An AI-based coloring system for concrete and masonry art

Abstract

The present invention relates to the technical field of coloring control, and specifically relates to an AI-based concrete and masonry art coloring system. The system includes a micro-structure induction module, a brightness adjustment control module, a path expansion module, a power partition regulation module, and a spraying regulation module. In the present invention, by extracting the micro-depression depth, roughness grade, particle distribution density, and refractive characteristic values on the surfaces of concrete and masonry, a reflection intensity reduction amount is constructed to avoid relying on image brightness to infer color deviation, and brightness regulation based on physical properties is realized. The difference between the brightness correction ratio and the gray-scale reference of the regional pattern is determined, and the ratio of pigment concentration is optimized to make the regional coloring more in line with the structural visual requirements. A calculation mechanism for adsorption level combining local reflectivity and pore surface area is constructed, and the pattern concentration deviation and microscopic absorption factor are introduced to dynamically divide the power level, realizing the synchronous matching of spraying power and coloring concentration, and improving regional adaptability and coating uniformity.

Description

Technical Field

[0001] The present invention relates to the technical field of coloring control, and particularly to an AI-based concrete and masonry art coloring system. Background Art

[0002] The technical field of coloring control involves methods and systems for controlling the color, hue, saturation, and their change processes on the surfaces of various substrates. This field is widely applied in multiple industries such as architectural decoration, manufacturing, and art design. The core lies in effectively managing the surface color distribution, coloring consistency, durability, and matching with the environment. Typical technologies include coloring agent ratio algorithms, coloring path control, coordinated control of spraying / brushing equipment, multi-channel color mixing strategies, etc. The goal is to make the coloring process controllable, repeatable, and efficient and consistent, especially emphasizing the regulation of coloring behavior and ensuring color stability on different materials (such as metals, woods, concretes, and masonries).

[0003] Among them, the concrete and masonry art coloring system is a system for achieving artistic patterns and color coverage on the surfaces of concrete structures or masonries. This system is mainly used in scenarios such as building facades, landscape facilities, and public art walls. By precisely controlling the pigment deposition path, coloring agent types, and application parameters, decorative, functional, or recognizable patterns and color layers are formed on the hard material surfaces. Through the coordinated control of software and hardware, the construction efficiency and coloring accuracy can be improved, manual differences can be reduced, and the customized and diverse architectural aesthetic requirements can be met.

[0004] Traditional coloring systems mainly rely on visual features such as image grayscale, pixel contrast, or edge detection to judge the coloring areas and pigment concentrations. When facing hard surfaces with large roughness and unstable reflectivity, coloring deviations and area mismatches are likely to occur. At the same time, the spraying path is limited by the two-dimensional pixel contour and it is difficult to identify the local drastic changes in the structural width in the masonry joints, resulting in incomplete edge spraying or pigment accumulation. Most of the existing power adjustment methods are based on average values or unified level settings and cannot perform fine zoning according to the adsorption capacity differences of the pore structures inside the patterns, resulting in uneven power distribution, abnormal local color saturation, or blurred patterns. This is particularly obvious in scenarios with high requirements for decorative consistency and pattern restoration such as public facades and landscape walls, and cannot meet the requirements of refined architectural aesthetic construction. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose an AI-based concrete and masonry art coloring system.

[0006] To achieve the above purpose, the present invention adopts the following technical solution: An AI-based concrete and masonry art coloring system, the system includes:

[0007] The microstructure induction module acquires the surface images of concrete and masonry components, performs image analysis through AI, extracts the micro-depression depth values, roughness grades, particle distribution densities, and material refraction characteristic values on the surfaces of each structural block, conducts weighted combination calculations, and generates reflection intensity reduction information;

[0008] The brightness adjustment control module, based on the reflection intensity reduction information, calls the pigment brightness coefficient and the reflection intensity reduction value to calculate the amplitude correction ratio, judges the difference between the amplitude correction ratio and the gray-scale brightness reference benchmark value, updates the pigment concentration adjustment ratio, and generates regional brightness adjustment ratio information;

[0009] The path extension module, based on the regional brightness adjustment ratio information, performs image analysis through AI, identifies the gap boundaries on the surfaces of concrete and masonry components, compares the width range of each segment with the spraying omission repair judgment threshold, and generates a set of masonry component gap painting paths;

[0010] The power partition control module calls the set of masonry component gap painting paths and the regional brightness adjustment ratio information, calculates the adsorption adaptation level value of each region, selects the corresponding spraying power level, constructs a spraying power setting sequence for each path segment, and generates local power adjustment information.

[0011] The improvements of the present invention are that the reflection intensity reduction information includes the regional light reflection interference level, the light offset intensity on the particle surface, and the local light scattering attenuation factor; the regional brightness adjustment ratio information specifically includes the pigment brightness adjustment amplitude, the regional color compensation coefficient, and the pigment ratio offset value; the set of masonry component gap painting paths specifically refers to the boundary expansion coordinate point set, the path segment coverage marking sequence, and the gap interval structure label; the local power adjustment information includes the path spraying power level identifier, the paragraph adsorption ability grading label, and the path voltage output correspondence table.

[0012] The improvements of the present invention are that the microstructure induction module includes:

[0013] The image analysis sub-module acquires the surface images of concrete and masonry components, performs image analysis through AI, processes each colored target area in the image into blocks, and performs edge sharpness detection, pixel gradient change extraction, and local area channel brightness change reading on each image block. Combining the three image features, it performs adjacent block comparison operations, filters out valid image blocks, and establishes a structural block number index to obtain an image block structure identification set;

[0014] Based on the image block structure identification, the feature extraction sub-module performs data collection on each image block corresponding to a number, collects the particle distribution density, micro-depression depth value, roughness level, and material refractive feature value, constructs a set of micro-feature parameter combinations for the structural block, and performs parameter normalization and judgment on the stability of the structural features within the block in sequence to obtain the block material structure parameter sequence;

[0015] The reflection calculation sub-module calls the block material structure parameter sequence, and according to the particle distribution density, depression depth value, roughness level, and refractive value of each block, uses the formula:

[0016] ;

[0017] Performs operations to obtain the reflection intensity reduction value of each structural block, and after comparing the reflection intensity reduction value with a preset reflection threshold, assigns an identification level to establish reflection intensity reduction information;

[0018] Among them, represents the particle distribution density, represents the micro-depression depth value, represents the roughness level, represents the material refractive value, represents the light absorption change rate per unit area, represents the compaction degree factor, represents the reflection intensity reduction value.

[0019] The improvement of the present invention is that the brightness adjustment control module includes:

[0020] The brightness reference extraction sub-module, according to the reflection intensity reduction information, calls the preset value of the pattern pigment brightness of the area to be colored, extracts the brightness level value of each layer, performs a mean integration operation on each level value, obtains the brightness reference value corresponding to the block, and generates the area brightness reference value;

[0021] The difference conversion sub-module, based on the area brightness reference value, extracts the pigment brightness coefficient and the reflection intensity reduction value of each structural block, and uses the formula:

[0022] ;

[0023] Performs operations to obtain the brightness conversion offset value;

[0024] Among them, represents the brightness conversion offset value, represents the pigment brightness coefficient, represents the reflection intensity reduction value, represents the area brightness reference value, represents the brightness level parameter of the current pattern layer;

[0025] The ratio parameter generation sub-module selects a pigment concentration adjustment section corresponding to the offset value according to the brightness conversion offset value, obtains the concentration factor and the adjustment ratio range within the current concentration section, performs interval ratio matching on the concentration factor and the offset value, calculates the adjustment ratio under the current structural block, and generates regional brightness adjustment ratio information.

[0026] The improvement of the present invention is that the path extension module includes:

[0027] The gap recognition sub-module performs image analysis through AI according to the regional brightness adjustment ratio information, recognizes the gap boundary on the surface of concrete and masonry components, obtains the gap boundary pixel line segment in the image, extracts the number of pixels in the widest segment and the number of pixels in the narrowest segment according to the line segment sequence, performs a difference operation and marks it as the width extreme difference value, and establishes a gap pixel extreme difference value group;

[0028] The boundary extreme difference judgment sub-module calls the gap pixel extreme difference value group, performs a difference judgment on each extreme difference value and the spraying omission repair judgment threshold, extracts the index of the gap segment greater than the judgment threshold, obtains the extended coordinates by performing a fixed value offset on the normal direction of the boundary corresponding to each index, and at the same time collects the roughness value of the extended area and the average roughness value of the adjacent brick joint area, performs a difference operation between the roughness difference and the critical value, and uses the formula:

[0029] ;

[0030] Performs an operation to obtain the rough expansion increase value, compares and screens and marks it with the rough difference adjustment critical threshold, and obtains an extended path screening index group;

[0031] Among them, represents the roughness value of the extended area, represents the average roughness of the adjacent brick joints, represents the rough adjustment reference value, represents the normal offset coefficient of the gap boundary pixel segment, represents the spraying concentration offset of the pattern area, represents the rough expansion increase value;

[0032] The path generation sub-module extracts the line segment combination in the corresponding regional normal direction based on the extended path screening index group, performs structural binding with the image coordinate mapping matrix, integrates it into a single image channel spraying path panel, and records the corresponding path number and coordinate coverage segment information, and generates a masonry component gap re-spraying path set.

[0033] The improvement of the present invention is that the power partition control module includes:

[0034] The area parameter acquisition sub-module calls the crack repair coating path set of the masonry component and the area brightness adjustment ratio information, extracts each coordinate point covered by the artistic pattern spraying path segment, acquires the local reflectivity and pore surface area value of the spraying grid area where the coordinate point is located, and establishes a coordinate area structure parameter set;

[0035] The adsorption level calculation sub-module extracts the local reflectivity value, pore surface area value, and area brightness adjustment ratio corresponding to each coordinate point according to the coordinate area structure parameter set, and uses the formula:

[0036] ;

[0037] Through calculation, the adsorption adaptation level value of each spraying area is obtained, mapped and classified according to the path segment, and a path adsorption level distribution set is generated;

[0038] Among them, represents the local reflectivity value, represents the pore surface area value, represents the area brightness adjustment ratio, represents the area concentration deviation value, represents the microscopic absorption constant, represents the adsorption stability index, represents the adsorption adaptation level value;

[0039] The power setting generation sub-module screens the sections according to the set power response interval based on the path adsorption level distribution set, assigns the corresponding spraying power level to each section, binds the power level label according to the path sequence number, constructs a matching mapping table of the path number and the power level, and generates local power adjustment information.

[0040] An improvement of the present invention is that the system further includes:

[0041] The spraying control module acquires the local power adjustment information and the area brightness adjustment ratio information, adjusts the pigment delivery concentration and the spraying power level corresponding to the path segment numbers in sequence, controls the spraying execution device to perform artistic pattern spraying, and generates a concrete and masonry pattern coloring execution record;

[0042] The concrete and masonry pattern coloring execution record is specifically a path segment pigment concentration matching record, a spraying execution number trajectory table, and an area pattern coloring mapping log.

[0043] An improvement of the present invention is that the spraying control module includes:

[0044] The parameter synchronization sub-module obtains the local power adjustment information and the regional brightness adjustment ratio information, extracts the brightness adjustment amount and the spraying power level value corresponding to each path segment number, aligns the two values according to the path segment number, and establishes a synchronization data structure through number indexing to obtain a path segment parameter correspondence table;

[0045] The execution matching sub-module, based on the path segment parameter correspondence table, respectively matches the pigment delivery concentration gear and the device power output voltage value according to the brightness adjustment amount and the spraying power level recorded in each segment, sets the working state of the nozzle array for each numbered path segment in sequence, and obtains a path segment spraying control sequence;

[0046] The pattern recording sub-module, according to the path segment spraying control sequence and the spraying execution instructions corresponding to each segment, controls the spraying execution device to perform artistic pattern spraying, collects the pattern number, spraying parameter value and coordinate position index of the current execution frame of the spraying device, establishes a binding relationship between the number record and the parameter trajectory, and generates a concrete and masonry pattern coloring execution record.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In the present invention, by extracting the micro-depression depth, roughness grade, particle distribution density and refractive characteristic value of the concrete and masonry surface, a reflection intensity reduction amount is constructed, avoiding relying on image brightness to infer color deviation, realizing brightness regulation based on physical properties, determining the difference between the brightness correction ratio and the gray-scale benchmark of the regional pattern, optimizing the ratio of the pigment concentration, making the regional coloring more in line with the structural visual requirements, introducing a gap width range difference recognition mechanism in the path processing link, and cooperating with the roughness ratio difference to make a path expansion decision, effectively solving the problem of pigment loss caused by the irregularity of the masonry structure. In the spraying regulation link, an adsorption level calculation mechanism combining local reflectivity and pore surface area is constructed, and a pattern concentration deviation and a microscopic absorption factor are introduced to dynamically divide the power level, realizing the synchronous matching of spraying power and coloring concentration, improving regional adaptability and coating uniformity, and significantly improving problems such as deposition deviation, color fracture and boundary blur in the pattern covering process of masonry components. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the system flow chart of the present invention;

[0050] Figure 2 is the flow chart of the microstructure induction module of the present invention;

[0051] Figure 3 is the flow chart of the brightness adjustment control module of the present invention;

[0052] Figure 4 is the flow chart of the path expansion module of the present invention;

[0053] Figure 5 is the flowchart of the power partition regulation module of the present invention;

[0054] Figure 6 is the flowchart of the spraying regulation module of the present invention. Specific embodiments

[0055] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, "a plurality of" means two or more unless otherwise specifically defined.

[0057] Please refer to Figure 1 , the present invention provides a technical solution: an AI-based concrete and masonry art coloring system, the system includes:

[0058] The micro-structure induction module acquires the surface images of concrete and masonry components, performs image analysis through AI, extracts the micro-depression depth values, roughness grades, particle distribution densities and material refraction characteristic values on the surfaces of each structural block, performs weighted combination calculations, obtains the light reflection weakening amplitude, and generates reflection intensity reduction information;

[0059] The lightness adjustment control module, according to the reflection intensity reduction information, combines the preset lightness value of the art pattern pigment in the area to be colored, calls the pigment lightness coefficient and the reflection intensity reduction value to calculate the amplitude correction ratio, judges the difference between the amplitude correction ratio and the gray-scale lightness reference benchmark value, and according to the judgment result, updates the pigment concentration adjustment ratio to generate area lightness adjustment ratio information;

[0060] Based on the regional brightness adjustment ratio information, the path expansion module performs image analysis through AI to identify the gap boundaries on the surfaces of concrete and masonry components, obtains the difference in the number of pixels between the widest and narrowest segments in the pixel sequence of the gap boundary segments on the masonry surface, compares the width range of each segment with the spraying omission repair determination threshold. If the range is greater than the threshold, a fixed displacement is performed in the normal direction of the gap to obtain the boundary expansion position coordinates, and the difference between the roughness value of the masonry surface in the expanded area and the average roughness of the adjacent brick joints is collected for judgment. If the difference is lower than the set critical value, the area is marked as a new spraying path segment, and a masonry component gap patching path set is generated;

[0061] The power partition control module calls the masonry component gap patching path set and the regional brightness adjustment ratio information, collects the local reflectance data and pore surface area values in the spraying area grid corresponding to each coordinate point of the artistic pattern, calculates the adsorption adaptation level value of each area, selects the corresponding spraying power level according to the adsorption adaptation level value, constructs a spraying power setting sequence for each path segment, and generates local power adjustment information;

[0062] The spraying control module obtains the local power adjustment information and the regional brightness adjustment ratio information, synchronously matches the brightness adjustment amount with the spraying power level, adjusts the pigment delivery concentration and the spraying power level in sequence according to the path segment number, controls the spraying execution device to spray the artistic pattern, and generates a concrete and masonry pattern coloring execution record;

[0063] The reflection intensity reduction information includes the regional light reflection interference level, the light offset intensity on the particle surface, and the local light scattering attenuation factor. The regional brightness adjustment ratio information specifically includes the pigment brightness adjustment amplitude, the regional color compensation coefficient, and the pigment ratio offset value. The masonry component gap patching path set specifically refers to the boundary expansion coordinate point set, the path segment coverage marking sequence, and the gap interval structure label. The local power adjustment information includes the path spraying power level identifier, the paragraph adsorption capacity classification label, and the path voltage output correspondence table. The concrete and masonry pattern coloring execution record specifically includes the path segment pigment concentration matching record, the spraying execution number trajectory table, and the regional pattern coloring mapping log.

[0064] Please refer to Figure 2 , the micro-structure induction module includes:

[0065] The image analysis sub-module obtains the surface images of concrete and masonry components, performs image analysis through AI, performs block processing on each colored target area in the image, and performs edge sharpness detection, pixel gradient change extraction, and local area channel brightness change reading on each image block. Combining the three image features, adjacent block comparison operations are performed, valid image blocks are screened, and a structural block number index is established to obtain an image block structure identification set;

[0066] The image analysis sub-module obtains the surface images of concrete and masonry components and conducts AI analysis. First, it divides the target areas of the images. For each divided image block, it first performs edge sharpness detection, that is, determines the sharpness of the image block by calculating the pixel gradient change at the edge of the image block. It uses an edge detection algorithm based on the gradient direction, such as the Sobel operator, to calculate the gradient value of each pixel in the image and analyzes the edge features of each area. The image blocks that do not meet the edge sharpness standard will be marked as low-quality image blocks and excluded. The setting of this standard is based on the sharpness threshold measured through experiments. This threshold is determined by calculating the sharpness of a large number of image blocks. The set sharpness threshold is set to 0.15, that is, when the gradient change value is less than 0.15, the image block is considered blurred and needs to be excluded. Next, it extracts the pixel gradient change within each block, that is, evaluates the gradient difference of pixels by calculating the change rate of pixel values in each image block. If the gradient change exceeds the set threshold, it is considered that the image block contains significant features. It reads the brightness change of the local area channels within the image block, reads and compares the brightness values in each image block segment by segment to confirm whether the area meets specific standards based on the degree of brightness fluctuation. The brightness change threshold is set based on the test data of multiple image areas, and the set threshold is set to 5%, that is, when the brightness change of a certain area exceeds 5%, the image block is considered to contain significant features and will continue to be processed. Finally, it performs an adjacent block comparison operation by combining these three image features, using the comprehensive calculation of pixel gradient, brightness change, and edge features. By calculating the similarity of adjacent image blocks, it screens out effective image blocks. The screening standard can be judged by setting a similarity threshold. If the similarity is lower than the set standard, the image block will be excluded. This similarity threshold is set to 0.8 through experiments, that is, the image blocks with a similarity lower than 0.8 will be excluded. After screening, the remaining image blocks will establish a structural block number index, assign a unique number to each compliant image block, thus forming an image block structure identification set, forming image blocks that can be used for subsequent feature extraction.

[0067] Based on the image block structure identification, the feature extraction sub-module performs data collection on each image block corresponding to the number, collects the particle distribution density, micro-depression depth value, roughness grade, and material refraction feature value, constructs a set of micro-feature parameter combinations for the structural block, and performs parameter normalization and judgment on the stability of the structural features within the block in sequence to obtain the sequence of block material structure parameters;

[0068] Based on the above image block structure identification set, the feature extraction sub-module collects data for each image block corresponding to a number. Data collection includes obtaining the particle distribution density, micro-indentation depth value, roughness level, and material refraction feature value. The particle distribution density is calculated by an image processing method to count the number of particles per unit area. In the example, if the number of particles in a unit area of the image is 50, the particle distribution density is 50 particles per square centimeter. The weight of the particle distribution density is set to 0.4, which means its importance in the overall feature judgment accounts for 40%. The micro-indentation depth value is calculated through three-dimensional depth analysis of the image block. Using a depth extraction algorithm based on light change, the image is converted into a depth map, and then the depth of the indented part is measured from the depth map. In the example, if the measured depth of an image block is 0.5 mm, the indentation depth of this image block is 0.5 mm. The weight of the micro-indentation depth value is set to 0.3, indicating its proportion in the overall structure judgment is 30%. The roughness level is evaluated by calculating the roughness index of the surface texture. Usually, the gray-level co-occurrence matrix method (GLCM) is used to evaluate the roughness, and the roughness index of the image block is calculated. If the calculation result is 0.3, it represents a relatively high roughness level of the image block. The weight of the roughness level is set to 0.2, indicating its relatively low weight in feature extraction. The material refraction feature value is obtained through analysis of the light reflection feature in the image. Usually, this value is obtained by calculating the reflectivity and refraction angle of the image. In the example, if the calculated reflectivity is 0.7, the material refraction value is 0.7. The weight of the refraction feature value is set to 0.1, indicating that the material refraction feature has a relatively small influence in the overall judgment. After these data are collected, a micro-feature parameter combination set of the structure block is further constructed. All data such as the particle distribution density, micro-indentation depth, roughness level, and material refraction feature value of all image blocks are collected into the parameter set and processed. Subsequently, a parameter normalization method is used to process these values to ensure the unity of their dimensions. For example, by proportionally normalizing the particle distribution density, micro-indentation depth, roughness, and refraction value with their respective maximum values, the normalized parameter data are obtained, thus avoiding the influence of dimensional differences between parameters on subsequent data analysis. Then, the stability of these normalized parameter data is judged to determine the stability of the structural features within each block. The stability can be judged by calculating the variance of each parameter. If the variance is small, it indicates that the structural parameters within the image block change stably. Image blocks with higher stability are considered to have more consistent material structures, and a material structure parameter sequence of the image block is generated.

[0069] In the reflection calculation sub-module, when calling the material structure parameter sequence of the block, according to the particle distribution density, indentation depth value, roughness level, and refraction value of each block, the formula:

[0070] ;

[0071] The operation obtains the reflection intensity reduction value of each structural block, compares the reflection intensity reduction value with a preset reflection threshold, assigns an identification level, and establishes reflection intensity reduction information;

[0072] Among them, represents the particle distribution density, represents the micro-depression depth value, represents the roughness level, represents the material refraction value, represents the light absorption change rate per unit area, represents the compaction degree factor, represents the reflection intensity reduction value.

[0073] Before the reflection calculation sub-module is called, the sequence of block material structure parameters obtained previously is used. According to the particle distribution density, micro-depression depth value, roughness level, and refraction value of each block, the reflection intensity reduction value is calculated using a formula. Taking the material of a certain block as an example, assume that the particle distribution density ( ) of this block is 50 particles per square centimeter, the micro-depression depth value ( ) is 0.5 mm, the roughness level ( ) is 0.3, the material refraction value ( ) is 0.7, the light absorption change rate per unit area ( ) is 0.1, and the compaction degree factor ( ) is 1.5. Substitute these data into the formula:

[0074] ;

[0075] The obtained reflection intensity reduction value is 36.527. Next, compare this reflection intensity reduction value with the preset reflection threshold. For example, set the threshold to 30. The reflection intensity reduction value of 36.527 is higher than this threshold. Therefore, this image block is assigned a higher reflection intensity level. The setting of the reflection intensity threshold of 30 is based on a series of image tests and the distribution analysis of the reflection intensity. When the reflection intensity value is greater than 30, it indicates that the material reflection characteristics of this image block are stronger.

[0076] Please refer to Figure 3 , the brightness adjustment control module includes:

[0077] The brightness reference extraction sub-module, based on the reflection intensity reduction information, calls the preset brightness value of the pattern pigment in the area to be colored, extracts the brightness level value of each layer, performs an average integration operation on each level value, obtains the brightness reference value corresponding to the block, and generates the area brightness reference value;

[0078] The brightness reference extraction sub-module obtains the brightness reference value of the area to be colored through the reflected intensity reduction information. Specifically, first, the preset pigment brightness values of each layer are called, which are usually determined according to the material and pigment type during the pattern design. For example, the preset pigment brightness value of a certain area is set to 30, while that of another area is set to 40. Next, according to the preset brightness values of each layer, the brightness level values of each layer are extracted, and these brightness levels are obtained by measuring the surface reflected light intensity of the layer. Suppose the brightness levels of a certain area are 30, 32, 33, 29, 31, etc. After the extracted brightness level data is subjected to a mean integration operation, the average brightness level of this area is calculated. For example, the calculation process is (30 + 32 + 33 + 29 + 31) / 5 = 31, and the average brightness value of this area is obtained as 31. At this time, the brightness reference value is set based on the average brightness data of all layers, and the overall brightness characteristics of an area are calculated through the mean of these data. Subsequently, this mean value will be used as the brightness reference value of this area, representing the overall brightness characteristics of this area for subsequent pigment ratio and color adjustment operations.

[0079] The difference conversion sub-module extracts the pigment brightness coefficient and the reflected intensity reduction value of each structural block based on the area brightness reference value, using the formula:

[0080] ;

[0081] Calculate to obtain the brightness conversion offset value;

[0082] Among them, represents the brightness conversion offset value, represents the pigment brightness coefficient, represents the reflected intensity reduction value, represents the area brightness reference value, represents the brightness level parameter of the current pattern layer;

[0083] The difference conversion sub-module extracts the pigment brightness coefficient and the reflected intensity reduction value of each structural block based on the area brightness reference value. The pigment brightness coefficient is a parameter that reflects the reflectivity of the pigment under specific lighting conditions, and this coefficient is determined according to the type of pigment, production process, and actual lighting conditions. Suppose the pigment brightness coefficient ( ) of a certain structural block is 0.9. The reflected intensity reduction value ( ) is calculated in the previous step, and the reflected intensity reduction value of this block is set to 36.527. The area brightness reference value ( ) is the mean value generated by the brightness reference extraction sub-module and is set to 31. The brightness level of the current pattern layer ( )(refers to the lightness characteristic of the pigments used in the pattern. The lightness level of this pattern layer is set to 33. According to these parameters, using the formula:

[0084] ;

[0085] the obtained lightness conversion offset value is 0.52. The calculation of this lightness conversion offset value depends on the pigment lightness coefficient ( ), the reflected intensity reduction value ( ), and the regional lightness reference value ( ). The pigment lightness coefficient is set according to the optical characteristics of specific pigments. The reflected intensity reduction value is closely related to the surface properties of this structural block, and the regional lightness reference value is related to the lightness characteristics of the entire region. These are all determined through the measurement and analysis of actual data. This result indicates that at the lightness level of the current pattern layer, the difference between the reflected intensity reduction value and the lightness reference value needs to be adjusted by an offset value of 0.52;

[0086] The ratio parameter generation sub-module selects the pigment concentration adjustment section corresponding to the offset value according to the lightness conversion offset value, obtains the concentration factor and the adjustment ratio range within the current concentration section, performs interval ratio matching on the concentration factor and the offset value, calculates the adjustment ratio under the current structural block, and generates the regional lightness adjustment ratio information;

[0087] The ratio parameter generation sub-module selects the pigment concentration adjustment section corresponding to the obtained lightness conversion offset value ( ). According to this offset value of 0.52, the concentration factor required for the current block can be determined according to the preset concentration section and the adjustment ratio range. It is set that in the pigment concentration section, the concentration factor corresponding to the offset value of 0.52 is 1.1, and the adjustment ratio range is (0.5, 1.5). Next, perform interval ratio matching on the concentration factor and the offset value. For example, if the concentration factor is 1.1 and the lightness offset value of this block is 0.52, the adjustment ratio of the current structural block is:

[0088] ;

[0089] The calculation of this adjustment ratio is closely related to the selection of the concentration factor. The concentration factor of 1.1 is selected based on the pigment concentration characteristics corresponding to this lightness level. This value reflects the change range of the pigment concentration during the adjustment process and is usually determined based on experimental data. This adjustment ratio of 0.572 represents the pigment concentration coefficient that needs to be used in this region, thereby generating the final regional lightness adjustment ratio information.

[0090] Please refer to Figure 4 , the path expansion module includes:

[0091] The gap recognition sub-module performs image analysis through AI based on the regional brightness adjustment ratio information, recognizes the gap boundaries on the surfaces of concrete and masonry components, obtains the pixel line segments of the gap boundaries in the image, extracts the number of pixels in the widest segment and the narrowest segment according to the line segment sequence, performs a difference operation and marks it as the width extreme difference value, and establishes a gap pixel extreme difference value group;

[0092] First, the gap recognition sub-module recognizes the gap boundaries on the surfaces of concrete and masonry components through AI image analysis technology based on the regional brightness adjustment ratio information. This process involves extracting the pixel line segments of the gap boundaries from the image, and these boundary line segments are recognized through the edge detection algorithm of the image. For example, in an image of a masonry wall, the AI will determine the specific position and shape of the gap according to different brightness intervals and pattern texture changes, and distinguish the boundary between the wall and the gap. Next, the line segment sequence of the gap boundaries in the image will be extracted. By extracting the line segments in the image, the system can identify the widest and narrowest gap segments. Suppose the number of pixels in the widest segment in an image is 120 pixels, and the number of pixels in the narrowest segment is 30 pixels. The system will perform a difference operation to obtain a width extreme difference value of 120 - 30 = 90 pixels. Thereafter, the system will mark this gap segment according to this difference value, and establish the width extreme difference values of all gap segments as the "gap pixel extreme difference value group", which will become the basis for subsequent judgment and processing.

[0093] The boundary extreme difference judgment sub-module calls the gap pixel extreme difference value group, performs a difference judgment on each extreme difference value and the spraying omission repair judgment threshold, extracts the index of the gap segment greater than the judgment threshold, obtains the extended coordinates by performing a fixed offset on the normal direction of the boundary corresponding to each index, and at the same time collects the roughness value of the extended area and the average roughness value of the adjacent brick joint area, performs a difference operation between the roughness difference and the critical value, and uses the formula:

[0094] ;

[0095] Performs an operation to obtain the rough extension increase value, compares and screens and marks it with the rough difference adjustment critical threshold, and obtains the extended path screening index group;

[0096] Among them, represents the roughness value of the extended area, represents the average roughness of the adjacent brick joints, represents the rough adjustment reference value, represents the normal offset coefficient of the gap boundary pixel segment, represents the spraying concentration offset of the pattern area, represents the rough extension increase value;

[0097] The boundary extreme difference judgment sub-module uses the established set of extreme differences of gap pixels to judge the spraying omission repair situation of the gap segment. The extreme difference of each gap segment will be judged by taking the difference with the spraying omission repair judgment threshold. The setting of the spraying omission repair judgment threshold refers to the specific construction requirements and the type of gap. In this embodiment, the threshold is set to 50 pixels, which means that only when the width extreme difference of the gap segment is greater than 50 pixels, it is considered as the target to be repaired. The selection of this threshold is based on the requirements in actual construction to ensure that those larger gaps can be effectively repaired without over-repairing the tiny gaps. With the set threshold of 50 pixels, the system will compare the extreme difference of each gap segment with this threshold. If the width extreme difference of the gap segment is greater than the threshold (for example, the width extreme difference of a certain segment is 90 pixels, which is greater than 50 pixels), this gap segment will be marked as the gap segment to be repaired. For each gap segment to be repaired, the system will extract the extended coordinates in the corresponding boundary normal direction, and this extended coordinate is obtained by performing a fixed-value offset calculation on the boundary normal direction of the gap. For example, if the offset amount in the normal direction of the gap boundary is set to 10 pixels, the system will offset the coordinates of the gap boundary along the normal direction according to this value to obtain the boundary coordinates of the extended area. In addition, the system will collect the roughness value of the extended area and the average roughness value of the adjacent brick joint area. Assuming the roughness value of the extended area is 2.5 and the average roughness value of the adjacent brick joint area is 2.0, the system will perform the difference operation between the roughness difference and the critical value. The setting basis of the roughness adjustment reference value is based on the conventional material roughness and construction standards in the construction environment, and this reference value is set to 0.3. The roughness adjustment reference value determines the acceptable range of different roughness differences. If the calculated roughness difference is too large, the system will take corresponding measures for correction. Assuming the difference calculated from the roughness difference is 0.5, substituting it into the formula:

[0098] ;

[0099] where, is the roughness value of the extended area, is the average roughness value of the adjacent brick joint area, is the normal offset coefficient of the gap boundary pixel segment, and its value is set to 1.2, is the spraying concentration offset amount in the pattern area, and its value is set to 0.4. Substituting the values for calculation, we get:

[0100] ;

[0101] Thus, the rough expansion increase value If the calculated growth rate value is 2.36, the system will compare it with the rough difference adjustment critical threshold, which is set to 2.0. If the calculated growth rate value is greater than the threshold, the system will mark the extended path as a valid path and continue with subsequent processing.

[0102] Based on the extended path screening index group, the path generation sub-module extracts the normal direction line segment combinations of the corresponding area, performs structural binding with the image coordinate mapping matrix, integrates them into a single image channel spraying path panel, and records the corresponding path number and coordinate coverage segment information to generate a brick and stone component gap touch-up path set.

[0103] The path generation sub-module extracts the normal direction line segment combinations of the corresponding area according to the extended path screening index group. Each screened line segment combination is structurally bound to the image coordinate mapping matrix to ensure the accuracy of path generation. Assuming that through screening, some valid extended paths are obtained, and the mapped coordinates of these paths in the image are (100, 200), (120, 220). After binding these coordinate points with the specific pixel values in the image, a complete path coordinate set is generated. Through the line segment combination of the path and the image coordinate mapping matrix, the system can determine the specific coordinate information of each path, record the path number and coverage segment information, and form a detailed brick and stone component gap touch-up path set. These path sets will provide accurate route guidance for subsequent spraying work and ensure that the spraying operation covers all the gap positions that need to be repaired.

[0104] Please refer to Figure 5 , the power partition regulation module includes:

[0105] The area parameter acquisition sub-module calls the brick and stone component gap touch-up path set and the area lightness adjustment ratio information, extracts each coordinate point covered by the artistic pattern spraying path segment, acquires the local reflectivity and pore surface area values of the spraying grid area where the coordinate point is located, and establishes a coordinate area structure parameter set.

[0106] The area parameter acquisition sub-module extracts each coordinate point covered by the artistic pattern spraying path segment according to the masonry component gap repair coating path set and the area brightness adjustment ratio information. For each coordinate point, the system first confirms whether the point is within the area to be sprayed through image recognition technology, and then acquires the local reflectivity and pore surface area value of the spraying grid area where the point is located. The local reflectivity is obtained by calculating the reflection intensity data of the point through an image processing algorithm, specifically by calculating the reflection ratio of the brightness value of the point in the image. The pore surface area value is deduced based on the pixel distribution of each grid area through image segmentation technology to obtain the porosity of the area. Suppose that in a certain area, the reflectivity of a coordinate point is 0.75, and the pore surface area value of the area is 20 square millimeters. The system will establish a set of structural parameters for the coordinate area based on this data. These parameter sets provide the necessary basic data for subsequent calculations to further analyze the spraying effect and adjust relevant operations.

[0107] The adsorption level calculation sub-module extracts the local reflectivity value, pore surface area value, and area brightness adjustment ratio corresponding to each coordinate point according to the coordinate area structure parameter set, and uses the formula:

[0108] ;

[0109] to calculate and obtain the adsorption adaptation level value for each spraying area, map and classify it by path segment, and generate a path adsorption level distribution set;

[0110] Among them, represents the local reflectivity value, represents the pore surface area value, represents the area brightness adjustment ratio, represents the area concentration deviation value, represents the microscopic absorption constant, represents the adsorption stability index, and represents the adsorption adaptation level value;

[0111] The adsorption level calculation sub-module extracts the local reflectivity value, pore surface area value, and area brightness adjustment ratio corresponding to each coordinate point based on the coordinate area structure parameter set. The area brightness adjustment ratio is determined according to the area brightness adjustment ratio information, which reflects the brightness adjustment degree of each area. For example, the area brightness adjustment ratio of an area may be 1.2, indicating that the brightness of this area is increased by 20% relative to the standard brightness. These parameters will be substituted into the adsorption level calculation formula:

[0112] ;

[0113] Among them, represents the local reflectivity value, represents the pore surface area value, Indicates the regional brightness adjustment ratio. is the regional concentration deviation value, is the microscopic absorption constant, is the adsorption stability index, Indicates the adsorption adaptation level value. Assume that the local reflectivity of a coordinate point is 0.75, the pore surface area is 20 square millimeters, the regional brightness adjustment ratio is 1.2, the concentration deviation is 0.8, the microscopic absorption constant is 1.5, and the adsorption stability index is 2. Substitute it into the formula for calculation:

[0114] ;

[0115] Therefore, the adsorption adaptation level value This value reflects the adsorption capacity of the material to the pigment during the spraying process. A higher adsorption grade value usually means that the area can better adsorb the spray pigment.

[0116] The power setting generation submodule selects sections according to the set power response interval based on the path adsorption level distribution set, assigns a corresponding spraying power level to each section, binds the power level label according to the path sequence number, builds a matching mapping table between path number and power level, and generates local power adjustment information;

[0117] The power setting generation submodule uses the path adsorption level distribution set and the set power response interval to select segments. Each segment's adsorption level value is assigned to a corresponding power level interval. For example, if the set power response intervals are 0-5, 5-10, and 10-15, the system matches each segment's adsorption level value with the power response interval based on the adsorption adaptation level value of each path segment. Setting a path segment's adsorption adaptation level value to 6.4 means it will be assigned to the power response interval of 5-10. The system then assigns a spraying power level to this segment and assigns a corresponding power level label. Setting the power level label to "medium power" means the spraying power for this path segment will be adjusted within the medium range. Finally, the system sequentially numbers each path segment and associates it with a power level label to generate a mapping table that matches path numbers to power levels. This mapping table provides detailed power adjustment information for subsequent spraying operations, ensuring that each area receives the appropriate spraying power to achieve the desired spraying effect.

[0118] See also Figure 6 , the spray control module includes:

[0119] The parameter synchronization sub-module obtains the local power adjustment information and the regional brightness adjustment ratio information, extracts the brightness adjustment amount and the spraying power level value corresponding to each path segment number, aligns the two values according to the path segment number, and establishes a synchronization data structure through number indexing to obtain the path segment parameter correspondence table;

[0120] The parameter synchronization sub-module obtains the local power adjustment information and the regional brightness adjustment ratio information. First, it extracts the brightness adjustment amount and the spraying power level value corresponding to each path segment number. Suppose the brightness adjustment amount of a certain path segment is 1.2 and the spraying power level value is 3. Next, the module aligns the two values according to the path segment number, that is, arranges the brightness adjustment amount and the spraying power level of each path segment in the position corresponding to its number. For example, when the path segment number is 1, the brightness adjustment amount is 1.2 and the power level is 3; when the path segment number is 2, the brightness adjustment amount is 0.9 and the power level is 2. Through number indexing, a synchronization data structure is established, and the brightness adjustment amount and the spraying power level value of each path segment are stored in a data structure according to the path segment number to obtain the path segment parameter correspondence table. This table effectively associates the spraying path with the corresponding adjustment information and provides standardized data support for the execution of subsequent steps.

[0121] The execution matching sub-module, based on the path segment parameter correspondence table, respectively matches the pigment delivery concentration gear and the device power output voltage value according to the brightness adjustment amount and the spraying power level recorded in each segment, sets the working state of the nozzle array in sequence for the numbered path segments, and obtains the path segment spraying control sequence;

[0122] The execution matching sub-module, based on the path segment parameter correspondence table, matches the pigment delivery concentration gear and the device power output voltage value respectively according to the brightness adjustment amount and the spraying power level recorded in each segment. Suppose for path segment number 1, the brightness adjustment amount is 1.2 and the spraying power level is 3. The module will find the corresponding pigment delivery concentration gear and the device power output voltage value according to the preset rules or by looking up a table. For example, it is set that the pigment delivery concentration gear is 4 and the device power output voltage value is 12V. Then, the module sets the working state of the nozzle array in the order of the path segment number, that is, assigns a specific working mode and state to the nozzle, such as the nozzle working mode being "high speed" or "low speed", etc., to ensure the accuracy of the spraying process. Through these settings, the module can obtain the spraying control sequence of the path segment, form a series of instructions and states for spraying operations, and guide the subsequent equipment to perform the spraying task.

[0123] The pattern recording sub-module controls the spraying execution device to perform artistic pattern spraying according to the path segment spraying control sequence and the spraying execution instructions corresponding to each segment, collects the pattern number, spraying parameter values, and coordinate position indexes of the current execution frame of the spraying device, establishes the binding relationship between the number record and the parameter trajectory, and generates the execution record of coloring for concrete and masonry patterns;

[0124] The pattern recording sub-module controls the spraying execution device to perform artistic pattern spraying according to the path segment spraying control sequence and executes the spraying execution instructions corresponding to each segment. Suppose the spraying control sequence of a certain path segment contains multiple control instructions of the spraying device, such as nozzle position, spraying time, spraying concentration, etc. The device will make real-time adjustments according to these instructions to ensure the accurate execution of the spraying process. The module also collects the pattern number, spraying parameter values, and coordinate position indexes of the current execution frame of the spraying device. Suppose the pattern number of the current execution frame is "ART123", the spraying parameter value is 10 (representing spraying intensity or concentration), and the coordinate position index is (50, 60). This information will be recorded and bound to the pattern number to establish the relationship between the number record and the parameter trajectory. Finally, the module generates the execution record of coloring for concrete and masonry patterns, recording the spraying parameters and execution conditions of each path segment at different coordinate positions, providing data support for subsequent quality evaluation and repair.

[0125] The above is only the preferred embodiment of the present invention and does not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An AI-based coloring system for concrete and masonry art, characterized in that, The system includes: The microstructure induction module acquires the surface images of concrete and masonry components, performs image analysis through AI, extracts the micro-depression depth values, roughness grades, particle distribution densities, and material refraction characteristic values on the surface of each structural block, conducts weighted combination calculations, and generates reflection intensity reduction information; The brightness adjustment control module, based on the reflection intensity reduction information, calls the pigment brightness coefficient and the reflection intensity reduction value to calculate the amplitude correction ratio, judges the difference between the amplitude correction ratio and the gray-scale brightness reference benchmark value, updates the pigment concentration adjustment ratio, and generates regional brightness adjustment ratio information; The path extension module, based on the regional brightness adjustment ratio information, performs image analysis through AI, identifies the gap boundaries on the surfaces of concrete and masonry components, compares the width range difference of each segment with the spraying omission repair judgment threshold, and generates a set of masonry component gap patching paths; The path extension module includes: The gap identification sub-module, based on the regional brightness adjustment ratio information, performs image analysis through AI, identifies the gap boundaries on the surfaces of concrete and masonry components, obtains the gap boundary pixel line segments in the image, extracts the number of pixels in the widest segment and the number of pixels in the narrowest segment according to the line segment sequence, performs a difference operation and marks it as the width range difference value, and establishes a gap pixel range difference value group; The boundary range difference judgment sub-module calls the gap pixel range difference value group, judges the difference between each range difference value and the spraying omission repair judgment threshold, extracts the indexes of the gap segments greater than the judgment threshold, obtains the extended coordinates by performing a fixed offset on the normal direction of the boundary corresponding to each index, and at the same time collects the roughness value of the extended area and the average roughness value of the adjacent brick joint area, performs a difference operation between the roughness difference and the critical value, and uses the formula: ； Calculate to obtain the rough extension increase value, compare and screen and mark it with the rough difference adjustment critical threshold, and obtain the extended path screening index group; Among them, represents the extended area roughness value, represents the mean roughness of adjacent brick joints, represents the roughness adjustment reference value, represents the normal offset coefficient of the gap boundary pixel segment, represents the spraying concentration offset of the pattern area, represents the rough extension increase value; The path generation sub-module, based on the extended path screening index group, extracts the line segment combinations in the corresponding regional normal direction, binds them to the image coordinate mapping matrix for structure, integrates them into a single image channel spraying path panel, and records the corresponding path number and coordinate coverage segment information, and generates a set of masonry component gap patching paths; The power partition control module calls the set of masonry component gap patching paths and the regional brightness adjustment ratio information, calculates the adsorption adaptation level value of each area, selects the corresponding spraying power level, constructs a spraying power setting sequence for each path segment, and generates local power adjustment information.

2. The AI-based concrete and masonry art coloring system according to claim 1, wherein The reflection intensity reduction information includes the regional light reflection interference level, the light offset intensity on the particle surface, and the local light scattering attenuation factor. The regional brightness adjustment ratio information specifically refers to the pigment brightness adjustment amplitude, the regional color compensation coefficient, and the pigment ratio offset value. The set of masonry component gap patching paths specifically refers to the boundary extended coordinate point set, the path segment coverage marking sequence, and the gap interval structure label. The local power adjustment information includes the path spraying power level identifier, the paragraph adsorption ability grading label, and the path voltage output corresponding table.

3. The AI-based concrete and masonry art coloring system according to claim 2, wherein The microstructure induction module includes: The image analysis sub-module acquires the surface images of concrete and masonry components, performs image analysis through AI, processes each colored target area in the image into blocks, and performs edge sharpness detection, pixel gradient change extraction, and local area channel brightness change reading on each image block. By combining these three image features, adjacent block comparison operations are performed, effective image blocks are screened, and a structural block number index is established to obtain an image block structure identification set; Based on the image block structure identification set, the feature extraction sub-module performs data acquisition on each image block corresponding to a number, collects the particle distribution density, micro-depression depth value, roughness level, and material refraction characteristic value, constructs a set of micro-feature parameter combinations for the structural blocks, and performs parameter normalization and judgment on the stability of the structural features within the block in sequence to obtain a sequence of block material structure parameters; The reflection calculation sub-module calls the sequence of block material structure parameters, and according to the particle distribution density, depression depth value, roughness level, and refraction value of each block, uses the formula: ； Performs calculations to obtain the reflection intensity reduction value for each structural block, compares the reflection intensity reduction value with a preset reflection threshold, assigns an identification level, and establishes reflection intensity reduction information; Among them, represents the particle distribution density, represents the micro-depression depth value, represents the roughness grade, represents the material refractive value, represents the light absorption change rate per unit area, represents the compaction degree factor, represents the reflected intensity reduction value.

4. The AI-based concrete and masonry art coloring system according to claim 3, characterized in that The brightness adjustment control module includes: The brightness reference extraction sub-module, based on the reflection intensity reduction information, calls the preset value of the pattern pigment brightness for the area to be colored, extracts the brightness level value of each layer, performs a mean integration operation on each level value, obtains the brightness reference value corresponding to the block, and generates the area brightness reference value; Based on the area brightness reference value, the difference conversion sub-module extracts the pigment brightness coefficient and the reflection intensity reduction value of each structural block, and uses the formula: ； Performs calculations to obtain the brightness conversion offset value; Among them, represents the brightness conversion offset value, represents the pigment brightness coefficient, represents the reflection intensity reduction value, represents the regional brightness reference value, represents the brightness level parameter of the current pattern layer; According to the brightness conversion offset value, the ratio parameter generation sub-module selects the pigment concentration adjustment section corresponding to the offset value, obtains the concentration factor and the adjustment ratio range within the current concentration section, performs an interval ratio matching between the concentration factor and the offset value, calculates the adjustment ratio for the current structural block, and generates the area brightness adjustment ratio information.

5. The AI-based concrete and masonry art coloring system according to claim 4, characterized in that, The power partition control module includes: The area parameter acquisition sub-module calls the masonry component gap repair coating path set and the area brightness adjustment ratio information, extracts each coordinate point covered by the art pattern spraying path segment, collects the local reflectivity and pore surface area value of the spraying grid area where the coordinate point is located, and establishes a set of coordinate area structure parameters; Based on the set of coordinate area structure parameters, the adsorption level calculation sub-module extracts the local reflectivity value, pore surface area value, and area brightness adjustment ratio corresponding to each coordinate point, and uses the formula: ； Performs calculations to obtain the adsorption adaptation level value for each spraying area, performs mapping classification according to the path segment, and generates a path adsorption level distribution set; Among them, represents the local reflectance value, represents the pore surface area value, represents the regional lightness adjustment ratio, represents the regional density deviation value, represents the microscopic absorption constant, represents the adsorption stability index, represents the adsorption adaptation level value; According to the path adsorption level distribution set, the power setting generation sub-module performs section screening according to the set power response interval, assigns the corresponding spraying power level to each section, binds the power level label according to the path sequence number, constructs a matching mapping table between the path number and the power level, and generates local power adjustment information.

6. The AI-based concrete and masonry art coloring system according to claim 5, wherein The system further includes: The spraying control module obtains the local power adjustment information and the regional brightness adjustment ratio information, adjusts the pigment delivery concentration and the spraying power level in sequence according to the path segment numbers, controls the spraying execution device to perform artistic pattern spraying, and generates the execution record of the coloring of concrete and masonry patterns; The execution record of the coloring of concrete and masonry patterns specifically includes the path segment pigment concentration matching record, the spraying execution number trajectory table, and the regional pattern coloring mapping log.

7. The AI-based concrete and masonry art coloring system according to claim 6, wherein The spraying control module includes: The parameter synchronization sub-module obtains the local power adjustment information and the regional brightness adjustment ratio information, extracts the brightness adjustment amount and the spraying power level value corresponding to each path segment number, aligns the two values in position according to the path segment number, and establishes a synchronous data structure through number indexing to obtain the path segment parameter correspondence table; The execution matching sub-module, based on the path segment parameter correspondence table, respectively matches the pigment delivery concentration gear and the device power output voltage value according to the brightness adjustment amount and the spraying power level recorded in each segment, sets the working state of the nozzle array in sequence for the numbered path segment, and obtains the path segment spraying control sequence; The pattern recording sub-module, according to the path segment spraying control sequence and the spraying execution instructions corresponding to each segment, controls the spraying execution device to perform artistic pattern spraying, collects the pattern number, spraying parameter value and coordinate position index of the current execution frame of the spraying device, establishes the binding relationship between the number record and the parameter trajectory, and generates the execution record of the coloring of concrete and masonry patterns.

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