data pre-processing unit 320 obtains continuous wavenumber and spectral intensity data from the spectral information data, and calculates the covariance using the Gaussian process kernel RBF kernel formula. The method for calculating the covariance using the Gaussian process kernel RBF kernel formula is as described above.

[0058] The pre-determination unit 330 is used to obtain spectral information of multiple yarn samples to be determined from the spectrum detection unit 310, construct a Gaussian process regression model based on the covariance, obtain the spectral information of the yarn sample to be detected from the spectrum detection unit 310, perform subtraction with the spectral information matrix of the Gaussian process regression model, and determine the dyeing performance of the yarn sample to be detected based on the data obtained by the subtraction. Furthermore, the pre-determination unit 330 transmits the dyeing performance determination result to the control unit 340.

[0059] The control unit 340 receives the judgment result from the pre-judgment unit 330, and based on the judgment result, sends the conveying direction and label of the yarn sample to be detected, i.e., the good product warehouse or the bad product warehouse, to the production line.

[0060] According to a third aspect of the present disclosure, there is provided an electronic device including at least one processing unit and a storage unit communicatively coupled to the at least one processing unit, the storage unit storing instructions (i.e., a computer program) executable by the processing unit, the computer program, when executed by the electronic device, causing the processing unit to perform the method.

[0061] The electronic device may further include a communication interface for communicating with an external device to perform data exchange transmission.

[0062] The processing unit may be selected from a central processor, other general-purpose processor, digital signal processor or dedicated integrated circuit etc. The general-purpose processor may be a microprocessor or any conventional processor etc.

[0063] The storage unit may include one or more of a read-only memory, a random access memory, and a non-volatile random access memory, and the memory may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory.

[0064] According to a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having stored thereon computer instructions, wherein the computer instructions are non-transitory and are used to cause the computer to perform the above method.

[0065] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. If implemented by software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed by a computer, the flow or function described in the embodiments of the present disclosure is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, Bluetooth, microwave, etc.). The computer-readable storage medium may be any available medium accessible by a computer, or may be a data storage device, such as a server or data center, that includes one or more available media. The available medium may be a magnetic medium (e.g., a floppy disk, hard disk, or magnetic tape), an optical medium (e.g., a Digital Versatile Disc (DVD)), a semiconductor medium (e.g., a Solid State Disk (SSD)), etc. Note that the computer-readable storage medium according to the present disclosure may be a non-volatile storage medium, that is, a non-transitory storage medium.

[0066] For specific functions and exemplary descriptions of each module and sub-module of the apparatus in the embodiments of the present disclosure, reference may be made to the relevant descriptions of the corresponding steps in the above method embodiments, and the description will be omitted here.

[0067] Those skilled in the art will understand that all or part of the steps for realizing the above embodiments may be completed by hardware, or may be completed by instructing related hardware by a program, and the program may be stored in a computer-readable storage medium, and the storage medium may be a read-only memory, a magnetic disk, an optical disk, etc.

[0068] In describing embodiments of the present disclosure, the use of the terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. Furthermore, the described specific features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, unless mutually inconsistent, a person skilled in the art may combine features from different embodiments or examples described herein.

[0069] In the description of the embodiments of the present disclosure, unless otherwise specified, " / " means "or," for example, "A / B" can represent "A" or "B." In this specification, "and / or" is merely a relation describing related objects, and means that three relationships may exist; for example, "A and / or B" can indicate three situations: "A" exists alone, "A" and "B" exist simultaneously, and "B" exists alone.

[0070] In describing the embodiments of the present disclosure, the terms "first" and "second" are descriptive only and should not be understood to denote or imply relative importance or the number of the designated elements. Thus, a feature qualified with "first" or "second" can explicitly or implicitly include one or more of that feature. In describing the embodiments of the present disclosure, unless otherwise specified, "plurality" means two or more.

[0071] The above are merely illustrative examples of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present disclosure should be included within the scope of the claims of the present disclosure.

Claims

1. 1. A method for pre-determining dye performance of a yarn, comprising: Identifying a normally dyed yarn sample in the same lot as the yarn sample to be judged; A spectrum is detected for a normally dyed yarn sample in the same lot, and the first spectrum information [x ｉ , y ｉ ] (however, x ｉ is the wavenumber sampling value, and y ｉ is the spectral information intensity, and i is a natural number from 1 to 400; Based on the first spectral information, a simulation is performed using a Gaussian process kernel RBF Kernel of the following calculation formula (I) to calculate the covariance (where σ is 0.5, l is 1.0, and t ｍ is y when i = m ｉ corresponds to the value of t ｎ is y when i = n ｉ where m and n are natural numbers from 1 to 400; [Equation 1] With a predetermined wavenumber sampling step size, a plurality of consecutive second spectral information [x ｊ , y ｊ ] and obtain second spectral information [x ｊ , y ｊ ] (wherein a plurality of consecutive second spectral information [x ｊ , y ｊ ] is 2×N １ matrix, j=1, 2, ..., N １ , N １ is the number of yarn samples sampled in the lot to be evaluated, and is a natural number greater than or equal to 200) For the yarn sample to be detected, the third spectral information [a ｊ , b ｊ ] is obtained (wherein the third spectrum information [a ｊ , b ｊ ] is 2×N ２ is a matrix, and N ２ is the number of samples to be detected, and N ２ is N １ (equal to The third spectral information [a ｊ , b ｊ ] 2 × N ２ matrix, and 2×N of the second spectral information after constructing a Gaussian process regression model. １ Subtract the matrix and to get 2×N ３ matrix [u ｊ , v ｊ ] (however, N ３ is N １ and N ２ (equal to v ｊ and determining the dyeing performance of the yarn sample to be detected based on the numerical value of Determining the dyeing performance of the yarn sample to be detected based on the value of v j is A method for pre-determining the dyeing performance of a yarn, comprising: determining that the yarn sample to be detected has been dyed normally when the numerical value of v j is within the interval [-0.01, 0.01]; determining that the dyeing of the yarn sample to be detected is strong when the numerical value of v j is greater than 0.01; or determining that the dyeing of the yarn sample to be detected is weak when the numerical value of v j is less than -0.

01.

2. The method of claim 1 , wherein the spectrum employs a Raman spectrum.

3. In the step of constructing the Gaussian process regression model, the wavenumber step size used for sampling is 2 to 10 cm －１ and the number of samples is 200 to 400.

4. The wavenumber step size used for sampling is 10 cm －１ , the sampling number N is 200, or the wavenumber step size used for sampling is 5 cm －１ The method of claim 3, wherein the number of samples N is 400.

5. 2. The method of claim 1, wherein determining the normally dyed yarn samples in the same lot is performed by manually judging the yarn samples to be judged using a sock dyeing method including the steps of knitting the yarn samples into socks, dyeing the yarn samples, and judging the color of the socks.

6. The method according to any one of claims 1 to 5, wherein the yarn is selected from pre-oriented yarn, fully drawn yarn, and draw-textured yarn.

7. An apparatus for carrying out the method according to any one of claims 1 to 5 and for determining in advance the dyeing performance of a yarn, Including a spectrum detection unit, a data pre-processing unit, a pre-determination unit and a control unit, The data pre-processing unit calculates a covariance according to the continuous spectral information data acquired from the spectrum detection unit using a Gaussian process kernel RBF Kernel formula; The pre-determination unit receives the spectral information from the spectral detection unit and constructs a Gaussian process regression model based on the covariance.

8. The apparatus of claim 7 , wherein the spectral detection unit is a Raman spectrometer.

9. 8. The apparatus according to claim 7, wherein the spectral detection unit is a spectrometer installed downstream of a winding machine and is used to spectrally detect the wound yarn package to be measured produced by the winding machine and obtain spectral information.

10. The device according to claim 7 , wherein the control unit receives a determination result from the pre-determination unit and issues a control command.

11. 1. An electronic device, comprising: at least one processing unit; a storage unit communicatively connected to the at least one processing unit; An electronic device, wherein the storage unit stores instructions executable by the at least one processing unit such that the at least one processing unit performs the method of any one of claims 1 to 5.

12. A computer-readable storage medium having computer instructions stored thereon, A computer-readable storage medium in which the computer instructions are non-transitory and cause a computer to perform the method of any one of claims 1 to 5.

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