Automatic spraying method for anticorrosive coating of iron accessory

Abstract

The invention relates to the technical field of intelligent control, and discloses an automatic spraying method for an anticorrosive coating of an iron accessory, which comprises the following steps: performing parameter mapping on the initial surface state of the iron accessory to obtain optimized spraying parameters; a coating state monitoring data set of the iron accessory in the spraying process is obtained, and a uniformity decision is made for the coating state monitoring data set to obtain a dynamic regulation and control instruction; performing real-time adjustment on output parameters of the spraying process based on the dynamic regulation and control instruction, and performing synchronous monitoring on the spraying process after real-time adjustment to obtain real-time coating distribution data; performing multi-mode real-time optimization on the spraying process based on the real-time coating distribution data to obtain a uniform wet coating; and curing joint inspection is conducted on the uniform wet coating, the iron accessories passing the curing joint inspection result are output as target iron accessories, and the automatic spraying efficiency of the anti-corrosion coating of the iron accessories can be improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to an automated method for spraying anti-corrosion coatings on iron accessories. Background Technology

[0002] The quality and efficiency of anti-corrosion coating spraying for iron fittings hinge on the precise control of the initial surface condition and the scientific matching of spraying parameters. Existing anti-corrosion spraying technologies for iron fittings lack systematic surface condition analysis capabilities in the initial stage. Most technologies fail to perform targeted optical scanning and feature extraction on the iron fittings, making it impossible to accurately obtain key data such as surface roughness distribution and oxidation area distribution. Spraying schemes are often set based solely on manual experience or general parameters, leading to a disconnect between spraying parameters and the actual surface condition of the iron fittings. This not only easily results in weak localized coating adhesion and uneven anti-corrosion performance but also necessitates repeated parameter adjustments to adapt to iron fittings with different surface conditions, significantly increasing labor and time costs and severely impacting the efficiency and accuracy of pre-spraying preparation.

[0003] In the spraying process control and curing quality inspection stages, the limitations of existing technologies become increasingly apparent. On the one hand, monitoring of coating condition is mostly limited to single-dimensional or stage-based inspections, making it difficult to capture real-time trends in coating thickness changes and surface coverage integrity. It also fails to promptly identify issues such as excessively thick or thin coatings, or coverage defects. The generation of dynamic control commands lacks real-time data support, leading to difficulties in ensuring coating uniformity. On the other hand, post-curing quality inspection lacks quantitative standards and a comprehensive joint inspection mechanism, relying heavily on visual observation or simple thickness measurement. This fails to accurately determine the consistency of the coating curing state, making it easy for iron accessories with hidden quality problems to be put into use, requiring frequent maintenance and replacement later. This reduces the overall reliability of the iron accessories and indirectly increases the ineffective costs of the entire spraying process. Therefore, improving the efficiency of automated spraying of anti-corrosion coatings for iron accessories has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides an automated spraying method for anti-corrosion coating of iron accessories to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an automated spraying method for anti-corrosion coating of iron accessories, comprising: S1. Perform parameter mapping on the initial surface state of the iron accessory to obtain the optimized spraying parameters of the iron accessory; S2. Obtain the coating condition monitoring dataset of the iron accessory during the spraying process, make a uniformity decision on the coating condition monitoring dataset, and obtain the dynamic control command of the spraying process. S3. Based on the dynamic control command, the output parameters of the spraying process are adjusted in real time, and the spraying process after real-time adjustment is monitored synchronously to obtain the real-time coating distribution data of the iron accessory. S4. Based on the real-time coating distribution data, perform multi-modal real-time optimization of the spraying process to obtain a uniform wet coating for the iron accessory; S5. Perform a curing inspection on the uniform wet coating, and output the iron accessories that pass the curing inspection as the target iron accessories.

[0006] In a preferred embodiment, the step of parameter mapping of the initial surface state of the iron accessory to obtain the optimized spraying parameters of the iron accessory includes: Optical scanning was performed on the iron fittings to obtain surface morphology data of the iron fittings; Feature extraction is performed on the surface morphology data to obtain the surface roughness distribution and oxidation region distribution of the surface morphology data; The surface roughness distribution and the oxide region distribution are matched using a parameter mapping table to obtain the optimized spraying parameters for the iron attachment.

[0007] In a preferred embodiment, the step of extracting features from the surface morphology data to obtain the surface roughness distribution and oxidation region distribution of the surface morphology data includes: The surface morphology data is analyzed for grayscale features to obtain the grayscale distribution information of the iron accessory surface; Texture feature extraction is performed on the grayscale distribution information to obtain the roughness distribution information of the iron accessory surface; Colorimetric information is collected from the surface morphology data to obtain the colorimetric distribution of the iron accessory surface; Regional feature identification is performed on the chromaticity distribution to obtain the distribution information of the oxide region on the surface of the iron accessory.

[0008] In a preferred embodiment, the step of performing a uniformity decision on the coating condition monitoring dataset to obtain dynamic control instructions for the spraying process includes: The thickness change trend of the coating condition monitoring dataset was analyzed to obtain the film thickness distribution characteristics of the coating in the iron accessory; By performing coverage defect identification on the coating condition monitoring dataset, the surface coverage integrity information of the coating is obtained; By jointly analyzing the film thickness distribution characteristics and the surface coverage integrity information, a comprehensive evaluation conclusion on the coating uniformity of the iron accessory coating is obtained. Based on the comprehensive evaluation conclusion of the coating uniformity, a strategy mapping and matching is performed on the preset control strategy library to obtain the dynamic control instructions for the spraying process.

[0009] In a preferred embodiment, the step of performing strategy mapping and matching on a preset control strategy library based on the comprehensive evaluation conclusion of coating uniformity to obtain dynamic control instructions for the spraying process includes: The coating uniformity comprehensive evaluation conclusion is mapped to a level to obtain the uniformity level identifier of the coating uniformity comprehensive evaluation conclusion; Based on the uniformity level identifier, a strategy hierarchy matching is performed on the preset control strategy library to obtain a preliminary control instruction set corresponding to the uniformity level identifier; The preliminary control instruction set is reconstructed into executable form to obtain the dynamic control instructions for the spraying process.

[0010] In a preferred embodiment, the step of adjusting the output parameters of the spraying process in real time based on the dynamic control command, and synchronously monitoring the adjusted spraying process to obtain real-time coating distribution data of the iron accessory includes: The control parameters of the dynamic control command are deconstructed to obtain the adjustment parameters in the dynamic control command. The adjustment parameters are remapped to output parameters to obtain the parameter adjustment instructions corresponding to the adjustment parameters; The spraying output parameters are adjusted in real time based on the parameter adjustment command to obtain the adjustment effect of the spraying output parameters. The coating state is sensed to obtain real-time coating distribution data of the iron accessory by sensing the control effect.

[0011] In a preferred embodiment, the step of performing multimodal real-time optimization of the spraying process based on the real-time coating distribution data to obtain a uniform wet coating on the iron accessory includes: Multi-source feature fusion is performed on the real-time coating distribution data to obtain a coating state comprehensive descriptor that characterizes the overall coating state; The coating state comprehensive descriptor is subjected to spraying parameter optimization matching to obtain the optimized spraying parameters corresponding to the coating state comprehensive descriptor; The spraying process is optimized and modulated in real time based on the optimized spraying parameters to obtain a uniform wet coating for the iron accessory.

[0012] In a preferred embodiment, the step of fusing multi-source features from the real-time coating distribution data to obtain a comprehensive coating state descriptor characterizing the overall coating state includes: The surface morphology of the real-time coating distribution data is analyzed to obtain the surface morphology feature information of the real-time coating distribution data. The material distribution of the real-time coating distribution data is analyzed to obtain the material distribution feature information of the real-time coating distribution data; Multi-source feature fusion is performed on the surface morphology feature information and the material distribution feature information to obtain a coating multimodal fusion descriptor of feature information; The coating multimodal fusion descriptor is refined to obtain the coating state comprehensive descriptor.

[0013] In a preferred embodiment, the step of performing a curing inspection on the uniform wet coating, and outputting the iron accessories that pass the curing inspection as target iron accessories, includes: The curing state of the uniform wet coating is characterized to obtain curing state data of the uniform wet coating; The quality consistency of the cured state data is determined to obtain the quality determination result of the cured state data. Based on the quality assessment results, a compliance decision is made for the iron attachments, and the iron attachments that are deemed compliant are output as the target iron attachments.

[0014] In a preferred embodiment, the step of determining the quality consistency of the cured state data to obtain the quality determination result of the cured state data includes: The curing state data is subjected to quantitative index calculation to obtain the quality consistency quantitative value of the curing state data, wherein the calculation formula of the quality consistency quantitative value is as follows: ; In the formula, This is a quantification value for quality consistency. For coating material property coefficients, The standard deviation of the cured state data is... This is the average value of the cured state data. This refers to the relative non-uniformity of the coating's cured state. This is a correction amount for process stability; The consistency of the quality consistency quantification value and the preset quality threshold are judged to obtain the quality judgment result of the solidification state data.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention uses optical scanning of the initial surface of iron accessories to extract surface roughness distribution and oxide area distribution characteristics. Then, through parameter mapping and matching, optimized spraying parameters are obtained, which accurately adapt the spraying parameters to the actual surface condition of the iron accessories. This enhances the adhesion between the coating and the substrate and reduces the problem of uneven local anti-corrosion performance. During the spraying process, the film thickness distribution and surface coverage integrity are obtained by analyzing the coating condition monitoring dataset. Dynamic control commands are generated and output parameters are adjusted in real time. Real-time coating distribution data is monitored and obtained simultaneously. Combined with multimodal optimization, a uniform wet coating is formed, effectively ensuring the accuracy of the spraying process and the uniformity of the coating.

[0016] 2. This invention characterizes the curing state of a uniform wet coating and calculates the quality consistency quantification value using a quantitative formula to accurately determine the coating curing quality, ensuring that the output target iron accessories meet quality standards and improving the reliability of the finished product. At the same time, the entire process from initial parameter optimization to curing inspection is automated, reducing manual intervention. This not only improves the efficiency of automated spraying of anti-corrosion coatings for iron accessories but also ensures stable coating quality, helping to extend the service life of iron accessories. Attached Figure Description

[0017] Figure 1 A schematic flowchart of an automated spraying method for anti-corrosion coating of iron accessories provided in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0018] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0019] This application provides an automated method for spraying an anti-corrosion coating on iron fittings. The executing entity of this automated anti-corrosion coating method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the automated anti-corrosion coating method for iron fittings can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0020] Reference Figure 1The diagram shown is a flowchart illustrating an automated anti-corrosion coating spraying method for iron fittings according to an embodiment of the present invention. In this embodiment, the automated anti-corrosion coating spraying method for iron fittings includes: S1. Perform parameter mapping on the initial surface state of the iron accessory to obtain the optimized spraying parameters of the iron accessory; In this embodiment of the invention, the step of parameter mapping of the initial surface state of the iron accessory to obtain the optimized spraying parameters of the iron accessory includes: Optical scanning was performed on the iron fittings to obtain surface morphology data of the iron fittings; Feature extraction is performed on the surface morphology data to obtain the surface roughness distribution and oxidation region distribution of the surface morphology data; The surface roughness distribution and the oxide region distribution are matched using a parameter mapping table to obtain the optimized spraying parameters for the iron attachment.

[0021] The step of extracting features from the surface morphology data to obtain the surface roughness distribution and oxidation region distribution of the surface morphology data includes: The surface morphology data is analyzed for grayscale features to obtain the grayscale distribution information of the iron accessory surface; Texture feature extraction is performed on the grayscale distribution information to obtain the roughness distribution information of the iron accessory surface; Colorimetric information is collected from the surface morphology data to obtain the colorimetric distribution of the iron accessory surface; Regional feature identification is performed on the chromaticity distribution to obtain the distribution information of the oxide region on the surface of the iron accessory.

[0022] Specifically, a high-precision laser optical scanner is selected, and the iron accessory is fixed on a scanning worktable with a positioning fixture. The worktable is adjusted so that the surface of the iron accessory is 200 mm vertically from the scanner lens, and the surface is completely within the scanning range of the scanner, including all corners, holes and protrusions.

[0023] Furthermore, the scanner is started, the scanning path is set to a grid pattern, and the horizontal and vertical scanning intervals are both 0.01 mm. The laser beam emitted by the scanner moves along the path and vertically illuminates the surface of the iron accessory. When the beam encounters the uneven parts of the surface, it is reflected. The reflected light is captured by the receiving lens of the scanner, converted into an electrical signal, and transmitted to the data acquisition card.

[0024] Furthermore, the data acquisition card filters the electrical signal to remove noise caused by ambient light interference, and then converts the processed signal into a digital signal, which contains depth information and reflected light intensity information of each point on the surface of the iron accessory. These digital signals are arranged according to their spatial positions to form complete surface morphology data.

[0025] Furthermore, for the depth information in the surface topography data, 100 consecutive adjacent data points are selected as a group, and the difference between each point in each group and the average depth in the group is calculated. Areas with a difference of less than 0.02 mm are marked as low roughness areas, those with a difference between 0.02 and 0.05 mm are marked as medium roughness areas, and those with a difference of more than 0.05 mm are marked as high roughness areas. The boundary coordinates and area of ​​each area are recorded to form the surface roughness distribution.

[0026] Furthermore, for the reflected light intensity information in the surface morphology data, the average reflected light intensity of all data points is calculated, and data points with an intensity lower than 70% of the average value are marked as oxidation points. Adjacent oxidation points are connected to form closed regions. After checking that the intensity of all points in the region conforms to the characteristics of oxidation points, the contour coordinates and coverage area of ​​the region are recorded to form the oxidation region distribution.

[0027] Furthermore, a preset parameter mapping table is invoked, which stores the correspondence between the proportion of different roughness regions, the area and location of the oxidation region and the spraying parameters. For example, when the proportion of low roughness regions exceeds 80% and the area of ​​oxidation regions is less than 5%, it corresponds to specific spraying pressure, paint flow rate and spraying distance.

[0028] Furthermore, the proportion of each level of surface roughness distribution to the total surface area of ​​the iron attachment is calculated, the total area ratio and main distribution location of the oxide region are measured, and these data are compared one by one with the entries in the parameter mapping table to find the entries that match perfectly. In practice, the entry that is closest to the match can also be selected.

[0029] Furthermore, the spraying pressure, paint flow rate, spraying distance, and moving speed corresponding to the matching entries are extracted. These data together constitute the optimized spraying parameters for the iron accessories.

[0030] Specifically, the surface morphology data is imported into an image analysis device that integrates grayscale analysis, texture extraction, colorimetric acquisition, and region recognition functions. The device first reads the reflected light intensity information corresponding to each spatial point in the data. According to the fixed correspondence rule that "the higher the reflected light intensity, the higher the grayscale value", the reflected light intensity of each point is converted into a unique grayscale value. Then, according to the actual spatial coordinates of the iron accessory surface, the grayscale values ​​of all points are sequentially filled into a preset two-dimensional grid. The cells of the two-dimensional grid correspond one-to-one with the surface area of ​​the iron accessory, and the value in the cell is the grayscale value of the corresponding area. Finally, a two-dimensional grayscale grid covering the entire surface of the iron accessory is formed. This two-dimensional grayscale grid is the grayscale distribution information of the iron accessory surface.

[0031] Furthermore, based on the grayscale distribution information, the device starts a point-by-point scanning mode. For each cell in the grayscale distribution information, it retrieves the grayscale value of the cell and the four adjacent cells above, below, left, and right. The grayscale value of each cell is subtracted from the grayscale value of the adjacent cells, and the absolute value is taken to obtain the grayscale value difference in each direction. The difference values ​​in the four directions are temporarily stored in the device's temporary data cache area.

[0032] Furthermore, the equipment sets up a fixed-size cluster of continuous cells as analysis units according to the spatial dimensions of the iron accessory surface. Each analysis unit contains several continuous cells in both the horizontal and vertical directions, ensuring that the unit size can cover local surface features without missing details. Then, the gray value differences in the temporary buffer area are classified according to the analysis units.

[0033] Furthermore, for each analysis unit, the device statistically summarizes the sum of all grayscale value differences after classification, compares the sum with the preset texture judgment boundary, and marks units with a sum less than the boundary as texture smooth areas and units with a sum greater than the boundary as texture rough areas. At the same time, the starting and ending coordinates of the boundary cells of each region are recorded.

[0034] Furthermore, the equipment counts the number of cells covered by each area, and calculates the actual coverage area of ​​the area by combining the size ratio of the cells to the actual surface of the iron attachment. Then, it integrates the boundary coordinates, actual area and corresponding roughness level of the area in spatial order to form the roughness distribution information of the iron attachment surface.

[0035] Furthermore, the device calls upon its built-in colorimetric acquisition module, which extracts the light reflection spectrum information of each spatial point from the surface topography data. The module then uses prism spectral splitting technology to decompose the spectrum into three monochromatic lights: red, green, and blue. The device's built-in light intensity sensor receives each of the three monochromatic lights and measures the light intensity value of each monochromatic light.

[0036] Furthermore, the device divides the intensity value of each monochromatic light by the sum of the intensities of the three monochromatic lights to obtain the intensity ratio of each color light in the total spectrum. Then, according to the colorimetric conversion rules stipulated by the International Commission on Illumination, the intensity ratios of red, green, and blue light are substituted into a fixed colorimetric conversion process to calculate the colorimetric coordinate value corresponding to each point.

[0037] Furthermore, the equipment initiates a three-dimensional coordinate input program, and according to the spatial position order of the iron accessory surface, associates the chromaticity coordinate value of each point with the corresponding spatial coordinate, and inputs them one by one into the preset three-dimensional coordinate system to form a dataset containing the chromaticity information and spatial position of all points. This dataset is the chromaticity distribution of the iron accessory surface.

[0038] Furthermore, the equipment retrieves a preset standard chromaticity coordinate library for unoxidized iron surfaces. The library stores the standard chromaticity coordinate range for unoxidized pure iron surfaces under the same illumination conditions. The equipment compares the chromaticity coordinate value of each point in the chromaticity distribution with the standard coordinate range one by one, and calculates the degree of deviation between the coordinates of each point and the standard coordinate range.

[0039] Furthermore, the device marks points that deviate beyond the set range and whose chromatic coordinates match the characteristics of iron oxide as candidate oxidation points, and records the spatial coordinates of each candidate point to avoid missing potential oxidation areas.

[0040] Furthermore, the device performs neighborhood search on the oxidation candidate points according to the principle of spatial continuity, connecting adjacent candidate points within a set distance with virtual line segments to gradually form a closed polygonal region, ensuring that all adjacent candidate points are included in the same region.

[0041] Furthermore, the equipment performs point-by-point checks on each closed polygonal region, verifying whether the chromaticity coordinates of all points within the region conform to the oxidation characteristics. If there are any points that do not conform, the region boundary is adjusted to remove those points until all points within the region meet the oxidation characteristics, thus confirming the region as an oxidation region.

[0042] Furthermore, the equipment records the boundary coordinates of each oxidized area, the total number of points within the area, and the converted actual coverage area, classifying and integrating the information of all oxidized areas according to their spatial location to form the distribution information of oxidized areas on the surface of iron accessories.

[0043] In summary, by acquiring surface morphology data through optical scanning, extracting features to obtain surface roughness distribution and oxide region distribution, and then determining optimized spraying parameters through parameter mapping table matching, the entire process achieves precise matching between the surface condition of iron accessories and spraying parameters, providing a reliable basis for efficient and high-quality spraying operations.

[0044] In summary, the entire process starts with surface morphology data and proceeds in stages and steps to perform grayscale feature analysis, texture feature extraction, color information acquisition, and oxidation area identification. Each step is carried out around a clear objective, ultimately generating complete grayscale distribution information, roughness distribution information, color distribution, and oxidation area distribution information. This provides accurate and comprehensive surface condition data support for subsequent matching and optimization of spraying parameters.

[0045] S2. Obtain the coating condition monitoring dataset of the iron accessory during the spraying process, make a uniformity decision on the coating condition monitoring dataset, and obtain the dynamic control command of the spraying process. In this embodiment of the invention, the step of performing uniformity decision-making on the coating condition monitoring dataset to obtain dynamic control instructions for the spraying process includes: The thickness change trend of the coating condition monitoring dataset was analyzed to obtain the film thickness distribution characteristics of the coating in the iron accessory; By performing coverage defect identification on the coating condition monitoring dataset, the surface coverage integrity information of the coating is obtained; By jointly analyzing the film thickness distribution characteristics and the surface coverage integrity information, a comprehensive evaluation conclusion on the coating uniformity of the iron accessory coating is obtained. Based on the comprehensive evaluation conclusion of the coating uniformity, a strategy mapping and matching is performed on the preset control strategy library to obtain the dynamic control instructions for the spraying process.

[0046] The step of performing strategy mapping and matching on a preset control strategy library based on the comprehensive evaluation conclusion of the coating uniformity to obtain dynamic control instructions for the spraying process includes: The coating uniformity comprehensive evaluation conclusion is mapped to a level to obtain the uniformity level identifier of the coating uniformity comprehensive evaluation conclusion; Based on the uniformity level identifier, a strategy hierarchy matching is performed on the preset control strategy library to obtain a preliminary control instruction set corresponding to the uniformity level identifier; The preliminary control instruction set is reconstructed into executable form to obtain the dynamic control instructions for the spraying process.

[0047] Specifically, a coating condition monitoring dataset is first obtained. This dataset contains real-time coating thickness measurements at different spatial points on the surface of the iron fittings, spatial coordinates of each point, and coating presence markers for each point. Then, based on the geometric features of the iron fitting surface, adjacent points with continuous spatial coordinates are grouped into the same analysis region to ensure that each region can reflect the coating condition of the local surface of the iron fittings, thus laying the foundation for subsequent analysis.

[0048] Furthermore, for each analysis area, the coating thickness measurements at all points within that area are arranged in chronological order of spraying time to form a unique thickness time series for each area. By observing the variation pattern of the thickness values ​​in the series, the variation patterns are divided into two categories: "increases over time and then remains stable" and "continues to fluctuate within a small range over time," and these are marked accordingly. At the same time, the final thickness value of each area after its thickness stabilizes is recorded.

[0049] Furthermore, the final thickness values ​​of all analyzed areas are summarized, and the differences in the final thickness values ​​between different areas are compared to clarify the magnitude of the thickness differences between areas. Then, the thickness variation pattern, final thickness value, and thickness differences between areas of each area are organized in the spatial order of the iron fitting surface to form a characteristic of the film thickness distribution state of the coating in the iron fitting that can comprehensively reflect the coating thickness distribution.

[0050] Further, the process switches to the coating defect identification stage. The coating presence marker information of each spatial point is extracted from the coating condition monitoring dataset. The points are checked one by one according to the spatial coordinates of the iron accessory surface. Points marked as "uncovered" are identified as defect points. Through neighborhood retrieval, adjacent defect points are connected to form closed defect regions. At the same time, the boundary coordinates of each defect region and the number of defect points contained in the region are recorded.

[0051] Furthermore, based on the conversion relationship between the coordinates of the centralized points in the data set and the actual surface dimensions of the iron fittings, the actual area of ​​each defect area is calculated. The actual areas of all defect areas are added together to obtain the total defect area on the surface of the iron fittings. The total defect area is then subtracted from the total surface area of ​​the iron fittings to obtain the actual coverage area of ​​the coating on the surface of the iron fittings. Finally, the actual coverage area is divided by the total surface area of ​​the iron fittings to obtain the coating coverage area ratio. The relevant information of the defect areas is integrated with the coverage area ratio to form the surface coverage integrity information of the coating.

[0052] Furthermore, a collaborative analysis of film thickness distribution characteristics and surface coverage integrity information is conducted. First, it is checked whether the final thickness value of each analysis area in the film thickness distribution characteristics is within the preset reasonable thickness range. The proportion of the number of areas with thickness values ​​within the reasonable range to the total number of analysis areas is counted. At the same time, combined with the coating coverage area ratio in the surface coverage integrity information, a preliminary judgment is made according to the standard that "a high proportion of qualified thickness areas and a high coverage area ratio indicates excellent uniformity, a medium proportion of qualified thickness areas and a medium coverage area ratio indicates good uniformity, and a low proportion of qualified thickness areas or a low coverage area ratio indicates poor uniformity".

[0053] Furthermore, the preliminary judgment results are revised: if the preliminary judgment is "excellent uniformity" but there are multiple adjacent small defect areas, it is revised to "good uniformity"; if the preliminary judgment is "poor uniformity" but all defect areas are isolated and small in area, it is revised to "good uniformity". Through the revision, the final comprehensive evaluation conclusion of the coating uniformity of the iron accessory coating is formed.

[0054] Furthermore, a preset control strategy library is invoked. This library stores corresponding spraying control measures according to three evaluation levels: "excellent uniformity," "good uniformity," and "poor uniformity." For example, "excellent uniformity" corresponds to "maintaining the current spraying parameters," "good uniformity" corresponds to "fine-tuning the spraying speed to reduce the thickness difference between areas," and "poor uniformity" corresponds to "adjusting the spraying pressure and re-spraying the defective areas." The control measure item that perfectly matches the current comprehensive evaluation conclusion of coating uniformity is retrieved from the strategy library.

[0055] Furthermore, the spraying control measures corresponding to the matching entries are extracted, the output parameters of the spraying process that need to be adjusted in the measures and the specific adjustment direction are identified, and then these adjustment information are organized according to the execution logic of the spraying equipment and the sequence of the spraying process to form dynamic control instructions for the spraying process that can directly guide the operation of the spraying equipment.

[0056] Specifically, the comprehensive evaluation conclusion of the coating uniformity of the iron fittings obtained in the previous stage is first obtained, and the preset level mapping rule is retrieved. This rule clearly stipulates the unique correspondence between the comprehensive evaluation conclusion of different types of coating uniformity and the corresponding uniformity level label. For example, "excellent coating uniformity" corresponds to the level label "G1", "good coating uniformity" corresponds to the level label "G2", and "poor coating uniformity" corresponds to the level label "G3", providing a clear basis for subsequent level mapping.

[0057] Furthermore, the current comprehensive evaluation conclusion on coating uniformity is analyzed to determine its specific evaluation type and whether it belongs to the "excellent", "good", or "poor" category, ensuring that the judgment of the conclusion is unbiased.

[0058] Furthermore, the evaluated type is compared one by one with the preset level mapping rules to find the rule entry that perfectly matches the evaluated type. The corresponding symbol or code is extracted from the level identifier marked in the entry. For example, when the evaluation conclusion is "good coating uniformity", the level identifier "G2" is extracted.

[0059] Furthermore, the extracted grade identifiers are verified to confirm that the correspondence between the identifiers and the evaluation conclusions conforms to the preset rules, with no mismatches or omissions. Finally, the uniformity grade identifiers of the comprehensive evaluation conclusion of the coating uniformity are determined and recorded.

[0060] Furthermore, a preset control strategy library is invoked. This strategy library is divided into different strategy levels according to the uniformity level identifier. Each level corresponds uniquely to a uniformity level identifier. The level stores all control strategies for the uniformity problem of that level. For example, the "G1" level stores the strategy of "maintaining the current spraying pressure, spraying speed and spraying distance", the "G2" level stores the strategy of "fine-tuning the spraying speed to reduce the difference in coating thickness", and the "G3" level stores the strategy of "increasing the spraying pressure, adjusting the spraying distance and re-spraying the defective area".

[0061] Furthermore, based on the determined uniformity level identifier, the corresponding strategy level is located in the preset control strategy library. For example, when the level identifier is "G2", the "G2" strategy level is located. Then, all control strategy contents under this level are extracted, and these strategy contents are classified and organized according to the control object to form a preliminary control instruction set corresponding to the uniformity level identifier.

[0062] Furthermore, the initial control instruction set is broken down into its contents, and each control strategy is decomposed into specific operational requirements. For example, "fine-tuning the spraying speed" is broken down into "adjusting the direction of the spraying speed" and "the object to be adjusted", ensuring that each operational requirement is clear and specific.

[0063] Furthermore, based on the actual operating parameter range and process execution logic of the spraying equipment, the disassembled operating requirements are adapted and adjusted. For example, it is confirmed that the direction and amplitude of the spraying speed adjustment are within the allowable operating range of the equipment to avoid exceeding the equipment's operating limits.

[0064] Furthermore, the adjusted operational requirements are reordered according to the sequence of the spraying process. For example, the spraying pressure is adjusted first, then the spraying speed is adjusted, and finally the touch-up spraying operation is performed. At the same time, the connection instructions between the operations are supplemented to ensure that the equipment can be executed sequentially and continuously.

[0065] Furthermore, the sorted operational requirements are converted into instruction formats that the spraying equipment can recognize. For example, "increase spraying pressure" is converted into "set the target pressure of the spraying pressure control module to the current pressure plus the specified adjustment amount". The format of each instruction is checked to ensure that the equipment can accurately read and execute it, and finally obtain the dynamic control instructions for the spraying process.

[0066] In summary, the entire process is based on the coating condition monitoring dataset. It completes the analysis of thickness change trends, identification of coverage defects, collaborative evaluation of uniformity, and matching of control strategies in steps. Gradually, it generates film thickness distribution characteristics, surface coverage integrity information, and comprehensive evaluation conclusions of coating uniformity. Finally, it obtains dynamic control instructions to achieve precise feedback and adjustment of the spraying process, effectively ensuring the uniformity of the coating on iron accessories and the overall spraying quality.

[0067] In summary, the entire process starts with the comprehensive evaluation conclusion of coating uniformity. Through grade mapping, the qualitative conclusion is transformed into a standardized uniformity grade identifier. Based on the grade identifier, a targeted preliminary control instruction set is matched from the layered design of the preset control strategy library. Then, through decomposition, adaptation, sorting and format conversion, an executable reconstruction is completed, and finally, dynamic control instructions that the equipment can directly execute are formed. This realizes a closed loop from coating uniformity evaluation to precise adjustment of the spraying process, ensuring the stable improvement of coating uniformity of iron accessories.

[0068] S3. Based on the dynamic control command, the output parameters of the spraying process are adjusted in real time, and the spraying process after real-time adjustment is monitored synchronously to obtain the real-time coating distribution data of the iron accessory. In this embodiment of the invention, the step of adjusting the output parameters of the spraying process in real time based on the dynamic control command, and synchronously monitoring the adjusted spraying process to obtain real-time coating distribution data of the iron accessory includes: The control parameters of the dynamic control command are deconstructed to obtain the adjustment parameters in the dynamic control command. The adjustment parameters are remapped to output parameters to obtain the parameter adjustment instructions corresponding to the adjustment parameters; The spraying output parameters are adjusted in real time based on the parameter adjustment command to obtain the adjustment effect of the spraying output parameters. The coating state is sensed to obtain real-time coating distribution data of the iron accessory by sensing the control effect.

[0069] Specifically, the dynamic control instructions for the spraying process generated earlier are first obtained. These instructions contain specific control requirements for the spraying process. Then, the text structure of the dynamic control instructions is parsed to extract the spraying output parameter control objects involved in the instructions. For each control object, the explicit adjustment requirements in the instructions are extracted, and the control objects are associated and integrated with the corresponding adjustment requirements, such as "spraying pressure - increase" and "spraying speed - fine-tune". These associated control objects and adjustment requirements together constitute the adjustment parameters in the dynamic control instructions.

[0070] Furthermore, the preset output parameter remapping rules are retrieved. These rules store the correspondence between adjustment parameters and each output module of the spraying equipment, as well as the execution logic corresponding to different adjustment requirements, providing a clear basis for subsequent parameter conversion.

[0071] Furthermore, each adjustment parameter is extracted one by one. First, the control object and adjustment requirements of each parameter are confirmed to avoid mapping errors caused by confusion of parameter information.

[0072] Furthermore, in the output parameter remapping rules, based on the combination of the control object and the adjustment requirements, a completely matching rule entry is found, and the corresponding device module identifier and specific execution details within the entry are extracted.

[0073] Furthermore, the equipment module identification, adjustment actions, and execution details are integrated into structured instructions, such as "Pressure control module - increase the opening of the air circuit valve - adjust gradually according to the preset range". Such structured instructions are the parameter adjustment instructions corresponding to the adjustment parameters.

[0074] Furthermore, the parameter adjustment command is sent to the corresponding spraying equipment output module through the industrial Ethernet interface. At the same time as sending, a command verification code is generated to ensure that the command is not damaged or tampered with during transmission.

[0075] Furthermore, after receiving the instruction, each device's output module first verifies the checksum. Once the verification is successful, it parses the instruction content to determine the adjustment actions it needs to perform.

[0076] Furthermore, after the pressure control module interprets the instruction to "increase the opening of the air circuit valve", it calls the internal valve controller to drive the valve to rotate gradually by a preset range to increase the opening; after the speed control module interprets the instruction to "fine adjust the speed", it starts the motor drive unit and adjusts the motor input current within a small range to change the speed.

[0077] Furthermore, by utilizing real-time monitoring sensors installed on each output module, the actual values ​​of the adjusted spraying output parameters are continuously collected, and the process of value change is recorded.

[0078] Furthermore, the collected adjusted actual values ​​are compared with the stable values ​​before adjustment to determine whether the parameter changes meet the requirements of the parameter adjustment command. At the same time, the time it takes for the adjusted parameters to reach a stable state is recorded. This information, including actual values, deviations, and stability time, together constitutes the control effect of the spray output parameters.

[0079] Furthermore, a laser confocal scanning device is activated as a coating condition sensing device. A grid-like scanning path is set according to the geometry of the iron attachment to ensure that the scanning points are evenly distributed and completely cover the entire coating surface of the iron attachment.

[0080] Furthermore, the equipment emits a laser signal suitable for metal coating detection. The laser shines vertically onto the coating surface, and part of the laser is reflected by the coating, forming a reflected signal.

[0081] Furthermore, the device's built-in photoelectric converter receives the reflected signal, converts the light signal into an electrical signal, and then calculates the coating thickness at the corresponding scanning point based on the intensity and propagation time of the reflected signal.

[0082] Furthermore, the coating coverage is determined by the difference in reflected signal intensity: the uncoated area has a significantly different reflected signal intensity compared to the coated area because the metal substrate of the iron accessory is exposed. Such areas are marked as "uncoated", while the coated area is marked as "coated".

[0083] Furthermore, by using the device's built-in positioning system, the three-dimensional spatial coordinates of each scanning point are obtained, and the coordinates are associated with the corresponding coating thickness value and coverage status one by one to avoid data misalignment.

[0084] Furthermore, according to the horizontal and vertical spatial order of the iron accessory surface, the associated "spatial coordinates-thickness value-coverage status" data is organized into a structured dataset, which is the real-time coating distribution data of the iron accessory.

[0085] In summary, the entire process starts with dynamic control commands and gradually completes the entire process from command parsing to data acquisition through multi-step detailed control parameter deconstruction, output parameter remapping, real-time control and status awareness operation. Each step revolves around clear operational goals and equipment interaction details, ensuring accurate extraction of adjustment parameters, reliable conversion of parameter commands, comprehensive monitoring of control effects, and accurate acquisition of coating data. Ultimately, it provides clear and controllable real-time status information for the continuous optimization of the spraying process.

[0086] S4. Based on the real-time coating distribution data, perform multi-modal real-time optimization of the spraying process to obtain a uniform wet coating for the iron accessory; In this embodiment of the invention, the step of performing multimodal real-time optimization of the spraying process based on the real-time coating distribution data to obtain a uniform wet coating on the iron accessory includes: Multi-source feature fusion is performed on the real-time coating distribution data to obtain a coating state comprehensive descriptor that characterizes the overall coating state; The coating state comprehensive descriptor is subjected to spraying parameter optimization matching to obtain the optimized spraying parameters corresponding to the coating state comprehensive descriptor; The spraying process is optimized and modulated in real time based on the optimized spraying parameters to obtain a uniform wet coating for the iron accessory.

[0087] The process of fusing multi-source features from the real-time coating distribution data to obtain a comprehensive coating state descriptor characterizing the overall coating state includes: The surface morphology of the real-time coating distribution data is analyzed to obtain the surface morphology feature information of the real-time coating distribution data. The material distribution of the real-time coating distribution data is analyzed to obtain the material distribution feature information of the real-time coating distribution data; Multi-source feature fusion is performed on the surface morphology feature information and the material distribution feature information to obtain a coating multimodal fusion descriptor of feature information; The coating multimodal fusion descriptor is refined to obtain the coating state comprehensive descriptor.

[0088] Specifically, real-time coating distribution data of iron accessories is first obtained. This data includes the thickness value, coverage status and thickness distribution differences in different regions corresponding to each spatial coordinate on the coating surface. Then, the multi-source feature fusion process is started to clarify the feature types to be fused as thickness features, coverage features and spatial distribution features, thus defining the scope for subsequent fusion operations.

[0089] Furthermore, thickness features are extracted from the real-time coating distribution data: according to the preset area division rules of the iron accessory surface, the points with adjacent coordinates are grouped into the same analysis area, the average, maximum and minimum values ​​of coating thickness in each area are statistically analyzed, and areas with thickness exceeding the preset reasonable range are marked to form a thickness feature set.

[0090] Furthermore, the coverage features are extracted: the proportion of points marked as "covered" in the real-time coating distribution data is statistically analyzed to determine the coverage integrity level, and the spatial coordinates of the points marked as "uncovered" are recorded to form a coverage feature set.

[0091] Furthermore, spatial distribution features are extracted: the spatial relationship between areas with abnormal thickness and uncovered areas is analyzed to determine whether there are concentrated problem areas, and the boundary coordinates and shapes of the problem areas are recorded to form a spatial distribution feature set.

[0092] Furthermore, according to the preset feature fusion rules, the thickness feature set, coverage feature set and spatial distribution feature set are associated: the thickness data, coverage status and whether it is a problem area in the same spatial area are integrated to avoid feature misalignment.

[0093] Furthermore, the associated features are organized according to the structure of "region identifier - thickness statistics - coverage status - problem type" to form structured data containing key status information of the coating on the entire surface of the iron accessories. This structured data is the coating status comprehensive descriptor that characterizes the overall status of the coating.

[0094] Furthermore, a preset spraying parameter optimization matching library is retrieved. This library stores a unique correspondence between comprehensive descriptors of different coating states and corresponding optimized spraying parameters. For example, "poor thickness uniformity and high coverage" corresponds to "increasing local spraying flow and maintaining spraying speed", and "medium thickness uniformity and low coverage" corresponds to "adjusting spraying angle and reducing spraying speed", providing a basis for parameter matching.

[0095] Furthermore, the content of the coating condition comprehensive descriptor is analyzed: the problem type corresponding to the area identifier, the uniformity level corresponding to the thickness statistics, and the integrity level corresponding to the coverage status are extracted from the descriptor to ensure that no information is omitted in the analysis.

[0096] Furthermore, in the coating parameter optimization matching library, a layer-by-layer search is performed according to the parsed problem type, uniformity level, and integrity level to find library entries that completely match the current coating state comprehensive descriptor information, thus avoiding matching deviations due to missing information.

[0097] Furthermore, the corresponding spraying parameters in the matching entries are extracted, including the adjustment direction of spraying pressure, the setting requirements of spraying speed, the control standard of spraying flow rate, and the adjustment value of spraying angle, to ensure that the parameters cover the key control items of the spraying process.

[0098] Furthermore, the extracted spraying parameters are verified: by comparing them with the rated operating parameter range of the spraying equipment, it is confirmed that the extracted parameter values ​​are within the adjustable range of the equipment. After verification, the optimized spraying parameters corresponding to the coating state comprehensive descriptor are formed.

[0099] Furthermore, the optimized spraying parameters are converted into electrical signals that can be recognized by each control module of the spraying equipment: the spraying pressure parameters are converted into voltage drive signals of the pressure control module, the spraying speed parameters are converted into pulse signals of the speed drive module, and the spraying flow parameters are converted into current signals of the flow control module, ensuring that the signal format matches the equipment interface.

[0100] Furthermore, the converted electrical signals are sent one by one from the main control interface to the corresponding control modules: the pressure signal is sent to the air pressure control unit, the speed signal is sent to the robotic arm drive unit, and the flow signal is sent to the paint delivery pump control unit, thus achieving precise transmission of parameter signals.

[0101] Furthermore, each control module activates its adjustment mechanism upon receiving an electrical signal: the pressure control unit changes the valve opening by rotating the valve core of the air circuit valve to bring the air circuit pressure to the optimized parameter setting value; the speed drive unit changes the movement speed of the robotic arm by adjusting the motor input frequency to match the optimized speed requirements; and the flow control unit changes the paint output by adjusting the pump speed to meet the optimized flow standard.

[0102] Furthermore, during parameter modulation, the equipment's built-in real-time monitoring system is activated: the pressure sensor collects the actual pressure value of the air path, the speed encoder collects the actual moving speed of the robotic arm, and the flow sensor collects the actual output of the coating. The collected actual values ​​are compared with the optimized parameter values ​​in real time. If there is a deviation, it is fed back to the control module, and the electrical signal is fine-tuned until the actual value matches the optimized value.

[0103] Furthermore, during the spraying process, a high-definition vision camera is used to capture the wet coating formation process on the surface of the iron accessories in real time to observe whether the coating is evenly covered and whether there are any drips or missed sprays. If uneven thickness of the wet coating is found in some areas, the spraying state is corrected by fine-tuning the electrical signal of the corresponding control module through the main control interface in combination with the adjustment logic of the optimization parameters.

[0104] Furthermore, the formation of the wet coating is continuously monitored until a wet coating of uniform thickness, without any missed sprays or drips, is formed on the entire surface of the iron accessory, ultimately resulting in a uniform wet coating on the iron accessory.

[0105] Specifically, we first obtain real-time coating distribution data of the iron fittings. This data includes the thickness value, reflectance spectrum information and spatial location correlation data corresponding to each spatial coordinate of the coating surface. We then identify the core fields in the data that are related to the surface morphology and material distribution of the coating, and define the data range for subsequent analysis.

[0106] Furthermore, for the analysis of the coating surface morphology, the thickness value corresponding to each spatial coordinate is extracted from the real-time coating distribution data. The thickness value is equated to the height value of the coating surface relative to the iron attachment substrate, and a correspondence between "spatial coordinates and height value" is established to ensure that each coordinate point has unique height data support.

[0107] Furthermore, based on the geometric structure of the iron fitting surface, adjacent points with continuous spatial coordinates are grouped into the same analysis region. Each region covers a local area of ​​the iron fitting surface, avoiding the omission of details due to an excessively large region or the fragmentation of data due to an excessively small region.

[0108] Furthermore, for each analysis area, the height values ​​of all points within the area are statistically analyzed to determine the trend of height value changes—areas with small height value fluctuations are marked as "flat areas," and areas with large fluctuations are marked as "undulating areas." At the same time, the maximum difference in height values ​​within the area is recorded to clarify the degree of undulation.

[0109] Furthermore, the "flat / undulating markers" and "maximum height difference" of all analysis areas are integrated and arranged in spatial order into structured information containing area boundary coordinates, morphology type, and undulation parameters. This structured information is the surface morphology feature information of the real-time coating distribution data.

[0110] Furthermore, the analysis shifts to the distribution of coating materials. The reflectance spectrum information corresponding to each spatial coordinate is extracted from the real-time coating distribution data. This information includes the reflectance data of the coating material to light of different wavelengths, which is the core basis for judging the material.

[0111] Furthermore, a pre-set standard spectral library of coating materials is retrieved. The library stores standard reflectance spectral curves of common spray coating materials under the same testing conditions to ensure the consistency of the comparison benchmark.

[0112] Furthermore, the reflectance spectral information of each spatial coordinate point is compared one by one with the curves in the standard spectral library. By observing the matching degree of the peak wavelength, trough wavelength and reflectance variation trend of reflectance, the coating material type corresponding to the coordinate point is determined. For example, the point where the peak wavelength of reflectance is consistent with the epoxy resin standard curve is marked as "epoxy resin material".

[0113] Furthermore, the material types of all spatial coordinate points are statistically analyzed, and adjacent points of the same material type are grouped into the same material region. The boundary coordinates and the number of points contained in each material region are recorded. At the same time, the proportion of the area of ​​each material region to the total surface area of ​​the coating is calculated. This information is integrated to form the material distribution characteristic information of the real-time coating distribution data.

[0114] Furthermore, multi-source feature fusion is carried out. First, an association rule with "spatial coordinates as the core" is established. The "regional morphology type and undulation parameters" corresponding to the same spatial coordinate in the surface morphology feature information are bound to the "material type and material region" corresponding to the same coordinate in the material distribution feature information, so as to ensure that the two types of features at the same coordinate point correspond accurately.

[0115] Furthermore, according to the spatial coordinate order of the iron attachment surface, the bound "spatial coordinates - morphological features - material features" information is organized one by one to form a dataset containing dual features of all coordinate points on the entire surface. This dataset is the coating multimodal fusion descriptor of feature information.

[0116] Furthermore, feature refinement is carried out on the multimodal fusion descriptor of the coating. First, key features are selected: core information such as "regional topography type, undulation parameters, material type, and material area proportion" are retained, and duplicate coordinate point basic data are removed to reduce data redundancy.

[0117] Furthermore, verify the consistency of the filtered features: check whether there are contradictions in the morphological features within the same material area. If there are contradictions, backtrack to the previous analysis steps, verify the original data and analysis process, and correct the contradictory features.

[0118] Furthermore, the verified key features are reorganized according to the structure of "material region - region morphology - fluctuation parameters - material proportion" to form logically clear and information-complete structured data. This structured data is the coating state comprehensive descriptor of the coating multimodal fusion descriptor.

[0119] In summary, the entire process is based on real-time coating distribution data. By fusing multi-source features, the dispersed coating data is transformed into a structured comprehensive descriptor of the coating state. Optimized spraying parameters are obtained through precise matching based on a preset matching library. Then, the spraying process is optimized and modulated through signal conversion, equipment control, and real-time monitoring. Each step revolves around the core objective of "improving coating uniformity" to ensure that the final wet coating meets uniformity requirements, thus guaranteeing stable performance after the coating has cured.

[0120] In summary, the entire process is based on real-time coating distribution data. It extracts two core features—surface morphology and material distribution—through step-by-step analysis, achieves precise correlation of multi-source features using spatial coordinates, and then refines the data through feature screening and consistency verification. Finally, it forms a comprehensive coating state descriptor that can fully characterize the coating state. Each step revolves around the accuracy of data analysis and the logic of feature fusion to ensure that the descriptor can truly reflect the actual state of the coating and provide a reliable basis for subsequent spraying parameter optimization.

[0121] S5. Perform a curing inspection on the uniform wet coating, and output the iron accessories that pass the curing inspection as the target iron accessories.

[0122] In this embodiment of the invention, the step of performing a curing inspection on the uniform wet coating, and outputting the iron accessories that pass the curing inspection as target iron accessories, includes: The curing state of the uniform wet coating is characterized to obtain curing state data of the uniform wet coating; The quality consistency of the cured state data is determined to obtain the quality determination result of the cured state data. Based on the quality assessment results, a compliance decision is made for the iron attachments, and the iron attachments that are deemed compliant are output as the target iron attachments.

[0123] The step of performing a quality consistency determination on the cured state data to obtain a quality determination result for the cured state data includes: The curing state data is subjected to quantitative index calculation to obtain the quality consistency quantitative value of the curing state data, wherein the calculation formula of the quality consistency quantitative value is as follows: ; In the formula, This is a quantification value for quality consistency. For coating material property coefficients, The standard deviation of the cured state data is... This is the average value of the cured state data. This refers to the relative non-uniformity of the coating's cured state. This is a correction amount for process stability; The consistency of the quality consistency quantification value and the preset quality threshold are judged to obtain the quality judgment result of the solidification state data.

[0124] Specifically, first, a uniform wet coating is formed on the surface of the iron accessory. The iron accessory is then fixed on the worktable of the curing state characterization equipment. The position of the worktable is adjusted so that the coating surface is perpendicular to the detection probe and completely within the detection range, ensuring that the detection signal can cover the entire surface of the coating and avoiding incomplete data due to omissions in the detection range.

[0125] Further, the differential scanning calorimeter is started, and the heating program of the device is set: the temperature of the detection area is gradually increased at a preset rate, and the thermal response signal of the coating at different temperatures is collected in real time. The progress of the cross-linking reaction of the resin inside the coating is judged by the change of the thermal response signal. If the signal is stable and there is no obvious heat change, it means that the cross-linking reaction is basically completed; if the signal shows obvious heat fluctuation, it means that the cross-linking reaction is still in progress. The thermal response characteristics corresponding to different temperature ranges are recorded to form thermal response data.

[0126] Furthermore, using a Shore hardness tester, multiple test points are selected on the coating surface according to the principle of uniform distribution. The same test pressure is applied to each point, and the pressure is kept stable until the hardness tester reading no longer changes. The hardness value of each point is recorded to avoid data deviation caused by uneven pressure or improper reading timing. The hardness values ​​of all points are then compiled to form hardness test data.

[0127] Furthermore, the thermal response data and hardness test data are correlated according to the test points: the thermal response characteristics and hardness values ​​of the same test point correspond one-to-one to avoid data misalignment. Then, the data is organized according to the structure of "test point - thermal response characteristics - hardness value" to form structured data that includes the degree of coating curing and the distribution of surface hardness. This structured data is the curing state data of the uniform wet coating.

[0128] Furthermore, a pre-set coating quality judgment standard library is retrieved. This library stores the acceptable ranges of key indicators of the curing state data. For example, "high degree of curing and consistent hardness values ​​at all points" is the acceptable standard, while "low degree of curing or large differences in hardness values" is the unacceptable standard. This clarifies the basis for quality judgment and avoids ambiguity in the judgment standards.

[0129] Furthermore, the content of the curing state data is analyzed: the curing degree level corresponding to the thermal response characteristics in the data is extracted, the differences in hardness values ​​at all test points are statistically analyzed, and it is confirmed whether the two key indicators meet the qualification requirements in the standard library to ensure that no information is omitted in the analysis.

[0130] Furthermore, the analyzed curing degree level and hardness difference are compared item by item with the acceptable range in the quality judgment standard library: if the curing degree level is "high" and the hardness value difference is within the acceptable range, it is judged as "consistent quality"; if any indicator does not meet the acceptable range, it is judged as "inconsistent quality". The judgment result is recorded to form the quality judgment result of the curing state data.

[0131] Furthermore, establish compliance decision-making rules: clearly define "quality judgment result of 'quality consistent'" as a "compliant" decision and "quality judgment result of 'quality inconsistent'" as a "non-compliant" decision, providing a clear basis for decision-making and avoiding confusion in decision-making logic.

[0132] Furthermore, the iron accessories are classified according to the quality judgment results: iron accessories judged as "compliant" are marked as "target iron accessories to be output", and the iron accessory number, coating inspection time and key inspection data are recorded for subsequent traceability; iron accessories judged as "non-compliant" are marked as "to be reworked", and the reasons for non-compliance are recorded simultaneously so that the curing process can be adjusted accordingly in the future.

[0133] Furthermore, a second verification is conducted on the iron accessories marked as "target iron accessories to be output": the solidification status data of some test points are checked again to confirm that the previous judgment results are without deviation, and to avoid misjudgment due to single test error. If the verification is passed, the mark is retained; if the verification fails, the solidification status characterization and quality judgment are carried out again.

[0134] Furthermore, the iron fittings that have passed the second verification and whose compliance decision results are compliant are classified and organized according to production batch and specifications to generate a target iron fitting list. The list includes information such as iron fitting number, test data, and compliance determination time. Finally, the iron fitting entity corresponding to the list is output, which is the target iron fitting.

[0135] Specifically, the curing state data of the uniform wet coating is first obtained. This data includes a description of the curing degree of each detection point on the coating surface and the hardness test results of the corresponding points. The core information dimensions in the data that can be used to calculate the quantitative value of quality consistency are identified, and the scope is defined for subsequent quantitative calculations.

[0136] Furthermore, for the curing degree description data, the preset curing degree mapping rule is retrieved. The rule clearly states that "complete cross-linking" corresponds to "high curing compliance", "partial cross-linking" corresponds to "medium curing compliance", and "insufficient cross-linking" corresponds to "low curing compliance". According to this rule, the curing degree description of each test point is converted into a unified compliance category to avoid calculation deviations caused by differences in description.

[0137] Furthermore, the number of "high curing compliance" points among all testing points is counted, and the proportion of this number to the total number of testing points is calculated. This proportion reflects the overall curing compliance of the coating and serves as the first core dimension for quantifying quality consistency.

[0138] Furthermore, the hardness test results are processed by sorting the hardness test results of all points according to their numerical values, identifying the highest and lowest values, and analyzing the difference between them. A small difference indicates that the hardness distribution is uniform, while a large difference indicates that the hardness distribution is uneven. The difference is then converted into a "hardness uniformity level" as the second core dimension for quantifying quality consistency.

[0139] Furthermore, the preset quantitative value integration rules are retrieved. The rules stipulate that "compliance rate of solidification" has the main consideration weight and "hardness uniformity grade" has the secondary consideration weight. This clarifies the weight allocation of each dimension for calculating the quantitative value of quality consistency, and at the same time, it is necessary to determine the source of the core parameters required for the calculation.

[0140] Furthermore, the property coefficient of the coating material The source of this value is the inherent performance parameter of the base material used in the coating. This parameter is obtained by consulting the coating material manufacturer's technical manual, or by using material performance testing equipment to test the physicochemical properties of the coating sample during the coating material testing stage. Based on the test results, a unique corresponding coefficient value is determined to ensure... It can reflect the influence of the coating material itself on the curing quality.

[0141] Furthermore, the standard deviation of the solidified state data The data is derived from calculations based on the obtained curing state data of a uniform wet coating. The calculation process involves first organizing all curing state data in sequence, and then calculating the average value of each data point relative to the total curing state data. The difference is calculated by squaring each difference, summing all squared differences, dividing the sum by the total number of solidified state data, and finally taking the square root of the result. The result is the standard deviation of the solidified state data. This is used to reflect the degree of dispersion of solidified state data.

[0142] Furthermore, the average value of the cured state data The average value of the curing state data is calculated from the curing state data of all testing points. The calculation involves summing the curing state data of all testing points, then dividing the sum by the total number of testing points to obtain the average value. This is used to reflect the average level of the overall curing state of the coating.

[0143] Furthermore, the process stability correction amount The correction value is determined based on process records of the spraying and curing process. During spraying and curing, changes in process parameters are recorded in real time. If the process parameters remain stable without significant fluctuations throughout the process, a preset positive correction value is used; if the process parameters fluctuate slightly but do not exceed the allowable range, a zero correction value is used; if the process parameters fluctuate beyond the allowable range, a preset negative correction value is used to ensure... It can reflect the corrective effect of process stability on curing quality.

[0144] Furthermore, based on the rules for integrating quantified values, combined with , , , The four parameters are combined by weighting the numerical value corresponding to "curing compliance rate" and the quantitative score corresponding to "hardness uniformity level" and incorporating the influence of each parameter on curing quality to obtain a comprehensive value that reflects the dual consistency of coating curing and hardness. This value is the quantitative value of quality consistency of curing state data.

[0145] Furthermore, the formula for the quantitative value of quality consistency is clarified: by integrating the coating material characteristics, the dispersion of curing state data, the average level of curing state, and process stability factors, the quantitative value of quality consistency is calculated. The part reflecting the relative uniformity of the coating curing state will increase as the dispersion of curing state data decreases and the average level increases. Combined with the coating material characteristic coefficient and the process stability correction amount, the final value can comprehensively and quantify the degree of consistency of coating curing quality. The higher the value, the better the consistency of coating curing quality; the lower the value, the worse the consistency of coating curing quality.

[0146] Furthermore, the trend of the formula for the quantitative value of quality consistency is analyzed: when the coating material characteristic coefficient When the value is increased, the quantification value of quality consistency will increase accordingly, assuming other parameters remain constant, because... The direct impact of coating materials on curing quality The larger the value, the stronger the material's support for high-quality curing, and the higher the final value.

[0147] Furthermore, when the standard deviation of the cured state data... When the value increases, the quantification value of quality consistency will decrease while other parameters remain unchanged, because... The larger the value, the higher the dispersion of the curing state data, the more obvious the difference in curing state at different points of the coating, the worse the overall consistency, which directly leads to a decrease in the value.

[0148] Furthermore, when the average value of the cured state data When the value is increased, the quantification value of quality consistency will increase accordingly, assuming other parameters remain constant, because... The larger the value, the higher the average level of the overall curing state of the coating, and the better the overall curing effect of the coating, which has a positive driving effect on the value.

[0149] Furthermore, when the process stability correction amount When the value is increased, the quantification value of quality consistency will increase accordingly, assuming other parameters remain constant, because... Increasing the value indicates better process stability during the spraying and curing process, or a more positive correction of process fluctuations, reducing the negative impact of process factors on curing quality and directly improving the value.

[0150] Furthermore, a preset quality threshold is retrieved. This threshold is set based on the quality standard of qualified coatings, clearly defining the quality level represented by the threshold. Quantitative values ​​above the threshold correspond to coating quality meeting consistency requirements, while values ​​below the threshold correspond to non-compliance, ensuring that the threshold determination criteria are directly related to the coating's performance requirements.

[0151] Furthermore, the calculated quality consistency quantification value is directly compared with the preset quality threshold to observe the relationship between the quantification value and the threshold, and to clarify whether the quantification value is above or below the threshold, thus avoiding ambiguous judgments during the comparison process.

[0152] Furthermore, a quality judgment result is generated based on the comparison results: if the quality consistency quantification value is greater than or equal to the preset quality threshold, it indicates that the coating meets the requirements in terms of curing compliance rate and hardness uniformity, and is judged as "quality consistent"; if the quality consistency quantification value is less than the preset quality threshold, it indicates that the coating has problems with unqualified curing or uneven hardness, and is judged as "quality inconsistent". At the same time, the difference between the quantification value and the threshold is recorded to provide a reference for subsequent improvement. This judgment result is the quality judgment result of the curing state data.

[0153] In summary, the entire process starts with a uniform wet coating, collects thermal response and hardness data step by step to form curing state data, completes quality consistency judgment based on a standard library, makes compliance decisions and verifications according to clear rules, and finally outputs qualified target iron accessories. Each step revolves around "ensuring that the coating curing quality meets the standards". Through the accuracy of data collection, the clarity of judgment standards and the rigor of the decision-making process, the coating performance of the target iron accessories is guaranteed to meet the requirements.

[0154] In summary, the entire process is based on solidified state data. It transforms qualitative descriptions into quantitative dimensions, clarifies the sources of calculation parameters, integrates multi-dimensional information according to weights to obtain a quantitative value for quality consistency, analyzes the meaning and trend of the formula, and completes the quality judgment by combining preset thresholds. Each step is carried out based on clear mapping rules, parameter acquisition logic, and integration logic to ensure the rationality of quantitative calculation and the accuracy of judgment results, providing accurate quality data support for subsequent compliance decisions of iron fittings.

[0155] In the several embodiments provided by this invention, it should be understood that the disclosed method can be implemented in other ways.

[0156] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0157] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, and technology that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0158] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. An automated method of applying a corrosion resistant coating to ferrous articles, characterized by, The method comprises: S1, parameter mapping of the initial surface state of the iron accessory, obtaining the optimized spraying parameters of the iron accessory; S2, obtaining the coating state monitoring data set of the iron accessory in the spraying process, making uniformity decision on the coating state monitoring data set, and obtaining the dynamic control instruction of the spraying process; S3, real-time adjustment of the output parameters of the spraying process based on the dynamic control instruction, synchronous monitoring of the real-time adjusted spraying process, and obtaining the real-time coating distribution data of the iron accessory; S4, multi-modal real-time optimization of the spraying process based on the real-time coating distribution data, and obtaining the uniform wet coating of the iron accessory; S5, curing joint inspection of the uniform wet coating, and outputting the iron accessory with the curing joint inspection result as passed as the target iron accessory.

2. The automated iron accessory anticorrosion coating spraying method according to claim 1, characterized in that, The parameter mapping of the initial surface state of the iron accessory to obtain the optimized spraying parameters of the iron accessory comprises: optical scanning of the iron accessory to obtain the surface topography data of the iron accessory; feature extraction of the surface topography data to obtain the surface roughness distribution and oxidation area distribution of the surface topography data; parameter mapping table matching of the surface roughness distribution and the oxidation area distribution to obtain the optimized spraying parameters of the iron accessory.

3. The automated iron accessory anticorrosion coating spraying method according to claim 2, wherein The feature extraction of the surface topography data to obtain the surface roughness distribution and oxidation area distribution of the surface topography data comprises: gray feature analysis of the surface topography data to obtain the gray distribution information of the iron accessory surface; texture feature extraction of the gray distribution information to obtain the roughness distribution information of the iron accessory surface; color information collection of the surface topography data to obtain the color distribution of the iron accessory surface; area feature recognition of the color distribution to obtain the oxidation area distribution information of the iron accessory surface.

4. The automated iron accessory anticorrosion coating spraying method according to claim 1, wherein The uniformity decision on the coating state monitoring data set to obtain the dynamic control instruction of the spraying process comprises: thickness change trend analysis of the coating state monitoring data set to obtain the film thickness distribution state feature of the coating in the iron accessory; covering defect recognition of the coating state monitoring data set to obtain the surface covering completeness information of the coating; cooperative analysis of the film thickness distribution state feature and the surface covering completeness information to obtain the coating uniformity comprehensive evaluation conclusion of the iron accessory coating; strategy mapping matching of the preset control strategy library based on the coating uniformity comprehensive evaluation conclusion to obtain the dynamic control instruction of the spraying process.

5. The automated iron accessory anticorrosion coating spraying method according to claim 4, wherein The strategy mapping matching of the preset control strategy library based on the coating uniformity comprehensive evaluation conclusion to obtain the dynamic control instruction of the spraying process comprises: grade mapping of the coating uniformity comprehensive evaluation conclusion to obtain the uniformity grade mark of the coating uniformity comprehensive evaluation conclusion; strategy level matching of the preset control strategy library based on the uniformity grade mark to obtain the preliminary control instruction set corresponding to the uniformity grade mark; The preliminary control instruction set is executable reconstruction, and the dynamic control instruction of the spraying process is obtained.

6. The automated iron accessory anticorrosion coating spraying method according to claim 1, wherein The output parameter of the spraying process is adjusted in real time based on the dynamic control instruction, and the real-time coating distribution data of the iron accessory is obtained by synchronously monitoring the spraying process after real-time adjustment, including: The control parameter of the dynamic control instruction is deconstructed, and the adjustment parameter in the dynamic control instruction is obtained. The output parameter of the adjustment parameter is remapped, and the parameter adjustment instruction corresponding to the adjustment parameter is obtained. The spraying output parameter is adjusted in real time based on the parameter adjustment instruction, and the control effect of the spraying output parameter is obtained. The coating state sensing is performed on the control effect, and the real-time coating distribution data of the iron accessory is obtained.

7. The automated iron accessory anticorrosion coating spraying method according to claim 1, wherein The spraying process is optimized in real time based on the real-time coating distribution data, and the uniform wet coating of the iron accessory is obtained, including: The multi-source feature fusion is performed on the real-time coating distribution data, and the coating state comprehensive descriptor representing the comprehensive state of the coating is obtained. The spraying parameter optimization matching is performed on the coating state comprehensive descriptor, and the optimized spraying parameter corresponding to the coating state comprehensive descriptor is obtained. The spraying process is optimized in real time based on the optimized spraying parameter, and the uniform wet coating of the iron accessory is obtained.

8. The automated iron accessory anticorrosion coating spraying method according to claim 7, wherein The multi-source feature fusion is performed on the real-time coating distribution data, and the coating state comprehensive descriptor representing the comprehensive state of the coating is obtained, including: The surface morphology of the real-time coating distribution data is analyzed, and the surface morphology feature information of the real-time coating distribution data is obtained. The coating material distribution of the real-time coating distribution data is analyzed, and the material distribution feature information of the real-time coating distribution data is obtained. The multi-source feature fusion is performed on the surface morphology feature information and the material distribution feature information, and the coating multi-modal fusion descriptor of the feature information is obtained. The coating state comprehensive descriptor of the coating multi-modal fusion descriptor is obtained by feature refining the coating multi-modal fusion descriptor.

9. The automated iron accessory anticorrosion coating spraying method according to claim 1, wherein The curing state of the uniform wet coating is characterized, and the curing state data of the uniform wet coating is obtained. The quality consistency of the curing state data is determined, and the quality determination result of the curing state data is obtained. Based on the quality determination result, the compliance decision of the iron accessory is made, and the iron accessory with the compliance decision result of compliance is output as the target iron accessory. The quality consistency of the curing state data is determined, and the quality determination result of the curing state data is obtained, including:

10. The automated iron accessory anticorrosion coating spraying method according to claim 9, wherein The quality consistency quantization value of the curing state data is calculated, and the quality consistency quantization value of the curing state data is calculated, wherein the quality consistency quantization value calculation formula is as follows: The quality consistency quantization value and the preset quality threshold are determined, and the quality determination result of the curing state data is obtained. ; wherein is a quality consistency quantification value, is a coating material characteristic coefficient, is a standard deviation of the curing state data, is an average value of the curing state data, is a relative unevenness of the coating curing state, is a process stability correction amount; The quality consistency quantization value and the preset quality threshold are determined, and the quality determination result of the curing state data is obtained.

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