A finished product tray code spraying recognition interaction method, system and medium

Abstract

The application provides a finished product tray code spraying recognition interaction method, system and medium, relating to the technical field of glass production management. The method provides an integrated main interface, enabling users to conveniently configure code spraying parameters, image acquisition configuration and handle code spraying recognition exceptions, realizing full-process digital management from barcode type selection, code spraying position setting to code spraying content template editing. The method not only supports accurate adjustment of camera recognition parameters, such as recognition area demarcation and exposure parameter adjustment, to optimize image acquisition effect, but also can automatically diagnose and repair barcode blur and offset based on real-time acquired code spraying image data, improve recognition accuracy through adaptive adjustment of camera angle and focal length, and generate detailed exception handling records and optimization suggestion reports, thereby improving production efficiency and automation level and ensuring data accuracy and system stability.

Description

Technical Field

[0001] This invention relates to the field of glass production management technology, and in particular to a finished product pallet inkjet printing recognition interactive method, system and medium. Background Technology

[0002] Traditional glass product production management processes lack effective means for real-time monitoring and optimization of the finished product production status. Existing technologies typically rely on manual input of one-dimensional or two-dimensional barcode information for each pallet of finished products. This method is not only inefficient and prone to errors, increasing labor costs, but also struggles to guarantee data accuracy and timeliness. Furthermore, while automatic barcode reading via cameras improves efficiency, traditional camera recognition systems struggle to provide timely diagnostic and handling mechanisms for anomalies such as blurred or misaligned barcodes. Recognition problems caused by factors such as camera angle and focal length often require manual intervention, increasing maintenance costs and time consumption. Summary of the Invention

[0003] The purpose of this invention is to provide a finished product pallet inkjet printing recognition interactive method, system and medium to solve one or more technical problems existing in the prior art, and at least provide a beneficial option or create conditions.

[0004] The solution to the technical problem of this invention is as follows: On the one hand, this invention provides a finished product pallet inkjet printing recognition and interaction method, including the following steps: The main interface for displaying the inkjet printing recognition of finished product pallets includes inkjet printing configuration controls, image acquisition configuration controls, and inkjet printing recognition exception handling controls. In response to the trigger command of the inkjet printing configuration control, the inkjet printing parameter configuration sub-interface is displayed, allowing for selection of the pallet barcode type, setting of inkjet printing position coordinates, and editing of the printing content template to generate basic inkjet printing configuration data; In response to the trigger command of the image acquisition configuration control, the camera parameter configuration sub-interface is displayed to perform recognition area delineation, exposure parameter adjustment and decoding algorithm parameter setting, and image acquisition control command is generated based on the inkjet printing basic configuration data; In response to the trigger command of the inkjet printing recognition anomaly handling control, the anomaly diagnosis sub-interface is displayed. Based on the real-time collected inkjet image data, the anomaly types of barcode blurring and barcode offset are identified. The self-repair operation of camera angle adjustment, focus calibration and parameter reset is automatically performed, and anomaly handling record and optimization suggestion report are generated.

[0005] Furthermore, the inkjet printing parameter configuration sub-interface includes a tray barcode type selection control, an inkjet printing position coordinate setting control, an inkjet printing content template editing control, and an inkjet printing configuration saving control; In the inkjet printing parameter configuration sub-interface, select the pallet barcode type, set the inkjet printing position coordinates, and edit the print content template to generate basic inkjet printing configuration data, including the following steps: In response to the trigger command of the tray barcode type selection control, a barcode type selection window is displayed, providing a variety of preset barcode type options, or special encoding rules can be configured through a custom format input box, and the corresponding decoding algorithm parameters are automatically loaded after selection; In response to the trigger command of the inkjet printing position coordinate setting control, a visual coordinate configuration window is displayed, which supports the input of coordinate parameters by dragging with the mouse or by entering them in the input box, and provides a preview function to display the relative relationship between the simulated inkjet printing position and the tray boundary in real time; In response to a trigger command on the print content template editing control, the template editor interface is displayed to edit the print content template, supporting the insertion of dynamic variables and static text; In response to the trigger command of the inkjet configuration save control, the selected barcode type, the coordinate parameters and the inkjet content template data are encapsulated into inkjet basic configuration data, uploaded to the inkjet controller and the image recognition system, and the decoding algorithm parameters are uploaded to the camera controller.

[0006] Furthermore, the camera parameter configuration sub-interface includes a recognition area delineation control, an exposure parameter adjustment control, an image decoding setting control, and a camera parameter configuration saving control; In the camera parameter configuration sub-interface, the recognition area is defined, exposure parameters are adjusted, and decoding algorithm parameters are set. Based on the basic inkjet printing configuration data, image acquisition control commands are generated, including the following steps: In response to the trigger command of the identification area delineation control, a visual area configuration window is displayed, which supports delineating the area of ​​interest by selecting with a rectangle or inputting coordinates, and automatically recommends the range of the area of ​​interest in conjunction with the coordinate parameters in the basic inkjet printing configuration data; In response to the trigger command of the exposure parameter adjustment control, the parameter adjustment panel is displayed, the exposure parameters are set, the exposure parameters can be adjusted by the slider or preset level, and a preview function is provided to display the grayscale value change of the adjusted image in real time. In response to the trigger command of the image decoding setting control, the algorithm configuration window is displayed, the decoding algorithm parameters corresponding to the barcode type in the inkjet printing basic configuration data are loaded, and the image recognition algorithm parameters are configured. In response to a trigger command to save the camera parameter configuration, the region of interest, the exposure parameters, and the image recognition algorithm parameters are encapsulated into an image acquisition control command and uploaded to the camera controller.

[0007] Furthermore, the anomaly diagnosis sub-interface includes an anomaly type identification control, a focus calibration control, and an anomaly handling completion control; In the anomaly diagnosis sub-interface, based on the real-time acquired inkjet image data, the system identifies anomalies such as barcode blurring and barcode offset, automatically performs self-repair operations including camera angle adjustment, focus calibration, and parameter reset, and generates anomaly handling records and optimization suggestion reports, including the following steps: In response to the trigger command of the anomaly type identification control, the anomaly diagnosis and analysis window is displayed, the real-time inkjet image data stream is loaded, and the anomaly type and its confidence level are output through the sharpness evaluation algorithm and the coordinate offset calculation algorithm. In response to the trigger command of the focal length calibration control, the lens parameter adjustment window is displayed, and the camera angle adjustment and focal length calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focal length are adjusted, and three consecutive image acquisitions are performed to verify the repair effect. In response to the trigger command of the exception handling completion control, the exception handling record and optimization suggestion report are automatically generated based on the exception type and its confidence level, repair measures, lens focal length parameter change log recorded during the exception handling process, and the verification result data of the repair effect.

[0008] Furthermore, in the anomaly diagnosis and analysis window, a real-time inkjet image data stream is loaded, and the anomaly type and its confidence level are output through a sharpness assessment algorithm and a coordinate offset calculation algorithm, including the following steps: According to the loading instruction of the real-time inkjet image data stream, continuous frame image data is obtained from the camera controller and displayed in real time in the anomaly diagnosis and analysis window as a dynamic preview box; The sharpness evaluation algorithm is invoked to perform grayscale processing on the current frame image. The gradient value of the image is calculated by the Laplacian operator and compared with a preset gradient threshold. If the gradient value of 3 consecutive frames is less than the preset gradient threshold, it is initially determined that the barcode is blurry, and the barcode blur confidence score is calculated. The value of the barcode blur confidence score is equal to 1 minus the gradient value divided by the preset gradient threshold. The coordinate offset calculation algorithm is invoked to extract the center coordinates of the smallest bounding rectangle of the inkjet area in the current frame, compare them with the coordinate parameters in the basic inkjet configuration data, calculate the offset value, and compare it with a preset offset threshold. If the offset value is greater than the preset offset threshold, it is determined to be a barcode offset, and the barcode offset confidence score is calculated. The barcode offset confidence score is equal to the ratio of the preset offset threshold to the offset value. The abnormal area is marked with a red border in the anomaly diagnosis and analysis window, and the gradient value, the offset value, the anomaly type and its confidence level are displayed.

[0009] Furthermore, in the lens parameter adjustment window, the camera angle adjustment and focal length calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focal length are adjusted, and three consecutive image acquisitions are performed to verify the repair effect, including the following steps: The anomaly type and its confidence level are obtained from the anomaly diagnosis and analysis window, and a priority determination mechanism is enabled in the lens parameter adjustment window, specifically including: If the barcode fuzziness confidence level is greater than the barcode offset confidence level, then the camera controller will first perform focus calibration and then perform camera angle compensation. If the barcode offset confidence level is greater than the barcode fuzziness confidence level, then the camera controller will first perform camera angle adjustment and then perform focus compensation. Perform three consecutive image acquisitions and verify the repair effect by combining preset gradient thresholds and preset offset thresholds. If at least two of the three acquisitions meet all the above conditions, the repair is considered successful; otherwise, the repair is considered unsuccessful, triggering a priority reversal retry mechanism to swap the order of focal length calibration and camera angle adjustment, and readjust the camera angle and lens focal length. After two cumulative failures, a pop-up window will prompt "Please check the mechanical structure stability of the camera".

[0010] Furthermore, the step of prioritizing focus calibration and then performing camera angle compensation via the camera controller includes the following steps: Based on the barcode fuzziness confidence level and the gradient value, a focal length graded search mechanism is initiated in the lens parameter adjustment window, specifically including: In the coarse adjustment stage, a bidirectional search is performed within the preset focal length range with a step size of 0.3mm. Images are acquired and real-time gradient values ​​are calculated at each step. When the increase in real-time gradient values ​​for three consecutive steps is less than the preset gradient threshold, the fine adjustment stage begins. During the fine-tuning phase, the step size is switched to 0.1mm. A one-way search is performed within ±0.5mm of the gradient value peak in the coarse-tuning phase to lock the position of the maximum gradient value as the optimal focal length. Lock the camera's optimal focal length and perform camera angle compensation, specifically including: Based on the offset value, the angle compensation amount is calculated, and the angle adjustment parameters are generated; According to the angle adjustment parameters, the gimbal motor is controlled to perform angle fine-tuning in steps of 0.02°. An image is acquired and the real-time offset value is calculated every 5 adjustment steps. When the real-time offset value is less than the preset offset threshold, the camera angle compensation is stopped and the corresponding camera angle parameters are locked.

[0011] Furthermore, the step of prioritizing camera angle adjustment and then performing focus compensation via the camera controller includes the following steps: Based on the barcode offset confidence level and the offset value, an angle-grading adjustment mechanism is initiated in the lens parameter adjustment window, specifically including: In the coarse adjustment stage, the initial angle compensation amount is calculated based on the offset value, and the gimbal motor is controlled to perform camera angle adjustment in 0.1° steps. An image is acquired and the real-time offset value is calculated every 3 adjustment steps. When the real-time offset value is less than the preset coarse adjustment offset threshold, the fine adjustment stage is entered. During the fine-tuning phase, the camera angle is adjusted in steps of 0.02°. An image is captured and the real-time offset value is calculated for each adjustment step. When the real-time offset value is less than the preset fine-tuning offset threshold, the angle adjustment amount is recorded, and the current camera angle parameters are recorded as the optimal angle. Lock the camera at its optimal angle and perform focus compensation, specifically including: Based on the angle adjustment amount, calculate the focal length compensation coefficient and determine the focal length compensation range; Within the focal length compensation range, focal length search is performed in steps of 0.1 mm, and the gradient value of the image is acquired synchronously. When the gradient value exceeds the preset gradient threshold, focal length compensation is stopped and the corresponding focal length is locked.

[0012] On the other hand, this application provides a finished product pallet inkjet printing recognition interactive system, including a main interface module, an inkjet printing configuration module, an image acquisition configuration module, and an inkjet printing recognition anomaly handling module; The main interface module is used to display the main interface for interactive inkjet coding recognition of finished product pallets; wherein, the main interface includes inkjet coding configuration controls, image acquisition configuration controls, and inkjet coding recognition exception handling controls; The inkjet printing configuration module is used to respond to the trigger command of the inkjet printing configuration control, display the inkjet printing parameter configuration sub-interface, select the pallet barcode type, set the inkjet printing position coordinates and edit the inkjet printing content template to generate basic inkjet printing configuration data; The image acquisition configuration module is used to respond to the trigger command of the image acquisition configuration control, display the camera parameter configuration sub-interface, perform recognition area delineation, exposure parameter adjustment and decoding algorithm parameter setting, and generate image acquisition control command based on the inkjet printing basic configuration data; The inkjet printing recognition anomaly handling module is used to respond to the trigger command of the inkjet printing recognition anomaly handling control, display an anomaly diagnosis sub-interface, identify the anomaly type of barcode blurring and barcode offset based on the real-time collected inkjet image data, automatically perform self-repair operations such as camera angle adjustment, focus calibration and parameter reset, and generate anomaly handling records and optimization suggestion reports.

[0013] On the other hand, this application provides a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the aforementioned finished product pallet inkjet printing recognition interaction method.

[0014] The beneficial effects of this invention are as follows: This application provides an interactive method for inkjet printing recognition of finished product pallets. By providing an integrated main interface, users can easily configure inkjet printing parameters, image acquisition, and handle inkjet printing recognition anomalies, realizing full-process digital management from barcode type selection and inkjet printing position setting to printing content template editing. This method not only supports precise adjustment of camera recognition parameters, such as recognition area delineation and exposure parameter adjustment, to optimize image acquisition effects, but also automatically diagnoses and repairs problems such as barcode blurring and offset based on real-time acquired inkjet printing image data. It improves recognition accuracy by adaptively adjusting the camera angle and focal length, and generates detailed anomaly handling records and optimization suggestion reports, thereby improving production efficiency and automation levels, and ensuring data accuracy and system stability. This application also provides a corresponding system and medium; the beneficial effects of the system and medium are similar to those of the method and will not be elaborated here.

[0015] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0016] The accompanying drawings are provided to further understand the technical solutions 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 technical solutions of the present invention, and do not constitute a limitation on the technical solutions of the present invention.

[0017] Figure 1 This is a flowchart of the finished product pallet inkjet printing recognition interaction method provided in this application; Figure 2 This is a schematic diagram of the main interface for the finished product pallet inkjet printing recognition interaction provided in this application; Figure 3 This is a schematic diagram of the inkjet printing parameter configuration sub-interface provided in this application; Figure 4 This is a schematic diagram of the camera parameter configuration sub-interface provided in this application; Figure 5 This is a schematic diagram of the anomaly diagnosis sub-interface provided in this application; Figure 6 This is a structural diagram of the finished product pallet inkjet printing recognition interactive system provided in this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0019] The present application will be further described below with reference to the accompanying drawings and specific embodiments. The described embodiments should not be considered as limitations on the present application, and all other embodiments obtained by those skilled in the art without inventive effort are within the scope of protection of the present application.

[0020] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] Traditional finished product pallet coding is crucial for production management and quality control. By assigning a unique one-dimensional or two-dimensional barcode to each palletized product, it automates the recording and tracking of information at every stage from production and distribution to warehousing, significantly reducing errors and costs associated with manual data entry. This identification method not only improves the accuracy and efficiency of data collection but also supports end-to-end product traceability, helping to promptly identify and resolve problems in the production process and ensuring product quality. Furthermore, it provides reliable data support for enterprise inventory management and logistics, promoting interconnectivity between upstream and downstream supply chains and improving overall operational efficiency and market responsiveness. However, traditional methods rely on manual operation and lack intelligent processing mechanisms, making them prone to recognition failures when faced with unclear barcodes or misaligned barcodes, thus limiting their further application and development.

[0023] Current technologies primarily rely on manual input or semi-automated equipment to record the barcode or QR code information of finished products. For example, in the post-packaging process, such as workshop transfers and warehouse warehousing, workers typically need to manually scan barcodes or use handheld scanners to collect data. In addition, some companies have adopted simple automated identification systems, such as installing fixed cameras in specific locations to read barcode information. However, these systems are often relatively simple in function, mainly focusing on the reading and storage of barcode information.

[0024] While existing technologies can meet production needs to some extent, they still have many shortcomings. Manual data entry not only consumes significant human resources but is also highly susceptible to human error, affecting data accuracy and timeliness. Traditional automated identification systems cannot provide effective solutions when encountering situations where labels are missing or wrinkled, leading to production line interruptions or product quality issues. Although using cameras to read barcodes improves efficiency, traditional systems struggle to accurately identify blurry or misaligned barcodes, directly impacting overall recognition rates and production efficiency. When anomalies occur (such as unclear or misaligned barcodes), manual intervention is required to adjust the camera angle or focus, increasing maintenance costs and time. Insufficient support for rapid deployment and parameter adjustment in new environments limits the system's widespread application.

[0025] To address the aforementioned issues, this application proposes an integrated and intelligent method, system, and medium for interactive coding and recognition of finished product pallets. This method not only automatically reads the one-dimensional or two-dimensional barcodes of each pallet-packaged product, avoiding errors caused by manual input, but also records photos of unidentified anomalies and triggers alarms, ensuring timely problem handling. Furthermore, by linking visual recognition technology with equipment such as coding machines, the accuracy of the coding content can be verified in real time, further guaranteeing product quality. More importantly, this system possesses self-diagnosis and repair capabilities, automatically adjusting the camera angle and focus based on real-time data acquisition to resolve issues such as barcode blurring and offset, thereby improving the recognition rate and system stability. Simultaneously, the system provides flexible basic configuration options, allowing users to quickly adjust settings according to actual needs and adapt to different production scenarios.

[0026] First, the finished product pallet inkjet printing recognition interaction method provided in this application embodiment will be described in detail below with reference to the accompanying drawings.

[0027] Reference Figures 1 to 5 The implementation process of the finished product pallet inkjet printing recognition interaction method provided in this application embodiment includes, but is not limited to, the following steps.

[0028] Step S110: Display the main interface 100 for finished product pallet inkjet printing recognition interaction.

[0029] Among them, reference Figure 2 The main interface 100 includes a coding configuration control 101, an image acquisition configuration control 102, and a coding recognition anomaly handling control 103.

[0030] In step S110, the main interface 100 for finished product pallet inkjet printing recognition interaction is displayed, serving as the fundamental starting point of the entire process. This step provides an integrated user interface, centrally displaying functional modules such as inkjet printing configuration, image acquisition configuration, and anomaly handling to the user, greatly improving user experience and operational efficiency. The inkjet printing configuration control 101, image acquisition configuration control 102, and inkjet printing recognition anomaly handling control 103 on the main interface 100 correspond to the entry points for subsequent detailed settings and operations, allowing users to intuitively perform various configuration and management tasks. This design not only simplifies the user's operation process but also ensures seamless integration between various functional modules of the system.

[0031] In step S120, in response to the trigger command of the inkjet configuration control 101, the inkjet parameter configuration sub-interface 200 is displayed to select the pallet barcode type, set the inkjet position coordinates, and edit the inkjet content template to generate basic inkjet configuration data.

[0032] In step S120, in response to the trigger command of the inkjet printing configuration control 101, the inkjet printing parameter configuration sub-interface 200 is displayed, allowing users to perform operations such as selecting the pallet barcode type, setting the inkjet printing position coordinates, and editing the printing content template to generate basic inkjet printing configuration data. The core significance of this step is to lay the foundation for the subsequent inkjet printing recognition process. By accurately selecting a suitable barcode type (such as a 1D or 2D barcode), determining the specific position of the inkjet print on the pallet, and customizing the printing content template, it can be ensured that the product information of each pallet package is accurately recorded. Furthermore, this configuration data will also serve as key parameters to guide the camera's image acquisition process, thereby improving the overall recognition rate and accuracy.

[0033] In step S130, in response to the trigger command of the image acquisition configuration control 102, the camera parameter configuration sub-interface 300 is displayed to perform recognition area delineation, exposure parameter adjustment and decoding algorithm parameter setting, and to generate image acquisition control command based on the inkjet printing basic configuration data.

[0034] In step S130, in response to the trigger command of the image acquisition configuration control 102, the camera parameter configuration sub-interface 300 is displayed, allowing the user to define the recognition area, adjust exposure parameters, and set decoding algorithm parameters according to actual needs. Image acquisition control commands are generated based on the previously generated basic inkjet printing configuration data. This process is crucial for ensuring image acquisition quality. Precise definition of the recognition area and fine adjustment of exposure and decoding algorithm parameters can effectively improve image clarity and recognition success rate, maintaining high-precision recognition even in poor lighting conditions or scenarios with complex barcode details. This step directly relates to the system's reliability and stability and is a key step in achieving efficient automatic recognition.

[0035] In step S140, in response to the trigger command of the inkjet printing recognition anomaly handling control 103, the anomaly diagnosis sub-interface 400 is displayed. Based on the real-time acquired inkjet printing image data, the anomaly type identification of barcode blurring and barcode offset is performed. The self-repair operation of camera angle adjustment, focus calibration and parameter reset is automatically executed, and anomaly handling record and optimization suggestion report are generated.

[0036] In step S140, in response to the trigger command of the inkjet printing recognition anomaly handling control 103, the anomaly diagnosis sub-interface 400 is displayed. Based on the real-time acquired inkjet image data, the system identifies anomalies such as barcode blurring and barcode offset, and automatically performs self-repair operations such as camera angle adjustment, focus calibration, and parameter reset. Finally, an anomaly handling record and optimization suggestion report are generated. This step aims to solve various problems that may occur during the recognition process and ensure the continuous and stable operation of the system. Through intelligent anomaly detection and self-repair mechanism, the system can quickly locate and solve problems without interrupting the production process, reducing the need for manual intervention. At the same time, it provides detailed processing records and optimization suggestions to help users further improve system performance. This automated processing method not only improves work efficiency but also significantly reduces maintenance costs.

[0037] In some embodiments of this application, reference is made to Figure 3 The inkjet printing parameter configuration sub-interface 200 includes a tray barcode type selection control 201, an inkjet printing position coordinate setting control 202, an inkjet printing content template editing control 203, and an inkjet printing configuration saving control 204. In step S110, the process of selecting the tray barcode type, setting the inkjet printing position coordinates, and editing the inkjet printing content template in the inkjet printing parameter configuration sub-interface 200 to generate basic inkjet printing configuration data includes, but is not limited to, the following steps.

[0038] In step S210, in response to the trigger command of the tray barcode type selection control 201, a barcode type selection window is displayed, providing a variety of preset barcode type options, or configuring special encoding rules through a custom format input box, and automatically loading the corresponding decoding algorithm parameters after selection.

[0039] In step S210, in response to the trigger command of the tray barcode type selection control 201, a barcode type selection window is displayed, providing multiple preset barcode type options (such as EAN-13, Code 128, QR Code, etc.), or configuring special encoding rules through a custom format input box. This step provides users with highly flexible and standardized barcode type configuration capabilities, ensuring the system can adapt to the labeling needs of different products, different customers, and even different national or industry standards. After selecting a barcode type, the system automatically loads the corresponding decoding algorithm parameters. This mechanism not only simplifies the user's operation process but also ensures the accuracy and efficiency of the decoding process in subsequent image recognition stages. By intelligently binding the barcode type with the decoding algorithm, recognition failures caused by incorrect parameter settings are avoided, improving the system's intelligence and compatibility.

[0040] In step S220, in response to the trigger command of the inkjet printing position coordinate setting control 202, a visual coordinate configuration window is displayed, which supports dragging and dropping with the mouse or entering coordinate parameters through an input box, and provides a preview function to display the relative relationship between the simulated inkjet printing position and the tray boundary in real time.

[0041] In step S220, in response to the trigger command of the inkjet printing position coordinate setting control 202, a visual coordinate configuration window is displayed, allowing users to intuitively set the inkjet printing position on the simulated pallet image by dragging and dropping with the mouse, or to accurately enter X and Y coordinate parameters through input boxes. Simultaneously, the system provides a real-time preview function, dynamically displaying the relative relationship between the set inkjet printing position and the pallet boundary, edge, or other key structures. This design greatly enhances the intuitiveness and accuracy of the configuration, effectively preventing problems such as inkjet printing overflow, obstruction, or recognition difficulties caused by improper coordinate settings. Especially in multi-specification pallet mixed-line production scenarios, visual configuration can quickly adapt to pallets of different sizes and layouts, improving changeover efficiency. Furthermore, accurate position setting also provides a reliable basis for the subsequent delineation of the camera recognition area, which is an important prerequisite for achieving a high recognition rate.

[0042] In step S230, in response to the trigger command of the inkjet content template editing control 203, the template editor interface is displayed to edit the inkjet content template, supporting the insertion of dynamic variables and static text.

[0043] In step S230, in response to the trigger command of the inkjet content template editing control 203, the template editor interface is displayed, allowing users to freely edit the inkjet content template. It supports inserting static text (such as product name, batch number prefix) and dynamic variables (such as production date, timestamp, serial number, shift information, work order number, etc.). This function achieves high customizability and data linkage capabilities for inkjet content, meeting diverse information identification needs in complex production environments. For example, a composite template containing "product model-batch number-production date@time" can be constructed to achieve structured information output. The introduction of dynamic variables ensures that each inkjet print reflects the real-time production status, enhancing product traceability. Simultaneously, template-based management facilitates unified standards, configuration reuse, reduces redundant settings, and improves configuration efficiency and consistency.

[0044] In step S240, in response to the trigger command of the inkjet configuration save control 204, the selected barcode type, coordinate parameters and inkjet content template data are encapsulated into inkjet basic configuration data, uploaded to the inkjet controller and image recognition system, and the decoding algorithm parameters are uploaded to the camera controller.

[0045] In step S240, in response to the trigger command to the inkjet configuration save control 204, the system encapsulates the currently selected barcode type, inkjet position coordinate parameters, and inkjet content template into a unified format of "inkjet basic configuration data," and uploads it to both the inkjet controller and the image recognition system. Simultaneously, the corresponding decoding algorithm parameters are sent to the camera controller. This step is a crucial node in the integration and distribution of the entire configuration process, ensuring data collaboration and consistency between the subsystems. The inkjet controller executes accurate inkjet printing based on this configuration, while the image recognition system presets recognition logic based on the same configuration information, forming a closed-loop control. In particular, synchronizing the decoding algorithm parameters to the camera controller gives the image acquisition device "prior knowledge," enabling targeted optimization of image processing strategies during the acquisition phase, significantly improving the first-time recognition success rate. This step achieves seamless integration from configuration to execution, ensuring the overall stability and reliability of the system.

[0046] In some embodiments of this application, reference is made to Figure 4 The camera parameter configuration sub-interface 300 includes a recognition area delineation control 301, an exposure parameter adjustment control 302, an image decoding setting control 303, and a camera parameter configuration saving control 304. In step S130, the recognition area is delineated, the exposure parameter is adjusted, and the decoding algorithm parameter is set in the camera parameter configuration sub-interface 300. The process of generating image acquisition control commands based on the inkjet printing basic configuration data includes, but is not limited to, the following steps.

[0047] In step S310, in response to the trigger command of the identification area delineation control 301, a visual area configuration window is displayed, which supports delineating the area of ​​interest by selecting with a rectangle or inputting coordinates, and automatically recommends the range of the area of ​​interest by linking the coordinate parameters in the basic inkjet printing configuration data.

[0048] In step S310, in response to the trigger command of the recognition area delineation control 301, the system displays a visual area configuration window, allowing users to intuitively delineate the Region of Interest (ROI) on real-time or static images by dragging and dropping rectangles with the mouse, or to precisely set the boundary of the region by manually inputting coordinate values. The core function of this step is to narrow the search range of image recognition, focusing on the actual local area where the inkjet code exists, thereby significantly improving image processing efficiency and recognition accuracy, and avoiding the decoding process being mistakenly triggered by background interference, other markings, or tray structures.

[0049] More importantly, the system can automatically recommend matching regions of interest based on the inkjet printing position coordinates configured in step S220, enabling intelligent cross-module collaboration of configuration data. This automatic recommendation mechanism based on prior information not only reduces the risk of errors from manual settings but also significantly improves the efficiency of system deployment and changeover debugging, making it particularly suitable for complex production environments with multiple production lines and frequent product switching.

[0050] In step S320, in response to the trigger command of the exposure parameter adjustment control 302, the parameter adjustment panel is displayed, the exposure parameters are set, and the exposure parameters can be adjusted by the slider or preset level. A preview function is provided to display the grayscale value change of the image after adjustment in real time.

[0051] In step S320, in response to the trigger command of the exposure parameter adjustment control 302, the system pops up a parameter adjustment panel, allowing the user to continuously adjust the parameters using a slider or select a preset exposure level (such as "low light mode," "standard mode," or "strong light suppression mode") to set the camera's exposure parameters. This step is directly related to the quality of image acquisition and is a crucial step in ensuring that the barcode is clear and legible.

[0052] In actual production environments, lighting conditions can fluctuate due to changes in ambient light, reflective materials, and low contrast in inkjet printing colors. Inappropriate exposure can lead to overexposure or underexposure, resulting in blurred barcodes, loss of detail, and reduced recognition success rates. To address this, the system provides a real-time preview function. Users can instantly observe the changing trends of image grayscale values ​​and the clarity of local details while adjusting exposure parameters, achieving precise "what you see is what you get" optimization. This visual and interactive parameter adjustment method significantly lowers the technical barrier to camera debugging, enabling operators to quickly adapt to different working conditions and ensuring the stability and consistency of image acquisition.

[0053] In step S330, in response to the trigger command of the image decoding setting control 303, the algorithm configuration window is displayed, the decoding algorithm parameters corresponding to the barcode type in the basic inkjet printing configuration data are loaded, and the image recognition algorithm parameters are configured.

[0054] In step S330, in response to the trigger command of the image decoding setting control 303, the system opens the algorithm configuration window and automatically loads the decoding algorithm parameters corresponding to the barcode type obtained from the basic inkjet printing configuration data, such as the error correction level, module size, and mask mode for QR codes; and the start character and check digit rules for Code 128. Based on this, the user can further configure image preprocessing algorithm parameters, such as binarization threshold, filtering intensity (denoising), edge enhancement, and tilt correction, to optimize image quality and improve decoding robustness.

[0055] The significance of this step lies in achieving both on-demand decoding and intelligent preprocessing capabilities: on the one hand, the system preloads the optimal decoding strategy based on known barcode types, avoiding blindly scanning all possible barcode types and improving decoding efficiency; on the other hand, through adjustable image processing parameters, the system can enhance processing for common problems such as blurriness, low contrast, and slight distortion, improving its recognition fault tolerance under complex working conditions. This context-based algorithm configuration mechanism makes the image recognition system more intelligent and adaptive.

[0056] In step S340, in response to the trigger command of the camera parameter configuration save control 304, the region of interest, exposure parameters and image recognition algorithm parameters are encapsulated into an image acquisition control command and uploaded to the camera controller.

[0057] In step S340, in response to the trigger command of the camera parameter configuration save control 304, the system encapsulates the currently set region of interest (ROI), exposure parameters and image recognition algorithm parameters into a structured image acquisition control command, and uploads it to the camera controller through industrial communication protocols (such as TCP / IP, Modbus, Profinet, etc.) to complete the final distribution and activation of the configuration.

[0058] This step is the closed-loop execution link in the entire camera configuration process, ensuring that all visual recognition-related parameters are accurately and completely transmitted to the execution device. Through standardized instruction encapsulation and transmission mechanisms, the consistency and coordination between system components are guaranteed, avoiding recognition failures or misidentifications caused by parameter asynchrony. Furthermore, the generation of this instruction is based on the prior inkjet printing configuration data, reflecting the integrated design concept of inkjet printing and recognition, and realizing full-process digital control from information generation to information reading. Once the configuration takes effect, the camera can perform stable and efficient image acquisition and decoding operations according to the set parameters, providing a reliable data foundation for subsequent anomaly diagnosis and self-repair functions.

[0059] In some embodiments of this application, reference is made to Figure 5 The anomaly diagnosis sub-interface 400 includes an anomaly type identification control 401, a focal length calibration control 402, and an anomaly processing completion control 403. In step S140, within the anomaly diagnosis sub-interface 400, based on real-time acquired inkjet image data, anomaly type identification of barcode blurring and barcode offset is performed. Automatic self-repair operations such as camera angle adjustment, focal length calibration, and parameter reset are executed. The process of generating an anomaly processing record and optimization suggestion report includes, but is not limited to, the following steps.

[0060] In step S410, in response to the trigger command of the anomaly type identification control 401, the anomaly diagnosis and analysis window is displayed, the real-time inkjet image data stream is loaded, and the anomaly type and its confidence level are output through the sharpness evaluation algorithm and the coordinate offset calculation algorithm.

[0061] In step S410, in response to the trigger command of the anomaly type identification control 401, the system displays an anomaly diagnosis and analysis window and loads the inkjet image data stream acquired by the industrial camera in real time. In this window, the system uses a preset sharpness evaluation algorithm (such as image sharpness scoring based on the Laplacian operator, frequency domain analysis, etc.) to quantitatively analyze the sharpness of the barcode area in the image and determine whether there is an anomaly of "barcode blur". At the same time, combined with the inkjet position coordinates set in step S220 and the region of interest defined in step S310, the system uses image registration and edge detection technology to calculate the offset between the actual inkjet position and the expected position, thereby identifying anomalies of "barcode offset".

[0062] This process not only outputs the specific anomaly type (fuzzy, offset, or both), but also quantifies the severity of the anomaly through a confidence scoring mechanism (such as fuzziness score, the ratio of offset distance to threshold). The significance of this step lies in achieving intelligent diagnosis and classification of the causes of identification failures, providing a scientific basis for subsequent precise execution of self-repair operations. It avoids the passive situation of traditional systems that can only issue alarms but cannot determine the root cause when an anomaly occurs, and is a key prerequisite for achieving intelligent closed-loop control.

[0063] In step S420, in response to the trigger command of the focus calibration control 402, the lens parameter adjustment window is displayed, and the camera angle adjustment and focus calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focus are adjusted, and three consecutive image acquisitions are performed to verify the repair effect.

[0064] In step S420, in response to the trigger command of the focus calibration control 402, the system pops up a lens parameter adjustment window and automatically performs camera angle adjustment and focus calibration operations through the camera controller. The core function of this step is to realize the "self-healing" capability of the vision system. When step S410 identifies a blurry or offset barcode, the system does not simply prompt for manual intervention, but intelligently decides to adjust the strategy based on the type of anomaly and its confidence level: for example, if it is determined to be slightly blurry, it may only need to fine-tune the lens focus; if it is a serious offset, it may need to coordinate with the servo mechanism to adjust the camera mounting angle or re-align the target area.

[0065] The system sends commands to the motorized zoom lens or gimbal via a control interface to achieve remote and automatic adjustment of physical parameters. Furthermore, after each adjustment, the system automatically performs three consecutive image acquisitions to verify the repair effect in real time—that is, rerunning the sharpness assessment and positional offset calculation to confirm whether the anomaly has been eliminated. This closed-loop mechanism from diagnosis and adjustment to verification effectively enhances the system's autonomous operation and maintenance capabilities, reduces downtime and manual maintenance costs, and ensures that the recognition system maintains optimal working condition in dynamic production environments.

[0066] Step S430: In response to the trigger command of the exception handling completion control 403, based on the exception type and its confidence level, repair measures, lens focal length parameter change log recorded during the exception handling process, and the verification result data of the repair effect, an exception handling record and optimization suggestion report are automatically generated.

[0067] In step S430, in response to the trigger command of the exception handling completion control 403, the system summarizes and structures all key data in the entire exception handling process, and automatically generates a detailed "Exception Handling Record and Optimization Suggestion Report". The report includes, but is not limited to: the original exception type and its confidence score, the conditions for triggering self-repair, the specific repair measures performed (such as "adjusting the focal length from 8.5mm to 9.2mm", "increasing the camera tilt angle by 1.5°"), the log records of parameter changes, and the success rate results of the three verification acquisitions.

[0068] Building upon this foundation, the system can also generate optimization suggestions based on trend analysis of historical anomaly data (such as frequent deviations in a production line), such as "It is recommended to check the stability of the pallet conveyor line" or "It is recommended to perform camera benchmark calibration once a month." The significance of this step lies not only in achieving traceability and auditability of anomalies, meeting the process recording requirements of quality management systems such as GMP and ISO, but more importantly, in transforming each anomaly handling into input for continuous system optimization through data accumulation and intelligent analysis. This drives the upgrade from a passive response to a proactive prevention-oriented operation and maintenance model, comprehensively improving the level of intelligent management of the production line.

[0069] In some embodiments of this application, in step S410, the real-time inkjet image data stream is loaded in the anomaly diagnosis and analysis window, and the anomaly type and its confidence level are output through a sharpness evaluation algorithm and a coordinate offset calculation algorithm. The implementation process includes, but is not limited to, the following steps.

[0070] Step S510: According to the loading instruction of the real-time inkjet image data stream, acquire continuous frame image data from the camera controller and display it in real time in the anomaly diagnosis and analysis window as a dynamic preview box.

[0071] In step S510, according to the loading instruction of the real-time inkjet printing image data stream, the system acquires continuous frame image data from the camera controller and displays these images in real time in the form of a dynamic preview box in the anomaly diagnosis and analysis window. This process provides users with intuitive visual feedback, enabling operators to instantly view the current inkjet printing status on the production line and understand the operating status of the recognition system.

[0072] This real-time monitoring mechanism not only helps to quickly locate and identify potential problems, but also lays the foundation for subsequent automated diagnosis and repair operations. Furthermore, the dynamic display method enhances the user experience, making the entire anomaly handling process more transparent and controllable, and improving the system's operability and maintenance efficiency.

[0073] Step S520: The sharpness assessment algorithm is invoked. After performing grayscale processing on the current frame image, the gradient value of the image is calculated using the Laplacian operator and compared with a preset gradient threshold. If the gradient value of three consecutive frames is less than the preset gradient threshold, it is initially determined that the barcode is blurry, and the barcode blur confidence score is calculated. The barcode blur confidence score is equal to 1 minus the gradient value divided by the preset gradient threshold.

[0074] In step S520, after the sharpness evaluation algorithm is invoked to perform grayscale processing on the current frame image, the Laplacian operator is used to calculate the gradient value of the image, and this gradient value is compared with a preset gradient threshold. If the gradient values ​​of three consecutive frames are all lower than the preset threshold, it is initially determined that the barcode is blurry. At the same time, the system calculates the barcode blur confidence level, which is defined as 1 minus the actual gradient value divided by the preset gradient threshold.

[0075] The significance of this step lies in providing a quantitative method for evaluating barcode sharpness. It detects barcode clarity and legibility by comparing the changing trends of gradient values. Using gradient values ​​from multiple consecutive frames as the basis for judgment effectively avoids misjudgments caused by accidental factors in a single frame, improving the accuracy and reliability of anomaly detection. Sharpness assessment not only helps in the timely detection of barcode blurring issues but also provides decision support for subsequent self-correcting measures such as focus calibration.

[0076] Step S530: Invoke the coordinate offset calculation algorithm to extract the center coordinates of the smallest bounding rectangle of the inkjet printing area in the current frame, compare them with the coordinate parameters in the basic inkjet printing configuration data, calculate the offset value, and compare it with a preset offset threshold. If the offset value is greater than the preset offset threshold, it is determined to be a barcode offset, and the barcode offset confidence score is calculated. The barcode offset confidence score is equal to the preset offset threshold and the ratio of the offset value.

[0077] In step S530, a coordinate offset calculation algorithm is invoked to extract the center coordinates of the smallest bounding rectangle of the inkjet printing area in the current frame, and these coordinates are compared with the inkjet printing position coordinate parameters set in step S220 to calculate the offset value between the two. If the offset value exceeds a preset offset threshold, it is determined to be a barcode offset. Based on this, the system also calculates the barcode offset confidence level, which is equal to the ratio of the preset offset threshold to the actual offset value.

[0078] The key function of this step is to accurately measure and quantify the degree of deviation of the barcode from its expected position, ensuring high-precision positioning even on high-speed production lines. By monitoring the offset value, not only can deviations in the inkjet printing position be quickly detected, but specific adjustment directions and magnitudes can also be provided for camera angle adjustments, thereby ensuring the accuracy of subsequent image acquisition and reducing the risk of recognition failures due to positional errors.

[0079] In step S540, the abnormal area is marked with a red border in the abnormal diagnosis and analysis window, and the gradient value, offset value, abnormal type and its confidence level are displayed.

[0080] In step S540, in the anomaly diagnosis and analysis window, the system uses a red border to mark the detected abnormal areas and displays detailed diagnostic information on the interface, including gradient values, offset values, anomaly types, and their confidence levels. This visual display method not only helps users intuitively understand the current problem but also provides operators with a convenient interactive interface, enabling them to quickly take appropriate measures.

[0081] By clearly marking the specific location of anomalies and related numerical indicators, such as the degree of barcode blurring or offset, the system significantly improves the efficiency of troubleshooting. It also provides necessary data support for generating detailed anomaly handling records and optimization suggestion reports. This intuitive and comprehensive information presentation method is of great significance for improving the system's usability and maintenance efficiency.

[0082] In some embodiments of this application, when the finished product pallet inkjet printing recognition system is running, the anomaly diagnosis and analysis window loads the continuous frame image data stream (frame rate 30fps) transmitted by the camera controller in real time and displays the current inkjet printing area in the dynamic preview box. If the inkjet printing of a pallet is blurred due to lens smudges, the system calls the sharpness evaluation algorithm to perform grayscale processing on the current frame, and calculates the gradient value as 45 using the Laplacian operator (the preset gradient threshold is 65). If the gradient value of three consecutive frames is lower than the threshold, it is initially determined to be "barcode blurry", with a blur confidence level of 1-45 / 65≈0.31 (31%). At the same time, the coordinate offset calculation algorithm extracts the center coordinates of the minimum bounding rectangle of the inkjet printing area (X=125.3mm, Y=88.6mm) and compares them with the standard coordinates (X=124.0mm, Y=88.0mm) in the basic inkjet printing configuration data to calculate the offset value. =1.3mm =0.6mm, because >A preset offset threshold of 0.8mm is determined to be "barcode offset," with an offset confidence level of 0.8mm / 1.3mm ≈ 0.62 (62%). At this point, the anomaly diagnosis analysis window marks the inkjet area with a red border and displays in real time: gradient value 45; offset value: =1.3mm, =0.6mm; Anomaly type: Barcode blurry (31%) + Barcode offset (62%)”, for operators to confirm the cause of the anomaly.

[0083] In some embodiments of this application, in step S420, in the lens parameter adjustment window, the camera angle adjustment and focal length calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focal length are adjusted, and the process of performing three consecutive image acquisitions to verify the repair effect includes, but is not limited to, the following steps.

[0084] Step S610: Obtain the anomaly type and its confidence level from the anomaly diagnosis and analysis window, and enable the priority determination mechanism in the lens parameter adjustment window, specifically including: If the confidence level of barcode ambiguity is greater than the confidence level of barcode offset, then the camera controller will first perform focus calibration and then perform camera angle compensation.

[0085] If the barcode offset confidence level is greater than the barcode blur confidence level, the camera angle adjustment is performed first by the camera controller, and then the focal length compensation is performed.

[0086] In step S610, after obtaining the anomaly type and its confidence level from the anomaly diagnosis and analysis window, the system activates a priority determination mechanism in the lens parameter adjustment window to decide whether to perform focus calibration or camera angle adjustment first. Specifically, if the confidence level of barcode blurriness is higher than that of barcode offset, focus calibration is performed first, followed by camera angle compensation as needed; conversely, if the confidence level of barcode offset is higher, camera angle adjustment is performed first, followed by focus compensation. This intelligent priority determination mechanism based on anomaly type can provide the most direct and effective solutions for the most significant problems, ensuring that the repair measures are targeted and efficient. In this way, the system can not only quickly locate and solve the main problems, but also gradually optimize other secondary problems, thereby maximizing image quality and recognition success rate.

[0087] Step S620: Perform 3 consecutive image acquisitions and verify the repair effect by combining the preset gradient threshold and preset offset threshold. Specifically: if at least 2 of the 3 acquisition results meet all the above conditions, the repair is determined to be successful; otherwise, the repair is determined to be unsuccessful, triggering the priority reversal retry mechanism, swapping the adjustment order of focal length calibration and camera angle, and readjusting the camera angle and lens focal length. After accumulating 2 repair failures, a pop-up window prompts "Please check the mechanical structure stability of the camera".

[0088] In step S620, after determining the priority processing order, the system performs three consecutive image acquisitions and verifies the repair effect by combining preset gradient thresholds (for evaluating sharpness) and offset thresholds (for evaluating positional accuracy). Specifically, if at least two of the three acquisitions simultaneously meet the standards for both sharpness and positional accuracy, the repair is considered successful; otherwise, the system determines the repair as a failure and triggers a priority reversal retry mechanism, that is, swapping the previously set order of focus calibration and camera angle adjustment and retrying the adjustment. Furthermore, if two repair attempts fail to achieve the expected results, the system will display a prompt suggesting "Please check the mechanical stability of the camera."

[0089] The purpose of this step is to ensure the effectiveness and reliability of the repair measures. By rigorously verifying the repair results, it avoids repeated debugging caused by single adjustments failing to completely resolve the problem. It also alerts users to potential hardware-level issues, further ensuring stable system operation. This method not only improves the scientific rigor and accuracy of the repair process but also provides clear direction for subsequent maintenance.

[0090] In some embodiments of this application, during the self-repair process of barcode blurring and offset anomalies, the system performs three consecutive image acquisitions (800ms interval / acquisition) to verify the repair effect: assuming a preset gradient threshold of 65 (clarity meets the standard) and an offset threshold of 0.8mm (positioning meets the standard). After the first repair, the results of the three acquisitions are as follows: First acquisition: gradient value 72 (>65), offset value 0.6mm (<0.8mm), meeting the standard; Second acquisition: gradient value 58 (<65), offset value 0.5mm (<0.8mm), not meeting the standard; Third acquisition: gradient value 70 (>65), offset value 0.7mm (<0.8mm), meeting the standard. Since two out of the three acquisitions meet all conditions, the repair is deemed successful. If only one out of three acquisitions after the initial repair meets the standard (e.g., gradient values ​​of 55 / 60 / 58, offset values ​​of 0.9 / 1.0 / 0.8mm), the priority reversal retry mechanism is triggered. The camera angle is adjusted first, then the focal length is compensated, and three more acquisitions are performed. If only one acquisition meets the standard after the retry, the total number of repair failures is two, and the system will display a pop-up message: "Please check the mechanical stability of the camera."

[0091] In some embodiments of this application, the implementation process of step S610, in which the camera controller first performs focal length calibration and then performs camera angle compensation, includes, but is not limited to, the following steps.

[0092] Step S710: Based on the barcode fuzziness confidence level and gradient value, initiate the focal length graded search mechanism in the lens parameter adjustment window, specifically including: (1) In the coarse adjustment stage, bidirectional search is performed within the preset focal length range with a step size of 0.3mm. Images are acquired and real-time gradient values ​​are calculated at each step. When the increase in real-time gradient values ​​for three consecutive steps is less than the preset gradient threshold, the fine adjustment stage is entered.

[0093] (2) In the fine-tuning stage, switch to a step size of 0.1mm and perform a one-way search within ±0.5mm of the gradient value peak in the coarse-tuning stage to lock the position of the maximum gradient value as the optimal focal length.

[0094] In step S710, based on the barcode fuzziness confidence level and gradient value, a focal length hierarchical search mechanism is initiated in the lens parameter adjustment window. This process is divided into two stages: coarse adjustment and fine adjustment. In the coarse adjustment stage, the system performs a bidirectional search within a preset focal length range with a step size of 0.3mm. Each time the focal length is adjusted, an image is acquired and its real-time gradient value is calculated. When the increase in the real-time gradient value after three consecutive adjustments is less than a preset gradient threshold, it indicates that the optimal focal length range is approaching, and the system enters the fine adjustment stage. In the fine adjustment stage, the system reduces the step size to 0.1mm and performs a unidirectional search within ±0.5mm of the gradient value peak found in the coarse adjustment stage, ultimately locking the position with the largest gradient value as the optimal focal length.

[0095] The significance of this step lies in ensuring that the focal length setting that maximizes image clarity is found through gradual and refined focal length adjustments, thereby effectively solving the problem of blurry barcodes caused by unsuitable focal length and improving recognition accuracy.

[0096] Step S720: Lock the camera's optimal focal length and perform camera angle compensation, specifically including: (1) Calculate the angle compensation amount based on the offset value and generate the angle adjustment parameters.

[0097] (2) According to the angle adjustment parameters, control the gimbal motor to perform angle fine adjustment in steps of 0.02°. Acquire an image and calculate the real-time offset value every 5 steps. Stop camera angle compensation when the real-time offset value is less than the preset offset threshold and lock the corresponding camera angle parameters.

[0098] In step S720, after locking the camera's optimal focal length, the system begins performing camera angle compensation. First, based on the previously calculated offset value, the system calculates the required angle compensation amount and generates the corresponding angle adjustment parameters. Next, the gimbal motor is controlled to fine-tune the angle in small steps of 0.02°. An image is acquired and the real-time offset value is recalculated every five fine-tuning operations. During this process, once the real-time offset value falls below a preset offset threshold, the camera angle is considered to be properly adjusted, further adjustments are stopped, and the current angle parameters are locked to the final set value.

[0099] The importance of this step lies in precisely correcting camera angle errors caused by installation deviations or environmental changes, ensuring that the coding area is centered in the image, and reducing the risk of recognition failure due to positional offset. This meticulous angle adjustment method not only improves the quality of image acquisition but also enhances the system's stability and adaptability, helping to maintain a consistently high recognition rate over the long term.

[0100] In some embodiments of this application, when the system detects that the barcode blur confidence level (70%) is higher than the offset confidence level (25%), focus calibration is initiated first: the preset focus range is a reference value (20mm) ± 1.5mm, and the gradient threshold is 50. In the coarse adjustment stage, a bidirectional search is performed starting from 18.5mm in 0.3mm steps. The gradient values ​​are 32 in the first step (18.8mm), 39 in the second step (19.1mm), 45 in the third step (19.4mm), and 48 in the fourth step (19.7mm). At this point, the gradient value increases for the three consecutive steps (7, 6, 3) are all less than the threshold of 50, and the system enters the fine adjustment stage. In the fine adjustment stage, a unidirectional search is performed in the range of 19.2mm-20.2mm in 0.1mm steps. The gradient value reaches 52 (peak) at 19.6mm, locking in the optimal focus length of 19.6mm. Angle compensation is then performed: the current offset value... =1.5mm, the calculated angle compensation is 1.2°, the gimbal motor is controlled to make fine adjustments in 0.02° steps, and an image is acquired every 5 adjustments (cumulative 0.1°), the offset value of the first (0.1°) is recorded. The offset value is 1.3 mm, the second offset (0.2°). The offset value is 1.0 mm, the third time (0.3°). It is 0.7mm (at this time) <Offset threshold 0.8mm), stop compensation and lock camera angle parameters to 0.3°.

[0101] In some embodiments of this application, the implementation process of step S610, in which the camera controller first performs camera angle adjustment and then performs focal length compensation, includes but is not limited to the following steps.

[0102] Step S810: Based on the barcode offset confidence level and offset value, activate the angle grading adjustment mechanism in the lens parameter adjustment window, specifically including: (1) In the coarse adjustment stage, the initial angle compensation amount is calculated based on the offset value, and the gimbal motor is controlled to perform camera angle adjustment in steps of 0.1°. An image is acquired and the real-time offset value is calculated every 3 adjustment steps. When the real-time offset value is less than the preset coarse adjustment offset threshold, the fine adjustment stage is entered.

[0103] (2) During the fine-tuning stage, switch to a step size of 0.02° to perform camera angle adjustment. Acquire an image and calculate the real-time offset value for each adjustment step. When the real-time offset value is less than the preset fine-tuning offset threshold, record the angle adjustment amount and record the current camera angle parameter as the optimal angle.

[0104] In step S810, based on the barcode offset confidence level and the actual offset value, the system initiates an angle-level adjustment mechanism in the lens parameter adjustment window to prioritize resolving the barcode position offset issue. This process is divided into two stages: coarse adjustment and fine adjustment. In the coarse adjustment stage, the system calculates the initial angle compensation amount based on the current offset value and controls the gimbal motor to perform rapid angle adjustments in 0.1° steps. An image is captured after every three adjustment steps, and the offset value of the inkjet printing area is calculated in real time to determine whether it is close to the target position. When the real-time offset value drops below the preset "coarse adjustment offset threshold," it indicates that the camera view is basically aligned with the inkjet printing area, and the system then enters the fine adjustment stage. In the fine adjustment stage, the adjustment step size is further reduced to 0.02°, achieving micron-level precision angle fine-tuning. An image is captured and the offset is evaluated after each adjustment step until the offset value is less than the more stringent "fine adjustment offset threshold." At this point, the system records the final angle adjustment amount and locks the current gimbal angle as the "optimal angle."

[0105] The significance of this step lies in its ability to efficiently and accurately correct barcode position offset issues caused by camera installation deviations, vibrations, or tray positioning fluctuations through a phased, coarse-to-fine dynamic adjustment strategy, laying a geometric alignment foundation for subsequent high-quality image acquisition.

[0106] Step S820: Lock the camera's optimal angle and perform focus compensation, specifically including: (1) Calculate the focal length compensation coefficient based on the angle adjustment amount and determine the focal length compensation range.

[0107] (2) Perform focal length search in steps of 0.1 mm within the focal length compensation range, and synchronously collect the gradient value of the image. When the gradient value exceeds the preset gradient threshold, stop focal length compensation and lock the corresponding focal length.

[0108] In step S820, after successfully locking the optimal camera angle, the system enters the focal length compensation stage to further optimize image sharpness. Since changes in camera angle can affect imaging distance and focus, a "focal length compensation coefficient" needs to be dynamically calculated based on the angle adjustment amount from the previous step. This coefficient determines a reasonable range for the next round of focal length search, avoiding blindly traversing the entire focal length interval and improving adjustment efficiency. Subsequently, the system performs a fine-grained focal length search within this compensation range in 0.1mm increments. An image is acquired after each focal length adjustment, and its gradient value is calculated using algorithms such as the Laplacian operator to evaluate sharpness. Once the gradient value exceeds a preset sharpness threshold (i.e., reaches a identifiable sharpness standard), the system immediately stops the search and locks the current focal length parameter as the optimal focal length.

[0109] This process embodies the intelligent repair logic of "geometric alignment first, then optical focusing," ensuring that image clarity is further restored after addressing positional misalignment. Through a linkage compensation mechanism between angle and focal length, the system can achieve highly robust adaptive optimization under complex operating conditions, significantly improving the recognition success rate, reducing the need for manual intervention, and ensuring continuous and stable operation of the production line.

[0110] In some embodiments of this application, when the system detects that the barcode offset confidence level (75%) is higher than the fuzziness confidence level (20%), it prioritizes initiating angle-level adjustment: initial offset value =2.0mm, calculate the initial angle compensation of 1.5°, and perform adjustments in 0.1° steps during the coarse adjustment stage, acquiring images every 3 steps (cumulative 0.3°). The offset value for the first adjustment (0.3°) is... The offset value is 1.7 mm, the second offset (0.6°). The offset value is 1.4 mm, the third time (0.9°). It is 1.0mm (at this time) <Coarse adjustment offset threshold 1.2mm), enter the fine adjustment stage; in the fine adjustment stage, switch to 0.02° step size, acquire images for each adjustment step, and record the offset value at 1.0° (cumulative adjustment 0.1°). Offset value at 0.8mm and 1.02° It is 0.6mm (at this time) <Fine-tuning offset threshold 0.7mm), record the angle adjustment amount of 1.02°, and lock the optimal angle parameters. Then perform focal length compensation: based on the angle adjustment amount of 1.02°, calculate the focal length compensation coefficient of 1.12, determine the compensation range as the reference focal length (18mm) ±0.8mm, search with a step size of 0.1mm, the gradient value at 18.5mm reaches 72 (exceeding the gradient threshold of 65), stop compensation and lock the focal length at 18.5mm.

[0111] In some embodiments of this application, the finished product pallet inkjet printing recognition interaction method introduces an intelligent error-proof detection mechanism, further enhancing the system's reliability and anomaly handling efficiency. This mechanism can automatically identify problems such as missing labels or wrinkled labels that prevent barcode reading. Specifically, when the system detects that a barcode cannot be correctly recognized, it immediately triggers a real-time snapshot function to capture a photo of the current anomaly as a basis for subsequent analysis and processing. At the same time, the system will activate the production line alarm lights to issue a warning signal, quickly attracting the attention of on-site personnel.

[0112] This end-to-end "detection-alarm-handling-closed-loop" management model goes beyond automated detection and alarm processes at the technical level. It also includes manual verification to ensure that every anomaly is properly handled. For example, upon receiving an alarm, operators can quickly locate the problem based on the anomaly image provided by the system and take appropriate corrective measures, such as relabeling or adjusting label positions. After processing, staff can provide feedback on the results through the system interface, forming a closed-loop management system that ensures all anomalies are recorded and effectively resolved. This not only improves the accuracy of label identification on the production line and reduces production interruptions due to label issues, but also significantly improves overall work efficiency through a timely and effective anomaly handling process. Furthermore, the system records each anomaly and its handling process in detail, providing data support for subsequent quality traceability and continuous improvement, which helps to continuously optimize production processes and improve management levels.

[0113] In summary, the finished product pallet inkjet coding recognition interactive method proposed in this application has the following technical effects.

[0114] This method integrates functions such as coding configuration, image acquisition configuration, and anomaly handling through a unified main interface, greatly improving user experience and operational efficiency. Users can intuitively select barcode types, set coding positions, and edit printing content templates. By precisely adjusting the camera's recognition area, exposure parameters, and decoding algorithm parameters, the system ensures image acquisition quality and recognition success rate. The system possesses real-time monitoring and intelligent diagnostic capabilities, quickly identifying and quantifying anomalies such as barcode blurring or misalignment, providing scientific evidence for subsequent self-repair mechanisms. This mechanism automatically decides whether to prioritize focus calibration or camera angle adjustment and verifies the repair effect through multiple consecutive image acquisitions, reducing downtime and manual maintenance costs.

[0115] Secondly, refer to Figure 6 This application provides a finished product pallet inkjet printing recognition interactive system, including a main interface module 910, an inkjet printing configuration module 920, an image acquisition configuration module 930, and an inkjet printing recognition anomaly handling module 940.

[0116] The main interface module 910 is used to display the main interface for the finished product pallet inkjet printing recognition interaction. The main interface includes inkjet printing configuration controls, image acquisition configuration controls, and inkjet printing recognition exception handling controls.

[0117] The inkjet printing configuration module 920 is used to respond to the trigger command of the inkjet printing configuration control, display the inkjet printing parameter configuration sub-interface, select the tray barcode type, set the inkjet printing position coordinates, and edit the inkjet printing content template to generate basic inkjet printing configuration data.

[0118] The image acquisition configuration module 930 is used to respond to the trigger command of the image acquisition configuration control, display the camera parameter configuration sub-interface, perform recognition area delineation, exposure time adjustment and decoding algorithm parameter setting, and generate image acquisition control commands based on the inkjet printing basic configuration data.

[0119] The inkjet printing recognition anomaly handling module 940 is used to respond to the trigger command of the inkjet printing recognition anomaly handling control, display the anomaly diagnosis sub-interface, perform anomaly type identification of barcode blur and barcode offset based on real-time acquired inkjet image data, automatically perform self-repair operations such as camera angle adjustment, focus calibration and parameter reset, and generate anomaly handling records and optimization suggestion reports.

[0120] Furthermore, embodiments of this application provide a computer-readable storage medium storing a processor-executable program, which, when executed by a processor, is used to implement the aforementioned finished product pallet inkjet printing recognition interaction method.

[0121] Similarly, the systems and media provided in this application have the same technical effects as those in the above method embodiments, and will not be described again here.

[0122] In some alternative embodiments, the functions / operations mentioned in the block diagrams may not occur in the order shown in the operation diagrams. For example, depending on the functions / operations involved, two consecutively shown blocks may actually be executed substantially simultaneously, or the blocks may sometimes be executed in reverse order. Furthermore, the embodiments presented and described in the flowcharts of this application are provided by way of example to provide a more comprehensive understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed and sub-operations described as part of a larger operation are executed independently.

[0123] Furthermore, although this application is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding this application. Rather, given the properties, functions, and internal relationships of the various functional modules in the apparatus disclosed herein, the actual implementation of the module will be understood within the scope of ordinary skill of an engineer. Therefore, those skilled in the art can implement the application set forth in the claims using ordinary skill. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of this application, which is determined by the full scope of the appended claims and their equivalents.

[0124] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several programs to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable programs for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can retrieve and execute a program from or in conjunction with such a program execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit a program for use by or in conjunction with a program execution system, apparatus, or device.

[0126] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or, if necessary, processing in a suitable manner, and then stored in computer memory.

[0127] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable program execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0128] In the foregoing description of this specification, the reference to terms such as "one embodiment / implementation," "another embodiment / implementation," or "certain embodiments / implementations," etc., indicates that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in an embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0129] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

[0130] The above is a detailed description of the preferred embodiments of the present invention. However, the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention. All such equivalent modifications or substitutions are included within the scope defined by the claims of the present invention.

Claims

1. A method for interactive recognition of finished product pallet inkjet printing, characterized in that, Includes the following steps: The main interface for displaying the inkjet printing recognition of finished product pallets includes inkjet printing configuration controls, image acquisition configuration controls, and inkjet printing recognition exception handling controls. In response to the trigger command of the inkjet printing configuration control, the inkjet printing parameter configuration sub-interface is displayed, allowing for selection of the pallet barcode type, setting of inkjet printing position coordinates, and editing of the printing content template to generate basic inkjet printing configuration data; In response to the trigger command of the image acquisition configuration control, the camera parameter configuration sub-interface is displayed to perform recognition area delineation, exposure parameter adjustment and decoding algorithm parameter setting, and image acquisition control command is generated based on the inkjet printing basic configuration data; In response to the trigger command of the inkjet printing recognition anomaly handling control, the anomaly diagnosis sub-interface is displayed. Based on the real-time collected inkjet image data, the anomaly types of barcode blurring and barcode offset are identified. The self-repair operation of camera angle adjustment, focus calibration and parameter reset is automatically performed, and anomaly handling record and optimization suggestion report are generated.

2. The finished product pallet inkjet printing recognition interactive method according to claim 1, characterized in that, The inkjet printing parameter configuration sub-interface includes a tray barcode type selection control, an inkjet printing position coordinate setting control, an inkjet printing content template editing control, and an inkjet printing configuration saving control. In the inkjet printing parameter configuration sub-interface, select the pallet barcode type, set the inkjet printing position coordinates, and edit the print content template to generate basic inkjet printing configuration data, including the following steps: In response to the trigger command of the tray barcode type selection control, a barcode type selection window is displayed, providing multiple preset barcode type options, or special encoding rules can be configured through a custom format input box, and the corresponding decoding algorithm parameters are automatically loaded after selection; In response to the trigger command of the inkjet printing position coordinate setting control, a visual coordinate configuration window is displayed, which supports the input of coordinate parameters by dragging with the mouse or by entering them in the input box, and provides a preview function to display the relative relationship between the simulated inkjet printing position and the pallet boundary in real time; In response to a trigger command on the print content template editing control, the template editor interface is displayed to edit the print content template, supporting the insertion of dynamic variables and static text; In response to the trigger command of the inkjet configuration save control, the selected barcode type, the coordinate parameters and the inkjet content template data are encapsulated into inkjet basic configuration data, uploaded to the inkjet controller and the image recognition system, and the decoding algorithm parameters are uploaded to the camera controller.

3. The finished product pallet inkjet printing recognition interactive method according to claim 1, characterized in that, The camera parameter configuration sub-interface includes a recognition area delineation control, an exposure parameter adjustment control, an image decoding setting control, and a camera parameter configuration saving control; In the camera parameter configuration sub-interface, the recognition area is defined, exposure parameters are adjusted, and decoding algorithm parameters are set. Based on the basic inkjet printing configuration data, image acquisition control commands are generated, including the following steps: In response to the trigger command of the identification area delineation control, a visual area configuration window is displayed, which supports delineating the area of ​​interest by selecting with a rectangle or inputting coordinates, and automatically recommends the range of the area of ​​interest in conjunction with the coordinate parameters in the basic inkjet printing configuration data; In response to the trigger command of the exposure parameter adjustment control, the parameter adjustment panel is displayed, the exposure parameters are set, the exposure parameters can be adjusted by the slider or preset level, and a preview function is provided to display the grayscale value change of the image after adjustment in real time. In response to the trigger command of the image decoding setting control, the algorithm configuration window is displayed, the decoding algorithm parameters corresponding to the barcode type in the inkjet printing basic configuration data are loaded, and the image recognition algorithm parameters are configured. In response to a trigger command to save the camera parameter configuration, the region of interest, the exposure parameters, and the image recognition algorithm parameters are encapsulated into an image acquisition control command and uploaded to the camera controller.

4. The finished product pallet inkjet printing recognition interactive method according to claim 1, characterized in that, The anomaly diagnosis sub-interface includes an anomaly type identification control, a focal length calibration control, and an anomaly processing completion control. In the anomaly diagnosis sub-interface, based on the real-time acquired inkjet image data, the system identifies anomalies such as barcode blurring and barcode offset, automatically performs self-repair operations including camera angle adjustment, focus calibration, and parameter reset, and generates anomaly handling records and optimization suggestion reports, including the following steps: In response to the trigger command of the anomaly type identification control, the anomaly diagnosis and analysis window is displayed, the real-time inkjet image data stream is loaded, and the anomaly type and its confidence level are output through the sharpness evaluation algorithm and the coordinate offset calculation algorithm. In response to the trigger command of the focal length calibration control, the lens parameter adjustment window is displayed, and the camera angle adjustment and focal length calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focal length are adjusted, and three consecutive image acquisitions are performed to verify the repair effect. In response to the trigger command of the exception handling completion control, the exception handling record and optimization suggestion report are automatically generated based on the exception type and its confidence level, repair measures, lens focal length parameter change log recorded during the exception handling process, and the verification result data of the repair effect.

5. The finished product pallet inkjet printing recognition interactive method according to claim 4, characterized in that, In the anomaly diagnosis and analysis window, the real-time inkjet image data stream is loaded, and the anomaly type and its confidence level are output through a sharpness assessment algorithm and a coordinate offset calculation algorithm, including the following steps: According to the loading instruction of the real-time inkjet image data stream, continuous frame image data is obtained from the camera controller and displayed in real time in the anomaly diagnosis and analysis window as a dynamic preview box; The sharpness evaluation algorithm is invoked to perform grayscale processing on the current frame image. The gradient value of the image is calculated by the Laplacian operator and compared with a preset gradient threshold. If the gradient value of 3 consecutive frames is less than the preset gradient threshold, it is initially determined that the barcode is blurry, and the barcode blur confidence score is calculated. The value of the barcode blur confidence score is equal to 1 minus the gradient value divided by the preset gradient threshold. The coordinate offset calculation algorithm is invoked to extract the center coordinates of the smallest bounding rectangle of the inkjet area in the current frame, compare them with the coordinate parameters in the basic inkjet configuration data, calculate the offset value, and compare it with a preset offset threshold. If the offset value is greater than the preset offset threshold, it is determined to be a barcode offset, and the barcode offset confidence score is calculated. The barcode offset confidence score is equal to the ratio of the preset offset threshold to the offset value. The abnormal area is marked with a red border in the anomaly diagnosis and analysis window, and the gradient value, the offset value, the anomaly type and its confidence level are displayed.

6. The finished product pallet inkjet printing recognition interactive method according to claim 4, characterized in that, In the lens parameter adjustment window, the camera angle adjustment and focal length calibration are automatically performed by the camera controller. Based on the anomaly type and its confidence level, the camera angle and lens focal length are adjusted, and three consecutive image acquisitions are performed to verify the repair effect, including the following steps: The anomaly type and its confidence level are obtained from the anomaly diagnosis and analysis window, and a priority determination mechanism is enabled in the lens parameter adjustment window, specifically including: If the barcode fuzziness confidence level is greater than the barcode offset confidence level, then the camera controller will first perform focus calibration and then perform camera angle compensation. If the barcode offset confidence level is greater than the barcode fuzziness confidence level, then the camera controller will first perform camera angle adjustment and then perform focus compensation. Perform three consecutive image acquisitions and verify the repair effect by combining preset gradient thresholds and preset offset thresholds. If at least two of the three acquisitions meet all the above conditions, the repair is considered successful; otherwise, the repair is considered unsuccessful, triggering a priority reversal retry mechanism, swapping the order of focal length calibration and camera angle adjustment, and readjusting the camera angle and lens focal length. After two cumulative unsuccessful repair attempts, a pop-up message will appear: "Please check the mechanical structure stability of the camera." 7. The finished product pallet inkjet printing recognition interactive method according to claim 6, characterized in that, The process of prioritizing focus calibration and then performing camera angle compensation via the camera controller includes the following steps: Based on the barcode fuzziness confidence level and the gradient value, a focal length graded search mechanism is initiated in the lens parameter adjustment window, specifically including: In the coarse adjustment stage, a bidirectional search is performed within the preset focal length range with a step size of 0.3mm. At each step, an image is acquired and the real-time gradient value is calculated. When the increase in the real-time gradient value for three consecutive steps is less than the preset gradient threshold, the fine adjustment stage begins. During the fine-tuning phase, the step size is switched to 0.1mm. A one-way search is performed within ±0.5mm of the gradient value peak in the coarse-tuning phase to lock the position of the maximum gradient value as the optimal focal length. Lock the camera's optimal focal length and perform camera angle compensation, specifically including: Based on the offset value, the angle compensation amount is calculated, and the angle adjustment parameters are generated; According to the angle adjustment parameters, the gimbal motor is controlled to perform angle fine-tuning in steps of 0.02°. An image is acquired and a real-time offset value is calculated every 5 adjustment steps. When the real-time offset value is less than the preset offset threshold, camera angle compensation is stopped and the corresponding camera angle parameters are locked.

8. The finished product pallet inkjet printing recognition interactive method according to claim 6, characterized in that, The step of prioritizing camera angle adjustment and then performing focus compensation via the camera controller includes the following steps: Based on the barcode offset confidence level and the offset value, an angle-grading adjustment mechanism is initiated in the lens parameter adjustment window, specifically including: In the coarse adjustment stage, the initial angle compensation amount is calculated based on the offset value, and the gimbal motor is controlled to perform camera angle adjustment in 0.1° steps. An image is acquired and the real-time offset value is calculated every 3 adjustment steps. When the real-time offset value is less than the preset coarse adjustment offset threshold, the fine adjustment stage is entered. During the fine-tuning phase, the camera angle is adjusted in steps of 0.02°. An image is captured and the real-time offset value is calculated for each adjustment step. When the real-time offset value is less than the preset fine-tuning offset threshold, the angle adjustment amount is recorded, and the current camera angle parameters are recorded as the optimal angle. Lock the camera at its optimal angle and perform focus compensation, specifically including: Based on the angle adjustment amount, calculate the focal length compensation coefficient and determine the focal length compensation range; Within the focal length compensation range, focal length search is performed in steps of 0.1 mm, and the gradient value of the image is acquired synchronously. When the gradient value exceeds the preset gradient threshold, focal length compensation is stopped and the corresponding focal length is locked.

9. A finished product pallet inkjet printing and recognition interactive system, characterized in that, It includes a main interface module, a coding configuration module, an image acquisition configuration module, and a coding recognition anomaly handling module; The main interface module is used to display the main interface for interactive inkjet coding recognition of finished product pallets; wherein, the main interface includes inkjet coding configuration controls, image acquisition configuration controls, and inkjet coding recognition exception handling controls; The inkjet printing configuration module is used to respond to the trigger command of the inkjet printing configuration control, display the inkjet printing parameter configuration sub-interface, select the pallet barcode type, set the inkjet printing position coordinates and edit the inkjet printing content template to generate basic inkjet printing configuration data; The image acquisition configuration module is used to respond to the trigger command of the image acquisition configuration control, display the camera parameter configuration sub-interface, perform recognition area delineation, exposure parameter adjustment and decoding algorithm parameter setting, and generate image acquisition control command based on the inkjet printing basic configuration data; The inkjet printing recognition anomaly handling module is used to respond to the trigger command of the inkjet printing recognition anomaly handling control, display an anomaly diagnosis sub-interface, identify the anomaly type of barcode blurring and barcode offset based on the real-time collected inkjet image data, automatically perform self-repair operations such as camera angle adjustment, focus calibration and parameter reset, and generate anomaly handling records and optimization suggestion reports.

10. A computer-readable storage medium storing a processor-executable program, characterized in that, The processor-executable program, when executed by the processor, is used to implement the finished product pallet inkjet printing recognition interactive method as described in any one of claims 1 to 8.

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