Glue and lamination-based automated production line, control method, device and medium

Abstract

The embodiments of this specification provide an automated production line, control method, device, and medium based on glue and lamination, belonging to the field of production technology. The automated production line includes production line units and a central control processor. The production line units include a gluing unit, a laminating unit, a baking unit, a laminating unit, and an image acquisition unit. The central control processor is configured to: determine gluing quality data based on a substrate image dataset; determine production line control parameters based on the gluing quality data; generate control instructions based on the production line control parameters, and issue the control instructions to the corresponding production line units so that the corresponding production line units execute the production line control parameters. By determining the gluing quality data and production line control parameters, the method can intelligently control the production line, while reducing production costs and improving lamination quality.

Description

Technical Field

[0001] This specification relates to the technical field of production lines, and in particular to an automated production line, control method, device and medium based on glue and lamination. Background Art

[0002] Laminating technology applies a protective film to the surface of a material, primarily for decorative and protective purposes. It enhances the gloss and strength of the surface, making the product more aesthetically pleasing and durable. Laminating technology is closely related to glue, which is often used for fastening and connection during the laminating process. The glue used must possess excellent adhesion and durability to ensure the durability and stability of the laminating effect. In an integrated automated production line for gluing and laminating, controlling each production step while ensuring the quality of the laminated product remains a pressing technical challenge.

[0003] Therefore, it is desired to provide an automated production line, control method, device and medium based on glue and lamination to intelligently control the production line while reducing production costs and improving lamination quality. Summary of the Invention

[0004] One or more embodiments of the present specification provide an automated production line based on glue and lamination. The automated line includes: a production line unit and a central control processor, wherein the production line unit includes a gluing unit, a laminating unit, a baking unit, a pressing unit, and an image acquisition unit; the gluing unit includes a gluing device and a gluing control subsystem, and the gluing device is configured to apply glue to the surface of the substrate based on the gluing instruction issued by the gluing control subsystem; the baking unit includes a baking device and a baking control subsystem, and the baking device is configured to bake the substrate based on the baking instruction issued by the baking control subsystem; the laminating unit includes a laminating device and a laminating control subsystem, and the laminating device is configured to adhere the film material to the surface coated with glue based on the laminating instruction issued by the laminating control subsystem; the pressing unit includes a pressing device and a pressing control subsystem, and the pressing The lamination device is configured to apply pressure to the substrate and the membrane material to achieve a tight fit based on the lamination instruction issued by the lamination control subsystem; the image acquisition unit includes a monitoring device deployed at at least one position of the automated production line, and the image acquisition unit is configured to collect glue image data and a substrate image data set of the substrate at at least one time point; the central control processor is configured to: determine the glue coating quality data based on the substrate image data set, and the substrate image data set includes an original image data set and a glue coating image data set; determine the production line control parameters based on the glue coating quality data; generate a control instruction based on the production line control parameters, and issue the control instruction to the corresponding production line unit so that the corresponding production line unit performs production control.

[0005] One or more embodiments of this specification provide a control method for an automated production line based on glue and lamination. The method includes: determining glue coating quality data based on a substrate image dataset, the substrate image dataset being acquired by an image acquisition unit of the automated production line, the substrate image dataset comprising an original image dataset and a glue coating image dataset; determining production line control parameters based on the glue coating quality data; generating control instructions based on the production line control parameters, and issuing the control instructions to corresponding production line units, such that the corresponding production line units execute production control, the corresponding production line units including at least one of a glue coating unit, a laminating unit, a drying unit, a laminating unit, and the image acquisition unit.

[0006] One or more embodiments of the present specification provide a control device for an automated production line based on glue and lamination, the device comprising at least one processor and at least one memory, the at least one memory being used to store computer instructions; the at least one processor being used to execute a control method for an automated production line based on glue and lamination as described in any embodiment of the present specification.

[0007] One or more embodiments of this specification provide a computer-readable storage medium that stores computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes a control method for an automated production line based on glue and lamination as described in any embodiment of this specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] This specification will be further described in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, like numbers represent like structures, wherein:

[0009] Figure 1 is a schematic diagram of an exemplary structure of an automated production line based on glue and lamination according to some embodiments of this specification;

[0010] Figure 2 is an exemplary flow chart of a control method applied to an automated production line based on glue and lamination according to some embodiments of this specification;

[0011] Figure 3 is an exemplary schematic diagram of a parameter determination model according to some embodiments of this specification. DETAILED DESCRIPTION

[0012] To more clearly illustrate the technical solutions of the embodiments of this specification, the following briefly describes the drawings required for describing the embodiments. Obviously, the drawings described below are merely examples or embodiments of this specification. Those skilled in the art can apply this specification to other similar scenarios based on these drawings without inventive effort. Unless otherwise apparent from the context or otherwise noted, the same reference numerals in the figures represent the same structure or operation.

[0013] It should be understood that the terms "system," "device," "unit," and / or "module" used herein are a method for distinguishing different components, elements, parts, portions, or assemblies at different levels. However, if other terms can achieve the same purpose, the terms may be replaced by other expressions.

[0014] As used in this specification and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not refer to the singular but also include the plural. Generally speaking, the terms "comprises" and "include" only indicate the inclusion of the steps and elements specifically identified, and these steps and elements do not constitute an exclusive list. A method or apparatus may also include other steps or elements.

[0015] Flowcharts are used throughout this specification to illustrate the operations performed by systems according to embodiments of this specification. It should be understood that preceding or following operations do not necessarily need to be performed in exact order. Instead, the steps may be processed in reverse order or simultaneously. Furthermore, other operations may be added to these processes, or one or more operations may be removed from these processes.

[0016] Figure 1 It is a schematic diagram of an exemplary structure of an automated production line based on glue and lamination according to some embodiments of this specification.

[0017] like Figure 1 As shown, in some embodiments, the glue and lamination-based automated production line 100 may include a production line unit 110 and a central control processor 120 .

[0018] In some embodiments, the production line unit 110 may include a gluing unit 111 , a baking unit 112 , a laminating unit 113 , a pressing unit 114 , and an image acquisition unit 115 .

[0019] The gluing unit 111 is configured to perform a gluing process. Gluing refers to applying glue to the surface of a material (e.g., the surface of a substrate). In some embodiments, the glue may include, but is not limited to, water-based glue and non-liquid glues such as paste glue, pasty glue, sheet glue, and film glue. In some embodiments, the water-based glue may include polyvinyl alcohol-based water-based adhesives, vinyl acetate-based water-based adhesives, acrylic water-based adhesives, polyurethane water-based adhesives, epoxy water-based adhesives, phenolic water-based adhesives, silicone water-based adhesives, and rubber water-based adhesives. The relevant descriptions of the above-mentioned glue types are for illustrative purposes only and are not intended to limit the scope of this specification.

[0020] In some embodiments, the gluing unit 111 may include a gluing device and a gluing control subsystem. The gluing control subsystem is configured to control the gluing device, and the gluing device is configured to apply glue to the surface of the substrate based on gluing instructions issued by the gluing control subsystem. The substrate refers to the material foundation required to manufacture a complete product. For example, the substrate can be used to make laminate flooring. In some embodiments, the gluing control subsystem can be in communication with the central control processor 120 to execute corresponding production control according to the control instructions issued by the central control processor 120.

[0021] In some embodiments, the glue application instructions may include one or more of a specific glue application amount, glue application speed, glue application frequency, and glue application pressure. Thus, the glue application control subsystem may control the glue application device to apply glue to the substrate surface according to one or more of the specific glue application amount, glue application speed, glue application frequency, and glue application pressure. In some embodiments, the glue application control subsystem may also control the glue application device to mix the glue with an appropriate amount of water to adjust the viscosity and solids content of the glue. Solids content is the mass percentage of the total amount of an emulsion or coating after drying under specified conditions.

[0022] In some embodiments, the gluing device may include a first conveyor belt and at least one gluing roller. In some embodiments, the gluing control subsystem may control the first conveyor belt to convey the substrate, and control the gluing roller to apply glue to the surface of the substrate on the first conveyor belt during the conveying process. In some embodiments, the gluing control subsystem may also control the first conveyor belt to convey the glued substrate to other parts of the production line unit 110 (e.g., the baking unit 112).

[0023] The baking unit 112 is configured to perform a baking process. In some embodiments, the baking unit 112 may include a baking device and a baking control subsystem. The baking control subsystem is configured to control the baking device, and the baking device is configured to bake the substrate based on baking instructions issued by the baking control subsystem. For example, the baking device can bake a substrate coated with glue.

[0024] In some embodiments, the baking control subsystem can be in communication with the central control processor 120 to execute corresponding production control according to control instructions issued by the central control processor 120. In some embodiments, the baking instructions can include one or more of a specific baking temperature, wind force, wind speed, baking time, and baking mode. Therefore, the baking control subsystem can control the baking device to bake the substrate according to one or more of the specific baking temperature, wind force, wind speed, baking time, and baking mode.

[0025] In some embodiments, the baking device may include a second conveyor belt and an intelligent baking channel, and the second conveyor belt may pass through the intelligent baking channel.

[0026] In some embodiments, the baking control subsystem can control the second conveyor belt to convey the substrate, and during the conveying process of the second conveyor belt, control the intelligent baking channel to bake the substrate placed on the second conveyor belt. The intelligent baking channel uses hot air or infrared rays to heat the substrate to solidify the glue applied to the substrate and enhance the bonding strength. In some embodiments, the baking temperature, wind force, wind speed time, baking mode and conveying speed of the intelligent baking channel can be controlled by the baking control subsystem. In some embodiments, the baking control subsystem can also control the second conveyor belt to convey the baked substrate to other parts of the production line unit (for example, the laminating unit 113).

[0027] In some embodiments, the baking control subsystem may include a multi-stage temperature control and wind speed adjustment system corresponding to the intelligent baking channel to adjust the baking temperature and wind speed of the intelligent baking channel.

[0028] The laminating unit 113 is configured to perform a laminating process. Laminating refers to applying a film to the surface of a material (e.g., a substrate). In some embodiments, the laminating unit 113 may include a laminating device and a laminating control subsystem. The laminating control subsystem is configured to control the laminating device, and the laminating device is configured to apply the film material to the surface coated with glue based on laminating instructions issued by the laminating control subsystem. For example, the laminating device can perform a laminating process on the surface of a substrate that has been coated with glue and baked.

[0029] In some embodiments, the laminating control subsystem can be in communication with the central control processor 120 to execute corresponding production control according to the control instructions issued by the central control processor 120. In some embodiments, the laminating instructions can include one or more of a specific amount of film material, a laminating speed, and a laminating pressure, so that the laminating control subsystem can control the laminating device to laminar the substrate according to one or more of the specific amount of film material, the laminating speed, and the laminating pressure.

[0030] In some embodiments, the laminating device may include a third conveyor belt and at least one laminating roller. In some embodiments, the laminating control subsystem may control the third conveyor belt to convey the substrate, and during the conveying process of the third conveyor belt, control the at least one laminating roller to apply the film material to the surface of the substrate on the third conveyor belt. In some embodiments, the laminating control subsystem may also control the third conveyor belt to convey the laminating substrate to other parts of the production line unit 110 (e.g., the laminating unit 114).

[0031] The lamination unit 114 is configured to perform a lamination process. Lamination refers to applying pressure to achieve a close fit between at least two materials. In some embodiments, the lamination unit 114 may include a lamination device and a lamination control subsystem, the lamination control subsystem being configured to control the lamination device, and the lamination device being configured to apply pressure to the substrate and the film material based on the lamination instruction issued by the lamination control subsystem to achieve a close fit. For example, the lamination device can perform a lamination process on the laminating substrate and the film material on the surface. The lamination process can help the adhesive layer on the surface of the substrate to better penetrate and adhere, ensuring that the lamination part is firm and reliable.

[0032] In some embodiments, the lamination control subsystem may be in communication with the central control processor 120 to execute corresponding production control according to control instructions issued by the central control processor 120. In some embodiments, the lamination instruction may include one or more of a specific conveying speed and a lamination pressure, and the lamination control subsystem may control the lamination device to laminarize the substrate according to one or more of the specific conveying speed and lamination pressure.

[0033] In some embodiments, the lamination device may include a fourth conveyor belt and at least one set of lamination rollers. In some embodiments, the lamination control subsystem may control the fourth conveyor belt to convey the substrate, and during the conveyance process of the fourth conveyor belt, control the at least one set of lamination rollers to apply pressure to the substrate and the film material to achieve a tight fit, thereby enhancing the tightness of the fit between the film material and the substrate surface and the surface smoothness of the substrate after lamination. In some embodiments, the lamination control subsystem may also control the fourth conveyor belt to convey the laminated substrate to other parts of the production line unit 110 (e.g., a subsequent processing unit).

[0034] In some embodiments, a set of laminating rollers may include two laminating rollers, an upper laminating roller and an lower laminating roller, and the set of laminating rollers may perform a laminating process on the material between the upper and lower laminating rollers.

[0035] In some embodiments, the first conveyor belt, the second conveyor belt, the third conveyor belt, and the fourth conveyor belt can be sequentially connected to each other to convey the substrate. In some embodiments, the first conveyor belt, the second conveyor belt, the third conveyor belt, and the fourth conveyor belt can also be partial conveyor belts of an integral conveyor belt, which is not limited in this description.

[0036] In some embodiments, the production line unit 110 may further include a cutting unit ( Figure 1(not shown), the cutting unit is configured to cut the substrate. In some embodiments, before laminating, the cutting unit can precisely cut the laminated substrate according to product specifications to obtain a cut substrate. The laminating unit 114 can perform a laminating process on the cut substrate, i.e., before laminating, the substrate can be cut into a plurality of partial substrates that meet the specifications. In some embodiments, the cutting unit can include automated cutting equipment.

[0037] In some embodiments, the production line unit 110 may further include a subsequent processing unit ( Figure 1 (not shown), the subsequent processing unit is configured to perform subsequent processing on the laminated substrate. In some embodiments, the subsequent processing may include one or more of cooling, grinding, and trimming. Cooling can stabilize the bonding between the film material and the substrate, grinding can smoothen the surface of the laminated substrate, and trimming can refine the edges of the laminated substrate.

[0038] The image acquisition unit 115 is configured to capture an image dataset. In some embodiments, the image acquisition unit 115 may include a monitoring device deployed at at least one location on the automated production line. The monitoring device may include a camera, a video camera, or the like. In some embodiments, the monitoring device may be deployed within the location area of each unit in the production line unit 110. For example, a monitoring device may be deployed at at least one point within the location area of each of the gluing unit 111, the baking unit 112, the laminating unit 113, and the laminating unit 114.

[0039] In some embodiments, the image acquisition unit 115 is configured to capture glue image data and a substrate image dataset of the substrate on the automated production line at at least one time point. In some embodiments, the substrate image dataset may include an original image dataset and a glue-coated image dataset. In some embodiments, the monitoring device may capture image data at a given imaging frequency.

[0040] For more information about glue image data and substrate image datasets, see Figure 2 and its related descriptions.

[0041] The central control processor 120 can be configured to control the production line unit 110. The central control processor 120 can be respectively connected to the control subsystems of other components in the production line unit 110 (for example, the gluing unit 111, the baking unit 112, the laminating unit 113, the pressing unit 114, and the image acquisition unit 115) to obtain data and / or information from other components in the production line unit 110. The central control processor 120 can process the data and / or information obtained from other components in the production line unit 110. The central control processor 120 can execute program instructions based on these data, information, and / or processing results to perform one or more functions described in the embodiments of this specification. For example, the central control processor 120 can be connected to the gluing control subsystem of the gluing unit 111 to send the target gluing parameters to the gluing control subsystem, and the gluing adjustment subsystem can control the gluing device to perform gluing processing based on the target gluing parameters. For example, the central control processor 120 can be connected to the baking control subsystem of the baking unit 112 in communication to send the target baking parameters to the baking control subsystem, and the baking adjustment subsystem can control the baking device to perform the baking process based on the target baking parameters. For another example, the central control processor 120 can be connected to the coating control subsystem of the laminating unit 113 in communication to send the target coating parameters to the coating control subsystem, and the coating adjustment subsystem can control the coating device to perform the laminating process based on the target coating parameters. For another example, the central control processor 120 can be connected to the pressing control subsystem of the pressing unit 114 in communication to send the target pressing parameters to the pressing control subsystem, and the pressing adjustment subsystem can control the pressing device to perform the pressing process based on the target pressing parameters.

[0042] For more information about target coating parameters, target baking parameters, target laminating parameters, target lamination parameters, and central control processor 120, please refer to Figures 2 and 3 and its related descriptions.

[0043] It should be noted that the above description of the glue and lamination-based automated production line 100 and its units is for convenience of description only and does not limit this specification to the scope of the embodiments cited.

[0044] Figure 2 This is an exemplary flow chart of a control method for an automated production line based on glue and lamination according to some embodiments of this specification. Figure 2 As shown, the process 200 includes the following steps: In some embodiments, the process 200 can be executed by the central control processor 120 in the automated production line 100 based on glue and lamination.

[0045] Step 210 : determining glue coating quality data based on the substrate image dataset.

[0046] A substrate image dataset is a dataset consisting of multiple images containing a substrate. The substrate image dataset can be acquired by an image acquisition unit. For example, a monitoring device installed at at least one location on an automated production line can acquire substrate images (i.e., images containing the substrate) from multiple angles and at multiple time points.

[0047] In some embodiments, the substrate image dataset may include an original image dataset and a gummed image dataset.

[0048] The raw image dataset may include at least one raw image, wherein the raw image is an image of a substrate that has not been processed by the automated production line. In some embodiments, the raw image dataset may be acquired by a monitoring device located at the start of the automated production line (e.g., the start of the first conveyor belt).

[0049] The glued image dataset may include at least one glued image, wherein the glued image is an image of the substrate after the glued image is processed. In some embodiments, the glued image dataset may be obtained by a monitoring device arranged at a glue coating unit (e.g., an end position of the first conveyor belt).

[0050] In some embodiments, the substrate image data acquired by the image acquisition unit may be pre-stored in a storage device, and the central control processor may retrieve the substrate image data set from the storage device. The storage device may be a storage device built into the central control processor or an external storage device connected to the central control processor, such as a hard disk or optical disk.

[0051] Glue coating quality data is data reflecting the quality of glue coating on a substrate. Glue coating quality refers to the coating effect on a substrate after the glue coating unit applies glue to the substrate. In some embodiments, the glue coating quality data may include the glue coating quality of a single substrate or the glue coating quality of multiple consecutive substrates.

[0052] In some embodiments, the quality of glue coating can be measured by glue coating indicators. Glue coating indicators are indicators used to assess glue coating quality. Glue coating indicators may include, but are not limited to, one or more of uniformity, bubbles and impurities, dry-wet unevenness, glue coating and stringing, coverage, and thickness deviation.

[0053] Uniformity refers to the distribution of the adhesive layer on the substrate. The adhesive layer is the colloid formed after the glue is applied to the substrate surface. If the adhesive layer is unevenly distributed on the substrate, including if there is too much or too little adhesive in some areas, it may lead to insufficient adhesion or overflow in some areas of the substrate.

[0054] The bubble and impurity situation refers to whether there are bubbles and impurities in the glue coating. If bubbles or impurities are mixed in during the glue coating process, it will affect the continuity and appearance of the glue coating.

[0055] Wet-drying unevenness refers to the presence of unevenness within the adhesive layer. This condition occurs when the adhesive layer on the substrate surface has partially dried out before the film is applied. This unevenness can affect the adhesive's adhesion. The occurrence of this unevenness is related to the adhesive and the environment.

[0056] Glue sticking and stringing refer to whether glue sticking or stringing occurs during the gluing process. Glue sticking refers to the adhesion of materials due to various reasons during the production process. Stringing refers to the phenomenon of glue being pulled into stringy shapes during the gluing process. Improper cleaning or improper setting of the gluing system can cause glue sticking.

[0057] Coverage refers to the ratio of the area of the substrate surface covered by the adhesive layer (i.e. the adhesive coating area) to the surface of the substrate to be coated. If the coverage is insufficient, some areas of the substrate surface will have no adhesion.

[0058] Thickness deviation refers to the thickness of the adhesive layer exceeding the specified range. If there is a thickness deviation, it will affect the dimensional stability and performance of the finished product.

[0059] In some embodiments, the evaluation content obtained for at least one gluing indicator can be used as gluing quality data. For example, when the gluing indicators include uniformity and the presence of bubbles and impurities, the evaluation content for uniformity, bubbles, and impurities can be used as gluing quality data. As an example only, the gluing quality data can include: uneven gluing layer, bubbles and impurities in the gluing layer, etc.

[0060] In some embodiments, the central control processor can determine the adhesive coating quality data based on the substrate image dataset in various ways. For example, the central control processor can use an image processing algorithm to identify the appearance of the adhesive coating layer on the substrate in the substrate image dataset to determine the adhesive coating quality data.

[0061] In some embodiments, the central control processor can determine glue application quality data based on the glue image data, the original image dataset, and the glue application image dataset. The glue image data is an image of the glue before it is applied. In some embodiments, the glue image data can be obtained by a monitoring device located at the glue barrel.

[0062] In some embodiments, the central control processor may determine the glue coating quality data based on the glue image data, the original image data set, and the glue coating image data set by using an image processing algorithm.

[0063] In some embodiments, the central control processor may compare each original image in the original image data set with the corresponding glued image through an image processing algorithm to determine the evaluation content for the glued index.

[0064] In some embodiments, the image processing algorithm may include an edge detection algorithm, and the central control processor may evaluate the coverage and uniformity of the glue through the edge detection algorithm. For example, the central control processor may extract the outline of the glue-coated area, calculate the area shape characteristics of the glue-coated area, and compare it with the standard template to determine whether the glue layer is uniform and completely covers the surface area of the substrate that needs to be glued. The standard template is the condition of the substrate surface after glue coating under a set ideal state. The area shape characteristics of the glue-coated area may include the ratio of the perimeter to the area, etc. In some embodiments, the edge detection algorithm may include but is not limited to edge detection algorithms such as Canny edge detection or Sobel operator.

[0065] In some embodiments, the image processing algorithm may include a texture analysis algorithm, and the central control processor can identify the color distribution of the glue-coated image through the texture analysis algorithm to determine the bubble situation and uniformity. For example, the central control processor can convert the glue-coated image into the HSV (Hue, Saturation, Value) color space and segment the glue-coated area according to the color threshold range unique to the glue. Bubbles usually appear as specific texture patterns, which are quite different from the uniform glue-coated area. Further, the central control processor can use texture analysis (e.g., GLCM (Gray Level Co-occurrence Matrix) texture features) algorithm to identify the number of bubbles in the glue-coated area and the distribution of bubbles or the distribution of glue. Among them, the HSV color space is easier to separate color and brightness information. The color threshold range unique to glue can be obtained based on glue image data. For example, the glue can be obtained as white through the glue image data, and the central control processor can obtain the color threshold range corresponding to white glue.

[0066] In some embodiments, the image processing algorithm may include a template matching algorithm, and the central control processor may detect specific defects in the glue-coated area through the template matching algorithm, such as unevenness caused by local glue deficiency or excessive glue. In some embodiments, the central control processor may establish a defect image template library, which may include a variety of defects (e.g., glue deficiency, excessive glue, etc.) and corresponding image features (the image features may include original image features corresponding to the reference original image of the same reference substrate and glued image features corresponding to the reference glued image). The central control processor may use the template matching algorithm to compare the original image features and glued image features corresponding to the same substrate with the image features in the defect image template library one by one to locate and mark potential defects. The original image features and glued image features may be extracted from the original image and glued image corresponding to the same substrate. In some embodiments, the template matching algorithm may include but is not limited to normalized cross-correlation (NCC) or square difference matching algorithm.

[0067] Step 220: Determine production line control parameters based on the glue coating quality data.

[0068] Production line control parameters are parameters used to control an automated production line based on glue and lamination. In some embodiments, the production line control parameters may include unit control parameters for controlling one or more units in the production line. In some embodiments, the unit control parameters may include target gluing parameters, target baking parameters, target laminating parameters, and target lamination parameters. For more information about target gluing parameters, target baking parameters, target laminating parameters, and target lamination parameters, please refer to the relevant description below.

[0069] In some embodiments, the central control processor can determine production line control parameters based on the glue coating quality data in various ways. In some embodiments, the central control processor can determine production line control parameters based on the corresponding relationship between the glue coating quality data and the production line control parameters. Different glue coating quality data can correspond to different production line control parameters. For example, when the glue coating quality data indicates insufficient coverage or uneven glue coating, the production line control parameters may include increasing the glue coating amount and increasing the pressure of the glue coating roller. The corresponding relationship between the glue coating quality data and the production line control parameters can be pre-set based on historical data or prior knowledge.

[0070] In order to detect possible problems in the automated production line in a timely manner and make adjustments, the automated production line can also be tested.

[0071] Testing can be performed periodically. For example, after an automated production line has been running for a certain period of time or after it has produced a certain number of finished products, the performance of each production unit on the production line can be checked using test materials. The time interval or number of finished products between two tests is called the intelligent test cycle.

[0072] The smart test period can be determined in a variety of ways. In some embodiments, the smart test period can be determined based on historical data or manually set.

[0073] In some embodiments, the substrate image data set may further include a coating image data set, the production line control parameters may include an intelligent test cycle, and the central control processor may obtain coating quality data based on the coating image data set; and determine the intelligent test cycle based on the glue quality data, the coating quality data, and the production quality data.

[0074] In some embodiments, the test can be a process of detecting the performance of the gluing unit and the baking unit by using a test material (for example, a substrate board with a larger area). For example, by gluing the test material, the gluing quality of the test material is checked, including whether the amount of glue applied is uniform, whether there is glue hanging, etc. If there is glue hanging, the gluing device can be adjusted and the gluing roller can be cleaned. For another example, by baking the test material after gluing, the baking quality of the test material (for example, judging the color of the glue) is detected, including whether the glue on the board surface becomes transparent. If the glue on the board surface of the test material is transparent, the baking unit can continue to be used; if the glue on the board surface of the test material is still a large piece of white, it is necessary to adjust the baking temperature and the speed of the conveyor belt until the glue on the board surface becomes transparent.

[0075] In some embodiments, the central control processor may execute a test process according to a determined intelligent test cycle to detect the gluing quality and baking quality of the test material, and adjust the production line control parameters or device conditions of the production line unit according to quality issues.

[0076] The lamination image dataset may include at least one lamination image, wherein the lamination image is an image of the substrate after lamination. In some embodiments, the lamination image dataset may be acquired at at least one time point by a monitoring device disposed at the lamination unit (e.g., at the end of the third conveyor belt).

[0077] Coating quality data is data reflecting the coating quality of a substrate. Coating quality refers to the coating effect after the coating unit coats the film material onto the substrate. In some embodiments, the coating quality data may include the coating quality of a single substrate or the coating quality of multiple consecutive substrates.

[0078] In some embodiments, lamination quality can be measured using lamination indicators. Lamination indicators refer to indicators for evaluating lamination quality. Lamination indicators may include, but are not limited to, one or more of: complete coverage, wrinkles and creases, misalignment, tears and damage, and uneven glue penetration.

[0079] Complete coverage refers to whether the film material is completely adhered to the substrate. For example, incomplete coverage occurs when bubbles remain or the edges curl up. Wrinkles and creases refer to wrinkles or creases produced during the lamination process. If wrinkles and creases exist on the laminated substrate, it will affect the flatness and appearance of the product. Misalignment refers to the misalignment of the pattern or size of the film material and the substrate, resulting in pattern offset or size mismatch between the film material and the substrate. Tearing and breakage refer to tears caused by excessive pressure from the laminating roller during lamination or quality problems with the film material itself. Uneven glue penetration refers to uneven glue penetration under the film material, resulting in poor adhesion or uneven light transmittance in some areas. For products that require transparency, the problem of uneven light transmittance caused by uneven glue penetration is particularly important.

[0080] In some embodiments, the evaluation content obtained for at least one lamination indicator can be used as lamination quality data. For example, if the lamination indicator includes whether the coverage is complete and whether there are wrinkles and creases, the evaluation content for whether the coverage is complete and whether there are wrinkles and creases can be used as lamination quality data. As an example only, the lamination quality data can be determined to include: incomplete coverage and the presence of wrinkles and creases.

[0081] In some embodiments, the determination of the coating quality data is similar to the determination of the coating image data.

[0082] In some embodiments, the central control processor may determine the lamination quality data based on the lamination image data set by using an image processing algorithm.

[0083] In some embodiments, the central control processor may determine the evaluation content for the film indicators based on the film image dataset through an image processing algorithm.

[0084] Because uneven surfaces cause variations in reflection patterns, the central control processor can assess gloss by analyzing the distribution and intensity of high-gloss reflective areas.

[0085] In some embodiments, the image processing algorithm may include image brightness analysis and contrast analysis algorithms (e.g., global contrast analysis and local contrast analysis). The central control processor may use the image brightness analysis and contrast analysis algorithms to identify uneven or wrinkled areas on the surface of the film material after lamination in the lamination image. Brightness analysis refers to calculating the average brightness or median brightness of the entire image.

[0086] In some embodiments, the image brightness analysis algorithm includes brightness analysis based on local brightness variance. For example, the central control processor can divide the coated image into multiple small areas (e.g., using a sliding window technique), calculate the average brightness for each small area, and then plot a brightness graph based on the average brightness of each small area to observe the brightness distribution. Since uneven or defective areas typically result in local brightness significantly different from that of the surrounding areas, uneven or wrinkled areas on the surface of the coated film material in the coated image can be identified based on the brightness distribution. Contrast analysis is similar to brightness analysis.

[0087] In some embodiments, the central control processor can convert the image to the frequency domain (e.g., via Fourier transform) and analyze the high-frequency components to identify lamination bubbles. Defects in lamination indicators (e.g., lamination bubbles) typically produce specific patterns in the frequency spectrum. By comparing the spectrum of a standard lamination with the detected spectrum, defects such as tiny bubbles that are difficult to observe directly in time domain images can be revealed. The standard lamination is the condition of the substrate after lamination under set ideal conditions.

[0088] Production quality data refers to data reflecting the quality of finished products. During operation on an automated production line, substrate images captured by monitoring devices cannot reveal all issues encountered during the production process. Therefore, in some embodiments, production quality data can be obtained through manual spot checks. In this embodiment, production quality data can be manually scored.

[0089] The sampling process can also be implemented by an automated production line. In some embodiments, the automated production line may further include a robotic arm configured to sample finished products based on sampling instructions issued by a central control processor. In some embodiments, the central control processor may be configured to: control the robotic arm to sample finished products based on sampling parameters, obtain at least one sample product, and determine production quality data based on the at least one sample product.

[0090] The robotic arm can be connected to a central control processor to receive sampling instructions from the central control processor. A finished product is a completed product, i.e., a product that has undergone gluing, baking, laminating, and lamination. A sample finished product is a product sampled from a finished product.

[0091] The sampling instruction is an instruction for controlling the robotic arm to sample the finished product. In some embodiments, the sampling instruction may include sampling parameters, which are parameters for sampling the finished product. In some embodiments, the sampling parameters may include a sampling frequency, a sampling interval, and a sampling quantity.

[0092] The sampling frequency refers to the number of samplings per unit time (e.g., 2 hours, 6 hours, or a day). The sampling quantity refers to the number of sample products taken during a single sampling operation. The number of sample products taken during a single sampling operation can include multiple samples. The sampling interval refers to the number of production products between two adjacent sample products taken during a single sampling operation.

[0093] In some embodiments, the central control process can determine the sampling parameters based on the glue coating quality data, the film coating quality data, and the coating and baking quality data. For more information about the coating and baking quality data, please refer to the relevant description below.

[0094] In some embodiments, the central control processor can determine a sampling reference score based on the gluing quality score corresponding to the gluing quality data, the laminating quality score corresponding to the laminating quality data, and the coating and baking quality score corresponding to the coating and baking quality data; and determine sampling parameters based on the sampling reference score.

[0095] The gluing quality score is used to measure the gluing quality. The laminating quality score is used to measure the laminating quality.

[0096] In some embodiments, the central control processor can determine the glue coating quality score based on the glue coating index. In some embodiments, the central control processor can set quantitative standards for the glue coating index. For example, the quantitative standard can be to regard the glue coating unevenness rate exceeding 5% as abnormal, and the coverage rate exceeding 5% as abnormal. The central control processor can set scoring standards for each glue coating index and determine the glue coating quality score based on the scoring standards. For example, different scores are assigned to specific values of situations such as unevenness rate and temperature deviation (for example, the higher the unevenness rate, the lower the score), and finally the scores of each glue coating index are weighted and summed to obtain the glue coating quality score, and the weighted weights can be pre-set. As an example only, if the coverage rate is higher, the uniformity is better, the bubbles are fewer, and the defects are fewer in the glue coating index, then the corresponding glue coating quality score is higher. In some embodiments, the central control processor can also perform a table lookup based on each glue coating index to determine the glue coating quality score.

[0097] In some embodiments, the central control processor can determine the coating quality score based on the coating index. In some embodiments, the central control processor can set quantitative standards and scoring standards for each coating index, and determine the coating quality score based on the scoring standard. For example, different scores are assigned to specific values of incomplete coverage, misalignment, etc. (for example, the more severe the misalignment, the lower the score), and finally the scores of each coating index are weighted and summed to obtain the coating quality score, and the weighted weights can be pre-set. As an example only, if the coating index is more complete, the less misalignment, the fewer wrinkles and creases, the better the glue penetration uniformity, and the fewer defects, then the corresponding coating quality score will be higher. In some embodiments, the central control processor can also look up a table based on each coating index to determine the coating quality score.

[0098] In some embodiments, if the glue coating quality score is less than a preset glue coating quality threshold or the film coating quality score is less than a preset film coating quality threshold, the central control processor can control the production line unit to stop production and perform repairs. This is because if the glue coating quality data or the film coating quality data is lower than the corresponding quality threshold, it means that the glue coating quality or the film coating quality is very poor, and the quality of the final product produced will also be poor. If production continues, it will only waste materials. The glue coating quality threshold and the film coating quality threshold are preset thresholds for the corresponding glue coating quality score and the film coating quality score used to determine whether to stop production.

[0099] The coating quality score is used to measure coating quality. For more information about coating quality data and coating quality scores, please refer to the relevant explanations below.

[0100] In some embodiments, the central control processor may perform a weighted summation of the gluing quality score, the laminating quality score, and the coating and baking quality score to determine a sampling reference score. The sampling reference score is a score used to determine sampling parameters. As an example only, the central control processor may determine the sampling reference score according to formula (1):

[0101] S=w1×x+w2×y+w3×z (1)

[0102] Where S represents the sampling reference score, x represents the gluing quality score, y represents the laminating quality score, and z represents the coating and baking quality score. w1, w2, and w3 are weights. w1, w2, and w3 can be obtained based on historical data analysis. For example, if historical data shows a 30% probability that gluing quality issues will result in substandard final production quality, and a 60% probability that laminating quality issues will result in substandard final production quality, then w2 can be set to > w1.

[0103] In some embodiments, if the sampling reference score is lower, the corresponding sampling parameters may be determined as a higher sampling frequency, a smaller sampling interval, and a larger number of samples; conversely, if the sampling reference score is higher, the corresponding sampling parameters may be determined as a lower sampling frequency, a larger sampling interval, and a smaller number of samples. This is because a lower reference score indicates lower quality of the sampled product, and more frequent sampling is required.

[0104] In some embodiments, at least one sample finished product may be manually scored to determine production quality data, which may include a score for each sample finished product.

[0105] In the embodiments of this specification, by sampling the finished products to determine the production quality data of the actual products, the intelligent test cycle can be determined more accurately to perform periodic testing on the production line units.

[0106] In some embodiments, the central control processor may determine the intelligent test cycle based on the glue coating quality data, the film coating quality data, and the production quality data through a vector database. In some embodiments, the central control processor may construct a cycle feature vector based on the glue coating quality data, the film coating quality data, and the production quality data, and match the closest reference feature vector from the vector database based on the cycle feature vector, and determine the reference test cycle corresponding to the reference feature vector as the intelligent test cycle corresponding to the aforementioned cycle feature vector.

[0107] The glue coating quality data, film coating quality data, and production quality data can include the corresponding quality of only one substrate or the corresponding quality of multiple substrates. The glue coating quality data and film coating quality data are corresponding, that is, they correspond to the same or multiple substrates. For example, if the glue coating quality data includes the glue coating quality of 10 substrates, the film coating quality data will be the film coating quality corresponding to these 10 substrates, and the production quality data can be the scores of sample finished products inspected within the corresponding time period for these 10 substrates.

[0108] The vector database is a database that includes preset reference feature vectors and corresponding reference test cycles. The vector database can be pre-set based on historical data or prior knowledge.

[0109] In the embodiments of this specification, by determining the glue coating quality data, the film coating quality data and the production quality data, the most appropriate intelligent testing cycle can be determined according to the actual production quality in the production process, so as to perform periodic testing on the production line unit during the production process, and timely detect possible problems in the production line unit and make adjustments.

[0110] In some embodiments, the automated production line may further include an environmental data monitoring device configured to collect environmental data from at least one location on the automated production line. The environmental data monitoring device may be located within the location area of each unit of the automated production line. For example, the environmental data monitoring device may be deployed at at least one location within the location area of each of the gluing unit 111, baking unit 112, laminating unit 113, and laminating unit 114.

[0111] Environmental data is data reflecting the environment inside the automated production line. In some embodiments, the environmental data can be obtained by an environmental data monitoring device. In some embodiments, the environmental data may include at least one of the environment, light, temperature, wind speed, and humidity.

[0112] In some embodiments, the central control processor may determine an acquisition parameter sequence of the image acquisition device based on environmental data, glue coating quality data, lamination quality data, and production quality data of at least one location of the automated production line.

[0113] The acquisition parameter sequence is a sequence of acquisition parameters corresponding to each monitoring device deployed at at least one location on the automated production line. Acquisition parameters are parameters used by the image acquisition device to capture image data. In some embodiments, acquisition parameters may include, but are not limited to, resolution, acquisition frequency, color depth, field of view, exposure time, gain, focus, depth of field, and the like.

[0114] In some embodiments, the central control processor may determine the acquisition parameter sequence in a variety of ways based on at least one of environmental data, glue coating quality data, lamination quality data, and production quality data of at least one location of the automated production line.

[0115] In some embodiments, the central control processor can dynamically adjust exposure time and gain based on lighting data from at least one location on the automated production line to ensure that the image capture device can capture clear, high-contrast images across the varying lighting conditions of the automated production line. Dynamically adjusting capture parameters such as exposure time and gain can effectively address lighting variations.

[0116] In some embodiments, the central control processor can determine focus and depth of field as part of the acquisition parameters. Accurate focus is crucial for detecting details such as adhesive uniformity, bubbles, and creases. In some embodiments, the central control processor can determine focus and depth of field using autofocus or depth sensing technology to ensure clear images at various working distances.

[0117] In some embodiments, when the glue quality score of a glue-coated substrate (i.e., a substrate after glue has been applied) is less than the glue monitoring threshold, the central control processor can instruct the image acquisition device to increase the shooting frequency and shooting resolution of the subsequent glue-coated substrates, so as to timely discover and locate the problem and quickly take measures to adjust it. Since the defects generated during the gluing and laminating process may be very subtle, high shooting resolution and high shooting frequency are required to capture the details in these images. In some embodiments, the central control processor can combine the production line speed and select an appropriate frame rate to ensure that each key production step can be captured in time to avoid missed inspections. The production line speed can be the conveying speed of the conveyor belt of each unit.

[0118] In some embodiments, when the gluing quality score of a glue-coated substrate (i.e., a substrate coated with glue) is less than a gluing monitoring threshold, the central control processor may further instruct the image acquisition device provided at the gluing unit to increase the shooting angle.

[0119] In some embodiments, the lower the adhesive coating quality score, the more the capture frequency, capture resolution, or capture angle is increased until the adhesive coating quality scores of multiple (e.g., n) consecutive substrates are all above the adhesive coating monitoring threshold. Here, n may be positively correlated to the speed of the conveyor belt (e.g., the first conveyor belt). The adhesive coating quality scores of the multiple consecutive substrates may be re-determined based on re-captured high-resolution images.

[0120] In some embodiments, the glue coating monitoring threshold is a preset threshold for determining whether it is necessary to strengthen monitoring of the glue coating substrate. The central control processor performs similar processing as the glue coating quality score based on the film coating quality score.

[0121] In some embodiments, when the production quality score is less than the production monitoring threshold, the central control processor may increase the shooting frequency and shooting resolution of the image acquisition devices installed at all positions on the current production line.

[0122] In some embodiments, when the glue quality score of a glue-coated substrate (i.e., a substrate coated with glue) is less than a glue monitoring threshold, the central control processor may further adjust the focal length or field of view according to adjustment rules to ensure optimal focus and clarity in the glue-coated area. Adjustment rules are pre-defined rules for adjusting acquisition parameters.

[0123] In some embodiments, the central control processor can determine a sequence of acquisition parameters based on environmental data from at least one location on the automated production line. For example, if the ambient temperature and wind speed at a certain location are high, and the humidity is low, the central control processor can increase the frequency and resolution of the monitoring device at that location. This is because when the ambient temperature, wind speed, and humidity are high, the glue may solidify prematurely, causing problems during the laminating process.

[0124] In the embodiments of this specification, the acquisition parameter sequence of the image acquisition device is determined by environmental data, etc., and the acquisition of the substrate image data set can be adjusted according to the acquisition parameters determined according to actual production conditions and environmental conditions to obtain more accurate or more informative image data.

[0125] Step 230 : Generate control instructions based on the production line control parameters, and send the control instructions to the corresponding production line units, so that the corresponding production line units perform production control.

[0126] Control instructions are instructions for controlling production line units. In some embodiments, the control instructions may include the corresponding production line unit name and production line control parameters. The central control processor can identify the type of production line control parameter and issue the control instruction to the corresponding production line unit based on the type of production line control parameter. The types of production line control parameters may include gluing parameters, baking parameters, laminating parameters, and lamination parameters. For example, for target gluing parameters, the control instruction may be issued to the gluing unit to cause the gluing unit to execute the target gluing parameters.

[0127] In the embodiments of this specification, by determining the glue coating quality data and the production line control parameters, the production line can be intelligently controlled, while reducing production costs and improving the lamination quality.

[0128] It should be noted that the above description of process 200 is for illustration and purpose only and does not limit the scope of application of this specification. Those skilled in the art may make various modifications and variations to process 200 under the guidance of this specification. However, such modifications and variations are still within the scope of this specification.

[0129] In some embodiments, the substrate image dataset may further include a baking image dataset, and the unit control parameters may include target coating parameters for controlling the coating unit and target baking parameters for controlling the baking unit. In some embodiments, the central control processor may determine a preset time point and acquire a stage image sequence corresponding to the preset time point; determine coating and baking quality data based on the stage image sequence and substrate raw material data; and determine target coating parameters and target baking parameters based on the coating and baking quality data.

[0130] The baking image dataset may include at least one baking image, wherein the baking image is an image of the substrate after baking. In some embodiments, the baking image dataset may be acquired by a monitoring device disposed at the baking unit (eg, at the end of the second conveyor belt).

[0131] The target gluing parameter is a parameter used to control the gluing unit. In some embodiments, the target gluing parameter may include at least one of a gluing amount, a gluing rate, and a gluing roller pressure.

[0132] The target baking parameters are parameters used to control the baking unit. In some embodiments, the target baking parameters may include at least one of baking temperature, wind speed, wind speed time, and baking mode. The baking mode may include hot air circulation, infrared baking, etc. Different glues are suitable for different baking modes.

[0133] The preset time point is a preset time point used to evaluate the comprehensive quality of gluing and baking. The preset time point may include multiple key time points during the gluing and baking process. For example, the preset time point may be the time point when the glue is just applied, the time point after baking for a period of time, etc. For example, the preset time point may be a key time point when the glue may prematurely transform from liquid to solid. For another example, the preset time point may be the time point when the glue viscosity reaches the standard during baking, the time point when the viscosity increases significantly, or the key time point when the glue may be overbaked, etc.

[0134] In some embodiments, the central control processor can extract predetermined preset time points. For example, the preset time points may include the 10th second after the start of gluing, the 10th second after the completion of gluing, the 20th second after the completion of gluing, the start of feeding into the baking device, the 15th second after the start of baking, and the completion of baking. By determining the preset time points, the image data of multiple time points are taken into account, and there is no need to process the images corresponding to each time point, which can improve the accuracy while ensuring the processing speed. Since gluing and baking are a series of continuous actions, it is more reliable to consider both gluing and baking than to consider only one of gluing or baking.

[0135] In some embodiments, the central control processor may determine a preset time point based on substrate material data, glue material data, current gluing parameters, and current baking parameters.

[0136] Substrate raw material data is data related to the substrate. Substrate raw material data may include the type of substrate, water absorption rate, surface roughness, etc. Substrates of different materials react differently to gluing and baking. Glue raw material data is data related to glue. Glue raw material data may include the type of glue, bonding strength, concentration, etc. The current gluing parameters are the parameters of the gluing process currently being performed by the gluing unit. For example, the current gluing parameters may include at least one of the current gluing amount, the current gluing speed, the current gluing frequency, and the current gluing roller pressure. The current baking parameters are the parameters of the baking process currently being performed by the baking unit. For example, the current baking parameters may include at least one of the current baking temperature, the current wind speed, the current wind speed time, and the current baking mode.

[0137] In some embodiments, the central control processor can determine a preset time point through a key point database based on the substrate raw material data, glue raw material data, current gluing parameters, and current baking parameters. In some embodiments, the central control processor can construct a key feature vector based on the substrate raw material data, glue raw material data, current gluing parameters, and current baking parameters; perform vector matching in the key point database based on the key feature vector, and obtain the reference time point corresponding to the reference feature vector closest to the key feature vector as the key time point. The key point database is a preset database that includes multiple reference feature vectors and corresponding reference time points. In some embodiments, the central control processor can conduct experiments in advance on different substrate raw material data, glue raw material data, current gluing parameters, and current baking parameters, and analyze and store the experimental data to construct a key point database.

[0138] In some embodiments, the central control processor may combine pre-set fixed time points with subsequently calculated key time points as the final preset time points. For example, if the preset time points for gluing start, gluing completion, or 10 seconds after gluing are fixed preset time points, then the final preset time points will also include these three time points.

[0139] A stage image sequence is a sequence consisting of at least one glued image and at least one baked image corresponding to a preset time point. In some embodiments, the stage image sequence may include at least one glued image and at least one baked image. In some embodiments, the central control processor may obtain the glued images and baked images corresponding to the preset time points (e.g., the aforementioned series of time points) as the stage image data sequence.

[0140] Coating and baking quality data takes into account both the gluing and baking quality. Since gluing and baking are the foundation for subsequent laminating processes, problems in either process will affect the quality of subsequent laminating processes, so a comprehensive analysis is necessary.

[0141] In some embodiments, the coating and baking quality data may include gluing quality data, baking quality data, and a comprehensive quality of the gluing quality data and baking quality data. The comprehensive quality of the gluing quality data and baking quality data may be referred to as a coating and baking quality score.

[0142] In some embodiments, the central control processor can determine the target coating parameters and target baking parameters through a parameter determination model based on the stage image sequence and substrate material data. For more information about the parameter determination model, please refer to Figure 3 and related instructions.

[0143] In some embodiments, the central control processor can dynamically adjust target gluing parameters and target baking parameters. In some embodiments, in response to a production quality score or a lamination quality score being less than an adjustment threshold, the central control processor can dynamically adjust the target gluing parameters and target baking parameters based on the lamination quality data and the production quality data. For example, the central control processor can adjust the target gluing parameters and target baking parameters for the next substrate based on the production quality score or lamination quality score of the previous substrate. For another example, the central control processor can adjust the target gluing parameters and target baking parameters for the next substrate based on the average of the production quality scores or the average of the lamination quality scores of multiple substrates.

[0144] The production quality score is a score that evaluates the production quality data. For more information about the lamination quality score, please refer to the relevant description above. The adjustment threshold is a threshold set to determine whether the target gluing parameters and target baking parameters need to be adjusted. The adjustment threshold can include a production adjustment threshold and a lamination adjustment threshold, which are used to determine the production quality score and the lamination quality score, respectively. If the production quality score is less than the production adjustment threshold or the lamination quality score is less than the lamination adjustment threshold, it may indicate that the quality of the finished product and the lamination quality do not meet the requirements and adjustments are required to improve the production quality and the lamination quality.

[0145] In some embodiments, the central control processor can determine abnormal indicators based on the coating quality data and the production quality data; determine the parameter adjustment direction based on the abnormal indicators; and dynamically adjust the target coating parameters and the target baking parameters based on the parameter adjustment direction.

[0146] Abnormal indicators are abnormal gluing or laminating indicators. For example, a gluing unevenness exceeding 5% is considered abnormal, a coverage exceeding ±5% is considered abnormal, or a final product with a low degree of glue hardening is considered abnormal.

[0147] In some embodiments, the central control processor can construct an exception rule table based on historical data. The exception rule table can include exception indicators and corresponding parameter adjustment directions. For example, if the exception indicator is uniformity or coverage, the parameter adjustment direction may be to increase the amount of glue used or increase the pressure of the glue roller. For another example, if the exception indicator is the degree of glue hardening, the parameter adjustment direction may be to increase the baking temperature, wind speed, etc.

[0148] In some embodiments, the central control processor can adjust the target gluing parameters and target baking parameters once or multiple times with a preset adjustment amplitude based on the parameter adjustment direction until no abnormal indicators appear or the production quality score and the lamination quality score are both greater than the adjustment threshold.

[0149] The preset adjustment range is the preset range for adjusting the parameters. The preset adjustment range can be a step-by-step process. For example, the first adjustment will increase the temperature by 5 degrees, and the second adjustment will only increase the temperature by 3 degrees to prevent over-adjustment.

[0150] In some embodiments, the central control processor can set a parameter adjustment range, setting upper and lower limits for each target gluing parameter and target baking parameter to ensure that the parameter adjustment does not exceed the safe operating range of the equipment.

[0151] In some embodiments, the unit control parameters further include target laminating parameters for controlling the laminating unit and target laminating parameters for controlling the laminating unit. In some embodiments, the central control processor may obtain candidate laminating parameters and candidate laminating parameters; and determine the target laminating parameters and target laminating parameters based on current coating parameters, current baking parameters, historical laminating quality data, historical coating and baking quality data, the candidate laminating parameters, and the candidate laminating parameters.

[0152] The target laminating parameter is a parameter used to control the laminating unit. In some embodiments, the target laminating parameter may include at least one of the following: the amount of film material used, the laminating speed, and the laminating roller pressure. The candidate laminating parameter is a parameter to be determined as the target laminating parameter.

[0153] The target lamination parameter is a parameter used to control the lamination unit. In some embodiments, the target lamination parameter may include at least one of a lamination conveying speed and a lamination roller pressure. The candidate lamination parameter is a parameter to be determined as the target lamination parameter.

[0154] In some embodiments, the central control processor may randomly adjust the current laminating parameters and the current laminating parameters to generate candidate laminating parameters and candidate laminating parameters. The current laminating parameters are the parameters currently being used by the laminating unit to perform the laminating process. The current laminating parameters are the parameters currently capable of laminating by the laminating unit. The range of the randomly generated candidate laminating parameters and candidate laminating parameters must not exceed a safety range. The safety range is a preset range for safe production on the automated production line.

[0155] In some embodiments, the central control processor may further determine a parameter adjustment range based on current lamination quality data and production quality data; and determine candidate lamination parameters and candidate pressing parameters based on the parameter adjustment range.

[0156] The parameter adjustment range consists of an upper and lower limit for parameter adjustment. For example, the film material usage can be adjusted within ±10% of the current usage, and the lamination speed can be adjusted within ±5%. In some embodiments, the central control processor can first determine a baseline range and then, based on current lamination quality data and production quality data, adjust the parameter adjustment range based on the baseline range. The baseline range is a pre-set parameter range. The baseline range can be determined based on historical experience.

[0157] In some embodiments, the parameter adjustment range is related to the current lamination quality data and production quality data. If both the current lamination quality data and the current production quality data are relatively poor, then the current lamination parameters and the current lamination parameters are inappropriate. In this case, if only a small parameter adjustment range is set, the final adjustment effect of the target lamination parameters and the target lamination parameters will be average. Therefore, the lower the current lamination quality data and production quality data, the larger the range can be, based on the baseline range, to set the parameter adjustment range to a larger value.

[0158] In some embodiments, the central control processor can extract a set of candidate parameters within the parameter adjustment range, including candidate lamination parameters and candidate lamination parameters, using a preset algorithm. In some embodiments, the preset algorithm can include Latin Hypercube Sampling (LHS) or a grid search method. LHS is an efficient sampling strategy that can ensure uniform distribution of samples throughout the space, which is conducive to exploring the parameter space.

[0159] In some embodiments, the central control processor may score the candidate laminating parameters and the candidate laminating parameters, and select a set of candidate laminating parameters and candidate laminating parameters with the highest scores as the target laminating parameters and the target laminating parameters.

[0160] In some embodiments, the central control processor may determine a score corresponding to each set of candidate lamination parameters and candidate lamination parameters based on a scoring model.

[0161] In some embodiments, the scoring model is a machine learning model. In some embodiments, the scoring model can be a machine learning model of a custom structure. The scoring model can also be a machine learning model of other structures, such as a neural network model. In some embodiments, the input of the scoring model can include current gluing parameters, environmental data of at least one position of the automated production line, substrate raw material data, current baking parameters, historical coating quality data, historical coating and baking quality data, and candidate coating parameters and candidate pressing parameters, and the output can include a set of scores corresponding to candidate coating parameters and candidate pressing parameters. For more information about current gluing parameters, environmental data, substrate raw material data, and current baking parameters, please refer to the above and its related descriptions, which will not be repeated here. Historical coating quality data and historical coating and baking quality data are coating quality data and coating and baking quality data in historical data. Historical data can be data from a previous preset time (for example, the previous 1 hour).

[0162] In some embodiments, the scoring model can be obtained by training multiple first training samples with first labels. The first training samples can be obtained from historical data. In some embodiments, the first training samples may include sample gluing parameters, sample environmental data, sample substrate raw material data, sample baking parameters, sample historical coating quality data, sample historical coating and baking quality data, as well as sample coating parameters and sample pressing parameters. The first label can be the score corresponding to the sample coating parameters and sample pressing parameters in the first training sample. For example, for each set of sample coating parameters and sample pressing parameters, the coating quality score or the production quality score of the final product can be used as the score of the group of candidate parameters. For another example, the weighted sum of the coating quality score and the production quality score of the final product can be used as the score of the group of candidate parameters.

[0163] In some embodiments, after determining the target lamination and lamination parameters, the central control processor can adjust these parameters based on the current coating and baking quality data. For example, if insufficient or slightly uneven adhesive coating is detected, the laminating roller pressure can be increased and the rotation speed reduced during the lamination process to ensure a tighter adhesion of the film material to the substrate.

[0164] In some embodiments, the central control processor can determine defect indicators based on current coating and baking quality data; dynamically adjust target coating and baking parameters based on the defect indicators; and dynamically adjust target lamination and lamination parameters based on the defect indicators and the corresponding parameter adjustment direction and magnitude from a defect rule table.

[0165] Defect indicators may include defective gluing indicators and coating indicators.

[0166] In some embodiments, the central control processor can search the defect rule table for the corresponding parameter adjustment direction and amplitude based on the defect indicator; and dynamically adjust the target laminating parameters and target lamination parameters based on the parameter adjustment direction and amplitude. The central control processor can construct a defect rule table based on historical data and determine the adjustment direction and amplitude of the target laminating parameters and target lamination parameters based on the defect rule table. The defect rule table can include the defect indicator and the corresponding parameter adjustment direction and amplitude. For example, if the glue coating uniformity is 70%, it may be necessary to increase the laminating pressure by 10% and reduce the laminating machine conveyor belt speed by 15%.

[0167] In some embodiments, the central control processor may adjust the target laminating parameters and the target pressing parameters based on the parameter adjustment direction and amplitude to determine the adjusted target laminating parameters and the target pressing parameters.

[0168] In some embodiments, the central control processor can set a parameter adjustment range, setting upper and lower limits for each target lamination parameter and target pressing parameter to ensure that the parameter adjustment does not exceed the safe operating range of the equipment.

[0169] In the embodiments of this specification, the target coating parameters and target baking parameters are determined by determining the coating and baking quality data, and the target coating parameters and target pressing parameters are determined by determining the candidate coating parameters and candidate pressing parameters. The unit control parameters of the production line unit can be determined according to the actual working conditions of the production line, and the unit control parameters can be adjusted in time to ensure the quality of the finished product.

[0170] Figure 3 is an exemplary schematic diagram of a parameter determination model according to some embodiments of this specification.

[0171] In some embodiments, the central control processor may determine the target gluing parameters and the target baking parameters based on a parameter determination model.

[0172] The parameter determination model is a machine learning model. In some embodiments, the parameter determination model can be a machine learning model with a custom structure as described below. The parameter determination model can also be a machine learning model with other structures, such as a neural network model, a convolutional neural network model, a recurrent neural network model, etc.

[0173] In some embodiments, the input of the parameter determination model may include a preset time point, at least one set of stage image data sequences, substrate raw material data, equipment maintenance data, current gluing parameters, and current baking parameters, and the output may include target gluing parameters and target baking parameters. Among them, multiple images in a set of stage image data sequences in at least one set of stage image data sequences correspond to the same substrate. More details about the preset time point, stage image data sequences, substrate raw material data, current gluing parameters, current baking parameters and coating and baking quality data, target gluing parameters and target baking parameters can be described above and will not be repeated here. Equipment maintenance data is the operating status data of equipment such as gluing devices and baking devices. For example, equipment maintenance data may include equipment aging, wear and tear, etc. Equipment maintenance data may affect the quality of finished products.

[0174] In some embodiments, the parameter determination model can be trained using multiple second training samples with second labels. The second training samples can be obtained from historical data. In some embodiments, the second training samples can include at least a sample preset time point, a sample stage image data sequence corresponding to the sample substrate, sample substrate raw material data, sample equipment maintenance data, sample coating reference, sample baking parameters, and sample coating and baking quality data. The second label can be the actual coating parameters and actual baking parameters corresponding to the sample substrate in the second training sample. The second label can be obtained through a test experiment corresponding to the sample substrate corresponding to the second training sample. For example, the central control processor can use a test substrate (the same as the sample substrate) to conduct an experiment, and during the experiment, multiple sets of coating parameters and baking parameters are used. If a set of coating parameters and baking parameters makes the coating and baking quality data obtained from the test substrate after coating and baking meet the conditions, the set of coating parameters and baking parameters can be used as the label corresponding to this set of samples. The coating and baking quality data meeting the conditions can be that there are no coating and baking quality issues or the coating and baking quality score is greater than a threshold.

[0175] In some embodiments, the parameter determination model may be composed of multiple processing layers. Figure 3 As shown, the parameter determination model 310 may include a quality determination layer 320 and a parameter determination layer 330 .

[0176] like Figure 3 As shown, the inputs of the quality determination layer 320 may include a preset time point 371, at least one set of stage image sequences 372 corresponding to at least one substrate, substrate raw material data 373, and equipment maintenance data 374. The output of the quality determination layer 320 may include at least one coating and baking quality data 340 corresponding to at least one substrate. A set of stage image sequences may correspond to one piece of coating and baking quality data.

[0177] like Figure 3As shown, the input of the parameter determination layer 330 may include the current gluing parameter 381, the current baking parameter 382 and at least one coating and baking quality data 340 output by the quality determination layer 320, and the output of the parameter determination layer 330 may include the target gluing parameter 350 and the target baking parameter 360.

[0178] In some embodiments, the quality determination layer 320 and the parameter determination layer 330 may be trained separately.

[0179] In some embodiments, the third training sample in the quality determination layer 320 may include a sample preset time point, a sample stage image data sequence corresponding to the sample substrate, sample substrate raw material data, and sample equipment maintenance data. The third label in the quality determination layer 320 may be the coating and baking quality data corresponding to the sample substrate in the third training sample. The third label may be obtained through actual measurement or evaluation by a professional.

[0180] In some embodiments, the fourth training sample in parameter determination layer 330 may include sample gluing parameters, sample baking parameters, and at least one sample coating and baking quality data. The fourth label in parameter determination layer 330 may be the actual gluing parameters and actual baking parameters corresponding to the sample substrate in the fourth training sample. The central control processor may perform gluing and baking on the sample substrate using the sample gluing parameters and sample baking parameters, and obtain corresponding coating and baking quality data as the sample coating and baking quality data. The fourth label may be obtained through testing experiments performed on the sample substrate corresponding to the fourth training sample.

[0181] In some embodiments, the output of the quality determination layer 320 may be used as the input of the parameter determination layer 330 , so the quality determination layer 320 and the parameter determination layer 330 in the parameter determination model 310 may be jointly trained.

[0182] An exemplary joint training process includes: inputting the sample preset time points, sample stage image data sequences, sample substrate material data, and sample equipment maintenance data from the third training sample into the initial quality determination layer, obtaining coating and baking quality data corresponding to the third training sample as output by the initial quality determination layer; using the output of the initial quality determination layer as the sample coating and baking quality data for the fourth training sample, and inputting the fourth training sample into the initial parameter determination layer, obtaining target coating parameters and target baking parameters output by the initial parameter determination layer. A loss function is constructed based on the fourth training label and the target coating parameters and target baking parameters output by the initial parameter determination layer, and the parameters of the initial quality determination layer and the initial parameter determination layer are simultaneously updated. Through parameter updates, the trained quality determination layer 320 and parameter determination layer 330 are obtained.

[0183] In some embodiments, the input to the quality determination layer 320 may also include environmental data 375 of at least one location of the automated production line, and the input to the parameter determination layer 330 may also include energy consumption indicators 383 and production efficiency indicators 384. For more information about environmental data, see Figure 2 and its related descriptions.

[0184] Just as an example, workshop temperature, humidity, air pressure, etc., these environmental variables may indirectly affect the gluing and baking effects, so environmental data can be further considered when determining target gluing parameters and target baking parameters.

[0185] The energy consumption index refers to the energy consumption, such as electricity consumption, steam consumption, etc. In some embodiments, the central control processor may consider energy efficiency optimization to reduce production costs while ensuring quality.

[0186] The production efficiency index refers to the production efficiency requirement. In some embodiments, the central control processor can consider the balance between production speed and quality when adjusting the target coating parameters and target baking parameters based on order requirements and delivery dates.

[0187] In some embodiments, the energy consumption index and the production efficiency index can be directly read from the system that manages the automated production line.

[0188] In the embodiments of this specification, target coating parameters and target baking parameters are determined using a parameter determination model. The self-learning capabilities of a machine learning model can be leveraged to identify patterns within a large amount of historical data, thereby obtaining relationships between preset time points, at least one set of stage image data sequences, substrate material data, equipment maintenance data, current coating parameters, current baking parameters, target coating parameters, and target baking parameters. This improves the accuracy and efficiency of determining the target coating parameters and target baking parameters. Jointly training the quality determination layer and the parameter determination layer of the parameter determination model can, in some cases, help address the difficulty of obtaining labels when training the quality determination layer alone, and also enable the quality determination layer to better obtain data reflecting coating and baking quality.

[0189] One or more embodiments of the present specification also provide a control device for an automated production line based on glue and lamination, the device comprising at least one processor and at least one memory; the at least one memory is used to store computer instructions; the at least one processor is used to execute at least part of the computer instructions to implement a control method for an automated production line based on glue and lamination as described in any embodiment of the present specification.

[0190] One or more embodiments of this specification also provide a computer-readable storage medium, which stores computer instructions, and is characterized in that when the computer reads the computer instructions in the storage medium, the computer executes a control method for an automated production line based on glue and lamination as described in any embodiment of this specification.

[0191] While the basic concepts have been described above, it will be apparent to those skilled in the art that the detailed disclosure is merely illustrative and does not limit this specification. Although not explicitly stated herein, various modifications, improvements, and revisions to this specification may be made by those skilled in the art. Such modifications, improvements, and revisions are suggested in this specification and remain within the spirit and scope of the exemplary embodiments of this specification.

[0192] This specification also uses specific terms to describe the embodiments of this specification. For example, "one embodiment," "an embodiment," and / or "some embodiments" refer to a feature, structure, or characteristic associated with at least one embodiment of this specification. Therefore, it should be emphasized and noted that references to "one embodiment," "an embodiment," or "an alternative embodiment" two or more times in different locations in this specification do not necessarily refer to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of this specification may be appropriately combined.

[0193] In addition, unless expressly stated in the claims, the order of the processing elements and sequences, the use of alphanumeric characters, or the use of other names described in this specification are not intended to limit the order of the processes and methods of this specification. Although the above disclosure discusses some of the invention embodiments currently considered useful through various examples, it should be understood that such details are for illustrative purposes only, and the appended claims are not limited to the disclosed embodiments. On the contrary, the claims are intended to cover all modifications and equivalent combinations that are consistent with the spirit and scope of the embodiments of this specification. For example, although the system components described above can be implemented by hardware devices, they can also be implemented only by software solutions, such as installing the described system on an existing server or mobile device.

[0194] Similarly, it should be noted that, in order to simplify the presentation of this specification and thus facilitate understanding of one or more embodiments of the invention, the foregoing descriptions of the embodiments of this specification sometimes combine multiple features into a single embodiment, figure, or description thereof. However, this disclosure method does not imply that the subject matter of this specification requires more features than those recited in the claims. In fact, an embodiment may have fewer features than all of the features of a single disclosed embodiment.

[0195] In some embodiments, numbers are used to describe the quantity of components and attributes. It should be understood that such numbers used in the description of the embodiments are modified by the modifiers "about", "approximately" or "substantially" in some examples. Unless otherwise stated, "about", "approximately" or "substantially" indicate that the numbers are allowed to vary by ±20%. Accordingly, in some embodiments, the numerical parameters used in the description and claims are approximate values, which may change according to the required characteristics of individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and adopt the general method of retaining digits. Although the numerical domains and parameters used to confirm the breadth of their range in some embodiments of this specification are approximate values, in specific embodiments, the settings of such numerical values are as accurate as possible within the feasible range.

[0196] Each patent, patent application, patent application publication, and other materials, such as articles, books, specifications, publications, and documents, cited in this specification is hereby incorporated by reference in its entirety. This includes application history documents that are inconsistent with or conflict with the content of this specification, as well as documents (currently or subsequently attached to this specification) that limit the broadest scope of the claims of this specification. It should be noted that if the descriptions, definitions, and / or terminology used in the accompanying materials are inconsistent or conflicting with the content of this specification, the descriptions, definitions, and / or terminology used in this specification will control.

[0197] Finally, it should be understood that the embodiments described in this specification are intended only to illustrate the principles of the embodiments of this specification. Other variations may also fall within the scope of this specification. Therefore, by way of example and not limitation, alternative configurations of the embodiments of this specification may be considered consistent with the teachings of this specification. Accordingly, the embodiments of this specification are not limited to the embodiments explicitly described and illustrated in this specification.

Claims

1. An automated production line based on glue and lamination, characterized in that: include: A production line unit and a central control processor, wherein the production line unit includes a gluing unit, a laminating unit, a baking unit, a pressing unit, and an image acquisition unit; the automated production line also includes an environmental data monitoring device and a robotic arm; The gluing unit includes a gluing device and a gluing control subsystem, wherein the gluing device is configured to apply glue to the surface of the substrate based on the gluing instruction issued by the gluing control subsystem; The baking unit includes a baking device and a baking control subsystem, wherein the baking device is configured to bake the substrate based on a baking instruction issued by the baking control subsystem; The laminating unit includes a laminating device and a laminating control subsystem, wherein the laminating device is configured to adhere the film material to the surface coated with glue based on the laminating instruction issued by the laminating control subsystem; The lamination unit includes a lamination device and a lamination control subsystem, wherein the lamination device is configured to apply pressure to the substrate and the membrane material based on the lamination instruction issued by the lamination control subsystem to achieve close fitting; The environmental data monitoring device is configured to collect environmental data of at least one location of the automated production line; the environmental data includes at least one of light, temperature, wind speed and humidity; The robotic arm is configured to sample the finished product based on the sampling instruction issued by the central control processor; The image acquisition unit includes a monitoring device deployed at at least one position of the automated production line, and the image acquisition unit is configured to collect glue image data and a substrate image data set of the substrate at at least one time point; The central control processor is configured to: Determining glue coating quality data based on the substrate image dataset, wherein the substrate image dataset includes a lamination image dataset, a baking image dataset, an original image dataset, and a glue coating image dataset; Determining production line control parameters based on the gluing quality data, the production line control parameters including an intelligent test cycle and unit control parameters; the unit control parameters including a target gluing parameter for controlling the gluing unit and a target baking parameter for controlling the baking unit; generating a control instruction based on the production line control parameter, and issuing the control instruction to a corresponding production line unit so that the corresponding production line unit performs production control; The central control processor is further configured to: determining the glue coating quality data based on the glue image data, the original image dataset, and the glue coating image dataset; Acquiring film quality data based on the film image dataset; Determining the intelligent test cycle based on the glue coating quality data, the film coating quality data and the production quality data; The central control processor is further configured to: Determining an acquisition parameter sequence of an image acquisition device based on the environmental data, the glue coating quality data, the laminating quality data, and the production quality data; the acquisition parameter sequence is a sequence composed of acquisition parameters corresponding to the monitoring device at at least one position; The central control processor is further configured to: Determining sampling parameters based on the glue coating quality data, the film coating quality data and the coating and baking quality data; controlling the robotic arm to sample finished products based on the sampling parameters to obtain at least one sample product; and determining the production quality data based on the at least one sample product; The central control processor is further configured to: Determine a preset time point based on substrate raw material data, glue raw material data, current gluing parameters, and current baking parameters; the preset time point includes multiple key time points in the gluing and baking process; Acquire a stage image sequence corresponding to the preset time point, wherein the stage image sequence includes at least one gluing image and at least one baking image; Determining coating and baking quality data based on the stage image sequence and substrate raw material data; Based on the coating and baking quality data, the target coating parameters and the target baking parameters are determined.

2. The automated production line according to claim 1, characterized in that: The unit control parameters further include a target laminating parameter for controlling the laminating unit and a target pressing parameter for controlling the pressing unit. The central control processor is further configured to: Obtain candidate lamination parameters and candidate pressing parameters; The target laminating parameters and the target pressing parameters are determined based on the current gluing parameters, the current baking parameters, the historical laminating quality data, the historical coating and baking quality data, the candidate laminating parameters and the candidate pressing parameters.

3. The control method of a glue and film-based automated production line according to claim 1, characterized in that: The method comprises: Determining glue coating quality data based on a substrate image dataset, wherein the substrate image dataset is acquired by an image acquisition unit of the automated production line, the substrate image dataset including a lamination image dataset, a baking image dataset, an original image dataset, and a glue coating image dataset; Determining production line control parameters based on the gluing quality data, the production line control parameters including an intelligent test cycle and unit control parameters; the unit control parameters including a target gluing parameter for controlling the gluing unit and a target baking parameter for controlling the baking unit; generating a control instruction based on the production line control parameter, and issuing the control instruction to a corresponding production line unit so that the corresponding production line unit performs production control, the corresponding production line unit including at least one of a gluing unit, a laminating unit, a drying unit, a laminating unit, and the image acquisition unit; The method further comprises: determining the glue coating quality data based on the glue image data, the original image dataset, and the glue coating image dataset; Acquiring film quality data based on the film image dataset; Determining the intelligent test cycle based on the glue coating quality data, the film coating quality data and the production quality data; The method further comprises: Determining an acquisition parameter sequence of an image acquisition device based on the environmental data, the glue coating quality data, the lamination quality data, and the production quality data; the acquisition parameter sequence is a sequence composed of acquisition parameters corresponding to the monitoring devices at at least one position; The method further comprises: Determining sampling parameters based on the glue coating quality data, the film coating quality data and the coating and baking quality data; Controlling the robotic arm to sample the finished products based on the sampling parameters to obtain at least one sample product; and determining the production quality data based on the at least one sample product; The method further comprises: Determine a preset time point based on substrate raw material data, glue raw material data, current gluing parameters, and current baking parameters; the preset time point includes multiple key time points in the gluing and baking process; Acquire a stage image sequence corresponding to the preset time point, wherein the stage image sequence includes at least one gluing image and at least one baking image; Determining coating and baking quality data based on the stage image sequence and substrate raw material data; Based on the coating and baking quality data, the target coating parameters and the target baking parameters are determined.

4. The method according to claim 3, characterized in that The unit control parameters further include a target laminating parameter for controlling the laminating unit and a target laminating parameter for controlling the laminating unit, and the method further includes: Obtain candidate lamination parameters and candidate pressing parameters; The target laminating parameters and the target pressing parameters are determined based on the current gluing parameters, the current baking parameters, the historical laminating quality data, the historical coating and baking quality data, the candidate laminating parameters and the candidate pressing parameters.

5. A control device for an automated production line based on glue and coating, comprising a processor, wherein the processor is configured to execute a control method for an automated production line based on glue and coating as claimed in any one of claims 3 to 4.

6. A computer-readable storage medium storing computer instructions. When a computer reads the computer instructions in the storage medium, the computer executes the control method for an automated production line based on glue and lamination as described in any one of claims 3 to 4.

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