Intelligent control method and system for building spraying robot

Abstract

The invention relates to the technical field of control, and discloses an intelligent control method and system for a building spraying robot, and the method comprises the steps: determining the point cloud data of a to-be-sprayed building based on a building drawing, or determining a plurality of VR images of the to-be-sprayed building based on a VR real scene model, constructing a virtual building model based on the target point cloud data or the target VR image, and carrying out the intelligent control of the to-be-sprayed building. The construction data of the virtual building model and the construction data of the building to be sprayed are compared, whether the virtual building model is updated or not is judged according to the comparison result, the reference spraying speed of each spraying area is determined according to the spraying identification, whether the reference spraying speed is adjusted or not is judged according to the meteorological area chain, and a spraying adjustment factor is determined based on the meteorological area chain; and the reference spraying speed is adjusted according to the spraying adjustment factor, and the building to be sprayed is sprayed at the adjusted reference spraying speed. The method effectively captures space details, and has the characteristic of good dynamic control.

Description

Technical Field

[0001] The present invention relates to the field of control technology, and in particular to an intelligent control method and system for a building spraying robot. Background Art

[0002] In the modern construction industry, spraying operations are an important part of building construction. Early construction spraying robots often set data such as building parameters and spraying parameters through manual programming. However, when faced with complex building structures, relying on human subjective interpretation of two-dimensional building drawings, it is difficult to fully capture spatial details, resulting in the inability of building parameters to adapt to the variability of building space. In addition, due to environmental changes at the construction site, the construction spraying robot lacks adaptability to the complex environment of the construction site and lacks feedback for adaptive adjustment, making it difficult to realize the spraying operation of the construction spraying robot.

[0003] Therefore, it is necessary to design an intelligent control method and system for a building spraying robot to solve the problems existing in the current technology. Summary of the Invention

[0004] In view of this, the present invention proposes an intelligent control method and system for a building spraying robot, aiming to solve the problem that it relies on human subjective interpretation of two-dimensional architectural drawings, is difficult to fully capture spatial details, resulting in the inability of architectural parameters to adapt to the variability of architectural space, and lacks feedback for adaptive adjustment, making it difficult to realize the spraying operation of the building spraying robot.

[0005] In one aspect, the present invention provides an intelligent control method for a construction spraying robot, comprising:

[0006] Determining point cloud data of a building to be painted based on architectural drawings, or determining several VR images of the building to be painted based on a VR real-scene model, performing a first preprocessing on the point cloud data to determine target point cloud data, and performing a second preprocessing on the several VR images to determine target VR images;

[0007] constructing a virtual building model based on the target point cloud data or the target VR image, comparing the virtual building model with the construction data of the building to be sprayed, determining whether to update the virtual building model based on the comparison result, and determining a target virtual building model based on the update result;

[0008] Determining a plurality of spraying areas according to the target virtual building model, establishing spraying identifications for the plurality of spraying areas, determining a driving path of the building spraying robot according to the spraying identifications, and determining a baseline spraying speed for each spraying area according to the spraying identifications; collecting meteorological data for each spraying area based on the driving path and constructing a meteorological area chain; determining whether to adjust the baseline spraying speed according to the meteorological area chain; and determining a spraying adjustment factor based on the meteorological area chain when it is determined that the baseline spraying speed should be adjusted;

[0009] The reference spraying speed is adjusted according to the spraying adjustment factor, and the building to be sprayed is sprayed at the adjusted reference spraying speed.

[0010] Furthermore, when the point cloud data is subjected to a first preprocessing to determine target point cloud data, and the plurality of VR images are subjected to a second preprocessing to determine a target VR image, the method includes:

[0011] The first preprocessing includes removing outliers and downsampling the point cloud;

[0012] The second preprocessing includes denoising and geometric correction, and extracting feature points of the VR image after the second preprocessing, matching the extracted feature points, and determining adaptation data between the VR images, wherein the adaptation data includes relative position and rotation relationship;

[0013] Image processing is performed on the plurality of VR images after the second preprocessing based on the adaptation data to determine the target VR image, wherein the image processing includes adjusting contrast and sharpening image edges.

[0014] Furthermore, when constructing a virtual building model based on the target point cloud data or the target VR image, the method includes:

[0015] According to the target point cloud data or the target VR image, a polygon mesh is created and edited based on a three-dimensional modeling engine to construct the virtual building model;

[0016] When constructing the virtual building model according to the target VR image, the method further includes:

[0017] The texture of the target VR image is mapped to the surface of the virtual building model according to UV mapping.

[0018] Furthermore, when comparing the virtual building model with the construction data of the building to be painted, determining whether to update the virtual building model based on the comparison result, and determining the target virtual building model based on the update result, the method includes:

[0019] When the size data of the virtual building model is consistent with the construction data of the building to be sprayed, it is determined that the virtual building model is not to be updated, and the virtual building model is determined as the target virtual building model;

[0020] When the size data of the virtual building model is inconsistent with the construction data of the building to be sprayed, determining to update the virtual building model;

[0021] When it is determined that the virtual building model is to be updated, the inconsistent dimension data is updated according to incremental modeling, and the virtual building model after the updated dimension data is determined as the target virtual building model.

[0022] Furthermore, when establishing spray markings for a plurality of the spraying areas, the method includes:

[0023] The spray marking includes a primary spray marking, a secondary spray marking and a fusion spray marking;

[0024] When the spraying area is a plane, the primary spraying mark is established for the spraying area;

[0025] When the spraying areas are all curved surfaces, the secondary spraying mark is established for the spraying areas;

[0026] When the spraying area has one or more planes and one or more curved surfaces, the fusion spraying mark is established for the spraying area.

[0027] Furthermore, when determining the reference spraying speed of each spraying area according to the spraying mark, it includes:

[0028] Presetting a first preset spraying speed, a second preset spraying speed, and a third preset spraying speed, wherein the first preset spraying speed is greater than the second preset spraying speed, and the second preset spraying speed is greater than the third preset spraying speed;

[0029] When the spraying area is the primary spraying mark, the first preset spraying speed is used as the reference spraying speed of the spraying area;

[0030] When the spraying area is the secondary spraying mark, the second preset spraying speed is used as the reference spraying speed of the spraying area;

[0031] When the spraying area is the fusion spraying mark, the third preset spraying speed is used as the baseline spraying speed of the spraying area.

[0032] Furthermore, when collecting meteorological data of each spraying area based on the driving path and constructing a meteorological area chain, and determining whether to adjust the reference spraying speed according to the meteorological area chain, the method includes:

[0033] Acquire meteorological characteristic parameters of meteorological data of each spraying area, and construct a meteorological area chain according to the meteorological characteristic parameters;

[0034] determining a standard meteorological region chain corresponding to the meteorological region chain, and comparing the meteorological region chain with the standard meteorological region chain;

[0035] When the meteorological characteristic parameters in the meteorological area chain are all equal to the standard meteorological characteristic parameters in the standard meteorological area chain, it is determined that the reference spraying speed is not adjusted, and the building to be sprayed is sprayed at the reference spraying speed;

[0036] When the meteorological characteristic parameters in the meteorological area chain are not equal to the standard meteorological characteristic parameters in the standard meteorological area chain, it is determined that the reference spraying speed is adjusted.

[0037] Furthermore, when it is determined to adjust the reference spraying speed, a spraying adjustment factor is determined based on the meteorological zone chain, including:

[0038] When the meteorological characteristic parameter in the meteorological region chain is less than the standard meteorological characteristic parameter in the standard meteorological region chain, the meteorological characteristic parameter is divided into a first meteorological set;

[0039] When the meteorological characteristic parameters in the meteorological area chain are equal to the standard meteorological characteristic parameters in the standard meteorological area chain, the meteorological characteristic parameters are divided into a second meteorological set;

[0040] When the meteorological characteristic parameter in the meteorological region chain is greater than the standard meteorological characteristic parameter in the standard meteorological region chain, the meteorological characteristic parameter is divided into a third meteorological set;

[0041] The spray adjustment factor is determined based on the first meteorological set, the second meteorological set, and the third meteorological set.

[0042] Furthermore, when adjusting the reference spraying speed according to the spraying adjustment factor, the method includes:

[0043] The reference spraying speed is directly proportional to the spraying adjustment factor.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: by constructing a virtual building model through target point cloud data or target VR images, errors in building parameters caused by deviations in subjective human interpretation are avoided, and the building parameters are made consistent with the actual building space to be sprayed. The virtual building model is compared with the construction data and dynamically updated to ensure that the target virtual building model is always consistent with the actual construction situation. Whether it is a change in building design or actual adjustments during the construction process, they can be reflected in the target virtual building model in a timely manner, improving the stability and accuracy of the building spraying robot during spraying operations. By dividing the spraying area and establishing spraying marks, a driving path is planned for the building spraying robot, reducing invalid movement and thus improving the spraying efficiency. At the same time, a baseline spraying speed is determined based on the spraying mark, and the baseline spraying speed is dynamically adjusted in combination with the meteorological zone chain, so that the building spraying robot can dynamically adjust according to changes in the construction environment, thereby improving environmental adaptability and ensuring the stability and reliability of the building spraying robot's spraying operations under different environmental conditions.

[0045] On the other hand, the present application also provides an intelligent control system for a construction spraying robot, which is applied to the intelligent control method of the above-mentioned construction spraying robot, including:

[0046] a data acquisition module configured to determine point cloud data of a building to be painted based on architectural drawings, or to determine a plurality of VR images of the building to be painted based on a VR real-scene model, perform a first preprocessing on the point cloud data to determine target point cloud data, and perform a second preprocessing on the plurality of VR images to determine a target VR image;

[0047] a model building module configured to construct a virtual building model based on the target point cloud data or the target VR image, compare the virtual building model with the construction data of the building to be sprayed, determine whether to update the virtual building model based on the comparison result, and determine a target virtual building model based on the update result;

[0048] a spray processing module configured to determine a plurality of spraying areas based on the target virtual building model, establish spraying identifiers for the plurality of spraying areas, determine a driving path of the building spraying robot based on the spraying identifiers, determine a baseline spraying speed for each spraying area based on the spraying identifiers, collect meteorological data for each spraying area based on the driving path and construct a meteorological area chain, determine whether to adjust the baseline spraying speed based on the meteorological area chain, and when it is determined that the baseline spraying speed is to be adjusted, determine a spraying adjustment factor based on the meteorological area chain;

[0049] The spraying adjustment module is configured to adjust the reference spraying speed according to the spraying adjustment factor, and spray the building to be sprayed at the adjusted reference spraying speed.

[0050] It is understandable that the above-mentioned intelligent control method and system of a construction spraying robot have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Various other advantages and benefits will become apparent to those skilled in the art upon reading the detailed description of the preferred embodiment below. The accompanying drawings are for illustration purposes only and are not to be considered as limiting the present invention. The same reference symbols are used throughout the drawings to represent the same components. In the drawings:

[0052] Figure 1 A flowchart of an intelligent control method for a building spraying robot provided by an embodiment of the present invention;

[0053] Figure 2 This is a functional block diagram of an intelligent control system for a building spraying robot provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, unless there is a conflict, the embodiments of the present disclosure and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0055] In some embodiments of the present application, see Figure 1 As shown, an intelligent control method for a building spraying robot includes:

[0056] S100: Determine the point cloud data of the building to be sprayed based on the architectural drawings, or determine several VR images of the building to be sprayed based on the VR real-scene model, perform a first preprocessing on the point cloud data to determine target point cloud data, and perform a second preprocessing on the several VR images to determine the target VR image.

[0057] S200: Construct a virtual building model based on target point cloud data or target VR image, compare the virtual building model with the construction data of the building to be sprayed, determine whether to update the virtual building model based on the comparison result, and determine the target virtual building model based on the update result.

[0058] S300: Determine several spraying areas based on the target virtual building model, establish spraying identifications for the several spraying areas, determine the driving path of the building spraying robot based on the spraying identifications, and determine the baseline spraying speed of each spraying area based on the spraying identifications, collect meteorological data of each spraying area based on the driving path and construct a meteorological area chain, determine whether to adjust the baseline spraying speed based on the meteorological area chain, and when it is determined to adjust the baseline spraying speed, determine the spraying adjustment factor based on the meteorological area chain.

[0059] S400: Adjusting the reference spraying speed according to the spraying adjustment factor, and spraying the building to be sprayed at the adjusted reference spraying speed.

[0060] Specifically, a 3D laser scanner and other equipment can be used to obtain point cloud data of the building to be painted based on architectural drawings. A VR reality model uses a VR device (such as the HTC Vive Pro) with a built-in high-definition camera (resolution up to 4K) and an inertial measurement unit (IMU) sensor to collect information on the building's surface texture, color, spatial structure, and motion posture, thereby determining a VR image of the building to be painted. Both the point cloud data and the VR image can present the 3D spatial structure of the building to be painted. Because point cloud data and VR images may be affected by factors such as data transmission, noise, and shooting exposure, the point cloud data undergoes a first preprocessing step, while several VR images undergo a second preprocessing step to facilitate subsequent analysis and processing. A virtual building model is constructed using the target point cloud data or target VR image. This model is a digital mapping of the building to be painted, containing information such as the building's geometry and spatial layout. The virtual building model is then compared with the construction data of the building to be painted. The construction data represents the actual measurement data of the building to be painted. This comparison can be used to determine whether the virtual building model needs to be updated. If new construction data is available, the virtual building model must be updated promptly to ensure that the target virtual building model is consistent with the actual building to be painted. This avoids errors caused by manual interpretation of two-dimensional drawings and can fully and accurately capture the spatial details of the building to be painted. At the same time, real-time updates of the virtual building model ensure that the building painting robot can cope with the variability of building spaces, thereby improving its adaptability to the painted buildings.

[0061] It is understandable that a number of spraying areas are determined based on the target virtual building model, and the spraying areas are evenly divided. The actual number of divisions is determined according to the size of the target virtual building model. Spraying marks are established for each spraying area. These spraying marks contain information such as the location of the corresponding area and spraying requirements. Based on the spraying marks, the Dijkstra algorithm or the A* algorithm is used to determine the length of the moving path between each spraying area, and then determine the driving path of the building spraying robot. The Dijkstra algorithm or the A* algorithm is mature and redundant, so I will not go into too much detail here. Using the Dijkstra algorithm or the A* algorithm can ensure that the building spraying robot can effectively reach each spraying area. At the same time, the baseline spraying speed of each spraying area is determined according to the spraying mark. The baseline spraying speed is determined based on the shape factor of the area (plane or curved surface). Moreover, during the driving process of the construction spraying robot, the meteorological data of each spraying area is collected according to the driving path, such as rainfall, dust, etc., so as to construct a meteorological area chain. Meteorological conditions have a certain influence on the spraying effect. For example, excessive wind speed may cause the paint spray to drift. The spraying adjustment factor is determined according to the meteorological area chain, and the baseline spraying speed is adjusted according to the spraying adjustment factor, so that the construction spraying robot can adapt to the complex environmental changes at the construction site and ensure the consistency and stability of the spraying effect under different meteorological conditions.

[0062] In some embodiments of the present application, when point cloud data is subjected to a first preprocessing to determine target point cloud data, and several VR images are subjected to a second preprocessing to determine a target VR image, it includes: the first preprocessing includes removing abnormal points and downsampling the point cloud, the second preprocessing includes denoising and geometric correction, and extracting feature points of the VR image after the second preprocessing, matching the extracted feature points, determining adaptation data between the VR images, the adaptation data including relative position and rotation relationship, performing image processing on the several VR images after the second preprocessing based on the adaptation data, and determining the target VR image, and the image processing includes adjusting contrast and sharpening image edges.

[0063] Specifically, outliers are removed through statistical filtering and radius filtering algorithms. These remove outliers (noise points) caused by factors such as equipment errors and environmental interference, preventing them from interfering with subsequent modeling and analysis, thereby ensuring the accuracy of the target point cloud data. Point cloud downsampling reduces the data volume without losing key data (point cloud data related to size), thereby reducing computational complexity and improving processing efficiency. The second preprocessing step, denoising, uses algorithms such as Gaussian filtering and median filtering to eliminate noise in VR images, making the images clearer. Geometric correction corrects image deformation caused by lens distortion, thereby restoring the true form of the building to be sprayed. Feature points are extracted through SIFT (Scale-Invariant Feature Transform) and SURF (Speeded Robust Features), with either one of these methods being used depending on the actual situation. The extracted feature points are matched using the RANSAC algorithm to determine the relative position and rotation relationship between the images, thereby improving the spatial adaptability of the building to be sprayed. Adjusting the contrast and sharpening the image edges improve the image quality of the target VR image. The first and second preprocessing steps lay the foundation for the subsequent construction of the virtual building model.

[0064] In some embodiments of the present application, when constructing a virtual building model based on target point cloud data or a target VR image, it includes: creating and editing a polygon mesh based on a three-dimensional modeling engine according to the target point cloud data or the target VR image to construct the virtual building model; when constructing the virtual building model according to the target VR image, it also includes: mapping the texture of the target VR image to the surface of the virtual building model according to UV mapping.

[0065] Specifically, the 3D modeling engine (Unity or Unreal Engine) uses the target point cloud data or target VR image as the modeling basis. By creating and editing polygonal meshes, it can construct a geometric model that fits the building to be sprayed to ensure accuracy in size, shape, etc. The target VR image after the second preprocessing not only corrects the image distortion and enhances the picture quality, but also obtains the spatial relationship between images. On this basis, combined with the UV mapping algorithm, the image texture is mapped to the surface of the virtual building model, achieving high-fidelity restoration of the appearance details of the building to be sprayed. With the help of the 3D modeling engine, the construction cycle of the virtual building model is shortened. At the same time, the error of manually setting the building parameters is reduced, and the spatial details of the building to be sprayed are effectively captured, thereby improving the reliability and stability of the control of the building spraying robot.

[0066] In some embodiments of the present application, when comparing the virtual building model with the construction data of the building to be sprayed, determining whether to update the virtual building model based on the comparison result, and determining the target virtual building model based on the update result, it includes: when the dimension data of the virtual building model is consistent with the construction data of the building to be sprayed, determining not to update the virtual building model, and determining the virtual building model as the target virtual building model; when the dimension data of the virtual building model is inconsistent with the construction data of the building to be sprayed, determining to update the virtual building model; when it is determined to update the virtual building model, the inconsistent dimension data is updated according to incremental modeling, and the virtual building model after the updated dimension data is determined as the target virtual building model.

[0067] Specifically, using dimensional data as a comparison factor, when the dimensional data of the virtual building model is consistent with the construction data of the building to be sprayed, the validity of the virtual building model is confirmed, unnecessary modification operations are avoided, and it is ensured that the building spraying robot can perform spraying operations based on stable and accurate model data, ensuring the reliability and accuracy of the control of the building spraying robot. When the data is inconsistent, the update mechanism is triggered in time, and the incremental modeling is used to accurately locate and correct the discrepant data, avoiding large-scale reconstruction of the entire model, ensuring the synchronization of the virtual building model and the actual construction data, and avoiding the risk of spraying errors of the building spraying robot due to inconsistency between the model and the actual. The spraying robot can flexibly respond to adjustments to the building structure, whether it is local size changes or overall spatial layout changes, and can accurately match the spraying requirements, thereby improving the adaptability of the building spraying robot to the building space.

[0068] In some embodiments of the present application, when establishing spray identification for several spray areas, it includes: the spray identification includes a primary spray identification, a secondary spray identification and a fused spray identification. When the spray areas are all planes, a primary spray identification is established for the spray area. When the spray areas are all curved surfaces, a secondary spray identification is established for the spray area. When there are one or more planes and one or more curved surfaces in the spray area, a fused spray identification is established for the spray area.

[0069] In some embodiments of the present application, when determining the baseline spraying speed of each spraying area according to the spraying mark, it includes: pre-setting a first preset spraying speed, a second preset spraying speed and a third preset spraying speed, the first preset spraying speed is greater than the second preset spraying speed, the second preset spraying speed is greater than the third preset spraying speed, when the spraying area is a primary spraying mark, the first preset spraying speed is used as the baseline spraying speed of the spraying area, when the spraying area is a secondary spraying mark, the second preset spraying speed is used as the baseline spraying speed of the spraying area, and when the spraying area is a fusion spraying mark, the third preset spraying speed is used as the baseline spraying speed of the spraying area.

[0070] Specifically, spraying areas of different shapes are accurately classified through spraying marks, so that the construction spraying robot can identify the spraying operation scene, and the reference spraying speed represents the spraying movement distance of the nozzle of the construction spraying robot per unit time. When facing the spraying area of ​​the primary spraying mark (plane), the construction spraying robot directly calls the first preset spraying speed. The first preset spraying speed is based on the characteristics of simple plane spraying operation and direct path planning, so that it passes over the surface of the building to be sprayed at a higher speed, thereby reducing the spraying time per unit area. When facing the spraying area of ​​the secondary spraying mark (curved surface), the construction spraying robot uses the second preset spraying speed as the reference spraying speed. The second preset spraying speed is relatively slow compared to the first preset spraying speed. The second preset spraying speed takes into account the complex trajectory planning and the need for precise control when spraying on curved surfaces. The demand for nozzle angle, when facing the spraying area of ​​​​the integrated spray mark (a combination of plane and curved surface), the building spraying robot uses the third preset spraying speed as the base spraying speed. The third preset spraying speed is a comprehensive consideration of both plane and curved surface characteristics. The third preset spraying speed is relatively slow compared to the second preset spraying speed. The spraying quality of the spraying area is ensured by running at a low speed. Whether facing a single-form spraying area or a relatively complex spraying area, the building spraying robot can adapt well to the spatial changes of the building to be sprayed, thereby improving the accuracy and reliability of the control of the building spraying robot.

[0071] In some embodiments of the present application, when collecting meteorological data of each spraying area based on the driving path and constructing a meteorological area chain, and judging whether to adjust the baseline spraying speed according to the meteorological area chain, it includes: obtaining meteorological characteristic parameters of the meteorological data of each spraying area, and constructing a meteorological area chain according to the meteorological characteristic parameters, determining a standard meteorological area chain corresponding to the meteorological area chain, and comparing the meteorological area chain with the standard meteorological area chain. When the meteorological characteristic parameters in the meteorological area chain are all equal to the standard meteorological characteristic parameters in the standard meteorological area chain, it is determined not to adjust the baseline spraying speed, and the building to be sprayed is sprayed at the baseline spraying speed. When there is a meteorological characteristic parameter in the meteorological area chain that is not equal to the standard meteorological characteristic parameter in the standard meteorological area chain, it is determined to adjust the baseline spraying speed.

[0072] Specifically, by building a meteorological regional chain and comparing it with a standard meteorological regional chain, construction spray painting robots can accurately perceive environmental changes at construction sites. Meteorological data includes weather data such as rainfall and dust. Meteorological characteristic parameters represent corresponding parameters, such as precipitation humidity, wind speed, and dust concentration. The meteorological characteristic parameters on the meteorological regional chain correspond one-to-one with the standard meteorological characteristic parameters on the standard meteorological regional chain. The standard meteorological regional chain is determined by the characteristics of the materials sprayed by the construction spray painting robot. For example, water-based paint relies on water evaporation to form a film. When the precipitation humidity is above 85%, water evaporation is hindered, which can easily lead to problems such as coating blooming, slow drying, and even mold. Therefore, the standard meteorological characteristic parameters on the standard meteorological regional chain are set at 85%. When the meteorological characteristic parameters deviate from the standard meteorological characteristic parameters, the baseline spraying speed is adjusted to avoid the incompatibility between the construction spraying robot and the construction environment, so that the construction spraying robot has the intelligent control ability of meteorological perception and strategy response. It is no longer limited to fixed spraying parameters, but autonomously controls and adjusts according to real-time meteorological data. Regardless of the meteorological differences between different spraying areas or the dynamic changes in meteorological conditions in the same spraying area, the construction spraying robot can identify and adjust the baseline spraying speed, thereby achieving reliability and flexibility in the control of the construction spraying robot.

[0073] In some embodiments of the present application, when it is determined to adjust the baseline spraying speed, the spraying adjustment factor is determined based on the meteorological area chain, including: when the meteorological characteristic parameters in the meteorological area chain are less than the standard meteorological characteristic parameters in the standard meteorological area chain, the meteorological characteristic parameters are divided into a first meteorological set; when the meteorological characteristic parameters in the meteorological area chain are equal to the standard meteorological characteristic parameters in the standard meteorological area chain, the meteorological characteristic parameters are divided into a second meteorological set; when the meteorological characteristic parameters in the meteorological area chain are greater than the standard meteorological characteristic parameters in the standard meteorological area chain, the meteorological characteristic parameters are divided into a third meteorological set; the spraying adjustment factor is determined based on the first meteorological set, the second meteorological set and the third meteorological set.

[0074] Specifically, by dividing the meteorological set into the first, second, and third meteorological sets, the accuracy of determining the spray adjustment factor is improved. The first number of meteorological characteristic parameters in the first meteorological set is counted, the second number of meteorological characteristic parameters in the second meteorological set is counted, and the third number of meteorological characteristic parameters in the third meteorological set is counted. The spray adjustment factor is obtained by the following formula:

[0075]

[0076] Among them, P represents the spraying adjustment factor, n represents the first quantity, m represents the third quantity, Hi represents the standard meteorological characteristic parameter corresponding to the i-th meteorological characteristic parameter in the first meteorological set, Fi represents the i-th meteorological characteristic parameter in the first meteorological set, Pj represents the j-th meteorological characteristic parameter in the third meteorological set, and Lj represents the standard meteorological characteristic parameter corresponding to the j-th meteorological characteristic parameter in the third meteorological set.

[0077] In some embodiments of the present application, when the reference spraying speed is adjusted according to the spraying adjustment factor, it includes: the reference spraying speed and the spraying adjustment factor are in direct proportion.

[0078] Specifically, the baseline spraying speed is adjusted according to the spraying adjustment factor P. Assuming that the baseline spraying speed is V, the adjusted baseline spraying speed is determined to be P*V. When a higher baseline spraying speed or a lower baseline spraying speed is required, the baseline spraying speed is directly proportional to the spraying adjustment factor, thereby achieving precise control of the baseline spraying speed. By calculating the spraying adjustment factor to adjust the baseline spraying speed, the stability of the spraying operation of the construction spraying robot is improved, and adaptive control of the construction spraying robot is achieved.

[0079] In summary, the beneficial effects of the present invention are as follows: by constructing a virtual building model through target point cloud data or target VR images, the deviation of building parameters caused by subjective human interpretation is avoided, so that the building parameters are consistent with the actual building space of the building to be sprayed. The virtual building model is compared with the construction data and dynamically updated to ensure that the target virtual building model is always consistent with the actual construction situation. Whether it is a change in building design or actual adjustments during the construction process, they can be reflected in the target virtual building model in a timely manner, thereby improving the stability and accuracy of the building spraying robot during spraying operations. By dividing the spraying area and establishing spraying marks, a driving path is planned for the building spraying robot, reducing invalid movement and thus improving the spraying efficiency. At the same time, the baseline spraying speed is determined according to the spraying mark, and the baseline spraying speed is dynamically adjusted in combination with the meteorological zone chain, so that the building spraying robot can be dynamically adjusted according to changes in the construction environment, thereby improving environmental adaptability and ensuring the stability and reliability of the building spraying robot's spraying operation under different environmental conditions.

[0080] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an intelligent control system for a building spraying robot, which is applied to the intelligent control method of the above-mentioned building spraying robot, including:

[0081] The data acquisition module is configured to determine point cloud data of the building to be painted based on the architectural drawings, or to determine multiple VR images of the building to be painted based on the VR real-scene model, perform a first pre-processing on the point cloud data to determine target point cloud data, and perform a second pre-processing on the multiple VR images to determine target VR images;

[0082] a model building module configured to build a virtual building model based on target point cloud data or target VR images, compare the virtual building model with construction data of the building to be sprayed, determine whether to update the virtual building model based on the comparison result, and determine the target virtual building model based on the update result;

[0083] a spray processing module configured to determine a plurality of spraying areas based on a target virtual building model, establish spraying identifiers for the plurality of spraying areas, determine a driving path of the building spraying robot based on the spraying identifiers, determine a baseline spraying speed for each spraying area based on the spraying identifiers, collect meteorological data for each spraying area based on the driving path and construct a meteorological area chain, determine whether to adjust the baseline spraying speed based on the meteorological area chain, and determine a spraying adjustment factor based on the meteorological area chain when it is determined that the baseline spraying speed should be adjusted;

[0084] The spraying adjustment module is configured to adjust the reference spraying speed according to the spraying adjustment factor, and spray the building to be sprayed at the adjusted reference spraying speed.

[0085] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or a combination of software and hardware embodiments. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0086] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0087] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0088] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0089] Finally, 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 the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An intelligent control method for a building spraying robot, characterized in that: include: Determining point cloud data of a building to be painted based on architectural drawings, or determining several VR images of the building to be painted based on a VR real-scene model, performing a first preprocessing on the point cloud data to determine target point cloud data, and performing a second preprocessing on the several VR images to determine target VR images; constructing a virtual building model based on the target point cloud data or the target VR image, comparing the virtual building model with the construction data of the building to be sprayed, determining whether to update the virtual building model based on the comparison result, and determining a target virtual building model based on the update result; Determining a plurality of spraying areas according to the target virtual building model, establishing spraying identifications for the plurality of spraying areas, determining a driving path of the building spraying robot according to the spraying identifications, and determining a baseline spraying speed for each spraying area according to the spraying identifications; collecting meteorological data for each spraying area based on the driving path and constructing a meteorological area chain; determining whether to adjust the baseline spraying speed according to the meteorological area chain; and determining a spraying adjustment factor based on the meteorological area chain when it is determined that the baseline spraying speed should be adjusted; The reference spraying speed is adjusted according to the spraying adjustment factor, and the building to be sprayed is sprayed at the adjusted reference spraying speed.

2. The intelligent control method of the building spraying robot according to claim 1, characterized in that: When the point cloud data is subjected to a first preprocessing to determine target point cloud data, and the plurality of VR images are subjected to a second preprocessing to determine a target VR image, the method includes: The first preprocessing includes removing outliers and downsampling the point cloud; The second preprocessing includes denoising and geometric correction, and extracting feature points of the VR image after the second preprocessing, matching the extracted feature points, and determining adaptation data between the VR images, wherein the adaptation data includes relative position and rotation relationship; Image processing is performed on the plurality of VR images after the second preprocessing based on the adaptation data to determine the target VR image, wherein the image processing includes adjusting contrast and sharpening image edges.

3. The intelligent control method of the building spraying robot according to claim 2, characterized in that: When constructing a virtual building model based on the target point cloud data or the target VR image, the method includes: According to the target point cloud data or the target VR image, a polygon mesh is created and edited based on a three-dimensional modeling engine to construct the virtual building model; When constructing the virtual building model according to the target VR image, the method further includes: The texture of the target VR image is mapped to the surface of the virtual building model according to UV mapping.

4. The intelligent control method of the building spraying robot according to claim 3 is characterized in that: When comparing the virtual building model with the construction data of the building to be sprayed, determining whether to update the virtual building model according to the comparison result, and determining the target virtual building model according to the update result, the method includes: When the size data of the virtual building model is consistent with the construction data of the building to be sprayed, it is determined that the virtual building model is not to be updated, and the virtual building model is determined as the target virtual building model; When the size data of the virtual building model is inconsistent with the construction data of the building to be sprayed, determining to update the virtual building model; When it is determined that the virtual building model is to be updated, the inconsistent dimension data is updated according to incremental modeling, and the virtual building model after the updated dimension data is determined as the target virtual building model.

5. The intelligent control method of the building spraying robot according to claim 4, characterized in that: When establishing spray markings for several of the spray areas, it includes: The spray marking includes a primary spray marking, a secondary spray marking and a fusion spray marking; When the spraying area is a plane, the primary spraying mark is established for the spraying area; When the spraying areas are all curved surfaces, the secondary spraying mark is established for the spraying areas; When the spraying area has one or more planes and one or more curved surfaces, the fusion spraying mark is established for the spraying area.

6. The intelligent control method of the building spraying robot according to claim 5, characterized in that: When determining the reference spraying speed of each spraying area according to the spraying mark, it includes: Presetting a first preset spraying speed, a second preset spraying speed, and a third preset spraying speed, wherein the first preset spraying speed is greater than the second preset spraying speed, and the second preset spraying speed is greater than the third preset spraying speed; When the spraying area is the primary spraying mark, the first preset spraying speed is used as the reference spraying speed of the spraying area; When the spraying area is the secondary spraying mark, the second preset spraying speed is used as the reference spraying speed of the spraying area; When the spraying area is the fusion spraying mark, the third preset spraying speed is used as the baseline spraying speed of the spraying area.

7. The intelligent control method for a building spraying robot according to claim 6, characterized in that: When collecting meteorological data of each spraying area based on the driving path and constructing a meteorological area chain, and determining whether to adjust the reference spraying speed according to the meteorological area chain, the method includes: Acquiring meteorological characteristic parameters of meteorological data of each spraying area, and constructing a meteorological area chain according to the meteorological characteristic parameters; determining a standard meteorological region chain corresponding to the meteorological region chain, and comparing the meteorological region chain with the standard meteorological region chain; When the meteorological characteristic parameters in the meteorological area chain are all equal to the standard meteorological characteristic parameters in the standard meteorological area chain, it is determined that the reference spraying speed is not adjusted, and the building to be sprayed is sprayed at the reference spraying speed; When the meteorological characteristic parameters in the meteorological area chain are not equal to the standard meteorological characteristic parameters in the standard meteorological area chain, it is determined that the reference spraying speed is adjusted.

8. The intelligent control method for a building spraying robot according to claim 7, characterized in that: When it is determined to adjust the reference spraying speed, a spraying adjustment factor is determined based on the meteorological zone chain, including: When the meteorological characteristic parameter in the meteorological region chain is less than the standard meteorological characteristic parameter in the standard meteorological region chain, the meteorological characteristic parameter is divided into a first meteorological set; When the meteorological characteristic parameters in the meteorological area chain are equal to the standard meteorological characteristic parameters in the standard meteorological area chain, the meteorological characteristic parameters are divided into a second meteorological set; When the meteorological characteristic parameter in the meteorological region chain is greater than the standard meteorological characteristic parameter in the standard meteorological region chain, the meteorological characteristic parameter is divided into a third meteorological set; The spray adjustment factor is determined based on the first meteorological set, the second meteorological set, and the third meteorological set.

9. The intelligent control method for a building spray painting robot according to claim 8, characterized in that: When adjusting the reference spraying speed according to the spraying adjustment factor, the method includes: The reference spraying speed is directly proportional to the spraying adjustment factor.

10. An intelligent control system for a construction spray painting robot, applied to the intelligent control method for a construction spray painting robot according to any one of claims 1 to 9, characterized in that: include: a data acquisition module configured to determine point cloud data of a building to be painted based on architectural drawings, or to determine a plurality of VR images of the building to be painted based on a VR real-scene model, perform a first preprocessing on the point cloud data to determine target point cloud data, and perform a second preprocessing on the plurality of VR images to determine a target VR image; a model building module configured to construct a virtual building model based on the target point cloud data or the target VR image, compare the virtual building model with the construction data of the building to be sprayed, determine whether to update the virtual building model based on the comparison result, and determine a target virtual building model based on the update result; a spray processing module configured to determine a plurality of spraying areas based on the target virtual building model, establish spraying identifiers for the plurality of spraying areas, determine a driving path of the building spraying robot based on the spraying identifiers, determine a baseline spraying speed for each spraying area based on the spraying identifiers, collect meteorological data for each spraying area based on the driving path and construct a meteorological area chain, determine whether to adjust the baseline spraying speed based on the meteorological area chain, and when it is determined that the baseline spraying speed is to be adjusted, determine a spraying adjustment factor based on the meteorological area chain; The spraying adjustment module is configured to adjust the reference spraying speed according to the spraying adjustment factor, and spray the building to be sprayed at the adjusted reference spraying speed.

Images