Method for seamless bonding of shoe soles

Abstract

The application is suitable for the field of leather shoe processing technology, and particularly relates to a seamless bonding method for leather shoe soles, which comprises the following steps: acquiring a first image and a second image; wherein the first object and the second object are leather shoe soles produced in the same batch; determining a plurality of transition regions based on the first image and the second image; wherein the transition region is used to reflect the transition region between different functional regions in the second object; determining a polishing degree change based on the plurality of transition regions, the first image and the second image; when the polishing degree change has at least one data which is not 0, determining an adjustment parameter based on the first image and the second image; wherein the adjustment parameter is used to reflect the adjustment of the glue application amount when bonding the second object. The seamless bonding method for leather shoe soles provided by the application can improve the adaptation degree of the glue application amount and the surface of the leather shoes after manual polishing, so as to make the bonding of the leather shoe soles firm.

Description

Technical Field

[0001] This application belongs to the field of leather shoe processing technology, and in particular relates to a seamless bonding method for leather shoe outsoles. Background Technology

[0002] Seamless bonding of leather shoe outsoles refers to the bonding process where the sole and upper of a leather shoe are glued together, resulting in a seamless or seamless joint. The process typically involves first sanding the surfaces where the upper and sole will bond, then applying a suitable amount of polyurethane glue or resin adhesive to these surfaces, and finally applying pressure and heat to cure the glued surfaces, thus completing the bonding. Due to the upgrading of modern lifestyles and the pursuit of quality, especially among male consumers who value a high-quality lifestyle and individuality, handcrafted leather shoes better meet the lifestyle and consumption needs of modern male consumers compared to mass-produced industrial shoes.

[0003] In related technologies, in the production of handmade leather shoes, hand sanding relies on the experience and judgment of craftsmen. Craftsmen need to use tools such as sandpaper and files to sand the bonding surface of the sole. Although the degree of sanding can be adjusted for different functional areas (such as the forefoot, heel, and midfoot), hand sanding cannot guarantee that the bonding surface of the sole in different batches has the same degree of sanding in the same functional area. This will result in different bonding strengths between different functional areas after the sanded upper and sole are pressurized and heat-cured. This difference in bonding strength between different areas will cause gaps to appear because the bonding strength of one area is insufficient. Summary of the Invention

[0004] This application provides a seamless bonding method for leather shoe outsoles, which can improve the problem of gaps that may occur in handmade leather shoes when bonding finished shoes because the amount of glue applied cannot be matched to the sanding differences in different areas of the outsole caused by hand sanding.

[0005] In a first aspect, embodiments of this application provide a seamless bonding method for leather shoe outsoles, including:

[0006] Acquire a first image and a second image; wherein the first image is used to reflect the surface image of the first object after a standard polishing operation, and the second image is used to reflect the surface image of the second object after a polishing operation, and the first object and the second object are both leather shoe soles produced in the same batch;

[0007] Based on the first image and the second image, multiple transition regions are determined; wherein, the transition regions are used to reflect the transition regions between different functional regions in the second object;

[0008] Based on multiple transition regions, the first image, and the second image, the degree of polishing is determined; wherein, the degree of polishing is used to reflect the change in the degree of polishing between the first object and the second object in different feature regions;

[0009] When at least one of the changes in the degree of polishing is not zero, adjustment parameters are determined based on the first image and the second image; wherein, the adjustment parameters are used to reflect the amount of adhesive applied when bonding the second object.

[0010] The technical solutions described in this application embodiment have at least the following technical effects:

[0011] The seamless bonding method for shoe outsoles provided in this application first acquires a surface image reflecting the surface image of a first object after a standard polishing operation and a second image reflecting the surface image of a second object after a polishing operation. Based on the first and second images, a transition area reflecting the second object between different foot functional areas is determined. Then, based on multiple transition areas, the first and second images, a change in the degree of polishing between the first and second objects is determined. When at least one of the changes in the degree of polishing is not zero, an adjustment parameter reflecting the amount of adhesive applied when bonding the second object is determined based on the first and second images.

[0012] This method effectively captures surface images of the soles of leather shoes produced in the same batch after standardized sanding operations. These images can serve as a comparison reference for subsequent batches. By comparing and analyzing the first and second images using the same acquisition method, focusing only on the sanding operation, the differences between the first and second images are determined solely by the sanding state itself. This allows for precise capture of subtle fluctuations during the sanding process, which can be compensated for through dynamic glue amount adjustment. Furthermore, by setting trigger conditions, it accurately identifies sanding deviations caused by minor variables, triggering subsequent steps to adjust the glue amount on the second object. In the production of handmade leather shoes, glue amount adjustments can compensate for deviations in the sanding process, thereby mitigating the bonding risks caused by sanding deviations. Ultimately, this improves the adaptability of handmade leather shoes to random sanding variations caused by human intervention in the bonding process, enhancing the compatibility between the adhesive and the sanding degree during bonding and reducing the possibility of the sole separating from the upper or detaching from the sole.

[0013] In one possible implementation of the first aspect, determining multiple transition regions based on the first image and the second image includes:

[0014] Based on the first image, the second image is divided into multiple feature regions; wherein, the feature regions are used to reflect the regions in the second image that correspond to different foot functional regions in the first image;

[0015] Multiple adjacent feature regions and multiple dividing lines corresponding to the adjacent feature regions are determined from the multiple feature regions; wherein, the adjacent feature regions are used to reflect two adjacent feature regions in the multiple feature regions, and the dividing lines are used to reflect the boundary lines between two adjacent feature regions in the multiple feature regions;

[0016] Based on the multiple adjacent feature regions, the second image, and the dividing line, multiple transition regions are determined.

[0017] In one possible implementation of the first aspect, determining multiple transition regions based on multiple adjacent feature regions, the second image, and the dividing line includes:

[0018] Based on the multiple dividing lines, multiple feature vectors are determined for each dividing line; wherein the feature vectors are used to reflect vector line segments perpendicular to the dividing lines;

[0019] The second image is traversed according to a preset window based on multiple feature vectors to obtain color value changes; wherein, the color value changes are used to reflect the average color value in the preset window;

[0020] The point where the color value changes for the first time is taken as the starting point, and the point where the color value changes for the second time is taken as the ending point.

[0021] Based on the starting point and the ending point corresponding to each feature vector, a transition range is determined; wherein, the transition range refers to the range covered by the line connecting the starting point and the ending point;

[0022] Multiple transition regions are constructed based on the multiple transition ranges corresponding to each of the adjacent feature regions.

[0023] In one possible implementation of the first aspect, determining the change in polishing degree based on multiple transition regions, the first image, and the second image includes:

[0024] Based on the first image and the multiple transition regions, a first polishing degree chain is determined; wherein, the first polishing degree chain is used to reflect the polishing degree of the first object between different foot functional areas under the standard polishing operation;

[0025] Based on the second image and the multiple transition regions, a second polishing degree chain is determined; wherein, the second polishing degree chain is used to reflect the polishing degree of the second object between different foot functional areas under the polishing operation;

[0026] The change in polishing degree is determined based on the data difference between each chain node between the first polishing degree chain and the second polishing degree chain.

[0027] In one possible implementation of the first aspect, determining the first polishing degree chain based on the first image and the plurality of said transition regions includes:

[0028] Multiple first average color values ​​corresponding to the transition region are extracted from the first image; wherein, the first average color values ​​are used to reflect the average color value between each transition range constituting the transition region in the first image;

[0029] A first broken line is determined based on multiple first average color values; wherein, the first broken line is used to reflect the broken line constructed by multiple first average color values ​​in the extraction order;

[0030] The first chain node is determined based on the standard deviation and variance of each of the first broken lines; wherein, the first chain node refers to the ratio between the standard deviation and variance of the first broken line;

[0031] Multiple nodes of the first chain are constructed into a first polishing degree chain.

[0032] In one possible implementation of the first aspect, determining the second polishing degree chain based on the second image and the plurality of said transition regions includes:

[0033] Multiple second average color values ​​corresponding to the transition region are extracted from the second image; wherein the second average color values ​​are used to reflect the average color value between each transition range constituting the transition region in the second image;

[0034] A second broken line is determined based on multiple second average color values; wherein the second broken line is used to reflect the broken line constructed by multiple first average color values ​​in the extraction order;

[0035] The second chain node is determined based on the standard deviation and variance of each second piecewise linear line; wherein, the second chain node refers to the ratio between the standard deviation and variance of the second piecewise linear line.

[0036] Multiple nodes of the second chain are constructed into a second polishing degree chain.

[0037] In one possible implementation of the first aspect, determining the adjustment parameters based on the first image and the second image when at least one of the changes in the polishing degree is not zero includes:

[0038] Based on the changes in the degree of polishing and the second image, an adjustment area is determined; wherein, the adjustment area is used to reflect the area in the second object where the amount of adhesive applied needs to be adjusted;

[0039] Based on the adjustment area, the first image, and the second image, adjustment parameters are determined.

[0040] In one possible implementation of the first aspect, determining the adjustment area based on the change in polishing degree and the first image includes:

[0041] The adjacent feature regions whose polishing degree is not 0 are determined as the first region group from the adjacent feature regions;

[0042] Based on the first region group, a second region group is determined from the first image; wherein the second region group is used to reflect two feature regions in the first image that correspond to the first region group;

[0043] The feature regions whose average color value differs from the average color value of the corresponding feature regions in the first region group are designated as adjustment regions.

[0044] In one possible implementation of the first aspect, determining the adjustment parameters based on the adjustment region, the first image, and the second image includes:

[0045] Based on the adjustment region and the corresponding transition region, a first sub-region and a second sub-region are determined; wherein, the first sub-region is used to reflect the part of the adjustment region that is not included by the transition region, and the second sub-region is used to reflect the part of the adjustment region that is included by the transition region.

[0046] Based on the first sub-region, the second sub-region, the first image, and the second image, adjustment parameters are determined.

[0047] In one possible implementation of the first aspect, determining the adjustment parameters based on the first sub-region, the second sub-region, the first image, and the second image includes:

[0048] A first texture ratio is determined based on the first texture of the first sub-region in the first image and the second texture of the first sub-region; wherein the first texture is used to reflect the texture degree matched by the first sub-region in the first image, and the second texture is used to reflect the texture degree of the first sub-region.

[0049] A second texture ratio is determined based on the second texture and the third texture of the second sub-region; wherein the third texture is used to reflect the texture degree of the second sub-region.

[0050] Based on the first texture ratio and the preset adhesive amount, the first adhesive amount of the adjustment parameter is determined; wherein, the first adhesive amount is used to reflect the amount of adhesive applied to the first sub-region;

[0051] Based on the first adhesive amount and the second texture ratio, the second adhesive amount of the adjustment parameter is determined; wherein, the second adhesive amount is used to reflect the amount of adhesive applied to the second sub-region.

[0052] Secondly, embodiments of this application provide a seamless bonding system for leather shoe outsoles, comprising:

[0053] An acquisition unit is used to acquire a first image and a second image; wherein the first image is used to reflect the surface image of the first object after a standard polishing operation, and the second image is used to reflect the surface image of the second object after a polishing operation, and the first object and the second object are both leather shoe soles produced in the same batch;

[0054] A transition region determination unit is used to determine multiple transition regions based on the first image and the second image; wherein the transition regions are used to reflect the transition regions between different functional regions of the second object;

[0055] A polishing degree confirmation unit is used to determine the polishing degree change based on multiple transition regions, the first image, and the second image; wherein the polishing degree change is used to reflect the change in polishing degree between the first object and the second object in different feature regions;

[0056] The parameter adjustment confirmation unit is used to determine adjustment parameters based on the first image and the second image when at least one of the data points in the change of the polishing degree is not zero; wherein the adjustment parameters are used to reflect the amount of adhesive applied when bonding the second object.

[0057] Thirdly, embodiments of this application provide a seamless bonding device for leather shoe soles, including an image acquisition device, an adhesive application device, and a control device. The image acquisition device and the adhesive application device are both electrically connected to the control device. The control device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the method described in any of the first aspects above.

[0058] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0059] Fifthly, embodiments of this application provide a computer program that, when run on a seamless bonding device for leather shoe outsoles, causes the seamless bonding device for leather shoe outsoles to perform the seamless bonding method for leather shoe outsoles described in any of the first aspects.

[0060] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0061] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0062] Figure 1 This is a schematic flowchart of a seamless bonding method for leather shoe outsoles provided in an embodiment of this application;

[0063] Figure 2 This is a schematic diagram illustrating the implementation process of a seamless bonding method for leather shoe outsoles provided in an embodiment of this application;

[0064] Figure 3 This is a schematic diagram of the structure of a seamless bonding system for leather shoe outsoles provided in an embodiment of this application;

[0065] Figure 4 This is a schematic diagram of the control device of a seamless bonding equipment for leather shoe outsoles provided in an embodiment of this application. Detailed Implementation

[0066] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0067] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0068] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0069] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0070] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0071] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0072] In related technologies, in the production of handmade leather shoes, hand sanding relies on the experience and judgment of craftsmen. Craftsmen need to use tools such as sandpaper and files to sand the bonding surface of the sole. Although the degree of sanding can be adjusted for different functional areas (such as the forefoot, heel, and midfoot), hand sanding cannot guarantee that the bonding surface of the sole in different batches has the same degree of sanding in the same functional area. This will result in different bonding strengths between different functional areas after the sanded upper and sole are pressurized and heat-cured. If the bonding strength of one area is insufficient, the area with insufficient bonding strength will affect other areas during normal walking, eventually leading to bonding failure. When the user wears the shoes, local delamination and sole separation will occur, and in severe cases, the upper and sole will separate directly.

[0073] To address the aforementioned issues, this application provides a seamless bonding method for leather shoe outsoles. This method first acquires a surface image reflecting the surface of a first object after a standard polishing operation and a second image reflecting the surface of a second object after a polishing operation. Based on the first and second images, a transition region reflecting the transition between different functional areas of the foot on the second object is determined. Then, based on multiple transition regions, the first and second images, a change in the degree of polishing between the first and second objects is determined. When at least one of the changes in the degree of polishing is non-zero, an adjustment parameter reflecting the amount of adhesive applied during bonding of the second object is determined based on the first and second images. This method effectively captures surface images of the soles of leather shoes produced in the same batch after standardized sanding operations. These images can serve as a comparison reference for subsequent batches. By comparing and analyzing the first and second images using the same acquisition method, focusing only on the sanding operation, the differences between the first and second images are determined solely by the sanding state itself. This allows for precise capture of subtle fluctuations during the sanding process, which can be compensated for through dynamic glue amount adjustment. Furthermore, by setting trigger conditions, it accurately identifies sanding deviations caused by minor variables, triggering subsequent steps to adjust the glue amount on the second object. In the production of handmade leather shoes, glue amount adjustments can compensate for deviations in the sanding process, thereby mitigating the bonding risks caused by sanding deviations. Ultimately, this improves the adaptability of handmade leather shoes to random sanding variations caused by human intervention in the bonding process, enhancing the compatibility between the adhesive and the sanding degree during bonding and reducing the possibility of the sole separating from the upper or detaching from the sole.

[0074] The seamless bonding method for leather shoe outsoles provided in this application embodiment can be applied to a seamless bonding equipment for leather shoe outsoles. In this case, the seamless bonding equipment for leather shoe outsoles is the executing entity of the seamless bonding method for leather shoe outsoles provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of seamless bonding equipment for leather shoe outsoles.

[0075] The seamless bonding equipment for shoe outsoles includes an image acquisition device, an adhesive application device, and a control device. The image acquisition device and adhesive application device are electrically connected to the control device. The image acquisition device is used to acquire images of the hand-polished surface of the shoe outsole. For example, the image acquisition device can be an industrial area array camera or a CMOS camera. The adhesive application device is used to apply adhesive to the polished surface of the shoe outsole. For example, the adhesive application device can be a dispensing machine or a spraying machine. The control device is used to monitor and control the adhesive application process of the shoe outsole.

[0076] For example, the control device can be a mobile phone, tablet computer, laptop computer, ultra-mobile personal computer (UMPC), netbook, smart screen, smart TV, handheld device with wireless communication function, desktop computer, handheld device with wireless communication function, computer, laptop computer, handheld computing device, etc.

[0077] To better understand the seamless bonding method for leather shoe outsoles provided in this application, the specific implementation process of the seamless bonding method for leather shoe outsoles provided in this application will be described below by way of example.

[0078] Figure 1 and Figure 2 A schematic flowchart illustrating the seamless bonding method for leather shoe outsoles provided in this application is shown. Please refer to [link / reference]. Figure 1 and Figure 2 Seamless bonding methods for leather shoe outsoles include:

[0079] S100, acquire a first image and a second image; wherein, the first image is used to reflect the surface image of the first object after a standard polishing operation, and the second image is used to reflect the surface image of the second object after a polishing operation, and the first object and the second object are both leather shoe soles produced in the same batch.

[0080] It is understandable that the first image can be obtained by photographing the surface of the first object after a standard polishing operation using an image acquisition device. Alternatively, simulation software can be used to simulate the polishing degree of an unpolished shoe sole according to a preset polishing degree, resulting in a simulated image, which is then used as the first image. The preset polishing degree can be manually input or obtained directly from a preset database. The second image can be obtained by photographing the surface of the second object after manual polishing using an image acquisition device. Shoe soles produced in the same batch use the same raw materials, production equipment, production processes, and are manufactured during the same production period, ensuring consistency in the initial material and structure of the shoe soles. Furthermore, the image acquisition device must photograph the first object after a standard polishing operation and the second object after a polishing operation from the same position. That is, the image acquisition device needs to photograph the entire polished surface of both the first and second objects from the same shooting angle, and the shooting parameters of the image acquisition device must be completely consistent, so that the first and second images only reflect the difference between the standard polishing operation and the polishing operation. The preset database refers to a database containing different functional areas of the foot and their corresponding polishing levels. This data can be obtained through laboratory experiments, on-site measurements and monitoring, and past experience. After acquisition, the collected data is organized, classified, and archived, useful information and patterns are extracted, and the relevant data is saved into the database to form the preset database.

[0081] S200, based on the first image and the second image, determine multiple transition regions; wherein, the transition regions are used to reflect the transition regions between different functional regions in the second object.

[0082] It's understandable that during hand sanding, different areas of the shoe sole need to be sanded to different degrees. This means that while sanding one area, you won't touch another, resulting in transition areas between two adjacent areas on the shoe sole.

[0083] For example, the second image can be divided into multiple regions corresponding to different foot functional areas in the first image, using a first image as a reference. Then, based on these multiple regions, combinations of adjacent regions and boundary lines between them are determined. Finally, a transition region is determined using the combination of adjacent regions, the second image, and the boundary lines between each adjacent region. Alternatively, the first and second images can be directly compared. Regions in the second image with different color values ​​corresponding to those in the first image are identified as difference regions. After traversing the entire second image, these difference regions collectively constitute the transition region.

[0084] In one possible implementation, in step S200, multiple transition regions are determined based on the first image and the second image, including:

[0085] S210, based on the first image, the second image is divided into multiple feature regions; wherein, the feature regions are used to reflect the regions in the second image that correspond to different foot functional regions in the first image.

[0086] It can be understood that the first image serves as a standard template image for manual polishing, and the feature region refers to the region in the second image that corresponds one-to-one with the different foot functional regions in the first image.

[0087] For example, firstly, all foot functional regions in the first image are identified and marked, and reference data such as the position coordinates and shape contours of each functional region are recorded. Then, the second image is aligned and calibrated with the first image. Finally, based on the position coordinates and shape contours of each foot functional region in the first image, the corresponding regions are divided in the aligned second image. These regions are the feature regions.

[0088] S220, determine multiple adjacent feature regions and multiple dividing lines corresponding to the adjacent feature regions from multiple feature regions; wherein, the adjacent feature regions are used to reflect two adjacent feature regions in the multiple feature regions, and the dividing lines are used to reflect the boundary lines between two adjacent feature regions in the multiple feature regions.

[0089] It can be understood that adjacent feature regions refer to two regions that are geographically adjacent and separated by other feature regions within the multiple feature regions already defined in the second image. The dividing line is the boundary line between adjacent feature regions, and its trend in the second image is the same as the design boundary of the corresponding foot functional region.

[0090] For example, all feature regions can be traversed to determine whether the contours of any two feature regions have a common edge or the shortest distance is less than a preset threshold. If the conditions are met, they are determined to be adjacent feature regions. After obtaining the adjacent feature regions, the common contour line segment of each group of adjacent feature regions is used as the dividing line.

[0091] S230, based on multiple adjacent feature regions, the second image, and the dividing line, determines multiple transition regions.

[0092] For example, one approach is to identify multiple vector segments perpendicular to each dividing line, then traverse the second image through a preset window according to the direction of each vector segment, and obtain the change in the average color value within the preset window. Based on the number of changes in the average color value, a sub-range constituting a transition region on that vector segment is determined. By traversing each dividing line multiple times, multiple sub-ranges can be obtained, thus constructing a transition region between adjacent feature regions corresponding to that dividing line. Repeating this step yields multiple transition regions. Alternatively, one approach is to analyze the average color value at the dividing line in the second image using the dividing line and the second image. Then, using a preset color value interval, traverse the adjacent feature regions in the second image corresponding to the dividing line according to the direction of the dividing line. Regions where the color value difference between the traversed point and the color value on the dividing line does not exceed the preset color value interval are considered transition regions.

[0093] This setup divides the second image into multiple feature regions based on the first image. Using the first image after standard polishing as a reliable reference benchmark reduces the segmentation deviation caused by human experience, thus laying an accurate regional foundation for the subsequent definition of transition regions. On this basis, multiple adjacent feature regions and their corresponding dividing lines are determined from the multiple feature regions. By identifying adjacent region pairs and their boundary lines, the boundary positions become quantifiable and repeatable, reducing the interference of boundary ambiguity on the definition of transition regions. Combining multiple adjacent feature regions, the second image, and the dividing lines, multiple transition regions are determined. The transition regions are defined as specific ranges on both sides of the boundary lines, achieving objective quantification of the transition regions. This makes the identification of transition regions unaffected by subjective factors, thereby significantly improving the accuracy of polishing degree change analysis.

[0094] In one possible implementation, in step S230, multiple transition regions are determined based on multiple adjacent feature regions, the second image, and the dividing line, including:

[0095] S231, based on multiple dividing lines, determine multiple feature vectors for each dividing line; wherein, the feature vectors are used to reflect the vector line segments perpendicular to the dividing lines.

[0096] It can be understood that the feature vector is a vector line segment with a direction perpendicular to the dividing line. Because the dividing line is a curve, different feature vectors exist at different positions of the dividing line. That is, the positions and directions of multiple feature vectors of each dividing line are different.

[0097] S232, based on multiple feature vectors, the second image is traversed according to a preset window to obtain color value changes; wherein, the color value changes are used to reflect the average color value in the preset window.

[0098] It's understandable that a preset window refers to a cropped window of a preset size, which can be manually entered or obtained directly from a window database.

[0099] For example, by using a preset window to traverse the path with the feature vector as the core, the preset window moves gradually from one region to another in the direction of the extension of the feature vector. The arithmetic mean of the color values ​​of all pixels in each window is calculated, and the average value and the position encompassed by the preset window are recorded.

[0100] S233, take the point where the color value changes for the first time as the starting point and the point where the color value changes for the second time as the ending point.

[0101] It can be understood that the starting point is the point where the color value changes for the first time, marking the boundary of entering the transition zone, and the ending point is the point where the color value changes for the second time, marking the boundary of leaving the transition zone. Together, they define the core range of the transition zone.

[0102] S234, Based on the starting point and ending point corresponding to each feature vector, determine the transition range; where the transition range refers to the range covered by the line connecting the starting point and the ending point.

[0103] It can be understood that after determining the start and end point data for each feature vector, the line connecting these two points is used as the core axis, and a rectangular or irregular strip-shaped area is generated according to the preset coverage width. This area is the transition range corresponding to the feature vector.

[0104] S235, multiple transition regions are constructed based on multiple transition ranges corresponding to each adjacent feature region.

[0105] It can be understood that the transition range is the area covered by the line connecting the start and end points of a single feature vector, while the transition region is a complete connecting area that covers the boundary of the adjacent feature regions by integrating all the transition ranges corresponding to the same adjacent feature regions.

[0106] This setup, by defining multiple feature vectors perpendicular to the dividing lines, effectively captures the color value gradation characteristics near the functional area boundary, solving the problem of inaccurate boundaries caused by relying solely on dividing lines. Furthermore, by using feature vectors to traverse the second image according to a preset window to obtain color value change data, this process not only smooths image noise but also highlights the color value change trend, making the identification of transition areas more reliable. By defining the two significant color value change points as the start and end points respectively, the boundary position of the transition area is precisely defined, providing an accurate basis for subsequent quantization. The transition range is determined based on the start and end points corresponding to each feature vector, and by integrating multiple transition ranges to form a complete transition area, a structured data foundation for the transition area is constructed.

[0107] S300, based on multiple transition regions, the first image, and the second image, determines the change in polishing degree; wherein, the change in polishing degree is used to reflect the change in polishing degree between the first object and the second object in different feature regions.

[0108] For example, the degree of polishing of a first object across different foot functional areas under standard polishing operations can be determined using a first image and multiple transition regions. Similarly, the degree of polishing of a second object across different foot functional areas under manual polishing operations can be determined using a second image and multiple transition regions. Finally, the change in polishing degree is determined based on the polishing degrees of the first and second objects across different foot functional areas. Alternatively, the change in polishing degree can be determined by directly comparing the average color values ​​of the first and second images based on the transition regions, using the difference between the average color value in the transition regions of the second image and the average color value in the same transition region of the first image.

[0109] In one possible implementation, in step S300, the change in polishing degree is determined based on multiple transition regions, the first image, and the second image, including:

[0110] S310, based on the first image and multiple transition regions, a first polishing degree chain is determined; wherein, the first polishing degree chain is used to reflect the polishing degree of the first object between different foot functional areas under standard polishing operation.

[0111] It can be understood that the first polishing degree chain is a data chain consisting of the polishing degree between different adjacent feature regions in the first image.

[0112] For example, the first polishing degree chain can be constructed by extracting the average color value corresponding to each transition region in the first image, reflecting the transition ranges constituting the transition regions, constructing a polyline based on the average color value in the extraction order, determining a node for each polyline, and finally constructing a chain through a node corresponding to each transition region. Alternatively, the first polishing degree chain can be constructed by directly using the average color value of each transition region in the first image as a chain node.

[0113] In one possible implementation, in step S310, determining a first polishing degree chain based on the first image and multiple transition regions includes:

[0114] S311, extract a plurality of first average color values ​​corresponding to the transition region from the first image; wherein, the first average color value is used to reflect the average color value between each transition range constituting the transition region in the first image.

[0115] This is understandable, because the first image is acquired after the standard polishing operation has been completed on the first object. When the acquisition conditions (shooting parameters of the image acquisition device) of the first image are fixed, the color values ​​in the first image are only affected by the standard polishing operation and thus differ. Therefore, by locating the standard ranges that correspond one-to-one with these transition ranges in the first image, then acquiring the pixel color values ​​for each standard range, calculating the arithmetic mean of the color values ​​of all pixels within that range, the first average color value is finally obtained.

[0116] S312, a first broken line is determined based on multiple first average color values; wherein, the first broken line is used to reflect the broken line constructed by multiple first average color values ​​in the extraction order.

[0117] It can be understood that each transition range in the transition region corresponds to a first average color value. The average color value corresponding to each transition range is arranged according to the extraction order of the first average color value to form a broken line. The horizontal axis of the broken line is the extraction order of the first average color value, and the vertical axis is the specific data of the first average color value. This broken line is called the first broken line.

[0118] S313, determine the first chain node based on the standard deviation and variance of each first piecewise linear line; wherein, the first chain node refers to the ratio between the standard deviation and variance of the first piecewise linear line.

[0119] It is understandable that the first line segment can reflect the continuous trend of color value changes in the transition area under standard polishing. The standard deviation can reflect the dispersion of the deviation of each first average color value from the mean in the first line segment. The first chain node refers to the chain node of the first polishing degree chain.

[0120] For example, the first chain node = standard deviation of the first broken line ÷ variance of the first broken line.

[0121] S314 constructs multiple first chain nodes into a first polishing degree chain.

[0122] It can be understood that there is a first broken line in the transition area between each adjacent feature region, and each first broken line corresponds to a chain node of the first polishing degree chain. That is, the chain nodes corresponding to multiple adjacent feature regions can form the first polishing degree chain.

[0123] This setup uses image processing technology to extract multiple first average color values ​​corresponding to the transition area from the image of the standard polished leather shoe outsole. These color values ​​directly reflect the uniformity of surface roughness. Based on the extracted first average color values, a first polygonal line is constructed sequentially. This sequence corresponds to the actual arrangement logic of the functional areas of the foot, allowing the gradual change in polishing degree to be visualized. By performing mathematical statistical analysis on the first polygonal line, the ratio of the standard deviation to the variance of each polygonal segment is calculated to determine the position of the first chain node. These positions precisely correspond to the key nodes at the boundaries of the functional areas. Multiple chain nodes are constructed into a first polishing degree chain, forming a structured sequence that makes the polishing degree distribution comparable and traceable. This achieves precise quantification of the changes in polishing degree in the transition area and solves the problem of fuzzy chain node positioning caused by the lack of an objective quantification mechanism.

[0124] S320, based on the second image and multiple transition regions, a second polishing degree chain is determined; wherein, the second polishing degree chain is used to reflect the polishing degree of the second object between different foot functional areas under polishing operation.

[0125] For example, the second polishing degree chain can be constructed by extracting the average color value corresponding to each transition region in the second image, reflecting the transition ranges constituting the transition regions, constructing a polyline based on the average color value in the order of its extraction, determining a node for each polyline, and finally constructing a chain through a node corresponding to each transition region. Alternatively, the second polishing degree chain can be constructed by directly using the average color value of each transition region in the second image as a chain node.

[0126] In one possible implementation, in step S320, a second polishing degree chain is determined based on the second image and multiple transition regions, including:

[0127] S321, extract multiple second average color values ​​corresponding to the transition region from the second image; wherein, the second average color values ​​are used to reflect the average color value between each transition range constituting the transition region in the second image.

[0128] It is understandable that the second image is acquired after the polishing operation of the second object has been completed. When the acquisition conditions of the second image are the same as those of the first image, that is, the color values ​​in the second image are only affected by the polishing operation and thus differ, multiple second average color values ​​can be obtained by obtaining multiple first average color values ​​in step S311. This will not be elaborated here.

[0129] S322, a second broken line is determined based on multiple second average color values; wherein, the second broken line is used to reflect the broken line constructed by multiple first average color values ​​in the extraction order.

[0130] It is understandable that the second broken line can be determined using the same method used to determine the first broken line in step S312, which will not be elaborated here.

[0131] S323, determine the second chain node based on the standard deviation and variance of each second piecewise linear line; where the second chain node refers to the ratio between the standard deviation and variance of the second piecewise linear line.

[0132] It is understandable that the second chain node can be determined using the method for determining the first chain node in step S313, which will not be elaborated here.

[0133] S324 constructs a second polishing chain by combining multiple second chain nodes.

[0134] It is understandable that the second polishing degree chain can be constructed using the method for constructing the first polishing degree chain in step S311, which will not be elaborated here.

[0135] This setup, by extracting multiple second average color values ​​from the second image, achieves a preliminary quantification of the actual polishing degree, reducing the bias of subjective experience judgment. Based on multiple second average color values, a second broken line is constructed, transforming discrete data into a continuous trend of change, thereby clearly reflecting the transition characteristics between functional areas and potential uneven polishing areas. By calculating the ratio of the standard deviation to the variance of each second broken line, significant change points are amplified and random noise interference is suppressed, enabling precise positioning of key change points. Multiple second chain nodes are integrated into a second polishing degree chain, forming a structured chain to fully characterize the distribution characteristics of the actual polishing degree between the functional areas of the sole.

[0136] S330, based on the data difference between each chain node between the first and second polishing degree chains, determine the change in polishing degree.

[0137] It can be understood that the first polishing degree chain is a coherent data chain constructed under standard polishing, containing the first chain nodes corresponding to each transition region. The second polishing degree chain is a data chain of the same type as the first polishing degree chain constructed under manual polishing, containing the second chain nodes corresponding to each transition region in the first polishing degree chain. The data difference corresponding to the chain nodes refers to the difference in the chain node values ​​of the same transition region in the two chains; the change in polishing degree is the data difference corresponding to each chain node.

[0138] This setup utilizes the first image as a standard surface reference after polishing. Combined with the precise definition of functional area boundaries via transition regions, it extracts the continuous distribution characteristics of polishing levels between different foot functional areas under standard operation, forming a quantifiable first polishing level chain. The same transition region is applied to the second image to capture the actual polishing level distribution of the current batch of shoe outsoles, forming a second polishing level chain. By comparing the numerical differences between the two chains node by node, the specific deviation at each functional area transition point can be accurately identified. This transforms abstract polishing inconsistencies into actionable data changes. This fine-grained difference quantification mechanism allows the system to locate local areas requiring adjustment, directly driving adaptive correction of subsequent adhesive application, thereby reducing the risk of adhesive failure due to accumulated polishing deviations.

[0139] S400, when at least one of the data points showing a non-zero change in the degree of polishing, an adjustment parameter is determined based on the first image and the second image; wherein, the adjustment parameter is used to reflect the amount of adhesive applied when bonding the second object.

[0140] It is understandable that if at least one of the data points showing a non-zero change in the degree of sanding, it indicates a difference between the sanding effect of the second object and the adjacent feature areas of the first object corresponding to the non-zero sanding degree change data. This means that the amount of adhesive applied to the second object needs to be adjusted. Once the adjusted adhesive amount for bonding the second object is obtained, the adhesive application equipment applies the corresponding amount of adhesive to different feature areas or different sub-areas of different feature areas of the second object.

[0141] For example, the area in the second object that requires adjustment of the amount of adhesive can be determined by the change in the degree of polishing and the second image, and then the adjustment parameters can be determined by the area, the first image, and the second image.

[0142] This setup, through comparative analysis of the first and second images, uses the same acquisition method only for differences in the polishing operation. This ensures that the difference between the first and second images stems solely from the polishing state itself, allowing for precise capture of subtle fluctuations during the polishing process. Compensation is achieved through dynamic glue amount adjustment, and trigger conditions accurately identify polishing deviations caused by minute variables, thus initiating subsequent glue amount adjustments on the second object. In the production of handmade leather shoes, glue amount adjustments can compensate for polishing deviations, mitigating the bonding risks associated with these deviations. Ultimately, this enhances the adaptability of handmade leather shoes to random polishing variations caused by human intervention in the bonding process, improving the compatibility between the adhesive and the polishing level during bonding, and reducing the likelihood of the sole and upper separating or detaching.

[0143] In one possible implementation, in step S400, when at least one of the data points showing a non-zero change in the degree of polishing, adjustment parameters are determined based on the first image and the second image, including:

[0144] S410, based on the change in the degree of polishing and the second image, determine the adjustment area; wherein, the adjustment area is used to reflect the area in the second object where the amount of adhesive needs to be adjusted.

[0145] It is understandable that the change in polishing degree refers to the difference in polishing effect between the second object and the first object, and the area where the change in polishing degree is non-zero data means that there are problems such as uneven polishing and abnormal roughness in that area.

[0146] For example, one can determine the adjacent feature regions corresponding to data where the degree of polishing change is not zero from multiple adjacent feature regions, then determine the corresponding region group in the first image based on the adjacent feature regions, and then perform average color value analysis on the adjacent feature regions and the region group. The region in the adjacent feature regions whose average color value is different from that of the region group is determined as the adjustment region. Alternatively, the adjustment region can be determined directly based on the sign of the degree of polishing change. When the sign of the degree of polishing change is "-", it indicates that the adjustment region is the region in the adjacent feature regions corresponding to the degree of polishing change that points in the direction of traversing the second image using a preset window. When the sign of the degree of polishing change is "+", it indicates that the adjustment region is the region in the adjacent feature regions corresponding to the degree of polishing change that points in the opposite direction of traversing the second image using a preset window.

[0147] In one possible implementation, in step S410, determining the adjustment area based on the polishing degree and the first image includes:

[0148] S411, determine the adjacent feature regions whose polishing degree is not 0 from the adjacent feature regions as the first region group.

[0149] It is understandable that the first region group is selected from all adjacent feature regions, and the combination of non-zero changes in the degree of polishing is used as the core region association object for subsequent glue application amount adjustment.

[0150] S412, based on the first region group, determine the second region group from the first image; wherein the second region group is used to reflect two feature regions in the first image that correspond to the first region group.

[0151] For example, after determining the first region group, key information such as the position coordinates and contour shape of the two feature regions in each group is extracted simultaneously. Then, the coordinates of each feature region in the first region group are mapped onto the first image to locate standard feature regions with the same function and aligned positions. These two feature regions obtained by matching coordinate positions form another standard combination. The combination of these two standard adjacent feature regions in the first image is the second region group.

[0152] S413, the feature regions whose average color value of the feature regions in the first region group is different from the average color value of the feature regions corresponding to the second region group are taken as adjustment regions.

[0153] It is understandable that when the average color value of the feature areas in the first region group differs from the average color value of the corresponding feature areas in the second region group, it means that there is a difference in the surface state of the two regions. These feature areas are precisely the areas where the amount of adhesive applied needs to be adjusted to accommodate the differences, i.e., the adjustment areas.

[0154] This setup utilizes the characteristic of adjacent feature regions as functional boundary transitions to filter out regions with a non-zero polishing degree, forming a first region group. This allows subsequent processing to focus on boundary regions where actual polishing changes occur. By mapping the first region group to the corresponding functional regions in the first image, a second region group is formed. This establishes a precise correspondence between the actual polishing state and the standard polishing state in the same functional region, reducing the risk of misjudgment due to region misalignment. By quantitatively comparing the average color values ​​of the corresponding feature regions in the first and second region groups, only regions with significant differences are marked as adjustment regions. This color value difference-based judgment mechanism fully utilizes the direct impact of polishing degree on surface texture, making adjustment region identification accurate and effectively solving the problem of ambiguous adjustment region identification. This provides a reliable guarantee for the precise adjustment of subsequent adhesive application.

[0155] S420, based on the adjustment area, the first image, and the second image, determines the adjustment parameters.

[0156] For example, by adjusting the region and the transition region, the portion of the adjustment region not included in the transition region and the portion of the adjustment region included in the transition region can be determined. Then, the adjustment parameters are determined by the first image, the second image, and the portions of the adjustment region not included in the transition region and the portions of the adjustment region included in the transition region. Alternatively, the amount of adhesive applied to the second object corresponding to the adjustment region can be determined by the amount of adhesive applied to the adjustment region on the first image and the ratio between the average color value of the adjustment region on the first image and the average color value of the adjustment region on the second image.

[0157] This setup solves the bonding failure problem caused by differences in sanding by dynamically adjusting the adhesive application parameters. When a non-zero data is detected in the change of sanding degree, the system automatically triggers the adjustment process, reducing redundant operations in the case of no difference. Based on the first image as a standard template, combined with the information on the change of sanding degree, the system can accurately locate the area where the amount of adhesive needs to be adjusted, thereby improving the accuracy of the adjustment range. By integrating the spatial information of the adjustment area, the ideal state data of the first image, and the actual surface features of the second image, the system can comprehensively evaluate the impact of sanding effect on bonding, and then calculate the amount of adhesive to suit local needs.

[0158] In one possible implementation, in step S420, adjustment parameters are determined based on the adjustment area, the first image, and the second image, including:

[0159] S421, based on the adjustment region and the corresponding transition region, determine the first sub-region and the second sub-region; wherein, the first sub-region is used to reflect the part of the non-adjustment region that is included by the transition region, and the second sub-region is used to reflect the part of the adjustment region that is included by the transition region.

[0160] It is understandable that the first sub-region and the second sub-region together constitute the adjustment area. During manual sanding, different areas of the shoe outsole need to be sanded to different degrees. This means that while sanding one area, the other area will not be touched. This results in the first and second sub-regions being sanded to different degrees, even though they are in the same sanding area.

[0161] S422, based on the first sub-region, the second sub-region, the first image, and the second image, determine the adjustment parameters.

[0162] It is understandable that the adhesive used for the second object in the first sub-region and the adhesive used for the second object in the second sub-region is the same type of adhesive.

[0163] For example, the texture intensity of the first sub-region in the corresponding area of ​​the first sub-region in the first image can be obtained by matching the first sub-region on the first image, as well as the texture intensity of the first sub-region itself. The amount of adhesive applied to the second object in the first sub-region can be determined by the ratio between the density texture intensity of the first sub-region in the corresponding area of ​​the first image and the texture intensity of the first sub-region itself, and the amount of adhesive applied to the second object in the first sub-region. Finally, the amount of adhesive applied to the second object in the second sub-region can be determined by the ratio between the texture intensity of the first sub-region itself and the texture intensity of the second sub-region itself, and the amount of adhesive applied to the second object in the first sub-region.

[0164] This setup, based on the adjustment area and the corresponding transition area, divides the adjustment area into a first sub-region and a second sub-region. When determining the bonding parameters, different processing strategies are adopted for the first and second sub-regions. For the first sub-region, since its surface texture is relatively uniform, the amount of adhesive can be directly calibrated based on the standard texture reference of the first image. For the second sub-region, since it is located in the transition zone of the functional area, the surface texture is more affected by the change in the degree of polishing. However, since it is in the same feature area as the first sub-region, the amount of adhesive needs to be dynamically adjusted in combination with the real-time texture data of the second image. Through this differentiated processing method, the amount of adhesive applied to non-boundary areas and boundary areas can be accurately matched to their respective surface conditions, reducing local bonding defects caused by uniform setting of overall parameters, and ultimately achieving a uniform distribution of the outsole bonding strength.

[0165] In one possible implementation, in step S422, the adjustment parameters are determined based on the first sub-region, the second sub-region, the first image, and the second image, including:

[0166] S4221, Based on the first texture of the first sub-region on the first image and the second texture of the first sub-region, determine the first texture ratio; wherein, the first texture is used to reflect the texture degree matched by the first sub-region in the first image, and the second texture is used to reflect the texture degree of the first sub-region.

[0167] It can be understood that the first texture ratio = first texture ÷ second texture. The first texture is the standard texture degree that matches the position of the first sub-region in the first image, reflecting the roughness of the region after polishing under standard conditions. The second texture is the actual texture degree of the first sub-region in the second image, reflecting the roughness of the first sub-region after actual polishing. The first texture ratio is the ratio of the first texture to the second texture, which can reflect the degree of deviation of the actual texture from the standard texture.

[0168] S4222, determine the second texture ratio based on the second texture and the third texture of the second sub-region; wherein the third texture is used to reflect the texture degree of the second sub-region.

[0169] It can be understood that the second texture ratio = second texture ÷ third texture. The third texture is the actual texture degree of the second sub-region in the second image, reflecting the roughness of the second sub-region after actual polishing. The second texture ratio is the ratio of the second texture to the third texture, which can reflect the degree of deviation of the actual texture of the two sub-regions within the same transition area.

[0170] S4223, based on the first texture ratio and the preset adhesive amount, determine the first adhesive amount of the adjustment parameter; wherein, the first adhesive amount is used to reflect the amount of adhesive applied to the first sub-region.

[0171] It can be understood that the preset adhesive amount is the standard adhesive application amount in the region corresponding to the first sub-region of the first object. The adhesive corresponding to the preset adhesive amount is the same adhesive as the adhesive applied to the first sub-region of the second object and the second sub-region of the second object. The first adhesive amount is the actual adhesive application amount of the first sub-region obtained after adjusting the preset adhesive amount according to the first texture ratio, used to adapt to the differences in surface adhesion characteristics caused by changes in the roughness of the sub-region. The preset adhesive amount can be manually input or obtained directly from a preset adhesive amount database.

[0172] S4224, based on the first adhesive amount and the second texture ratio, determine the second adhesive amount of the adjustment parameter; wherein, the second adhesive amount is used to reflect the amount of adhesive applied to the second sub-region.

[0173] It is understood that after determining the amount of adhesive applied to the first sub-region in the adjustment area in step S4223, the amount of adhesive applied to the second sub-region is determined by the first amount of adhesive, the texture of the first sub-region itself, and the texture of the second sub-region itself.

[0174] This setup first compares the non-transition areas in the standard polishing image with the actual polishing image by calculating the first texture ratio. This comparison can accurately capture the changes in surface characteristics caused by the polishing operation. Then, by calculating the second texture ratio, the correlation between surface characteristics between the transition area and the non-transition area is analyzed, and special texture features at the boundary of the functional area are identified. The first adhesive amount is determined based on the first texture ratio and the preset adhesive amount, so that the adhesive amount can be dynamically adjusted according to the actual texture state. The second adhesive amount is determined by using the first adhesive amount and the second texture ratio, so as to achieve a smooth transition from the non-transition area to the transition area.

[0175] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0176] Corresponding to the seamless bonding method for leather shoe outsoles described in the above embodiments, this application also provides a seamless bonding system for leather shoe outsoles, wherein each module of the seamless bonding system can realize each step of the seamless bonding method for leather shoe outsoles. Figure 3 The diagram shows a structural block diagram of a seamless bonding system for leather shoe outsoles provided in an embodiment of this application. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0177] Reference Figure 3 The seamless bonding system for leather shoe outsoles includes:

[0178] The acquisition unit is used to acquire a first image and a second image; wherein the first image is used to reflect the surface image of the first object after a standard polishing operation, and the second image is used to reflect the surface image of the second object after a polishing operation, and the first object and the second object are both leather shoe soles produced in the same batch.

[0179] The transition region determination unit is used to determine multiple transition regions based on the first image and the second image; wherein the transition regions are used to reflect the transition regions between different functional regions in the second object.

[0180] The polishing degree confirmation unit is used to determine the polishing degree change based on multiple transition areas, a first image, and a second image; wherein the polishing degree change is used to reflect the change in polishing degree between the first object and the second object in different feature areas.

[0181] The adjustment parameter confirmation unit is used to determine the adjustment parameters based on the first image and the second image when there is at least one non-zero data point in the change of the degree of polishing; wherein the adjustment parameters are used to reflect the amount of adhesive applied when bonding the second object.

[0182] It should be noted that the information interaction and execution process between the above systems / units are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, and they will not be repeated here.

[0183] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0184] This application also provides a seamless bonding device for leather shoe outsoles, which includes a seamless bonding apparatus and a control device, wherein the seamless bonding apparatus and the control device are electrically connected. Figure 4 This is a schematic diagram of the structure of the control device 4 provided in one embodiment of this application. Figure 4 As shown, the control device 4 in this embodiment includes: at least one processor 40 ( Figure 4 Only one is shown in the image), at least one memory 41 ( Figure 4 (Only one is shown in the image) and a computer program 42 stored in the at least one memory 41 and executable on the at least one processor 40, wherein when the processor 40 executes the computer program 42, it causes the control device 4 to perform the steps in any of the above embodiments of the seamless bonding method for shoe outsoles, or causes the control device 4 to perform the functions of each module / unit in the above embodiments of the system.

[0185] For example, the computer program 42 may be divided into one or more modules / units, which are stored in the memory 41 and executed by the processor 40 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 42 in the control device 4.

[0186] The control device 4 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The control device 4 may include, but is not limited to, a processor 40 and a memory 41. Those skilled in the art will understand that... Figure 4This is merely an example of control device 4 and does not constitute a limitation on control device 4. It may include more or fewer components than shown, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0187] The processor 40 can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0188] In some embodiments, the memory 41 may be an internal storage unit of the control device 4, such as a hard disk or memory of the control device 4. In other embodiments, the memory 41 may be an external storage device of the control device 4, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the control device 4. Furthermore, the memory 41 may include both internal storage units and external storage devices of the control device 4. The memory 41 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 41 can also be used to temporarily store data that has been output or will be output.

[0189] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0190] This application provides a computer program product that, when run on a seamless bonding device for leather shoe outsoles, enables the seamless bonding device for leather shoe outsoles to perform the steps described in any of the above method embodiments.

[0191] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to the seamless bonding device for shoe soles, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, such as USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0192] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0193] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0194] In the embodiments provided in this application, it should be understood that the disclosed seamless bonding system for leather shoe outsoles can be implemented in other ways. For example, the embodiments of the seamless bonding system for leather shoe outsoles described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0195] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0196] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for seamless bonding of leather shoe outsoles, characterized in that, include: Acquire a first image and a second image; wherein the first image is used to reflect the surface image of the first object after a standard polishing operation, and the second image is used to reflect the surface image of the second object after a polishing operation, and the first object and the second object are both leather shoe soles produced in the same batch; Based on the first image and the second image, multiple transition regions are determined; wherein, the transition regions are used to reflect the transition regions between different foot functional regions in the second object; Based on multiple transition regions, the first image, and the second image, a change in the degree of polishing is determined; wherein, the change in the degree of polishing is used to reflect the change in the degree of polishing between the first object and the second object in different feature regions; wherein, the feature regions are used to reflect the regions in the second image that correspond to different foot functional regions in the first image; When at least one of the changes in the degree of polishing is not zero, adjustment parameters are determined based on the first image and the second image; wherein, the adjustment parameters are used to reflect the amount of adhesive applied when bonding the second object; Wherein, when at least one of the changes in the polishing degree is not zero, determining the adjustment parameters based on the first image and the second image includes: Based on the changes in the degree of polishing and the second image, an adjustment area is determined; wherein, the adjustment area is used to reflect the area in the second object where the amount of adhesive applied needs to be adjusted; Based on the adjustment area, the first image, and the second image, the adjustment parameters are determined; The step of determining the adjustment area based on the change in polishing degree and the second image includes: The adjacent feature regions whose polishing degree change is not zero are identified as the first region group; wherein, the adjacent feature regions are used to reflect two adjacent feature regions among the multiple feature regions. Based on the first region group, a second region group is determined from the first image; wherein the second region group is used to reflect two feature regions in the first image that correspond to the first region group; The feature regions whose average color value is different from the average color value of the corresponding feature regions in the first region group are taken as the adjustment regions; The step of determining adjustment parameters based on the adjustment region, the first image, and the second image includes: Based on the adjustment region and the corresponding transition region, a first sub-region and a second sub-region are determined; wherein, the first sub-region is used to reflect the part of the adjustment region that is not included by the transition region, and the second sub-region is used to reflect the part of the adjustment region that is included by the transition region. Based on the first sub-region, the second sub-region, the first image, and the second image, adjustment parameters are determined.

2. The seamless bonding method for leather shoe outsoles as described in claim 1, characterized in that, The determination of multiple transition regions based on the first image and the second image includes: Based on the first image, the second image is divided into multiple feature regions; Multiple adjacent feature regions and multiple dividing lines corresponding to the adjacent feature regions are determined from the multiple feature regions; wherein the dividing lines are used to reflect the boundary lines between two adjacent feature regions in the multiple feature regions; Based on the multiple adjacent feature regions, the second image, and the dividing line, multiple transition regions are determined.

3. The seamless bonding method for leather shoe outsoles as described in claim 2, characterized in that, The determination of multiple transition regions based on multiple adjacent feature regions, the second image, and the dividing line includes: Based on the multiple dividing lines, multiple feature vectors are determined for each dividing line; wherein the feature vectors are used to reflect vector line segments perpendicular to the dividing lines; The second image is traversed according to a preset window based on multiple feature vectors to obtain color value changes; wherein, the color value is used to reflect the average color value in the preset window; wherein, the preset window refers to a cropping window of a preset size; The point where the color value changes for the first time is taken as the starting point, and the point where the color value changes for the second time is taken as the ending point; Based on the starting point and the ending point corresponding to each feature vector, a transition range is determined; wherein, the transition range refers to the range covered by the line connecting the starting point and the ending point; Multiple transition regions are constructed based on the multiple transition ranges corresponding to each of the adjacent feature regions.

4. The seamless bonding method for leather shoe outsoles as described in claim 3, characterized in that, The determination of the polishing degree change based on multiple transition regions, the first image, and the second image includes: Based on the first image and the plurality of transition regions, a first polishing degree chain is determined; wherein, the first polishing degree chain is used to reflect the polishing degree of the first object between different foot functional areas under the standard polishing operation; Based on the second image and the multiple transition regions, a second polishing degree chain is determined; wherein, the second polishing degree chain is used to reflect the polishing degree of the second object between different foot functional areas under the polishing operation; The change in polishing degree is determined based on the data difference between each chain node between the first polishing degree chain and the second polishing degree chain.

5. The seamless bonding method for leather shoe outsoles as described in claim 4, characterized in that, The step of determining the first polishing degree chain based on the first image and the plurality of transition regions includes: Multiple first average color values ​​corresponding to the transition region are extracted from the first image; wherein, the first average color values ​​are used to reflect the average color value between each transition range of the transition region constituting the first object in the first image; A first broken line is determined based on multiple first average color values; wherein, the first broken line is used to reflect the broken line constructed by multiple first average color values ​​in the extraction order; A first chain node is determined based on the standard deviation and variance of each of the first piecewise linear lines; wherein, the first chain node refers to the ratio between the standard deviation and variance of the first piecewise linear lines; Multiple nodes of the first chain are constructed into a first polishing degree chain.

6. The seamless bonding method for leather shoe outsoles as described in claim 4, characterized in that, The step of determining the second polishing degree chain based on the second image and the plurality of transition regions includes: Multiple second average color values ​​corresponding to the transition region are extracted from the second image; wherein the second average color values ​​are used to reflect the average color value between each transition range constituting the transition region in the second image; A second broken line is determined based on multiple second average color values; wherein the second broken line is used to reflect the broken line constructed by multiple second average color values ​​in the extraction order; The second chain node is determined based on the standard deviation and variance of each second piecewise linear line; wherein, the second chain node refers to the ratio between the standard deviation and variance of the second piecewise linear line. Multiple nodes of the second chain are constructed into a second polishing degree chain.

7. The seamless bonding method for leather shoe outsoles as described in claim 1, characterized in that, The step of determining adjustment parameters based on the first sub-region, the second sub-region, the first image, and the second image includes: A first texture ratio is determined based on the first texture of the first sub-region on the first image and the second texture of the first sub-region; wherein the first texture is used to reflect the texture degree matched by the first sub-region in the first image, and the second texture is used to reflect the texture degree of the first sub-region. A second texture ratio is determined based on the second texture and the third texture of the second sub-region; wherein the third texture is used to reflect the texture degree of the second sub-region. Based on the first texture ratio and the preset adhesive amount, the first adhesive amount of the adjustment parameter is determined; wherein, the first adhesive amount is used to reflect the amount of adhesive applied to the first sub-region; Based on the first adhesive amount and the second texture ratio, the second adhesive amount of the adjustment parameter is determined; wherein, the second adhesive amount is used to reflect the amount of adhesive applied to the second sub-region.

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