Intelligent spraying construction control method, system and medium based on image processing

Abstract

This application relates to the field of image processing technology and discloses an intelligent spray coating construction control method, system, and medium based on image processing. The method includes: performing image acquisition and defect analysis on the exterior wall base layer to generate benchmark data; performing grid optimization and path planning based on the benchmark data; acquiring environmental parameters, establishing a compensation database, and generating a process control matrix; monitoring coating quality in real time to generate a data set and early warning library; optimizing spray trajectories to generate control instructions; and evaluating construction quality to form an evaluation system. This application implements real-time monitoring and adaptive adjustment of intelligent spray coating quality based on image processing, thereby improving the automation level and quality control accuracy of exterior wall spray coating construction.

Description

Technical Field

[0001] The present application relates to the field of image processing, and in particular to an intelligent spraying construction control method, system and medium based on image processing. Background Art

[0002] In the field of exterior wall spraying, traditional manual spraying methods suffer from low efficiency and inconsistent quality. With the development of automation technology, spraying robots are beginning to be used in exterior wall construction, automating the process by following pre-set spray paths and process parameters. Current intelligent spraying systems are already capable of basic automated spraying functions, including automatic adjustment of spray pressure, control of spray distance and speed, and basic monitoring and control of spray quality.

[0003] However, existing intelligent spraying technology still has many shortcomings in practical application. First, the lack of accurate assessment and analysis of substrate conditions leads to a lack of targeted spray parameter settings. Second, the inability to make real-time adjustments based on environmental changes and quality fluctuations during construction results in uneven coating quality. Third, the limited quality monitoring methods make it difficult to form a complete quality assessment system, which cannot effectively guide the optimization and adjustment of the construction process. These issues seriously affect the quality and efficiency of intelligent spraying applications. Summary of the Invention

[0004] The present application provides an intelligent spraying construction control method, system and medium based on image processing, which are used to realize real-time monitoring and adaptive adjustment of intelligent spraying quality based on image processing, thereby improving the automation level and quality control accuracy of exterior wall spraying construction.

[0005] In the first aspect, the present application provides an intelligent spraying construction control method based on image processing, and the intelligent spraying construction control method based on image processing includes: performing image acquisition on the base surface of the building exterior wall, locating the hollow area and analyzing the crack distribution of the acquired image data, generating a base defect feature map and a surface parameter matrix, and extracting intelligent spraying benchmark data based on the base defect feature map; performing grid preprocessing on the building exterior wall according to the intelligent spraying benchmark data and the surface parameter matrix to obtain an initial grid scheme, and optimizing the initial grid scheme in combination with the building facade characteristics to generate a grid control parameter set and a spraying path planning library; collecting temperature, humidity and wind parameters in the construction environment, and performing temperature, humidity and wind parameters on the construction environment according to the temperature, humidity and wind parameters. The coating characteristics are dynamically detected, and an environmental compensation database is established in combination with the grid control parameter set to generate a spraying process control matrix; the coating formation data during the spraying process are collected, the coating uniformity index and coverage parameter are calculated based on the coating formation data, and a quality monitoring data set and a defect warning parameter library are generated in combination with the spraying process control matrix; based on the quality monitoring data set and the environmental compensation database, in combination with the spraying path planning library and the defect warning parameter library, the spraying trajectory is optimized in real time to generate intelligent compensation control instructions; the surface feature information of the spraying completion area is collected, the construction quality index is calculated based on the surface feature information and the intelligent compensation control instructions, a construction file containing the quality monitoring data set is established, and a quality assessment system is formed.

[0006] In a second aspect, the present application provides an intelligent spraying construction control system based on image processing, the intelligent spraying construction control system based on image processing comprising:

[0007] An acquisition module is used to acquire images of the base surface of the building's exterior wall, locate hollow areas and analyze crack distribution based on the acquired image data, generate a base defect feature map and a surface parameter matrix, and extract intelligent spraying benchmark data based on the base defect feature map;

[0008] A processing module is used to perform grid preprocessing on the building exterior wall based on the intelligent spraying benchmark data and the surface parameter matrix to obtain an initial grid scheme, and optimize the initial grid scheme in combination with the building facade characteristics to generate a grid control parameter set and a spraying path planning library;

[0009] A detection module is used to collect temperature, humidity and wind parameters in the construction environment, dynamically detect coating characteristics based on the temperature, humidity and wind parameters, establish an environmental compensation database based on the grid control parameter set, and generate a spraying process control matrix;

[0010] A generation module is used to collect coating formation data during the spraying process, calculate coating uniformity index and coverage parameter based on the coating formation data, and generate a quality monitoring data set and a defect warning parameter library in combination with the spraying process control matrix;

[0011] An optimization module, configured to optimize the spray trajectory in real time and generate intelligent compensation control instructions based on the quality monitoring data set and the environmental compensation database, in combination with the spray path planning library and the defect warning parameter library;

[0012] The calculation module is used to collect surface feature information of the sprayed area, calculate the construction quality index based on the surface feature information and intelligent compensation control instructions, establish a construction file containing the quality monitoring data set, and form a quality assessment system.

[0013] A third aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned intelligent spray construction control method based on image processing.

[0014] In the technical solution provided by the present application, by collecting and analyzing images of the base surface of the building's exterior wall, the precise positioning of hollow areas and cracks is achieved, and a base defect feature map and a surface parameter matrix are generated, providing an accurate basic data basis for subsequent construction; by pre-processing the building's exterior wall in grids and optimizing it in combination with the building's facade features, an accurate grid control parameter set and a spray path planning library are generated, which realizes the reasonable division of the construction area and the optimized planning of the spray path; the temperature, humidity and wind parameters in the construction environment are collected and dynamically detected, and an environmental compensation database is established in combination with the grid control parameter set to generate a spray process control matrix, which effectively solves the impact of environmental factors on construction quality; through real-time The coating formation data during the spraying process is collected, the coating uniformity index and coverage parameters are calculated, and the quality monitoring data set and defect warning parameter library are generated in combination with the spraying process control matrix, thereby realizing real-time monitoring of the spraying quality; based on the quality monitoring data set and the environmental compensation database, the spraying trajectory is optimized in real time in combination with the spraying path planning library and the defect warning parameter library, and intelligent compensation control instructions are generated to ensure the accuracy of the spraying construction; finally, by collecting the surface feature information of the sprayed area, the construction quality index is calculated according to the surface feature information and the intelligent compensation control instructions, and a construction file containing the quality monitoring data set is established to form a quality assessment system, thereby realizing quality traceability and assessment of the entire construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0016] Figure 1 This is a schematic diagram of an embodiment of an intelligent spraying construction control method based on image processing in an embodiment of the present application;

[0017] Figure 2 This is a schematic diagram of a quality change trend diagram in an embodiment of the present application;

[0018] Figure 3 This is a schematic diagram of the arrangement of measuring points on the coating surface in the embodiment of this application;

[0019] Figure 4 This is a schematic diagram of an embodiment of an intelligent spraying construction control system based on image processing in an embodiment of the present application. DETAILED DESCRIPTION

[0020] The embodiments of the present application provide an intelligent spraying construction control method, system and medium based on image processing. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0021] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the intelligent spraying construction control method based on image processing includes:

[0022] Step S101: Capture images of the base surface of the building exterior wall, perform hollow area location and crack distribution analysis on the acquired image data, generate a base defect feature map and a surface parameter matrix, and extract intelligent spraying benchmark data based on the base defect feature map;

[0023] Step S102: Based on the intelligent spraying benchmark data and the surface parameter matrix, the building exterior wall is pre-processed to obtain an initial gridding scheme, and the initial gridding scheme is optimized in combination with the building facade characteristics to generate a gridding control parameter set and a spraying path planning library;

[0024] Step S103: collecting temperature, humidity and wind parameters in the construction environment, dynamically detecting coating characteristics based on the temperature, humidity and wind parameters, establishing an environmental compensation database in combination with the grid control parameter set, and generating a spraying process control matrix;

[0025] Step S104: Collect coating formation data during the spraying process, calculate coating uniformity index and coverage parameter based on the coating formation data, and generate a quality monitoring data set and defect warning parameter library in combination with the spraying process control matrix;

[0026] Step S105: Based on the quality monitoring data set and the environmental compensation database, combined with the spray path planning library and the defect warning parameter library, the spray trajectory is optimized in real time to generate intelligent compensation control instructions;

[0027] Step S106: Collect surface feature information of the sprayed area, calculate construction quality indicators based on the surface feature information and intelligent compensation control instructions, establish a construction file containing a quality monitoring data set, and form a quality assessment system.

[0028] It is understandable that the execution subject of this application can be an intelligent spraying construction control system based on image processing, or a terminal or a server, which is not limited here. The embodiment of this application is described by taking the server as the execution subject as an example.

[0029] Specifically, during spray painting on building exterior walls, a multi-channel high-definition imaging device is used to scan the base surface of the exterior wall. During the scanning process, the camera captures images along a preset trajectory at a speed of 0.5 meters per second. The acquired image data is preprocessed to form a standardized digital image. To locate hollow areas, infrared thermal imaging technology is used to detect the internal structure of the wall. The detected data is then Fourier transformed to generate a spectral feature map. Threshold segmentation is performed on the spectral feature map to identify areas of temperature anomalies, which are potential hollowing locations. Crack distribution analysis is then performed, using an edge detection algorithm to extract crack outlines and calculate crack length, width, and distribution density. This process generates a base defect feature map containing information on the hollow area ratio and crack distribution. Surface roughness and flatness are also measured to construct a surface parameter matrix.

[0030] Based on the obtained base defect feature map and surface parameter matrix, the building exterior wall is pre-processed for grid division. The grid density is set using the hollow area distribution information in the intelligent spraying benchmark data, and the base grid data is weighted to generate weight values ​​for each partition. These weight values ​​determine the size and shape of the grid, and areas with higher weights require more detailed grid processing. Areas are merged according to the standard of 1.5 meters by 1.5 meters to form an initial grid division scheme. Subsequently, feature extraction is performed on the building facade to identify structural features such as windows and balconies, and the grid boundaries are adjusted and optimized based on these features. By calculating the area, position, and boundary feature values ​​of each grid area, a grid control parameter set is formed, and the motion trajectory of the spray robot is planned accordingly, and the trajectory point sequence is stored in the spray path planning library.

[0031] During the construction process, sensors collect temperature, humidity, and wind speed data from the construction environment. These environmental parameters are segmented and processed at time intervals to obtain average environmental parameter values ​​for each time period. Paint samples are then tested, and the relationship between environmental parameters and paint viscosity is analyzed to establish a dynamic environment-paint correlation database. This data is integrated with the grid control parameter set to ultimately generate a spray process control matrix. During the spraying operation, coating formation data is acquired through real-time image acquisition. This data is then segmented and processed to extract color difference and texture features. Based on these features, coating uniformity indicators are calculated to assess coating quality. Statistical analysis derives coverage parameters for each segment, which are then correlated with process parameters in the spray process control matrix to generate a quality monitoring dataset. Based on parameter change trends, warning thresholds are established, and a defect warning parameter library is constructed.

[0032] To optimize the spray trajectory in real time, the coating uniformity and coverage parameters in the quality monitoring dataset are sorted by partition to generate a quality parameter sequence. The compensation coefficient for the current time period is extracted from the environmental compensation database and matched to the quality parameter sequence. Simultaneously, the overlap ratio is calculated based on the trajectory points in the spray path planning library to obtain trajectory compensation parameters. The compensation parameters are constrained by combining the warning thresholds in the defect warning parameter library, ultimately generating intelligent compensation control instructions.

[0033] Multi-angle imaging of the painted area captures surface feature information, which is then compared with the compensation data in the intelligent compensation control instructions to generate construction quality deviation values. A digital level is used to measure the coating surface at specific points. After comparison with the construction quality deviation values, the surface is filtered within a 2mm error band to generate flatness data. Glossiness data is measured at a 60-degree angle of incidence, and acceptable range values ​​are extracted. This data, combined with the weather resistance evaluation matrix, is used to generate construction quality indicators. Historical data from the quality monitoring dataset is organized into quality trend charts, and regional quality assessment records are established to generate construction archives, ultimately forming a quality assessment system.

[0034] In the embodiment of the present application, by collecting and analyzing images of the base surface of the building's exterior wall, the precise positioning of hollow areas and cracks is achieved, and a base defect feature map and a surface parameter matrix are generated, providing an accurate basic data basis for subsequent construction; by pre-processing the building's exterior wall in grids and optimizing it in combination with the building's facade features, an accurate grid control parameter set and a spray path planning library are generated, which realizes the reasonable division of the construction area and the optimized planning of the spray path; the temperature, humidity and wind parameters in the construction environment are collected and dynamically detected, and an environmental compensation database is established in combination with the grid control parameter set to generate a spray process control matrix, which effectively solves the impact of environmental factors on construction quality; by real-time collection The coating formation data during the spraying process is used to calculate the coating uniformity index and coverage parameters, and the quality monitoring data set and defect warning parameter library are generated in combination with the spraying process control matrix, thereby realizing real-time monitoring of the spraying quality; based on the quality monitoring data set and the environmental compensation database, the spraying trajectory is optimized in real time in combination with the spraying path planning library and the defect warning parameter library, and intelligent compensation control instructions are generated to ensure the accuracy of the spraying construction; finally, by collecting the surface feature information of the sprayed area, the construction quality index is calculated according to the surface feature information and the intelligent compensation control instructions, and a construction file containing the quality monitoring data set is established to form a quality assessment system, thereby realizing quality traceability and assessment of the entire construction process.

[0035] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0036] (1) Scan the surface of the building's exterior wall base at multiple angles using a high-definition camera to obtain the original image data of the exterior wall base;

[0037] (2) The original image data of the exterior wall base layer is partitioned and sliced ​​according to the size of 1.5 meters × 1.5 meters to obtain base layer image partition data;

[0038] (3) grayscale and edge enhancement processing is performed on the base image partition data to obtain an enhanced base image;

[0039] (4) Using image segmentation methods, the enhanced base layer image is used to identify hollow areas and extract crack edges to generate a base layer defect feature map;

[0040] (5) Calculate the surface roughness, flatness and color difference value of each partition based on the enhanced base layer image and construct a surface parameter matrix;

[0041] (6) The hollow area ratio, crack length density and surface parameter matrix data in the base defect feature map are weighted and fused to generate intelligent spraying benchmark data.

[0042] Specifically, a high-resolution camera array is used to capture the exterior wall basement surface from multiple angles. The camera array consists of three high-definition cameras spaced at a 120-degree angle, each with a resolution of 4096 x 3072 pixels. During acquisition, the cameras maintain a fixed distance of 0.8 meters from the wall, with simultaneous acquisitions every 0.5 meters. Adjacent images maintain a 30% overlap. This multi-angle acquisition effectively captures the wall's surface contours and surface details, generating raw image data of the exterior wall basement. After image acquisition, the raw image data is divided into multiple sub-areas of a standard size of 1.5 meters x 1.5 meters. A grid segmentation algorithm is used in the segmentation process, matching feature points in overlapping image regions to eliminate image stitching errors. Each sub-area is fused at the overlapping portion to ensure the continuity of the partition boundaries, resulting in the basement image partition data.

[0043] Digital image preprocessing and grayscale conversion are performed on the base image partition data, converting the RGB three-channel image into a single-channel grayscale image to reduce data processing. Adaptive histogram equalization is then used to enhance image contrast and highlight edges and texture information. Edge enhancement is then performed using the Difference of Gaussian operator to improve image clarity and detail, resulting in an enhanced base image.

[0044] Defect detection is performed on the enhanced base layer image, where the mathematical model for hollow area recognition and crack edge extraction is expressed as:

[0045]

[0046] Where D(x,y) represents the defect characteristic value, ω i is the weight coefficient of different types of defects, G(x, y0 is the image gradient value, H(x, y) is the local grayscale mean, μ x and μ y represents the defect center coordinates, σ x and σ y

[0047] represents the spatial distribution parameter of the defect area, and n is the number of defect types. The defect feature map is calculated using this model.

[0048] In terms of surface parameter calculation, a comprehensive evaluation model was established:

[0049]

[0050] Wherein, M(r,p,c) is the comprehensive evaluation value of surface parameters; r is the surface roughness parameter; p is the flatness parameter; c is the color difference parameter; α is the roughness weight coefficient, ranging from [0, 1]; β is the flatness weight coefficient, ranging from [0, 1]; γ is the color difference weight coefficient, ranging from [0, 1]; Z ij is the surface height value of the sampling point in row i and column j; is the average height value of all sampling points; m is the number of rows of surface height sampling points; n is the number of columns of surface height sampling points; P k is the value of the kth flatness measurement point; P k-1 is the value of the k-1th flatness measurement point; l is the total number of flatness measurement points; L i is the Lab color space component value of the current measurement point (i=1, 2, 3 correspond to L, a, b respectively); is the reference value of the standard Lab color space component; i is the sampling point row index; j is the sampling point column index; k is the flatness measurement point index.

[0051] A layered, weighted fusion method is used to generate intelligent spray benchmark data based on the base defect feature map and the surface parameter matrix. The hollowing area ratio and crack length density are normalized and then fused with the roughness, flatness, and color difference data in the surface parameter matrix at multiple levels. Weight coefficients are assigned to each level, and the final intelligent spray benchmark data is derived through iterative calculation.

[0052] For example, a standard 1.5 m x 1.5 m area was selected for analysis. During the image acquisition phase, three high-definition cameras captured image data from different angles. After image segmentation processing, two hollow defects, measuring 200 square centimeters and 150 square centimeters respectively, and three cracks with a total length of 80 centimeters were detected. Surface roughness measurements revealed numerous irregularities in the area, and data processing led to the calculation of specific surface parameters. These raw data were then weighted and fused to form the baseline spray parameters for the area.

[0053] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0054] (1) Divide the intelligent spraying benchmark data into grid density according to the hollow area distribution and output the base grid data;

[0055] (2) Perform weighted calculation on the base grid data through the surface parameter matrix to obtain the wall partition weight value;

[0056] (3) Binarize the wall partition weights and merge the regions according to the 1.5m × 1.5m standard to generate an initial partitioning scheme;

[0057] (4) Scan the structural features of the building's exterior walls and extract the location information of windows and balconies, and correct the grid boundaries in the initial grid scheme;

[0058] (5) Calculating the area, position, and boundary characteristic values ​​of each grid region based on the corrected grid boundaries to form a grid control parameter set;

[0059] (6) Plan the motion trajectory points of the spraying robot according to the boundary characteristic values ​​of each grid area, and store the trajectory point sequence in the spraying path planning library.

[0060] Specifically, the grid density of the hollow area is divided according to the intelligent spraying benchmark data. The grid density division adopts the adaptive grid method, that is, denser grid points are set around the hollow area, and the grid point spacing gradually increases with the distance from the hollow area. For example, at the edge of the hollow area, the grid point spacing is set to 5 cm, and the grid spacing increases by 5 cm every 20 cm outward until the maximum grid spacing of 25 cm is reached. This dynamically changing grid density distribution forms the base grid data. The surface parameter matrix is ​​then used to perform weighted calculations on the base grid data. The specific approach is to assign different weight coefficients to the three index values ​​of roughness, flatness and color difference in the surface parameter matrix, and obtain the comprehensive weight value of each grid point by weighted summation. For each grid point in an area of ​​1.5 meters by 1.5 meters, its average weight value is calculated as the wall partition weight value of the area.

[0061] The obtained wall partition weights are binarized, with the threshold set to the weight mean. Areas above the threshold are marked as 1, and areas below the threshold are marked as 0. Adjacent areas with the same value are then merged according to a standard 1.5 m x 1.5 m grid size to generate an initial gridding scheme. Next, the building's exterior structural features are scanned, and the location information of features such as windows and balconies is extracted using an edge detection algorithm. For each identified feature, the coordinates of its four corner points and the boundary contour are recorded. Based on this feature information, the grid boundaries in the initial gridding scheme are corrected to ensure that the grid boundaries do not pass through windows or balconies.

[0062] The parameters of the corrected grid boundaries were calculated, and a grid control parameter calculation model was established:

[0063]

[0064] Where: F(A,B,C) is the comprehensive characteristic value of the grid area; t is the total number of grid areas; V i is the area of ​​the i-th grid area; Q i is the position weight of the i-th grid area; s is the number of boundary nodes; E ij is the curvature value of the jth boundary node in the i-th grid area; R ij is the relative position of the jth boundary node in the i-th grid area; B k are the coordinates of the four main inflection points of the boundary; B k-1 Indicates the coordinate value of the k-1th inflection point; is the feature weight coefficient; i is the grid area number; j is the boundary node number; k is the inflection point number.

[0065] The spray robot's trajectory is planned based on the calculated grid control parameter set. Reference track points are generated within each grid area, and the track point density is dynamically adjusted based on the area and boundary eigenvalues ​​of the area. For regions with complex boundaries, the track point density is increased; for regular areas, a standard point spacing is used. Finally, all track points are arranged in spatial order to form a track point sequence, which is stored in the spray path planning library.

[0066] For example, during wall construction, a standard area has windows and two hollow defects. Grid division is performed according to the location of the hollows, and dense grid points with a spacing of 5 cm are set in the area around the hollows. The grid spacing gradually increases with the distance. Then the surface roughness data (value 0.8), flatness data (value 1.2) and color difference data (value 0.6) of the area are substituted into the weight calculation to obtain the regional weight value. After binarization, the grid points with similar weight values ​​are merged into a spray partition. The identified window position information is used to adjust the partition boundary to ensure that the partition boundary is set along the window outline. Finally, the trajectory point sequence is calculated based on the geometric characteristics of the partition to complete the entire partition planning process.

[0067] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0068] (1) Collect temperature, humidity, and wind speed data in the construction environment through sensors to form an environmental parameter sequence;

[0069] (2) Divide the environmental parameter sequence into segments according to time intervals, and calculate the average temperature, humidity and wind parameters for each time period;

[0070] (3) Sampling and testing the paint, correlating the average temperature, humidity and wind parameters with the paint viscosity data to obtain the environmental impact characteristic value;

[0071] (4) Fusing the environmental impact characteristic values ​​with the grid control parameter sets to construct an environmental compensation database;

[0072] (5) Calculate the compensation factor based on the data in the environmental compensation database to obtain the environmental compensation coefficient;

[0073] (6) Convert the environmental compensation coefficient into spraying process parameters and generate a spraying process control matrix according to the partition number.

[0074] Specifically, temperature sensors, humidity sensors, and wind speed sensors were deployed throughout the construction site. The temperature sensors had a measurement accuracy of 0.1°C and a measurement range of -30°C to 70°C; the humidity sensors had a measurement accuracy of 1% RH and a measurement range of 0-100% RH; and the wind speed sensors had a measurement accuracy of 0.1 m / s and a measurement range of 0-30 m / s. These sensors collected data at 10-second intervals. Each data point contained a timestamp, sensor ID, and measurement value, forming an environmental parameter sequence. The collected environmental parameter sequence was segmented into 15-minute time windows for processing. Statistical analysis was performed on the data within each time window to calculate the average temperature, average relative humidity, and average wind speed. The fluctuation range of each parameter was also recorded, and outliers were removed to ensure data accuracy. The processed data formed the average temperature, humidity, and wind speed parameters for each time period.

[0075] The paint is sampled and tested regularly, and its dynamic viscosity is measured using a rotational viscometer. The measured viscosity values ​​are correlated with the average temperature, humidity, and wind speed parameters over the same time period. Data fitting is used to determine the environmental impact characteristic value. This characteristic value reflects the degree to which environmental changes affect the paint's performance. This characteristic value is then fused with the grid control parameter set, which includes characteristic values ​​for each grid area, location, and boundary. These parameters are combined with the environmental impact characteristic values ​​to construct an environmental compensation database. This database records the compensation parameters required for each grid area under different environmental conditions.

[0076] The compensation factor is calculated based on the information in the environmental compensation database. The calculation of the compensation factor takes into account the effect of temperature on the viscosity of the paint, the effect of humidity on the drying speed, and the effect of wind speed on the deviation of the spray trajectory, resulting in the environmental compensation coefficient.

[0077] For the transformation of spraying process parameters, the following calculation model was established:

[0078]

[0079] Where: T(N,U,W) is the spraying process parameter matrix; n is the number of partitions; X i is the spraying pressure value of the i-th partition; Y iis the spraying distance value of the i-th partition; J i is the spraying speed value of the i-th partition; K ij is the jth environmental compensation coefficient of the i-th partition; S ij is the jth standard parameter value of the i-th partition; W k is the kth coating performance parameter; U k is the kth environmental influencing factor; θ1, θ2, θ3 are process parameter weight coefficients; m is the number of environmental compensation parameters; S is the number of coating performance parameters; i is the partition number; j is the compensation parameter number; k is the performance parameter number;

[0080] For example, sensors detect a temperature of 25°C, relative humidity of 65%, and wind speed of 2.5 m / s during a certain period. Paint sampling and testing reveal that under these conditions, the viscosity of the paint increases compared to the standard value. This change is entered into the environmental compensation database, which calculates a compensation strategy that requires increasing spray pressure and reducing spray speed. Depending on the location of the zones, a larger compensation factor is assigned to higher zones, which are more affected by wind speed. Finally, all these parameters are integrated into a process control matrix to guide the robot for precise spraying.

[0081] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0082] (1) Real-time image acquisition of the spraying area, obtaining coating formation data, and marking the data according to the partition number;

[0083] (2) Segmenting the coating forming data according to a preset sampling time interval to extract the color difference and texture features of each coating segment;

[0084] (3) Calculate the uniformity index of the coating in each area based on the color difference and texture characteristics to form the coating uniformity evaluation data;

[0085] (4) Statistically analyze the coating uniformity evaluation data and calculate the coverage parameters of each partition coating;

[0086] (5) Correlation analysis is performed between the coverage parameters and the process parameters corresponding to the spraying process control matrix to generate a quality monitoring data set;

[0087] (6) Establish parameter warning thresholds based on parameter change trends in the quality monitoring data set and build a defect warning parameter library.

[0088] Specifically, during the spraying process, a high-resolution imaging device captures real-time images of the spraying area. The imaging device consists of an RGB camera with a resolution of 4096 × 3072 pixels, used to capture color information on the coating surface; and an infrared camera with a resolution of 1920 × 1080 pixels, used to monitor the coating's curing state. The cameras are mounted on the end of the spray robot's arm and move synchronously with the spraying process to ensure continuous image acquisition. Each captured image is automatically timestamped and partitioned to facilitate subsequent data processing. The captured image data is segmented into 5-second time windows. Within each time window, color difference features of the coating are extracted. The RGB image is converted to Lab color space, and the color difference with a standard color sample is calculated. Simultaneously, texture analysis is performed on the image, using the gray-level co-occurrence matrix method to extract feature parameters such as contrast, entropy, energy, and correlation. To improve analysis accuracy, the image is decomposed at multiple scales, extracting features at 1 × 1, 2 × 2, and 4 × 4 scales. The final texture feature values ​​are then synthesized using a weighted approach.

[0089] Based on the extracted color difference and texture features, the uniformity index of the coating is calculated. The color difference value is normalized to obtain a uniformity index between 0 and 1. The texture features are then used to calculate the surface texture uniformity, including the texture consistency of the local area and the global texture distribution characteristics. By combining these indicators, the coating uniformity evaluation data is formed. The coating uniformity evaluation data is statistically analyzed to calculate the coverage parameters of the coating in each partition. The coverage parameter reflects the degree of coverage of the coating on the base layer and is evaluated by analyzing the grayscale distribution, edge continuity and texture integrity of the image. Multiple sampling points are set for each partition, the coverage values ​​of the sampling points are calculated, and the coverage distribution map of the entire partition is obtained by interpolation.

[0090] The calculated coverage parameters are correlated with the parameters in the spraying process control matrix. The process parameters include spraying pressure, spraying distance, spraying speed, etc. By establishing a mapping relationship between the parameters, the influence of different process parameter combinations on the coating quality is analyzed. The correlation analysis uses the sliding time window method to calculate the correlation between parameter changes and quality changes in real time to generate a quality monitoring data set. Based on the quality monitoring data set, a parameter early warning mechanism is established. By analyzing the distribution characteristics of historical data, the normal fluctuation range of each parameter is calculated. Set three-level early warning thresholds: when the parameter deviates from the mean by 1 standard deviation, a prompt warning is issued; when it deviates by 2 standard deviations, a warning warning is issued; when it deviates by 3 standard deviations, a serious warning is issued. These thresholds and corresponding early warning rules are stored in the defect early warning parameter library.

[0091] For example, during real-time monitoring, the image acquisition system detected slight quality fluctuations in a certain area of ​​the coating. Analysis revealed that the color difference values ​​in this area fluctuated within the standard deviation, but the contrast parameter in the texture features showed an abnormal increase. A query of the process parameters revealed slight fluctuations in the spray pressure at this time. The system immediately activated a Level 1 warning, prompting adjustments to the spray pressure parameters. With timely parameter adjustments, the coating quality quickly returned to normal.

[0092] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0093] (1) Sort the coating uniformity index and coverage parameters in the quality monitoring data set according to the partition number to obtain the quality parameter sequence;

[0094] (2) extracting the environmental compensation coefficient of the current time period from the environmental compensation database and performing data matching with the quality parameter sequence;

[0095] (3) Calculating the spray overlap rate of each partition based on the trajectory point sequence in the spray path planning library to obtain the trajectory compensation parameters;

[0096] (4) Extract the warning threshold from the defect warning parameter library and perform boundary constraint processing on the trajectory compensation parameters;

[0097] (5) Perform weighted calculation on the environmental compensation coefficient and the trajectory compensation parameter to form a compensation data stream;

[0098] (6) Calculate the adjusted spraying speed and spraying distance based on the compensation data stream and generate intelligent compensation control instructions.

[0099] Specifically, during the spray coating process, coating uniformity and coverage parameters are extracted from the quality monitoring dataset. The uniformity index reflects the consistency of the coating surface, while the coverage parameter indicates the coating's ability to cover the substrate. This data is sorted from smallest to largest partition number to generate an ordered sequence of quality parameters. Each sequence item contains the partition number, uniformity index value, and coverage value.

[0100] Retrieve the current period's environmental data from the environmental compensation database based on the timestamp. Environmental compensation coefficients include temperature, humidity, and wind speed compensation factors. These compensation coefficients are mapped to the data in the quality parameter sequence to form a parameter-environment mapping table.

[0101] For the calculation of the overlap rate of spraying trajectories, the following mathematical model was established:

[0102]

[0103] Where: Z(x,y,z) is the trajectory overlap compensation parameter; g is the number of partitions; U e is the spraying width of the e-th partition; V e is the track spacing of the e-th partition; Y ef is the offset of the fth track point in the eth partition; X ef is the standard spacing of the f-th track point in the e-th partition; G d is the characteristic parameter of the d-th trajectory; F d is the dth influencing factor; ξ1, ξ2, ξ3 are the overlap rate weight coefficients; h is the number of trajectory points; s is the number of feature parameters; e is the partition number; f is the trajectory point number; and d is the feature number.

[0104] Warning thresholds for various parameters, including the maximum allowable overlap rate and the minimum required overlap rate, are extracted from the defect warning parameter library. These thresholds are used to constrain the calculated trajectory compensation parameters to ensure they remain within a reasonable range. The environmental compensation coefficient and trajectory compensation parameters are weighted and fused according to preset weights. The environmental compensation coefficient primarily affects spray pressure and paint flow, while the trajectory compensation parameters primarily affect spray distance and speed. This weighted calculation yields a complete compensation data stream.

[0105] Specific control instructions are generated based on the compensation data stream. The adjusted spray speed is the product of the baseline speed and the compensation factor, and the spray distance is fine-tuned based on the overlap ratio requirements. These parameters are packaged into a standard control instruction format and sent to the spray robot for execution.

[0106] For example, real-time monitoring revealed a downward trend in coating uniformity in zone 3. Environmental monitoring data also indicated high wind speeds in this area. Trajectory planning analysis revealed that this zone was located at a corner, requiring a high trajectory overlap ratio. Taking these factors into account, the compensation control module calculated adjustments requiring a reduced spray speed and shorter spray distance. By executing these compensation control instructions, coating quality in this zone was effectively improved.

[0107] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0108] (1) Collect multi-angle images of the sprayed area and process the images to obtain surface feature information;

[0109] (2) Compare and analyze the surface feature information with the compensation data in the intelligent compensation control instruction to generate a construction quality deviation value;

[0110] (3) Calculate the coating flatness, glossiness and weather resistance data based on the construction quality deviation value to form the construction quality index;

[0111] (4) Arrange the historical monitoring data in the quality monitoring data set in chronological order to obtain a quality change trend graph;

[0112] (5) Establish zoning quality assessment records based on construction quality indicators and quality change trend charts, and generate construction files;

[0113] (6) Conduct statistical analysis on the data in the construction files and form a quality assessment system according to the assessment dimensions.

[0114] Specifically, for the areas where spraying has been completed, a multi-angle image acquisition device is used to acquire surface information. The device contains three high-definition cameras, which shoot the wall from angles of 0 degrees, 45 degrees, and 90 degrees respectively. The acquired images are preprocessed, including geometric correction, lighting equalization, and noise elimination. Feature extraction is performed on the processed images, including surface texture, color distribution, and edge features, to obtain surface feature information. The acquired surface feature information is compared and analyzed with the compensation data in the intelligent compensation control instructions. The compensation data includes compensation values ​​for process parameters such as spraying pressure, spraying distance, and spraying speed. By comparing the difference between the actual construction effect and the expected effect, the construction quality deviation value is calculated. This deviation value reflects the adjustment effect of various parameters during the actual construction process.

[0115] According to the construction quality deviation value, a digital level is used to measure the flatness of the coating surface. During the measurement, 9 measuring points are set in each 1.5m x 1.5m area to form a measuring point grid. The data obtained at each measuring point is compared with the standard plane to calculate the deviation value. At the same time, a gloss meter is used to measure the coating surface at a 60-degree incident angle to obtain surface gloss data. Weather resistance data is evaluated by analyzing the density and bonding strength of the coating surface. These data together constitute the construction quality index. A time series analysis is performed on the historical data in the quality monitoring data set, which contains all monitoring records from the start of construction to the current moment. These records are arranged in chronological order, and a quality change trend chart is drawn using data visualization technology. Figure 2 As shown, it is a quality change trend diagram in the embodiment of the present application, which shows the change trend of the three main quality indicators during the construction process of the exterior wall coating. The flatness index (solid line) remains relatively stable throughout the construction process, with only slight fluctuations, which reflects the intelligent spraying system's ability to accurately control the coating thickness; the glossiness index (dashed line) shows large fluctuations but an overall positive trend, which is related to the dynamic changes of environmental factors and the adjustment process of compensation control; the weather resistance index (dotted line) increases rapidly in the early stage of construction and then tends to stabilize, indicating that the coating performance gradually meets the design requirements during the construction process. The horizontal axis represents the construction time (min), and the vertical axis represents the quality index value (dimensionless). The trend chart intuitively shows the change pattern of various quality indicators over time.

[0116] Combine construction quality indicators with quality trend charts to establish zone-by-zone quality assessment records. Each zone's record contains basic information (zone number, construction time, construction personnel), quality indicator data (flatness, glossiness, weather resistance), and quality change trends. These records are organized and archived to form a complete construction archive. Finally, a multi-dimensional statistical analysis is performed on the data in the construction archive. From a technical perspective, the physical performance indicators of the coating are analyzed; from a process perspective, the control effects of construction parameters are evaluated; and from an environmental perspective, the impact of external conditions on construction quality is studied. Through this multi-dimensional analysis, a systematic quality assessment system is formed.

[0117] For example, a quality assessment of a completed subdivision was conducted. Surface images of the area were captured from multiple angles, and after image processing, subtle surface unevenness was detected in some localized areas. Querying intelligent compensation control instructions revealed that the wind speed in this area was high during construction, and the system automatically increased the spray pressure and reduced the spray speed. Measurements revealed that the flatness index of this area was within a 2mm error range, the glossiness met the required standard, and the weather resistance index was good. Comparing this data with historical monitoring records revealed that although environmental factors interfered with the construction process, the construction quality met the expected standards through intelligent compensation control.

[0118] In a specific embodiment, the process of calculating the coating flatness, glossiness, and weather resistance data based on the construction quality deviation value may specifically include the following steps:

[0119] (1) Use a digital level to measure the coating surface at different points, compare the measured data with the construction quality deviation value, and calculate the flatness reference value;

[0120] (2) The flatness reference value is filtered with a 2 mm error band to obtain the coating flatness data;

[0121] (3) Use a gloss meter to measure the sprayed surface at a 60-degree angle, correct the measurement results with the construction quality deviation value, and calculate the gloss data;

[0122] (4) Compare the glossiness data with the process standard value and extract the glossiness qualified interval value;

[0123] (5) Construct a coating durability evaluation matrix based on the construction quality deviation value and extract weather resistance data from it;

[0124] (6) The coating flatness data, glossiness qualified interval value and weather resistance data are weighted and integrated to form the construction quality index.

[0125] Specifically, a digital level is used to measure the coating surface at different points. The nine-point method is used, that is, 9 measuring points are evenly arranged in each standard area of ​​1.5 meters by 1.5 meters to form a 3×3 measurement grid. Figure 3 The figure shows a schematic diagram of the arrangement of measuring points on the coating surface in an embodiment of the present application, which shows the arrangement of 9 measuring points within a standard detection area of ​​1.5m×1.5m. The dotted line represents the area bisector, which divides the entire area into 9 equal small areas; the circle represents the position of the measuring point; and the solid outer frame represents the boundary of the detection area. The measuring points are evenly distributed within the area to ensure the representativeness of the measurement data. The accuracy of the digital level is 0.1mm, and the measurement range is ±10mm. The difference between the elevation data obtained at each measuring point and the reference plane is recorded as the measuring point deviation value. These deviation values ​​are compared and analyzed with the construction quality deviation values ​​to calculate a preliminary flatness reference value. The obtained flatness reference value is filtered with a 2mm error band. A bandpass filter is used in the filtering process to truncate deviation values ​​greater than 2mm and smooth small fluctuations less than 0.1mm. Through this filtering method, outliers and noise interference are eliminated to obtain more accurate coating flatness data.

[0126] During gloss measurement, a standard gloss meter is used to inspect the coating surface. The gloss meter's incident angle is fixed at 60 degrees. This is because it is most suitable for measuring the gloss of exterior building coatings and accurately reflects the microstructural characteristics of the coating surface. Five measurement points are taken within each standard area, and each point is measured three times. The average value is taken as the gloss reading for that point. These readings are corrected and calculated with the construction quality deviation value to obtain the actual gloss data. The measured gloss data is then compared with the process standard value. The process standard value is a pre-set reference range based on the coating type and construction requirements, typically consisting of an upper and lower limit. Through comparative analysis, it is determined whether the measured gloss falls within the acceptable range, and the acceptable range value is recorded. This range value reflects the acceptable range of gloss for the coating surface.

[0127] A coating durability assessment matrix is ​​constructed based on construction quality deviation values. This assessment matrix encompasses multiple evaluation dimensions: coating-substrate bonding strength, surface density, crack resistance, and water resistance. Through a comprehensive analysis of these performance indicators, weatherability data is extracted from the assessment matrix, reflecting the long-term performance of the coating. Coating flatness data, glossiness acceptance interval values, and weatherability data are weighted accordingly. Flatness data reflects the coating's macroscopic quality, gloss data reflects the coating's surface quality, and weatherability data reflects the coating's service life. Through weighted calculations, comprehensive construction quality indicators are generated.

[0128] For example, within a standard inspection area, elevation measurements were first performed at nine points using a digital level. Processing the measured data revealed that the maximum deviation occurred at the edge of the area. Comparing this data with the construction quality deviation value determined that this deviation was related to environmental factors during construction. Filtering with a 2mm error band yielded the flatness data for that area. Simultaneously, a gloss meter was used to measure the five points, and the averaged data fell within the acceptable range specified by the process standards. Combined with the coating durability assessment results, the overall quality index for that area was ultimately calculated.

[0129] The above describes the intelligent spraying construction control method based on image processing in the embodiment of the present application. The following describes the intelligent spraying construction control system based on image processing in the embodiment of the present application. Figure 4 In the embodiment of the present application, an embodiment of the intelligent spraying construction control system based on image processing includes:

[0130] The acquisition module 201 is used to acquire images of the base surface of the building exterior wall, locate hollow areas and analyze crack distribution on the acquired image data, generate a base defect feature map and a surface parameter matrix, and extract intelligent spraying benchmark data based on the base defect feature map;

[0131] Processing module 202 is used to perform grid preprocessing on the building exterior wall based on the intelligent spraying benchmark data and the surface parameter matrix to obtain an initial grid scheme, and optimize the initial grid scheme in combination with the building facade characteristics to generate a grid control parameter set and a spraying path planning library;

[0132] Detection module 203, for collecting temperature, humidity and wind parameters in the construction environment, dynamically detecting coating characteristics based on the temperature, humidity and wind parameters, establishing an environmental compensation database in combination with the grid control parameter set, and generating a spraying process control matrix;

[0133] A generation module 204 is used to collect coating formation data during the spraying process, calculate coating uniformity index and coverage parameter based on the coating formation data, and generate a quality monitoring data set and a defect warning parameter library in combination with the spraying process control matrix;

[0134] An optimization module 205 is configured to optimize the spraying trajectory in real time and generate intelligent compensation control instructions based on the quality monitoring data set and the environmental compensation database, in combination with the spraying path planning library and the defect warning parameter library;

[0135] The calculation module 206 is used to collect surface feature information of the sprayed area, calculate the construction quality index based on the surface feature information and the intelligent compensation control instructions, establish a construction file containing the quality monitoring data set, and form a quality evaluation system.

[0136] Through the coordinated cooperation of the above-mentioned components, by collecting and analyzing images of the base surface of the building's exterior wall, the precise positioning of hollow areas and cracks is achieved, and a base defect feature map and surface parameter matrix are generated, providing accurate basic data basis for subsequent construction; by pre-processing the building's exterior wall in grids and optimizing it in combination with the building's facade characteristics, an accurate grid control parameter set and a spray path planning library are generated, achieving a reasonable division of the construction area and optimized planning of the spray path; the temperature, humidity and wind parameters in the construction environment are collected and dynamically detected, and an environmental compensation database is established in combination with the grid control parameter set to generate a spray process control matrix, effectively solving the impact of environmental factors on construction quality; The coating formation data during the spraying process is collected in real time, the coating uniformity index and coverage parameters are calculated, and the quality monitoring data set and defect warning parameter library are generated in combination with the spraying process control matrix, thereby realizing real-time monitoring of the spraying quality; based on the quality monitoring data set and the environmental compensation database, the spraying trajectory is optimized in real time in combination with the spraying path planning library and the defect warning parameter library, and intelligent compensation control instructions are generated to ensure the accuracy of the spraying construction; finally, by collecting the surface feature information of the sprayed area, the construction quality index is calculated according to the surface feature information and the intelligent compensation control instructions, and a construction file containing the quality monitoring data set is established to form a quality assessment system, thereby realizing quality traceability and assessment of the entire construction process.

[0137] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the steps of the image processing-based intelligent spray construction control method.

[0138] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0140] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent spraying construction control method based on image processing, characterized in that: The intelligent spraying construction control method based on image processing includes: Capture images of the building exterior wall base surface, locate hollow areas and analyze crack distribution on the acquired image data, generate a base defect feature map and a surface parameter matrix, and extract intelligent spraying benchmark data based on the base defect feature map; Based on the intelligent spraying benchmark data and the surface parameter matrix, the building exterior wall is pre-processed to obtain an initial gridding scheme, and the initial gridding scheme is optimized in combination with the building facade characteristics to generate a gridding control parameter set and a spraying path planning library; Collecting temperature, humidity and wind parameters in the construction environment, dynamically detecting coating characteristics based on the temperature, humidity and wind parameters, establishing an environmental compensation database in combination with the grid control parameter set, and generating a spraying process control matrix; Collecting coating formation data during the spraying process, calculating coating uniformity index and coverage parameter based on the coating formation data, and generating a quality monitoring data set and a defect warning parameter library in combination with the spraying process control matrix; Based on the quality monitoring data set and the environmental compensation database, combined with the spray path planning library and the defect warning parameter library, the spray trajectory is optimized in real time to generate intelligent compensation control instructions; Surface feature information of the sprayed area is collected, construction quality indicators are calculated based on the surface feature information and intelligent compensation control instructions, a construction file containing the quality monitoring data set is established, and a quality assessment system is formed.

2. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: The method includes collecting images of the surface of the base layer of the building exterior wall, locating hollow areas and analyzing crack distribution on the acquired image data, generating a base layer defect feature map and a surface parameter matrix, and extracting intelligent spraying benchmark data based on the base layer defect feature map, including: Use a high-definition camera to scan the surface of the building's exterior wall base at multiple angles to obtain the original image data of the exterior wall base; The original image data of the exterior wall base layer is partitioned and sliced ​​according to a size of 1.5 meters × 1.5 meters to obtain base layer image partition data; grayscale and edge enhancement are performed on the base image partition data to obtain an enhanced base image; Perform hollow area recognition and crack edge extraction on the enhanced base layer image by image segmentation method to generate a base layer defect feature map; Calculate the surface roughness, flatness and color difference value of each partition according to the enhanced base layer image, and construct a surface parameter matrix; The hollowing area ratio, crack length density in the base defect characteristic map and the data in the surface parameter matrix are weightedly fused to generate intelligent spraying benchmark data.

3. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: The method comprises: performing grid preprocessing on the building exterior wall according to the intelligent spraying benchmark data and the surface parameter matrix to obtain an initial grid scheme, optimizing the initial grid scheme in combination with the building facade features, and generating a grid control parameter set and a spraying path planning library, including: Divide the intelligent spraying benchmark data into grid density according to the hollow area distribution, and output base grid data; Performing weighted calculation on the base grid data using the surface parameter matrix to obtain a wall partition weight value; Binarize the wall partition weight values, merge the regions according to the 1.5m×1.5m standard, and generate an initial partitioning scheme; Scanning the structural features of the building's exterior walls and extracting window and balcony location information, and correcting the grid boundaries in the initial gridding scheme; The area, position and boundary characteristic value of each grid region are calculated according to the corrected grid boundary to form a grid control parameter set; The motion trajectory points of the spraying robot are planned according to the boundary characteristic values ​​of each grid area, and the trajectory point sequence is stored in the spraying path planning library.

4. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: The temperature, humidity and wind parameters in the construction environment are collected, the coating characteristics are dynamically detected according to the temperature, humidity and wind parameters, an environmental compensation database is established in combination with the grid control parameter set, and a spraying process control matrix is ​​generated, including: The temperature, humidity and wind speed data of the construction environment are collected through sensors to form an environmental parameter sequence; Divide the environmental parameter sequence into segments according to time intervals, and calculate the average temperature, humidity and wind parameters for each time period; Sampling and testing the paint, correlating the average temperature, humidity and wind parameters with the paint viscosity data to obtain an environmental impact characteristic value; Performing data fusion on the environmental impact characteristic value and the grid control parameter set to construct an environmental compensation database; Calculating the compensation factor based on the data in the environmental compensation database to obtain an environmental compensation coefficient; The environmental compensation coefficient is converted into a spraying process parameter, and the spraying process control matrix is ​​generated by sorting the parameters according to the partition number.

5. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: The method includes collecting coating forming data during the spraying process, calculating coating uniformity index and coverage parameter based on the coating forming data, and generating a quality monitoring data set and a defect warning parameter library in combination with the spraying process control matrix, including: Collect real-time images of the spraying area, obtain coating formation data, and mark the data according to the partition number; Segmenting the coating forming data according to a preset sampling time interval to extract the color difference and texture features of each coating segment; Calculating the uniformity index of the coating in each area based on the color difference and texture characteristics to form coating uniformity evaluation data; Performing statistical analysis on the coating uniformity evaluation data to calculate the coverage parameters of the coating in each partition; Perform correlation analysis on the coverage parameter and the process parameters corresponding to the spraying process control matrix to generate a quality monitoring data set; Parameter warning thresholds are established based on parameter change trends in the quality monitoring data set, and a defect warning parameter library is constructed.

6. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: Based on the quality monitoring data set and the environmental compensation database, combined with the spray path planning library and the defect warning parameter library, the spray trajectory is optimized in real time to generate intelligent compensation control instructions, including: Sorting the coating uniformity index and coverage parameter in the quality monitoring data set according to the partition number to obtain a quality parameter sequence; Extracting the environmental compensation coefficient for the current time period from the environmental compensation database and performing data matching with the quality parameter sequence; Calculating the spray overlap rate of each partition according to the trajectory point sequence in the spray path planning library to obtain a trajectory compensation parameter; Extracting a warning threshold from the defect warning parameter library and performing boundary constraint processing on the trajectory compensation parameter; Performing weighted calculation on the environmental compensation coefficient and the trajectory compensation parameter to form a compensation data stream; The adjusted spraying speed and spraying distance are calculated according to the compensation data stream, and an intelligent compensation control instruction is generated.

7. The intelligent spraying construction control method based on image processing according to claim 1 is characterized in that: The surface feature information of the sprayed area is collected, the construction quality index is calculated based on the surface feature information and the intelligent compensation control instruction, a construction file containing the quality monitoring data set is established, and a quality assessment system is formed, including: Collect multi-angle images of the sprayed area and process the images to obtain surface feature information; Comparing and analyzing the surface feature information with the compensation data in the intelligent compensation control instruction to generate a construction quality deviation value; Calculating coating flatness, glossiness, and weather resistance data based on the construction quality deviation value to form a construction quality index; Arrange the historical monitoring data in the quality monitoring data set in chronological order to obtain a quality change trend graph; Establishing a zoning quality assessment record based on the construction quality indicators and quality change trend chart, and generating a construction file; Conduct statistical analysis on the data in the construction files and form a quality assessment system according to the assessment dimensions.

8. The intelligent spraying construction control method based on image processing according to claim 7 is characterized in that: The coating flatness, glossiness and weather resistance data are calculated based on the construction quality deviation value to form a construction quality index, including: Measure the coating surface at different points using a digital level, compare the measured data with the construction quality deviation value, and calculate the flatness reference value; Performing 2 mm error band filtering on the flatness reference value to obtain coating flatness data; Use a gloss meter to measure the sprayed surface at a 60-degree angle of incidence, correct the measurement results with the construction quality deviation value, and calculate the gloss data; Comparing the glossiness data with the process standard value to extract the glossiness qualified interval value; constructing a coating durability evaluation matrix based on the construction quality deviation values ​​and extracting weather resistance data therefrom; The coating flatness data, glossiness qualified interval value and weather resistance data are weighted and integrated to form a construction quality index.

9. An intelligent spraying construction control system based on image processing, used to implement the intelligent spraying construction control method based on image processing according to any one of claims 1 to 8, characterized in that: The intelligent spraying construction control system based on image processing includes: An acquisition module is used to acquire images of the base surface of the building's exterior wall, locate hollow areas and analyze crack distribution based on the acquired image data, generate a base defect feature map and a surface parameter matrix, and extract intelligent spraying benchmark data based on the base defect feature map; A processing module is used to perform grid preprocessing on the building exterior wall based on the intelligent spraying benchmark data and the surface parameter matrix to obtain an initial grid scheme, and optimize the initial grid scheme in combination with the building facade characteristics to generate a grid control parameter set and a spraying path planning library; A detection module is used to collect temperature, humidity and wind parameters in the construction environment, dynamically detect coating characteristics based on the temperature, humidity and wind parameters, establish an environmental compensation database in combination with the grid control parameter set, and generate a spraying process control matrix; A generation module is used to collect coating formation data during the spraying process, calculate coating uniformity index and coverage parameter based on the coating formation data, and generate a quality monitoring data set and a defect warning parameter library in combination with the spraying process control matrix; An optimization module, configured to optimize the spray trajectory in real time and generate intelligent compensation control instructions based on the quality monitoring data set and the environmental compensation database, in combination with the spray path planning library and the defect warning parameter library; The calculation module is used to collect surface feature information of the sprayed area, calculate the construction quality index based on the surface feature information and intelligent compensation control instructions, establish a construction file containing the quality monitoring data set, and form a quality assessment system.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the intelligent spray construction control method based on image processing according to any one of claims 1 to 8 is implemented.

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