Control method, device and storage medium for a glue spraying system

Abstract

The application discloses a control method and device of a glue spraying system and a storage medium, relates to the field of automatic spraying technology, and comprises the following steps: acquiring a distance parameter of a spraying head from a workpiece surface at the end of an nth time step and spraying area image data of the nth time step, generating workpiece curvature features and spraying state features; substituting the spraying state features, the workpiece curvature features and a material type of a corresponding area of an (n+1)th time step into a compensation algorithm to generate a difference parameter value; and generating and executing a target control parameter of the (n+1)th time step according to the difference parameter value and an initial control parameter. The application overcomes the technical problem of uneven coating thickness by monitoring the changes of the workpiece surface in real time and automatically adjusting the spraying parameters according to the changes, and improves the quality and efficiency of spraying.

Description

Technical Field

[0001] This application relates to the field of automated spraying technology, and more particularly to a control method, device and storage medium for a glue spraying system. Background Technology

[0002] In various precision manufacturing fields, the spot spraying mode of adhesive spraying systems is a key technology for achieving precise local adhesive application. The quality of the resulting adhesive layer directly affects the connection strength, sealing performance, and service life of the workpiece. Related technologies require manual static pre-setting of spraying parameters before executing the dynamic spraying process. However, the operating conditions are prone to fluctuations during spraying, forcing operators to frequently adjust parameters based on experience, resulting in uneven coating thickness.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main objective of this application is to provide a control method, device, and storage medium for a spray adhesive system, which aims to solve the technical problem of uneven coating thickness.

[0005] To achieve the above objectives, this application proposes a method for controlling a glue spraying system, the method comprising: Obtain the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, as well as the spraying area image data at the nth time step, and generate workpiece curvature features and spraying state features. Substitute the spraying state characteristics, the workpiece curvature characteristics, and the material type of the region corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values; The target control parameters for the (n+1)th time step are generated and executed based on the difference parameter values ​​and the initial control parameters.

[0006] In one embodiment, at the end of the nth time step, the distance parameter between the nozzle and the workpiece surface and the image data of the spraying area corresponding to the nth time step are collected. The distance parameter is extracted to generate the workpiece curvature feature that characterizes the surface morphology of the workpiece. The spraying area image data is processed by image analysis to generate the spraying state features that reflect the state of the spraying process.

[0007] In one embodiment, the data type is classified and analyzed to determine the numerical set of the features, namely the spraying state features, the workpiece curvature features, and the material type of the region corresponding to the nth time step; The numerical set is compared item by item with the corresponding threshold parameters in the database, and the parameter difference is calculated. Based on the compensation algorithm, the parameter difference is compensated and adjusted, and the difference parameter value corresponding to the (n+1)th time step is generated.

[0008] In one embodiment, the curvature threshold, glue distribution uniformity threshold, and newly added glued area deviation threshold are retrieved from the threshold parameters. The numerical set is compared with the curvature threshold, the glue distribution uniformity threshold, and the newly added glued area deviation threshold one by one, and the comparison result data is output. Based on the comparison results, the difference between each parameter in the numerical set and the corresponding threshold parameter is calculated to generate parameter difference values.

[0009] In one embodiment, the difference parameter value corresponding to the (n+1)th time step is associated and matched with the initial control parameter according to the parameter type to determine the operation correspondence; Based on the operational correspondence, the parameter fusion logic corresponding to the difference parameter value and the initial control parameter is obtained by filtering. Based on the parameter fusion logic, the correlated difference parameter values ​​are superimposed with the initial control parameters to generate the target control parameters for the (n+1)th time step.

[0010] In one embodiment, in response to the start command at the first time step, the nozzle is controlled to reset to the initial spraying position, and a mode selection command is triggered at the initial spraying position; Based on the mode selection instruction, load the initial parameter input and generate a parameter confirmation instruction; Based on the parameter confirmation command, the nozzle is driven to perform the spraying action according to the set initial parameters.

[0011] In one embodiment, if the (n+1)th time step is the cutoff time step, the nozzle is controlled to run to the end of the spraying path; The nozzle is controlled to stop at the end of the spraying path, and the complete coating is acquired, generating coating inspection data; The coating test data is compared with the preset coating quality requirements to generate coating test results.

[0012] In one embodiment, if the coating inspection result is unqualified, then the unqualified items are extracted from the coating inspection result; Based on the control parameters corresponding to the nonconformities, determine the types of defect parameters that need to be adjusted; Based on the deviation magnitude corresponding to the defect parameter type and the spraying path, a coating compensation strategy is generated through the compensation algorithm.

[0013] In addition, to achieve the above objectives, this application also proposes a glue spraying device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the glue spraying system as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the control method of the glue spraying system as described above.

[0015] This application provides a control method for a glue spraying system, including acquiring the distance parameter between the nozzle and the workpiece surface, as well as image data of the spraying area within that time step, at the end of the nth time step. Based on the distance parameter, a workpiece curvature feature reflecting the undulations of the workpiece surface is generated through feature extraction logic; relying on the image data, a spraying state feature containing information such as glue line shape and coverage area is generated through image analysis. Next, the above-mentioned spraying state feature, workpiece curvature feature, and material type of the corresponding area at the (n+1)th time step are input into a compensation algorithm. The algorithm calculates a difference parameter value based on the correlation between the three, which quantifies the deviation between the current state and the ideal state. Finally, using the difference parameter value as a correction basis, combined with the initial control parameters, a target control parameter for the (n+1)th time step is generated through parameter fusion logic and transmitted to the actuator for execution, realizing dynamic adaptation and adjustment of the spraying process. This application overcomes the technical problems of poor coating adaptability caused by the lag in real-time perception of workpiece status and lack of data support for parameter adjustment in traditional spraying processes, as well as the problems of excessive manual intervention and insufficient precision. It realizes the full-process automated control from data acquisition and status analysis to dynamic parameter optimization and quality closed-loop confirmation. It accurately transforms the key status information in the spraying process into the basis for motion parameter adjustment, improves the real-time performance and accuracy of parameter adjustment, effectively optimizes the uniformity and consistency of coating, and thus improves the quality stability and production efficiency of spraying operations.

[0016] In summary, this application overcomes the technical problem of uneven coating thickness by monitoring changes on the workpiece surface in real time and automatically adjusting the spraying parameters based on these changes, thereby improving the quality and efficiency of spraying. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the first embodiment of the control method for the adhesive spraying system of this application; Figure 2 This is a flowchart illustrating the sixth embodiment of the control method for the adhesive spraying system of this application; Figure 3 This is a flowchart illustrating the seventh embodiment of the control method for the adhesive spraying system of this application; Figure 4 This is a flowchart illustrating the eighth embodiment of the control method for the adhesive spraying system of this application; Figure 5 This is a schematic diagram of the adhesive spraying equipment of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] In related technologies, spraying parameters need to be preset manually and statically before the dynamic spraying process is executed. However, the working conditions are prone to fluctuations during the spraying process, which requires operators to frequently adjust the parameters based on experience, resulting in uneven coating thickness.

[0023] This application provides a solution: First, obtain the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, and the image data of the spraying area at the nth time step, to generate workpiece curvature features and spraying state features. Then, substitute the spraying state features, the workpiece curvature features, and the material type of the area corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values. Finally, generate and execute the target control parameters for the (n+1)th time step based on the difference parameter values ​​and the initial control parameters.

[0024] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device or adhesive spraying device capable of performing the above functions. The following description uses an adhesive spraying device as an example to illustrate this embodiment and the subsequent embodiments.

[0025] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0026] This application provides a control method for a glue spraying system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the control method for the adhesive spraying system of this application.

[0027] In this embodiment, the control method of the adhesive spraying system includes steps S10~S30: Step S10: Obtain the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, and the spraying area image data at the nth time step, and generate workpiece curvature features and spraying state features.

[0028] In this embodiment, the end point of the nth time step refers to the time point at the end of the nth time cycle. The distance parameter between the nozzle and the workpiece surface refers to the numerical information of the distance between the nozzle and the workpiece surface. The spraying area image data of the nth time step refers to the image information of the spraying area captured within the nth time cycle. The workpiece curvature feature refers to the feature information reflecting the degree and shape of the workpiece surface curvature. The spraying state feature refers to the feature information reflecting the distribution, shape, and other states of the paint during the spraying process.

[0029] As an optional implementation, at the end of the nth time step, the distance parameters between the nozzle and the workpiece surface and the image data of the sprayed area at that time step are simultaneously acquired without data preprocessing. The workpiece curvature features are generated directly based on the distance parameters by calculating the rate of change of distance between adjacent points through fixed-interval sampling. The image data of the sprayed area is then converted to grayscale to extract the contour and area information of the sprayed area, which are used as the spraying state features. This method is suitable for scenarios requiring high feature generation speed and stable environments, and can meet basic spraying control needs.

[0030] As an alternative implementation, at the end of the nth time step, the collected distance parameter between the nozzle and the workpiece surface is first filtered to remove abnormal fluctuation values. Based on the processed distance parameter, the curvature change trend is calculated through continuous curve fitting, generating workpiece curvature features that include the magnitude and rate of change of curvature. The image data of the sprayed area is then denoised and enhanced to extract detailed information such as the thickness distribution, uniformity, and boundary clarity of the sprayed area, integrating them into multi-dimensional spraying state features. These features are then output after validity verification. This method is suitable for scenarios with high feature accuracy requirements and complex environments, providing a more reliable basis for precise adjustment of spraying parameters.

[0031] Step S20: Substitute the spraying state characteristics, the workpiece curvature characteristics, and the material type of the region corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values.

[0032] In this embodiment, the material type of the region corresponding to the (n+1)th time step refers to the material attribute information of the workpiece region to be sprayed by the nozzle in the (n+1)th time period. The compensation algorithm refers to the preset computational logic used to calculate the parameter correction amount based on the input feature and attribute information. The difference parameter value refers to the deviation value obtained after the compensation algorithm calculation, used to correct the initial control parameters.

[0033] As an optional implementation, the core indicators from the spraying state features and the basic parameters from the workpiece curvature features are first extracted and then associated with the material type of the region corresponding to the (n+1)th time step. The three types of associated information are then input into a compensation algorithm in a fixed order, and the algorithm calls a preset single calculation rule. During the calculation process, only key intermediate results are retained, secondary calculation steps are ignored, and the integrated difference parameter values ​​are directly output. This method is suitable for scenarios where the feature and material associations are simple and the timeliness of parameter generation is critical, and can meet basic parameter correction needs.

[0034] As an alternative implementation, the spraying state characteristics and workpiece curvature characteristics are first analyzed from multiple dimensions. Invalid features are eliminated, and the weights of core features are marked. Simultaneously, material type attribute details are supplemented for the region corresponding to the (n+1)th time step. Based on the feature weights and material details, suitable computational logic is matched from the rule base of the compensation algorithm. After the algorithm is started, the dynamic influence relationship of the three types of information is correlated in real time, and the calculation process and feature adaptation basis at each step are recorded. After the calculation is completed, difference parameter values ​​containing details of deviations in each dimension are generated. This method is suitable for scenarios with complex features, diverse materials, and stringent requirements for parameter accuracy. It can provide a precise basis for subsequent parameter adjustments and reduce fluctuations in spraying quality.

[0035] Step S30: Generate and execute the target control parameters for the (n+1)th time step based on the difference parameter values ​​and the initial control parameters.

[0036] In this embodiment, the initial control parameters refer to the basic parameters preset when the spraying system starts, which control the operation and glue dispensing status of the nozzle. The target control parameters for the (n+1)th time step refer to the final parameters customized for the (n+1)th time period, used for precise control of the spraying operation.

[0037] As an optional implementation, the core deviation items in the difference parameter values ​​are extracted and their corresponding basic items in the initial control parameters. A direct association is established according to parameter type, without additional parameter weighting. A fixed superposition logic is used to process the associated parameters, directly merging the difference parameter values ​​with the initial control parameters. After the calculation, the target control parameters for the (n+1)th time step are directly generated without parameter validity verification. The target control parameters are then transmitted to the actuator, triggering immediate execution, without retaining records of parameter generation and transmission. This method features a simple parameter processing and calculation flow, fast generation and execution response, and can quickly adapt to time-sensitive scenarios.

[0038] As an alternative implementation, the difference parameter values ​​are decomposed dimensionally, and the influence weight of each deviation item is marked. Simultaneously, the hierarchical structure of the initial control parameters is analyzed. Based on the weights and hierarchy, a dynamic adaptation operation logic is used to merge the difference parameter values ​​with the corresponding initial control parameters according to their weight ratios. The compatibility between parameters is verified in real time during the operation. After generating the target control parameters, a secondary verification is initiated to confirm whether the target control parameters conform to the preset operating range. Upon successful verification, the target control parameters, along with the generated log, are transmitted to the execution mechanism. This method, through weight partitioning and adaptation verification, generates target control parameters with high accuracy, strong stability, and low execution risk.

[0039] For example, in a scenario involving spraying a workpiece, the control sequence of the adhesive spraying system is as follows: System Startup: The operator presses the start button, the system initializes, including the detection and calibration of various sensors and actuators. Mode Selection: The operator selects either "continuous mode" or "spot spray mode" according to the spraying requirements, and the system enters the parameter setting interface for the corresponding mode. Parameter Setting: If "continuous mode" is selected, parameters such as the nozzle movement speed (V) are set; the system defaults to high-speed spraying (V≥50mm / s). If "spot spray mode" is selected, parameters such as the spot spray opening time (Ton), closing time (Toff), spot spray speed (V), and adhesive output (Q) are set, where the adhesive output is dynamically calibrated through a flow sensor and a stepper motor. Spraying Start: The system begins spraying according to the selected mode and set parameters. Real-time Monitoring and Adjustment: During the spraying process, the system monitors information such as the spraying distance and the curvature of the workpiece surface in real time. In "continuous mode," the nozzle movement speed (V) and spot spray opening time (Ton) are dynamically adjusted based on the monitoring data to maintain uniform coating thickness. In "spot spray mode," a closed-loop feedback mechanism using Ton and Q ensures high-precision glue output. Simultaneously, if Ton exceeds a threshold (e.g., >0.5s), an intermittent cooling mechanism is automatically activated. Spraying completion: Once the spraying task is finished, the system stops dispensing glue, the nozzle returns to its initial position, and the operator can perform subsequent inspections or cleaning.

[0040] By using closed-loop control through real-time data acquisition, feature analysis, and dynamic compensation, the problem of excessive coating uniformity error caused by traditional manual parameter adjustment in curved surface spraying is solved, thus improving the quality and efficiency of spraying.

[0041] Based on any of the above embodiments, in Embodiment 2 of this application, step S10 includes steps A11 to A13: Step A11: At the end of the nth time step, collect the distance parameter between the nozzle and the workpiece surface and the image data of the spraying area corresponding to the nth time step.

[0042] In this embodiment, the nth time step deadline refers to a specific time node at the end of the nth preset time period. Acquisition refers to the operation process of obtaining and capturing relevant information about the target object using specialized equipment. The distance parameter between the nozzle and the workpiece surface refers to numerical information characterizing the spatial interval between the nozzle and the workpiece surface.

[0043] As an optional implementation, a synchronization command for distance detection and image acquisition is triggered at the instant the nth time step ends. Distance parameter acquisition only captures a single value at the current instant. Image data acquisition directly targets the sprayed area for a single image capture. After acquisition, the distance parameters and image data are directly packaged, and then, after preprocessing or quality verification, verified distance parameters and sprayed area image data are generated. This method features a simple acquisition process, fast response speed, and can quickly provide data input for subsequent processing.

[0044] As an alternative implementation, a pre-preparation process is initiated during a preset period before the deadline of the nth time step to calibrate the synchronization between the distance detection device and the image acquisition device. Upon reaching the deadline, the distance parameters are sampled multiple times consecutively, outliers are removed, and the average value is taken as valid data. Image acquisition employs multi-angle continuous imaging, and the images undergo noise reduction and stitching processing to generate a complete image of the sprayed area. After acquisition, the stability of the distance parameters and the clarity of the image data are verified; if they do not meet the standards, re-acquisition is triggered. After successful verification, the data is appended with identification information such as acquisition time and device status before being transmitted to subsequent modules and the original data is stored. This method, through pre-calibration, multiple sampling, preprocessing, and verification, results in high-precision, complete, and reliable data acquisition.

[0045] Step A12: By extracting the distance parameter, the workpiece curvature feature representing the surface morphology of the workpiece is generated.

[0046] In this embodiment, feature extraction refers to the process of filtering and refining information that reflects the key attributes of the target object from the raw data. The distance parameter refers to the numerical information characterizing the spatial interval between the nozzle and the workpiece surface. Characterizing the workpiece surface morphology refers to reflecting the geometric state of the workpiece surface, such as undulations and curvatures, through feature information. Workpiece curvature features refer to feature information used to describe the degree of curvature, direction of curvature, and trend of change of the workpiece surface.

[0047] As an optional implementation, after receiving the distance parameter, data preprocessing is performed to remove outliers exceeding reasonable ranges and interpolate missing data. Abrupt changes in the surface morphology and smooth transition segments with uniform shape are captured according to the workpiece surface morphology. Combined with a preset morphological change threshold, smooth segments are merged into basic segments using abrupt changes as boundaries. Trend analysis is then used to determine the morphological type corresponding to the distance parameter, optimizing the segmentation to unify the morphological patterns within the same segment, generating clear-bounded continuous segments. For different continuous segments, an appropriate curve fitting logic is used to establish a continuous mapping relationship between the distance parameter and the surface position. The curvature value at each position is calculated based on the fitted curve, while simultaneously extracting detailed information. Multi-dimensional information is integrated into a complete workpiece curvature feature. This method is suitable for scenarios with complex workpiece surfaces and stringent requirements for curvature feature accuracy. It can provide accurate morphological basis for subsequent coating parameter compensation, reducing coating defects caused by morphological judgment errors.

[0048] Step A13: Based on image analysis and processing, the image data of the sprayed area is processed to generate the spraying state features that reflect the state of the spraying process.

[0049] In this embodiment, image analysis and processing refers to the process of parsing, transforming, and refining the acquired image data to obtain effective information.

[0050] As an optional implementation, after receiving the image data of the sprayed area, multi-step preprocessing is performed, including noise reduction to remove interfering pixels, enhancing the contrast between the paint and the background, and correcting image distortion. A multi-threshold segmentation combined with an edge detection algorithm is used to accurately extract the complete contour of the sprayed area, while simultaneously marking breakpoints, burrs, and other abnormal parts of the contour. The paint thickness distribution is inferred through image grayscale value analysis, and the uniformity index of different areas is calculated, integrating multi-dimensional information such as contour integrity, thickness consistency, and the number of abnormal points. The integrated information forms a complete feature set, and the matching degree between the features and the original image is verified. After confirmation, the spraying state features are output. This method is suitable for scenarios with stringent requirements for spraying quality and precise control of the process state, providing accurate basis for parameter adjustment and reducing the spraying defect rate.

[0051] For example, at the end of the nth time step, the distance parameter between the nozzle and the workpiece surface and the corresponding spraying area image data are simultaneously acquired. After receiving the distance parameter, outliers are removed and missing data is filled in after preprocessing. The surface morphology is divided into segments for curve fitting, and the curvature values ​​and rates of change at each position are calculated to generate workpiece curvature features that characterize the workpiece surface morphology. After receiving the spraying area image data, noise reduction, contrast enhancement, and distortion correction are performed sequentially. Multi-threshold segmentation combined with edge detection is used to extract the spraying area contour, abnormal parts are marked, and thickness distribution and uniformity are estimated through grayscale value analysis. These are integrated to form a spraying state feature that reflects the spraying process status.

[0052] By accurately collecting data and generating a closed loop through dual-feature collaboration, the problems of large deviations in workpiece shape recognition and lagging monitoring of spraying status in traditional spraying are solved. At the same time, the use of multi-step preprocessing and segmented fitting technology improves the efficiency and quality of spraying.

[0053] Based on any of the above embodiments, in Embodiment 3 of this application, step S20 includes steps B11 to B13: Step B11: Classify and analyze the data types for the spraying state features, workpiece curvature features, and material type of the region corresponding to the nth time step, and determine the set of feature values.

[0054] In this embodiment, classifying and analyzing data types refers to the process of categorizing and analyzing information according to its attributes, form, and representational meaning. The numerical set of features refers to the information set formed after converting various features and material types into calculable numerical forms.

[0055] As an optional implementation, this method first receives the spraying state features, workpiece curvature features, and material type of the corresponding region at the n+1th time step. The data types are then directly categorized into three main types: state, morphology, and attribute. Spraying state features are classified as state, workpiece curvature features as morphology, and material type as attribute. Only core representational values ​​are extracted from state and morphology features, while attribute-type material types are converted into single values ​​according to a preset mapping rule. The matching between values ​​and features is not verified; the three types of values ​​are directly integrated to form a feature value set and output. This method has simple classification logic, fast data processing speed, and can quickly generate value sets.

[0056] As an alternative implementation, the spraying state features at the nth time step are first decomposed into sub-features such as contour integrity and thickness uniformity. The workpiece curvature features are decomposed into sub-features such as curvature magnitude and rate of change. For the material type of the region corresponding to the (n+1)th time step, related attributes such as surface adhesion and roughness are added. Data types are further subdivided into discrete numerical types, continuous variation types, and attribute-coded types. Discrete numerical types correspond to the number of outliers in the spraying state sub-features, continuous variation types correspond to the rate of curvature change, and attribute-coded types correspond to the material type and related attributes. Each sub-feature and attribute is converted into a numerical value of the appropriate type. The rationality of the numerical values ​​and the correlation between the values ​​are verified. Invalid values ​​are removed, and the data is integrated to form a structured set of feature values. Classification labels are then added before output. This method offers detailed classification dimensions, strong numerical conversion adaptability, comprehensive and accurate generated set information, and high reliability.

[0057] Step B12: Compare the numerical set with the corresponding threshold parameters in the database item by item, and calculate the parameter difference.

[0058] In this embodiment, the threshold parameter in the database refers to a reference value pre-stored in the database to measure whether each feature meets the standard. Item-by-item comparison refers to the operation of comparing each value in the value set with its corresponding threshold parameter in the database one by one in sequence. Calculating the parameter difference refers to calculating the difference between each value in the value set and its corresponding threshold parameter.

[0059] As an optional implementation, the numerical values ​​in the set are first labeled with classification tags according to state, form, and attribute. Simultaneously, threshold parameter sets for the corresponding categories are retrieved from the database to establish a hierarchical matching relationship. Each numerical value in the set is precisely paired with its corresponding threshold parameter within the same category according to its classification tag, and the feature correlation and rationality of the pairing are verified. An appropriate difference calculation logic is adopted for different categories of numerical values: the absolute difference is calculated for continuous values, and the deviation is calculated for discrete values. Invalid differences without corresponding thresholds are filtered out, and classification labels and comparison results are added to valid differences. These are then integrated to form a structured parameter difference set and output. This method is suitable for scenarios with complex numerical types and where subsequent parameter adjustments rely on precise differences, providing a reliable basis for deviation quantification for compensation algorithms.

[0060] Step B13: Based on the compensation algorithm, compensate and adjust the parameter difference, and integrate to generate the difference parameter value corresponding to the (n+1)th time step.

[0061] As an optional implementation, the parameter differences are first retrieved and compared with a preset compensation algorithm. Without distinguishing the classification attributes or degree of deviation of the differences, all parameter differences are directly substituted into the fixed operation rules of the compensation algorithm according to a uniform ratio, without prioritizing the differences. This compensation algorithm only amplifies or reduces the differences in a single dimension. After processing, the reasonableness of the compensation result is not verified; all adjusted differences are directly integrated in their original order to generate and output the difference parameter value corresponding to the (n+1)th time step. This method has a simple compensation process, fast algorithm speed, and can quickly generate difference parameter values.

[0062] As an alternative implementation, parameter differences are first categorized into state, morphology, and attribute types, and the influence weight and deviation level of each type of difference are marked. Simultaneously, the corresponding adaptation rule library for the compensation algorithm is loaded. Based on the weight and level, parameter differences of different categories are substituted into the matching compensation logic in the algorithm. Fine-grained correction rules are applied to high-weight differences, and related parameter differences are adjusted collaboratively. During the algorithm operation, the correlation between the adjustment results and features is verified in real time, and compensation values ​​exceeding a reasonable range are removed. The verified compensated differences are integrated by category, with added weight identifiers and adjustment logic descriptions, forming a structured difference parameter value corresponding to the (n+1)th time step. After consistency verification, this difference parameter value is output. This method, through classification weight division and adaptation rule matching, achieves high compensation adjustment accuracy, strong parameter value correlation, and good reliability.

[0063] For example, in the application scenario of the dynamic compensation algorithm on curved workpieces, the initial position of the workpiece and the nozzle is as follows: The nozzle is located at the starting spraying position of the curved workpiece, and the laser rangefinder begins to measure the distance between the nozzle and the workpiece surface. Real-time monitoring and data feedback: As the nozzle moves along the workpiece surface, the laser rangefinder continuously measures the spraying distance and feeds the data back to the control system in real time. The system calculates the curvature change of the workpiece surface based on the distance measurement data. Parameter adjustment: The system automatically adjusts the nozzle moving speed (V) and the spot spraying time (Ton) according to the curvature change and the spraying distance. For example, when the spraying distance increases, the system appropriately increases Ton to compensate for the amount of adhesive; when the curvature increases, the nozzle moving speed (V) is reduced to ensure uniform coating thickness. Spraying process: The nozzle continues to move along the workpiece surface and spray according to the adjusted parameters, and the dynamic compensation algorithm corrects the adhesive amount error caused by the curvature change in real time. Final effect: After spraying, the coating thickness on the workpiece surface is uniform, with no obvious adhesive accumulation or omission, adapting to the spraying requirements of complex curved workpieces, and improving the uniformity of coating thickness. The key to ensuring that the parameters of the adhesive spraying system are suitable for different workpieces lies in flexibly adjusting them according to the shape, material, size, and spraying requirements of the workpiece. The following are the specific methods and steps, along with workpiece characteristic analysis: Shape and Size: For workpieces with complex shapes (such as curved surfaces or porous structures), the nozzle's movement speed and spraying path need to be adjusted. For example, curved workpieces require a slower movement speed and a shorter spraying interval to ensure uniform adhesive coverage. Material: Different materials have different adhesive adhesion and heat sensitivity. For heat-sensitive materials, the adhesive dispensing rate and spraying time need to be reduced to minimize heat damage. Parameter Settings: Spraying Speed ​​(V): Adjusted according to the complexity of the workpiece and spraying requirements. For complex curved surfaces, the speed should be controlled within a lower range (e.g., ≤20mm / s) to ensure uniform adhesive distribution. Dot Spray Open Time (Ton) and Close Time (Toff): Set according to the spraying area and accuracy requirements of the workpiece. For high-precision dispensing, Ton needs precise control, while Toff is used to avoid adhesive buildup. Dispensing Glue (Q): Dynamic calibration using a flow sensor and stepper motor ensures consistent dispensing volume for each application. Dynamic Adjustment: Real-time Monitoring and Feedback: Utilizing a laser rangefinder and vision inspection device, the spraying distance and glue distribution are monitored in real-time. The nozzle movement speed and dispensing time are dynamically adjusted based on the monitoring data. Automatic Compensation: The system automatically adjusts parameters based on monitoring data; for example, increasing Ton appropriately when the spraying distance increases. The core of the dynamic compensation algorithm lies in real-time monitoring of changes on the workpiece surface and automatically adjusting spraying parameters accordingly. The specific implementation of the dynamic compensation algorithm is as follows: Real-time Monitoring: Laser Rangefinder: Monitors the distance between the nozzle and the workpiece surface in real-time, calculating changes in the curvature of the workpiece surface. Vision Inspection Device: Captures the spraying area using a camera, identifies glue distribution, and calculates the newly added glued area per unit time.Parameter Adjustment: Nozzle Movement Speed ​​(V): The nozzle movement speed is dynamically adjusted based on changes in the curvature of the workpiece surface. For example, the nozzle movement speed is reduced when the curvature increases. Spray Opening Time (Ton): Ton is dynamically adjusted based on changes in the spraying distance. For example, Ton is appropriately increased when the spraying distance increases. Adhesive Dispensing Volume (Q): Adhesive dispensing data fed back from the flow sensor is combined with a dynamic compensation algorithm to ensure consistent spraying volume for each application. Algorithm Logic includes Data Acquisition and Analysis: The dynamic compensation algorithm acquires sensor data in real time and analyzes changes in the workpiece surface. Automatic Adjustment: Based on the analysis results, the spraying parameters are automatically adjusted to ensure uniformity and consistency of the spraying effect. Offline Parameter Tuning: Offline parameter tuning avoids the cumbersome process of online adjustment, improving the real-time performance of the control system. Through these methods, the dynamic compensation algorithm can accurately adjust spraying parameters to adapt to the complex requirements of different workpieces, ensuring spraying quality and efficiency.

[0064] By employing a mechanism of command linkage, status verification, and synchronized startup, the problems of initial spraying position deviation and missing data caused by the asynchronous movement of the nozzle and the activation of the sensor in traditional spraying are resolved. Real-time data acquisition improves data integrity, thereby enhancing the efficiency of the spraying preparation stage.

[0065] Based on any of the above embodiments, in Embodiment 4 of this application, step B12 includes steps C11 to C13: Step C11: Retrieve the corresponding curvature threshold, glue distribution uniformity threshold, and newly added glue application area deviation threshold from the threshold parameters.

[0066] In this embodiment, the curvature threshold refers to a critical value defined in the preset compensation rules for determining whether the curvature change exceeds the normal range. The adhesive distribution uniformity threshold refers to a critical value defined in the preset compensation rules for determining whether the adhesive distribution uniformity meets the standard. The newly added adhesive application area deviation threshold refers to a critical value defined in the preset compensation rules for determining whether the deviation of the newly added adhesive application area from the preset path is acceptable.

[0067] As an optional implementation, the main storage directory of the preset compensation rules is first located, and rule version verification is initiated to confirm that the currently invoked rule is the latest effective version. After successful verification, the corresponding parameter entries within the rules are retrieved sequentially in a fixed order: curvature threshold, glue distribution uniformity threshold, and newly added glue application area deviation threshold. Each entry must be matched with a unique identifier code. During the extraction process, the numerical format of the thresholds is checked in real time to ensure consistency with the preset data type. If the format is abnormal, the current extraction is paused, a format error message is returned, and the abnormal entry is recorded. After all thresholds are extracted, a list containing the specific values ​​of the three thresholds, the extraction time, and the rule version is generated, and the original extraction log is stored synchronously for traceability. This method, which combines extraction with format verification, has high threshold accuracy and is suitable for scenarios with fixed rule structures.

[0068] As an alternative implementation, the index table of preset compensation rules is parsed to determine the associated storage locations and interdependencies of the curvature threshold, adhesive distribution uniformity threshold, and newly added adhesive area deviation threshold within the rules. A multi-threaded parallel retrieval mechanism is initiated, simultaneously sending retrieval requests to the storage addresses of the three thresholds, without restricting the retrieval order. After all thresholds are returned, cross-validation is initiated to check whether the numerical ranges of the three thresholds conform to preset collaborative constraints. If the validation passes, the thresholds are categorized and encapsulated according to their types, generating a threshold set marked with association relationships. If the retrieval of a certain threshold fails, a substitute value is estimated based on the association relationship, and an estimation identifier is marked. This method features fast parallel retrieval speed, and the association validation ensures logical coordination between thresholds, exhibiting strong adaptability.

[0069] Step C12: Compare the numerical set with the curvature threshold, the glue distribution uniformity threshold, and the newly added glued area deviation threshold one by one, and output the comparison result data.

[0070] In this embodiment, the comparison result data refers to the structured information that records the comparison status, deviation direction, and magnitude of each key indicator value with the corresponding threshold.

[0071] As an optional implementation method, the specific values ​​corresponding to curvature, glue distribution uniformity, and deviation of newly applied glue areas in the key indicator values ​​are first identified, and a comparison sequence is established in a fixed order of curvature, glue distribution uniformity, and deviation of newly applied glue areas. A single-path comparison process is initiated, first comparing the curvature indicator value with the curvature threshold, recording whether it exceeds the threshold range and the direction of deviation. If it exceeds the standard, the curvature is marked as abnormal and the degree of deviation is noted. After completion, the glue distribution uniformity indicator value is compared with the corresponding threshold, similarly recording the status and details. Finally, the deviation of newly applied glue areas is compared with the deviation threshold. After all comparisons are completed, each result is integrated according to the comparison order, supplementing attributes such as comparison time and indicator identifier, generating time-series comparison result data and outputting it. This method has a clear comparison order, detailed records of each step's results, facilitates tracing the root cause of single indicator anomalies, and has high accuracy.

[0072] Step C13: Based on the comparison result data, calculate the difference between each parameter in the numerical set and the corresponding threshold parameter to generate parameter difference.

[0073] In this embodiment, parameter difference refers to the set formed by integrating the differences between all individual parameters and their corresponding thresholds.

[0074] As an optional implementation, the comparison result data is first parsed to filter out valid matching parameter-threshold parameter pairs, while invalid matching parameters are marked and recorded separately. Valid matching pairs are categorized by parameter type, and appropriate difference calculation logic is assigned to different types of matching pairs. The difference between each parameter and the threshold parameter is calculated according to the logic corresponding to the category, while simultaneously verifying whether the difference is within a reasonable range. Differences exceeding the range are marked as abnormal, and matching problems are traced. Valid differences are categorized and integrated according to parameter type, with added matching status, calculation logic identifiers, and abnormal records to form structured parameter differences. After secondary verification, the parameter differences are output. This method is suitable for scenarios with complex parameter types and where subsequent compensation algorithms rely on precise differences, providing reliable deviation data support for precise parameter adjustments.

[0075] For example, first, the corresponding curvature threshold, glue distribution uniformity threshold, and newly added glued area deviation threshold are retrieved from the threshold parameters. The system receives a set of categorized and parsed values ​​(including values ​​corresponding to glue distribution uniformity and newly added area deviation in workpiece curvature features and spraying status features). It then compares each value according to the correspondence between "curvature value and curvature threshold," "glue distribution uniformity value and glue distribution uniformity threshold," and "newly added area deviation value and newly added glued area deviation threshold," recording the matching status of each parameter pair (e.g., meeting or exceeding the standard), and outputs the comparison result data. Based on this comparison result data, validly matched parameter and threshold pairs are extracted. Adaptation logic is used to calculate the difference between each parameter and its corresponding threshold, invalid matches are marked, and the results are integrated to generate a parameter difference value containing each difference and its matching status.

[0076] By employing a process of synchronous data reception, specialized analysis, and logical verification, the problems of asynchronous distance and image data processing and low accuracy in glue distribution recognition in traditional spraying have been solved, thus improving the stability of spraying quality.

[0077] Based on any of the above embodiments, in Embodiment 5 of this application, step S30 includes steps D11 to D13: Step D11: Associate and match the difference parameter value corresponding to the (n+1)th time step with the initial control parameter according to the parameter type to determine the operation correspondence.

[0078] In this embodiment, parameter type refers to the attribute category to which the parameter belongs, such as curvature adaptation, adhesive volume control, or region positioning. Association matching refers to the operation of establishing a correspondence between the difference parameter values ​​and the initial control parameters according to the same attribute category. Operational correspondence refers to the rules and logic followed when determining the fusion calculation between the difference parameter values ​​and the initial control parameters.

[0079] As an optional implementation, the difference parameter values ​​corresponding to the (n+1)th time step are labeled with basic category tags according to parameter type, while the type attributes of the initial control parameters are parsed. Based on the type tags, the difference parameter values ​​are matched one-to-one with the initial control parameters to form a basic correspondence. For the matched parameter pairs, the operational correspondence is determined according to preset general rules, with consistent operational logic applied to parameters of the same type. A simple check is performed on the matching results and operational relationships to ensure no obvious mismatches, and then the associated matching results and corresponding operational correspondences are output. This method is suitable for scenarios with simple parameter types and clear correlations, meeting basic parameter fusion requirements and ensuring a smooth operational flow.

[0080] As an alternative implementation, the difference parameter values ​​corresponding to the (n+1)th time step are analyzed in multiple dimensions, refining the parameter type labels to include core and auxiliary attributes. Simultaneously, the initial control parameters are hierarchically divided, clarifying the main parameters and associated sub-parameters. Combining the core attributes and hierarchical relationships, the difference parameter values ​​are precisely associated with the initial control parameters, establishing a matching network containing master-slave relationships. For parameter pairs of different levels and attributes, historical matching records and parameter characteristics are referenced to determine suitable operational correspondences, distinguishing between different logics such as incremental adjustment and proportional correction. The matching network and operational relationships are cross-validated to ensure the uniqueness of parameter associations and the adaptability of operational logic, forming a structured operational correspondence. This method is suitable for scenarios with diverse parameter types and complex associations, providing reliable logical support for subsequent accurate parameter fusion and reducing operational deviations.

[0081] Step D12: Based on the operation correspondence, filter to obtain the parameter fusion logic corresponding to the difference parameter value and the initial control parameter.

[0082] In this embodiment, parameter fusion logic refers to the specific operational rules for integrating and calculating the difference parameter values ​​with the initial control parameters.

[0083] As an optional implementation, the established operation correspondences are retrieved, and the parameter type tags and basic operation rules contained therein are identified. From a pre-defined parameter fusion logic library, a search is performed according to the direct correspondence between parameter types and operation rules, matching logic content that matches the operation correspondence tags. A basic check is performed on the matched fusion logic to confirm that it does not conflict with the rule descriptions in the operation correspondences. The verified fusion logic is then organized in parameter pair order to form a logic set that matches the operation correspondences one-to-one. This method relies on tag matching to achieve fast retrieval and efficiently obtains fusion logic.

[0084] Step D13: Based on the parameter fusion logic, the correlated difference parameter values ​​are superimposed with the initial control parameters to generate the target control parameters for the (n+1)th time step.

[0085] In this embodiment, superposition calculation refers to the process of integrating the difference parameter values ​​with the initial control parameters according to the fusion logic.

[0086] As an optional implementation, the parameter fusion logic is first decomposed hierarchically to clarify the operational priorities of main parameters and sub-parameters, while also identifying the interrelationships between related parameters. Superposition calculations are initiated according to priority, with the potential impact on sub-parameters considered concurrently during main parameter calculations, and operational details adjusted based on the coordination rules in the fusion logic. After each set of parameter calculations is completed, the reasonableness of the results is immediately verified by comparing the adjustment range of the initial control parameters with the deviation magnitude of the differing parameter values. After all parameter calculations and verifications are completed, the results of parameters with interrelationships are collaboratively verified to ensure that there are no conflicts in parameter adaptation, ultimately integrating to form a structured target control parameter for the (n+1)th time step. This method is suitable for scenarios with complex parameter hierarchies and close interrelationships, and the generated target control parameters can accurately adapt to spraying requirements, significantly reducing quality problems caused by parameter anomalies.

[0087] For example, in a spraying scenario, specifically a dot-spray coating scenario, the differential parameter values, such as nozzle height correction values, movement speed compensation values, and adhesive flow rate adjustment values, are analyzed. Through format validation and type matching, specific adjustment parameter values ​​are obtained, such as height +0.1mm, speed -2%, and flow rate +5%. These specific adjustment parameter values ​​are then superimposed with the current parameter values: current height 50mm, current speed 100mm / s, and current flow rate 20ml / min (50 + 0.1 = 50.1mm, 100 × (1 - 2%) = 98mm / s, 20 × (1 + 5%) = 21ml / min), generating new parameter values ​​which are then output. Based on the new parameter values, the corresponding height, speed, and flow rate parameter entries in the nozzle motion parameter library are located, and the original values ​​are replaced to complete the update. The integrated data is extracted from the parameter library to generate the updated target control parameters: height 50.1mm, speed 98mm / s, and flow rate 21ml / min.

[0088] By using a closed loop of precise association, logical adaptation, and standardized calculation, the problems of high parameter mismatch rate, poor adaptability of fusion logic, and large error in target parameter generation in traditional spraying are solved, thereby improving the accuracy of key indicator extraction and thus improving the quality of spraying.

[0089] Based on any of the above embodiments, in Embodiment Six of this application, referring to Figure 2 , Figure 2 This is a flowchart illustrating the sixth embodiment of the control method for the adhesive spraying system of this application. Before step S10, steps E11-E13 are also included: Step E11: In response to the start command of the first time step, control the nozzle to reset to the initial spraying position, and trigger the mode selection command at the initial spraying position.

[0090] In this embodiment, the start command for the first time step refers to the command signal that triggers the start of the first time cycle spraying operation. The initial spraying position refers to the preset reference position of the nozzle before the start of the spraying operation. The mode selection command refers to the command signal used to select the operating mode of the spraying operation.

[0091] As an optional implementation, upon receiving the start command at the first time step, the nozzle reset process is immediately initiated, driving the nozzle to move towards the initial spraying position along a preset trajectory. The nozzle position coordinates are monitored in real time, and reset completion is confirmed when the coordinates match the preset value of the initial spraying position. After the reset completion signal is generated, a mode selection command is immediately triggered, which by default calls the basic spraying mode parameters. The reset trajectory and command trigger time are recorded to form an initial start-up process log, synchronously outputting the reset completion and mode selection commands. This method is suitable for conventional spraying scenarios, ensuring rapid entry into the work state and meeting basic start-up requirements.

[0092] As an alternative implementation, upon receiving the start command for the first time step, the deviation between the current nozzle position and the initial spraying position is checked, and the shortest reset trajectory is planned. The nozzle is driven to move according to the planned trajectory, and the trajectory deviation is dynamically corrected during the movement to ensure movement accuracy. After the nozzle reaches the initial spraying position, a position stability test is initiated, and the reset is completed after confirming that there is no position deviation. Subsequently, historical spraying mode records and the current job type identifier are retrieved to generate a suitable mode recommendation list. A mode selection command is triggered based on the recommendation list, and the command includes a preview of mode parameters. The reset deviation data, stability test results, and mode recommendation basis are recorded to form a detailed initial process report before the mode selection command is output. This method is suitable for high-precision spraying scenarios, can reduce the risk of initial position deviation, improve the accuracy of mode matching, and lay the foundation for subsequent spraying quality.

[0093] Step E12: Based on the mode selection instruction, load the input of initial parameters and generate a parameter confirmation instruction.

[0094] In this embodiment, the initial parameters refer to the basic control parameters that match the selected spraying mode and are used to start the spraying operation. The parameter confirmation command is a command signal used to confirm that the initial parameters meet the operation requirements and that subsequent processes can be started.

[0095] As an optional implementation, after receiving the mode selection command, the mode type, job identifier, and recommended parameter information are extracted from the command. This information is then combined with the basic attributes of the current spraying scenario to select a suitable initial parameter set. The parameter set is hierarchically decomposed, and loaded and input into the control module in batches according to the order of core parameters and auxiliary parameters, marking the correlation between each parameter during input. After each batch of parameters is input, the compatibility of the parameter values ​​with the mode requirements is verified, and deviation parameters are fine-tuned. After all parameters are input and corrected, a parameter confirmation command containing parameter details, adaptation basis, and correction records is generated. After internal cross-verification confirms accuracy, a parameter correlation graph is output. This method is suitable for spraying operations with diverse modes and high parameter accuracy requirements, reducing the risk of parameter mismatch and providing reliable initial parameter support for precise spraying.

[0096] Step E13: Based on the parameter confirmation command, drive the nozzle to perform the spraying action according to the set initial parameters.

[0097] As an optional implementation, upon receiving a parameter confirmation command, the parameter details, relationships, and verification records are extracted from the command, and the logical hierarchy of the initial parameters is analyzed using a parameter relationship graph. Following the principle of prioritizing core parameters and coordinating the startup of related parameters, the parameters are transmitted to the nozzle's drive system in stages. First, the position positioning module is activated to achieve precise alignment, and then the paint output module is started. During the drive process, the deviation between the nozzle's operating parameters and the initial settings is dynamically monitored, and the drive signal is adjusted in real time based on the relationships. After each spraying sub-action is completed, the consistency between the action effect and the parameter requirements is verified. The drive and verification process is continuously cycled until the complete spraying action is completed according to the parameters. This method is suitable for scenarios with complex parameter relationships and stringent requirements for spraying accuracy, reducing quality problems caused by action deviations and improving the consistency of spraying effects.

[0098] For example, in the scenario of spray coating, in response to the start command at the first time step, the nozzle is controlled to reset to the initial spraying position. Upon reaching the position, a mode selection command is triggered. The spraying mode identifier is parsed according to the mode selection command, the corresponding initial parameters are retrieved from the parameter library and input into the control module. After verifying the completeness and compatibility of the parameters, a parameter confirmation command is generated. Based on the parameter confirmation command, the set initial parameters are retrieved, and the corresponding modules are started in the order of position drive and paint output. Parameters such as position coordinates and glue dispensing rate are input into the modules, and the operating status is monitored. Once the nozzle reaches the first spraying position and stabilizes glue dispensing, it is driven to execute the complete spraying action according to the parameters.

[0099] By implementing a closed loop of reset, mode selection, parameters, and drive, the problems of large nozzle reset deviation, high mode and parameter mismatch rate, and slow action start-up response during traditional spraying are solved, thus improving the quality and efficiency of spraying.

[0100] Based on any of the above embodiments, in Embodiment Seven of this application, referring to Figure 3 , Figure 3 This is a flowchart illustrating the seventh embodiment of the control method for the adhesive spraying system of this application. Following step S30, steps F11-F13 are also included: Step F11: If the (n+1)th time step is the end time step, control the nozzle to run to the end of the spraying path.

[0101] In this embodiment, the (n+1)th time step being the cutoff time step means that the (n+1)th time period is set as the final time node for the termination of the spraying operation. Reaching the end of the spraying path means driving the nozzle to move along the preset spraying trajectory and reach the end of the trajectory. The end of the spraying path refers to the operation termination position defined in the preset spraying trajectory.

[0102] As an optional implementation, first determine whether the (n+1)th time step is the cutoff time step. If confirmed, retrieve the complete trajectory data of the current spraying path and locate the coordinates of the trajectory endpoint. Extract the current position information of the nozzle and plan a direct trajectory segment from the current position to the endpoint. Drive the nozzle along the planned trajectory segment at a preset constant rate, comparing the deviation between the current coordinates and the endpoint coordinates in real time during the movement. When the nozzle coordinates perfectly match the endpoint coordinates, stop the driving action, confirm that the nozzle has reached the endpoint of the spraying path, record the movement trajectory and arrival time, and output a position arrival signal. This method features simple trajectory planning, direct driving logic, and fast response speed for the nozzle to reach the endpoint.

[0103] As an alternative implementation, the cutoff attribute of the (n+1)th time step is first checked. After confirmation, the trajectory parameters and endpoint feature information of the spraying path are retrieved. Combining the current nozzle operating parameters and remaining time, a suitable deceleration-type movement trajectory is planned to ensure a smooth transition to the endpoint. The nozzle is driven in stages according to the planned trajectory, first maintaining the current speed, then gradually reducing the speed until a preset distance before the endpoint. Upon reaching the endpoint area, a position fine-tuning process is initiated to precisely match the nozzle position with the endpoint features. After confirming that the position matching is correct, the operation stops, and the trajectory planning logic, speed change curve, and fine-tuning results are recorded. A status report including the endpoint positioning accuracy is output. This method, through deceleration trajectory planning and fine-tuning processes, ensures smooth nozzle operation and high endpoint position accuracy.

[0104] Step F12: Control the nozzle to stop at the end of the spraying path, acquire the complete coating, and generate coating inspection data.

[0105] In this embodiment, a complete coating refers to the overall coating that completely covers the target area after the spraying operation is completed. Coating inspection data refers to the set of information reflecting the quality status of the coating generated by the vision inspection device after inspecting the complete coating.

[0106] As an optional implementation, upon receiving the arrival signal, a nozzle movement stop command is immediately output, cutting off the signal source driving the nozzle operation without waiting for additional status feedback. Simultaneously, a detection trigger signal is sent to the vision inspection device, directly initiating imaging inspection of the complete coating without adjusting device parameters. The inspection process captures coating images according to a preset fixed pattern, extracting features such as appearance integrity and boundary clarity, and converting these features into structured data. Coating inspection data containing inspection time and basic coating condition is generated and output directly without adding additional verification information. This method is suitable for scenarios where inspection timeliness is more important than accuracy, enabling rapid completion of the post-spraying inspection process and ensuring operational efficiency.

[0107] Step F13: Compare the coating test data with the preset coating quality requirements to generate coating test results.

[0108] In this embodiment, the preset coating quality requirement refers to a pre-set standard for measuring whether the coating is qualified.

[0109] As an optional implementation, upon receiving coating inspection data, the core quality indicators are immediately extracted, ignoring secondary detailed indicators. The core indicators are then rapidly compared with the corresponding core standards in the preset coating quality requirements, without redundant verification. If all core indicators are within the preset range, the requirements are directly deemed met without further confirmation. A test result is generated containing only a task completion indicator and a brief description of the core indicator's compliance, without adding extra data or records, and the coating inspection result is output quickly. This method features a simple comparison process, fast signal generation, and can quickly terminate the task process.

[0110] As an alternative implementation, after receiving coating inspection data, all quality indicators are analyzed, without distinguishing between core and secondary indicators. Each indicator is compared against a pre-defined full-item standard for coating quality requirements, and the deviation of each indicator's specific value from the standard is recorded. If all indicators meet the requirements, a second cross-validation is initiated to check the correlation between different indicators and confirm that there are no hidden non-compliance issues. After successful validation, a coating inspection result containing proof of compliance for all indicators, comparison time, and validation results is generated, and the complete comparison record is simultaneously stored and output. This method is suitable for scenarios with stringent coating quality requirements and the need for complete quality traceability. It ensures that the output task completion signal is accurate and reliable, providing a comprehensive basis for quality archiving.

[0111] For example, in a spray coating scenario, based on real-time updated data transmitted from a laser sensor, including dynamic values ​​of coating thickness and nozzle position deviation, the updated motion parameters (speed 100mm / s, nozzle height 50mm, trajectory coordinates) are substituted into a dynamic compensation algorithm for iterative calculation. Each round optimizes the algorithm by combining the previous results with the new data until the deviation is <0.01mm, outputting the final updated motion parameters: speed 98mm / s, height 50.1mm. Based on these parameters, the nozzle is driven to the end of the spraying path, outputting an arrival signal. The nozzle movement stops upon receiving the arrival signal, triggering a vision inspection device to detect the complete coating and generating coating inspection data including thickness distribution, uniformity, and defects. The data is compared with preset coating quality requirements: thickness 0.1–0.15mm, uniformity ≥95%, and no defects >0.5mm. Once the requirements are confirmed, a coating inspection result is generated.

[0112] By using a closed loop of dynamic iterative compensation, precise endpoint drive, and full-volume quality verification, the problems of lagging dynamic parameter adjustment, large endpoint positioning deviation, low coating detection accuracy, and missed detection in task completion judgment in traditional spraying are solved, thereby improving the coating spraying quality.

[0113] Based on any of the above embodiments, in Embodiment Eight of this application, referring to Figure 4 , Figure 4 This is a flowchart illustrating the eighth embodiment of the control method for the adhesive spraying system of this application. Following step F13, steps G11~G13 are also included: Step G11: If the coating test result is unqualified, then extract the unqualified items from the coating test result.

[0114] In this embodiment, "quality non-compliance" refers to a coating inspection result that fails to meet the preset quality standard. Non-compliance items refer to the specific types of defects in the coating that do not meet the quality standard, along with related information.

[0115] As an optional implementation, the coating inspection results are first received, and the quality judgment identifiers within them are parsed to confirm whether the quality is unqualified. If unqualified, a preset list of unqualified items is retrieved. The descriptive content and data indicators in the inspection results are compared one by one according to the list categories, and information matching the classification characteristics is matched. The matched information is organized by category, clarifying the basic type of each unqualified item and its corresponding inspection data, forming a list of unqualified items. The classification matching process and matching basis are recorded, and the unqualified item extraction results containing basic information are output. This method relies on classification list matching, has a clear extraction process, high operational efficiency, and can quickly identify the basic unqualified type.

[0116] Step G12: Based on the control parameters corresponding to the non-conforming items, determine the type of defect parameter that needs to be adjusted.

[0117] In this embodiment, the corresponding control parameters refer to parameters associated with various coating defects and used to regulate the spraying process. The defect parameter type that needs adjustment refers to the attribute category to which the control parameter needs to be modified to resolve nonconformities.

[0118] As an optional implementation, the method receives an extracted list of nonconforming items and parses the basic type of each nonconforming item. It retrieves a pre-defined list of correspondences between defect types and control parameters, and matches the control parameters in the list one by one according to the basic type of the nonconforming item. For the matched control parameters, they are categorized based on parameter attributes. The nonconforming items and their corresponding parameter types are sequentially associated and integrated to form a correspondence table between nonconforming items and parameter types. The matching criteria and parameter type categorization logic are recorded, and the result of determining the defect parameter type, including the basic correspondence, is output. This method relies on a fixed list matching process, is simple and intuitive, and quickly determines the parameter type.

[0119] Step G13: Based on the deviation magnitude corresponding to the defect parameter type and the spraying path, generate a coating compensation strategy using the compensation algorithm.

[0120] In this embodiment, the deviation amplitude refers to the degree of difference between the actual value and the standard value corresponding to the defect parameter type. Spraying path: refers to the preset movement trajectory of the spray head when performing the spraying operation. Coating compensation strategy: refers to the parameter adjustment and path adaptation scheme formulated to correct coating defects and optimize the spraying effect.

[0121] As an optional implementation method, this approach first analyzes the core attributes and dynamic changes in deviation amplitude of defect parameter types, then constructs a comprehensive analysis dataset by combining detailed information such as the curvature and node distribution of the spraying path. The multi-dimensional computation module adapted to the compensation algorithm is invoked, and the dataset is input hierarchically according to the defect-affected area, path characteristics, and parameter sensitivity. The algorithm references historical compensation effect data to accurately calculate the parameter correction requirements for different path segments, while considering the synergy of parameter adjustments and path adaptability. Compensation priorities are defined, clarifying the key adjustment schemes for core defect areas and the collaborative correction logic for related areas. The generated strategies are then validated for feasibility, correcting any conflicts with paths or parameters. Finally, a structured coating compensation strategy is formed, including adjustment logic, priority division, and validation results, along with an output of strategy adaptation criteria. This method, by combining multi-dimensional data and historical experience, achieves high strategy accuracy and strong adaptability.

[0122] For example, in the scenario of spray coating, if the coating inspection result is unqualified, the unqualified items are extracted from the coating inspection result, and the defect type, location, deviation data, and scope of influence are identified. Based on the unqualified items, a pre-set associated database is retrieved, and the corresponding control parameters are matched. The type of defect parameter that needs adjustment is determined through parameter attribute analysis. The deviation amplitude corresponding to the defect parameter type is obtained, and information such as the trajectory segmentation, curvature change, and node coordinates of the spraying path is retrieved. This data is synchronously input into the compensation algorithm. The algorithm combines parameter sensitivity analysis and historical compensation cases to calculate the parameter correction amount for each path segment, classify compensation priorities, and verify the adaptability with the spraying path. Finally, a coating compensation strategy containing adjustment logic, correction amplitude, and priority classification is generated.

[0123] Further, after initiating spraying, the target coating thickness parameters are retrieved, and an initial spraying threshold thickness less than the target value is determined according to preset rules. Corresponding initial control parameters are loaded, and the nozzle is driven to perform the first pass spraying along the spraying path. The thickness of the first pass spraying is strictly controlled within the preset threshold range. After the first pass spraying is completed, coating detection is initiated to obtain coating thickness data for the entire area, comparing the consistency of the thickness at each location with the target value. If the detection results show inconsistent thickness, thickness deviation-related non-conformities are extracted from the detection results, clarifying the deviation location, value, and range. Based on the non-conformities, the corresponding control parameters such as glue dispensing rate and moving speed are matched to determine the type of defect parameters that need adjustment. The deviation amplitude corresponding to the defect parameter type is obtained, and combined with the trajectory segmentation information of the spraying path, a coating compensation strategy is generated through a compensation algorithm, clarifying the parameter correction amount and adjustment timing for each path segment. The control parameters are adjusted according to the compensation strategy, and the nozzle is driven to perform a second spraying on the entire coating, ensuring that the coating thickness of the entire area reaches the target thickness value after the second spraying. If the test results show that the thickness is consistent, the nozzle is controlled to spray another layer of coating with a preset threshold thickness according to the preset superposition rules in the coating compensation strategy, thereby completing the entire spraying process.

[0124] Furthermore, before starting the spraying process, the spraying area is divided into multiple independent segments according to path nodes. An appropriate initial spraying threshold thickness is set for each segment, and segment-specific initial control parameters are matched. After starting the spraying process, the nozzles are driven to perform the first pass of spraying in segment order, and the spraying thickness of each segment is monitored in real time to ensure it meets the preset threshold requirements. After the first pass of spraying is completed, a zone detection mode is used to obtain the coating thickness data of each segment, and the consistency between the segment thickness and the target value is compared one by one. If the thickness of a certain segment is inconsistent, the thickness deviation of that segment is extracted, and the segment identifier, deviation magnitude, and distribution characteristics are marked. The corresponding control parameters such as nozzle height and dispensing pressure for that segment are matched to determine the defect parameter type. Combining details such as the path curvature and spraying sequence of that segment, a segment-specific compensation strategy is generated through a compensation algorithm to clarify the parameter correction logic. The corresponding control parameters are adjusted according to the segment compensation strategy. For segments with inconsistent thickness, a second spraying is performed first, and then the remaining segments are calibrated with overall respraying until the thickness of the entire area meets the standard. If all sections have the same thickness, a global overlay strategy is adopted, and coatings with a preset threshold thickness are sprayed sequentially according to the original parameters of each section. After completion, the coatings of each section are integrated to form a complete target coating.

[0125] By accurately identifying defects, targeting parameters, and scientifically generating a closed loop, the problems of incomplete non-conforming information, high parameter mismatch rate, and poor adaptation of compensation strategies and paths in traditional spraying defect handling are solved, thereby improving the spraying quality of the coating.

[0126] This application provides a glue spraying device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the control method of the glue spraying system in the first embodiment described above.

[0127] The following is for reference. Figure 5 The diagram illustrates a structural schematic of a glue spraying device suitable for implementing embodiments of this application. The glue spraying device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, spraying actuators, personal digital assistants (PDAs), tablet computers (PADs), portable media players (PMPs), sensors, etc., as well as fixed terminals such as laser rangefinders, desktop computers, etc. Figure 5 The adhesive spraying equipment shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0128] like Figure 5 As shown, the glue spraying device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the glue spraying device. The processing unit 1001, the ROM 1002, and the RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the glue spraying equipment to communicate wirelessly or wiredly with other devices to exchange data. Although glue spraying equipment with various systems is shown in the figures, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0129] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from read-only memory 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0130] The adhesive spraying equipment provided in this application, employing the control method of the adhesive spraying system in the above embodiments, can solve the technical problem of uneven coating thickness. Compared with the prior art, the beneficial effects of the adhesive spraying equipment provided in this application are the same as those of the control method of the adhesive spraying system provided in the above embodiments, and other technical features of this adhesive spraying equipment are the same as those disclosed in the method of the previous embodiment, and will not be repeated here.

[0131] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0133] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the control method of the glue spraying system in the above embodiments.

[0134] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, radio frequency (RF), etc., or any suitable combination thereof.

[0135] The aforementioned computer-readable storage medium may be included in the adhesive spraying equipment; or it may exist independently and not assembled into the adhesive spraying equipment.

[0136] The aforementioned computer-readable storage medium carries one or more programs. When the aforementioned one or more programs are executed by the glue spraying device, the glue spraying device: acquires the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, and the image data of the spraying area at the nth time step, and generates workpiece curvature features and spraying state features; substitutes the spraying state features, the workpiece curvature features, and the material type of the area corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values; and generates and executes the target control parameters for the (n+1)th time step based on the difference parameter values ​​and the initial control parameters.

[0137] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0140] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the control method of the above-described adhesive spraying system, which can solve the technical problem of uneven coating thickness. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the control method of the adhesive spraying system provided in the above embodiments, and will not be repeated here.

[0141] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A control method for a glue spraying system, characterized in that, Applied to the (n+1)th time step, the method includes: Obtain the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, as well as the spraying area image data at the nth time step, and generate workpiece curvature features and spraying state features. Substitute the spraying state characteristics, the workpiece curvature characteristics, and the material type of the region corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values; The difference parameter value corresponding to the (n+1)th time step is associated and matched with the initial control parameter according to the parameter type to determine the operation correspondence; Based on the operational correspondence, the parameter fusion logic corresponding to the difference parameter value and the initial control parameter is obtained by filtering. Based on the parameter fusion logic, the correlated difference parameter values ​​are superimposed with the initial control parameters to generate the target control parameters for the (n+1)th time step.

2. The control method for the adhesive spraying system as described in claim 1, characterized in that, The steps of obtaining the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, and the image data of the sprayed area at the nth time step, and generating the workpiece curvature features and spraying state features include: At the end of the nth time step, the distance parameter between the nozzle and the workpiece surface and the image data of the spraying area corresponding to the nth time step are collected. The distance parameter is extracted to generate the workpiece curvature feature that characterizes the surface morphology of the workpiece. The spraying area image data is processed by image analysis to generate the spraying state features that reflect the state of the spraying process.

3. The control method for the adhesive spraying system as described in claim 1, characterized in that, The step of substituting the spraying state characteristics, the workpiece curvature characteristics, and the material type of the region corresponding to the (n+1)th time step into the compensation algorithm to generate difference parameter values ​​includes: The data types are classified and analyzed to determine the numerical set of the features, including the spraying state features, the workpiece curvature features, and the material type of the region corresponding to the n+1th time step. The numerical set is compared item by item with the corresponding threshold parameters in the database, and the parameter difference is calculated. Based on the compensation algorithm, the parameter difference is compensated and adjusted, and the difference parameter value corresponding to the (n+1)th time step is generated.

4. The control method for the adhesive spraying system as described in claim 3, characterized in that, The step of comparing the numerical set with the corresponding threshold parameters in the database item by item and calculating the parameter difference includes: Retrieve the corresponding curvature threshold, glue distribution uniformity threshold, and newly applied glue area deviation threshold from the threshold parameters; The numerical set is compared with the curvature threshold, the glue distribution uniformity threshold, and the newly added glued area deviation threshold one by one, and the comparison result data is output. Based on the comparison results, the difference between each parameter in the numerical set and the corresponding threshold parameter is calculated to generate parameter difference values.

5. The control method for the adhesive spraying system as described in claim 1, characterized in that, Before the steps of obtaining the distance parameter between the nozzle and the workpiece surface at the end of the nth time step, and the image data of the spraying area at the nth time step, and generating the workpiece curvature features and spraying state features, the control method of the adhesive spraying system further includes: In response to the start command at the first time step, the nozzle is controlled to reset to the initial spraying position, and a mode selection command is triggered at the initial spraying position; Based on the mode selection instruction, load the initial parameter input and generate a parameter confirmation instruction; Based on the parameter confirmation command, the nozzle is driven to perform the spraying action according to the set initial parameters.

6. The control method for the adhesive spraying system as described in claim 1, characterized in that, After the step of calculating the target control parameter for the (n+1)th time step by superimposing the correlated difference parameter value with the initial control parameter based on the parameter fusion logic, the control method of the glue spraying system further includes: If the (n+1)th time step is the end time step, control the nozzle to run to the end of the spraying path; The nozzle is controlled to stop at the end of the spraying path, and the complete coating is acquired, generating coating inspection data; The coating test data is compared with the preset coating quality requirements to generate coating test results.

7. The control method for the adhesive spraying system as described in claim 6, characterized in that, After the step of comparing the coating inspection data with preset coating quality requirements to generate coating inspection results, the control method of the adhesive spraying system further includes: If the coating inspection result is unqualified, then the unqualified items are extracted from the coating inspection result; Based on the control parameters corresponding to the nonconformities, determine the types of defect parameters that need to be adjusted; Based on the deviation magnitude corresponding to the defect parameter type and the spraying path, a coating compensation strategy is generated through the compensation algorithm.

8. A glue spraying device, characterized in that, The adhesive spraying device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the control method for the adhesive spraying system as described in any one of claims 1 to 7.

9. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the control method for the glue spraying system as described in any one of claims 1 to 7.

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