An intelligent spraying method, system, device and storage medium for a building wall

Abstract

The application discloses a kind of building wall intelligent spraying method, system, equipment and storage medium, it is related to building wall intelligent spraying technical field, including to wall surface is primed and collects spraying robot operation data and wall surface data;Drying time calculation is carried out in combination with putty material parameters and preliminary coating polishing is carried out, and preliminary polishing coating is scanned and output spraying decision.The method described in the application achieves better results in terms of spraying thickness precision control, intelligent prediction of polishing time and wall flatness detection feedback.

Description

Technical Field

[0001] This invention relates to the field of intelligent spraying technology for building walls, specifically to an intelligent spraying method, system, equipment, and storage medium for building walls. Background Technology

[0002] Wall putty spraying is generally done manually or with semi-automatic equipment. Workers need to adjust the spray gun angle, spray pressure, and flow rate based on their subjective experience. Due to insufficient operational stability, the putty layer thickness often becomes uneven. If it is too thick in some areas, it can easily lead to cracking and bubbling, while if it is too thin in some areas, it will affect the coverage and smoothness, thus increasing the amount of sanding work required later.

[0003] The drying time of putty is usually judged by the construction worker by touching the wall surface to determine whether it is dry. This experience-based judgment does not fully take into account factors such as the temperature and humidity of the construction environment, ventilation conditions, and the putty material ratio. If sanding is done too early, the putty will not be fully cured, which can easily cause the surface layer to peel off; if sanding is done too late, the putty will be too hard and difficult to sand, affecting the construction period and surface quality, and even causing batch differences.

[0004] In terms of construction quality inspection, most projects still rely on manual measurement of wall flatness using a 2-meter straightedge and feeler gauge. This manual inspection method is not only inaccurate but also highly susceptible to the operator's technique, angle, and subjective judgment. Inspection results often cannot be fed back to the construction end in real time, leading to delays in touch-up painting and a lack of data support for adjustments. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing building wall putty spraying and testing methods suffer from poor spraying uniformity, low coating thickness control accuracy, low construction efficiency, and delayed testing feedback. The invention also addresses the problem of how to achieve intelligent closed-loop control of putty spraying, drying time calculation, flatness detection, and spraying decision output by linking a spraying robot with a measurement robot.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent spraying method for building walls, comprising applying a primer to the wall and collecting spraying robot operation data and wall data.

[0008] The drying time is calculated based on the putty material parameters, and a preliminary coating sanding is performed. The preliminary sanded coating is then scanned, and a spraying decision is output. Smoothness is judged in each preliminary sanding coating. If it does not meet the smoothness requirements of the topcoat, the sanding coating that does not meet the smoothness requirements of the preliminary topcoat is set as the intermediate coating area. The intermediate coating area is recoated, the putty sanding time of the intermediate coating area is calculated and the intermediate coating sanding coating is scanned, and the smoothness is judged again.

[0009] When the surface coating smoothness requirement is met, the surface coating putty is applied according to the standard that the spray thickness is not greater than 1mm and the gun speed is greater than 0.8m / s and less than 1m / s. The sanding time of the surface coating putty is calculated and the surface coating sanding coating 202 is scanned for inspection.

[0010] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the step of applying a primer to the wall surface includes determining the wall surface to be sprayed.

[0011] Set the maximum coating thickness to no more than 2mm and the minimum coating thickness to no less than 1mm.

[0012] A spraying robot applies a primer to the entire wall surface.

[0013] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the preliminary coating sanding includes constructing a putty drying time prediction model using physical calculation formulas.

[0014] The drying time is calculated as the grinding point.

[0015] Determine whether the coating meets the requirements for sanding.

[0016] The coating is sanded using an electric sander.

[0017] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the preliminary sanding of the coating includes calling a measurement robot to perform a three-dimensional scan of the wall surface.

[0018] Mark inspection points at key corners and joints of the wall.

[0019] Set the scanning accuracy to no more than 1.5mm.

[0020] Calculate the flatness of the wall surface.

[0021] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the flatness determination includes setting the preliminary sanding coating as the intermediate coating area when the flatness is greater than 3mm.

[0022] When the flatness is less than or equal to 3mm, the initial sanding coating is set as the topcoat area.

[0023] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the method of recoating the intermediate coating area includes taking the local area of ​​the sanded coating with a deviation greater than 3mm as the intermediate coating area.

[0024] The drying time of the intermediate coating area is calculated as the sanding node.

[0025] Repeat the initial sanding process and check the flatness again.

[0026] Continue until the flatness is less than or equal to 3mm.

[0027] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the calculation of the topcoat putty sanding time includes setting the sanding coating that meets the initial topcoat smoothness requirements as the topcoat area.

[0028] Collect current spraying robot operation data, wall data, and putty material parameters.

[0029] As a preferred embodiment of the intelligent spraying method for building walls described in this invention, the output spraying decision includes calling the actual measurement robot again to perform a three-dimensional scan of the wall.

[0030] Compare with specific construction and acceptance standards.

[0031] When the specific construction and acceptance standards are met, the current actual wall surface data will be output.

[0032] When specific construction and acceptance standards are not met, localized scraping and patching should be carried out on the areas that do not meet the standards.

[0033] The actual wall surface data and the wall surface data according to the specific construction and acceptance standards are used as the construction report.

[0034] Another objective of this invention is to provide an intelligent spraying system for building walls, which solves the problems of insufficient spraying thickness control precision, reliance on manual experience for sanding timing, delayed detection feedback, and fragmented construction processes in current building wall construction technologies by integrating a spraying robot, a measurement robot, and an intelligent control terminal for collaborative operation.

[0035] As a preferred embodiment of the intelligent spraying system for building walls described in this invention, it includes a primer spraying and data acquisition module, and a sanding detection and spraying decision module.

[0036] The primer spraying and data acquisition module is used to apply a primer to the wall surface and collect data on the spraying robot's operation and the wall surface.

[0037] The sanding detection and spraying decision module is used to calculate the drying time by combining the putty material parameters and to perform preliminary coating sanding, scan the preliminary sanded coating and output the spraying decision.

[0038] Another object of the present invention is to provide an intelligent spraying device for building walls, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of an intelligent spraying method for building walls.

[0039] Another object of the present invention is to provide a smart spraying storage medium for building walls, wherein a computer program is stored thereon, and when the computer program is executed by a processor, the steps of the smart spraying method for building walls are implemented.

[0040] The beneficial effects of this invention are as follows: The intelligent spraying method for building walls provided by this invention calculates the drying time using physical formulas by inputting parameters such as putty density, moisture content, ambient temperature and humidity, ventilation rate, and spray thickness, thus avoiding premature or delayed sanding caused by manual judgment. It utilizes a measurement robot employing laser triangulation and structured light scanning technology to acquire three-dimensional data of the wall surface, achieving high-precision judgment of wall flatness, verticality, and local deviations. This invention achieves significantly better results in spray thickness accuracy control, intelligent prediction of sanding time, and feedback on wall flatness detection. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is an overall flowchart of an intelligent spraying method for building walls provided in Embodiment 1 of the present invention.

[0043] Figure 2 This is a field operation diagram of an intelligent spraying method for building walls provided in Embodiment 1 of the present invention.

[0044] Figure 3 This is a cloud modeling and flatness deviation identification diagram for an intelligent spraying method for building walls provided in Embodiment 1 of the present invention.

[0045] Figure 4 This is a spray layer diagram of an intelligent spraying method for building walls provided in Embodiment 1 of the present invention. Detailed Implementation

[0046] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0047] Example 1, referring to Figure 1-3 As one embodiment of the present invention, a smart spraying method for building walls is provided, comprising: S1: Apply a primer to the wall surface and collect data on the spraying robot's operation and the wall surface.

[0048] Determine the wall surface to be sprayed.

[0049] Set the maximum coating thickness to no more than 2mm and the minimum coating thickness to no less than 1mm.

[0050] A spraying robot applies a primer to the entire wall surface.

[0051] Furthermore, data on the spraying robot's operation, wall surface data, and putty material parameters were collected.

[0052] Among them, the type of putty material, curing mechanism, proportion of cementitious material, and moisture content are used as parameters of the putty material.

[0053] Spraying pressure, flow rate, thickness, temperature and humidity of the construction area, and ventilation rate are used as data for the operation of the spraying robot.

[0054] The type of the base layer and the original wall surface flatness deviation are used as wall surface data.

[0055] The temperature, humidity, and ventilation rate of the construction area in the spraying robot's operation data are used as the current environmental data.

[0056] Specifically, refer to Figure 2 The exhibition showcased a real-world application scenario where multiple putty spraying robots simultaneously performed wall primer coating operations at a building construction site.

[0057] The painting robot is positioned at a designated height via an automatic lifting mechanism, and a built-in painting system applies a uniform primer to the wall surface. During the operation, the operator provides auxiliary monitoring and parameter confirmation.

[0058] Install a laser thickness measuring device on the spraying equipment to measure the operation data of the spraying robot, wall data, and putty material parameters.

[0059] S2: Calculate the drying time based on the putty material parameters and perform preliminary coating sanding. Scan the preliminary sanded coating and output the spraying decision.

[0060] A putty drying time prediction model was constructed using physical calculation formulas.

[0061] The drying time is calculated as the grinding point.

[0062] A preferred method for calculating drying time is: , in, Indicates drying time. The values ​​represent putty density, W0 represents initial moisture content, We represents critical drying moisture content, k represents empirical coefficient, T0 represents reference temperature, H represents ambient relative humidity, D represents coating thickness, T represents ambient temperature, and V represents ambient ventilation rate.

[0063] Specifically, the drying time prediction model is executed in the following order: parameter acquisition, batch calibration, time calculation, and on-time verification. Before spraying begins, the control terminal reads the material identification, density, and initial moisture content provided by the manufacturer for this batch of putty. Samples of the same batch of putty are weighed, and the initial moisture content is checked by the change in mass before and after drying. Putty samples are prepared on a test board identical to the base material of the wall surface to be coated, according to the thickness of the primer. They are dried under reference temperature and reference ventilation conditions. The moisture content of the samples is checked every 30 minutes, and local test grinding is performed in an independent test grinding area. When there is no putty adhering to the sandpaper surface, no roughening or large-scale peeling occurs in the test grinding area, and the grinding debris is uniformly powdery, the moisture content detected at that moment is determined as the critical drying moisture content of this batch of putty.

[0064] The empirical coefficient is calibrated based on the actual drying process of the same batch of test panels. The control terminal inputs the putty density, initial moisture content, critical drying moisture content, coating thickness, and the temperature, relative humidity, and ventilation rate during the test into the aforementioned drying time prediction formula, adjusts the empirical coefficient, and makes the drying time output by the model consistent with the actual time from the completion of spraying to meeting the test grinding conditions of the test panel. The calibrated empirical coefficient is then associated with and stored with the putty material identification, ratio, and production batch. When the putty material ratio or production batch changes, the test panel calibration is re-executed.

[0065] After the base coat is applied, a laser thickness measuring device reads the actual coating thickness of each work area, a temperature and humidity sensor reads the ambient temperature and relative humidity, and a wind speed sensor reads the ambient ventilation rate near the wall. The control terminal retrieves the corresponding batch of putty density, initial moisture content, critical drying moisture content, and empirical coefficients for each work area, inputs this data into the aforementioned drying time prediction formula, and obtains the estimated drying time for each work area. It then generates the estimated sanding time starting from the completion time of spraying in each area. When multiple spraying robots are used to apply the coating to different sections of the wall, each robot sends its area number, spraying completion time, and actual coating thickness to the control terminal, which then calculates the estimated sanding time for each area.

[0066] Before the expected sanding time arrives, the control terminal collects ambient temperature, relative humidity, ventilation rate, and current coating moisture content every 30 minutes. If the environmental parameters change compared to the previous calculation, the most recently detected coating moisture content is used as the initial moisture content for the current drying stage, and the remaining drying time is recalculated using the latest environmental parameters. The expected sanding time is then updated accordingly, thus avoiding deviations in sanding timing caused by changes in temperature, humidity, or ventilation conditions during construction.

[0067] Determine whether the coating meets the requirements for sanding.

[0068] Specifically, upon reaching the scheduled sanding time, detection points are set up at the center, edges, and corners of each work area to check the coating moisture content. Simultaneously, trial sanding is conducted in localized areas that do not affect the finished product quality. If the moisture content at each detection point is not greater than the critical drying moisture content, and during the trial sanding process, no putty adheres to the sandpaper, the coating does not exhibit roughening or peeling, and the sanding debris is uniformly powdery, the coating is deemed to meet the sanding conditions. If the moisture content at any detection point exceeds the critical drying moisture content, or if sand adheres, roughening, or peeling occurs during the trial sanding, the maximum moisture content among all detection points is taken as the initial moisture content for the current drying stage. The remaining drying time is recalculated using the latest temperature, relative humidity, and ventilation rate, and the sanding schedule is postponed until both the moisture content and trial sanding conditions are met simultaneously.

[0069] Select sandpaper with a grit of 240-360 and use an electric sander to sand the coating.

[0070] Furthermore, a measurement robot was used to perform a 3D scan of the wall surface.

[0071] Set the scanning accuracy to no more than 1.5mm.

[0072] The flatness of the wall surface is calculated using laser triangulation and structured light 3D scanning technology.

[0073] Wall flatness includes the maximum gap value within a 2-meter range and the overall deviation distribution cloud map.

[0074] Reference Figure 3 It showcases a visual interface that demonstrates how a real-time measurement robot can perform 3D scanning and flatness assessment of the wall space during the wall decoration construction process.

[0075] The left side of the screen shows a 3D point cloud model of the interior wall structure, displaying multiple wall areas that have been scanned, with detection points marked on the key corners (300°) and splicing surfaces (400°).

[0076] The right side is the test results panel, which lists the name, coordinate number and corresponding flatness deviation value of each test point.

[0077] Deviation values ​​are distinguished by color.

[0078] When the flatness is greater than 3mm, the initial sanding coating 100 is set as the intermediate coating area.

[0079] When the flatness is less than or equal to 3mm, the initial sanding coating 100 is set as the topcoat area.

[0080] It should be noted that the wall flatness requirement is a fixed indicator and there is no need to calculate the flatness requirement based on specific construction requirements.

[0081] The initial sanding coating 100 area with a deviation greater than 3mm is designated as the intermediate coating area.

[0082] The drying time of the intermediate coating area is calculated as the sanding node.

[0083] For the intermediate coating area, the control terminal uses the completion time of each local area's recoating as the drying start point, replaces the coating thickness in the drying time prediction model with the measured coating thickness after recoating, and calls up the density, initial moisture content, critical drying moisture content, and empirical coefficients corresponding to this batch of putty; the temperature, ambient relative humidity, and ambient ventilation rate are adopted from the actual measured values ​​at the time of recoating completion. When the intermediate coating area includes multiple discontinuous local areas, an estimated sanding time is generated for each; after reaching the corresponding time, each local area independently performs moisture content detection and local trial sanding. Local areas that do not meet the sanding conditions continue drying and the estimated sanding time is updated, while local areas that have met the sanding conditions are sanded.

[0084] Repeat the initial sanding process and check the flatness again.

[0085] Continue until the flatness is less than or equal to 3mm.

[0086] Furthermore, the drying time of the intermediate coating area is calculated as the grinding node.

[0087] The specific parameters for touch-up coating are: coating thickness of 1-1.5mm, spraying parameters of 0.4MPa, and flow rate of 6L / min.

[0088] The initial sanding coating 100, which meets the initial surface smoothness requirements, is set as the surface coating area.

[0089] Collect current spraying robot operation data, wall data, and putty material parameters.

[0090] Calculate the sanding time for the top coat of putty.

[0091] A preferred method for calculating the sanding time of topcoat putty is as follows: , in, This indicates the time required for sanding the putty. This indicates the critical moisture content of the topcoat.

[0092] The sanding time for the topcoat putty follows the same process of obtaining parameters, batch calibration, dynamic correction, and on-time verification. The control terminal uses the actual spray thickness measured after the topcoat is completed as the coating thickness, and uses the critical moisture content of the topcoat determined by the same batch of test panels to generate the expected sanding time for the topcoat putty using the aforementioned formula. After reaching this time, the moisture content is checked at the center, edge, and corner of the topcoat area, and local test sanding is performed. Sanding of the topcoat is only initiated when the moisture content at each detection point is not greater than the critical moisture content of the topcoat, and there is no sand adhesion, roughening, or large-scale peeling during the test sanding process; otherwise, the remaining drying time is re-determined using the current maximum moisture content and the latest environmental parameters.

[0093] like Figure 4 As shown, the intermediate sanding coating 201 and the topcoat sanding coating 202 are shown.

[0094] The measurement robot was used again to perform a 3D scan of the wall.

[0095] Compare with specific construction and acceptance standards.

[0096] When the specific construction and acceptance standards are met, the current actual wall surface data will be output.

[0097] When specific construction and acceptance standards are not met, localized scraping and patching should be carried out on the areas that do not meet the standards.

[0098] The actual wall surface data and the wall surface data according to the specific construction and acceptance standards are used as the construction report.

[0099] Example 2 is an embodiment of the present invention, which provides an intelligent spraying system for building walls, including a primer spraying and data acquisition module, and a sanding detection and spraying decision module.

[0100] Among them, the primer spraying and data acquisition module is used to apply primer to the wall and collect the operation data of the spraying robot and the wall data; The sanding inspection and spraying decision module is used to calculate the drying time by combining putty material parameters and perform preliminary coating sanding, scan the preliminary sanded coating and output the spraying decision.

[0101] This embodiment also provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the intelligent spraying method for building walls as proposed in the above embodiment.

[0102] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the intelligent spraying method for building walls as proposed in the above embodiments.

[0103] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0104] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0105] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0106] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0107] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent spraying of building walls, characterized in that, include Apply a primer to the wall surface and collect data on the spraying robot's operation and the wall surface. The drying time is calculated based on the putty material parameters, and preliminary coating sanding is performed. The preliminary sanded coating is scanned, and a spraying decision is output. Smoothness is judged in each preliminary sanding coating (100). If it does not meet the smoothness requirements of the topcoat, the preliminary sanding coating (100) is set as the intermediate coating area, the intermediate coating area is recoated, the putty sanding time of the intermediate coating area is calculated and the intermediate sanding coating (201) is scanned, and smoothness is judged again. When the surface coating flatness requirement is met, the surface coating putty is applied according to the standard that the spray thickness is not greater than 1mm and the gun speed is greater than 0.8m / s and less than 1m / s. The surface coating putty sanding time is calculated and the surface coating sanding coating (202) is scanned for testing.

2. The intelligent spraying method for building walls as described in claim 1, characterized in that: The process of applying a primer to the wall surface includes... Determine the wall surface to be sprayed; The maximum coating thickness is set to be no more than 2mm and the minimum thickness is no less than 1mm. A spraying robot applies a primer to the entire wall surface.

3. The intelligent spraying method for building walls as described in claim 2, characterized in that: The preliminary coating polishing includes... A putty drying time prediction model was constructed using physical calculation formulas. Calculate the drying time as the grinding point; Determine if the coating meets the polishing requirements; The coating is sanded using an electric sander.

4. The intelligent spraying method for building walls as described in any one of claims 1 to 3, characterized in that: The scanning preliminary polishing coating includes, A real-world measurement robot was used to perform a 3D scan of the wall surface; Mark the inspection points on the key corners (300) and splicing surfaces (400) of the wall; Set the scanning precision to no more than 1.5mm; Calculate the flatness of the wall surface.

5. The intelligent spraying method for building walls as described in claim 4, characterized in that: The flatness judgment includes, When the flatness is greater than 3mm, the initial sanding coating (100) is set as the intermediate coating area; When the flatness is less than or equal to 3mm, the initial sanding coating (100) is set as the topcoat area.

6. The intelligent spraying method for building walls as described in any one of claims 1 to 3 and 5, characterized in that: The process of applying additional coating to the intermediate coating area includes... The local area with a deviation greater than 3mm in the initial polished coating (100) is designated as the intermediate coating area; Calculate the drying time of the intermediate coating area as the sanding node; Repeat the initial sanding process and check the smoothness again; Continue until the flatness is less than or equal to 3mm.

7. The intelligent spraying method for building walls as described in any one of claims 1 to 3 and 5, characterized in that: The calculation of the putty sanding time includes... The initial sanding coating (100) that meets the initial surface smoothness requirements is set as the surface coating area; Collect current spraying robot operation data, wall data, and putty material parameters.

8. The intelligent spraying method for building walls as described in claim 7, characterized in that: The output spraying decision includes, The actual measurement robot was used again to perform a 3D scan of the wall surface; Compare with specific construction and acceptance standards; When specific construction and acceptance standards are met, output the current actual wall surface data; If the specific construction and acceptance standards are not met, localized scraping and patching should be carried out on the areas that do not meet the standards. The actual wall surface data and the wall surface data according to the specific construction and acceptance standards are used as the construction report.

9. A building wall intelligent spraying system, employing the building wall intelligent spraying method as described in any one of claims 1 to 8, characterized in that: Includes a primer spraying and data acquisition module, and a grinding inspection and spraying decision module; The primer spraying and data acquisition module is used to apply a primer to the wall surface and collect data on the operation of the spraying robot and the wall surface. The sanding detection and spraying decision module is used to calculate the drying time by combining the putty material parameters and to perform preliminary coating sanding, scan the preliminary sanded coating and output the spraying decision.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent spraying method for building walls as described in any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent spraying method for building walls as described in any one of claims 1 to 8.

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