Intelligent control system for NEA leveling gel internal thermal insulation construction of building external wall

Abstract

The invention discloses an intelligent control system for NEA leveling gel internal thermal insulation construction of a building external wall, particularly relates to the technical field of intelligent control of construction, and comprises functional modules such as external wall data acquisition and modeling, model introduction and coordinate alignment, wall expansion and gridding, construction risk calculation, intelligent path planning and spraying optimization and the like. The system is fused with the BIM model through the laser point cloud, the geometric attributes of the wall surface are obtained in real time, and a two-dimensional u-v grid is generated; calculating a gravity shear ratio, a spraying stage factor, a construction momentum factor and an environment sensitive factor in combination with material physical properties, environmental parameters and spraying working conditions to form a dynamic risk heat map; and according to the risk level, a block dislocation spraying strategy and self-adaptive process parameter combination are adopted, and low-first and high-second partition construction and work-following closed-loop quality control are achieved. The system can remarkably reduce the risk of gel sliding and falling and film thickness deviation, improves the spraying efficiency and the material utilization rate, and is suitable for internal thermal insulation engineering under complex vertical faces and extreme environments.

Description

Technical Field

[0001] The present invention relates to the technical field of construction intelligent control, and more particularly to an intelligent control system for NEA leveling gel internal insulation construction of building exterior walls. Background Art

[0002] NEA leveling gel, due to its excellent thixotropy and low thermal conductivity, has been widely used as an internal insulation leveling layer on building exterior walls. However, existing construction methods rely primarily on manual experience or simple semi-automatic equipment. First, the spraying thickness and path lack precise control, which can easily lead to localized thickening, shrinkage cracks, and thermal bridges. Second, uncured gel is prone to sliding under gravity, especially on large, high-rise walls or when temperature and humidity deviate from the optimal range. Third, existing systems lack comprehensive assessment of multiple spraying stages, real-time nozzle speed, and environmental changes, making it impossible to form quantitative risk indicators, resulting in high rework rates and significant quality fluctuations.

[0003] Meanwhile, while point cloud-BIM integration technology has been used for curtain wall installation and quality inspection, it has yet to be implemented as a closed-loop risk control solution for internal insulation spraying. Spraying path planning algorithms often prioritize geometric minimization, failing to integrate material rheological behavior with the on-site environment, making it difficult to meet the high safety and uniformity requirements of construction. Therefore, an intelligent control system capable of sensing, evaluating, and dynamically adjusting the entire process before, during, and after construction is urgently needed to address film thickness consistency and slippage risks during gel internal insulation construction, while also improving overall construction efficiency and safety. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an intelligent control system for NEA leveling gel internal insulation construction of building exterior walls to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls includes the following modules:

[0007] The exterior wall data acquisition and modeling module is used to obtain building facade point clouds through benchmark point layout, laser scanning or drone LiDAR, and generate the corresponding exterior wall BIM model in the modeling software;

[0008] The model import and coordinate alignment module is used to import the BIM model into the edge computing unit using the IFC interface, and align the model coordinate system with the construction site coordinate system to obtain a global-site coordinate conversion relationship;

[0009] The wall unfolding and meshing module is used to map the three-dimensional wall surface to the two-dimensional UV plane and divide the grid units according to the preset resolution;

[0010] The construction risk calculation module is used to calculate the risk weight of each grid unit by comprehensively considering the gravity shear ratio, spraying stage factor, construction momentum factor, and environmental sensitivity factor;

[0011] The intelligent path planning and spray optimization module is used to risk classify grid units according to the risk weights of the grid units, and generate spray paths in the order of low risk to high risk according to preset rules; when a continuous high-risk area meets the trigger conditions, the module further divides the area into multiple small blocks and adopts a staggered spraying strategy, where the area of ​​each small block is determined according to the area of ​​the area and the local risk weight of the area.

[0012] In a preferred embodiment, the exterior wall data acquisition and modeling module registers a multi-site cloud using a reference sphere or reference plate, denoises and crops the point cloud, and automatically generates a parametric wall model with insulation thickness attributes on a Revit or Rhino platform.

[0013] In a preferred embodiment, the construction risk calculation module is used to calculate the risk weight of each grid unit by weighted summation according to the gravity shear ratio, the spraying stage factor, the construction momentum factor, and the environmental sensitivity factor.

[0014] In a preferred embodiment, the gravity shear ratio is: ; ρ is the volume density of NEA leveling gel; g is the gravitational acceleration constant; Design thickness of each spray for this grid; is the yield shear stress at the current temperature and shear conditions.

[0015] In a preferred embodiment, the spraying stage factor is: ; n is the current number of spraying passes; is the total number of design passes.

[0016] In a preferred embodiment, the construction momentum factor is: is the instantaneous linear velocity of the nozzle; This is the safety reference spray rate given by the manufacturer.

[0017] In a preferred embodiment, the environmental sensitive factor: ; is the real-time ambient temperature at the grid; The optimal construction temperature for the material; is the upper limit of the allowable temperature difference; is the relative humidity at the point; is the normalized upper limit of humidity.

[0018] In a preferred embodiment, the distribution of risk weights of each grid unit of the entire wall is based on a clustering algorithm to automatically group continuous high-value areas and grade the risks according to preset thresholds.

[0019] In a preferred embodiment, the staggered spraying strategy is that two adjacent small blocks do not have completely aligned boundaries, but rather have a certain horizontal or vertical offset from each other.

[0020] In a preferred embodiment, the area of ​​the region is the product of the number of grid units contained in the region and the area of ​​a single grid unit; the local risk weight of the region is the average risk weight of each grid unit contained in the region.

[0021] The technical effects and advantages of the present invention are as follows:

[0022] The present invention uses a point cloud-BIM model to be imported through the IFC interface and aligned with the least squares or ICP algorithm, so that the exterior wall model maintains a millimeter-level error with the on-site coordinate system, laying a precise spatial benchmark for subsequent spray path planning and defect detection; a risk weight model composed of gravity shear ratio, spray stage factor, construction momentum factor and environmental sensitivity factor is used to form a dynamic risk heat map; when the area of ​​continuous high-risk areas exceeds the threshold, the system automatically triggers the block dislocation strategy and links the parameters to reduce the edge spray speed and spray width to avoid the accumulation of material weight on the same shear line; the small block scale is adaptively determined based on the area-risk dual factor to further reduce the incidence of large wall sliding events. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings;

[0024] Figure 1 This is a schematic diagram of the structure of the intelligent control system for NEA leveling gel internal insulation construction of building exterior walls of the present invention;

[0025] Figure 2 This is a flow chart of the intelligent path planning and spray optimization module. DETAILED DESCRIPTION

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0027] Example 1: The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls of the present invention is as follows: Figure 1As shown, it includes the following modules:

[0028] The exterior wall data acquisition and modeling module is used to perform multi-station laser scanning or drone LiDAR scanning of building facades, generate high-density point clouds, and automatically or semi-automatically reconstruct the exterior wall geometry and attribute models in the Building Information Modeling (BIM) platform;

[0029] The model import and coordinate alignment module is used to import the exterior wall BIM model into the edge computing unit through the IFC interface, and obtain the rotation matrix and translation vector from the global coordinate system to the site coordinate system based on the reference point registration algorithm to achieve high-precision alignment between the model and the site;

[0030] The wall expansion and meshing module is used to map the three-dimensional exterior wall surface to a two-dimensional UV plane and generate a regular grid according to the preset resolution, recording the projected coordinates of the center point of each grid unit in the site coordinate system and the designed insulation thickness and other properties;

[0031] The construction risk calculation module is used to calculate the risk weight of each grid unit by comprehensively considering the gravity shear ratio, spraying stage factor, construction momentum factor, and environmental sensitivity factor;

[0032] The intelligent path planning and spray optimization module is used to risk-classify grid units according to the risk weights of the grid units, and generate spray paths in order from low risk to high risk according to preset rules; when a continuous high-risk area meets the trigger conditions, the module further divides the area into multiple small blocks and adopts a staggered spraying strategy, where the area of ​​each small block is determined according to the overall area of ​​the continuous area and the local risk distribution.

[0033] Specifically,

[0034] The exterior wall data acquisition and modeling module is mainly used to obtain high-density point clouds by laying out a small number of benchmarks on site and determining the coordinates with a total station, and then using a ground laser scanner or drone LiDAR to perform multi-station scanning of the building facade; in software (such as Cyclone, Scene or CloudCompare), the data of each station is aligned to a unified coordinate system through a benchmark sphere or benchmark plate, and global bundle adjustment is performed based on the benchmark point coordinates to make the point cloud consistent with the project benchmark; then the original point cloud is denoised, cropped and gridded, retaining only the relevant parts of the wall and partitioned by floor and facade direction; in Revit, BricsC The "purified" point cloud is loaded into modeling platforms such as AD or Rhino-VisualARQ, and the main plane of the point cloud is used to fit the parametric wall through automatic plane detection or manual wall pulling command (thickness, elevation, material layer and other properties can be entered at one time). For window openings, door openings and curtain wall units, the point cloud void area is used to automatically generate opening objects or curtain wall meshes; after all walls are completed, the error is checked by overlapping with the point cloud (the out-of-plane deviation is generally controlled at ±10mm), and finally the IFC or RVT file is exported with attribute fields such as coordinate system, layer number, and designed insulation thickness, so as to obtain a directly callable exterior wall BIM model.

[0035] The model import and coordinate alignment module primarily uses the IFC interface to import the exterior wall BIM model generated by the exterior wall data acquisition and modeling module into the edge computing unit, achieving a seamless connection between the model and construction site data. IFC (Industry Foundation Classes) is an open, internationally standardized BIM data exchange format that can fully carry the geometric information, spatial location, attribute fields, and relationships between exterior wall components. This frees the model from the proprietary format restrictions of any software vendor and ensures free flow between different platforms and different stages.

[0036] During the import process, the system uses IFC parsing tools to extract the geometry, coordinate information, and key properties of each exterior wall component, such as the designed insulation thickness and material type, and converts them into a data structure that can be recognized within the edge computing unit to support subsequent algorithm analysis and calculations.

[0037] However, IFC is usually in a global or design coordinate system, while the construction site establishes its own local coordinate system based on the benchmark points laid out by the total station, and there is a slight spatial deviation between the two.

[0038] To address this issue, we further utilize spatial registration algorithms such as least squares fitting, Procrustes analysis, or ICP, using reference points in the BIM model and corresponding point pairs obtained from on-site measurements to calculate the rotation matrix and translation vector between the global and on-site coordinate systems, thereby constructing an accurate coordinate transformation matrix. Using this matrix, the system batch-converts the coordinates of each wall face and grid cell center point in the BIM model to the on-site coordinate system, ensuring precise alignment between the design model and the actual construction environment.

[0039] This coordinate alignment not only makes the BIM model the spatial benchmark for on-site construction control, spray path planning, and deviation detection, but also ensures that subsequent intelligent control processes such as point cloud comparison, film thickness detection, and repair planning can run with high precision within a unified spatial system.

[0040] The wall development and meshing module is mainly used to convert the three-dimensional exterior wall surface in the BIM model into a two-dimensional development surface that is convenient for construction analysis and path planning, and generate a regular grid on this basis to support subsequent film thickness calculation, risk analysis and intelligent spray path optimization.

[0041] First, the system uses a geometric kernel function to parameterize the geometry of each wall in the BIM model, mapping the 3D wall surface to a 2D u-v plane coordinate system to achieve a planar unfolding of the wall. This unfolding not only preserves the wall's true dimensions but also annotates the unfolded drawing with key building information, such as the wall's layer number, designed insulation thickness, structural joints, and door and window openings, ensuring that the 2D unfolded drawing fully corresponds to the actual 3D building structure.

[0042] After unfolding, the system generates a regular grid on the u–v plane at a preset resolution (typically 100 mm × 100 mm), forming a grid cell matrix. Each grid cell center point, pᵢ, is recorded with its u–v coordinates and its precise projection coordinates in the original 3D space. These projection coordinates are then batch-transformed to the construction site coordinate system using a global-to-site coordinate conversion matrix, seamlessly integrating the BIM model with the actual construction environment.

[0043] When gridding is completed, the attribute fields such as "design insulation thickness" and "wall slope" embedded in the BIM model will be directly mapped to each grid unit to generate the design insulation thickness and wall slope of each grid.

[0044] The construction risk calculation module provides real-time physical properties of NEA leveling gel construction batches, including bulk density ρ, yield stress, etc., based on the material database after wall expansion and meshing. (Dynamically updated with temperature and consistency, the offline calibration value is 120Pa.) The system calculates the gravity shear ratio online for each grid: ; ρ is the volume density of NEA leveling gel; g is the gravitational acceleration constant; Design thickness of each spray for this grid; The yield shear stress under the current temperature-shear conditions. The gravity shear ratio measures the ratio of the shear stress generated by the weight of the adhesive layer to the yield stress of the material itself to resist flow. The larger it is, the easier it is for the uncured adhesive layer to slide down due to its own weight.

[0045] The volume density of the NEA leveling gel, the designed spraying thickness of the grid each time, and the yield shear stress under the current temperature and shear conditions are all input in advance by the staff as needed.

[0046] Spraying stage factor: ; n is the current number of spraying passes; = is the total number of passes designed; intermediate passes present the greatest risk. For example, in a three-pass spraying process, the first pass is the thinnest, has a low weight, and presents a lower risk of slippage. Intermediate passes, with increased thickness and less-cured adhesive layers, also bear greater weight, presenting the highest risk of slippage. The final pass, with a smaller increase in thickness and the underlying layer already partially cured, presents a slightly lower risk. Therefore, the construction risks associated with different passes vary.

[0047] During multiple spray passes, the weight of the adhesive layer increases with each coat. During application, the thinner first coat has a lower weight and a lower risk of slippage. However, with subsequent coats, particularly the intermediate coats, the weight of the adhesive layer increases rapidly, and each additional coat places additional pressure on the underlying coat. The intermediate coats often coincide with the intersection of two critical factors: increased weight and the fact that the underlying coat has yet to reach stable cure strength, creating areas most susceptible to pressure and slippage. Materials like NEA leveling gel are typically thixotropic. After the initial spray application, the gel's structure is disrupted by shear, temporarily reducing the material's flowability and yield stress. During the intermediate coats, the material has not yet fully recovered its thixotropic structure, and new, thicker coats are applied over it, increasing the risk of flow. Before the underlying coat has fully recovered its strength, spraying the intermediate coats can easily cause sagging or slippage, potentially leading to slippage.

[0048] Construction Momentum Factor: is the instantaneous linear velocity of the nozzle; The spray speed is the safety reference spray speed given by the manufacturer (the reference value of the joint calibration of materials and equipment). The instantaneous linear velocity of the spray head is obtained by the flow rate monitoring sensor set at the gel discharge port.

[0049] Vᵢ reflects the ratio of the current spray speed to the safe spray speed. The higher the speed, the greater the momentum of the material bundle, the thicker the initial wet film thickness, and the greater the risk of slippage.

[0050] Environmental sensitive factors: ; is the real-time ambient temperature at the grid; It is the optimal construction temperature of the material. The upper limit of the allowed temperature difference is used to normalize the temperature deviation to 0–1; is the relative humidity at that point; The real-time temperature environment and the relative humidity at this point are obtained by the temperature and humidity sensors at the gel discharge port.

[0051] The further the temperature deviates from the optimum and the higher the humidity, the lower the yield stress or the slower the drying, thus amplifying the probability of slippage. The bigger the better.

[0052] The risk weight of each grid cell is calculated by comprehensively considering the gravity shear ratio, spraying stage factor, construction momentum factor, and environmental sensitivity factor; ;in is the risk weight of each grid unit, They are the weight coefficients of gravity shear ratio, spraying stage factor, construction momentum factor, and environmental sensitivity factor respectively.

[0053] Intelligent path planning and spraying optimization module, such as Figure 2 As shown, first of all, the system is based on the whole wall Using clustering algorithms such as DBSCAN or K-Means, the system automatically groups consecutive high-value areas and categorizes the risks into low, medium, and high levels based on preset thresholds, enabling precise identification of areas requiring key control. The system then dynamically replans the spray path, prioritizing low-risk areas to avoid rework in high-risk areas. Furthermore, within large, high-risk areas, the system employs a staggered spraying strategy, breaking down large areas into smaller areas to reduce the risk of localized material accumulation.

[0054] When a continuous distribution of high-risk grid cells is identified in a certain area, especially when the area exceeds a set threshold (for example, 2m² or larger), a block-by-block staggered spraying strategy is automatically triggered to reduce local material accumulation and the risk of slipping due to the weight of the adhesive layer. Specifically, the geometric boundaries of the high-risk area are first accurately located through heat map analysis and clustering algorithms. Then, based on the u-v coordinate system of the wall expansion diagram, the entire high-risk area is divided into several non-overlapping small blocks. The area of ​​each small block is determined based on the area of ​​the area (i.e., the product of the number of grid cells contained in the area and the area of ​​each grid cell) and the local risk weight of the area (i.e., the average risk weight of each grid cell contained in the area). The calculation formula is as follows: ; Where M represents the area of ​​the small piece, mj represents the area of ​​the region, and qz represents the local risk weight of the region; 、 They are the area of ​​the region and the weight coefficient of the local risk weight of the region, which can be set as needed.

[0055] This ensures a small single-pass spraying volume, thereby reducing the deadweight burden caused by localized buildup. This not only allows small areas to dynamically adapt to the risk concentration in different regions, but also ensures a more refined staggered spraying strategy in high-risk areas, effectively preventing quality issues such as sagging and slipping caused by localized buildup, thereby maximizing operational efficiency while ensuring construction safety.

[0056] However, this division isn't simply a simple geometric segmentation; it employs a staggered spraying strategy. Instead of perfectly aligned boundaries, adjacent patches are offset horizontally or vertically by a certain amount, Δu or Δv. This arrangement avoids continuous longitudinal or transverse construction seams. If high-risk areas are sprayed along the same line for extended periods, stress concentration and sagging channels can easily form at the boundaries. This staggered distribution spatially staggers the spraying sequence, preventing material accumulation from occurring simultaneously on the same plane. This significantly reduces the concentrated pressure exerted on the underlying, uncured adhesive layer by the accumulated vertical or horizontal weight. Δu and Δv are designed on demand and pre-entered into the system.

[0057] For example, if the wall is unfolded into a rectangular area, high-risk areas can be sprayed first with several small patches on the left side of the even-numbered rows, while the odd-numbered rows are staggered to the right by a distance of Δu, thus forming a "zigzag" construction layout on the overall surface. The construction sequence is also alternating in time. That is, adjacent small patches in the same area are not sprayed continuously in the same spray pass. Instead, several passes or time periods are left in between to allow the sprayed small patches to achieve initial curing during this period. This staggered spraying sequence not only reduces thickness accumulation in a single area within a short period of time, but also provides time for the thixotropic structure of the adhesive layer to recover, thereby enhancing interlaminar shear resistance.

[0058] Then a spraying path is generated, with construction in low-risk areas completed first, then in medium-risk areas, and finally in high-risk areas. For high-risk areas, construction is carried out according to the "zigzag" path generated by the staggered strategy, and the area of ​​each small area and the local risk weight of the area are comprehensively determined.

[0059] For example, the wall can be displayed on a display screen and then calibrated with color; for example, the low-risk area can be marked green first, and construction in the low-risk area can be completed; then the medium-risk area can be marked green again, and construction in the medium-risk area can be completed; and then construction can be carried out according to the generated small areas and the "zigzag" path generated by the staggered strategy until the entire wall is completed.

[0060] Furthermore, the block-by-block staggered spraying strategy is linked to process parameters. For example, spray speed and width are further reduced at the edges of small blocks, while leaving tiny gaps between blocks. After the initial solidification of the adhesive layer, the edges are then patched and trimmed to prevent excessive thickness of the material at the boundaries due to multiple cross-spraying. This block-by-block staggered spraying strategy enables more uniform and safer material distribution during construction, even in large, high-risk areas. This effectively reduces quality defects caused by localized thick accumulation, concentrated stress, or sagging, ultimately ensuring that NEA leveling gel internal insulation construction maintains a high degree of reliability and precision even in extreme environments or on complex facades.

[0061] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0062] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0063] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0064] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0065] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls is characterized by: Includes the following modules: The exterior wall data acquisition and modeling module is used to obtain building facade point clouds through benchmark point layout, laser scanning or drone LiDAR, and generate the corresponding exterior wall BIM model in the modeling software; The model import and coordinate alignment module is used to import the BIM model into the edge computing unit using the IFC interface, and align the model coordinate system with the construction site coordinate system to obtain a global-site coordinate conversion relationship; The wall unfolding and meshing module is used to map the three-dimensional wall surface to the two-dimensional UV plane and divide the grid units according to the preset resolution; The construction risk calculation module is used to calculate the risk weight of each grid unit by comprehensively considering the gravity shear ratio, spraying stage factor, construction momentum factor, and environmental sensitivity factor; The intelligent path planning and spraying optimization module is used to classify the risk of the grid units according to the risk weights of the grid units, and generate spraying paths in the order of low risk to high risk according to preset rules; When a continuous high-risk area meets the triggering conditions, the module further divides the area into multiple small blocks and adopts a staggered spraying strategy, where the area of ​​each small block is determined according to the area of ​​the area and the local risk weight of the area.

2. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 1 is characterized by: The exterior wall data acquisition and modeling module registers the multi-site cloud through a reference sphere or reference plate, denoises and crops the point cloud, and automatically generates a parametric wall model with insulation thickness attributes on the Revit or Rhino platform.

3. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 1 is characterized by: The construction risk calculation module is used to calculate the risk weight of each grid unit by weighted summation based on the gravity shear ratio, the spraying stage factor, the construction momentum factor, and the environmental sensitivity factor.

4. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 3 is characterized by: The gravity shear ratio: ; ρ is the volume density of NEA leveling gel; g is the gravitational acceleration constant; Design thickness of each spray for this grid; is the yield shear stress at the current temperature and shear conditions.

5. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 3 is characterized by: The spraying stage factor: ; n is the current number of spraying passes; is the total number of design passes.

6. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 3 is characterized by: The construction momentum factor: is the instantaneous linear velocity of the nozzle; This is the safety reference spray rate given by the manufacturer.

7. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 3 is characterized by: The environmental sensitive factors: ; is the real-time ambient temperature at the grid; The optimal construction temperature for the material; is the upper limit of the allowable temperature difference; is the relative humidity at the point; is the normalized upper limit of humidity.

8. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 1 is characterized by: Based on the distribution of risk weights of each grid unit in the entire wall, continuous high-value areas are automatically grouped through a clustering algorithm, and the risks are graded according to preset thresholds.

9. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 1 is characterized by: The staggered spraying strategy means that two adjacent small blocks do not have completely aligned boundaries, but have a certain horizontal or vertical offset from each other.

10. The intelligent control system for NEA leveling gel internal insulation construction of building exterior walls according to claim 1 is characterized by: The area of ​​the region is the product of the number of grid units contained in the region and the area of ​​a single grid unit; the local risk weight of the region is the average risk weight of each grid unit contained in the region.

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