Control method and apparatus for a spray gun, storage medium

Abstract

This application discloses a spray gun control method, device, and storage medium. The method includes: acquiring a set of calibration parameters for an experimental spray gun group when spraying with a calibrated paint thickness. The calibration parameter set includes at least one calibration parameter group, each used to record any type of calibration parameter corresponding to each wear level node of the experimental spray gun group; calculating various types of application parameters corresponding to the applied spray gun group based on the applied wear level of the applied spray gun group and the various types of calibration parameters corresponding to each wear level node of the experimental spray gun group; determining various types of working parameters of the applied spray gun group based on the various types of application parameters; and controlling the applied spray gun group to perform spraying based on the various types of working parameters. The technical solution of this application achieves a precise mapping between wear state and process parameters, maintains stable paint film thickness through automatic and coordinated parameter adjustment, and improves spraying efficiency while reducing manual dependence.

Description

Technical Field

[0001] This application belongs to the field of spray painting, specifically relating to spray gun control methods and equipment, and storage media. Background Technology

[0002] With the large-scale development of the logistics industry, containers, as the core carriers of cargo transportation, have their surface coating quality directly determining their corrosion resistance and service life. To meet the needs of mass production, container coating has gradually entered the era of automation. Water-based paints, with their advantages of being environmentally friendly and low-pollution, have become the mainstream coating material. The process of evenly spraying water-based paint onto the container surface using automated spray guns has become an industry standard.

[0003] The stability of paint film thickness (paint thickness) is a core indicator of coating quality. However, in the continuous operation of automated spraying, the spray gun nozzles inevitably wear down due to long-term erosion by the paint, leading to fluctuations in paint output and distortion of the spray pattern, directly disrupting the uniformity of the paint film thickness. To mitigate this problem, current technologies generally rely on operators manually adjusting the parameters of the pressure pump connected to the nozzle based on their experience, and simultaneously changing the moving speed of the container at the spraying station to compensate for the impact of nozzle wear.

[0004] However, traditional manual adjustment methods have many insurmountable limitations: workers cannot perceive the actual wear condition of the nozzles in real time and with precision; parameter adjustments exhibit significant lag and are difficult to respond quickly to changes in wear; the adjustment effect relies entirely on personal experience, and the coordination and matching accuracy of multiple parameters such as pressure and movement speed is extremely low, easily leading to problems such as uneven paint film thickness, local overspray, or missed spraying; manual adjustment is inefficient in large-scale production, cannot adapt to the continuous operation rhythm of automated production lines, and labor costs remain high. Currently, the industry has not yet developed a mature solution that can automatically identify and collaboratively optimize the adjustment of relevant process parameters based on the nozzle wear condition.

[0005] Therefore, how to address the objective pain point of nozzle wear in automated container painting, achieve precise mapping between wear status and process parameters, maintain stable paint film thickness through automatic and collaborative parameter adjustment, and simultaneously improve painting efficiency and reduce manual reliance are technical problems that the industry urgently needs to solve. Summary of the Invention

[0006] The purpose of this application is to address the objective pain point of nozzle wear in automated container painting, to achieve precise mapping between wear status and process parameters, to maintain stable paint film thickness through automatic and coordinated parameter adjustment, and to improve painting efficiency and reduce manual reliance.

[0007] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0008] According to one aspect of the embodiments of this application, a method for controlling a spray gun is provided, the method comprising:

[0009] Obtain a set of calibration parameters for the experimental spray gun group when spraying with a calibrated paint thickness. The set of calibration parameters includes at least one calibration parameter group, and each calibration parameter group is used to record any type of calibration parameter corresponding to each wear degree node of the experimental spray gun group. Based on the wear degree of the applied spray gun group and the calibration parameters of each type corresponding to each wear degree node of the experimental spray gun group, calculate the application parameters of each type corresponding to the applied spray gun group. The working parameters of each type of the application spray gun group are determined according to the application parameters of each type. The application spray gun assembly is controlled to perform painting according to the working parameters described for each type.

[0010] According to one aspect of the embodiments of this application, the method further includes: Each experimental spray gun group is acquired when spraying paint with a calibrated paint thickness. Each experimental data group includes at least one sub-data group, and each sub-data group corresponds to a parameter type of the experimental spray gun group. For any parameter type of the experimental spray gun group, obtain multiple sub-data groups corresponding to the parameter type; A calibration parameter group is determined based on multiple sub-data groups; wherein, each calibration parameter group is used to describe the calibration parameters of the parameter type corresponding to the experimental spray gun group at different wear levels; By combining the calibration parameter sets corresponding to each type of parameter, a calibration parameter set is obtained; the calibration parameter set is used to calibrate the calibration parameters of each type of spray gun group at different wear nodes under the calibration paint thickness.

[0011] According to one aspect of the embodiments of this application, determining a calibration parameter set based on a plurality of sub-data sets includes: Calculate the variance of each sub-data group, and retain the sub-data groups with variances less than a first threshold as reference data groups; The ratio of the variance of the reference data group to the sum of variances is used as the weighting coefficient of the reference data group. For each wear degree node, the product of the parameters of each reference data group and the weighting coefficient is used as the sub-calibration parameter of the wear degree node; The sum of the sub-calibration parameters corresponding to the wear degree node is used as the calibration parameter of the wear degree node; The calibration parameter set is determined based on the calibration parameters corresponding to each wear node.

[0012] According to one aspect of the embodiments of this application, there exists a set of calibration parameters corresponding to different calibrated paint thicknesses. The set of calibration parameters for obtaining the experimental spray gun group when spraying with calibrated paint thicknesses includes: Calculate the absolute difference between the applied paint thickness of the spray gun assembly and each calibrated paint thickness; The calibration paint thickness with the smallest absolute difference from the applied paint thickness is retained, along with the calibration parameter set corresponding to the calibration paint thickness.

[0013] According to one aspect of the embodiments of this application, based on the application wear degree of the application spray gun group and the calibration parameters of each type corresponding to each wear degree node of the experimental spray gun group, the application parameters of each type are calculated respectively, including: Identify the wear node adjacent to the applied wear level and select it as the selected node; For any set of calibration parameters, determine the calibration parameter corresponding to the selected node as the target calibration parameter; Based on the selected node and target calibration parameters, a prediction equation is established, and the application wear degree is substituted into the prediction equation to obtain the application parameters; Obtain the application parameters determined according to each calibration parameter group to obtain the application parameters corresponding to each type of application spray gun group.

[0014] According to one aspect of the embodiments of this application, determining a wear node adjacent to the applied wear level as a selected node includes: Obtain the absolute difference between the applied wear level and each adjacent wear level node; If the minimum absolute difference is greater than or equal to the second threshold, then the wear nodes adjacent to the applied wear degree on both sides are used as seed nodes; if the minimum absolute difference is less than the second threshold, then the wear node whose absolute difference with the applied wear degree is less than the second threshold is used as a seed node. The seed node and the wear node adjacent to the seed node are selected as the selected nodes.

[0015] According to one aspect of the embodiments of this application, determining the working parameters of each type of application spray gun group based on the application parameters of each type includes: For each type of application parameter, the first coefficient is determined based on the difference between the applied paint thickness of the application spray gun group and the calibrated paint thickness; For each type of application parameter, a second coefficient is determined based on the difference in the properties of the coatings used in the application spray gun group and the experimental spray gun group; Based on the first and second coefficients, as well as the application parameters for each type, the corresponding working parameters for each type of application parameter are calculated to obtain the working parameters for each type.

[0016] According to one aspect of the embodiments of this application, the method further includes: The application wear degree is calculated based on the number of objects already sprayed by the application spray gun group, the wear coefficient of the sprayed objects, and the wear coefficient of the sprayed paint.

[0017] According to one aspect of the embodiments of this application, a control device for a spray gun is provided, including a memory, a processor, and a readable program stored in the memory, wherein the processor executes the readable program to implement the method described in any of the above.

[0018] According to one aspect of the embodiments of this application, a readable storage medium is provided, on which a readable program / instruction is stored, which, when executed by a processor, implements the method described in any one of the above-described embodiments.

[0019] In this application, a set of calibration parameters is obtained when an experimental spray gun group performs painting with a calibrated paint thickness. The set of calibration parameters includes at least one calibration parameter group, each of which is used to record any type of calibration parameter corresponding to each wear degree node of the experimental spray gun group. Based on the application wear degree of the applied spray gun group and the various types of calibration parameters corresponding to each wear degree node of the experimental spray gun group, various types of application parameters corresponding to the applied spray gun group are calculated respectively. Based on the various types of application parameters, various types of working parameters of the applied spray gun group are determined. Based on the various types of working parameters, the applied spray gun group is controlled to perform painting.

[0020] In this embodiment, by acquiring the calibration parameter set of the experimental spray gun group under the calibrated paint thickness, and combining it with the application wear degree of the applied spray gun group, the system automatically calculates various application parameters and determines the working parameters, achieving automatic adjustment of the working parameters of the applied spray gun group without manual intervention. This effectively solves the problems of traditional manual adjustment, which struggles to accurately match nozzle wear conditions and exhibits lag. Through the correspondence between the calibration parameter set and the application wear degree, it accurately compensates for changes in paint output and spraying pattern caused by nozzle wear, ensuring stable paint film thickness and improving the consistency of spraying quality. Simultaneously, it adapts to the continuous operation requirements of automated spraying, avoids human error, significantly improves spraying efficiency, and meets the process requirements of large-scale container spraying. In other words, it addresses the objective pain point of nozzle wear in automated container spraying, achieving precise mapping between wear state and process parameters. Through automatic and collaborative parameter adjustment, it maintains stable paint film thickness while improving spraying efficiency and reducing reliance on manual labor.

[0021] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0022] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0023] 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. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0024] Figure 1 A schematic diagram of a spray gun control method according to an embodiment of this application is shown.

[0025] Figure 2 A flowchart illustrating the acquisition of a calibration dataset according to an embodiment of this application is shown.

[0026] Figure 3 A schematic diagram illustrating the determination of a calibration parameter set based on a plurality of sub-data sets according to an embodiment of the present application is shown.

[0027] Figure 4 A schematic diagram of a calibration parameter set according to an embodiment of this application is shown.

[0028] Figure 5 A flowchart is shown illustrating the process of obtaining the calibration parameter set when an experimental spray gun group performs painting with a calibrated paint thickness, based on a calibration parameter set corresponding to different calibrated paint thicknesses according to an embodiment of this application.

[0029] Figure 6 A flowchart illustrating the calculation of various application parameters corresponding to the application spray gun group based on the application wear degree of the application spray gun group and the various types of calibration parameters corresponding to each wear degree node of the experimental spray gun group according to one embodiment of the present application is shown.

[0030] Figure 7 A flowchart illustrating the determination of wear nodes adjacent to the applied wear degree as selected nodes according to one embodiment of this application is shown.

[0031] Figure 8 A flowchart illustrating the determination of various types of working parameters for an application spray gun assembly based on various application parameters, according to one embodiment of this application, is shown.

[0032] Figure 9 A block diagram of a computer device for executing a spray gun control method according to an embodiment of this application is shown. Detailed Implementation

[0033] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art.

[0034] Furthermore, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. Numerous specific details are provided in the following description to give a thorough understanding of embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced without one or more of the specific details, or other methods, components, apparatuses, steps, etc., can be employed. In other instances, well-known methods, apparatuses, implementations, or operations are not shown or described in detail to avoid obscuring various aspects of this application.

[0035] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0036] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0037] In the embodiments of this application, the terms "module" or "unit" refer to a computer program or part of a computer program that has a predetermined function and works with other related parts to achieve a predetermined goal, and can be implemented wholly or partially using software, hardware (such as processing circuitry or memory), or a combination thereof. Similarly, a processor (or multiple processors or memory) can be used to implement one or more modules or units. Furthermore, each module or unit can be part of an overall module or unit that includes the functionality of that module or unit.

[0038] Please see Figure 1 , Figure 1 A schematic diagram of a spray gun control method according to an embodiment of this application is shown. This application provides the execution steps of a spray gun control method, including: Step S110: Obtain the calibration parameter set when the experimental spray gun group sprays paint with the calibration paint thickness. The calibration parameter set includes at least one calibration parameter set. Each calibration parameter set is used to record any type of calibration parameter corresponding to each wear node of the experimental spray gun group. Step S120: Based on the application wear degree of the application spray gun group and the calibration parameters of each type corresponding to each wear degree node of the experimental spray gun group, calculate the application parameters of each type corresponding to the application spray gun group. Step S130: Determine the working parameters of each type of application spray gun group according to the application parameters of each type. Step S140: Control the application of spray gun assembly for painting according to the working parameters of each type.

[0039] The four steps described above are described in detail below.

[0040] First, it needs to be clarified that the container painting device includes several pressure pumps, each of which is connected to a set of spray guns, forming a corresponding relationship structure between pressure pumps and spray gun sets. Each set of spray guns adopts a synchronous batch replacement strategy, that is, the start-up time, working time and replacement cycle of all spray guns in the same set are completely consistent, ensuring that the wear history of each spray gun in the set remains synchronized, thereby ensuring that the spray guns in the same set always have a uniform degree of wear and achieving consistency in the wear state of the spray guns in the set.

[0041] The technical solution described in this application can be used to control each spray gun group for various applications. That is, the same operating parameters (such as pump pressure, moving speed, and spraying distance) are used to control each spray gun in the spray gun group to achieve fully automated spraying of containers.

[0042] In some embodiments, the technical solution described in this application is executed by triggering a calibration event. The calibration event may be that the number of coatings applied by the spray gun group reaches a certain threshold; it may be triggered based on user operation behavior; or it may be that the working time of the spray gun group reaches any time threshold. These are not limited to any specific event.

[0043] In step S110, the experimental spray gun group serves as a reference spray gun group for conducting full lifecycle testing and obtaining calibration parameters. This reference spray gun group can be the same model as the application spray gun group used in actual operations, and it should be positioned in the same location on the container being painted. In other words, the reference spray gun group and the application spray gun group should have the same location and quantity in application, and share the same application scenario. It should be noted that the reference spray gun group can contain only one spray gun. The application spray gun group can contain only one application spray gun.

[0044] The calibrated paint thickness refers to the fixed standard paint film thickness set during the experiment (serving as the reference thickness for parameter calibration) (paint thickness refers to film thickness or paint film thickness). The calibration parameter set is the collection of data corresponding to all parameter types for the experimental spray gun group under the calibrated paint thickness, serving as the reference for subsequent parameter calculations. The calibration parameter group is a subset of the calibration parameter set, corresponding to a single type of parameter (such as pump pressure, travel speed, spraying distance), recording the specific values ​​of that type of parameter at each wear degree node. Wear degree nodes refer to the spray gun wear degree markers recorded at fixed intervals during the experiment (such as 1 TEU, 100 TEU, used to divide wear stages). Pump pressure refers to the pump pressure of the pressure pump used in the experimental or applied spray gun group, with units of MPa. Travel speed refers to the travel speed of each spray gun in the experimental or applied spray gun group relative to the sprayed object (such as a container), with units of m / s. Spraying distance refers to the distance of each spray gun in the experimental or applied spray gun group relative to the sprayed object, with units of mm. Wear degree (wear degree node) refers to the degree of wear of each nozzle in the test spray gun group or the application spray gun group, and the unit can be TEU.

[0045] A fixed calibrated paint thickness is pre-set for the experimental spray gun assembly, allowing it to be used from brand new to wear-out (full lifecycle). Throughout this process, the paint film thickness is maintained at the calibrated thickness. Different types of parameters (pump pressure, travel speed, spraying distance, etc.) are recorded at each wear level. Each type of parameter forms a calibration parameter group, and all calibration parameter groups are summarized into a calibration parameter set. A calibration parameter set adapted to the applied spray gun assembly is then obtained.

[0046] In step S120, the application spray gun set refers to the spray guns actually used for container painting operations. Application wear refers to the current actual wear level of the application spray gun set. This can be obtained based on user operation behavior or calculated using historical application data of the application spray gun set. Application parameters refer to intermediate parameters calculated based on the application wear and calibration parameters, adapted to the current wear state.

[0047] First, determine the current wear level of the applied spray gun assembly. Then, for each type of parameter (such as pump pressure, travel speed, and spraying distance), find the corresponding calibration parameter group from the calibration parameter set. Based on the calibration parameters of each wear level node in that group, and combined with the applied wear level, calculate the application parameters for that type of parameter individually (ensuring the parameters are compatible with the current wear state). It is important to note that (different calibration parameter groups correspond to different types of calibration parameters; the calibration parameter group can be any of the following types: pump pressure, travel speed, or spraying distance). In some embodiments, the application abrasion degree is calculated based on the number of objects already sprayed by the application spray gun group, the abrasion coefficient of the sprayed objects, and the abrasion coefficient of the sprayed paint.

[0048] The number of objects already sprayed refers to the actual number of objects (e.g., containers) to be sprayed using the spray gun assembly (e.g., 100 units). The wear coefficient of the object being sprayed is: e.g., 1.0 for a 20-foot container and 1.5 for a 40-foot container (preset fixed values ​​stored in the control equipment); the wear coefficient of the sprayed paint is: e.g., 1.2 for Class A water-based paint and 1.0 for Class B water-based paint (preset fixed values ​​determined based on paint property testing); the application wear degree is a specific numerical value quantifying the wear state of the spray gun (e.g., in TEU units). In some embodiments, the three factors are integrated through multiplication to derive the application wear degree.

[0049] First, count the number of containers that have been painted using the spray gun group, i.e., the number of objects painted (e.g., 80 containers). Second, retrieve the wear coefficient of the object being painted (e.g., 1.5 for 40-foot containers). Then, retrieve the wear coefficient of the paint used (e.g., 1.2 for Class A water-based paint). Finally, substitute these three data points into the preset calculation formula: Application Wear Degree = Number of Painted Objects × Wear Coefficient of Painted Objects × Wear Coefficient of Paint (e.g., 80 × 1.5 × 1.2 = 144 TEU). This calculation directly yields the current application wear degree of the spray gun group. This step requires no manual intervention, and all data are objective and statistically verifiable values, ensuring the accuracy and consistency of wear degree calculation.

[0050] In step S130, the application parameters of each type are checked. If there are no additional special requirements for actual spraying (such as the required film thickness of the application spray gun group, or the paint used is consistent with the experimental spray gun group), the application parameters are directly used as the working parameters. If there are differences that need to be adjusted (such as the required film thickness of the application spray gun group, or the paint used is inconsistent with the experimental spray gun group), the working parameters are determined after correction according to the corresponding rules.

[0051] In step S140, the final determined working parameters are transmitted to the spray gun control device, and the device automatically adjusts the operating status of the spray gun (such as adjusting the pressure of the pressure pump, the moving speed, the spraying distance, etc.) to ensure that the spray gun completes the painting operation according to the set parameters.

[0052] It should be clarified that since the application spray gun set and the experimental spray gun set use the same or similar model of spray guns, each type of calibration parameter set corresponds to a type of application parameter, and each type of application parameter corresponds to a type of operating parameter. The operating parameters of each type can control the application spray gun set to spray at the actual required film thickness.

[0053] This application embodiment obtains a calibration parameter set containing multiple parameter types through experiments, providing a precise benchmark for application parameter calculation and avoiding the problems of traditional manual adjustment relying on experience and lacking unified standards. By applying wear degree and corresponding calculation with the calibration parameter set, it achieves precise matching of working parameters under different wear states, effectively compensating for problems such as paint output fluctuations and spray pattern distortion caused by nozzle wear of the spray gun assembly, ensuring the uniformity and stability of paint film thickness. Various parameters form separate calibration parameter sets and calculate application parameters separately, ensuring the coordinated adaptation of multiple parameters such as pump pressure, moving speed, and spraying distance, further improving the consistency of spraying quality. The entire process requires no manual intervention, realizing automatic calculation and adjustment of working parameters, solving the lag problem of traditional manual adjustment, adapting to the continuous operation rhythm of automated production lines, improving spraying efficiency, reducing human operation errors, lowering labor costs, and meeting the process requirements of large-scale, high-precision spraying of containers.

[0054] Please see Figure 2 , Figure 2 A flowchart illustrating the process of obtaining a calibration dataset according to an embodiment of this application is shown. Embodiments of this application provide steps for obtaining a calibration dataset, including: Step S201: Obtain the experimental data set of each experimental spray gun group when spraying with calibrated paint thickness. Each experimental data set includes at least one sub-data set, and each sub-data set corresponds to a parameter type of the experimental spray gun group. Step S202: For any parameter type of the experimental spray gun group, obtain multiple sub-data groups corresponding to the parameter type; Step S203: Determine calibration parameter groups based on multiple sub-data groups; wherein, each calibration parameter group is used to describe the calibration parameters of the parameter type corresponding to the experimental spray gun group at different wear nodes; Step S204: Combine the calibration parameter sets corresponding to each type of parameter to obtain the calibration parameter set; the calibration parameter set is used to calibrate the calibration parameters of each type of test spray gun group at different wear nodes under the calibration paint thickness.

[0055] The above four steps are described in detail below.

[0056] In step S201, the experimental data set refers to the complete data collection formed by the experimental spray gun set during the full life cycle test, containing test data for all parameter types of the experimental spray gun set; the sub-data set refers to the subdivided unit of the experimental data set, corresponding to only one parameter type (such as pump pressure, travel speed, spraying distance), recording the change data of that type of parameter as the spray gun wears. The parameter type refers to the key control indicators of the spray gun painting operation, including pump pressure, travel speed, spraying distance, etc.

[0057] Multiple sets of experimental spray guns with the same or similar application scenarios and models were selected. A uniform calibrated paint thickness (e.g., 50 μm) was set for all sets, and the sets were continuously used from brand new to wear-out (full lifecycle testing). During the test, the paint film thickness was maintained at the calibrated thickness, and data for various parameter types were recorded simultaneously: the wear degree of the spray gun was recorded at fixed intervals (i.e., wear nodes), and the corresponding parameter values ​​were recorded, such as pump pressure, travel speed, and spraying distance at each wear node. The test data for each set of spray guns was integrated into an experimental data set, which contained multiple sub-data sets. Each sub-data set corresponded to a single parameter type, and the specific values ​​of that parameter type at each wear node were fully recorded.

[0058] In some embodiments, each sub-data group can be represented by a line chart.

[0059] In step S202, based on all the experimental data sets obtained in step S201, the data is categorized and organized according to parameter type. For each parameter type (e.g., only data related to "pump pressure" is selected), sub-data sets corresponding to that parameter type are collected for all experimental spray gun groups—for example, if there are 10 experimental spray gun groups, the "pump pressure" type corresponds to 10 sub-data sets, the "movement speed" type also corresponds to 10 sub-data sets, and so on. The core of this step is to aggregate the data of the same type scattered in each experimental data set, providing a data foundation for subsequent calibration parameter set selection or fusion processing.

[0060] In step S203, the calibration parameter group refers to a standard reference data group for a certain parameter type, which is used to describe the standard values ​​(i.e., calibration parameters) corresponding to the parameter type at different wear degree nodes; the wear degree node refers to the spray gun wear stage marker (such as 1TEU, 100TEU) divided at fixed intervals in the experiment, which is the reference for the correspondence between parameters and wear state; the calibration parameter refers to the specific value recorded in the calibration parameter group, which is the optimal parameter value for maintaining the calibration paint thickness under the wear degree node.

[0061] For the multiple sub-data sets of a certain parameter type summarized in step S202, a preset method is used to determine the calibration parameter set: First, the sub-data set with the smallest variance can be directly selected as the calibration parameter set (the smaller the variance, the stronger the data stability and the higher the reference value); Second, sub-data sets with variance less than a first threshold can be selected as reference data sets, the weight coefficient (variance ratio) of each reference data set can be calculated, and then the calibration parameters of each wear degree node can be obtained by summing the product of the parameter value and the weight coefficient, thus forming the calibration parameter set. This calibration parameter set clearly records the standard parameter values ​​required to maintain the calibration paint thickness under different wear degree nodes for the corresponding parameter type, realizing a precise mapping between wear state and parameters.

[0062] In some embodiments, the calibration parameter set can be displayed using a line graph.

[0063] In step S204, the calibration parameter set refers to the collection of calibration parameter groups corresponding to all parameter types, which is a complete benchmark library for subsequent application of spray gun group parameter calculation; the calibration parameter groups of different parameter types are integrated and summarized into a unified data set.

[0064] Repeat step S203 to determine the corresponding calibration parameter set for each parameter type, such as pump pressure, moving speed, and spraying distance. Then, integrate and summarize the calibration parameter sets for all parameter types to form a complete calibration parameter set. The core function of this calibration parameter set is to comprehensively record all types of calibration parameters corresponding to different wear levels of the experimental spray gun group under a preset calibration paint thickness. This provides a complete and unified reference for calculating the adaptation parameters based on the actual wear level of the applied spray gun group, ensuring the comprehensiveness and accuracy of subsequent parameter calculations.

[0065] This application embodiment comprehensively collects raw data of different parameter types through full lifecycle testing of multiple experimental spray gun groups, avoiding the random errors caused by single-experiment spray gun group testing. By summarizing sub-data groups according to parameter type, it ensures centralized processing of data of the same type, facilitating subsequent screening or fusion. The calibration parameter group is determined by using variance screening or weighted fusion, effectively improving the stability and reliability of the calibration parameters and avoiding interference factors in the raw data. The final compiled calibration parameter set completely covers the correspondence between different wear degree nodes and various types of parameters, providing accurate and comprehensive benchmarks for subsequent parameter calculations of the application spray gun group. This method solves the problems of lack of unified standards and insufficient data reliability in traditional calibration data, ensuring that subsequent application spray gun groups can achieve accurate matching between wear state and working parameters based on accurate calibration parameters, thereby ensuring the stability of paint film thickness, reducing errors caused by manual intervention, adapting to the large-scale production needs of automated container spraying, and improving the consistency of spraying quality and production efficiency.

[0066] Please see Figure 3 , Figure 3 A schematic diagram illustrating the determination of a calibration parameter set based on multiple sub-data sets according to an embodiment of this application is shown. This application embodiment provides step S202 for determining a calibration parameter set based on multiple sub-data sets, including: Step S301: Calculate the variance of each sub-data group, and retain the sub-data groups with variances less than the first threshold as reference data groups; Step S302: The ratio of the variance of the reference data group to the sum of variances is used as the weighting coefficient of the reference data group. Step S303: For each wear degree node, the product of the parameters of each reference data group and the weighting coefficient is used as the sub-calibration parameter of the wear degree node. Step S304: The sum of the sub-calibration parameters corresponding to the wear degree node is used as the calibration parameter of the wear degree node; Step S305: Determine the calibration parameter group based on the calibration parameters corresponding to each wear node.

[0067] The following is a detailed description of the five steps described above.

[0068] In step S301, a sub-data set refers to the original test data set corresponding to a certain parameter type (such as pump pressure or moving speed) of a single experimental spray gun group, recording the value of the parameter at each wear degree node; variance is a statistical index used to measure the dispersion of parameter values ​​in the sub-data set. The smaller the variance, the stronger the data stability and the higher the reliability; the first threshold refers to the preset variance judgment standard (such as 0.05), used to screen out sub-data sets that meet the stability standard; the reference data set refers to the sub-data set retained after variance screening, which is the basic data for subsequent calculation and calibration of parameters.

[0069] For multiple sub-data sets summarizing the same parameter type (e.g., pump pressure), the variance of each sub-data set is first calculated to assess the parameter stability of each sub-data set. A first threshold is preset as the pass / fail line for data stability. Sub-data sets with variances below this threshold are selected as reference data sets; sub-data sets with variances greater than or equal to the first threshold are discarded due to insufficient stability. This step filters out abnormal or excessively fluctuating raw data, ensuring the reliability of the data used in subsequent calculations.

[0070] In step S302, the weighting coefficient refers to the proportion of the credibility of each reference data group. The larger the weight, the greater the influence of the corresponding data group on the final calibration parameters. The variance sum refers to the sum of the variances of all reference data groups.

[0071] First, calculate the sum of variances (i.e., the variance sum) of all reference data groups. Then, for each reference data group, divide its own variance by the variance sum to obtain the weight coefficient for that reference data group. Since smaller variances indicate more stable data, this calculation method will give reference data groups with stronger stability a smaller variance proportion (weight coefficient), making its impact on the final result more reasonable in subsequent calculations and achieving a more accurate fit where higher stability corresponds to a better weight proportion.

[0072] In step S303, the sub-calibration parameter refers to the intermediate parameter value corresponding to a certain wear degree node after the parameters of a single reference data group have been weighted and adjusted.

[0073] Processing step-by-step according to wear level nodes, for each wear level node (e.g., 100 TEU), extract the parameter values ​​corresponding to all reference data groups at that wear level node. Multiply each parameter value by the weight coefficient of the reference data group to obtain the "sub-calibration parameter" for each reference data group at that wear level node. This step adjusts the parameters of each reference data group through weight coefficients to make the influence of stable data more prominent.

[0074] In step S304, the calibration parameter refers to the standard parameter value determined after weighted fusion at a certain wear level node, which is the optimal reference value for maintaining the calibrated paint thickness. For each wear level node, the sub-calibration parameters corresponding to all reference data sets for that node are collected, and these sub-calibration parameters are summed. The sum is the final calibration parameter for that wear level node. This step achieves the fusion of multiple sets of stable data through summation, avoiding the random errors of single sets of data and improving the accuracy of the calibration parameters.

[0075] In step S305, the calibration parameter group refers to the standard data group corresponding to a certain parameter type, which fully records the calibration parameters of that parameter type at all wear degree nodes and is a precise mapping table between wear degree nodes and calibration parameters.

[0076] All calibration parameters corresponding to wear levels are compiled and summarized in ascending or descending order of wear degree to form a calibration parameter group for that parameter type (e.g., pump pressure). This calibration parameter group clearly presents the standard parameters required to maintain the calibrated paint thickness under different wear conditions, providing a precise and unified reference for subsequent application of spray gun groups to calculate adaptation parameters based on actual wear.

[0077] Please see Figure 4 , Figure 4 A schematic diagram of a calibration parameter set according to an embodiment of this application is shown. Figure 4 The data records the calibration parameters of the test spray gun group at different wear levels under the specified paint thickness.

[0078] This application embodiment effectively eliminates raw data with excessive fluctuations and insufficient stability by calculating variance and combining it with a first threshold to filter reference data groups, thus avoiding interference from abnormal data on the accuracy of subsequent parameters. By allocating weight coefficients according to variance proportions, more stable sub-data groups play a more reasonable role in the fusion process, ensuring the scientific nature of data fusion. By obtaining calibration parameters through weighted summation according to wear degree nodes, the advantages of multiple reliable data groups are complemented, reducing the random errors of single data groups and significantly improving the accuracy and stability of calibration parameters for each wear degree node. The final calibration parameter group establishes a complete and accurate correspondence between wear degree nodes and calibration parameters, providing a high-quality benchmark for the automatic calculation of subsequent spray gun group parameters. This ensures that the wear state and working parameters of the application spray gun group can be accurately matched based on this calibration parameter group, effectively compensating for spraying deviations caused by nozzle wear, ensuring the uniformity and consistency of paint film thickness, adapting to the large-scale production needs of automated container spraying, reducing errors caused by manual intervention, and improving production efficiency and spraying quality.

[0079] In some embodiments, there are calibration parameter sets corresponding to different calibrated paint thicknesses. That is, different calibration parameter sets are repeatedly obtained based on different calibrated paint thicknesses. Each calibrated paint thickness corresponds to one calibration parameter set. For this situation, please refer to [link to relevant documentation]. Figure 5 , Figure 5 A flowchart illustrating the process of obtaining the calibration parameter set of an experimental spray gun group when spraying with a calibrated paint thickness, based on a given embodiment of this application, is provided. The embodiment of this application provides step S110 of obtaining the calibration parameter set of an experimental spray gun group when spraying with a calibrated paint thickness, given the given calibrated paint thickness and the existence of different calibrated paint thicknesses. The step includes: Step S111: Calculate the absolute difference between the applied paint thickness of the spray gun group and each calibrated paint thickness. Step S112: Retain the calibration paint thickness with the smallest absolute difference from the applied paint thickness, and the calibration parameter set corresponding to the calibration paint thickness.

[0080] The two steps above will be described in detail below.

[0081] In step S111, the absolute difference refers to the result of taking the absolute value of the difference between the applied paint thickness and a certain calibrated paint thickness. It is the core indicator for measuring the degree of closeness between the two (the smaller the difference, the stronger the compatibility).

[0082] First, determine the required paint thickness for this spraying operation using the spray gun set (e.g., 48μm in actual demand). Then, retrieve all the calibrated paint thicknesses established during the experimental phase (e.g., 40μm, 50μm, 60μm, etc.). For each calibrated paint thickness, calculate the absolute difference between it and the required paint thickness (e.g., the absolute difference between 48μm and 40μm is 8μm, the absolute difference between 48μm and 50μm is 2μm, and the absolute difference between 48μm and 60μm is 12μm). The core of this step is to clarify the degree of fit between each calibrated paint thickness and the actual demand through quantitative calculation, providing data support for subsequent selection.

[0083] In step S112, all absolute differences calculated in step S111 are compared, and the smallest absolute difference is identified (e.g., in the previous example, the smallest absolute difference is 2μm between 48μm and 50μm). Only the calibration paint thickness (50μm) corresponding to this smallest difference and its corresponding calibration parameter set are retained. Other calibration paint thicknesses (40μm, 60μm, etc.) and their corresponding calibration parameter sets are no longer used in subsequent processes. This step, through precise screening, ensures that the benchmark data (calibration parameter set) used to calculate application parameters is best matched with the actual application paint thickness, reducing parameter deviations caused by paint thickness differences from the source.

[0084] This application embodiment achieves coverage of spraying requirements for different paint film thicknesses by pre-setting multiple sets of calibration parameter sets corresponding to different calibrated paint thicknesses, thus improving the versatility of the technical solution. By calculating the absolute difference between the applied paint thickness and each calibrated paint thickness, the suitability of each calibration parameter set to actual needs can be quantitatively measured, avoiding the subjectivity and error of traditional manual selection of benchmark parameter sets. Only the calibrated paint thickness with the smallest absolute difference and its corresponding calibration parameter set are retained, ensuring that the benchmark data for subsequent application parameter calculations are highly consistent with the actual applied paint thickness, fundamentally reducing spraying parameter deviations caused by paint thickness mismatch, thereby ensuring the stability and uniformity of paint film thickness. This screening process requires no manual intervention, has a high degree of automation, is compatible with the large-scale production rhythm of automated container spraying, reduces efficiency losses and errors caused by manual operation, and provides a reliable benchmark for subsequent accurate calculation of parameters based on wear degree, further improving the consistency of spraying quality and production efficiency.

[0085] Please see Figure 6 , Figure 6 A flowchart illustrating an embodiment of this application is provided, showing how to calculate the application parameters of an application spray gun group based on the application wear degree of the application spray gun group and the calibration parameters of the experimental spray gun group at each wear degree node. The embodiment of this application provides step S120, which involves calculating the application parameters of an application spray gun group based on the application wear degree of the application spray gun group and the calibration parameters of the experimental spray gun group at each wear degree node, including: Step S121: Determine the wear node adjacent to the applied wear degree as the selected node; Step S122: For any set of calibration parameters, determine the calibration parameters corresponding to the selected node as the target calibration parameters; Step S123: Based on the selected node and target calibration parameters, establish a prediction equation, and substitute the applied wear degree into the prediction equation to obtain the application parameters; Step S124: Obtain the application parameters determined according to each calibration parameter group, and obtain the application parameters of each type corresponding to the application spray gun group.

[0086] The above four steps are described in detail below.

[0087] In some embodiments, if there is a wear degree node with the same magnitude as the application wear, the calibration parameters corresponding to the wear degree node are directly used as the application parameters corresponding to the application spray gun group.

[0088] If there is no wear node with the same size as the application wear, proceed to steps S121 to S124.

[0089] In step S121, the selected node refers to the wear node that is directly adjacent to the applied wear degree (such as the wear node with the largest left side and the smallest right side of the applied wear degree), which is the reference node for subsequent calculations.

[0090] In some embodiments, among the wear nodes with wear levels less than the applied wear level, a first number of wear nodes are selected as selected nodes in descending order. If the number of wear nodes with wear levels less than the applied wear level is less than the first number, then all wear nodes with wear levels less than the applied wear level are selected. Among the wear nodes with wear levels greater than the applied wear level, a second number of wear nodes are selected as selected nodes in ascending order. If the number of wear nodes with wear levels greater than the applied wear level is greater than the first number, then all wear nodes with wear levels greater than the applied wear level are selected. Wherein, the first number and the second number are positive integers.

[0091] In some embodiments, among wear nodes that are less than the applied wear level, the largest wear node is selected as the selected node in descending order; among wear nodes that are greater than the applied wear level, the smallest wear node is selected as the selected node in ascending order.

[0092] For example, first determine the current wear level of the application spray gun group (e.g., 90 TEU), then retrieve all wear level nodes of the experimental spray gun group (e.g., 1 TEU, 100 TEU, etc.). Find the nodes adjacent to the application wear level by numerical comparison: if there is no node that completely corresponds to the application wear level (e.g., 90 TEU), then select the largest node (1 TEU) less than the wear level and the smallest node (100 TEU) greater than the wear level as the selected nodes.

[0093] In step S122, the calibration parameter group refers to the standard data group corresponding to a single parameter type (such as pump pressure, moving speed, spraying distance), which records the calibration parameters corresponding to each wear node; the target calibration parameter refers to the specific calibration parameter value corresponding to the selected node in a certain calibration parameter group, which is the core data for establishing the prediction equation.

[0094] For each calibration parameter group (such as the "pump pressure-wear degree node" calibration parameter group and the "moving speed-wear degree node" calibration parameter group), the calibration parameters corresponding to the selected node determined in step S121 are found within that group. For example, for the pump pressure calibration parameter group, the selected node has a calibration parameter of 4.00 MPa for 1 TEU and 3.50 MPa for 100 TEU; these two parameter values ​​are the target calibration parameters for that calibration parameter group. This step extracts suitable reference data for each parameter type separately to ensure the relevance of subsequent calculations.

[0095] In step S123, the prediction equation refers to the mathematical model that quantifies the relationship between the wear degree node and the calibration parameter (it can be a linear equation, i.e., the linear equation y=ax+b, where y is the parameter value and x is the wear degree).

[0096] Using the wear degree of the selected node as the independent variable (x) and the corresponding target calibration parameter as the dependent variable (y), a prediction equation is established. For example, selecting a node with 1 TEU (x1) corresponds to a pump pressure of 4.00 MPa (y1), and 100 TEU (x2) corresponds to 3.50 MPa (y2). Substituting these values ​​into the linear equation y = ax + b, we obtain a = -0.005 and b = 4.005, resulting in the prediction equation y = -0.005x + 4.005. Then, substituting the applied wear degree (e.g., 90 TEU) into this equation, we calculate the applied parameter corresponding to the pump pressure (y = -0.005 × 90 + 4.005 = 3.555 MPa). This step achieves a precise quantitative mapping between wear degree and parameters through mathematical modeling, avoiding errors from empirical estimation.

[0097] In step S124, a set of application parameters covering all parameter types such as pump pressure, moving speed, and spraying distance is obtained.

[0098] Repeat steps S122 to S123, establishing corresponding prediction equations and calculating application parameters for each calibration parameter group, including pump pressure, travel speed, and spraying distance. Then, summarize the application parameters corresponding to all parameter types to form the application parameters for each type of spray gun group. This step ensures that all key control parameters are adapted to the current wear state, providing comprehensive and accurate intermediate data support for subsequently determining the final working parameters.

[0099] This application's embodiments ensure that the reference node for parameter calculation closely matches the actual wear state by identifying selected nodes adjacent to the applied wear degree, reducing errors caused by node deviation. Target calibration parameters are extracted separately for each calibration parameter group, and predictive equations are established, achieving precise adaptation of different types of parameters and avoiding the blindness of multi-parameter coordinated adjustments. The correspondence between wear degree and parameters is quantified through mathematical modeling, significantly improving the calculation accuracy and consistency of application parameters compared to traditional manual estimation. Finally, all types of application parameters are summarized, ensuring the comprehensiveness of subsequent working parameter determination and covering key control dimensions of spray gun painting. This method effectively solves the problem of traditional manual adjustment's difficulty in accurately matching nozzle wear state, laying a reliable foundation for subsequent working parameter optimization, thereby ensuring the stability of paint film thickness, adapting to the large-scale production needs of automated container painting, reducing manual operation errors, and improving painting quality and production efficiency.

[0100] Please see Figure 7 , Figure 7 A flowchart illustrating the determination of wear nodes adjacent to the applied wear level as selected nodes according to an embodiment of this application is shown. The embodiment of this application provides step S121 of determining wear nodes adjacent to the applied wear level as selected nodes, including: Step S401: Obtain the absolute difference between the applied wear degree and each adjacent wear degree node; Step S402: If the minimum absolute difference is greater than or equal to the second threshold, then the wear nodes adjacent to the applied wear degree on both sides are selected nodes; if the minimum absolute difference is less than the second threshold, then the wear node with the applied wear degree absolute difference less than the first threshold is selected as the seed node. Step S403: Select the seed node and the wear node adjacent to the seed node as the selected nodes.

[0101] The above three steps are described in detail below.

[0102] In step S401, adjacent wear degree nodes refer to the several wear degree nodes (including wear degree nodes that are less than or greater than the applied wear degree) that are numerically closest to the applied wear degree among all wear degree nodes.

[0103] For example, first, the specific value of the current wear level of the spray gun group is determined (e.g., 95 TEU). Then, all the wear level nodes preset in the experimental phase are retrieved (e.g., 1 TEU, 100 TEU, 200 TEU, 300 TEU, etc.). The nodes adjacent to the wear level value are selected (e.g., 100 TEU and 1 TEU in the example), and the absolute difference between the wear level and each adjacent node is calculated (e.g., the absolute difference between 95 TEU and 100 TEU is 5 TEU, and the absolute difference between 95 TEU and 1 TEU is 94 TEU). This step, by quantifying the proximity of the wear level to each node, provides a core basis for subsequent judgment on determining the selected nodes based on different scenarios.

[0104] In step S402, the minimum absolute difference refers to the smallest value among all the absolute differences calculated in step S401, which best reflects the degree of closeness between the applied wear and a certain node. The second threshold refers to a preset numerical judgment standard (such as 20 TEU) used to distinguish the degree of proximity between the application wear and adjacent nodes. Seed nodes are wear nodes whose absolute difference with the application wear is less than the second threshold, and they are the core benchmarks for subsequent expansion of selected nodes; selected nodes refer to the final set of benchmark nodes determined for subsequent parameter calculations.

[0105] First, extract the minimum absolute difference from all the absolute differences in step S401 and compare it with the preset second threshold. If the minimum absolute difference is greater than or equal to the second threshold (e.g., the minimum difference is 15 TEU and the second threshold is 10 TEU), it means that the applied wear degree is not too close to each adjacent node. Then, directly determine the wear degree nodes adjacent to the applied wear degree on both sides (the largest node less than the applied wear degree and the smallest node greater than the applied wear degree) as the selected nodes.

[0106] In some embodiments, among wear nodes that are less than the applied wear level, the largest wear node is selected as the selected node in descending order; among wear nodes that are greater than the applied wear level, the smallest wear node is selected as the selected node in ascending order.

[0107] In some embodiments, among the wear nodes with wear levels less than the applied wear level, a first number of wear nodes are selected as selected nodes in descending order. If the number of wear nodes with wear levels less than the applied wear level is less than the first number, then all wear nodes with wear levels less than the applied wear level are selected. Among the wear nodes with wear levels greater than the applied wear level, a second number of wear nodes are selected as selected nodes in ascending order. If the number of wear nodes with wear levels greater than the applied wear level is greater than the first number, then all wear nodes with wear levels greater than the applied wear level are selected. Wherein, the first number and the second number are positive integers.

[0108] If the minimum absolute difference is less than the second threshold (e.g., minimum difference 8 TEU, second threshold 10 TEU), it indicates the existence of nodes closely related to the application wear level. In this case, all nodes whose absolute difference with the application wear level is less than the second threshold are selected and used as seed nodes (e.g., if the application wear level is 85 TEU, the second threshold is 10 TEU, and the 100 TEU nodes with a difference of less than 10 TEU from 85 TEU are seed nodes). This step, through case-by-case processing, ensures that the selection of nodes conforms to both data logic and actual wear status.

[0109] In step S403, the wear nodes adjacent to the seed node are the two wear nodes that are numerically adjacent to the seed node (e.g., if the seed node is 100 TEU, the adjacent nodes are 1 TEU and 200 TEU).

[0110] The process involves retrieving the preceding and following adjacent nodes of the seed node from all wear level nodes, and then integrating the seed node with these adjacent nodes to form the selected nodes. For example, if the seed node is 100 TEU and its adjacent nodes are 1 TEU and 200 TEU, then the final selected nodes will be 1 TEU, 100 TEU, and 200 TEU. This step, by expanding the adjacent nodes of the seed node, ensures that the selected nodes closely match the applied wear level and provides sufficient benchmark data for subsequent prediction equation development, thus improving the reliability of parameter calculations.

[0111] In some embodiments, among the wear nodes smaller than the seed node, a first number of wear nodes are selected as selected nodes in descending order of wear degree. If the number of wear nodes smaller than the applied wear degree is less than the first number, then all wear nodes smaller than the applied wear degree are selected as selected nodes. Among the wear nodes larger than the seed node, a second number of wear nodes are selected as selected nodes in ascending order of wear degree. If the number of wear nodes larger than the applied wear degree is greater than the first number, then all wear nodes larger than the applied wear degree are selected as selected nodes. Wherein, the first number and the second number are positive integers.

[0112] This application embodiment achieves quantitative determination of node proximity by calculating the absolute difference between the application wear degree and each adjacent node, avoiding the subjective error of traditional node selection that relies on experience. It handles different cases based on the relationship between the minimum absolute difference and the second threshold. When the difference between a node and the application wear degree is large, it locks the nodes on both sides of the core to ensure accurate benchmarking. When the node and the application wear degree are very close, it expands the selected nodes by combining seed nodes and adjacent nodes, ensuring comprehensive data coverage. The final selected nodes are highly consistent with the application wear degree, providing a reliable benchmark for subsequent extraction of target calibration parameters and establishment of prediction equations, effectively reducing application parameter calculation errors caused by node deviation. The entire screening process is automated, requiring no manual intervention, adapting to the large-scale production rhythm of automated container painting, laying a solid foundation for subsequent accurate calculation of application parameters and stable paint film thickness, thereby improving the consistency of painting quality and production efficiency.

[0113] Please see Figure 8 , Figure 8 A flowchart illustrating the determination of various types of operating parameters for an application spray gun group based on various application parameters according to an embodiment of this application is shown. This embodiment of the application provides step S130 for determining various types of operating parameters for an application spray gun group based on various application parameters, including: Step S131: For each type of application parameter, determine the first coefficient based on the difference between the applied paint thickness and the calibrated paint thickness of the application spray gun group; Step S132: For each type of application parameter, determine the second coefficient based on the difference in paint properties between the application spray gun group and the experimental spray gun group; Step S133: Based on the first coefficient and the second coefficient, as well as the application parameters of each type, calculate the working parameters corresponding to each type of application parameter to obtain the working parameters of each type.

[0114] The above three steps are described in detail below.

[0115] In step S131, the applied paint thickness refers to the target paint film thickness that the spray gun assembly needs to achieve in the actual spraying operation (i.e., the film thickness required for the actual operation). The first coefficient is a correction coefficient determined based on the difference between the applied paint thickness and the calibrated paint thickness, used to compensate for parameter deviations caused by different film thickness requirements.

[0116] For each type of parameter (such as pump pressure, travel speed, spraying distance, etc.), first determine the actual paint thickness used in this application (e.g., 60μm) and the calibration paint thickness used in the experimental phase (e.g., 50μm). Then, determine the first coefficient based on the difference between the two. For example, using the formula "first coefficient = applied paint thickness / calibration paint thickness", if the applied paint thickness is 60μm and the calibration paint thickness is 50μm, then the first coefficient is 1.2; if both are the same (e.g., both are 50μm), then the first coefficient is 1.0. This step can be used to set or calculate different first coefficients for each type of application parameter, or all types of application parameters can share the same first coefficient, flexibly adapting to different film thickness requirements.

[0117] In step S132, coating properties refer to the core characteristics of the coating that affect the spraying effect (such as film-forming efficiency, viscosity, flowability, etc., and the direct relationship between parameters and film thickness). The second coefficient is a correction coefficient determined based on the differences in properties between the applied coating and the experimental coating, used to compensate for the deviation in film-forming effect caused by different coatings.

[0118] For each type of application parameter, retrieve the properties of the paint actually used in the application spray gun group (e.g., the film-forming efficiency of the application paint is 90%) and the properties of the experimental paint used in the experimental spray gun group (e.g., the film-forming efficiency of the experimental paint is 100%). Determine the second coefficient based on the difference between the two properties. For example, using film-forming efficiency as the core criterion, the second coefficient = experimental paint film-forming efficiency / application paint film-forming efficiency, i.e., 100% / 90% ≈ 1.11; if the paint properties are the same, the second coefficient is 1.0. Similarly, this step can set different second coefficients for each type of application parameter, or all types of application parameters can share the same second coefficient to ensure adaptation to different paint characteristics.

[0119] In step S133, for each type of application parameter, the corresponding application parameter, first coefficient, and second coefficient are substituted into a preset calculation formula (e.g., working parameter = application parameter × first coefficient × second coefficient) to calculate the final working parameter for that type of parameter. For example, if a certain type of application parameter (pump pressure) is 3.5 MPa, the first coefficient is 1.2, and the second coefficient is 1.11, then the corresponding working parameter = 3.5 × 1.2 × 1.11 ≈ 4.66 MPa. This calculation process is repeated to correct all types of application parameters, ultimately obtaining various types of working parameters covering pump pressure, moving speed, spraying distance, etc., providing a precise execution basis for spray gun control.

[0120] It should be clarified that different types of application parameters can correspond to different first and second coefficients, or they can all use the same first and second coefficients.

[0121] This application embodiment, by setting a first coefficient individually or uniformly for each type of application parameter, can accurately compensate for spraying deviations caused by the difference between the applied paint thickness and the calibrated paint thickness, ensuring the stability of the paint film thickness under different film thickness requirements. By setting a second coefficient, it effectively adapts to the differences in properties between the applied coating and the experimental coating, avoiding problems such as uneven film application and insufficient thickness caused by different coating characteristics. It supports different coefficients for each type of parameter or the use of the same coefficient, improving the flexibility and adaptability of the technical solution. Through the combined calculation of coefficient correction and application parameters, it realizes the accurate conversion of application parameters into working parameters adapted to actual working conditions, ensuring that the working parameters not only fit the current wear state of the spray gun, but also match the actual film thickness and coating requirements, significantly improving the adaptation accuracy of spraying parameters. The entire process requires no manual intervention, and the parameter correction is completed automatically, adapting to the large-scale production rhythm of automated container spraying, reducing human operation errors, ensuring the consistency and stability of spraying quality, and further improving production efficiency.

[0122] Application scenarios: This technical solution addresses the problem of unstable paint film thickness caused by nozzle wear in automated container spraying. It constructs a comprehensive spray gun group control system that integrates experimental baseline establishment, precise application adaptation, and automated equipment execution. The core of this system is to establish the correlation between wear and parameters through experimental data, and dynamically adjust spraying parameters based on actual working conditions, without requiring manual intervention. Details are as follows: I. Experimental Phase: Constructing a Precise Calibration Parameter Set Multiple sets of experimental spray guns were selected, and a fixed calibrated paint thickness was set to conduct full life cycle testing: the pump pressure, moving speed, spraying distance and other parameters required to maintain the calibrated paint thickness at different wear nodes of each experimental spray gun set were continuously recorded to form an experimental data set for a single experimental spray gun set (including sub-data sets corresponding to each parameter type).

[0123] For multiple sub-data sets of the same parameter type, the calibration parameter set is determined in two ways: one is to directly select the sub-data set with the smallest variance; the other is to screen the reference data set with variance less than the first threshold, allocate weight coefficients according to the variance ratio, and sum the parameters of each wear node by weight to obtain the calibration parameters, thus forming the calibration parameter set.

[0124] Summarize all calibration parameter sets of all parameter types to obtain the calibration parameter set corresponding to the calibrated paint thickness; if there are multiple calibration parameter sets with different calibrated paint thicknesses, the absolute difference between the applied paint thickness and each calibrated paint thickness will be calculated, and only the calibrated paint thickness with the smallest difference and its corresponding calibration parameter set will be retained to ensure that the benchmark data matches the actual needs.

[0125] II. Application Phase: Accurate Calculation of Adaptation Working Parameters Calculate the actual wear condition (application wear) of the spray gun assembly: The current wear degree of the spray gun is accurately calculated using the formula "application wear degree = number of objects sprayed × wear coefficient of objects sprayed × wear coefficient of paint".

[0126] Determine the reference node (selected node) for parameter calculation: Calculate the absolute difference between the applied wear degree and each wear degree node. If the minimum absolute difference is greater than or equal to the second threshold, directly select the adjacent nodes on both sides of the applied wear degree as selected nodes; if the minimum absolute difference is less than the second threshold, first filter out the seed nodes whose difference with the applied wear degree is less than the second threshold, and then select the seed nodes and their adjacent nodes as selected nodes (the first number and the second number can be preset to flexibly adjust the node selection range).

[0127] Calculate application parameters: For each type of calibration parameter group, extract the target calibration parameters corresponding to the selected node, establish a prediction equation with wear degree as the independent variable and calibration parameter as the dependent variable, substitute the application wear degree into the equation, and obtain the application parameters corresponding to each parameter type.

[0128] The final working parameters are obtained by correction: For each application parameter, the first coefficient is determined based on the difference between the applied paint thickness and the calibrated paint thickness, and the second coefficient is determined based on the difference in properties between the applied paint and the experimental paint (each parameter type can have its own coefficient or share coefficients). The working parameters of each type that are suitable for the actual working conditions are obtained by calculating "working parameter = application parameter × first coefficient × second coefficient".

[0129] III. Execution Phase: Automatic Control of Spraying Operation The finalized working parameters for each type are sent to the spray gun control device, which includes a memory (stores readable programs corresponding to the above methods) and a processor (executes programs to implement control logic). The device automatically adjusts the working status of the application spray gun group, such as pump pressure, moving speed, and spraying distance, to ensure stable paint film thickness.

[0130] Meanwhile, the readable storage medium stores a program / instruction that implements the above control method. When the program / instruction is executed by the processor, the entire spray gun control process can be fully implemented.

[0131] In summary, the entire technical solution establishes a reliable benchmark through experimental data, dynamically corrects parameters based on actual wear, film thickness requirements, and coating properties, and achieves automated execution with the help of dedicated control equipment. It comprehensively covers all aspects of parameter calibration, wear calculation, parameter calculation, correction, and control, and completely solves the problems of lagging and low precision in traditional manual adjustment.

[0132] Figure 9 A block diagram of a computer device for executing a spray gun control method according to an embodiment of this application is shown.

[0133] It should be noted that, Figure 9 The computer device 800 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0134] like Figure 9 As shown, the computer device 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 802 or programs loaded from storage section 808 into random access memory (RAM). The RAM 803 also stores various programs and data required for device operation. The CPU 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output interface 805 (I / O interface) is also connected to the bus 804.

[0135] The following components are connected to the input / output interface 805: an input section 806 including a keyboard, mouse, etc.; an output section 807 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a local area network card, modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the input / output interface 805 as needed. A removable medium 811, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 810 as needed so that computer programs read from it can be installed into the storage section 808 as needed.

[0136] Specifically, according to embodiments of this application, the processes described in the various method flowcharts can be implemented as computer software programs. For example, embodiments of 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 communication section 809, and / or installed from removable medium 811. When the computer program is executed by central processing unit 801, it performs the various functions defined in the device of this application.

[0137] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination thereof. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor device, apparatus, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution device, apparatus, or apparatus. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such transmitted data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution device, apparatus, or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0138] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based device that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0139] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0140] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0141] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0142] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for controlling a spray gun, characterized in that, The method includes: Obtain a set of calibration parameters for the experimental spray gun group when spraying with a calibrated paint thickness. The set of calibration parameters includes at least one calibration parameter group, and each calibration parameter group is used to record any type of calibration parameter corresponding to each wear degree node of the experimental spray gun group. Based on the wear degree of the applied spray gun group and the calibration parameters of each type corresponding to each wear degree node of the experimental spray gun group, calculate the application parameters of each type corresponding to the applied spray gun group. The working parameters of each type of the application spray gun group are determined according to the application parameters of each type. The application spray gun assembly is controlled to perform painting according to the working parameters described for each type.

2. The method according to claim 1, characterized in that, The method further includes: Each experimental spray gun group is acquired when spraying paint with a calibrated paint thickness. Each experimental data group includes at least one sub-data group, and each sub-data group corresponds to a parameter type of the experimental spray gun group. For any parameter type of the experimental spray gun group, obtain multiple sub-data groups corresponding to the parameter type; A calibration parameter group is determined based on multiple sub-data groups; wherein, each calibration parameter group is used to describe the calibration parameters of the parameter type corresponding to the experimental spray gun group at different wear levels; By combining the calibration parameter sets corresponding to each type of parameter, a calibration parameter set is obtained; the calibration parameter set is used to calibrate the calibration parameters of each type of spray gun group at different wear nodes under the calibration paint thickness.

3. The method according to claim 2, characterized in that, The calibration parameter set is determined based on multiple sub-data sets, including: Calculate the variance of each sub-data group, and retain the sub-data groups with variances less than a first threshold as reference data groups; The ratio of the variance of the reference data group to the sum of variances is used as the weighting coefficient of the reference data group. For each wear degree node, the product of the parameters of each reference data group and the weighting coefficient is used as the sub-calibration parameter of the wear degree node; The sum of the sub-calibration parameters corresponding to the wear degree node is used as the calibration parameter of the wear degree node; The calibration parameter set is determined based on the calibration parameters corresponding to each wear node.

4. The method according to claim 1, characterized in that, There exists a set of calibration parameters corresponding to different calibrated paint thicknesses. The set of calibration parameters for the experimental spray gun group when spraying at the calibrated paint thickness is obtained, including: Calculate the absolute difference between the applied paint thickness of the spray gun assembly and each calibrated paint thickness; The calibration paint thickness with the smallest absolute difference from the applied paint thickness is retained, along with the calibration parameter set corresponding to the calibration paint thickness.

5. The method according to claim 1, characterized in that, Based on the wear degree of the applied spray gun assembly and the calibration parameters of each type corresponding to each wear degree node of the experimental spray gun assembly, the application parameters of each type corresponding to the applied spray gun assembly are calculated, including: Identify the wear node adjacent to the applied wear level and select it as the selected node; For any set of calibration parameters, determine the calibration parameter corresponding to the selected node as the target calibration parameter; Based on the selected node and target calibration parameters, a prediction equation is established, and the application wear degree is substituted into the prediction equation to obtain the application parameters; Obtain the application parameters determined according to each calibration parameter group to obtain the application parameters corresponding to each type of application spray gun group.

6. The method according to claim 5, characterized in that, Identify wear nodes adjacent to the applied wear level as selected nodes, including: Obtain the absolute difference between the applied wear level and each adjacent wear level node; If the minimum absolute difference is greater than or equal to the second threshold, then the wear nodes adjacent to the applied wear degree on both sides are used as seed nodes; if the minimum absolute difference is less than the second threshold, then the wear node whose absolute difference with the applied wear degree is less than the second threshold is used as a seed node. The seed node and the wear node adjacent to the seed node are selected as the selected nodes.

7. The method according to claim 5, characterized in that, The operating parameters for each type of application spray gun assembly are determined based on the application parameters described for each type, including: For each type of application parameter, the first coefficient is determined based on the difference between the applied paint thickness of the application spray gun group and the calibrated paint thickness; For each type of application parameter, a second coefficient is determined based on the difference in the properties of the coatings used in the application spray gun group and the experimental spray gun group; Based on the first and second coefficients, as well as the application parameters for each type, the corresponding working parameters for each type of application parameter are calculated to obtain the working parameters for each type.

8. The method according to claim 1, characterized in that, The method further includes: The application wear degree is calculated based on the number of objects already sprayed by the application spray gun group, the wear coefficient of the sprayed objects, and the wear coefficient of the sprayed paint.

9. A control device for a spray gun, comprising a memory, a processor, and a readable program stored in the memory, characterized in that, The processor executes the readable program to implement the control method according to any one of claims 1 to 8.

10. A readable storage medium, characterized in that, It stores a readable program / instruction, which, when executed by a processor, implements the control method according to any one of claims 1 to 8.

Images