On-line stacking method with multiple error adjustment

Abstract

The invention relates to the technical field of paper diaper production, in particular to an online stacking method with multiple error adjustment. The multi-error-adjustment online stacking method comprises the steps that S1, materials are prepared; s2, collecting an image and adjusting a preliminary error; s3, performing a clustering algorithm; s4, performing secondary error adjustment; s5, packaging data entry; and S6, production line application. The industrial camera and the TOF depth camera are arranged on the assembly line for preliminarily stacking the paper diapers, the real-time image of the current paper diaper stack is obtained, excessively-protruding paper diaper pieces are recognized through a target detection algorithm and removed, after new paper diaper pieces are supplemented, the stacked paper diaper stacks are pushed into the packaging assembly line, and the paper diaper stacking efficiency is improved. And the packaging assembly line correspondingly adjusts mechanism data of secondary stacking adjustment according to the identified paper diaper types, stacks are aligned, and then corresponding packaging data are called, so that the subsequent packaging process is carried out in a programmed mode, and multiple types of paper diapers can be identified and packaged on the same paper diaper production assembly line.

Description

Technical Field

[0001] The invention relates to the technical field of diaper production, and in particular to an online palletizing method with multiple error adjustments. Background Art

[0002] Diapers, also known as wet diapers, are an indispensable daily necessity in the process of raising infants and young children. In the prior art, diaper products are usually packaged in bags, and the same packaging production line can only produce diaper stacks of the same size and specification as the corresponding packaging. If the production line needs to switch to packaging diaper stacks of another specification, the entire production line needs to be shut down, and the producer needs to readjust the corresponding packaging data based on production experience. The production line cannot identify the type of diapers currently packaged, and the producer also needs to manually adjust the production data of the production line. Summary of the invention

[0003] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the description and other drawings of the description.

[0004] The purpose of the present invention is to overcome the above-mentioned shortcomings and provide an online stacking method with multiple error adjustments. An industrial camera and a TOF depth camera are set on the production line for preliminary stacking of diapers to obtain a real-time image of the current diaper stack. The overly protruding diaper pieces are identified and removed through the target detection algorithm. After the new diaper pieces are added, the stacked diaper stacks are pushed into the packaging production line. The packaging production line adjusts the secondary stacking adjustment mechanism data according to the identified diaper types, aligns the stacks and retrieves the corresponding packaging data, so that the subsequent packaging process is programmed, so that multiple types of diapers can be identified and packaged on the same diaper production line, and the stacks are arranged neatly and the packaging data corresponds.

[0005] The present invention provides an online palletizing method with multiple error adjustments, comprising: S1. Material preparation: input multiple groups of diaper stacks of different types into the production line for preliminary stacking of diapers, and the number of groups of each type of diaper stack is at least three; S2. Image acquisition and preliminary error adjustment: An industrial camera and a TOF depth camera are set up on the production line for preliminary stacking of diapers to collect real-time images of each group of diaper stacks and input them to the control center A. A target detection model is established in the control center A to perform preliminary stacking adjustments and remove diaper pieces that are too protruding; S3, clustering algorithm: collect the real-time image of the diaper stack after preliminary stacking adjustment and input it to the control center B, which performs K-means clustering algorithm on the real-time image, classifies the diaper stack according to the cluster center, and records the volume and number of stacking layers corresponding to each group of cluster centers; S4, secondary error adjustment: Multiple groups of diaper stacks of different types are pushed out of the initial stacking line one by one and enter the packaging line. The stacking robot arm is controlled by the fuzzy control algorithm to perform secondary stacking adjustment on the diaper stacks, so that the diaper pieces in a group of diaper stacks are arranged neatly; S5. Packaging data entry: according to production experience, corresponding packaging data is set for different types of multiple groups of diaper stacks, and the packaging data is fed back to the control center B, and combined with the clustering results to form a diaper stacking data set group; S6. Production line application: Input the diaper stacking data set group established in steps S3-S5 into control center B. When there are multiple types of diaper stacks in the initial stacking line, the target detection algorithm in control center A monitors the diaper stacks online and collects real-time images, identifies the corresponding diaper stack types, removes overly protruding diaper pieces and automatically fills them. After the diaper stacks are pushed into the packaging line, the diaper stacking data set group in control center B is called to perform secondary stacking adjustments and packaging according to the diaper stack types.

[0006] In some embodiments, in the S2 step, the specific steps of collecting real-time images through the industrial camera are: collecting RGB images of the current diaper stack on the assembly line through the industrial camera, inputting the RGB image in the loading state to the control center A, adding rectangular frames and labels to diaper pieces of different sizes through the labelme software, defining the diaper type and identifying the corresponding quantity, inputting the processed image into the Mask RCNN instance segmentation network, learning image features and establishing a target detection model, marking diaper pieces that are too protruding, and controlling the rejection robot arm to remove the protruding diaper pieces from the assembly line.

[0007] In some embodiments, in the process of establishing a target detection model and marking diaper pieces that are too protruding, an alignment algorithm is involved. After the target detection model detects each independent diaper piece in the current diaper stack, a rectangular frame mark is added to the diaper piece, and the center point of each rectangular frame and the slope between the center points are calculated. If the slope exceeds a set threshold, it is judged as a diaper piece that is too protruding, and the rejection robot arm is controlled to remove the protruding diaper piece from the assembly line. In combination with the number of diaper pieces rejected and production needs, the missing diaper pieces in the current diaper stack are supplemented, and the alignment is recalculated until the alignment is within the threshold range, and step S3 is entered.

[0008] In some embodiments, in step S3, the specific steps of performing a clustering algorithm operation on the diaper stack after the preliminary stacking adjustment are: S31, the industrial camera collects the RGB image of the preliminary stacking pipeline in the no-load state, the TOF depth camera collects the depth image of the preliminary stacking pipeline in the no-load state, aligns the RGB image and the depth image, and records the real-time image samples in the no-load state; S32, the industrial camera collects the RGB image of the current diaper stack on the preliminary stacking line, transmits the RGB image in the loading state to the control center B, and the TOF depth camera synchronously collects the depth image of the diaper stack in the loading state, corrects the depth image, retains the outer frame and obtains the area of ​​the current diaper stack; S33, aligning the RGB image with the depth image, obtaining a comprehensive image of the arrangement state of the diaper pieces in the diaper stack in the current state, repeatedly obtaining comprehensive images of multiple groups of diaper stacks of different types, performing a K-means clustering algorithm, classifying the diaper stacks according to the cluster centers, and obtaining multiple cluster centers; S34, combining the depth image in the empty state with the depth image in the loaded state, calculating the height and volume of the current diaper stack, and synchronously inputting the volume information of the current diaper stack and the comprehensive image into the K-means clustering algorithm, so that each data point carries the volume information; S35. In control center B, the obtained cluster center is combined with the actual production information, and the type, number of stacking layers and volume information labels of the diaper stack are added according to the cluster center. At the same time, the information is imported into control center A with a target detection model, so that the classification in the target detection model corresponds to the classification of the cluster center.

[0009] In some embodiments, in step S4, after the current diaper stack is pushed into the packaging line, the specific steps of performing secondary stacking adjustment on the diaper stack are as follows: S41, the stack of diapers slides into the front end of the packaging assembly line, and the left clamping arm, the right clamping arm and the rear clamping arm extend to form a U-shaped frame, so that the stack of diapers is located in the U-shaped frame, and the left clamping arm, the right clamping arm and the rear clamping arm are all plate-shaped mechanisms controlled by hydraulic telescopic rods, which are used to limit the stack of diapers; S42, a stacking robot arm extends from the front side of the diaper stack, and the end of the stacking robot arm is connected to a limit plate through a rotating shaft, and the rotating shaft is connected to the control center A. The control center A stores the center points of each diaper piece in the current diaper stack according to the target detection algorithm, and performs a least squares calculation on the center points to obtain the slope of the current diaper stack; S43. A fuzzy control algorithm module is also provided in the control center A. The rotating shaft is connected to the fuzzy control algorithm module. After the slope of the current diaper stack is input, the corresponding initial rotation angle and rotation rate of the rotating shaft are output through the fuzzy control algorithm. The limit plate rotates in the initial state to adapt to the slope of the current diaper stack. In the process of gradually pushing, adjusting and aligning the diaper stack, the rotating shaft gradually rotates until it is parallel to the rear clamping arm, thereby realizing the secondary stacking adjustment of the diaper stack.

[0010] In some embodiments, in step S4, the extension amounts of the left clamping arm, the right clamping arm, the rear clamping arm and the palletizing robot arm are set by the production personnel according to the current diaper stack type, and the set extension amount data is synchronized to control center A and control center B.

[0011] In some embodiments, after the secondary stacking adjustment of the diaper stack is completed, the rear clamping arm and the stacking robot arm are withdrawn, the left clamping arm and the right clamping arm press the diaper stack inward, the vacuum pump arranged on the rear side of the packaging line unfolds the corresponding type of diaper packaging bag, the left clamping arm and the right clamping arm move backward, the diaper stack is sent into the packaging bag and then pulled out, and the plastic sealing machine plastic seals the opening of the packaging bag; the clamping distance and pressure of the left clamping arm and the right clamping arm, the moving distance of the left clamping arm and the right clamping arm, the operating data of the vacuum pump and the operating data of the plastic sealing machine are all packaging data, which are set by the production personnel according to the current diaper stack type, and the set packaging data are synchronously input into the control center B, and combined with the corresponding clustering results to form a diaper stacking data set group.

[0012] In some embodiments, in step S6, the specific operation of automatic replenishment is that after removing the diaper pieces that are too protruding, the initial stacking assembly line controls the replenishment robot arm to push the diaper pieces on the rear side forward to replenish, and when it runs to the last stack of diapers of this type, there is a shortage of diapers, and the control center A issues an alarm, and the producer chooses to remove the stack of diapers or add new diapers to the stack of diapers.

[0013] By adopting the above technical solution, the beneficial effects of the present invention are: The present invention arranges an industrial camera and a TOF depth camera on an assembly line for preliminary stacking of diapers, obtains a real-time image of the current diaper stack, identifies overly protruding diaper pieces through a target detection algorithm and removes them, and after adding new diaper pieces, pushes the stacked diaper stack into a packaging assembly line. The packaging assembly line adjusts the mechanism data of the secondary stacking adjustment according to the identified diaper types, aligns the stacks and retrieves the corresponding packaging data, so that the subsequent packaging process is programmed, so that multiple types of diapers can be identified and packaged on the same diaper assembly line, and the stacks are arranged neatly and the packaging data are corresponding.

[0014] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure.

[0015] Undoubtedly, these and other objects of the present invention will become more apparent after the following detailed description of the preferred embodiment described with reference to various figures and drawings.

[0016] In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, one or several preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used together with the embodiments of the present invention to explain the present invention and do not constitute a limitation of the present invention.

[0018] In the drawings, the same reference numerals are used for the same components and the drawings are schematic and not necessarily drawn to scale.

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only one or several embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on such drawings without paying creative work.

[0020] Figure 1 It is a schematic diagram of the overall process of the online palletizing method in some embodiments of the present invention; Figure 2 A schematic diagram of a target detection model identifying a center point and a slope in some embodiments of the present invention; Figure 3 A schematic diagram of diaper classification obtained by a clustering algorithm in some embodiments of the present invention; Figure 4 Schematic diagram of the packaging line structure in some embodiments of the present invention.

[0021] Description of main reference numerals: 1. Left side clamping arm; 2. Right side clamping arm; 3. Rear side clamping arm; 4. Palletizing robot arm. DETAILED DESCRIPTION

[0022] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but are not used to limit the present invention.

[0023] In addition, in the description of the present invention, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "axial", "radial", "circumferential" and the like indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0024] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral body; it can be directly connected or indirectly connected through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. However, if it is indicated as a direct connection, it means that the two connected bodies are not connected through a transition structure, but are connected to form a whole through a connecting structure. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0025] In the present invention, unless otherwise clearly specified and limited, the first feature "on" or "under" the second feature may be that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in an appropriate manner in any one or more embodiments or examples.

[0026] Reference 1- Figure 4 , Figure 1 It is a schematic diagram of the overall process of the online palletizing method in some embodiments of the present invention; Figure 2 A schematic diagram of a target detection model identifying a center point and a slope in some embodiments of the present invention; Figure 3 A schematic diagram of diaper classification obtained by a clustering algorithm in some embodiments of the present invention; Figure 4 Schematic diagram of the packaging line structure in some embodiments of the present invention.

[0027] According to some embodiments of the present invention, the present invention provides an online palletizing method with multiple error adjustments, comprising: S1. Material preparation: input multiple groups of diaper stacks of different types into the production line for preliminary stacking of diapers, and the number of groups of each type of diaper stack is at least three; S2. Image acquisition and preliminary error adjustment: An industrial camera and a TOF depth camera are set up on the production line for preliminary stacking of diapers to collect real-time images of each group of diaper stacks and input them to the control center A. A target detection model is established in the control center A to perform preliminary stacking adjustments and remove diaper pieces that are too protruding; The specific steps of collecting real-time images through industrial cameras are as follows: collect RGB images of the current diaper stack on the assembly line through industrial cameras, input the RGB images in the loading state to the control center A, add rectangular frames and labels to diaper pieces of different sizes through labelme software, define diaper types and identify corresponding quantities, input the processed images into the Mask RCNN instance segmentation network, learn image features and establish a target detection model, mark diaper pieces that are too protruding, and control the rejection robot arm to remove the protruding diaper pieces from the assembly line; like Figure 2 As shown, in the process of establishing the target detection model and marking the overly protruding diaper pieces, an alignment algorithm is involved. After the target detection model detects the independent diaper pieces in the current diaper stack, a rectangular frame mark is added to the diaper piece, and the center point of each rectangular frame and the slope between the center points are calculated. If the slope exceeds the set threshold, it is judged as a diaper piece that is too protruding, and the rejection robot arm is controlled to remove the protruding diaper piece from the assembly line. Combined with the number of rejected diaper pieces and production needs, the missing diaper pieces in the current diaper stack are supplemented, and the alignment is recalculated until the alignment is within the threshold range, and then the step S3 is entered.

[0028] S3, clustering algorithm: collect the real-time image of the diaper stack after preliminary stacking adjustment and input it to the control center B, which performs K-means clustering algorithm on the real-time image, classifies the diaper stack according to the cluster center, and records the volume and number of stacking layers corresponding to each group of cluster centers; The specific steps of clustering algorithm operation on the diaper stack after preliminary stacking adjustment are as follows: S31, the industrial camera collects the RGB image of the preliminary stacking pipeline in the no-load state, the TOF depth camera collects the depth image of the preliminary stacking pipeline in the no-load state, aligns the RGB image and the depth image, and records the real-time image samples in the no-load state; S32, the industrial camera collects the RGB image of the current diaper stack on the preliminary stacking line, transmits the RGB image in the loading state to the control center B, and the TOF depth camera synchronously collects the depth image of the diaper stack in the loading state, corrects the depth image, retains the outer frame and obtains the area of ​​the current diaper stack; S33, aligning the RGB image with the depth image, obtaining a comprehensive image of the arrangement state between the diaper pieces in the diaper stack in the current state, repeatedly obtaining comprehensive images of multiple groups of diaper stacks of different types, performing a K-means clustering algorithm, and classifying the diaper stacks according to the cluster centers, such as Figure 3 As shown, multiple cluster centers are obtained; S34, combining the depth image in the empty state with the depth image in the loaded state, calculating the height and volume of the current diaper stack, and synchronously inputting the volume information of the current diaper stack and the comprehensive image into the K-means clustering algorithm, so that each data point carries the volume information; S35. In control center B, the obtained cluster center is combined with the actual production information, and the type, number of stacking layers and volume information labels of the diaper stack are added according to the cluster center. At the same time, the information is imported into control center A with a target detection model, so that the classification in the target detection model corresponds to the classification of the cluster center.

[0029] S4, secondary error adjustment: push different types of multiple groups of diaper stacks out of the preliminary stacking line one by one, and enter the packaging line. The fuzzy control algorithm is used to control the stacking robot 4 to perform secondary stacking adjustment on the diaper stacks, so that the diaper pieces in a group of diaper stacks are arranged neatly; After the current diaper stack is pushed into the packaging line, the specific steps for secondary stacking adjustment of the diaper stack are as follows: S41, the diaper stack slides into the front end of the packaging line, such as Figure 4 As shown, the left clamping arm 1, the right clamping arm 2 and the rear clamping arm 3 extend to form a U-shaped frame, so that the diaper stack is located in the U-shaped frame, and the left clamping arm 1, the right clamping arm 2 and the rear clamping arm 3 are all plate-shaped mechanisms controlled by hydraulic telescopic rods, which are used to limit the diaper stack; S42, a stacking robot arm 4 extends from the front side of the diaper stack, and the end of the stacking robot arm 4 is connected to a limit plate through a rotating shaft, and the rotating shaft is connected to the control center A. The control center A stores the center points of each diaper piece in the current diaper stack according to the target detection algorithm, and performs a least squares calculation on the center points to obtain the slope of the current diaper stack; S43, the control center A is also provided with a fuzzy control algorithm module, the rotating shaft is connected to the fuzzy control algorithm module, after the slope of the current diaper stack is input, the corresponding initial rotation angle and rotation rate of the rotating shaft are output through the fuzzy control algorithm, the limit plate rotates a certain angle in the initial state to adapt to the slope of the current diaper stack, and in the process of gradually pushing and adjusting the diaper stack, the rotating shaft gradually rotates until it is parallel to the rear clamping arm 3, thereby realizing the secondary stacking adjustment of the diaper stack; Among them, the extension amounts of the left clamping arm 1, the right clamping arm 2, the rear clamping arm 3 and the palletizing robot arm 4 are set by the production personnel according to the current diaper stack type, and the set extension amount data are synchronized to the control center A and the control center B.

[0030] S5. Packaging data entry: according to production experience, corresponding packaging data is set for different types of multiple groups of diaper stacks, and the packaging data is fed back to the control center B, and combined with the clustering results to form a diaper stacking data set group; After the secondary stacking adjustment of the diaper stack is completed, the rear clamping arm 3 and the stacking robot arm 4 are withdrawn, the left clamping arm 1 and the right clamping arm 2 press the diaper stack inward, and the vacuum pump arranged at the rear side of the packaging line unfolds the corresponding type of diaper packaging bag, and the left clamping arm 1 and the right clamping arm 2 move backward to send the diaper stack into the packaging bag and then pull it out, and the plastic sealing machine plastic seals the opening of the packaging bag; the clamping distance and pressure of the left clamping arm 1 and the right clamping arm 2, the moving distance of the left clamping arm 1 and the right clamping arm 2, the operating data of the vacuum pump and the operating data of the plastic sealing machine are all packaging data, which are set by the production personnel according to the current diaper stack type, and the set packaging data are synchronously input into the control center B, and combined with the corresponding clustering results to form a diaper stacking data set group.

[0031] S6, production line application: input the diaper stacking data set group established in steps S3-S5 into the control center B. When there are multiple types of diaper stacks in the preliminary stacking line, the target detection algorithm in the control center A monitors the diaper stacks online and collects real-time images, identifies the corresponding diaper stack types, removes the overly protruding diaper pieces and automatically fills them. After the diaper stacks are pushed into the packaging line, the diaper stacking data set group in the control center B is called to perform secondary stacking adjustment and packaging according to the diaper stack types; The specific operation of automatic replenishment is that after removing the diaper pieces that are too protruding, the initial stacking production line controls the replenishment robot arm to push the diaper pieces on the back side forward to replenish. When it runs to the last stack of diapers of this type, there is a shortage of diapers. The control center A issues an alarm and the producer chooses to remove the stack of diapers or add new diapers to the stack of diapers. Example

[0032] Reference 1- Figure 4 , Figure 1 It is a schematic diagram of the overall process of the online palletizing method in some embodiments of the present invention; Figure 2 A schematic diagram of a target detection model identifying a center point and a slope in some embodiments of the present invention; Figure 3 A schematic diagram of diaper classification obtained by a clustering algorithm in some embodiments of the present invention; Figure 4Schematic diagram of the packaging line structure in some embodiments of the present invention.

[0033] According to some embodiments of the present invention, the present invention provides an online palletizing method with multiple error adjustments, comprising: S1. Material preparation: Input multiple groups of diaper stacks of different types into the production line for preliminary stacking of diapers, and there are three groups of each type of diaper stack; specifically, the production line produces a total of three categories of diapers, namely A, B, and C. Under the three categories of diapers A, B, and C, each category also has three stacking specifications, namely 10 pieces, 20 pieces, and 30 pieces. Therefore, there are a total of 9 types of diaper stacks running on the production line, and each type of diaper stack is repeated in three groups, which are sequentially input to the industrial camera and TOF depth camera; S2. Image acquisition and preliminary error adjustment: An industrial camera and a TOF depth camera are set up on the production line for preliminary stacking of diapers to collect real-time images of each group of diaper stacks and input them to the control center A. A target detection model is established in the control center A to perform preliminary stacking adjustments and remove diaper pieces that are too protruding; The specific steps of collecting real-time images through industrial cameras are as follows: collect RGB images of the current diaper stack on the assembly line through industrial cameras, input the RGB images in the loading state to the control center A, add rectangular frames and labels to diaper pieces of different sizes through labelme software, define diaper types and identify corresponding quantities, input the processed images into the Mask RCNN instance segmentation network, learn image features and establish a target detection model, mark diaper pieces that are too protruding, and control the rejection robot arm to remove the protruding diaper pieces from the assembly line; Labels are added to the diaper sheets of categories A, B, and C, and input into the Mask RCNN instance segmentation network to learn image features and build a target detection model. Through the target detection model, the control center A can identify the current diaper category and its corresponding stacking quantity. like Figure 2 As shown, in the process of establishing the target detection model and marking the overly protruding diaper pieces, an alignment algorithm is involved. After the target detection model detects the independent diaper pieces in the current diaper stack, a rectangular frame mark is added to the diaper piece, and the center point of each rectangular frame and the slope between the center points are calculated. If the slope exceeds 30 degrees, it is judged as a diaper piece that is too protruding, and the rejection robot arm is controlled to remove the protruding diaper piece from the assembly line. Combined with the number of rejected diaper pieces and production needs, the missing diaper pieces in the current diaper stack are supplemented, and the alignment is recalculated until the alignment is within the threshold range before entering step S3. For example, if two diapers are removed from a 10-piece diaper stack, two diapers are added from the back and the 10-piece stack is pushed into the packaging assembly line.

[0034] S3, clustering algorithm: collect the real-time image of the diaper stack after preliminary stacking adjustment and input it to the control center B, which performs K-means clustering algorithm on the real-time image, classifies the diaper stack according to the cluster center, and records the volume and number of stacking layers corresponding to each group of cluster centers; The specific steps of clustering algorithm operation on the diaper stack after preliminary stacking adjustment are as follows: S31, the industrial camera collects the RGB image of the preliminary stacking pipeline in the no-load state, the TOF depth camera collects the depth image of the preliminary stacking pipeline in the no-load state, aligns the RGB image and the depth image, and records the real-time image samples in the no-load state; S32, the industrial camera collects the RGB image of the current diaper stack on the preliminary stacking line, transmits the RGB image in the loading state to the control center B, and the TOF depth camera synchronously collects the depth image of the diaper stack in the loading state, corrects the depth image, retains the outer frame and obtains the area of ​​the current diaper stack; The specific steps of the correction process are: extracting the hot spot area from the depth image, screening and correcting the hot spot area data, retaining the rectangular hot spot area of ​​the outer frame, deleting the irrelevant data inside, performing noise processing on the rectangular hot spot area of ​​the outer frame, and finally calculating the outer frame area of ​​the current diaper stack; S33, aligning the RGB image with the depth image, obtaining a comprehensive image of the arrangement state between the diaper pieces in the diaper stack in the current state, repeatedly obtaining comprehensive images of multiple groups of diaper stacks of different types, performing a K-means clustering algorithm, and classifying the diaper stacks according to the cluster centers, such as Figure 3 As shown, 9 cluster centers are set in the K-means clustering algorithm, and 9 categories are formed after clustering. Classification labels are added to the currently obtained cluster centers, such as A1, 10 pieces per package; A2, 20 pieces per package; A3, 30 pieces per package; B1, 10 pieces per package, etc.; S34, combining the depth image in the empty state with the depth image in the loaded state, calculating the height and volume of the current diaper stack, and synchronously inputting the volume information of the current diaper stack and the comprehensive image into the K-means clustering algorithm, so that each data point carries the volume information; The distance measurement principle of the TOF depth camera is to continuously send light pulses to the target, and then use the sensor to receive the light returned from the object, and obtain the distance of the target object by detecting the round-trip time of the light pulse. The TOF camera thus obtains the depth information of the entire image. The relevant calculation method for calculating the height of the TOF camera can refer to the existing technology, which will not be elaborated here. The height is combined with the outer frame area of ​​the detected diaper stack to obtain the volume information of the diaper stack; S35. In control center B, the obtained cluster center is combined with the actual production information, and the type, number of stacking layers and volume information labels of the diaper stack are added according to the cluster center. At the same time, the information is imported into control center A with a target detection model, so that the classification in the target detection model corresponds to the classification of the cluster center.

[0035] The obtained cluster centers are manually identified, and the cluster centers and labels that conform to the actual production information are retained. The erroneous cluster center information is re-clustered and re-imported. In this embodiment, 9 cluster centers are obtained. The relevant information of the 9 cluster centers is imported into the control center A, so that when the early target detection model detects the corresponding diaper category, the control center B jumps to the cluster center under the corresponding category and calls the corresponding production information. For example, the target detection model detects that the type of the current diaper stack on the assembly line is A1, 10 pieces, and calls the corresponding cluster center volume label to select the packaging bag corresponding to the type.

[0036] S4, secondary error adjustment: push different types of multiple groups of diaper stacks out of the preliminary stacking line one by one, and enter the packaging line. The fuzzy control algorithm is used to control the stacking robot 4 to perform secondary stacking adjustment on the diaper stacks, so that the diaper pieces in a group of diaper stacks are arranged neatly; After the current diaper stack is pushed into the packaging line, the specific steps for secondary stacking adjustment of the diaper stack are as follows: S41, the diaper stack slides into the front end of the packaging line, such as Figure 4 As shown, the left clamping arm 1, the right clamping arm 2 and the rear clamping arm 3 extend to form a U-shaped frame, so that the diaper stack is located in the U-shaped frame, and the left clamping arm 1, the right clamping arm 2 and the rear clamping arm 3 are all plate-shaped mechanisms controlled by hydraulic telescopic rods, which are used to limit the diaper stack; S42, a stacking robot arm 4 extends from the front side of the diaper stack, and the end of the stacking robot arm 4 is connected to a limit plate through a rotating shaft, and the rotating shaft is connected to the control center A. The control center A stores the center points of each diaper piece in the current diaper stack according to the target detection algorithm, and performs a least squares calculation on the center points to obtain the slope of the current diaper stack; S43, the control center A is also provided with a fuzzy control algorithm module, the rotating shaft is connected to the fuzzy control algorithm module, after the slope of the current diaper stack is input, the corresponding initial rotation angle and rotation rate of the rotating shaft are output through the fuzzy control algorithm, the limit plate rotates a certain angle in the initial state to adapt to the slope of the current diaper stack, and in the process of gradually pushing and adjusting the diaper stack, the rotating shaft gradually rotates until it is parallel to the rear clamping arm 3, thereby realizing the secondary stacking adjustment of the diaper stack; Among them, the elongation of the left clamping arm 1, the right clamping arm 2, the rear clamping arm 3 and the stacking robot arm 4 is set by the production personnel according to the current diaper stack type, and the set elongation data is synchronized to the control center A and the control center B. It should be understood that the specific structure of the left clamping arm 1, the right clamping arm 2, the rear clamping arm 3 and the stacking robot arm 4 can refer to the existing technology. The component structure in the accompanying drawings is only for reference and is used to assist understanding. It does not represent its actual structure as shown in the figure. Its actual structure can be adaptively changed according to the combination of the realized function and the existing technology.

[0037] S5. Packaging data entry: according to production experience, corresponding packaging data is set for different types of multiple groups of diaper stacks, and the packaging data is fed back to the control center B, and combined with the clustering results to form a diaper stacking data set group; After the secondary stacking adjustment of the diaper stack is completed, the rear clamping arm 3 and the stacking robot arm 4 are withdrawn, the left clamping arm 1 and the right clamping arm 2 press the diaper stack inward, and the vacuum pump arranged at the rear side of the packaging line unfolds the corresponding type of diaper packaging bag, and the left clamping arm 1 and the right clamping arm 2 move backward to send the diaper stack into the packaging bag and then pull it out, and the plastic sealing machine plastic seals the opening of the packaging bag; the clamping distance and pressure of the left clamping arm 1 and the right clamping arm 2, the moving distance of the left clamping arm 1 and the right clamping arm 2, the operating data of the vacuum pump and the operating data of the plastic sealing machine are all packaging data, which are set by the production personnel according to the current diaper stack type, and the set packaging data are synchronously input into the control center B, and combined with the corresponding clustering results to form a diaper stacking data set group.

[0038] S6, production line application: input the diaper stacking data set group established in steps S3-S5 into the control center B. When there are multiple types of diaper stacks in the preliminary stacking line, the target detection algorithm in the control center A monitors the diaper stacks online and collects real-time images, identifies the corresponding diaper stack types, removes the overly protruding diaper pieces and automatically fills them. After the diaper stacks are pushed into the packaging line, the diaper stacking data set group in the control center B is called to perform secondary stacking adjustment and packaging according to the diaper stack types; The specific operation of automatic filling is that after removing the diaper pieces that are too protruding, the initial stacking production line controls the filling robot arm to push the diaper pieces on the rear side forward to fill. When it runs to the last stack of diapers of this type, if the number of diapers is insufficient, the control center A will issue an alarm, and the producer can choose to remove the stack of diapers or add new diapers to the stack of diapers. During the actual production line operation, industrial cameras and TOF depth cameras perform target detection algorithms while acquiring real-time images of the diaper stacks after preliminary error adjustment. The real-time images are added as new data points to the K-means clustering algorithm. When enough data points are obtained, the cluster center of the current type of diaper stack will be offset. When the offset path exceeds the threshold, an alarm is issued to remind producers to adjust the production data on the packaging line in a timely manner.

[0039] It should be understood that the embodiments disclosed in the present invention are not limited to the specific processing steps or materials disclosed herein, but should be extended to equivalent substitutions of such features understood by ordinary technicians in the relevant field. It should also be understood that the terms used herein are only used for the purpose of describing specific embodiments and are not meant to be limiting.

[0040] The "embodiment" mentioned in the specification means that the specific features or characteristics described in conjunction with the embodiment are included in at least one embodiment of the present invention. Therefore, phrases or "embodiment" appearing in various places throughout the specification do not necessarily refer to the same embodiment.

[0041] In addition, the described features or characteristics may be combined in one or more embodiments in any other suitable manner. In the above description, some specific details, such as thickness, quantity, etc., are provided to provide a comprehensive understanding of the embodiments of the present invention. However, those skilled in the relevant art will understand that the present invention can be implemented without one or more of the above specific details or can also be implemented using other methods, components, materials, etc.

Claims

1. An online palletizing method with multiple error adjustments, characterized in that: include: S1. Material preparation: inputting multiple groups of diaper stacks of different types into the production line for preliminary stacking of diapers, with the number of groups of each type of diaper stack being at least three; S2. Image acquisition and preliminary error adjustment: An industrial camera and a TOF depth camera are set up on the production line for preliminary stacking of diapers to collect real-time images of each group of diaper stacks and input them to the control center A. A target detection model is established in the control center A to perform preliminary stacking adjustments and remove diaper pieces that are too protruding; S3, clustering algorithm: collect the real-time image of the diaper stack after preliminary stacking adjustment and input it to the control center B, which performs K-means clustering algorithm on the real-time image, classifies the diaper stack according to the cluster center, and records the volume and number of stacking layers corresponding to each group of cluster centers; S4, secondary error adjustment: Multiple groups of diaper stacks of different types are pushed out of the initial stacking line one by one and enter the packaging line. The stacking robot arm is controlled by the fuzzy control algorithm to perform secondary stacking adjustment on the diaper stacks, so that the diaper pieces in a group of diaper stacks are arranged neatly; S5. Packaging data entry: according to production experience, corresponding packaging data is set for different types of multiple groups of diaper stacks, and the packaging data is fed back to the control center B, and combined with the clustering results to form a diaper stacking data set group; S6. Production line application: Input the diaper stacking data set group established in steps S3-S5 into control center B. When there are multiple types of diaper stacks in the initial stacking line, the target detection algorithm in control center A monitors the diaper stacks online and collects real-time images, identifies the corresponding diaper stack types, removes overly protruding diaper pieces and automatically fills them. After the diaper stacks are pushed into the packaging line, the diaper stacking data set group in control center B is called to perform secondary stacking adjustments and packaging according to the diaper stack types.

2. The online palletizing method with multiple error adjustments according to claim 1, characterized in that: In the S2 step, the specific steps of collecting real-time images through the industrial camera are as follows: collecting RGB images of the current diaper stack on the assembly line through the industrial camera, inputting the RGB image in the loading state to the control center A, adding rectangular frames and labels to diaper pieces of different sizes through the labelme software, defining the diaper type and identifying the corresponding quantity, inputting the processed image into the Mask RCNN instance segmentation network, learning image features and establishing a target detection model, marking diaper pieces that are too protruding, and controlling the rejection robot arm to remove the protruding diaper pieces from the assembly line.

3. The online palletizing method with multiple error adjustments according to claim 2, characterized in that: In the process of establishing the target detection model and marking the diaper pieces that are too protruding, an alignment algorithm is involved. After the target detection model detects the independent diaper pieces in the current diaper stack, a rectangular frame mark is added to the diaper piece, and the center point of each rectangular frame and the slope between the center points are calculated. If the slope exceeds the set threshold, it is judged as a diaper piece that is too protruding, and the rejection robot arm is controlled to remove the protruding diaper piece from the assembly line. Combined with the number of diaper pieces rejected and production needs, the missing diaper pieces in the current diaper stack are supplemented, and the alignment is recalculated until the alignment is within the threshold range, and then the S3 step is entered.

4. The online palletizing method with multiple error adjustments according to claim 3 is characterized in that: In step S3, the specific steps of performing clustering algorithm operation on the diaper stack after preliminary stacking adjustment are as follows: S31, the industrial camera collects the RGB image of the preliminary stacking pipeline in the no-load state, the TOF depth camera collects the depth image of the preliminary stacking pipeline in the no-load state, aligns the RGB image and the depth image, and records the real-time image samples in the no-load state; S32, the industrial camera collects the RGB image of the current diaper stack on the preliminary stacking line, transmits the RGB image in the loading state to the control center B, and the TOF depth camera synchronously collects the depth image of the diaper stack in the loading state, corrects the depth image, retains the outer frame and obtains the area of ​​the current diaper stack; S33, aligning the RGB image with the depth image, obtaining a comprehensive image of the arrangement state of the diaper pieces in the diaper stack in the current state, repeatedly obtaining comprehensive images of multiple groups of diaper stacks of different types, performing a K-means clustering algorithm, classifying the diaper stacks according to the cluster centers, and obtaining multiple cluster centers; S34, combining the depth image in the empty state with the depth image in the loaded state, calculating the height and volume of the current diaper stack, and synchronously inputting the volume information of the current diaper stack and the comprehensive image into the K-means clustering algorithm, so that each data point carries the volume information; S35. In control center B, the obtained cluster center is combined with the actual production information, and the type, number of stacking layers and volume information labels of the diaper stack are added according to the cluster center. At the same time, the information is imported into control center A with a target detection model, so that the classification in the target detection model corresponds to the classification of the cluster center.

5. The online palletizing method with multiple error adjustments according to claim 4, characterized in that: In step S4, after the current diaper stack is pushed into the packaging line, the specific steps of performing secondary stacking adjustment on the diaper stack are as follows: S41, the stack of diapers slides into the front end of the packaging assembly line, and the left clamping arm, the right clamping arm and the rear clamping arm extend to form a U-shaped frame, so that the stack of diapers is located in the U-shaped frame, and the left clamping arm, the right clamping arm and the rear clamping arm are all plate-shaped mechanisms controlled by hydraulic telescopic rods, which are used to limit the stack of diapers; S42, a stacking robot arm extends from the front side of the diaper stack, and the end of the stacking robot arm is connected to a limit plate through a rotating shaft, and the rotating shaft is connected to the control center A. The control center A stores the center points of each diaper piece in the current diaper stack according to the target detection algorithm, and performs a least squares calculation on the center points to obtain the slope of the current diaper stack; S43. A fuzzy control algorithm module is also provided in the control center A. The rotating shaft is connected to the fuzzy control algorithm module. After the slope of the current diaper stack is input, the corresponding initial rotation angle and rotation rate of the rotating shaft are output through the fuzzy control algorithm. The limit plate rotates in the initial state to adapt to the slope of the current diaper stack. In the process of gradually pushing, adjusting and aligning the diaper stack, the rotating shaft gradually rotates until it is parallel to the rear clamping arm, thereby realizing the secondary stacking adjustment of the diaper stack.

6. The online palletizing method with multiple error adjustments according to claim 5, characterized in that: In step S4, the extension amounts of the left clamping arm, the right clamping arm, the rear clamping arm and the palletizing robot arm are set by the production personnel according to the current diaper stack type, and the set extension amount data is synchronized to control center A and control center B.

7. The online palletizing method with multiple error adjustments according to claim 6, characterized in that: After the secondary stacking adjustment of the diaper stack is completed, the rear clamping arm and the stacking robot arm are withdrawn, the left clamping arm and the right clamping arm press the diaper stack inward, and the vacuum pump set at the rear side of the packaging line unfolds the corresponding type of diaper packaging bag, and the left clamping arm and the right clamping arm move backward to send the diaper stack into the packaging bag and then pull it out, and the plastic sealing machine plastic seals the opening of the packaging bag; the clamping distance and pressure of the left clamping arm and the right clamping arm, the moving distance of the left clamping arm and the right clamping arm, the operating data of the vacuum pump and the operating data of the plastic sealing machine are all packaging data, which are set by the production personnel according to the current diaper stack type, and the set packaging data are synchronously input into the control center B, and combined with the corresponding clustering results to form a diaper stacking data set group.

8. The online palletizing method with multiple error adjustments according to claim 7, characterized in that: In step S6, the specific operation of automatic replenishment is that after removing the diaper pieces that are too protruding, the initial stacking production line controls the replenishment robot arm to push the diaper pieces on the rear side forward to replenish. When it runs to the last stack of diapers of this type, there is a shortage of diapers. The control center A issues an alarm, and the producer chooses to remove the stack of diapers or add new diapers to the stack of diapers.

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