A method and system for detecting pulp distribution uniformity in toilet paper production

Abstract

The application provides a method and system for detecting the uniformity of pulp distribution in toilet paper production, and relates to the fields of papermaking process and detection technology.The method comprises the following steps: extracting an edge distribution diagram of pulp texture from a filtered image to obtain a pulp texture edge distribution diagram; calculating a width index of the macrostructure of the pulp surface according to the pulp texture edge distribution diagram to obtain a preliminary evaluation result of the uniformity of the pulp distribution of the toilet paper; and subdividing the image area and calculating a local index according to the preliminary evaluation result of the pulp surface to obtain the local index.The method combines the detection of the width index of the macrostructure of the pulp surface and the uneven coefficient of the internal structure, and provides more comprehensive quality evaluation indexes for toilet paper production and more accurate control of product quality.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of papermaking process and detection technology, in particular to a method and system for detecting the uniformity of pulp distribution in the production of toilet paper. BACKGROUND

[0002] In the production process of toilet paper, the uniform distribution of pulp is a key factor to ensure product quality. The uniformity of pulp distribution not only affects the core performance of toilet paper such as thickness, strength and water absorption, but also directly relates to the appearance and user experience of the product. If the pulp distribution is uneven, it may cause problems such as uneven thickness, easy tearing or poor water absorption of toilet paper, thereby reducing the market competitiveness of the product.

[0003] Traditional methods for detecting the uniformity of pulp distribution mainly rely on manual observation and simple physical tests. Manual observation method usually depends on the experience and subjective judgment of the operator, which is not only low in efficiency, but also severely limited in accuracy and reliability. At the same time, although simple physical tests can provide certain quantitative data, they often cannot fully reflect the true situation of pulp distribution, especially the distribution differences at the microscopic level. SUMMARY

[0004] The technical problem to be solved by the present application is to provide a method and system for detecting the uniformity of pulp distribution in the production of toilet paper, which combines the detection of the width index of the macrostructure on the surface of the pulp and the unevenness coefficient of the internal structure, to provide more comprehensive quality evaluation indicators for toilet paper production and more accurately control product quality.

[0005] To solve the above technical problems, the technical solution of the present application is as follows:

[0006] In a first aspect, a method for detecting the uniformity of pulp distribution in the production of toilet paper is provided, the method comprising:

[0007] capturing a texture image of the surface of the pulp in the production process of toilet paper;

[0008] separating the background of the pulp image and the pulp texture to obtain a separated pulp texture image;

[0009] filtering the separated pulp texture image to obtain a filtered image;

[0010] extracting an edge distribution map of the pulp texture from the filtered image to obtain a pulp texture edge distribution map;

[0011] calculating a width index of the macrostructure on the surface of the pulp according to the pulp texture edge distribution map to obtain a preliminary evaluation result of the uniformity of pulp distribution on the surface of the toilet paper pulp;

[0012] Based on the preliminary evaluation results of the pulp surface, the image region is subdivided and local indices are calculated to obtain the local indices;

[0013] The uniform complexity of the pulp surface image is obtained by subdividing the image regions and local metrics.

[0014] Pulp samples from the production line were selected, and the tomographic images of the samples were analyzed to obtain the internal structure of the pulp, including the distribution of fibers and fillers.

[0015] Calculate the non-uniformity coefficient of the internal structure of the pulp, including the horizontal non-uniformity coefficient and the vertical non-uniformity coefficient;

[0016] The uniformity of pulp distribution in toilet paper is comprehensively evaluated based on the non-uniformity coefficient and uniformity complexity, so as to realize the detection of pulp distribution uniformity in toilet paper production.

[0017] Furthermore, the background and pulp texture of the pulp image are separated to obtain a separated pulp texture image, including:

[0018] Acquire each pixel of the image data and set the window size according to the image;

[0019] In each window, calculate the local mean and local standard deviation for each pixel;

[0020] Based on local mean and local standard deviation, through The adaptive threshold for each pixel is obtained, where W is the window radius. It is the horizontal position of a pixel in the image. It is the vertical position of a pixel in the image. It is the horizontal offset within the window. It is the vertical offset within the window. It is the location in the image ( The pixel value of ), where k is an empirical constant, ranging from -0.2 to -0.5; It is an adaptive threshold for each pixel;

[0021] Based on an adaptive threshold, each pixel is thresholded to separate the background and pulp texture of the pulp image, resulting in a separated pulp texture image.

[0022] Furthermore, based on the filtered image, the edge distribution map of the pulp texture is extracted to obtain the pulp texture edge distribution map, including:

[0023] The filtered image is convolved using horizontal and vertical Sobel matrices to obtain the horizontal and vertical gradients of each pixel.

[0024] The gradient amplitude and direction of each pixel point are calculated according to the horizontal gradient and the vertical gradient, and a gradient amplitude map of the image is obtained.

[0025] According to the gradient amplitude map of the image, a threshold is set for edge determination to extract an edge distribution map of the pulp texture.

[0026] Further, according to the edge distribution map of the pulp texture, a width index of the macrostructure of the pulp surface is calculated to obtain a preliminary evaluation result of the uniformity of the pulp distribution on the surface of the tissue paper, including:

[0027] On the edge distribution map of the pulp texture, all edges are identified and marked using image processing technology to obtain accurate position and shape information of the edges;

[0028] For each marked edge, its width is measured, and all measured edge widths are averaged to obtain an average edge width;

[0029] The standard deviation or coefficient of variation of the edge width is calculated to obtain the dispersion degree of the width;

[0030] Based on the average edge width and the dispersion degree of the width, the width index is determined;

[0031] According to the value of the width index, the preliminary evaluation result of the uniformity of the pulp distribution on the surface of the tissue paper is obtained.

[0032] Further, according to the preliminary evaluation result of the pulp surface, the image area is subdivided and local indexes are calculated to obtain local indexes, including:

[0033] According to the preliminary evaluation result of the pulp surface, the subgraph division method is determined;

[0034] According to the subgraph division method, the global edge distribution map of the pulp texture is divided into several subgraphs, wherein each subgraph should contain a part of the pulp texture;

[0035] The width index of the subgraph texture edge distribution is calculated, including identifying edges, measuring widths, averaging widths, and dispersion degree of widths;

[0036] The width index is normalized to obtain the local index.

[0037] Further, pulp samples from the production line are selected, and the tomographic images of the samples are analyzed to obtain the internal structure of the pulp, including the distribution of fibers and fillers, including:

[0038] Randomly select pulp samples from the production line;

[0039] Use a high-resolution tomographic scanning device to scan the processed pulp samples to obtain tomographic image data;

[0040] Based on the tomographic image data, the pixels in the image are divided into two or more categories by iteratively calculating the threshold, so as to obtain the internal structure of the pulp, including the distribution of fibers and fillers.

[0041] Furthermore, the non-uniformity coefficient of the internal structure of the pulp is calculated, including the horizontal non-uniformity coefficient and the vertical non-uniformity coefficient, including:

[0042] Based on the tomographic images, the analysis area is determined and divided into a regular grid.

[0043] For each grid, the fibers and filler within the grid are quantized to obtain quantized data for each grid;

[0044] Based on the quantized data of each grid, for all grids on the same horizontal layer, through... The fiber non-uniformity coefficient of the horizontal layer is obtained by... The filler non-uniformity coefficient of the horizontal layer is obtained, where, and They are the first The non-uniformity of horizontal layer fibers and fillers, It is the first The first in the horizontal layer Fiber quantification data for each grid, It is the first The first in the horizontal layer The filler quantization data for the Nth grid, where N is the number of grid cells. The total number of grids in the horizontal layer, and It is an index;

[0045] Based on the quantized data of each grid, for grids at different horizontal levels, through... The fiber vertical non-uniformity coefficient is obtained by... The vertical non-uniformity coefficient of the packing is obtained, where, and These are the non-uniformity of fibers and fillers across multiple horizontal layers. It is the first The first in the horizontal layer Fiber quantification data for each grid, It is the first The first in the horizontal layer The filler quantization data for the Nth grid, where N is the number of grid cells. The total number of grids in the horizontal layer, and It is an index. It is the total number of horizontal layers. It represents the weight of each horizontal layer.

[0046] In a second aspect, a system for detecting pulp distribution uniformity in toilet paper production includes:

[0047] An acquisition module configured to capture a texture image of a pulp surface in a toilet paper production process;

[0048] A surface processing module configured to separate a background from the pulp texture in the pulp image to obtain a separated pulp texture image, to filter the separated pulp texture image to obtain a filtered image, to extract an edge distribution map of the pulp texture from the filtered image to obtain a pulp texture edge distribution map, to calculate a width index of a macrostructure of the pulp surface from the pulp texture edge distribution map to obtain a preliminary evaluation result of the pulp surface in the toilet paper production, to subdivide the image area and calculate a local index from the preliminary evaluation result to obtain a uniform complexity of the pulp surface image;

[0049] An internal processing module configured to select a pulp sample from a production line, to analyze a tomographic image of the sample to obtain an internal structure of the pulp, including fiber and filler distribution, and to calculate an unevenness coefficient of the internal structure, including a horizontal unevenness coefficient and a vertical unevenness coefficient;

[0050] A comprehensive processing module configured to comprehensively evaluate the pulp distribution uniformity in the toilet paper production according to the unevenness coefficient and the uniform complexity to achieve the detection of the pulp distribution uniformity in the toilet paper production.

[0051] In a third aspect, a computing device includes:

[0052] One or more processors;

[0053] A storage device configured to store one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method.

[0054] In a fourth aspect, a computer readable storage medium has stored therein a program, which, when executed by a processor, implements the method.

[0055] The above-mentioned scheme of the present application at least has the following beneficial effects:

[0056] By capturing and analyzing the texture image of the pulp surface, the uniformity of the pulp distribution can be accurately evaluated. By using the background and texture separation technology of the pulp image, combined with filtering and edge extraction, the flow and distribution of the pulp in the production process of the toilet paper can be deeply understood. By real-time monitoring the uniformity of the pulp distribution, problems in the production process can be found in time and adjusted, avoiding unnecessary downtime and material waste. By analyzing the tomographic image of the pulp sample on the production line, the distribution of the pulp internal fibers and fillers can be deeply understood. Combined with the width index of the macrostructure of the pulp surface and the non-uniformity coefficient of the internal structure, the method provides comprehensive quality evaluation index for the production of toilet paper, which helps to control the product quality more accurately and meets the diversified needs of the market and consumers. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a flowchart of a method for detecting the uniformity of pulp distribution in toilet paper production provided by an embodiment of the present application.

[0058] Figure 2 is a schematic diagram of a system for detecting the uniformity of pulp distribution in toilet paper production provided by an embodiment of the present application. DETAILED DESCRIPTION

[0059] Exemplary embodiments of the present disclosure will be described in greater detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be accurately conveyed to those skilled in the art.

[0060] As Figure 1 shown, an embodiment of the present application proposes a method for detecting the uniformity of pulp distribution in toilet paper production, which comprises the following steps:

[0061] Step 11, capturing the texture image of the pulp surface in the production process of toilet paper;

[0062] Step 12, separating the background of the pulp image and the pulp texture to obtain a separated pulp texture image;

[0063] Step 13, filtering the separated pulp texture image to obtain a filtered image;

[0064] Step 14, extracting the edge distribution map of the pulp texture according to the filtered image to obtain a pulp texture edge distribution map;

[0065] Step 15, according to the edge distribution map of the pulp texture, calculate the width index of the pulp surface macrostructure to obtain the preliminary evaluation result of the pulp surface uniformity of the toilet paper;

[0066] Step 16, according to the preliminary evaluation result of the pulp surface, subdivide the image area and calculate the local index to obtain the local index;

[0067] Step 17, according to the subdivided image area and the local index, to obtain the uniform complexity of the pulp surface image;

[0068] Step 18, select the pulp sample of the production line, analyze the tomographic image of the sample to obtain the internal structure of the pulp, including the distribution of fibers and fillers;

[0069] Step 19, calculate the unevenness coefficient of the internal structure of the pulp, including the horizontal unevenness coefficient and the vertical unevenness coefficient;

[0070] Step 20, according to the unevenness coefficient and the uniform complexity, comprehensively evaluate the distribution uniformity of the toilet paper pulp to realize the detection of the distribution uniformity of the toilet paper pulp in the production of toilet paper.

[0071] In the embodiment of the present application, by capturing and analyzing the texture image of the pulp surface, the surface quality of the toilet paper can be monitored in real time, the image processing technology is used to automatically separate the pulp texture from the background, the detection efficiency and accuracy are improved, and the error of manual detection is reduced. The filtering processing can remove the noise and interference in the image, so that the subsequent edge detection and width index calculation are more accurate. By extracting the edge distribution map of the pulp texture, the distribution of the pulp on the surface of the toilet paper can be accurately understood, and data support is provided for uniformity evaluation. The width index of the macrostructure of the pulp surface provides a fast and practical preliminary evaluation result of the pulp surface for quality control in the production process. By analyzing the tomographic image of the pulp sample, the distribution of fibers and fillers in the pulp can be understood in depth, so that the production formula and process can be optimized. The unevenness coefficient, including the horizontal and vertical, provides a quantitative index for evaluating the uniformity of the internal structure of the pulp. Combined with the surface uniform complexity and the unevenness coefficient of the internal structure, the distribution uniformity of the toilet paper pulp can be more comprehensively evaluated to ensure product quality. According to the comprehensive evaluation result, the production process parameters can be adjusted in time to improve the uniformity of the pulp distribution, and then the product quality is improved.

[0072] In a preferred embodiment of the present application, the above step 11 comprises:

[0073] Align the camera lens to the pulp surface, trigger the camera to take pictures in real time or at regular intervals during the production of toilet paper, and capture the texture image of the pulp surface.

[0074] In this embodiment of the invention, by capturing images in real-time or at set intervals, texture images of the pulp surface can be acquired instantly, thereby enabling real-time monitoring of the toilet paper production process. This helps to promptly identify problems in production, such as uneven pulp distribution and fiber aggregation, so that corrective measures can be taken quickly. The texture image of the pulp surface is an important indicator of toilet paper quality. By capturing and analyzing these images, the surface quality of toilet paper can be precisely controlled, ensuring that the product meets quality standards and reducing the defect rate.

[0075] The specific implementation steps of this invention include: setting camera parameters and triggering mode for real-time triggering; in real-time triggering mode, a fixed time interval is set, and the camera will continuously capture images of the pulp surface; previewing the images captured by the camera on the software interface; and adjusting the camera position and parameters according to the actual situation; during the toilet paper production process, activating the image capture function, the camera will begin capturing texture images of the pulp surface, and the captured image data will be transmitted to a computer or image processing system in real time for storage.

[0076] In a preferred embodiment of the present invention, step 12 includes:

[0077] Step 121: Obtain each pixel of the image data and set the window size according to the image;

[0078] Step 122: In each window, calculate the local mean and local standard deviation for each pixel;

[0079] Step 123, based on the local mean and local standard deviation, through... The adaptive threshold for each pixel is obtained, where W is the window radius. It is the horizontal position of a pixel in the image. It is the vertical position of a pixel in the image. It is the horizontal offset within the window. It is the vertical offset within the window. It is the location in the image ( The pixel value of ), where k is an empirical constant, ranging from -0.2 to -0.5; It is an adaptive threshold for each pixel;

[0080] Step 124: Threshold each pixel according to the adaptive threshold to separate the background and pulp texture of the pulp image, and obtain the separated pulp texture image.

[0081] In the embodiments of the present application, the local mean and standard deviation are calculated pixel by pixel, and the adaptive threshold is set accordingly, which can more accurately segment the pulp texture and background. This method is more flexible and accurate than using a global threshold, especially suitable for cases where the contrast between background and texture changes greatly. The adaptive thresholding method can adjust the threshold according to the local characteristics of different regions, thus better preserving the subtle features of the pulp texture. By accurately separating the background and texture, the interference of background noise on texture analysis can be eliminated, and this method can reduce the dependence on manual intervention and achieve higher level of automation. Accurate separation of background and texture is the basis for subsequent complex image processing tasks such as pulp uniformity evaluation and quality detection. Using the adaptive thresholding method can ensure the consistency of image segmentation under different lighting conditions and pulp color changes.

[0082] The specific implementation steps of the present application include:

[0083] Step 121, the captured pulp surface texture image is read into the computer memory through the image processing library, and the window radius W is determined according to the characteristics of the pulp texture and the image resolution to ensure that enough local information can be captured for calculating the local statistical information of each pixel point.

[0084] Step 122, use nested loop structure to traverse each pixel point in the image. For each pixel point, create a window of size (2W+1) x (2W+1) centered on it. In the current window, accumulate the gray values of all pixel points and divide by the total number of pixels in the window (i.e. (2W+1)²) to get the local mean of the current pixel point, where the local mean reflects the average brightness level of the surrounding area of the pixel point. According to the local mean of the pixel point, the local standard deviation of the current pixel point is calculated. The local standard deviation reflects the fluctuation degree of the brightness of the surrounding area of the pixel point.

[0085] Steps 123-124, for each pixel point, use the formula to calculate its adaptive threshold. Again, traverse each pixel point in the image, compare the gray value of each pixel point with the corresponding adaptive threshold. If the gray value is greater than or equal to the threshold, set the pixel point to foreground color (usually white, representing pulp texture); otherwise, set it to background color (usually black, representing background). After the above processing, the pulp texture and background in the original pulp image will be effectively separated.

[0086] In a preferred embodiment of the present application, step 13 above includes:

[0087] According to the selected Sobel filter and parameters, a filter kernel is created, the kernel is convolved with each local region of the separated pulp texture image to obtain new pixel values, and a filtered image is obtained.

[0088] In the embodiment of the present application, the Sobel filter is a commonly used edge detection operator that can effectively detect edge information in an image. In a pulp texture image, the edges of pulp fibers can be accurately identified through Sobel filtering. Filtering can effectively suppress noise in an image, especially high-frequency noise. The Sobel filter can smooth the image while enhancing edge information, reducing noise interference in subsequent analysis. Through filtering, the edge features in the pulp texture image are enhanced, which helps subsequent feature extraction and analysis. Clear edge information can make the distribution, direction and density of pulp fibers more obvious. Compared with other complex image processing algorithms, the Sobel filter has the characteristics of simple calculation and high efficiency. Therefore, using the Sobel filter for filtering can improve the computational efficiency of image processing and meet the needs of real-time or large-scale data processing. The edge information of the image after Sobel filtering is more explicit. In the production of toilet paper, the texture and background of pulp may change due to factors such as raw materials and process. Using the Sobel filter for filtering processing can improve the overall robustness and stability.

[0089] In a preferred embodiment of the present application, the above step 14 comprises:

[0090] Step 141, convolve the filtered image with the horizontal and vertical Sobel matrices to obtain the horizontal gradient and vertical gradient of each pixel point;

[0091] Step 142, calculate the gradient amplitude and direction of each pixel point according to the horizontal gradient and vertical gradient, and obtain the gradient amplitude map of the image.

[0092] Step 143, according to the gradient amplitude map of the image, set a threshold for edge determination to extract the edge distribution map of the pulp texture.

[0093] In the embodiment of the present application, the horizontal gradient and the vertical gradient of each pixel point can be accurately calculated by convolving the image with the horizontal and vertical Sobel matrices. The gradient amplitude and direction of each pixel point can be further calculated by combining the horizontal gradient and the vertical gradient, forming the gradient amplitude map of the image, which helps to more meticulously describe the structure and characteristics of the pulp texture. By setting the threshold for edge determination, the sensitivity of edge extraction can be flexibly adjusted according to actual needs, and the edge distribution map of the pulp texture can be customized according to different product standards or production environments. The Sobel operator has a certain inhibitory effect on noise, and through gradient calculation and threshold determination, the interference of non-edge regions can be further reduced, the real pulp texture edge can be highlighted, and the signal-to-noise ratio of the analysis can be improved. The accurately extracted pulp texture edge distribution map is an important basis for subsequent complex image processing tasks such as pulp uniformity evaluation, quality detection, fault identification, etc. Through accurate extraction and analysis of the pulp texture edge, problems in the production process can be found in time, such as uneven fiber distribution, impurity mixing, etc., so as to timely adjust the production parameters, optimize the production process, and ensure the product quality.

[0094] The specific implementation steps of the present application include:

[0095] Step 141, first, standard horizontal and vertical Sobel matrices are used. The horizontal Sobel matrix is used to detect the horizontal edges in the image, and the vertical Sobel matrix is used to detect the vertical edges. The two matrices are respectively convolved with the filtered image. In the convolution process, each element of the Sobel matrix is multiplied by the pixel value at the corresponding position in the image, and all the products are added to obtain the horizontal gradient and the vertical gradient of each pixel point.

[0096] Step 142, using the horizontal gradient and the vertical gradient obtained in step 141, the gradient amplitude and direction of each pixel point are calculated, wherein the gradient amplitude is obtained by calculating the square root of the sum of the squares of the horizontal gradient and the vertical gradient, and the gradient amplitude reflects the strength of the edge. At the same time, the gradient direction can be obtained by calculating the inverse tangent function of the ratio of the vertical gradient to the horizontal gradient, and the gradient direction represents the direction of the edge. The gradient amplitude value of each pixel point is taken as the gray value or color value of the pixel in the gradient amplitude map. In this way, the gradient amplitude map intuitively shows the change intensity of each region in the image, and the edge region usually has a higher gradient amplitude value. In the gradient amplitude map, the brighter area represents a larger gradient amplitude, i.e. an edge or a region with obvious changes; while the darker area represents a smaller gradient amplitude, i.e. a region with gentle or uniform changes in the image.

[0097] Step 143, determine the edge according to the gradient amplitude map of the image. First, set a suitable threshold, setting the threshold too high may result in loss of edge information, and setting too low may introduce too much noise. By comparing the gradient amplitude of each pixel point with the threshold, it can be determined which pixel points belong to the edge. Specifically, if the gradient amplitude of a pixel point is greater than or equal to the threshold, it is marked as an edge pixel point; otherwise, it is considered as a non-edge pixel point. Finally, all the pixel points marked as edge constitute the edge distribution map of the pulp texture.

[0098] In a preferred embodiment of the present application, step 15 comprises:

[0099] Step 151, on the edge distribution map of the pulp texture, use image processing techniques to identify and mark all the edges to obtain the accurate position and shape information of the edges;

[0100] Step 152, for each edge marked, measure its width, and average all the measured edge widths to obtain the average edge width;

[0101] Step 153, calculate the standard deviation or coefficient of variation of the edge width to obtain the dispersion degree of the width;

[0102] Step 154, determine the width index based on the average edge width and the dispersion degree of the width;

[0103] Step 155, according to the value of the width index, obtain the preliminary evaluation result of the pulp surface uniformity of the toilet paper.

[0104] In the embodiments of the present application, all edges of the pulp texture are identified and marked by image processing techniques, which can obtain accurate position and shape information of the edges, help to accurately analyze the uniformity of pulp distribution, and avoid the subjectivity and errors of manual identification. By measuring and averaging the width of all marked edges, a specific average edge width value is obtained, which provides an objective basis for evaluating the uniformity of pulp distribution, making it possible to more accurately understand the product quality. By calculating the standard deviation or coefficient of variation of the edge width, the dispersion degree of the width can be understood, which helps to identify the uneven areas in the pulp distribution and provides guidance for improving the production process. Based on the average edge width and the dispersion degree of the width, the width index is determined, which provides a comprehensive evaluation standard for the uniformity of the pulp distribution of the toilet paper. According to the value of the width index, the preliminary evaluation result of the uniformity of the pulp distribution of the toilet paper can be obtained, which allows the products that do not meet the uniformity requirements to be quickly screened out during the production process, and timely adjustments are made, thereby improving the product quality and the pass rate. Through accurate evaluation of the uniformity of the pulp distribution, the production process can be more effectively optimized, raw material waste can be reduced, and production costs can be reduced. At the same time, improving product quality also helps to enhance brand image and market competitiveness.

[0105] The specific implementation steps of the present application include:

[0106] Step 151, based on the edge distribution map obtained by the Sobel operator before, perform thinning processing and adopt some morphological operations (such as dilation, erosion, opening operation and closing operation) to optimize the continuity and accuracy of the edges, and use image processing techniques to identify and mark all edges on the edge distribution map of the pulp texture, the purpose of identification and marking is to obtain accurate position and shape information of the edges.

[0107] Step 152, after the edges are accurately marked, scan the image row by row or column by column, and record the width value of each edge to determine the starting and ending positions of the edges, so as to calculate the width. After completing the width measurement of all edges, average the width values to obtain the average edge width, wherein the average value represents the average size of the edge of the pulp texture and is one of the important parameters for evaluating the uniformity of the pulp distribution.

[0108] Step 153, in order to more comprehensively evaluate the uniformity of the pulp distribution, calculate the dispersion degree of the edge width by calculating the standard deviation or coefficient of variation of the edge width. The standard deviation is a statistical quantity that measures the dispersion degree of data distribution, which reflects the fluctuation of data points relative to the average value. The coefficient of variation is the ratio of the standard deviation to the average value, which eliminates the influence of the average value size on the measurement of the dispersion degree, so that data sets with different average values can be compared. The standard deviation or coefficient of variation is selected to quantify the range of variation of the edge width, so as to evaluate the uniformity of the pulp distribution.

[0109] Step 154, based on the average edge width and the dispersion degree (standard deviation or coefficient of variation) of the width in step 153, a comprehensive width index is determined, which can comprehensively reflect the average size and consistency of the size of the pulp texture edge. For example, the average edge width and the standard deviation (or coefficient of variation) can be combined to obtain a single width index value. The larger the value, the worse the uniformity of the pulp distribution; otherwise, the better the uniformity.

[0110] Step 155, according to the calculated width index value, the preliminary evaluation result of the pulp surface of the tissue paper pulp distribution uniformity is obtained. The preliminary evaluation result is a series of threshold values for mapping the width index value to a specific uniformity level. For example, several threshold values can be set to divide the width index value into "excellent", "good", "general" and "poor" levels. When the width index value is lower than a certain threshold value, the pulp distribution uniformity is considered excellent; as the index value increases, the uniformity level gradually decreases.

[0111] In a preferred embodiment of the present application, step 16 comprises:

[0112] Step 161, according to the preliminary evaluation result of the pulp surface, the subgraph division method is determined;

[0113] Step 162, according to the subgraph division method, the global pulp texture edge distribution graph is divided into several subgraphs, wherein each subgraph should contain a part of the pulp texture;

[0114] Step 163, calculate the width index of the subgraph texture edge distribution, the uniformity index includes identifying edges, measuring width, average width and dispersion degree of width;

[0115] Step 164, normalize the width index to obtain a local index.

[0116] In the embodiments of the present application, by subgraph division, the global pulp texture edge distribution map can be subdivided into smaller areas, so that each area is evaluated more finely, which helps to find local differences and unevenness in pulp distribution. When determining the subgraph division method, the size and number of subgraphs can be flexibly adjusted according to the preliminary evaluation results of the pulp surface, so that the evaluation process can adapt to pulp texture images of different sizes and resolutions, improving the universality and practicality of the evaluation method. The width index of the subgraph texture edge distribution can highlight the characteristics of each local area. By comparing the index values of different subgraphs, abnormal areas or potential problem points in the pulp distribution can be more easily identified, providing strong support for quality control in the production process. Normalizing the width index can eliminate the dimensional differences between different subgraphs caused by factors such as size and resolution, making the local index comparable and helping to uniformly quantify the uniformity of the pulp distribution in the global range. Through detailed analysis of the local index, the uniformity of the tissue paper pulp distribution can be more accurately evaluated. The subgraph division and local index calculation process can be automated through programming, reducing the influence of manual intervention and subjective judgment. This helps to improve evaluation efficiency, reduce evaluation cost, and lay the foundation for intelligent management of tissue paper production.

[0117] The specific implementation steps of the present application include:

[0118] Step 161, analyze the preliminary evaluation results of the pulp surface, according to the index (such as the average edge width and the dispersion degree of the width) in the preliminary evaluation results of the pulp surface, determine which areas may exhibit higher unevenness, consider the characteristics of the pulp texture and the image size, determine the appropriate subgraph size, the subgraph should be large enough to contain enough texture information, while small enough to facilitate local feature analysis. A simple grid division can be used to uniformly divide the global image into several subgraphs, if the preliminary evaluation results of the pulp surface indicate that certain specific areas need more detailed analysis, more intensive division can be used in these areas, while less sparse division can be used in other areas.

[0119] Step 162, according to the subgraph division method determined in step 161, use image processing software or functions in programming libraries to actually divide the global pulp texture edge distribution map, cut the global image into several subgraphs according to the division method, each subgraph should contain a part of the pulp texture, and the size should meet the determination in step 161, ensure the clear boundary between subgraphs, save the generated subgraphs in a proper format for subsequent analysis and processing.

[0120] Step 163, for each subgraph, repeat the process of steps 151 to 154, i.e. identify edges, measure width, calculate average width and dispersion of width. Record the calculation results (average edge width, standard deviation or coefficient of variation, etc.) of each subgraph to form the width index data at the subgraph level. By comparing the width indices of different subgraphs, the distribution uniformity of pulp texture in different local areas can be analyzed.

[0121] Step 164, in order to facilitate comparison between different subgraphs, it is necessary to normalize the width index, convert the width index of each subgraph to a unified numerical range (such as between 0 and 1), and the normalized width index is the local index, which reflects the relative uniformity of pulp texture distribution in each subgraph. By comparing the local indices of different subgraphs, areas with more uniform or less uniform pulp distribution can be more easily identified.

[0122] In a preferred embodiment of the present application, the above-mentioned step 17 comprises:

[0123] The calculated local indices (i.e. normalized width indices) of each subgraph are collected, the index data is cleaned and formatted, the processed local index data is statistically analyzed, the distribution in different subgraphs is obtained, and according to the distribution of local indices, those areas that deviate significantly from the normal range are identified, which correspond to the parts with extremely uneven pulp distribution or defects. Based on the change range and distribution of local indices, the uniform complexity is calculated, which is a comprehensive index for quantifying the uniformity of texture distribution in the pulp surface image. The change range of local indices in different subgraphs is analyzed by calculating the difference between the maximum and minimum values, standard deviation or interquartile range, etc. Combined with the average value, change range and distribution of local indices, the uniform complexity is calculated using weighted average.

[0124] In the specific implementation steps of the present application, they include:

[0125] By collecting the local indices of each subgraph and performing data cleaning and formatting, the accuracy and consistency of the data can be ensured, and by statistically analyzing the processed local index data, those areas that deviate significantly from the normal range can be accurately identified, which helps to quickly locate the parts with extremely uneven pulp distribution or defects, so as to timely adjust and improve. By calculating the comprehensive index of uniform complexity, the uniformity of texture distribution in the pulp surface image can be quantified, providing an objective and comparable measurement standard, which helps to evaluate product quality and consistency. By analyzing the distribution of local indices in different subgraphs, the overall situation of pulp distribution can be fully understood, which helps to find potential systemic problems or trends, and through the above analysis, production parameters and processes can be more accurately adjusted to improve production efficiency and product quality.

[0126] In a preferred embodiment of the present application, the above-mentioned step 18 comprises:

[0127] Step 181, randomly selecting a pulp sample from the production line;

[0128] Step 182, using a high-resolution tomography device to scan the processed pulp sample and obtain tomographic image data;

[0129] Step 183, according to the tomographic image data, the pixels in the image are divided into two or more categories by iterative threshold calculation to obtain the internal structure of the pulp, including the distribution of fibers and fillers.

[0130] In the embodiment of the present application, randomly selecting a pulp sample from the production line can ensure that the selected sample is representative and can reflect the actual situation in the production process, which helps to obtain accurate and reliable data; using a high-resolution tomography device to scan the pulp sample can obtain high-precision tomographic image data, which can show the microstructure of the pulp in detail, including the distribution of fibers and fillers, providing intuitive and comprehensive visual information for in-depth understanding of the properties of the pulp. By iterative threshold calculation, the pixels in the image are divided into two or more categories, which can realize the quantitative analysis and classification of the internal structure of the pulp. This classification ability helps to accurately identify different types of fibers and fillers and quantify their proportion and distribution in the pulp. Understanding the detailed information of the internal structure of the pulp can better control the quality of the product. By monitoring the distribution of fibers and fillers, problems in the production process such as uneven fiber distribution and filler aggregation can be found in time, and production process parameters can be adjusted accordingly to optimize the production process. In-depth analysis of the internal structure of the pulp can provide strong support for the development of new products. By understanding the influence of different combinations of fibers and fillers on the performance of the pulp, new products with specific performance can be developed to meet market demand. Optimizing the production process and formula of the pulp can not only improve product quality, but also help to achieve energy saving and emission reduction and efficient use of resources. By reducing waste and unreasonable use in the production process, production costs can be reduced, and the impact on the environment can be reduced. With in-depth understanding and optimization of the internal structure of the pulp, higher quality and more innovative products can be provided to enhance market competitiveness. This helps to expand market share, improve customer satisfaction and loyalty.

[0131] The specific implementation steps of the present application include:

[0132] Step 181, according to the characteristics of the production line and the uniformity of the pulp flow, set sampling points at different stages of the production line (such as mixing, stirring, conveying, etc.), ensure that the sampling points can represent the pulp quality of the whole production line, at each sampling point, use appropriate tools (such as sampling spoon, sampling tube, etc.) to randomly grab a certain amount of pulp as sample, and properly process the sampled pulp.

[0133] Step 182, select a high-resolution tomography device such as an X-ray tomography or a nuclear magnetic resonance imaging instrument; according to the characteristics of the pulp sample and the analysis requirements, set appropriate scanning parameters such as scanning speed, layer thickness, resolution, etc.; place the processed pulp sample in the appropriate position of the scanning device, start the scanning device, and perform high-resolution tomography on the pulp sample. During scanning, the device will penetrate the sample layer by layer and record the image data of each layer.

[0134] Step 183, pre-process the tomographic image data obtained by scanning, use Otsu algorithm to determine the optimal segmentation threshold of different organizations (such as fibers and fillers) in the image, through multiple iteration calculations, find the threshold that can maximize the inter-class variance, according to the calculated threshold, use the segmentation tool in the image processing software to divide the pixels in the image into two or more classes, respectively corresponding to different components (such as fibers, fillers, etc.) in the pulp, visualize the classified image to observe the internal structure of the pulp more intuitively, and statistically analyze the classification results, such as calculating the volume fraction and distribution of each component, to quantitatively evaluate the uniformity and quality of the pulp.

[0135] In a preferred embodiment of the present application, the above step 19 comprises:

[0136] Step 191, according to the tomographic image, determine the analysis area, and divide the analysis area into regular grids;

[0137] Step 192, for each grid, quantify the fibers and fillers in the grid to obtain the quantization data of each grid;

[0138] Step 193, according to the quantization data of each grid, for all grids on the same horizontal layer, by get the fiber unevenness coefficient of the horizontal layer, by get the filler unevenness coefficient of the horizontal layer, wherein, and are the unevenness of the fibers and fillers of the horizontal layer, respectively, is the fiber quantization data of the grid in the horizontal layer, is the filler quantization data of the grid in the quantitative data of the filler in the grid, N is the total number of grids in the total number of grids in the horizontal layer, and is the index;

[0139] Step 194, according to the quantitative data of each grid, for the grids on different horizontal layers, by obtaining the vertical unevenness coefficient of the fiber, by obtaining the vertical unevenness coefficient of the filler, wherein, and are the unevenness of the fiber and the filler on multiple horizontal layers, respectively, is the fiber quantitative data of the grid in the horizontal layer, is the fiber quantitative data of the grid in the horizontal layer, is the filler quantitative data of the grid in the horizontal layer, N is the total number of grids in the horizontal layer, and is the index, is the total number of horizontal layers, is the weight of each horizontal layer.

[0140] In the embodiment of the present application, by dividing the analysis area into regular grids, the internal structure of the pulp can be analyzed more finely, which helps to accurately capture the distribution of fiber and filler at different positions; by quantifying the fiber and filler in each grid, comprehensive quantitative data can be obtained, which can objectively and accurately reflect the content and distribution of each component in the pulp, avoiding errors caused by subjective judgment and improving the accuracy and reliability of the analysis. By calculating the fiber and filler unevenness coefficients of the horizontal layers, the distribution uniformity of the fiber and filler on the same horizontal layer can be evaluated, which helps to identify the distribution differences and problem areas in the horizontal direction and provides a strong basis for optimizing the pulp formula and production process. By calculating the vertical unevenness coefficients of the fiber and filler, the distribution uniformity of the fiber and filler between different horizontal layers can be evaluated, which helps to understand the structural changes of the pulp in the thickness direction and find potential interlayer differences, providing guidance for improving the overall performance of the product. Combined with the unevenness evaluation results of the horizontal layers and the vertical direction, the distribution of the fiber and filler in the pulp can be comprehensively evaluated at multiple levels, which can more comprehensively reveal the characteristics and existing problems of the pulp structure; based on the quantitative analysis results, the production process can be optimized and the ratio and distribution of the fiber and filler can be adjusted to improve the uniformity and quality stability of the product. At the same time, this also helps to realize the quality control and cost optimization in the production process. By improving the distribution uniformity of the fiber and filler in the pulp, the overall performance and use experience of the product can be improved.

[0141] The specific implementation steps of the present application include:

[0142] Step 191, according to the characteristics of the tomographic image, a representative area is selected as the analysis object, which can reflect the overall situation of the internal structure of the pulp, the selected analysis area is divided into a series of regular grids, the shape of the grid can be square or rectangular; the size of the grid is determined to ensure that each grid contains enough pixel points for subsequent quantitative analysis.

[0143] Step 192, select the quantification method, including pixel count, gray value statistics, etc., for fibers, the shape, size and gray value can be quantified according to the characteristics; for fillers, the gray value and distribution density can be quantified according to the characteristics; the quantification data of fibers and fillers in each grid is recorded to form a data set.

[0144] Step 193, extract the fiber and filler quantification data of all grids on the same horizontal layer from the quantification data set, for fibers, use the provided formula to calculate the fiber unevenness coefficient of the horizontal layer, which reflects the unevenness of the fiber distribution on the same horizontal layer. For fillers, the same formula is used to calculate the filler unevenness coefficient of the horizontal layer, which reflects the unevenness of the filler distribution on the same horizontal layer.

[0145] Step 194, extract the fiber and filler quantification data of the grids on different horizontal layers from the quantification data set, assign a weight to each horizontal layer, the weight can be determined according to the importance, thickness or other related factors of the horizontal layer, use the provided formula to calculate the vertical unevenness coefficient of the fiber, the coefficient reflects the unevenness of the fiber distribution between different horizontal layers. Similarly, the vertical unevenness coefficient of the filler is calculated.

[0146] In a preferred embodiment of the present application, the above step 20 includes:

[0147] In the production of toilet paper, uniform complexity is used as a surface evaluation index, which reflects the smoothness of the surface of the toilet paper and the consistency of the distribution of fibers and fillers; and unevenness coefficient is used as an internal structure evaluation index, which reveals the dispersion uniformity of the fibers and fillers in the pulp. Combined with the two indexes, the distribution uniformity of the pulp of the toilet paper can be comprehensively evaluated. The following is the specific implementation process:

[0148] According to the calculation of the uniform complexity index, the index can quantify the uniformity of the surface of the toilet paper, and according to the quantification data, the horizontal unevenness coefficient and the vertical unevenness coefficient are calculated respectively to evaluate the uniformity of the distribution of the fibers and fillers in the pulp.

[0149] The collected uniform complexity and non-uniformity coefficient data are standardized to eliminate the dimensional differences between different indicators. A comprehensive evaluation index is constructed by combining the uniform complexity and non-uniformity coefficient and through weighted average. In constructing the comprehensive evaluation index, appropriate weights are assigned to the uniform complexity and non-uniformity coefficient based on historical data to reflect their importance in evaluating the uniformity of pulp distribution. According to historical data, threshold values and evaluation criteria are set for the comprehensive evaluation index. These threshold values and criteria will be used to judge the degree of uniformity of the pulp distribution of the toilet paper, for example, evaluation levels such as "excellent", "good", "general" and "poor" can be set.

[0150] The calculated comprehensive evaluation index is compared with the set evaluation criteria to determine the evaluation level of the uniformity of the pulp distribution of the toilet paper, so as to obtain the uniformity of the pulp distribution.

[0151] In the embodiments of the present application, by considering the surface uniformity and internal structure uniformity of the toilet paper at the same time, a more comprehensive and accurate evaluation of the product quality can be obtained. The uniform complexity and non-uniformity coefficient are both quantitative evaluation indicators, which can provide objective and accurate data support, eliminate the uncertainty caused by subjective evaluation, and make the evaluation results more comparable and reliable. By monitoring the changes of the uniform complexity and non-uniformity coefficient, problems in the production process such as uneven fiber distribution and rough surface can be found in time, which helps to take corresponding corrective measures quickly and avoid the problem from expanding and affecting the product quality. Based on the feedback of the comprehensive evaluation index, the production process can be optimized, the raw material ratio can be adjusted, and the process parameters can be improved, which helps to improve the production efficiency, reduce the production cost and the waste rate, and provide higher quality products to consumers, which helps to improve the brand image and enhance the market competitiveness of the products, thereby expanding the market share. The comprehensive evaluation index and its threshold values and evaluation criteria provide a basis for continuous improvement.

[0152] As shown in Figure 2 The embodiments of the present application also provide a detection system 20 for the uniformity of pulp distribution in toilet paper production, which comprises:

[0153] An acquisition module 21 is configured to capture a texture image of the surface of the pulp in the toilet paper production process.

[0154] The surface processing module 22 is used for separating the background and the pulp texture of the pulp image to obtain a separated pulp texture image, filtering the separated pulp texture image to obtain a filtered image, extracting an edge distribution map of the pulp texture according to the filtered image to obtain a pulp texture edge distribution map, calculating a width index of the pulp surface macrostructure according to the pulp texture edge distribution map to obtain a preliminary evaluation result of the pulp surface of the tissue paper, subdividing the image area and calculating a local index according to the preliminary evaluation result of the pulp surface to obtain a local index, and obtaining the uniform complexity of the pulp surface image according to the subdivided image area and the local index.

[0155] The internal processing module 23 is used for selecting a pulp sample of a production line, analyzing the tomographic image of the sample to obtain the internal structure of the pulp, including the fiber and filler distribution, and calculating the unevenness coefficient of the internal structure of the pulp, including the horizontal unevenness coefficient and the vertical unevenness coefficient.

[0156] The comprehensive processing module 24 is used for comprehensively evaluating the distribution uniformity of the tissue paper pulp according to the unevenness coefficient and the uniform complexity to realize the detection of the distribution uniformity of the tissue paper pulp in the production of the tissue paper.

[0157] The above describes the preferred embodiments of the present application. It should be noted that those skilled in the art can make several improvements and refinements without departing from the principles of the present application, and these improvements and refinements should also be considered as the protection scope of the present application.

Claims

1. A method for detecting the uniformity of pulp distribution in sanitary paper production, characterized in that The method comprises: capturing a texture image of a pulp surface in a toilet paper production process; separating a background of the pulp image and a pulp texture to obtain a separated pulp texture image, comprising: acquiring each pixel point of the image data, and setting a window size according to the image; in each window, calculating a local mean and a local standard deviation of each pixel point; Based on local mean and local standard deviation, through The adaptive threshold for each pixel is obtained, where W is the window radius. It is the horizontal position of a pixel in the image. It is the vertical position of a pixel in the image. It is the horizontal offset within the window. It is the vertical offset within the window. It is the location in the image ( The pixel value of ), where k is an empirical constant, ranging from -0.2 to -0.5; It is an adaptive threshold for each pixel; according to an adaptive threshold, performing thresholding processing on each pixel point to separate the background of the pulp image and the pulp texture, and obtain the separated pulp texture image; performing filtering processing on the separated pulp texture image to obtain a filtered image; according to the filtered image, extracting an edge distribution map of the pulp texture to obtain a pulp texture edge distribution map, comprising: convolving the filtered image through a horizontal and vertical Sobel matrix to obtain a horizontal gradient and a vertical gradient of each pixel point; calculating a gradient amplitude and a direction of each pixel point according to the horizontal gradient and the vertical gradient to obtain a gradient amplitude map of the image; according to the gradient amplitude map of the image, setting a threshold for edge determination to extract the edge distribution map of the pulp texture; according to the pulp texture edge distribution map, calculating a width index of a macrostructure of the pulp surface to obtain a preliminary evaluation result of the pulp surface uniformity of the toilet paper, comprising: on the pulp texture edge distribution map, using image processing technology to identify and mark all edges to obtain accurate position and shape information of the edges; for each marked edge, measuring the width, and averaging all measured edge widths to obtain an average edge width; calculating a standard deviation or a coefficient of variation of the edge width to obtain a dispersion degree of the width; determining the width index based on the average edge width and the dispersion degree of the width; according to the value of the width index, obtaining the preliminary evaluation result of the pulp surface uniformity of the toilet paper; according to the preliminary evaluation result of the pulp surface, subdividing the image area and calculating a local index to obtain the local index, comprising: determining a subgraph division mode according to the preliminary evaluation result of the pulp surface; dividing the global pulp texture edge distribution map into a plurality of subgraphs according to the subgraph division mode, wherein each subgraph should contain a part of the pulp texture; calculating a width index of a subgraph texture edge distribution, the uniformity index comprising identifying edges, measuring widths, averaging widths and a dispersion degree of the width; and normalizing the width index to obtain the local index; according to the subdivided image area and the local index, obtaining a uniform complexity of the pulp surface image; selecting a production line pulp sample, analyzing a tomographic image of the sample to obtain a pulp internal structure, including fiber and filler distribution; The unevenness coefficients of the internal structure of the pulp, including the horizontal unevenness coefficient and the vertical unevenness coefficient, are calculated, including: determining an analysis region according to a tomographic image, dividing the analysis region into regular grids; for each grid, quantifying the fibers and fillers in the grid to obtain quantification data of each grid; according to the quantification data of each grid, for all grids on the same horizontal layer, obtaining the fiber unevenness coefficient of the horizontal layer by obtaining the fiber unevenness coefficient of the horizontal layer by obtaining the filler unevenness coefficient of the horizontal layer, wherein, and are the first horizontal layer fiber and filler unevenness, is the fiber quantification data of the first grid in the first horizontal layer, is the filler quantification data of the first grid in the first horizontal layer, N is the total number of grids in the first horizontal layer, and are indexes; Based on the quantized data of each grid, for grids at different horizontal levels, through... The fiber vertical non-uniformity coefficient is obtained by... The vertical non-uniformity coefficient of the packing is obtained, where, and These are the non-uniformity of fibers and fillers across multiple horizontal layers. It is the first The first in the horizontal layer Fiber quantification data for each grid, It is the first The first in the horizontal layer The filler quantization data for the Nth grid, where N is the number of grid cells. The total number of grids in the horizontal layer, and It is an index. It is the total number of horizontal layers. These are the weights of each horizontal layer; comprehensively evaluating the distribution uniformity of the toilet paper pulp according to the non-uniformity coefficient and the uniform complexity to realize the detection of the pulp distribution uniformity in the toilet paper production.

2. The method for detecting the pulp distribution uniformity in sanitary paper production according to claim 1, characterized in that, selecting a production line pulp sample, analyzing a tomographic image of the sample to obtain a pulp internal structure, including fiber and filler distribution, comprising: randomly selecting a pulp sample from the production line; scanning the processed pulp sample using a high-resolution tomographic scanning device to obtain tomographic image data; According to the tomographic image data, pixels in the image are classified into two or more classes by iteratively computing a threshold value to obtain the internal structure of the pulp, including fiber and filler distribution.

3. A system for detecting the uniformity of pulp distribution in sanitary paper production, characterized by The system performs the method of any one of claims 1-2, comprising: an acquisition module configured to capture a texture image of a surface of the pulp during the production of the sanitary paper; a surface processing module configured to separate a background of the pulp image from a pulp texture to obtain a separated pulp texture image, to filter the separated pulp texture image to obtain a filtered image, to extract an edge distribution map of the pulp texture from the filtered image to obtain a pulp texture edge distribution map, to calculate a width index of a macrostructure of the surface of the pulp from the pulp texture edge distribution map to obtain a preliminary evaluation result of the distribution uniformity of the pulp of the sanitary paper, and to subdivide the image area and calculate a local index from the preliminary evaluation result of the surface of the pulp to obtain a uniform complexity of the surface of the pulp image; an internal processing module configured to select a pulp sample from the production line, to analyze a tomographic image of the sample to obtain an internal structure of the pulp, including fiber and filler distribution, and to calculate a non-uniformity coefficient of the internal structure of the pulp, including a horizontal non-uniformity coefficient and a vertical non-uniformity coefficient; a comprehensive processing module configured to comprehensively evaluate the distribution uniformity of the pulp of the sanitary paper from the non-uniformity coefficient and the uniform complexity to achieve the detection of the distribution uniformity of the pulp in the production of the sanitary paper.

4. A computing device, comprising: comprising: one or more processors; a storage device storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method of any one of claims 1-2.

5. A computer readable storage medium, characterized in that, The computer readable storage medium stores a program which, when executed by a processor, implements the method of any one of claims 1-2.

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