Closed-loop control method, device and storage medium for segmented construction of special-shaped curtain wall steel structure

Abstract

The application relates to the technical field of building steel structure construction, and provides a closed-loop control method for sectional construction of a special-shaped curtain wall steel structure, which comprises the following steps: step S101: three-dimensional scanning is performed on a to-be-mounted component and a supporting jig frame, a coordinate system is established through positioning of a target, and point cloud data are acquired; step S102: key points for component splicing are extracted, the point cloud is matched with a design model, collision detection is performed, and pre-splicing positioning data are generated; step S103: the positioning data are converted into visual instructions, real-time hoisting data are combined, and the component is accurately positioned; step S104: a joint area is scanned after installation, and a newly-arrived component is re-inspected; step S105: steps S102 to S104 are repeated until all components are installed; and step S106: an overall scan is performed to generate a completion model, the completion model is compared with a design model to find differences, and a construction quality acceptance report is output. According to the technical scheme, a closed-loop control process is constructed, and the construction precision, efficiency and construction quality of the special-shaped curtain wall steel structure are cooperatively optimized.

Description

Technical Field

[0001] This application relates to the field of building steel structure construction technology, and in particular to a closed-loop control method, equipment and storage medium for segmented construction of irregular curtain wall steel structures. Background Technology

[0002] With the development of artistic design in modern architecture, irregularly shaped curved curtain walls are increasingly used in large public buildings. These curtain walls typically employ lightweight steel structures as their support system, with components featuring complex spatial curved surfaces or asymmetrical cross-sectional characteristics, requiring segmented assembly to achieve the overall structure. During assembly, precise control of the spatial orientation of the components is crucial for ensuring both the smoothness of the curtain wall's curvature and its structural safety.

[0003] Current mainstream methods for assembling irregularly shaped curtain walls employ a technology system based on single-point measurement instruments: construction workers use equipment such as total stations and levels to perform discrete point-by-point measurements and manual corrections on key points of component splicing (e.g., the center of bolt hole groups, weld joint surfaces). However, the aforementioned existing technical solutions suffer from drawbacks such as incomplete three-dimensional spatial data measurement, low construction collaboration efficiency, and uncontrollable risk of error accumulation.

[0004] The aforementioned defects all stem from the disconnect between "discrete measurement data" and "continuous construction control." That is, the construction process relies on fragmented point information, which makes it impossible to fully perceive the true spatial form of the components and to establish an error suppression mechanism across processes, ultimately resulting in fluctuations in assembly accuracy and uncontrollable construction period. Summary of the Invention

[0005] This application provides a closed-loop control method, equipment, and storage medium for segmented construction of irregular-shaped curtain wall steel structures. By constructing a closed-loop control process, the construction accuracy, efficiency, and construction quality of irregular-shaped curtain wall steel structures are optimized collaboratively.

[0006] On the one hand, this application provides a closed-loop control method for segmented construction of irregularly shaped curtain wall steel structures, the method comprising:

[0007] Step S101: Perform multi-angle three-dimensional scanning of the component to be installed and the support frame at the construction site, establish a unified spatial coordinate system by setting up positioning targets, and obtain point cloud data including the surface morphology of the component to be installed and the position of the support frame.

[0008] Step S102: Extract the splicing key points of the component to be assembled based on the point cloud data, and perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model according to the splicing key points. Perform collision detection in the virtual environment and output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled.

[0009] Step S103: Convert the pre-assembled positioning dataset into a visual instruction to indicate the theoretical installation position, and combine it with the real-time positioning and tracking data of the hoisting process to guide the component to be installed into the installed component.

[0010] Step S104: After the current component installation is completed, a local fine scan is performed on the construction status, including the joint area of ​​the installed component, and a re-inspection scan is performed on the newly arrived components to be installed.

[0011] Step S105: For subsequent components to be installed, repeat steps S102 to S104 until all segmented components are installed.

[0012] Step S106: Perform a 3D scan of the completed overall structure to obtain the as-built point cloud model. Compare the spatial morphological differences between the as-built point cloud model and the design model to generate an acceptance report for quantitatively evaluating the construction quality.

[0013] On the other hand, this application provides a closed-loop control device for segmented construction of irregular-shaped curtain wall steel structures, the device comprising:

[0014] The acquisition module is used to perform multi-angle three-dimensional scanning of the components to be installed and the support frame at the construction site. By setting up positioning targets, a unified spatial coordinate system is established to acquire point cloud data containing the surface morphology of the components to be installed and the position of the support frame.

[0015] The registration module is used to extract the splicing key points of the component to be assembled based on the point cloud data, and to perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model based on the splicing key points, to perform collision detection in the virtual environment, and to output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled.

[0016] The conversion module is used to convert the pre-assembled positioning dataset into visual instructions for indicating the theoretical installation position, and combine it with real-time positioning and tracking data during the hoisting process to guide the component to be installed into the installed component.

[0017] The scanning module is used to perform a local fine scan of the construction status, including the joint area of ​​the installed components, after the current component installation is completed, and to perform a re-inspection scan on newly arrived components to be installed.

[0018] The loop module is used to repeatedly execute the registration module, conversion module, and scanning module for subsequent components to be installed until all segmented components are installed.

[0019] The report generation module is used to perform a 3D scan of the overall structure after completion, obtain the as-built point cloud model, compare the spatial morphological differences between the as-built point cloud model and the design model, and generate an acceptance report for quantitatively evaluating the construction quality.

[0020] Thirdly, this application provides an electronic device, the device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the technical solution of the closed-loop control method for segmented construction of irregular curtain wall steel structures as described above.

[0021] Fourthly, this application provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the closed-loop control method for segmented construction of irregular curtain wall steel structures as described above.

[0022] As can be seen from the technical solution provided in this application, by establishing a unified coordinate system through multi-angle 3D scanning, generating pre-assembly positioning data through point cloud registration, dynamically correcting installation parameters, and comparing and accepting the as-built model, a closed-loop control of the entire process is formed. This has the advantages of improving the integrity of 3D spatial data, enhancing construction collaboration efficiency, and making the risk of error accumulation controllable. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of the closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures provided in the embodiments of this application;

[0025] Figure 2 This is a schematic diagram of detecting the edge of the component to be installed according to an embodiment of this application;

[0026] Figure 3 This is a schematic diagram of the fitting of the boundary of the component to be installed according to an embodiment of this application;

[0027] Figure 4 This is a schematic diagram of the closed-loop control device for segmented construction of irregular-shaped curtain wall steel structure provided in the embodiments of this application;

[0028] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0029] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] In this specification, adjectives such as "first" and "second" are used only to distinguish one element or action from another, without necessarily requiring or implying any actual such relationship or order. Where circumstances permit, reference to an element, component, or step (etc.) should not be construed as limited to only one element, component, or step, but may include one or more of the elements, components, or steps, etc.

[0031] For ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn to actual scale.

[0032] In existing technologies, with the development of modern architectural aesthetics, irregularly shaped curved curtain walls are increasingly widely used in large public buildings. These curtain walls typically employ lightweight steel structures as their support system, with components featuring complex spatial curved surfaces or asymmetrical cross-sectional characteristics, requiring segmented assembly to achieve the overall structure. During assembly, precise control of the spatial orientation of the components is crucial for ensuring the smoothness of the curtain wall's curvature and structural safety. Current mainstream irregularly shaped curtain wall assembly construction utilizes a technology system based on single-point measuring instruments, which suffers from drawbacks such as incomplete three-dimensional spatial data, low efficiency in measurement and construction collaboration, and uncontrollable risk of error accumulation. For example, single-point measurement can only obtain coordinate data of isolated locations, failing to construct an overall curved surface model of the component, resulting in insufficient basis for adapting complex geometric shapes; the measurement process requires repeated instrument setup, creating a serial delay of "measurement-adjustment-remeasurement," slowing down construction progress; and the lack of closed-loop verification of the installed component status during segmented installation allows local deviations to be continuously amplified and propagated in subsequent assembly stages.

[0033] To address the aforementioned issues, the applicant discovered a disconnect between discrete measurement data and continuous construction control in existing technologies. This leads to a reliance on fragmented point information during construction, hindering a comprehensive understanding of the true spatial morphology of components. Analysis revealed that acquiring continuous three-dimensional spatial data throughout the construction process and establishing a dynamic error correction mechanism can effectively resolve the problems of insufficient data integrity and error accumulation. Furthermore, deeply integrating 3D scanning technology with the construction process to form a closed-loop control system enables visualized and precise guidance and real-time error compensation during construction. Based on this, the applicant proposes a closed-loop control method covering the entire construction cycle by constructing a holographic data model through multi-angle 3D scanning and combining it with virtual pre-assembly and dynamic parameter correction.

[0034] Specifically, this application proposes a closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures, the flowchart of which is attached. Figure 1 As shown, the main steps include S101 to S106, which are detailed below:

[0035] Step S101: Perform multi-angle three-dimensional scanning of the component to be installed and the support frame at the construction site. Establish a unified spatial coordinate system by setting up positioning targets and obtain point cloud data containing the surface morphology of the component to be installed and the position of the support frame.

[0036] In this embodiment, multi-angle three-dimensional scanning refers to acquiring geometric information of the component surface from different directions using a multi-station scanning device. Specifically, a laser scanner combined with a rotating gimbal can be used to completely obtain the spatial relationship between the component's curved surface shape and the supporting frame. The positioning target refers to a spatial reference marker with specific reflective properties. Specifically, a spherical or cubic target array can be used to establish a unified coordinate system transformation reference between each scanning station.

[0037] Furthermore, considering that, on the one hand, traditional single-point measurement methods can only acquire discrete point data and cannot eliminate environmental interference; on the other hand, existing technologies typically employ global smoothing processing for weld deformation areas, resulting in the loss of geometric features. Therefore, in order to effectively solve the problem of point cloud data distortion caused by environmental noise interference and component welding deformation during 3D scanning, and to ensure the acquisition of high-fidelity surface morphology and jig position data of the component to be installed, so as to provide an accurate original data basis for subsequent spatial matching and registration operations, as an embodiment of this application, multi-angle 3D scanning of the component to be installed and the supporting jig is performed at the construction site. By arranging positioning targets to establish a unified spatial coordinate system, point cloud data containing the surface morphology of the component to be installed and the position of the supporting jig can be obtained by: setting up several scanning stations around the component unloading area to achieve coverage without dead angles; using a pass-through filtering algorithm to remove environmental noise, and using feature preservation technology to process the welding deformation area in the point cloud to retain the original geometric features. The pass-through filtering algorithm refers to automatically filtering point cloud data of non-target objects by setting a 3D coordinate threshold range. Specifically, it can be implemented by setting the X / Y / Z axis coordinate interval. This algorithm can effectively eliminate interference point clouds generated by scaffolding and temporary equipment in the construction environment. Feature preservation technology refers to a method for local optimization of the welding deformation area in the point cloud. Specifically, it can be implemented using the curvature-constrained Laplacian smoothing algorithm. This technology preserves the true deformation characteristics of the component while suppressing measurement noise.

[0038] Specifically, in the above embodiment, several scanning stations are set up around the component unloading area to achieve coverage without blind spots. A pass-through filtering algorithm is used to remove environmental noise, and feature preservation technology is used to process the welding deformation area in the point cloud to retain the original geometric features. This can be achieved by: multiple scanning stations arranged around the component unloading area forming a cross-covering scanning network, with each station collecting component surface data from different angles to eliminate blind spots from a single viewpoint; the original point cloud obtained by scanning is preprocessed using a pass-through filtering algorithm, and a coordinate filtering range is set according to the spatial relationship between the support frame and the component to be installed, automatically eliminating environmental noise that exceeds the set range; for component areas with welding deformation, a Laplace smoothing algorithm based on curvature constraints is used to calculate the curvature distribution characteristics of the local point cloud, maintaining the curvature continuity of the original geometric contour during the smoothing of noise points.

[0039] Because existing methods rely on the relative coordinate systems of each scanning station, coordinate alignment requires feature matching of overlapping areas between adjacent stations. This is susceptible to environmental noise and missing features, leading to accumulated stitching errors. To establish a globally unified absolute coordinate system directly without relying on feature matching between stations, and fundamentally eliminate the coordinate system inconsistency problem in multi-station scanning, the above embodiment establishes a unified spatial coordinate system by arranging positioning targets. This can be achieved by: fixing a set of positioning targets with known spatial relationships within the scanning area, using the positioning targets as spatial references; and using a spatial analysis algorithm to calculate coordinate transformation parameters based on the target position data captured by each scanning station, so that all... The point cloud data acquired at each site is uniformly aligned to a reference coordinate system fixedly associated with the supporting frame. The positioning target refers to a physical marker with fixed geometric features and a known spatial position, specifically a spherical or prismatic target with a specific reflective material. Its known spatial relationships are obtained through pre-measurement or calibration, providing a unified absolute spatial reference for multi-site scanning. The spatial analysis algorithm refers to a mathematical calculation method based on the principle of spatial geometric transformation, specifically using the least squares method or singular value decomposition algorithm. By calculating the relative positional relationships of the targets at different scanning sites, the coordinate transformation matrix is ​​solved, eliminating coordinate system differences between multi-site scans. The reference coordinate system fixedly associated with the supporting frame refers to a three-dimensional coordinate system rigidly bound to the physical structure of the supporting frame. This is achieved by setting permanent measurement control points at key nodes of the frame, ensuring the stability of the coordinate reference during construction.

[0040] As one embodiment of this application, a set of positioning targets with known spatial relationships are fixedly arranged within the scanning area, and the positioning targets are used as spatial references. Using a spatial analysis algorithm, coordinate transformation parameters are calculated based on the target position data captured by each scanning station, so that the point cloud data acquired by all stations are uniformly aligned to a reference coordinate system fixedly associated with the support frame. This can be achieved by: arranging multiple positioning targets within the scanning area, with the relative positional relationships between the targets pre-determined; during multi-angle 3D scanning, each scanning station captures point cloud data from at least three targets; using a spatial analysis algorithm, the target position data collected by each station is matched with the known spatial relationships to calculate the rotation matrix and translation vector that transforms the current station's coordinate system to the reference coordinate system; after coordinate transformation, the point cloud data from all stations are unified to the reference coordinate system fixed with the support frame, achieving seamless stitching of multi-source point cloud data. Through the above technical solution of this application, the problem of point cloud stitching errors caused by inconsistent coordinate systems of different stations during multi-angle 3D scanning is effectively solved, while avoiding spatial reference drift caused by changes in scanning stations. By fixing the reference coordinate system to the support frame, the spatial positioning accuracy of the installation of irregular curtain wall components is significantly improved, ensuring that the construction positioning process is always based on a stable physical reference.

[0041] Step S102: Extract the splicing key points of the component to be assembled based on the point cloud data containing the surface morphology of the component to be assembled and the position of the support frame, and perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model according to the splicing key points. Perform collision detection in the virtual environment and output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled.

[0042] While the reliability of physical trial assembly is relatively easy to guarantee, it is time-consuming and cannot predict cumulative errors. Compared to the high cost of physical trial assembly, virtual pre-assembly can significantly reduce calibration time through dynamic guidance and can predict cumulative errors. To resolve the conflict between the reliability of physical trial assembly and the timeliness of construction progress, key splicing points of the components to be assembled can be extracted based on point cloud data containing the surface morphology of the components and the position of the supporting frame. Then, based on these key splicing points, the point cloud of the components to be assembled is spatially matched and registered with the design model. Collision detection is performed in a virtual environment, and a pre-assembly positioning dataset containing the theoretical pose of the components to be assembled is output. This pre-assembly positioning dataset includes the three-dimensional spatial position information and attitude angle information of the components to be assembled in a unified spatial coordinate system, i.e., P... pre ={x i ,y i ,z i ,θ x ,θ y ,θ z}, i = 1, 2, ..., n, the pre-assembled positioning dataset is the most direct basis for construction guidance. Key splicing points refer to the feature points of the connection points between components. Specifically, the center points of bolt hole groups can be extracted using edge detection algorithms, or the contour lines of the welded end faces can be obtained using boundary recognition technology, serving as the matching benchmark for point cloud registration. Spatial matching and registration refers to spatially aligning the measured point cloud with the design model. Specifically, the iterative nearest point algorithm can be used to optimize the rotation and translation matrix, enabling the point cloud and model to achieve the best fit.

[0043] Furthermore, existing methods rely on manual point-by-point measurement of bolt hole positions and cannot obtain the overall contour of the welded end face, resulting in incomplete key point data and low efficiency. To avoid random errors in manual measurement and achieve full-coverage feature acquisition of the component docking area, in the above embodiments, the extraction method for splicing key points can be: locating the center point of the bolt hole group on the component to be assembled using edge detection technology; and extracting the theoretical contact area contour line of the welded end face of the component to be assembled using boundary recognition technology. The edge detection technology can be an algorithm based on the abrupt change features of the gray-level gradient of point cloud data to identify the bolt hole edge contour. Specifically, it can be implemented using the Canny operator combined with morphological filtering, automatically segmenting the hole area and calculating its geometric center by setting a gradient threshold. The boundary recognition technology can be an algorithm based on the curvature change features of point cloud to extract the continuous boundary of the welded end face. Specifically, it can be implemented using the region growing method combined with curvature clustering analysis, identifying the theoretical boundary line of the contact surface by constructing a curvature distribution map. Figure 2 The diagram shown is a schematic diagram of detecting the edge of the component to be installed according to an embodiment of this application. Figure 3 This is a schematic diagram of the fitting of the boundary of the component to be installed provided in the embodiment of this application.

[0044] Specifically, in the above embodiments, locating the center point of the bolt hole group on the component to be assembled using edge detection technology can be achieved by: establishing a three-dimensional grayscale field by scanning point cloud data, capturing gradient abrupt change points in the area surrounding the holes using an edge detection algorithm, eliminating noise interference through morphological filtering, fitting the hole edge ring using the least squares method, and calculating the center coordinates; extracting the theoretical contact area contour line of the welding end face of the component to be assembled using boundary recognition technology can be achieved by: calculating the curvature value of each point in the point cloud, clustering continuous points with similar curvature into boundary line segments using a region growing method, and generating a smooth and closed contact surface contour line after topology optimization. The synergistic application of the two technologies ensures that the geometric features of both discrete connection points and continuous contact surfaces are fully captured, forming a key point dataset covering features of different dimensions, providing multi-level constraints for subsequent registration. As can be seen from the above embodiments, the corresponding technical solutions solve the problem of missing key point data caused by discrete point measurement and achieve a complete representation of the geometric features of the component splicing area. Automatic positioning of the center point of the bolt hole group improves the identification efficiency and accuracy of discrete connection points, and continuous extraction of the weld end face contour ensures accurate adaptation of the contact surface shape. The combination of the two provides highly reliable input data for subsequent registration operations and effectively suppresses the accumulation of installation errors caused by incomplete key point data.

[0045] Because existing rigid registration can only align point clouds through translation and rotation operations, it cannot adapt to the local processing deformation of actual components, resulting in residual errors after registration. Therefore, in order to eliminate the problem of local errors, in the above embodiments, the point cloud of the component to be installed is spatially matched and registered with the design model based on the splicing key points. The spatial matching and registration operation can be non-rigid registration, that is, a registration method that allows the point cloud data to undergo elastic deformation during the registration process. Specifically, it can be implemented by using an iterative nearest point algorithm combined with an elastic deformation model. By iteratively adjusting the point cloud deformation parameters, the spatial distance between the measured component point cloud and the corresponding point in the design model gradually converges to a minimum value, thereby compensating for the local geometric deviations generated during component processing. In this embodiment, the RANSAC plane fitting algorithm can be used to extract the positioning reference plane of the support frame, calculate its normal vector deviation, and perform non-rigid registration between the component point cloud and the BIM design model. The optimization objective is to minimize the distance deviation between the measured component point cloud and the corresponding point in the design model, while introducing a constraint mechanism to control non-physical deformation. Its mathematical expression is:

[0046]

[0047] Where, p i For the measured point, q iLet λ be the theoretical point, Reg(T) be the regularization coefficient, and Reg(T) be the constraint to suppress non-physical deformation, ensuring that the deformation does not exceed the yield strength of the steel while avoiding excessive local compression / tension (Poisson effect). This makes the registration process mathematically optimal and physically feasible, conforming to the mechanical behavior of steel. In other words, the aforementioned constraint on non-physical deformation is a restriction imposed in the registration algorithm to prevent deformation that does not conform to actual physical laws. Specifically, this can be achieved by setting a threshold for the material's elastic modulus or geometric continuity conditions. For example, during the point cloud deformation process, the maximum displacement gradient between adjacent points can be limited to not exceed the material's yield strength, thereby avoiding abrupt distortions caused by measurement noise or local errors. This mechanism ensures that the deformation process conforms to the mechanical properties of the actual component.

[0048] Specifically, based on the key points of the splicing, the point cloud of the component to be assembled is spatially matched and registered with the design model to minimize the distance deviation between the measured component point cloud and the corresponding points in the design model. Simultaneously, a constraint mechanism to control non-physical deformation is introduced: First, an elastic deformation mapping relationship is established between the component point clouds using a non-rigid registration algorithm, allowing each measured point to adjust its position along multiple degrees of freedom, thereby eliminating local morphological differences caused by processing errors. During this process, by minimizing the distance deviation between corresponding points, the overall pose of the component is ensured to maintain spatial consistency with the design model. At the same time, deformation constraints based on material properties are introduced into the algorithm. For example, the point cloud deformation is treated as an elastic thin plate model, limiting its curvature change to no more than a preset threshold, thereby suppressing non-physical deformation caused by measurement noise or local errors. Therefore, the measured component point cloud can both compensate for actual processing errors through elastic deformation and avoid distortion caused by overfitting outliers. As can be seen from the above embodiments, the corresponding technical solutions can effectively distinguish between actual processing errors and abnormal deformations caused by measurement noise. While improving the accuracy of point cloud registration, they ensure that the deformation process conforms to the true physical characteristics of the component, thereby avoiding subsequent installation parameter calculation errors caused by non-physical deformation interference.

[0049] Step S103: Convert the pre-assembled positioning dataset into visual instructions to indicate the theoretical installation position, and combine it with real-time positioning and tracking data during the hoisting process to guide the components to be installed into the installed components.

[0050] If the components to be installed are directly hoisted, repeated trial and error adjustments are required, especially for components with abrupt curvature changes, where the physical adjustment time increases significantly. Therefore, in order to quantify the manufacturing error and design deviation of the components, predict installation conflicts, and thus provide quantitative input for dynamic guidance, this application can convert the pre-assembly positioning dataset into a visual instruction for indicating the theoretical installation position. Combined with the real-time positioning and tracking data of the hoisting process, it can guide the components to be installed into the installed components. The visual instruction is a way to convert the theoretical installation posture into a recognizable spatial mark. Specifically, it can be achieved by projecting a positioning crosshair onto the surface of the jig using a laser projection device, or by displaying a virtual assembly guide line using an augmented reality device.

[0051] Furthermore, considering that existing technologies heavily rely on total stations for discrete point measurements, each adjustment requires re-setting up the instrument and manually recording data, leading to process interruptions and operational delays, in order to eliminate the time loss from repeated setting up of measuring equipment and avoid random errors from manual visual adjustments, the visualization instructions in the above embodiment can be to project laser marks at predetermined positions on the support frame to indicate the theoretical installation position; and to track and display the deviation vector between the spatial position of the component during hoisting and the theoretical installation position in real time. Specifically, projecting laser marks involves using a laser emitting device to form optical marks corresponding to the theoretical installation position on the surface of the support frame or in the surrounding space. This can be achieved using a laser projector with a three-dimensional coordinate transformation module. This projector receives the three-dimensional coordinate information from the pre-assembled positioning dataset and converts it into projection control instructions. Real-time tracking and display of the deviation vector involves collecting real-time pose data of the hoisted component from positioning sensors and performing spatial vector calculations with the theoretical installation position to obtain vector data. This can be achieved using a tracking device that integrates an inertial measurement unit and a visual recognition system, dynamically generating a visual graphic of the deviation direction and distance.

[0052] The projection of laser markers transforms the theoretical installation position from a digital model into a visible physical marker, allowing construction personnel to directly observe the target area without relying on repeated measurements and positioning with a total station. During hoisting, the tracking device continuously acquires the spatial coordinates of the component, calculates the deviation between the current position and the theoretical position through vector calculations, and displays this deviation as an arrow superimposed on the hoisting monitoring interface. Operators adjust the hoisting equipment's motion parameters in real time based on the arrow's direction and length, moving the component along the direction of decreasing deviation vector until the laser marker coincides with the component's actual contact surface. In other words, the technical solution described above solves the problems of low hoisting positioning efficiency and high reliance on manual labor caused by the lack of visual guidance in traditional construction, achieving real-time visual feedback of component posture deviation. Operators can directly make precise adjustments based on optical markers and dynamic vectors, reducing the frequency of total station use and manual measurement interventions, significantly improving the hoisting efficiency and first-time positioning accuracy of complex and irregularly shaped components.

[0053] Step S104: After the current component installation is completed, perform a local fine scan of the construction status, including the joint area of ​​the installed components, and perform a re-inspection scan of the newly arrived components to be installed.

[0054] Although converting pre-assembled positioning datasets into visual instructions to indicate theoretical installation positions, combined with real-time positioning and tracking data during the hoisting process to guide the placement of components to be installed as already installed components, solves the problems of low hoisting positioning efficiency and high reliance on manual labor caused by the lack of visual guidance methods in traditional construction, there is an irreconcilable contradiction between local optimization of single-segment accuracy and global degradation of overall cumulative error. The traditional technical solution of independent acceptance of segments causes the cumulative error to grow exponentially, and the cumulative deviation rate also increases significantly. Therefore, in order to pre-inspect deformation and block error propagation, after the current component is installed, a local fine scan of the construction status, including the joint area of ​​the already installed components, is performed, and a re-inspection scan is performed on newly arrived components to be installed. Meanwhile, in order to achieve feedforward-feedback composite control, adjustment instructions can be generated based on the error propagation model after local fine scanning and retest scanning. That is, based on the difference data between the current scanning result (i.e., the scanning result of local fine scanning and / or retest scanning) and the expected state, dynamic correction instructions for subsequent component hoisting parameters are generated. Here, the dynamic correction instructions are adjustment commands generated according to the real-time error state. Specifically, PID control algorithms can be used to generate displacement compensation or angle correction values ​​to guide the adjustment of the hoisting equipment's action parameters.

[0055] As one embodiment of this application, generating dynamic correction instructions for subsequent component hoisting parameters based on the difference data between the scan results of the re-inspection scan and the expected state can be as follows: establishing a transmission relationship model between the current installation error and subsequent installation parameters; based on this transmission relationship model, when the detected installation error is less than a set threshold, fine-tuning the installation parameters of subsequent components; when the accumulated installation error exceeds the design allowable range, suspending construction and triggering a reverse adjustment process to adjust the pose of the installed components; wherein, the transmission relationship model is a mathematical relationship describing the influence of the installed component error on subsequent installation parameters, and can be implemented using a regression analysis model or a finite element simulation model based on historical installation data, used to predict the error transmission path and calculate compensation parameters. For example, ε k+1 =A εk +B uk +w k , where ε is the error vector, u is the control input, w is the process noise, and matrices A and B can be obtained through system identification.

[0056] As another embodiment of this application, the dynamic correction instruction for subsequent component hoisting parameters based on the difference data between the scan results of the re-inspection scan and the expected state can be as follows: During the segmented installation process, the actual pose data of the installed components is obtained through 3D scanning, and the difference data is generated by comparing it with the design model; the transmission relationship model establishes the influence coefficient matrix of the current installation error on the subsequent installation parameters by analyzing the error distribution law; when a single installation error is detected to be within the threshold range, only the hoisting parameters of the subsequent components are locally corrected, such as adjusting the position of the lifting point or the rotation angle, to offset the error transmission effect; when the vector superposition of continuous installation errors exceeds the design allowable value, the system automatically suspends the hoisting operation and starts the reverse adjustment process, using the adjustment device on the support frame to perform reverse compensation on the spatial coordinates of the installed components, such as lifting or translating the target component, until its pose returns to the theoretical position. Through the technical solutions corresponding to the above embodiments, the transmission path of installation errors can be identified in real time, and the dynamic optimization of the construction process can be achieved through a hierarchical control strategy. When the installation error is small, the construction efficiency is maintained by fine-tuning the parameters. When the installation error exceeds the limit, the error chain is blocked by reverse adjustment, ensuring that the installation accuracy of each segment component is always within a controllable range, and finally achieving a high degree of consistency between the overall spatial form of the irregular curtain wall and the design model.

[0057] Furthermore, existing methods require disassembling and repositioning the installed components when the accumulated error exceeds the limit, leading to construction interruption and the risk of secondary installation errors. To avoid disassembly and ensure the continuity of the construction process by completing the adjustment online, the reverse adjustment process for adjusting the pose of the installed components in the above embodiment can be as follows: using the adjustment device on the support frame to perform compensatory adjustment on the pose of the target installed component; wherein, the adjustment device on the support frame is a multi-degree-of-freedom pose adjustment mechanism integrated into the main structure of the support frame, which can be implemented by a hydraulic jack assembly or a lead screw mechanism driven by a servo motor. Its function is to indirectly drive the installed component to produce translational or rotational motion by changing the spatial coordinates of the contact point between the frame and the component. The compensatory adjustment is a reverse correction operation based on the error transmission direction, which can be implemented by calculating the adjustment amount using an inverse kinematics algorithm. Its function is to actively eliminate the pose deviation of the installed component and block the transmission path of error to subsequent components.

[0058] As one embodiment of this application, the compensatory adjustment of the pose of the target installed component using the adjustment device on the support frame can be as follows: When the cumulative installation error is detected to exceed the design allowable range, the control system determines the adjustment direction of the target installed component based on the current error vector direction; after receiving the control command, the adjustment device on the support frame synchronously changes the spatial coordinates of the support point by linking multiple execution units, causing the installed component to undergo a small displacement along a predetermined trajectory. During this process, the inverse kinematics algorithm calculates the displacement of each adjustment device in real time to ensure that the component pose adjustment amount accurately matches the error correction requirements; after the adjustment is completed, the system automatically triggers a local retest process to verify the correction effect. If the residual error is within the allowable range, the subsequent component installation continues. In the above embodiment, the adjustment device of the support frame can be configured as four independently controlled hydraulic lifting modules, each module containing three hydraulic cylinders arranged in an orthogonal direction; when it is necessary to adjust the horizontal position of the component, the corresponding hydraulic cylinders are controlled to extend and retract synchronously to push the component to translate; when it is necessary to adjust the pitch angle of the component, the adjacent two sets of hydraulic cylinders are controlled to generate differential stroke. Through the technical solutions corresponding to the above embodiments, reverse correction can be quickly implemented when the cumulative error exceeds the limit, effectively eliminating the impact of the positional deviation of the installed components on subsequent installation, ensuring that the overall construction accuracy meets the design requirements. This solution achieves active blocking of the error transmission path during construction through the synergistic effect of physical adjustment devices and algorithm control, solving the problem of systematic accuracy loss of control caused by the inability to reverse adjust in existing methods.

[0059] Step S105: For subsequent components to be installed, repeat steps S102 to S104 until all segmented components are installed.

[0060] Step S106: Perform a 3D scan of the completed overall structure to obtain the as-built point cloud model. Compare the spatial morphological differences between the as-built point cloud model and the design model to generate an acceptance report for quantitatively evaluating the construction quality.

[0061] Relying on human experience leads to high disputes over construction quality and makes it impossible to trace the root cause of defects. To provide an objective chain of evidence to replace subjective sampling and thus form a complete data loop to verify the effectiveness of the method, this application can perform a 3D scan of the completed overall structure to obtain an as-built point cloud model. The as-built point cloud model is then compared with the design model to identify spatial morphological differences, generating an acceptance report for quantitatively assessing the construction quality.

[0062] Because existing construction acceptance relies on the comparison of discrete point coordinates, it can only detect positional deviations and cannot assess the continuity of the surface, making it difficult to effectively identify local deformations in the weld area. Therefore, in order to detect higher-order deviations of the weld path from the perspective of geometric continuity and overcome the limitations of discrete point measurement in evaluating surface morphology, as an embodiment of this application, in the above embodiment, comparing the spatial morphological differences between the as-built point cloud model and the design model can be done by calculating the consistency of curvature changes on the weld path between adjacent components. The consistency of curvature changes refers to the degree of matching between the distribution of curvature values ​​at each point on the weld path and the corresponding path in the design model. Specifically, it can be achieved by using differential geometry algorithms to extract the curvature feature sequence of the weld path and using dynamic time warping algorithms to calculate similarity. This feature, by quantifying the matching degree of the second derivative of the surface, can identify the microscopic deformation of the weld area from the differential geometry level. Specifically, calculating the consistency of curvature changes along the weld path between adjacent components can be achieved by: extracting a continuous set of three-dimensional coordinate points of the weld path from the as-built point cloud model; calculating the path curvature value point by point using a curvature estimation algorithm to form a measured curvature distribution curve; simultaneously deriving the curvature distribution curve of the theoretical weld path from the design model; normalizing the two curves; calculating their morphological similarity using a dynamic time warping algorithm; and outputting a curvature change consistency coefficient. This curvature change consistency coefficient characterizes the degree of deviation in the continuity of the weld area surface. When the curvature change consistency coefficient is lower than a set threshold, it is determined that the area has geometric discontinuities caused by installation errors or welding deformation. Through the technical solutions corresponding to the above embodiments, a quantitative assessment of the geometric continuity of the weld area is achieved, avoiding the destruction of the overall surface smoothness of the curtain wall due to sudden changes in local curvature, and providing an evaluation basis that is more consistent with the actual stress state for construction quality acceptance.

[0063] The aforementioned acceptance report may include a color-coded distribution map showing the differences in the consistency of curvature variations.

[0064] From the above appendix Figure 1The closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures, as illustrated in the example, demonstrates several key aspects. First, spatial registration is achieved by extracting key splicing points from point clouds, enabling precise matching and collision prediction between components and the design model in a virtual environment. This transforms traditional on-site trial assembly into digital simulation, significantly reducing the number of physical adjustments. Second, the virtual pre-assembly results are converted into visual instructions to guide precise component placement, achieving a controlled transition from pre-installed to installed components. Through phased scanning comparisons and dynamic parameter corrections, errors in installed components are fed back to subsequent process controls, forming an iterative and extended closed-loop control mechanism that effectively blocks the transmission of error chains. Third, by comparing the spatial morphological differences between the as-built model and the design model, an objective acceptance report is generated, overcoming the limitations of subjective evaluation in traditional manual sampling inspections. In summary, the technical solution of this application, by constructing a closed-loop control process, collaboratively optimizes the construction accuracy, efficiency, and quality of irregular-shaped curtain wall steel structures.

[0065] Please see the appendix Figure 4 This application provides a closed-loop control device for segmented construction of irregularly shaped curtain wall steel structures. The device may include an acquisition module 401, a registration module 402, a conversion module 403, a scanning module 404, a loop module 405, and a report generation module 406, as detailed below:

[0066] The acquisition module 401 is used to perform multi-angle three-dimensional scanning of the components to be installed and the support frame at the construction site. By arranging positioning targets, a unified spatial coordinate system is established to acquire point cloud data containing the surface morphology of the components to be installed and the position of the support frame.

[0067] The registration module 402 is used to extract the splicing key points of the component to be assembled based on the point cloud data, and to perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model based on the splicing key points. It performs collision detection in the virtual environment and outputs a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled.

[0068] The conversion module 403 is used to convert the pre-assembled positioning dataset into a visual instruction for indicating the theoretical installation position, and to guide the component to be installed into the installed component by combining the real-time positioning and tracking data of the hoisting process.

[0069] The scanning module 404 is used to perform a local fine scan of the construction status, including the joint area of ​​the installed components, after the current component installation is completed, and to perform a re-inspection scan of newly arrived components to be installed.

[0070] The loop module 405 is used to repeatedly execute the registration module, conversion module and scanning module for subsequent components to be installed until all segmented components are installed.

[0071] The report generation module 406 is used to perform a 3D scan of the overall structure after completion, obtain the as-built point cloud model, compare the spatial morphological differences between the as-built point cloud model and the design model, and generate an acceptance report for quantitatively evaluating the construction quality.

[0072] From the above appendix Figure 4 As illustrated by the closed-loop control device for segmented construction of irregular-shaped curtain wall steel structures, on the one hand, spatial registration is performed based on point cloud extraction of key splicing points, achieving precise matching and collision prediction between components and the design model in a virtual environment. This transforms traditional on-site trial assembly into digital simulation, significantly reducing the number of physical adjustments. On the other hand, the virtual pre-assembly results are converted into visual instructions to guide the precise placement of components, realizing the controlled transition from components to installed components. Through phased scanning comparison and dynamic parameter correction, errors in installed components are fed back to subsequent process control, forming an iterative and extended closed-loop control mechanism that effectively blocks the transmission of error chains. Thirdly, by comparing the spatial morphological differences between the as-built model and the design model, an objective acceptance report is generated, ending the limitations of subjective evaluation in traditional manual sampling inspections. In summary, the technical solution of this application, by constructing a closed-loop control process, collaboratively optimizes the construction accuracy, efficiency, and construction quality of irregular-shaped curtain wall steel structures.

[0073] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. For example... Figure 5 As shown, the electronic device 5 in this embodiment mainly includes: a processor 50, a memory 51, and a computer program 52 stored in the memory 51 and executable on the processor 50, such as a program for a closed-loop control method for segmented construction of irregularly shaped curtain wall steel structures. When the processor 50 executes the computer program 52, it implements the steps in the above-described embodiment of the closed-loop control method for segmented construction of irregularly shaped curtain wall steel structures, for example... Figure 1 The steps S101 to S106 are shown. Alternatively, when the processor 50 executes the computer program 52, it implements the functions of each module / unit in the above-described device embodiments, for example... Figure 4 The functions of the acquisition module 401, registration module 402, conversion module 403, scanning module 404, loop module 405, and report generation module 406 are shown.

[0074] For example, the computer program 52 of the closed-loop control method for segmented construction of irregular curtain wall steel structure mainly includes: Step S101: Perform multi-angle three-dimensional scanning of the component to be installed and the supporting frame at the construction site, establish a unified spatial coordinate system by arranging positioning targets, and obtain point cloud data containing the surface morphology of the component to be installed and the position of the supporting frame; Step S102: Extract the splicing key points of the component to be installed based on the point cloud data containing the surface morphology of the component to be installed and the position of the supporting frame, and perform spatial matching and registration operation between the point cloud of the component to be installed and the design model according to the splicing key points, perform collision detection in the virtual environment, and output a pre-assembly positioning dataset containing the theoretical pose of the component to be installed; Step S103: Set the pre-assembly positioning data into a pre-assembly positioning dataset. The bit dataset is converted into visual instructions to indicate the theoretical installation position, and combined with real-time positioning and tracking data during the hoisting process to guide the placement of the components to be installed as installed components; Step S104: After the current component installation is completed, a local fine scan of the construction status, including the joint area of ​​the installed components, is performed, and a re-inspection scan is performed on the newly arrived components to be installed; Step S105: For subsequent components to be installed, steps S102 to S104 are repeated until all segmented components are installed; Step S106: A three-dimensional scan of the completed overall structure is performed to obtain the as-built point cloud model, and the spatial morphological differences between the as-built point cloud model and the design model are compared to generate an acceptance report for quantitatively evaluating the construction quality. The computer program 52 can be divided into one or more modules / units, one or more modules / units are stored in the memory 51 and executed by the processor 50 to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 52 in the electronic device 5.For example, the computer program 52 can be divided into the functions of an acquisition module 401, a registration module 402, a conversion module 403, a scanning module 404, a loop module 405, and a report generation module 406 (a module in the virtual device). The specific functions of each module are as follows: The acquisition module 401 is used to perform multi-angle three-dimensional scanning of the component to be assembled and the support frame at the construction site, establish a unified spatial coordinate system by arranging positioning targets, and acquire point cloud data containing the surface morphology of the component to be assembled and the position of the support frame; The registration module 402 is used to extract the splicing key points of the component to be assembled based on the point cloud data, and perform spatial matching and registration operations between the point cloud of the component to be assembled and the design model based on the splicing key points, perform collision detection in the virtual environment, and output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled. The conversion module 403 is used to convert the pre-assembly positioning dataset into visual instructions for indicating the theoretical installation position, and guides the components to be installed into the installed components by combining real-time positioning and tracking data during the hoisting process; the scanning module 404 is used to perform a local fine scan of the construction status, including the joint area of ​​the installed components, after the current component is installed, and to perform a re-inspection scan on newly arrived components to be installed; the loop module 405 is used to repeatedly execute the registration module, conversion module, and scanning module for subsequent components to be installed until all segmented components are installed; the report generation module 406 is used to perform a three-dimensional scan of the overall structure after completion, obtain the as-built point cloud model, compare the spatial morphological differences between the as-built point cloud model and the design model, and generate an acceptance report for quantitatively evaluating the construction quality.

[0075] Electronic device 5 may include, but is not limited to, processor 50 and memory 51. Those skilled in the art will understand that... Figure 5 This is merely an example of electronic device 5 and does not constitute a limitation on electronic device 5. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device may also include input / output devices, network access devices, buses, etc.

[0076] The processor 50 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0077] The memory 51 can be an internal storage unit of the electronic device 5, such as a hard disk or RAM. The memory 51 can also be an external storage device of the electronic device 5, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 51 can include both internal and external storage units of the electronic device 5. The memory 51 is used to store computer programs and other programs and data required by the electronic device. The memory 51 can also be used to temporarily store data that has been output or will be output.

[0078] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed. That is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above-described device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0079] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0080] Those skilled in the art will recognize that the units and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art 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.

[0081] In the embodiments provided in this application, it should be understood that the disclosed apparatus / device and method can be implemented in other ways. For example, the apparatus / device embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0082] The units described as separate components may or may not be physically separate. 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 the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0083] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0084] If integrated modules / units are implemented as software functional units and sold or used as independent products, they can be stored in a storage medium. Based on this understanding, all or part of the processes in the above-described embodiments of this application can also be implemented by a computer program instructing related hardware. The computer program for the closed-loop control method of segmented construction of irregular curtain wall steel structure can be stored in a storage medium. When the computer program is executed by a processor, it can implement the steps of the above-described method embodiments, namely, step S101: perform multi-angle three-dimensional scanning of the component to be installed and the supporting frame at the construction site, establish a unified spatial coordinate system by arranging positioning targets, and obtain point cloud data containing the surface morphology of the component to be installed and the position of the supporting frame; step S102: extract the splicing key points of the component to be installed based on the point cloud data containing the surface morphology of the component to be installed and the position of the supporting frame, and perform spatial matching and registration operation between the point cloud of the component to be installed and the design model based on the splicing key points in a virtual environment. Step S103: Perform collision detection and output a pre-assembly positioning dataset containing the theoretical pose of the component to be installed; Step S104: Convert the pre-assembly positioning dataset into a visual instruction to indicate the theoretical installation position, and combine it with real-time positioning and tracking data during the hoisting process to guide the component to be installed into the installed component; Step S105: After the current component is installed, perform a local fine scan of the construction status, including the joint area of ​​the installed component, and perform a re-inspection scan on the newly arrived component to be installed; Step S106: For subsequent components to be installed, repeat steps S102 to S104 until all segmented components are installed; Step S107: Perform a 3D scan of the completed overall structure to obtain the as-built point cloud model, compare the spatial morphological differences between the as-built point cloud model and the design model, and generate an acceptance report for quantitatively evaluating the construction quality. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. Storage media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the contents of storage media can be appropriately added to or removed according to the requirements of legislation and patent practice in a jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, storage media may not include electrical carrier signals and telecommunication signals.

[0085] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application. The specific embodiments described above further illustrate the purpose, technical solutions, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the protection scope of this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this invention.

Claims

1. A closed-loop control method for segmented construction of irregularly shaped curtain wall steel structures, characterized in that, The method includes: Step S101: Perform multi-angle 3D scanning of the component to be installed and the supporting jig at the construction site. Establish a unified spatial coordinate system by arranging positioning targets to obtain point cloud data containing the surface morphology of the component to be installed and the position of the supporting jig. The multi-angle 3D scanning of the component to be installed and the supporting jig at the construction site, and the establishment of a unified spatial coordinate system by arranging positioning targets to obtain point cloud data containing the surface morphology of the component to be installed and the position of the supporting jig, includes: setting up several scanning stations around the component unloading area to achieve coverage without blind spots; using a pass-through filtering algorithm to remove environmental noise, and using feature preservation technology to process the welding deformation area in the point cloud to retain the original geometric features; the establishment of a unified spatial coordinate system by arranging positioning targets includes: fixing a set of positioning targets with known spatial relationships in the scanning area, and using the positioning targets as spatial references; using a spatial analysis algorithm to calculate coordinate transformation parameters based on the target position data captured by each scanning station, so that the point cloud data acquired by all stations are uniformly aligned to the reference coordinate system fixedly associated with the supporting jig. Step S102: Extract the splicing key points of the component to be assembled based on the point cloud data, and perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model according to the splicing key points. Perform collision detection in the virtual environment and output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled. Step S103: Convert the pre-assembled positioning dataset into a visual instruction to indicate the theoretical installation position, and combine it with the real-time positioning and tracking data of the hoisting process to guide the component to be installed into the installed component. Step S104: After the current component installation is completed, a local fine scan is performed on the construction status, including the joint area of ​​the installed component, and a re-inspection scan is performed on the newly arrived components to be installed. Step S105: For subsequent components to be installed, repeat steps S102 to S104 until all segmented components are installed. Step S106: Perform a 3D scan of the completed overall structure to obtain the as-built point cloud model. Compare the spatial morphological differences between the as-built point cloud model and the design model to generate an acceptance report for quantitatively evaluating the construction quality.

2. The closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures according to claim 1, characterized in that, The method for extracting the key points of splicing in step S102 is as follows: The center point of the bolt hole group on the component to be installed is located using edge detection technology; and The theoretical contact area contour line of the welding end face of the component to be installed is extracted by boundary recognition technology.

3. The closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures according to claim 1, characterized in that, The spatial matching and registration operation is a non-rigid registration, and its optimization objective is: Minimize the distance deviation between the measured component point cloud and the corresponding points in the design model, while introducing a constraint mechanism to control non-physical deformation.

4. The closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures according to claim 1, characterized in that, The visualization instructions in step S103 include: Laser marks are projected at predetermined positions supporting the jig to indicate the theoretical installation position; and The deviation vector between the spatial position of the component during hoisting and the theoretical installation position is tracked and displayed in real time.

5. The closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures according to claim 1, characterized in that, The comparison of spatial morphological differences between the as-built point cloud model and the design model includes: calculating the degree of consistency of curvature changes on the weld paths between adjacent components.

6. A closed-loop control device for segmented construction of irregularly shaped curtain wall steel structures, characterized in that, The device is used to implement the closed-loop control method for segmented construction of irregular-shaped curtain wall steel structures as described in any one of claims 1 to 5, and the device comprises: The acquisition module is used to perform multi-angle three-dimensional scanning of the components to be installed and the support frame at the construction site. By setting up positioning targets, a unified spatial coordinate system is established to acquire point cloud data containing the surface morphology of the components to be installed and the position of the support frame. The registration module is used to extract the splicing key points of the component to be assembled based on the point cloud data, and to perform spatial matching and registration operation between the point cloud of the component to be assembled and the design model based on the splicing key points, to perform collision detection in the virtual environment, and to output a pre-assembly positioning dataset containing the theoretical pose of the component to be assembled. The conversion module is used to convert the pre-assembled positioning dataset into visual instructions for indicating the theoretical installation position, and combine it with real-time positioning and tracking data during the hoisting process to guide the component to be installed into the installed component. The scanning module is used to perform a local fine scan of the construction status, including the joint area of ​​the installed components, after the current component installation is completed, and to perform a re-inspection scan on newly arrived components to be installed. The loop module is used to repeatedly execute the registration module, conversion module, and scanning module for subsequent components to be installed until all segmented components are installed. The report generation module is used to perform a 3D scan of the overall structure after completion, obtain the as-built point cloud model, compare the spatial morphological differences between the as-built point cloud model and the design model, and generate an acceptance report for quantitatively evaluating the construction quality.

7. An electronic device, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 5.

8. A storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 5.

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