Identification method and system for carrier tape packaging machine

Abstract

The invention discloses an identification method and system for a carrier tape packaging machine, and the method comprises the steps: obtaining a working principle of the carrier tape packaging machine, determining a plurality of working process identification elements according to the working principle, and obtaining an identification mechanism of each working process identification element; determining identification logic and an identification analysis mechanism of each working process identification element according to the identification mechanism, and selecting identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and the identification analysis mechanism; target identification equipment is selected based on the identification equipment specification and the identification equipment mode configuration parameters, and the working process of the carrier tape packaging machine is identified and identified; and determining working defect parameters according to an identification result, and performing feedback control based on the working defect parameters through a preset defect judgment and linkage control model. High-matching-degree identification work can be carried out according to the working characteristics of different working components through multiple types of identification devices, and the identification and detection stability and reliability and the working efficiency are improved.

Description

Technical Field

[0001] This invention relates to the field of equipment identification technology, and in particular to an identification method and system for a carrier packaging machine. Background Technology

[0002] Currently, carrier tape packaging is one of the mainstream packaging methods for SMT (Surface Mount Technology) components. The process typically includes: automatic feeding and orientation of bulk components via a vibratory feeder; a robotic arm picking up the components and placing them into the carrier tape compartments; and finally, heat sealing to encapsulate the cover tape and carrier tape. During this process, components may be missing, misaligned, or inherently defective (such as "groove blockage" in irregularly shaped nuts). Therefore, rigorous inspection is essential before packaging. Existing carrier tape packaging machine identification solutions primarily rely on photoelectric sensors installed above or on the side of the carrier tape to detect whether components are blocking light, or on an industrial camera placed at the robotic arm placement station or pre-packaging station to photograph and identify abnormal robotic arm picking behavior. These techniques only provide basic presence checks and cannot meet the high standard of "zero tolerance" for defects. Furthermore, the need for multiple inspection tasks on the same equipment leads to a high rate of missed detections, reducing inspection efficiency and stability. Summary of the Invention

[0003] To address the problems mentioned above, this invention provides an identification method and system for a carrier packaging machine to solve the problems mentioned in the background art. Traditional identification methods can only complete the most basic existence judgment, which cannot meet the high standard requirement of "zero tolerance" for defects. Furthermore, the same equipment needs to complete multiple detection tasks, resulting in a high rate of missed detections and reduced detection efficiency and stability.

[0004] A method for identifying a carrier packaging machine includes the following steps:

[0005] Obtain the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and obtain the identification mechanism of each identification element of the working process;

[0006] Based on the identification mechanism, determine the identification logic and identification analysis mechanism for each work process identification element, and select the identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and identification analysis mechanism;

[0007] The target identification device is selected based on the identification device specifications and identification device mode configuration parameters, and the workflow of the carrier packaging machine is identified and verified.

[0008] Based on the identification and assessment results, the working defect parameters are determined, and feedback control is carried out based on the working defect parameters through a preset defect judgment and linkage control model.

[0009] Preferably, the step of obtaining the working principle of the carrier packaging machine, determining multiple process identification elements based on the working principle, and obtaining the identification mechanism for each process identification element includes:

[0010] Obtain the working principle of the carrier packaging machine from the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle.

[0011] The work objectives of each work stage are determined based on the work task description of each work stage, and the identification elements of each work stage are determined based on the work objectives.

[0012] Obtain the explicit expression parameters corresponding to the identification elements of each work stage, and determine the identification method for each work stage based on the explicit expression parameters;

[0013] Based on the identification method, determine the identification process for each work stage, and based on the identification process, determine the identification mechanism for the identification elements of each work stage.

[0014] Preferably, the step of determining the identification logic and identification analysis mechanism for each work process identification element based on the identification mechanism, and selecting the identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and identification analysis mechanism, includes:

[0015] Based on the identification mechanism, the identification indicators of each work process identification element are determined, and the identification logic and identification analysis mechanism are determined based on the identification indicators;

[0016] The type of identification device is determined based on the identification logic, the data accuracy requirement is determined based on the identification analysis mechanism, and the specifications of the identification device are selected based on the data accuracy requirement and the type of identification device.

[0017] Determine the feature representation of each identification element in the work process, and determine the data feedback indicator requirements based on the feature representation;

[0018] Configure the mode parameters of the recognition device based on the data feedback indicators.

[0019] Preferably, the step of selecting the target identification device based on the identification device specifications and identification device mode configuration parameters, and identifying and verifying the workflow of the carrier packaging machine, includes:

[0020] Obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters;

[0021] Obtain the performance parameters of each recommended recognition device, and select the best recommended recognition device as the target recognition device based on the performance parameters;

[0022] Determine the standard acceptance parameters for each work process element, and generate an acceptance judgment model based on the standard acceptance parameters;

[0023] The acceptance judgment model identifies, evaluates, and accepts the workflow based on the real-time detection parameters of each target recognition device.

[0024] Preferably, the step of determining the work defect parameters based on the identification and assessment results, and performing feedback control based on the work defect parameters through a preset defect judgment and linkage control model, includes:

[0025] Based on the identification and assessment results, determine the abnormal workflow and its status parameters, and based on the abnormal workflow and its status parameters, determine the work defect parameters.

[0026] The preset defect judgment and linkage control model determines the control parameters based on the working defect parameters and generates control commands according to the control parameters.

[0027] The parameters of the control body are adjusted according to the control command to achieve feedback control.

[0028] An identification system for a carrier packaging machine, the system comprising:

[0029] The acquisition module is used to acquire the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and acquire the identification mechanism of each identification element of the working process.

[0030] The selection module is used to determine the recognition logic and recognition analysis mechanism for each work process recognition element according to the recognition mechanism, and select the recognition equipment specifications and recognition equipment mode configuration parameters based on the recognition logic and recognition analysis mechanism.

[0031] The identification and authentication module is used to select the target identification device based on the identification device specifications and identification device mode configuration parameters, and to identify and authenticate the workflow of the carrier packaging machine.

[0032] The control module is used to determine the working defect parameters based on the identification and assessment results, and to perform feedback control based on the working defect parameters through a preset defect judgment and linkage control model.

[0033] Preferably, the acquisition module includes:

[0034] The first determination submodule is used to obtain the working principle of the carrier packaging machine through the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle.

[0035] The second determination submodule is used to determine the work objective of each work stage based on the work task description of each work stage, and to determine the identification elements of each work stage based on the work objective.

[0036] The third determination submodule is used to obtain the explicit expression parameters corresponding to the identification elements of each work stage, and determine the identification method for each work stage based on the explicit expression parameters;

[0037] The fourth determination submodule is used to determine the identification process for each work stage based on the identification method, and to determine the identification mechanism for the identification elements of each work stage according to the identification process.

[0038] Preferably, the selection module includes:

[0039] The fifth determination submodule is used to determine the identification indicators of each work process identification element according to the identification mechanism, and to determine the identification logic and identification analysis mechanism based on the identification indicators;

[0040] The first selection submodule is used to determine the type of recognition device based on the recognition logic, determine the data accuracy requirements based on the recognition analysis mechanism, and select the recognition device specifications based on the data accuracy requirements and the type of recognition device.

[0041] The sixth submodule is used to determine the feature representation of each identification element in the work process, and to determine the data feedback indicator requirements based on the feature representation.

[0042] The settings submodule is used to set the mode configuration parameters of the recognition device based on the data feedback indicator requirements.

[0043] Preferably, the identification module includes:

[0044] The second selection submodule is used to obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters;

[0045] The third selection submodule is used to obtain the performance parameters of each recommended recognition device and select the best recommended recognition device as the target recognition device based on the performance parameters.

[0046] The first generation submodule is used to determine the standard acceptance parameters for each work process element and generate an acceptance judgment model based on the standard acceptance parameters.

[0047] The identification and authentication submodule is used to identify, authenticate, and accept the workflow based on the real-time detection parameters of each target identification device through the acceptance judgment model.

[0048] Preferably, the control module includes:

[0049] The seventh determination submodule is used to determine the abnormal workflow and its status parameters based on the identification and assessment results, and to determine the work defect parameters based on the abnormal workflow and its status parameters.

[0050] The second generation submodule is used to determine control parameters based on working defect parameters through a preset defect judgment and linkage control model, and generate control instructions according to the control parameters.

[0051] The adjustment submodule is used to adjust the parameters of the control body according to the control command to achieve feedback control.

[0052] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings.

[0053] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0054] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0055] Figure 1 A flowchart illustrating the identification method for a carrier packaging machine provided by the present invention;

[0056] Figure 2 Another flowchart of the identification method for a carrier packaging machine provided by the present invention;

[0057] Figure 3 A schematic diagram of the structure of an identification system for a carrier packaging machine provided by the present invention;

[0058] Figure 4 This is a schematic diagram of the structure of the acquisition module in the identification system of a carrier packaging machine provided by the present invention. Detailed Implementation

[0059] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0060] Currently, carrier tape packaging is one of the mainstream packaging methods for SMT (Surface Mount Technology) components. The process typically includes: automatic feeding and orientation of bulk components via a vibratory feeder; a robotic arm picking up the components and placing them into the carrier tape compartments; and finally, heat sealing to encapsulate the cover tape and carrier tape. During this process, components may be missing, misoriented, or inherently defective (such as "groove blockage" in irregularly shaped nuts). Therefore, rigorous inspection is essential before packaging. Existing carrier tape packaging machine identification solutions mainly rely on photoelectric sensors installed above or on the side of the carrier tape to detect whether components are blocking light, or on an industrial camera placed at the robotic arm placement station or pre-packaging station to photograph and identify components, thus identifying abnormal robotic arm picking behavior. These techniques only provide basic presence checks and cannot meet the high standard of "zero tolerance" for defects. Furthermore, the need for multiple inspection tasks on the same equipment leads to a high rate of missed detections, reducing inspection efficiency and stability. To address these issues, this embodiment discloses an identification method for carrier tape packaging machines.

[0061] A method for identifying carrier packaging machines, such as Figure 1 As shown, it includes the following steps:

[0062] Step S101: Obtain the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and obtain the identification mechanism of each identification element of the working process.

[0063] Step S102: Determine the identification logic and identification analysis mechanism for each work process identification element according to the identification mechanism, and select the identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and identification analysis mechanism;

[0064] Step S103: Select the target identification device based on the identification device specifications and identification device mode configuration parameters, and identify and authenticate the workflow of the carrier packaging machine.

[0065] Step S104: Determine the working defect parameters based on the identification and assessment results, and perform feedback control based on the working defect parameters through the preset defect judgment and linkage control model.

[0066] The working principle of the above technical solution is as follows: The working principle of the carrier packaging machine is obtained; multiple process identification elements are determined based on the working principle; the identification mechanism of each process identification element is obtained; the identification logic and identification analysis mechanism for each process identification element are determined based on the identification mechanism; the identification equipment specifications and identification equipment mode configuration parameters are selected based on the identification logic and identification analysis mechanism; the target identification equipment is selected based on the identification equipment specifications and identification equipment mode configuration parameters, and the process flow of the carrier packaging machine is identified and assessed; the work defect parameters are determined based on the identification and assessment results; and feedback control is performed based on the work defect parameters through a preset defect judgment and linkage control model.

[0067] The beneficial effects of the above technical solution are as follows: By determining multiple identification elements and evaluating their identification logic and analysis mechanism, and then selecting suitable identification equipment for process identification and authentication of the workflow, high-matching identification can be achieved through multiple types of identification equipment targeting the working characteristics of different working components. This improves the stability, reliability, and efficiency of identification and detection. Furthermore, by using a defect judgment and linkage control model for response control, relevant control of work defects can be implemented in the first instance to ensure the stable operation of the packaging machine, thus improving practicality. This solves the problems mentioned in the background technology, where traditional identification methods can only complete the most basic existence judgment, failing to meet the high standard requirement of "zero tolerance" for defects, and the high risk of missed detection due to the need for the same equipment to complete multiple detection tasks, which reduces detection efficiency and stability.

[0068] In one embodiment, such as Figure 2 As shown, the process of obtaining the working principle of the carrier packaging machine, determining multiple process identification elements based on the working principle, and obtaining the identification mechanism for each process identification element includes:

[0069] Step S201: Obtain the working principle of the carrier packaging machine through the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle;

[0070] Step S202: Determine the work objective of each work stage based on the work task description of each work stage, and determine the identification elements of each work stage based on the work objective;

[0071] Step S203: Obtain the explicit expression parameters corresponding to the identification elements of each work stage, and determine the identification method for each work stage based on the explicit expression parameters;

[0072] Step S204: Determine the identification process for each work stage based on the identification method, and determine the identification mechanism for the identification elements of each work stage according to the identification process.

[0073] In this embodiment, multiple working stages include: feeding aluminum alloy vibratory feeder, picking up material with swing arm robot and placing it into tape track, CCD detection of material direction, missing material, etc., and automatic packaging;

[0074] In this embodiment, the identification time points for each identification element are different. For example, the identification elements for feeding aluminum alloy vibratory feeder are: material presence and rough material posture. The identification time point is when the material is at the end of the vibratory feeder's straight track. The purpose of identification is to ensure that the robot arm has material to grasp and that the general direction is correct, thus improving the grasping success rate. The identification elements for the swing arm robot arm picking up material and placing it into the tape track are: grasping success rate and post-grab posture. The identification time point is after the robot arm has grasped the material. The purpose of identification is to confirm that the material has been reliably picked up and to provide a corrective basis for subsequent placement. The identification elements for CCD detection of material direction are: material presence (missing material), material direction / polarity, slag blockage defects in the tank, and whether the position is in place. The identification time point is after the robot arm places the material onto the carrier belt. The purpose of identification is to ensure that each product placed onto the carrier belt is qualified and in the correct state. The identification element for automatic packaging is: the quality of the cover tape packaging. The identification time point is after packaging. The purpose of identification is to check whether the heat seal is firm and free of bubbles, ensuring the packaging is airtight and ensuring usability after transportation.

[0075] In this embodiment, a specific identification mechanism is determined based on the identification elements, wherein:

[0076] 1. Identification mechanism for the end of a linear vibration track

[0077] Sensor: A high frame rate, medium resolution CCD camera, mounted above the end of the track.

[0078] Light source: Ring-shaped shadowless white light source, providing uniform illumination and reducing shadows.

[0079] Recognition logic:

[0080] Existence: By using the background difference method, the image difference between the presence and absence of material is compared. If the area is greater than the threshold, it is determined that there is material.

[0081] Coarse attitude: Extract the material profile, calculate its smallest bounding rectangle, and the rotation angle of this rectangle is the coarse attitude angle θ.

[0082] Data application: The coordinate offset (Δx, Δy) and angle θ of the material are sent to the robot for gripping compensation.

[0083] 2. Recognition mechanism after grasping by the robotic arm

[0084] Sensor: Small photoelectric sensor or vacuum pressure sensor.

[0085] Recognition logic:

[0086] Vacuum sensor: Detects whether the vacuum level inside the nozzle has reached a preset threshold; if it has, the grab is considered successful.

[0087] Photoelectric sensor: detects whether the nozzle position is blocked by material; if blocked, it is determined that there is material.

[0088] Data application: If the grasping fails, the robotic arm is triggered to grasp again, and the number of failures is recorded. If the limit is exceeded, an alarm is triggered.

[0089] 3. Identification mechanism after the carrier tape is placed (core)

[0090] Sensor: A high-resolution, high-precision CCD camera, mounted directly above the carrier tape.

[0091] Light source: Low-angle ring-shaped red light source. This is crucial for detecting slag blockage in the tank!

[0092] Principle: Low-angle light will cause protrusions (such as waste residue) to cast obvious shadows, while depressions (such as clean grooves) will be darker in color, thus greatly enhancing the contrast of defects.

[0093] Recognition logic (image processing algorithm):

[0094] Positioning and segmentation: First, through template matching or Blob analysis, each carrier tube cavity and the material within it are accurately located.

[0095] Presence detection: Grayscale values ​​are statistically analyzed in the expected area of ​​the material, and if the value is below the threshold, it is determined to be a missed release.

[0096] Orientation / polarity detection: Extract material contours or internal features, match them with a standard template, calculate the rotation angle difference, and if it exceeds the tolerance, it is NG.

[0097] Slag blockage defect detection:

[0098] Traditional algorithm: After material positioning, focus on the region of interest (ROI) in the tank, use the imaging features under low-angle light to perform threshold segmentation and spot analysis, calculate the defect pixel area, and judge it as NG if it is greater than the threshold T (e.g., 0.1 mm2).

[0099] Deep learning algorithm: Collect a large number of OK and NG samples, train a lightweight CNN classification model (such as MobileNet), and directly classify the in-slot regions end-to-end (OK / NG), which has stronger anti-interference ability.

[0100] 4. Carrier self-identification mechanism

[0101] Sensor: slotted photoelectric sensor or small field-of-view barcode reader camera.

[0102] Recognition logic:

[0103] The traction hole at the edge of the carrier tape is itself a type of optical marker.

[0104] When the sensor detects that the traction hole has passed through, it will generate a pulse signal.

[0105] The system indirectly controls the carrier belt travel distance (number of stations) by counting the number of pulses.

[0106] Data application: Enables closed-loop control of the carrier tape. If the number of pulses detected is insufficient or excessive, it indicates that the carrier tape is slipping or stretched, and the system can compensate or issue an alarm.

[0107] 5. Post-encapsulation identification mechanism

[0108] Sensors: Telecentric lenses paired with high-resolution cameras, or dedicated transparent material sensors.

[0109] Light source: backlight or coaxial light.

[0110] Recognition logic: Detect the fit between the cover tape and the carrier tape.

[0111] Bubbles / Wrinkles: Under backlighting, bubble areas will appear as bright spots.

[0112] Poor sealing: Image processing algorithms are used to analyze the uniformity of light transmission of the cover strip. Uneven areas may indicate sealing problems.

[0113] The beneficial effects of the above technical solution are as follows: by determining the identification mechanism for each work stage, the identification mechanism can be used to accurately and efficiently identify the identification objects in each work stage, thereby improving identification efficiency and work stability.

[0114] In this embodiment, obtaining the explicit expression parameters corresponding to the identification elements at each working stage includes:

[0115] Acquire standard working signals for each working stage, and determine the spatiotemporal synchronized visual features for each working stage based on the standard working signals;

[0116] Based on the spatiotemporal synchronous visual features, the identification focus bias mechanism for each working stage is determined, and the key monitoring spatial domain is determined according to the identification focus bias mechanism.

[0117] Obtain the dynamic change parameters of visual recognition elements at each working stage within the key monitoring spatial domain, and determine the change rate characteristics of visual recognition elements based on the dynamic change parameters of visual recognition elements.

[0118] The scanning frequency of the key monitoring spatial domain for each work stage is determined based on the rate change characteristics of visual recognition elements.

[0119] Based on the scanning frequency, the ability to perceive modal features of visual recognition elements in each working stage is determined, and based on the ability to perceive modal features, the ability to control the quality details of visual recognition elements in each working stage is determined.

[0120] The feature discrimination capability of each visual recognition element in each work stage is determined based on the ability to control quality details and the correlation weight parameters between different visual recognition elements in each work stage.

[0121] The scanning frequency setting parameters are determined to be qualified based on the feature discrimination capability. If not, the scanning frequency setting parameters are down-processed to obtain the final scanning frequency.

[0122] The corresponding explicit expression parameters are obtained by scanning the identification elements of each working stage using the final scanning frequency.

[0123] In this embodiment, the spatiotemporal synchronized visual feature represents the synchronized visual display feature between visual elements in the temporal and spatial domains of each working stage.

[0124] In this embodiment, the center-of-gravity identification mechanism is represented as a regional center-of-gravity control mechanism that focuses on identifying key areas within each work stage.

[0125] In this embodiment, the visual element dynamic change parameter is represented as the visual feature dynamic change parameter of the identified element within the midpoint monitoring space of each working stage.

[0126] In this embodiment, the visual change rate feature of the recognition element is represented as the visual change rate feature of the recognition element.

[0127] In this embodiment, modal feature perception capability is expressed as the accuracy perception capability of visual modal features of the identified element at the current scanning frequency;

[0128] In this embodiment, the quality detail control capability refers to the ability to control the quality detail of the image acquired for visual recognition elements at the current scanning frequency.

[0129] The beneficial effects of the above technical solution are as follows: by setting reasonable scanning frequency parameters, it is possible to capture and quantify the micro-dynamics and detail differences of visual recognition elements that are difficult for the human eye to perceive, which greatly improves the accuracy and consistency of quality detection. At the same time, it can also ensure the high-quality control of the detail quality and features of the explicit expression parameters of the recognition elements, thus ensuring the effectiveness and stability of monitoring.

[0130] In one embodiment, determining the identification logic and analysis mechanism for each work process identification element based on the identification mechanism, and selecting the identification device specifications and identification device mode configuration parameters based on the identification logic and analysis mechanism, includes:

[0131] Based on the identification mechanism, the identification indicators of each work process identification element are determined, and the identification logic and identification analysis mechanism are determined based on the identification indicators;

[0132] The type of identification device is determined based on the identification logic, the data accuracy requirement is determined based on the identification analysis mechanism, and the specifications of the identification device are selected based on the data accuracy requirement and the type of identification device.

[0133] Determine the feature representation of each identification element in the work process, and determine the data feedback indicator requirements based on the feature representation;

[0134] Configure the mode parameters of the recognition device based on the data feedback indicators.

[0135] In this embodiment, the end of the linear vibration track identifies: the presence of material and its approximate orientation.

[0136] 1. Recognition Logic and Recognition Analysis Mechanism

[0137] Recognition logic: Determine whether there is material at a specified position on the track, and output its approximate center coordinates and angle.

[0138] Analysis mechanism:

[0139] Image preprocessing: After acquiring the image, Gaussian filtering is performed to reduce noise interference.

[0140] Target segmentation: Using background subtraction. A "background image" of an empty track is taken beforehand. The current image is subtracted from the background image, and areas where the difference exceeds a threshold are identified as potential materials.

[0141] Feature extraction: Perform Blob analysis (connected region analysis) on the segmented target region to calculate its centroid coordinates (Cx, Cy), circumscribed rectangle, and rectangle orientation angle θ.

[0142] 2. Identify equipment specifications and mode configuration

[0143] Camera selection:

[0144] Resolution: Not necessary. VGA (640x480) or 1.3 megapixels (1280x1024) is sufficient to cover the track width.

[0145] Sensor type: Global shutter CMOS. This avoids motion blur caused by rolling shutter speeds.

[0146] Frame rate: ≥200fps. Requires matching the high-frequency operation of the robotic arm.

[0147] Lens selection:

[0148] Focal length: Short focal length lens (e.g., 6mm) to ensure the field of view covers the entire waiting area at the end of the track.

[0149] Interface: Port C.

[0150] Light source selection:

[0151] Type: Ring-shaped shadowless white LED light source.

[0152] Objective: To provide uniform lighting, reduce shadows, and ensure stable contrast between materials and background.

[0153] Configuration parameters:

[0154] Exposure time: ≤1ms (short exposure freezes motion).

[0155] Gain: Low gain (e.g., 0dB) to avoid introducing noise.

[0156] Trigger mode: Hard trigger. The photo is triggered by a robotic arm or sensor signal, ensuring that a picture is taken each time the material is in place.

[0157] Identification after placement of the carrier tape (core re-inspection station): blockage, leakage, and orientation.

[0158] 1. Recognition Logic and Recognition Analysis Mechanism

[0159] Identification logic: Accurately determine whether the material in the carrier belt cavity is qualified (no leakage, correct orientation, no blockage).

[0160] Analysis mechanism:

[0161] Precise positioning (Template Matching): First, a pattern matching algorithm is used to accurately locate each carrier cavity and material in the image.

[0162] Missed Pixel / Existence Detection: Within the located cavity area, calculate the sum or variance of pixel grayscale values; if it is below the threshold, it is considered a missed pixel.

[0163] Orientation detection: Extract the material contour, match it with a standard template, and calculate the rotation angle difference.

[0164] Slag plugging defect detection (core):

[0165] Method 1 (Traditional Algorithm): After precise localization, define the Region of Interest (ROI) within the groove. Utilizing low-angle light imaging characteristics, perform threshold segmentation (e.g., adaptive thresholding) on ​​the ROI to separate the defect from the background. Then, perform morphological opening operations to remove noise. Finally, calculate the area of ​​the connected region; if the area is greater than the threshold T (e.g., 0.1 mm), the defect is removed. 2 That is, it is judged as NG.

[0166] Method 2 (Deep Learning): After cropping the ROI region, input it into a pre-trained lightweight CNN classification network (such as SqueezeNet, MobileNet), and directly output the OK / NG probabilities. This method has strong anti-interference capabilities, but requires a large amount of data for training.

[0167] 2. Identify equipment specifications and mode configuration

[0168] Camera selection:

[0169] Resolution: A key parameter. Determined by detection accuracy.

[0170] Pixel accuracy calculation formula: PixelAccuracy = FOV (long side of the field of view) / Camera Resolution (number of pixels on the long side).

[0171] Requirement: To inspect 0.1mm 2 The defect has a feature size of approximately 0.3 mm. According to the Nyquist sampling theorem, at least two pixels are required to cover the feature; therefore, the pixel accuracy must be ≤0.15 mm / pixel. For reliability, ≤0.05 mm / pixel is typically required.

[0172] Calculation example: If the field of view (FOV) is 20mm x 20mm and the pixel accuracy needs to be 0.05mm, then the camera resolution needs to be at least: 20mm / 0.05mm = 400 pixels. Choosing a camera with 5 megapixels (2448x2048) or higher is more reliable.

[0173] Sensor type: Global shutter CMOS.

[0174] Color depth: Monochrome (black and white) camera recommended. Higher resolution, faster image processing, and better suited for detecting contours and contrast.

[0175] Lens selection:

[0176] Focal length: Determined by working distance (WD) and field of view (FOV). Focal length is calculated using the formula: f = (WD × SensorSize) / FOV.

[0177] Magnification: A telecentric lens is recommended. Its advantages include no perspective error; even with slight changes in the material's height within the carrier belt, the measured dimensions remain unchanged, greatly improving detection stability.

[0178] Light source selection (the key to success or failure):

[0179] Type: Low-angle ring LED light source.

[0180] Color: Red (620-630nm). Longer wavelength results in less scattering and higher contrast; and CMOS sensors are more sensitive to red light.

[0181] Angle: Extremely low angle (5°-15°). This creates strong shadows on tiny raised debris, while flat metal surfaces form a specular reflection, appearing dark in the camera, thus creating extremely high contrast.

[0182] Configuration parameters:

[0183] Exposure time: Adjusted according to the brightness of the light source and the image effect, usually short (1-5ms).

[0184] Trigger mode: Triggered synchronously with the carrier belt stepper motor to ensure that the photo is taken after the carrier belt has come to a complete stop.

[0185] III. Carrier belt self-identification: Traction hole positioning

[0186] 1. Recognition Logic and Recognition Analysis Mechanism

[0187] Recognition logic: Detect whether the carrier belt traction hole has passed through and count it to achieve closed-loop control of the carrier belt's travel position.

[0188] Analysis Mechanism: This is a binary judgment, very simple. The sensor outputs a digital signal (0 or 1), which changes when the hole is in position. The PLC or controller counts the number of changes.

[0189] 2. Identify equipment specifications and mode configuration

[0190] Equipment selection: slotted photoelectric sensor (through-beam type).

[0191] Principle: Infrared light is emitted from one side and received from the other. When the carrier tape traction hole passes through, the light passes through, the receiver receives the signal, and outputs a high level; when the physical part of the carrier tape passes through, the light is blocked, and outputs a low level.

[0192] Configuration parameters:

[0193] Response time: Select a sensor with a response time of <1ms to ensure reliability at high speeds.

[0194] Installation position: precisely aligned with the movement trajectory of the traction hole.

[0195] Sensitivity: Adjust to only respond to the passage of the traction hole to avoid false triggering.

[0196] The beneficial effects of the above technical solution are as follows: by selecting the identification equipment specifications and configuring the mode parameters, the best and most suitable identification equipment can be selected according to the working characteristics of different working stages for effective identification and detection, which improves stability and practicality. Furthermore, by configuring the mode parameters, the high quality and high value of the detection data can be guaranteed, thereby improving the accuracy of data detection.

[0197] In one embodiment, the step of selecting a target identification device based on identification device specifications and identification device mode configuration parameters, and identifying and verifying the workflow of the carrier packaging machine, includes:

[0198] Obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters;

[0199] Obtain the performance parameters of each recommended recognition device, and select the best recommended recognition device as the target recognition device based on the performance parameters;

[0200] Determine the standard acceptance parameters for each work process element, and generate an acceptance judgment model based on the standard acceptance parameters;

[0201] The acceptance judgment model identifies, evaluates, and accepts the workflow based on the real-time detection parameters of each target recognition device.

[0202] The beneficial effects of the above technical solution are as follows: by selecting the target recognition device with the best performance, the recognition performance of the device can be maximized, further improving stability and reliability. Furthermore, by constructing an acceptance judgment model, the workflow qualification judgment and acceptance can be quickly and accurately determined based on real-time detection parameters, further improving detection efficiency and accuracy.

[0203] In one embodiment, determining the work defect parameters based on the identification and assessment results, and performing feedback control based on the work defect parameters through a preset defect judgment and linkage control model, includes:

[0204] Based on the identification and assessment results, determine the abnormal workflow and its status parameters, and based on the abnormal workflow and its status parameters, determine the work defect parameters.

[0205] The preset defect judgment and linkage control model determines the control parameters based on the working defect parameters and generates control commands according to the control parameters.

[0206] The parameters of the control body are adjusted according to the control command to achieve feedback control.

[0207] The beneficial effects of the above technical solution are: by generating control commands, the control body can be quickly controlled to repair and correct defects, ensuring the operational stability of the packaging machine and improving work efficiency.

[0208] In one embodiment, this embodiment also discloses an identification system for a carrier packaging machine, such as... Figure 3 As shown, the system includes:

[0209] The acquisition module 301 is used to acquire the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and acquire the identification mechanism of each identification element of the working process.

[0210] The selection module 302 is used to determine the recognition logic and recognition analysis mechanism for each work process recognition element according to the recognition mechanism, and select the recognition equipment specifications and recognition equipment mode configuration parameters based on the recognition logic and recognition analysis mechanism.

[0211] The identification and authentication module 303 is used to select the target identification device based on the identification device specifications and identification device mode configuration parameters, and to identify and authenticate the workflow of the carrier packaging machine.

[0212] The control module 304 is used to determine the working defect parameters based on the identification and assessment results, and to perform feedback control based on the working defect parameters through a preset defect judgment and linkage control model.

[0213] The working principle and beneficial effects of the above technical solution have been explained in the method embodiments, and will not be repeated here.

[0214] In one embodiment, such as Figure 4 As shown, the acquisition module 301 includes:

[0215] The first determining submodule 3011 is used to obtain the working principle of the carrier packaging machine through the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle.

[0216] The second determining submodule 3012 is used to determine the work objective of each work stage based on the work task description of each work stage, and to determine the identification elements of each work stage based on the work objective.

[0217] The third determining submodule 3013 is used to obtain the explicit expression parameters corresponding to the identification elements of each working stage, and to determine the identification method for each working stage based on the explicit expression parameters;

[0218] The fourth determination submodule 3014 is used to determine the identification process for each work stage based on the identification method, and to determine the identification mechanism for the identification elements of each work stage according to the identification process.

[0219] In one embodiment, the selection module includes:

[0220] The fifth determination submodule is used to determine the identification indicators of each work process identification element according to the identification mechanism, and to determine the identification logic and identification analysis mechanism based on the identification indicators;

[0221] The first selection submodule is used to determine the type of recognition device based on the recognition logic, determine the data accuracy requirements based on the recognition analysis mechanism, and select the recognition device specifications based on the data accuracy requirements and the type of recognition device.

[0222] The sixth submodule is used to determine the feature representation of each identification element in the work process, and to determine the data feedback indicator requirements based on the feature representation.

[0223] The settings submodule is used to set the mode configuration parameters of the recognition device based on the data feedback indicator requirements.

[0224] In one embodiment, the identification module includes:

[0225] The second selection submodule is used to obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters;

[0226] The third selection submodule is used to obtain the performance parameters of each recommended recognition device and select the best recommended recognition device as the target recognition device based on the performance parameters.

[0227] The first generation submodule is used to determine the standard acceptance parameters for each work process element and generate an acceptance judgment model based on the standard acceptance parameters.

[0228] The identification and authentication submodule is used to identify, authenticate, and accept the workflow based on the real-time detection parameters of each target identification device through the acceptance judgment model.

[0229] In one embodiment, the control module includes:

[0230] The seventh determination submodule is used to determine the abnormal workflow and its status parameters based on the identification and assessment results, and to determine the work defect parameters based on the abnormal workflow and its status parameters.

[0231] The second generation submodule is used to determine control parameters based on working defect parameters through a preset defect judgment and linkage control model, and generate control instructions according to the control parameters.

[0232] The adjustment submodule is used to adjust the parameters of the control body according to the control command to achieve feedback control.

[0233] Those skilled in the art should understand that the "first" and "second" in this invention simply refer to different application stages.

[0234] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0235] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for identifying a carrier packaging machine, characterized in that, Includes the following steps: Obtain the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and obtain the identification mechanism of each identification element of the working process; Based on the identification mechanism, determine the identification logic and identification analysis mechanism for each work process identification element, and select the identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and identification analysis mechanism; The target identification device is selected based on the identification device specifications and identification device mode configuration parameters, and the workflow of the carrier packaging machine is identified and verified. Based on the identification and assessment results, the working defect parameters are determined, and feedback control is carried out based on the working defect parameters through a preset defect judgment and linkage control model.

2. The identification method of the carrier packaging machine according to claim 1, characterized in that, The process involves obtaining the working principle of the carrier packaging machine, determining multiple process identification elements based on the working principle, and obtaining the identification mechanism for each process identification element, including: Obtain the working principle of the carrier packaging machine from the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle. The work objectives of each work stage are determined based on the work task description of each work stage, and the identification elements of each work stage are determined based on the work objectives. Obtain the explicit expression parameters corresponding to the identification elements of each work stage, and determine the identification method for each work stage based on the explicit expression parameters; Based on the identification method, determine the identification process for each work stage, and based on the identification process, determine the identification mechanism for the identification elements of each work stage.

3. The identification method for a carrier packaging machine according to claim 1, characterized in that, The process of determining the identification logic and analysis mechanism for each work process identification element based on the identification mechanism, and selecting identification equipment specifications and identification equipment mode configuration parameters based on the identification logic and analysis mechanism, includes: Based on the identification mechanism, the identification indicators of each work process identification element are determined, and the identification logic and identification analysis mechanism are determined based on the identification indicators; The type of identification device is determined based on the identification logic, the data accuracy requirement is determined based on the identification analysis mechanism, and the specifications of the identification device are selected based on the data accuracy requirement and the type of identification device. Determine the feature representation of each identification element in the work process, and determine the data feedback indicator requirements based on the feature representation; Configure the mode parameters of the recognition device based on the data feedback indicators.

4. The identification method for a carrier packaging machine according to claim 1, characterized in that, The process of selecting the target identification device based on the identification device specifications and identification device mode configuration parameters, and identifying and verifying the workflow of the carrier packaging machine, includes: Obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters; Obtain the performance parameters of each recommended recognition device, and select the best recommended recognition device as the target recognition device based on the performance parameters; Determine the standard acceptance parameters for each work process element, and generate an acceptance judgment model based on the standard acceptance parameters; The acceptance judgment model identifies, evaluates, and accepts the workflow based on the real-time detection parameters of each target recognition device.

5. The identification method for a carrier packaging machine according to claim 1, characterized in that, The step of determining work defect parameters based on the identification and assessment results, and performing feedback control based on the work defect parameters through a preset defect judgment and linkage control model, includes: Based on the identification and assessment results, determine the abnormal workflow and its status parameters, and based on the abnormal workflow and its status parameters, determine the work defect parameters. The preset defect judgment and linkage control model determines the control parameters based on the working defect parameters and generates control commands according to the control parameters. The parameters of the control body are adjusted according to the control command to achieve feedback control.

6. An identification system for a carrier packaging machine, characterized in that, The system includes: The acquisition module is used to acquire the working principle of the carrier packaging machine, determine multiple identification elements of the working process based on the working principle, and acquire the identification mechanism of each identification element of the working process. The selection module is used to determine the recognition logic and recognition analysis mechanism for each work process recognition element according to the recognition mechanism, and select the recognition equipment specifications and recognition equipment mode configuration parameters based on the recognition logic and recognition analysis mechanism. The identification and authentication module is used to select the target identification device based on the identification device specifications and identification device mode configuration parameters, and to identify and authenticate the workflow of the carrier packaging machine. The control module is used to determine the working defect parameters based on the identification and assessment results, and to perform feedback control based on the working defect parameters through a preset defect judgment and linkage control model.

7. The identification system for the carrier packaging machine according to claim 6, characterized in that, The acquisition module includes: The first determination submodule is used to obtain the working principle of the carrier packaging machine through the equipment manual, and determine multiple working stages and the work task description of each working stage based on the working principle. The second determination submodule is used to determine the work objective of each work stage based on the work task description of each work stage, and to determine the identification elements of each work stage based on the work objective. The third determination submodule is used to obtain the explicit expression parameters corresponding to the identification elements of each work stage, and determine the identification method for each work stage based on the explicit expression parameters; The fourth determination submodule is used to determine the identification process for each work stage based on the identification method, and to determine the identification mechanism for the identification elements of each work stage according to the identification process.

8. The identification system for the carrier packaging machine according to claim 6, characterized in that, The selection module includes: The fifth determination submodule is used to determine the identification indicators of each work process identification element according to the identification mechanism, and to determine the identification logic and identification analysis mechanism based on the identification indicators; The first selection submodule is used to determine the type of recognition device based on the recognition logic, determine the data accuracy requirements based on the recognition analysis mechanism, and select the recognition device specifications based on the data accuracy requirements and the type of recognition device. The sixth submodule is used to determine the feature representation of each identification element in the work process, and to determine the data feedback indicator requirements based on the feature representation. The settings submodule is used to set the mode configuration parameters of the recognition device based on the data feedback indicator requirements.

9. The identification system for the carrier packaging machine according to claim 6, characterized in that, The identification and authentication module includes: The second selection submodule is used to obtain user budget and supplier preference information, and select multiple recommended recognition devices based on user budget and supplier preference information, as well as recognition device specifications and recognition device mode configuration parameters; The third selection submodule is used to obtain the performance parameters of each recommended recognition device and select the best recommended recognition device as the target recognition device based on the performance parameters. The first generation submodule is used to determine the standard acceptance parameters for each work process element and generate an acceptance judgment model based on the standard acceptance parameters. The identification and authentication submodule is used to identify, authenticate, and accept the workflow based on the real-time detection parameters of each target identification device through the acceptance judgment model.

10. The identification system for the carrier packaging machine according to claim 6, characterized in that, The control module includes: The seventh determination submodule is used to determine the abnormal workflow and its status parameters based on the identification and assessment results, and to determine the work defect parameters based on the abnormal workflow and its status parameters. The second generation submodule is used to determine control parameters based on working defect parameters through a preset defect judgment and linkage control model, and generate control instructions according to the control parameters. The adjustment submodule is used to adjust the parameters of the control body according to the control command to achieve feedback control.

Images