An e-commerce clothing sales value evaluation system based on cloud computing

Abstract

The present invention discloses a cloud computing-based e-commerce clothing sales value assessment system, comprising a data acquisition module, a sampling module, a quality assessment module, and an output module. The sampling module is used to obtain relevant image features of a target shirt; the quality assessment module is used to perform feature detection on the target shirt. The data acquisition module, sampling module, quality assessment module, and output module are interconnected and communicated with each other. The present invention utilizes a feature overlap detection module and a pattern coincidence detection module to divide the pattern area of ​​the shirt and compare the slopes of the lines in the sampling detector, thereby visualizing the specific parameters of all pattern lines within the shirt, greatly improving detection accuracy. Pattern lines at different locations on the shirt are located, marked, and disassembled, thereby sorting out the line features within the shirt and effectively reducing the difficulty of detection caused by irregularities in the line features. The present invention has the characteristics of high feature testing efficiency and strong precision monitoring capabilities.

Description

Technical Field

[0001] The present invention relates to the technical field of clothing production management, and in particular to an e-commerce clothing sales value evaluation system based on cloud computing. Background Art

[0002] In modern clothing design, plaid pattern is an important source of inspiration and design element for fashion designers. With the development and evolution of popular trends, the application form of plaid pattern has been constantly updated and has become the production choice of many clothing manufacturers.

[0003] As consumers' demands for clothing coordination increase, the patterns and styles of clothing also influence sales prices and sales rates. Existing technology uses feature detection of different shirt designs as a method that accurately predicts the design value of clothing patterns and is widely used by e-commerce merchants to evaluate shirt quality. However, with the emergence of an increasing number of clothing styles, simply testing the color distribution and pattern types of clothing can no longer meet the diverse needs of shirt design evaluation. Therefore, a cloud computing-based e-commerce clothing sales value assessment system with high efficiency in design feature testing and strong precision monitoring capabilities is highly necessary. Summary of the Invention

[0004] The purpose of the present invention is to provide an e-commerce clothing sales value evaluation system based on cloud computing to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a cloud computing-based e-commerce clothing sales value assessment system, comprising a data acquisition module, a sampling module, a quality assessment module and an output module, wherein the data acquisition module is used to obtain relevant information about shirts sold in shops; the sampling module is used to obtain relevant image features of a target shirt through a detector; the quality assessment module is used to perform feature detection on the target shirt and obtain relevant parameter information of the shirt pattern; the output module is used to output the detection results of the shirt design scheme, and the data acquisition module, the sampling module, the quality assessment module and the output module are communicatively connected to each other.

[0006] According to the above technical solution, the data acquisition module includes a shirt information acquisition module and a detector information acquisition module. The shirt information acquisition module is used to obtain relevant information about shirt production; the detector information acquisition module is used to obtain detector information for detecting shirt features.

[0007] According to the above technical solution, the quality assessment module includes a pattern overlap detection module and a feature overlap detection module. The pattern overlap detection module is used to detect the overlap of patterns set on the shirts for sale; the feature overlap detection module is used to detect and determine whether feature overlap occurs in the target shirt.

[0008] According to the above technical solution, the pattern overlap detection module further includes an arrangement analysis submodule and a line detection submodule. The arrangement analysis submodule is used to analyze the overall arrangement of the patterns on the shirts for sale; the line detection submodule is used to detect the characteristic information presented by the pattern arrangement of the shirts for sale through the slope and degree of concavity of the pattern lines on the shirts for sale.

[0009] According to the above technical solution, the operation method of the sales value evaluation system mainly includes the following steps:

[0010] Step S1: Extracting clothing information authorized online by the clothing manufacturer in the shirt purchasing and selling inspection software connected to the sales value assessment system, and extracting the maximum and minimum values ​​of the clothing size information through data analysis and processing, thereby obtaining, for the target shirt to be inspected, a distance range from the left cuff to the right cuff of the shirt as [a1, a2], and a distance range from the shirt collar to the bottom button of the shirt as [b1, b2];

[0011] Step S2: placing the produced garment on the detection instrument and analyzing the shirt style image captured by the camera;

[0012] Step S3: obtaining the pattern lines of the shirt in the detection instrument, calculating the overlap rate of the pattern on the current production garment, and determining whether the pattern lines of the currently selected sampled shirt have feature overlap;

[0013] Step S4: Test different sample shirts in sequence, and output clothing solutions with different clothing but overlapping style features through the output module.

[0014] According to the above technical solution, step S2 further includes:

[0015] Step S21: a detector is set up to detect patterns on produced clothing, a robotic arm places a target shirt on the detector, and an automatic ironing machine is used to remove wrinkles on the target shirt;

[0016] Step S22: In the first image captured by the camera, the system establishes a plane rectangular coordinate system with the center point of the image as the coordinate origin, a straight line through the coordinate origin parallel to the upper limit of the first image as the X-axis, and a straight line rotated 90° as the Y-axis. The coordinates of the center point of the image are (0, 0), the length of the X-axis is the distance a2 from the left cuff to the right cuff of the production shirt, the length of the Y-axis is the distance b2 from the collar of the production shirt to the bottom button of the shirt, and the unit length of the coordinate system is 1.

[0017] According to the above technical solution, in step S21, the specific steps of placing the target shirt for inspection by the robotic arm are as follows:

[0018] Step S211: The system adjusts the direction of the camera located at the highest point of the detector to be perpendicular to the ground. The camera is connected to the loading platform below via a mechanical arm. The camera's direction is controlled to coincide with the center point of the loading platform located at the bottom of the detector. The image captured by the camera is set as the first image.

[0019] Step S212: The robotic arm adjusts the bottom button, top button, left cuff, and right cuff of the target shirt to corresponding positions in the coordinate system according to the front view position in the plane rectangular coordinate system in step S2;

[0020] The corresponding position in the coordinate system is: the coordinate of the button with the smallest vertical coordinate among all the identified buttons The coordinate of the button with the largest vertical coordinate among all identified buttons Coordinates of the midpoint of the left cuff Coordinates of the midpoint of the right cuff The button with the smallest vertical coordinate among the buttons corresponds to the bottom button of the detection target shirt, and the button with the largest vertical coordinate among the identified buttons corresponds to the top button of the detection target shirt, A∈[a1, a2], B∈[b1, b2].

[0021] According to the above technical solution, in step S212, the specific method for the robotic arm to identify the target shirt is:

[0022] The camera sends the captured image of the target shirt to the sampling module;

[0023] The sampling module preprocesses the image;

[0024] Image segmentation obtains the target area and extracts the external features of the shirt;

[0025] The image is input into the recognition model obtained through big data training, the cuff and button features of the shirt in the image are extracted, the location of the features is located and uploaded to the device collaboration system.

[0026] According to the above technical solution, step S3 further includes:

[0027] Step S31: Using infrared rays from a camera, the camera detects the features of the shirts stacked on the detector's loading platform. Image feature analysis technology is used to identify the initial point coordinates (X1, Y1) and the end point coordinates (X2, Y2) of the pattern lines arranged on each single-layer shirt within the coordinate system, and the number of overlaps at the end of each line is obtained.

[0028] Step S32: Arrange the coordinate points in descending order according to the number of overlaps, extract the two coordinates with the largest number of overlaps, the two coordinates with the smallest number of overlaps, and S coordinates with the number of overlaps at the midpoint of the sequence. The number S to be extracted at the midpoint of the sequence is determined by the total number of coordinate points. When n is an odd number, the number to be extracted is 1; when n is an even number, the number to be extracted is 2.

[0029] Step S33: Obtain the slope of the (K+1) pattern lines corresponding to each extracted coordinate in turn. Determine the slope difference between the two pattern lines among the slopes of the pattern lines corresponding to the five extracted coordinate points Where L1 and L2 are the corresponding slopes of two lines randomly selected from the pattern lines corresponding to the five coordinate points. When (100%-μ%)<η<(100%+μ%), it is judged that the currently selected pattern lines have feature overlap, where μ is the maximum error limit ratio value of the slope difference corresponding to the feature overlap.

[0030] According to the above technical solution, in step S31, the method for identifying the pattern lines arranged on each single-layer shirt mainly includes the following steps:

[0031] Step S311: For each shirt inner pattern, the image area is cut into k1*k2 rectangular small detection areas, where k1 and k2 are the corresponding numbers of the length and width of the image area, respectively, so that the area of ​​each small detection area is the same, and n areas of different colors in the small detection area are divided into "first pattern area", "second pattern area", ... "nth pattern area", where the boundary between two adjacent areas is the pattern line, and the pattern lines dividing each area are marked as "first x1 line", ... "nth line". G X H Lines", where X1, X G and X H are the numbers corresponding to two adjacent divided areas within the monitoring area, X1, X G and X H are all integers, min{X1, X K 、X G}≥1, max{X1, X K 、X G}≤n;

[0032] Step S312: Mark the initial position of the pattern line in the coordinate system (X1, Y1) and the end point coordinate (X2, Y2) of the pattern line in each area, and obtain the initial position coordinate point (X1, Y1) of the pattern line in the small detection area in turn. 11 , Y 11 ), (X 12 , Y 12 )……(X 1n , Y 1n ) and the end point coordinates (X 21 , Y 21 ), (X 22 , Y 22 )……(X 2n , Y 2n ), the number of times each coordinate among all the acquired coordinate points coincides with other initial position coordinate points and end position coordinate points is K, then there are "(K+1)" coordinate points intersecting at this coordinate point.

[0033] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention processes the clothing information of the shirt in advance through the sampling module, and uses the detector to locate and detect the relevant information of the target shirt, determines the length range of the target shirt, and improves the applicability of the clothing feature detection system and the feature detection efficiency; through the feature overlap detection module, the pattern area of ​​the shirt is divided and the slopes of the lines are compared in the sampling detector, and the various visual parameters in the shirt are visualized, which increases the way to identify the technical features of the shirt, greatly improves the detection accuracy, provides a new and powerful basis for e-commerce merchants to choose clothing styles, and effectively avoids the risk of mass production of low-quality clothing that damages corporate sales; at the same time, through the pattern overlap detection module, the pattern lines at different positions of the shirt are located, marked and disassembled, and the features formed by all the pattern lines are arranged, the line features in the shirt are sorted out, and the difficulty of detection caused by the irregularity of the line features is effectively reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0035] Figure 1 It is a schematic diagram of the system module composition of the present invention. DETAILED DESCRIPTION

[0036] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0037] See also Figure 1 The present invention provides a technical solution: an e-commerce clothing sales value evaluation system based on cloud computing, comprising:

[0038] The data acquisition module, sampling module, quality assessment module and output module are connected to each other. The data acquisition module is used to obtain relevant information about shirts sold in shops; the sampling module is used to obtain relevant image features of the target shirt through a detector; the quality assessment module is used to perform feature detection on the target shirt and obtain relevant parameter information of the shirt pattern; the output module is used to output the detection results of the shirt design scheme. The data acquisition module, sampling module, quality assessment module and output module are connected to each other in communication.

[0039] The present invention processes the clothing information of the shirt in advance through a sampling module, and uses a detector to locate and detect relevant information of the target shirt, determines the length range of the target shirt, and improves the applicability of the clothing feature detection system and the feature detection efficiency; through the feature overlap detection module, the pattern area of ​​the shirt is divided and the slopes of the lines are compared in the sampling detector, and various visual parameters in the shirt are visualized, which increases the way to identify the technical features of the shirt and greatly improves the detection accuracy, provides a new and powerful basis for e-commerce merchants to select clothing styles, and effectively avoids the risk of mass production of low-quality clothing that damages corporate sales; at the same time, through the pattern overlap detection module, the pattern lines at different positions of the shirt are located, marked and disassembled, and the features formed by all the pattern lines are arranged, so as to sort out the line features in the shirt and effectively reduce the difficulty of detection caused by the irregularity of the line features.

[0040] The data acquisition module includes a shirt information acquisition module and a detector information acquisition module. The shirt information acquisition module is used to obtain relevant information about shirt production; the detector information acquisition module is used to obtain detector information for detecting shirt features.

[0041] The quality assessment module includes a pattern overlap detection module and a feature overlap detection module. The pattern overlap detection module is used to detect the overlap of patterns on the shirts for sale; the feature overlap detection module is used to detect whether feature overlap occurs in the target shirt.

[0042] The pattern overlap detection module further includes an arrangement analysis submodule and a line detection submodule. The arrangement analysis submodule is used to analyze the overall arrangement of the patterns on the shirts for sale; the line detection submodule is used to detect the characteristic information presented by the pattern arrangement of the shirts through the slope and convexity of the pattern lines on the shirts for sale.

[0043] In a preferred embodiment, the operating method of the sales value evaluation system mainly includes the following steps:

[0044] Step S1: Extracting clothing information authorized online by the clothing manufacturer in the shirt purchasing and selling inspection software connected to the sales value assessment system, and extracting the maximum and minimum values ​​of the clothing size information through data analysis and processing, thereby obtaining, for the target shirt to be inspected, a distance range from the left cuff to the right cuff of the shirt as [a1, a2], and a distance range from the shirt collar to the bottom button of the shirt as [b1, b2];

[0045] Step S2: placing the produced garment on the detection instrument and analyzing the shirt style image captured by the camera;

[0046] Step S3: obtaining the pattern lines of the shirt in the detection instrument, calculating the overlap rate of the pattern on the current production garment, and determining whether the pattern lines of the currently selected sampled shirt have feature overlap;

[0047] Step S4: Test different sample shirts in sequence, and output clothing solutions with different clothing but overlapping style features through the output module.

[0048] Step S2 further comprises:

[0049] Step S21: a detector is set up to detect patterns on produced clothing, a robotic arm places a target shirt on the detector, and an automatic ironing machine is used to remove wrinkles on the target shirt;

[0050] Step S22: In the first image captured by the camera, the system establishes a plane rectangular coordinate system with the center point of the image as the coordinate origin, a straight line through the coordinate origin parallel to the upper limit of the first image as the X-axis, and a straight line rotated 90° as the Y-axis. The coordinates of the center point of the image are (0, 0), the length of the X-axis is the distance a2 from the left cuff to the right cuff of the production shirt, the length of the Y-axis is the distance b2 from the collar of the production shirt to the bottom button of the shirt, and the unit length of the coordinate system is 1.

[0051] In step S21, the specific steps of placing the robot arm on the target shirt for inspection are as follows:

[0052] Step S211: The system adjusts the direction of the camera located at the highest point of the detector to be perpendicular to the ground. The camera is connected to the loading platform below via a mechanical arm. The camera's direction is controlled to coincide with the center point of the loading platform located at the bottom of the detector. The image captured by the camera is set as the first image.

[0053] Step S212: The robotic arm adjusts the bottom button, top button, left cuff, and right cuff of the target shirt to corresponding positions in the coordinate system according to the front view position in the plane rectangular coordinate system in step S2;

[0054] The corresponding position in the coordinate system is: the coordinate of the button with the smallest vertical coordinate among all the identified buttons The coordinate of the button with the largest vertical coordinate among all identified buttons Coordinates of the midpoint of the left cuff Coordinates of the midpoint of the right cuff The button with the smallest vertical coordinate among the buttons corresponds to the bottom button of the detection target shirt, and the button with the largest vertical coordinate among the identified buttons corresponds to the top button of the detection target shirt, A∈[a1, a2], B∈[b1, b2].

[0055] Manufacturers often produce shirts with different sizes for the same style, or different sizes for the same size for different styles. This makes it difficult for a single detection system to be used for multiple target styles when testing shirt features. This technical solution pre-processes the shirt's clothing information to determine the target shirt's length range, improving the applicability of the clothing feature detection system while also accelerating feature detection efficiency.

[0056] In step S212 of this embodiment, the specific method for the robotic arm to identify the target shirt is:

[0057] The camera sends the captured image of the target shirt to the sampling module;

[0058] The sampling module preprocesses the image;

[0059] Image segmentation obtains the target area and extracts the external features of the shirt;

[0060] The image is input into the recognition model obtained through big data training, the cuff and button features of the shirt in the image are extracted, the location of the features is located and uploaded to the device collaboration system.

[0061] In this embodiment, step S3 further includes:

[0062] Step S31: Using infrared rays from a camera, the camera detects the features of the shirts stacked on the detector's loading platform. Image feature analysis technology is used to identify the initial point coordinates (X1, Y1) and the end point coordinates (X2, Y2) of the pattern lines arranged on each single-layer shirt within the coordinate system, and the number of overlaps at the end of each line is obtained.

[0063] Step S32: Arrange the coordinate points in descending order according to the number of overlaps, extract the two coordinates with the largest number of overlaps, the two coordinates with the smallest number of overlaps, and S coordinates with the number of overlaps at the midpoint of the sequence. The number S to be extracted at the midpoint of the sequence is determined by the total number of coordinate points. When n is an odd number, the number to be extracted is 1; when n is an even number, the number to be extracted is 2.

[0064] Step S33: Obtain the slope of the (K+1) pattern lines corresponding to each extracted coordinate in turn. Determine the slope difference between the two pattern lines among the slopes of the pattern lines corresponding to the five extracted coordinate points Where L1 and L2 are the corresponding slopes of two lines randomly selected from the pattern lines corresponding to the five coordinate points. When (100%-μ%)<η<(100%+μ%), it is judged that the currently selected pattern lines have feature overlap, where μ is the maximum error limit ratio value of the slope difference corresponding to the feature overlap.

[0065] The value of a feature is directly related to its occurrence rate and repetition rate. The higher the occurrence rate and the greater the repetition rate, the lower the value of the feature; the lower the occurrence rate and the less repetition rate, the higher the value. In other words, the more unique and accidental a feature is when it is formed, the higher its value; the more regular and universal a feature is when it is formed, the lower its value.

[0066] This technical solution solves the problem of determining shirt pattern designs by identifying the tonal modulation of different shirts, which results in an overly simplistic identification method, a single analysis channel, and severely limited analytical results. It also addresses the difficulty in quantifying the patterns and lines of shirt pattern designs, which leads to a lack of screening capabilities for unsold shirts and excess production capacity. By dividing the pattern areas of a shirt and comparing the slopes of the lines in the sampling detector, the various visual parameters within the shirt are visualized, increasing the number of ways to identify the technical characteristics of the shirt and significantly improving detection accuracy.

[0067] By testing the slope of patterns on shirts, the company's assessment of shirt pattern production quality has been further improved, providing a new and powerful basis for e-commerce merchants to choose clothing styles, and effectively avoiding the risk of mass production of low-quality clothing damaging e-commerce merchants' sales.

[0068] In step S31 of this embodiment, the method for identifying the pattern lines arranged on each single-layer shirt mainly includes the following steps:

[0069] Step S311: For each shirt inner pattern, the image area is cut into k1*k2 rectangular small detection areas, where k1 and k2 are the corresponding numbers of the length and width of the image area, respectively, so that the area of ​​each small detection area is the same, and n areas of different colors in the small detection area are divided into "first pattern area", "second pattern area", ... "nth pattern area", where the boundary between two adjacent areas is the pattern line, and the pattern lines dividing each area are marked as "first x1 line", ... "nth line". G X H Lines", where X1, X G and X H are the numbers corresponding to two adjacent divided areas within the monitoring area, X1, X G and X H are all integers, min{X1, X K 、X G}≥1, max{X1, X K 、X G}≤n;

[0070] Step S312: Mark the initial position of the pattern line in the coordinate system (X1, Y1) and the end point coordinate (X2, Y2) of the pattern line in each area, and obtain the initial position coordinate point (X1, Y1) of the pattern line in the small detection area in turn. 11 , Y 11 ), (X 12 , Y 12 )……(X 1n , Y 1n ) and the end point coordinates (X 21 , Y 21 ), (X 22 , Y 22 )……(X 2n , Y 2n ), the number of times each coordinate among all the acquired coordinate points coincides with other initial position coordinate points and end position coordinate points is K, then there are "(K+1)" coordinate points intersecting at this coordinate point.

[0071] This technical solution solves the problem of difficulty in detecting the pattern layout of a target shirt due to varying shapes and sizes, varying pattern lengths, and even distorted pattern lines within each area. By locating, marking, and deconstructing pattern lines at different locations on the shirt, the line features within the shirt are identified, and the features corresponding to the specific parameters of all pattern lines are arranged and combined, effectively reducing the difficulty of detection caused by irregular line features.

[0072] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0073] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A cloud computing-based e-commerce clothing sales value assessment system, characterized by: The system comprises a data acquisition module, a sampling module, a quality assessment module, and an output module. The data acquisition module is used to obtain information about shirts sold in shops; the sampling module is used to obtain relevant image features of target shirts through a detector; the quality assessment module is used to perform feature detection on the target shirt and obtain relevant parameter information of the shirt pattern; and the output module is used to output the detection results of the shirt design scheme. The data acquisition module, the sampling module, the quality assessment module, and the output module are communicatively connected to each other. The data acquisition module includes a shirt information acquisition module and a detector information acquisition module. The shirt information acquisition module is used to acquire relevant information about shirt production; the detector information acquisition module is used to acquire detector information for detecting shirt features. The quality assessment module includes a pattern overlap detection module and a feature overlap detection module. The pattern overlap detection module is used to detect the overlap of patterns on the shirts being sold; the feature overlap detection module is used to detect whether feature overlap occurs in the target shirt. The pattern overlap detection module further includes an arrangement analysis submodule and a line detection submodule, wherein the arrangement analysis submodule is used to analyze the overall arrangement of patterns on the shirts for sale; The line detection submodule is used to detect characteristic information presented by the pattern arrangement of the shirt by measuring the slope and concave-convex degree of the pattern lines on the shirt; The operation method of the sales value evaluation system mainly includes the following steps: Step S1: Extracting clothing information authorized online by the clothing manufacturer in the shirt purchasing and selling inspection software connected to the sales value assessment system, and extracting the maximum and minimum values ​​of the clothing size information through data analysis and processing, thereby obtaining, for the target shirt to be inspected, a distance range from the left cuff to the right cuff of the shirt as [a1, a2], and a distance range from the shirt collar to the bottom button of the shirt as [b1, b2]; Step S2: placing the produced garment on the detection instrument and analyzing the shirt style image captured by the camera; Step S3: obtaining the pattern lines of the shirt in the detection instrument, calculating the overlap rate of the pattern on the current production garment, and determining whether the pattern lines of the currently selected sampled shirt have feature overlap; Step S4: testing different sample shirts in sequence, and outputting clothing solutions with different clothing but overlapping style features through the output module; It is characterized in that: the step S3 further includes: Step S31: Using infrared rays from a camera, the camera detects the features of the shirts stacked on the detector's loading platform. Image feature analysis technology is used to identify the initial point coordinates (X1, Y1) and the end point coordinates (X2, Y2) of the pattern lines arranged on each single-layer shirt within the coordinate system, and the number of overlaps at the end of each line is obtained. Step S32: Arrange the coordinate points in descending order according to the number of overlaps, extract the two coordinates with the largest number of overlaps, the two coordinates with the smallest number of overlaps, and S coordinates with the number of overlaps at the midpoint of the sequence. The number S to be extracted at the midpoint of the sequence is determined by the total number of coordinate points. When n is an odd number, the number to be extracted is 1; when n is an even number, the number to be extracted is 2. Step S33: Obtain the slope of the (K+1) pattern lines corresponding to each extracted coordinate in turn. Determine the slope difference between the two pattern lines among the slopes of the pattern lines corresponding to the five extracted coordinate points Where L1 and L2 are the corresponding slopes of two lines randomly selected from the pattern lines corresponding to the five coordinate points. When (100%-μ%)<η<(100%+μ%), it is judged that the currently selected pattern lines have feature overlap, where μ is the maximum error limit ratio value of the slope difference corresponding to the feature overlap.

2. The cloud computing-based e-commerce clothing sales value evaluation system according to claim 1 is characterized by: In step S31, the method for identifying the pattern lines arranged on each single-layer shirt mainly includes the following steps: Step S311: For each shirt inner pattern, the image area is cut into k1*k2 rectangular small detection areas, where k1 and k2 are the corresponding numbers of divisions of the length and width of the image area, respectively, so that the area of ​​each small detection area is the same. The n areas of different colors within the small detection area are divided into "first pattern area", "second pattern area", ... "nth pattern area", where the boundary between two adjacent areas is the pattern line, and the pattern lines dividing each area are marked as "first x1 line", ... "nth pattern area". G X H Lines", where X1, X G and X H are the numbers corresponding to two adjacent divided areas within the monitoring area, X1, X G and X H are all integers, min{X1, X K 、X G }≥1, max{X1, X K 、X G }≤n; Step S312: Mark the initial position of the pattern line in the coordinate system (X1, Y1) and the end point coordinate (X2, Y2) of the pattern line in each area, and obtain the initial position coordinate point (X1, Y1) of the pattern line in the small detection area in turn. 11 , Y 11 ), (X 12 , Y 12 )……(X 1n , Y 1n ) and the end point coordinates (X 21 , Y 21 ), (X 22 , Y 22 )……(X 2n , Y 2n ), the number of times each coordinate of all the acquired coordinate points overlaps with other initial position coordinate points and end position coordinate points is K, then there are "(K+1)" coordinate points intersecting at this coordinate point.

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