Self-adaptive multi-scene special-shaped structure scaffold building system and method

Abstract

The invention provides a self-adaptive multi-scene special-shaped structure scaffold building system and method. The core lies in collecting scene parameters through multi-source sensing fusion, precisely modeling a special-shaped structure, intelligently generating self-adaptive nodes, dynamically matching components and optimizing multiple targets. The method comprises the following steps: acquiring a special-shaped structure parameter and an environment constraint parameter of a target scene; constructing a three-dimensional model based on the parameters; generating an adaptive node distribution scheme; a scaffold assembly is matched, and an initial building model is constructed; an optimal building scheme is obtained through a multi-objective optimization algorithm; and performing automatic / semi-automatic building. Through deep fusion of mechanical modeling and an intelligent optimization algorithm, the problems that a traditional scaffold is poor in adaptability to a special-shaped structure, low in building efficiency and insufficient in safety are solved, personalized, efficient and safe building of the special-shaped structure scaffold under multiple scenes is achieved, and building flexibility and reusability are remarkably improved.

Description

Technical Field

[0001] This invention relates to the fields of building construction and temporary erection technology, specifically to an adaptive multi-scaffolding system and method for constructing irregular structures. Background Technology

[0002] As a core temporary support structure in building construction, stage construction, and temporary exhibitions, the quality of scaffolding construction directly affects construction safety, construction efficiency, and usage costs. With the diversified development of modern engineering design, the application of irregular structures (such as curved building facades, irregularly shaped stages, and customized exhibition installations) is becoming increasingly widespread, placing higher demands on the adaptability, flexibility, and safety of scaffolding.

[0003] However, existing technologies have many shortcomings:

[0004] Poor adaptability: Traditional scaffolding (such as coupler type and cup buckle type) is mainly designed for regular structures such as rectangles and cylinders. The components have low versatility. For irregular structures, special components need to be customized, resulting in high construction costs, long cycle and low component reuse rate.

[0005] Low construction efficiency: The construction of scaffolding for irregular structures relies on manual on-site measurement and hand-drawn plans to plan the distribution of nodes. This is not only labor-intensive, but also prone to insufficient construction accuracy due to human error, requiring repeated adjustments later.

[0006] Insufficient safety: Traditional construction methods rely on manual experience for node layout and component selection, lacking precise analysis of the mechanical properties of irregular structures. This can easily lead to problems such as uneven stress on nodes and insufficient overall stability, posing safety hazards.

[0007] Limitations in scenario adaptation: Existing technologies are unable to simultaneously meet the differentiated needs of different scenarios such as building construction (high load-bearing capacity, high stability), stage construction (rapid assembly and disassembly, lightweight), and temporary exhibitions (flexible adjustment, aesthetics), and lack an adaptive adjustment mechanism.

[0008] Therefore, how to provide a scaffolding construction solution that can accurately adapt to irregular structures, meet the needs of multiple scenarios, and improve construction efficiency and safety has become a technical problem that the industry urgently needs to solve. Summary of the Invention

[0009] This invention provides an adaptive multi-scaffolding system and method for constructing irregularly shaped structures, which can realize personalized, efficient, and safe construction of irregularly shaped scaffolding in various scenarios, solving the problems of poor adaptability, low efficiency, and insufficient safety of existing technologies.

[0010] To achieve the above objectives, the present invention is implemented through the following technical solution:

[0011] In a first aspect, the present invention provides an adaptive multi-scene irregular structure scaffolding erection method, comprising the following steps:

[0012] Collect the irregular structure parameters and environmental constraint parameters of the target scene;

[0013] A three-dimensional model of the irregular structure is constructed based on the aforementioned irregular structure parameters;

[0014] Based on the three-dimensional model of the irregular structure and environmental constraint parameters, a scaffolding node distribution scheme adapted to the irregular structure is generated.

[0015] Based on the node distribution scheme, match the corresponding scaffolding components and construct an initial scaffolding construction model;

[0016] The initial scaffolding model is dynamically optimized using a multi-objective optimization algorithm to obtain the optimal construction scheme.

[0017] The scaffolding is assembled automatically or semi-automatically according to the optimal assembly scheme.

[0018] As a further improvement to the technical solution of this invention, the collection of irregular structure parameters and environmental constraint parameters of the target scene specifically includes:

[0019] Three-dimensional point cloud data of the irregular structure is collected by a lidar sensor, and geometric parameters are extracted, including contour dimensions, surface curvature, and coordinates of key control points.

[0020] Environmental image data of the target scene is collected by a visual sensor, and environmental constraint parameters are extracted. The environmental constraint parameters include the location of spatial obstacles, the available building space range, and the lighting conditions.

[0021] Mechanical parameters of the irregularly shaped base structure are collected by strain gauge sensors. These mechanical parameters include load-bearing limit and vibration frequency.

[0022] The Kalman filter algorithm is used to fuse the geometric parameters, environmental constraint parameters, and base mechanical parameters to remove noise data and obtain a standardized parameter set.

[0023] As a further improvement to the technical solution of the present invention, constructing a three-dimensional model of the irregular structure based on the aforementioned irregular structure parameters specifically includes:

[0024] Extract feature control points of the irregular structure from the standardized parameter set. , ; Where m and n are the number of control points in the u and v directions, respectively;

[0025] The three-dimensional model of the irregular structure is constructed using the Bézier surface equation, which is:

[0026] ;

[0027] in, , Let be m-th degree Bessel basis functions, satisfying:

[0028] ;

[0029] in, Let be a combination number, satisfying: ;

[0030] The 3D point cloud data is fitted to a surface using the least squares method, and the feature control points are iteratively optimized. The coordinates of the model reduce the geometric error between the 3D model and the actual irregular structure. 0.5mm.

[0031] As a further improvement to the technical solution of the present invention, the generation of a scaffold node distribution scheme adapted to the irregular structure specifically includes:

[0032] Surface curvature based on the three-dimensional model Determine node density It satisfies the dynamic density equation:

[0033] ;

[0034] in, Reference node density ( ), The curvature coefficient ( );

[0035] Establish a set of force equilibrium equations for the nodes, the set of equations being:

[0036] , , , , , ;

[0037] in, , , The nodes are respectively , , The resultant external force in the direction, , , For each node , , The net external torque on the shaft;

[0038] Solving the system of force equilibrium equations yields the optimal spatial coordinates of each node. ;

[0039] Construct a node connection matrix ,in Represents a node With nodes There is a connection. This indicates no connection relationship, and the connection relationship satisfies the overall stability constraint of the scaffold (overturning coefficient). Bearing capacity safety factor ).

[0040] As a further improvement to the technical solution of the present invention, the matching of the corresponding scaffolding components based on the node distribution scheme specifically includes:

[0041] Establish a database of scaffolding components, including telescopic support rods (length adjustment range 1-3m, load-bearing capacity 1-5t) and adaptive rotating connectors (rotation angle). Torsional stiffness Adjustable base (height adjustment range 0-20cm, horizontal adjustment accuracy) 0.1mm);

[0042] Based on the node type (corner node, load-bearing node, ordinary node) and the force on the node. The K-nearest neighbor algorithm (K=3) is used to match the optimal component from the component database. The matching criteria are as follows:

[0043] ;

[0044] in, Weighting coefficients ( , , ), For the k-th parameter of the component, For the k-th requirement parameter of the node;

[0045] Based on the matching results, an initial scaffolding model is constructed to clarify the installation positions, connection methods, and tightening torques of the components.

[0046] As a further improvement to the technical solution of this invention, the dynamic optimization of the initial scaffolding construction model using a multi-objective optimization algorithm specifically includes:

[0047] The multi-objective optimization function is determined, with optimization objectives including safety (S), stability (T), economy (C), and construction efficiency (E). The fitness function is:

[0048] ;

[0049] in, , , , ,and ;

[0050] The security S satisfies:

[0051] ;

[0052] in; For the maximum force on the node, Rated load capacity of the component;

[0053] The stability T satisfies:

[0054] ;

[0055] Where d is the offset of the scaffold's center of gravity. To allow the maximum offset ( );

[0056] The economic efficiency C satisfies:

[0057] ;

[0058] in, Let $\frac{k}{k}$ be the unit price of the component of type k. The number of components of type k used;

[0059] The construction efficiency E satisfies:

[0060] ;

[0061] in; Total setup time;

[0062] use A multi-objective optimization algorithm is used to find the Pareto optimal solution, and the scheme with the largest overall fitness F is selected as the optimal construction scheme.

[0063] As a further improvement to the technical solution of the present invention, the automated or semi-automated construction of the scaffolding according to the optimal construction scheme specifically includes:

[0064] During automated assembly, the robotic arm performs component installation. Based on the coordinate information of the optimal assembly scheme, the attitude parameters of the robotic arm's end effector are calculated using inverse kinematics. The inverse kinematics equations are as follows: ;

[0065] in, This is the joint angle vector of the robotic arm. For Jacobian matrices, for The pseudo-inverse matrix, This is the spatial position vector of the end effector;

[0066] During semi-automated assembly, AR devices are used to overlay a 3D model of the optimal assembly plan onto the real-world scene, guiding workers to install step by step in real time.

[0067] The installation accuracy is detected in real time using a vision sensor, and when the component installation deviation occurs... When the deviation is 1mm, an audible and visual warning will be issued, and a deviation correction suggestion will be output.

[0068] A second aspect of the present invention provides an adaptive multi-scene irregular structure scaffolding erection system, comprising:

[0069] The parameter acquisition unit is used to acquire the irregular structure parameters and environmental constraint parameters of the target scene. The parameter acquisition unit includes a lidar module, a vision sensor module, a strain sensor module and a data fusion module.

[0070] A modeling unit is used to construct a three-dimensional model of the irregular structure based on the irregular structure parameters.

[0071] The node generation unit is used to generate a scaffold node distribution scheme that is adaptive to the irregular structure based on the three-dimensional model and environmental constraint parameters.

[0072] The component matching unit is used to match the corresponding scaffold components based on the node distribution scheme and to construct an initial scaffolding construction model.

[0073] The optimization unit is used to dynamically optimize the initial scaffolding construction model using a multi-objective optimization algorithm to obtain the optimal construction scheme.

[0074] An execution unit is set up to perform automated or semi-automated scaffolding construction according to the optimal construction scheme. The execution unit includes an automated construction module, an AR guidance module, and an accuracy detection module.

[0075] A third aspect of the present invention provides a computer device, the computer device including a memory and a processor, the memory storing code, characterized in that the processor is configured to acquire the code and execute the adaptive multi-scene irregular structure scaffolding construction method as described above.

[0076] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive multi-scene irregular structure scaffolding construction method described above.

[0077] The technical solution of the present invention has the following advantages over the prior art:

[0078] This invention presents an adaptive multi-scaffolding construction method for irregular structures. It accurately collects parameters of the irregular structure and environmental constraints through multi-source sensor fusion, and combines this with Bézier surface modeling to achieve high-precision fitting of complex irregular structures. Then, through dynamic node density calculation and force balance analysis, it generates an adaptive node distribution scheme. Combined with intelligent component matching and the NSGA-Ⅲ multi-objective optimization algorithm, it balances construction safety, stability, economy, and efficiency. Finally, it is implemented through automated or semi-automated construction modes. This method not only overcomes the limitations of traditional scaffolding in terms of poor adaptability to irregular structures and reliance on manual experience, enabling flexible adaptation to various scenarios such as building construction, stage construction, and temporary exhibitions, but also significantly improves construction accuracy and efficiency while reducing construction costs. Furthermore, through mechanical analysis and multi-objective optimization, it ensures a high safety standard for the scaffolding, with an overturning resistance coefficient ≥1.5 and a load-bearing capacity safety factor ≥2.0. This achieves an integrated solution for personalized, efficient, and safe construction of irregular structure scaffolding. Attached Figure Description

[0079] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0080] Figure 1 This is a schematic diagram of the framework process of an adaptive multi-scene irregular structure scaffolding construction method according to an embodiment of the present invention;

[0081] Figure 2 This is a schematic diagram of the modular framework of an adaptive multi-scene irregular structure scaffolding erection system according to an embodiment of the present invention;

[0082] Figure 3 This is a schematic diagram of the composition of a computing device according to an embodiment of the present invention. Detailed Implementation

[0083] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0084] The present invention will be further described in detail below with reference to the accompanying drawings.

[0085] Reference Figure 1 In a first aspect, the present invention provides an adaptive multi-scene irregular structure scaffolding erection method, comprising the following steps:

[0086] Collect the irregular structure parameters and environmental constraint parameters of the target scene;

[0087] A three-dimensional model of the irregular structure is constructed based on the aforementioned irregular structure parameters;

[0088] Based on the three-dimensional model of the irregular structure and environmental constraint parameters, a scaffolding node distribution scheme adapted to the irregular structure is generated.

[0089] Based on the node distribution scheme, match the corresponding scaffolding components and construct an initial scaffolding construction model;

[0090] The initial scaffolding model is dynamically optimized using a multi-objective optimization algorithm to obtain the optimal construction scheme.

[0091] The scaffolding is assembled automatically or semi-automatically according to the optimal assembly scheme.

[0092] It should be noted that this invention achieves adaptive construction of irregularly shaped scaffolding in multiple scenarios through six core steps. First, it collects the irregularly shaped structural parameters and environmental constraint parameters of the target scenario to obtain the structural geometric characteristics, mechanical properties, and scene limitations. Based on the collected parameters, a 3D model of the irregularly shaped structure is constructed to accurately reproduce its form. Then, based on the 3D model and environmental constraints, a scaffolding node distribution scheme adapted to the irregularly shaped structure is generated, clarifying the node positions and connection relationships. Based on the node distribution scheme, suitable scaffolding components are matched from the component database to construct an initial construction model. The initial model is dynamically optimized using a multi-objective optimization algorithm to balance safety, stability, economy, and construction efficiency, resulting in the optimal construction scheme. Finally, based on the optimal scheme, automated equipment or AR-guided manual execution of the scaffolding construction operation is used.

[0093] This invention breaks through the limitations of traditional scaffolding construction, which relies on manual experience and is only suitable for regular structures. Through an integrated process of parameter collection, precise modeling, intelligent node generation, component matching, and multi-objective optimization, it achieves adaptive adaptation to irregular structures in various scenarios such as building construction, stage construction, and temporary exhibitions. It significantly improves the personalization and accuracy of construction while taking into account safety, efficiency, and cost. It provides a systematic and intelligent solution for the construction of irregular structure scaffolding, solving the core problems of poor adaptability, low efficiency, and insufficient safety of existing technologies.

[0094] In some embodiments, collecting the irregular structure parameters and environmental constraint parameters of the target scene specifically includes:

[0095] Three-dimensional point cloud data of the irregular structure is collected by a lidar sensor, and geometric parameters are extracted, including contour dimensions, surface curvature, and coordinates of key control points.

[0096] Environmental image data of the target scene is collected by a visual sensor, and environmental constraint parameters are extracted. The environmental constraint parameters include the location of spatial obstacles, the available building space range, and the lighting conditions.

[0097] Mechanical parameters of the irregularly shaped base structure are collected by strain gauge sensors. These mechanical parameters include load-bearing limit and vibration frequency.

[0098] The Kalman filter algorithm is used to fuse the geometric parameters, environmental constraint parameters, and base mechanical parameters to remove noise data and obtain a standardized parameter set.

[0099] In practice, a multi-source sensor collaborative data acquisition method is adopted. The lidar sensor collects 3D point cloud data of the irregular structure and extracts geometric parameters such as contour size and surface curvature. The vision sensor collects scene environment image data and obtains environmental constraint parameters such as obstacle position and available space. The strain gauge sensor collects mechanical parameters such as the load-bearing limit and vibration frequency of the structural base. Then, the Kalman filter algorithm is used to fuse the data collected by the multi-source sensors. Data noise is removed through state update and measurement update equations, and a standardized parameter set is output to provide accurate and reliable data support for subsequent modeling and scheme design.

[0100] This invention achieves comprehensive coverage of information on irregular structures and scenes through the collaborative acquisition of data from multiple sources of sensors, avoiding the limitations of data collected by a single sensor. The application of the Kalman filter algorithm effectively filters out data noise, improves the accuracy and reliability of parameter data, ensures the precision of subsequent steps such as 3D modeling and node planning, lays a data foundation for the rationality and safety of the overall construction scheme, and reduces construction adjustments or safety hazards caused by data errors.

[0101] In some embodiments, constructing a three-dimensional model of the irregular structure based on the irregular structure parameters specifically includes:

[0102] Extract feature control points of the irregular structure from the standardized parameter set. , ; Where m and n are the number of control points in the u and v directions, respectively;

[0103] The three-dimensional model of the irregular structure is constructed using the Bézier surface equation, which is:

[0104] ;

[0105] in, , Let be m-th degree Bessel basis functions, satisfying:

[0106] ;

[0107] in, Let be a combination number, satisfying: ;

[0108] The 3D point cloud data is fitted to a surface using the least squares method, and the feature control points are iteratively optimized. The coordinates of the model reduce the geometric error between the 3D model and the actual irregular structure. 0.5mm.

[0109] It should be noted that feature control points of the irregular structure are extracted from the standardized parameter set. Based on the control points, a three-dimensional model is constructed using the Bézier surface equation. By calculating the Bézier basis function and combinatorial number, flexible fitting of the complex surface is achieved. The least squares method is used to optimize the surface fitting of the three-dimensional point cloud data. The coordinates of the feature control points are iteratively adjusted to minimize the geometric error between the model and the actual structure, ensuring that the three-dimensional model can accurately reproduce the actual shape of the irregular structure, and the geometric error is controlled within the preset range.

[0110] This invention utilizes Bézier surface modeling technology to accurately fit irregularly shaped structures with any complex form, solving the problem that traditional modeling methods struggle to reproduce irregular surfaces. The fitting optimization process further improves model accuracy, ensuring that the geometric errors between the 3D model and the actual structure meet the construction requirements. This provides a precise structural reference for subsequent node distribution and component matching, ensuring the fit between the scaffolding and the irregular structure, and enhancing the stability and reliability of the construction.

[0111] In some embodiments, generating an adaptive scaffold node distribution scheme for the irregular structure specifically includes:

[0112] Surface curvature based on the three-dimensional model Determine node density It satisfies the dynamic density equation:

[0113] ;

[0114] in, Reference node density ( ), The curvature coefficient ( );

[0115] Establish a set of force equilibrium equations for the nodes, the set of equations being:

[0116] , , , , , ;

[0117] in, , , The nodes are respectively , , The resultant external force in the direction, , , For each node , , The net external torque on the shaft;

[0118] Solving the system of force equilibrium equations yields the optimal spatial coordinates of each node. ;

[0119] Construct a node connection matrix ,in Represents a node With nodes There is a connection. This indicates no connection relationship, and the connection relationship satisfies the overall stability constraint of the scaffold (overturning coefficient). Bearing capacity safety factor ).

[0120] In practical implementation, based on the surface curvature of the 3D model, the node density is determined through a dynamic density equation. The greater the surface curvature, the higher the node density, ensuring the support strength in complex structural areas. A set of node force balance equations is established, considering various forces such as gravity, wind load, and construction load, to obtain the optimal spatial coordinates of each node. A node connection relationship matrix is ​​constructed to clarify the connection logic between nodes, while simultaneously meeting the constraints of the overturning resistance coefficient and the bearing capacity safety factor, ensuring that the node distribution adapts to the irregular structural form and guarantees the overall stability of the scaffolding. The dynamic adjustment of node density enables precise adaptation to the support needs of different areas of the irregular structure, avoiding insufficient local support or resource waste caused by uniform distribution. Force balance analysis and stability constraints ensure the mechanical rationality of the node distribution, keeping the force on each node in a balanced state, improving the overall stability and bearing capacity of the scaffolding, reducing the risk of overturning, and providing core protection for safe use after construction.

[0121] In some embodiments, matching the corresponding scaffolding components based on the node distribution scheme specifically includes:

[0122] Establish a database of scaffolding components, including telescopic support rods (length adjustment range 1-3m, load-bearing capacity 1-5t) and adaptive rotating connectors (rotation angle). Torsional stiffness Adjustable base (height adjustment range 0-20cm, horizontal adjustment accuracy) 0.1mm);

[0123] Based on the node type (corner node, load-bearing node, ordinary node) and the force on the node. The K-nearest neighbor algorithm (K=3) is used to match the optimal component from the component database. The matching criteria are as follows:

[0124] ;

[0125] in, Weighting coefficients ( , , ), For the k-th parameter of the component, For the k-th requirement parameter of the node;

[0126] Based on the matching results, an initial scaffolding model is constructed to clarify the installation positions, connection methods, and tightening torques of the components.

[0127] In practical implementation, a scaffolding component database is pre-established, containing core components such as retractable support rods, adaptive rotating connectors, and adjustable bases. Each component is associated with parameters such as dimensions, load-bearing capacity, stiffness, and compatible node type. Based on the node type (corner node, load-bearing node, ordinary node) and stress conditions, the K-nearest neighbor algorithm (K=3) is used for component matching. With load-bearing capacity, size, and stiffness compatibility as core criteria, the compatibility deviation between the component and node requirements is calculated through weight allocation, and the component with the smallest deviation is selected as the optimal matching component. Based on the matching results, the component installation position, connection method, and tightening torque are determined, and an initial scaffolding erection model is constructed. The establishment of the component database enables standardized management of scaffolding components and improves component reusability. The application of the K-nearest neighbor algorithm ensures accurate matching between components and node requirements, avoiding insufficient support or resource waste caused by improper component selection. The clear initial erection model provides a clear operational basis for subsequent optimization and erection execution, reducing uncertainty in the erection process, improving erection efficiency and quality, and simultaneously reducing component wear and erection costs.

[0128] In some embodiments, dynamically optimizing the initial scaffolding model using a multi-objective optimization algorithm specifically includes:

[0129] The multi-objective optimization function is determined, with optimization objectives including safety (S), stability (T), economy (C), and construction efficiency (E). The fitness function is:

[0130] ;

[0131] in, , , , ,and ;

[0132] The security S satisfies:

[0133] ;

[0134] in; For the maximum force on the node, Rated load capacity of the component;

[0135] The stability T satisfies:

[0136] ;

[0137] Where d is the offset of the scaffold's center of gravity. To allow the maximum offset ( );

[0138] The economic efficiency C satisfies:

[0139] ;

[0140] in, Let $\frac{k}{k}$ be the unit price of the component of type k. The number of components of type k used;

[0141] The construction efficiency E satisfies:

[0142] ;

[0143] in; Total setup time;

[0144] use A multi-objective optimization algorithm is used to find the Pareto optimal solution, and the scheme with the largest overall fitness F is selected as the optimal construction scheme.

[0145] It should be noted that the core objectives of multi-objective optimization are clearly defined, including safety, stability, economy, and construction efficiency. A comprehensive fitness function is constructed, and the importance of each objective is balanced through weight allocation. Safety is calculated based on the ratio of the maximum force on a node to the rated load of a component; stability is calculated based on the scaffolding's center of gravity offset; economy is the total procurement cost of components; and construction efficiency is the total construction time. A multi-objective optimization algorithm is used to find the Pareto optimal solution. By setting parameters such as population size, number of iterations, crossover probability, and mutation probability, the algorithm selects the scheme with the highest overall fitness as the optimal construction scheme. This invention achieves a balance between the core performance aspects of the construction scheme through a multi-objective optimization algorithm, avoiding the trade-offs caused by single-objective optimization (such as sacrificing security for the sake of minimum cost). The algorithm can efficiently solve multi-objective optimization problems. The optimal solution output takes into account safety, stability, economy and construction efficiency, ensuring that the overall performance of the construction solution is optimal. It not only meets the safety requirements, but also controls costs, improves efficiency, and enhances the practicality and promotion value of the solution.

[0146] In some embodiments, the automated or semi-automated erection of the scaffolding according to the optimal erection scheme specifically includes:

[0147] During automated assembly, the robotic arm performs component installation. Based on the coordinate information of the optimal assembly scheme, the attitude parameters of the robotic arm's end effector are calculated using inverse kinematics. The inverse kinematics equations are as follows: ;

[0148] in, This is the joint angle vector of the robotic arm. For Jacobian matrices, for The pseudo-inverse matrix, This is the spatial position vector of the end effector;

[0149] During semi-automated assembly, AR devices are used to overlay a 3D model of the optimal assembly plan onto the real-world scene, guiding workers to install step by step in real time.

[0150] The installation accuracy is detected in real time using a vision sensor, and when the component installation deviation occurs... When the deviation is 1mm, an audible and visual warning will be issued, and a deviation correction suggestion will be output.

[0151] It should be noted that during automated assembly, based on the coordinate information of the optimal assembly plan, the joint angle vectors of the robotic arm are calculated through inverse kinematics equations. This controls the end effector of the robotic arm to install components according to preset postures and positions, ensuring installation accuracy. During semi-automated assembly, an AR device overlays a 3D model of the optimal assembly plan onto the real-world scene, displaying the component installation sequence, position markers, and operation instructions in real time, guiding workers to perform the installation step by step. During the assembly process, visual sensors monitor the component installation accuracy in real time. When the deviation exceeds a preset threshold, an audible and visual warning is issued, along with deviation correction suggestions, ensuring installation quality. The automated assembly mode significantly improves assembly efficiency and reduces manual labor intensity, while the high-precision operation of the robotic arm ensures the accuracy of component installation. The AR-guided semi-automated assembly mode balances flexibility and accuracy, making it suitable for complex areas or scenarios where automated equipment is difficult to operate, reducing reliance on workers' professional skills. The real-time accuracy detection and warning mechanism can promptly detect and correct installation deviations, avoiding the accumulation of deviations that could lead to overall assembly quality problems, and ensuring the installation accuracy and safety of the scaffolding.

[0152] Reference Figure 2The second aspect of the present invention provides an adaptive multi-scene irregular structure scaffolding erection system, comprising:

[0153] The parameter acquisition unit is used to acquire the irregular structure parameters and environmental constraint parameters of the target scene. The parameter acquisition unit includes a lidar module, a vision sensor module, a strain sensor module and a data fusion module.

[0154] A modeling unit is used to construct a three-dimensional model of the irregular structure based on the irregular structure parameters.

[0155] The node generation unit is used to generate a scaffold node distribution scheme that is adaptive to the irregular structure based on the three-dimensional model and environmental constraint parameters.

[0156] The component matching unit is used to match the corresponding scaffold components based on the node distribution scheme and to construct an initial scaffolding construction model.

[0157] The optimization unit is used to dynamically optimize the initial scaffolding construction model using a multi-objective optimization algorithm to obtain the optimal construction scheme.

[0158] An execution unit is set up to perform automated or semi-automated scaffolding construction according to the optimal construction scheme. The execution unit includes an automated construction module, an AR guidance module, and an accuracy detection module.

[0159] It should be noted that this system achieves adaptive assembly of irregularly shaped scaffolding through the collaborative work of its various functional units. The parameter acquisition unit collects data through a multi-source sensor module and processes it through a data fusion module to output standardized parameters; the modeling unit constructs a 3D model of the irregular structure based on these parameters; the node generation unit generates an adaptive node distribution scheme based on the model and environmental constraints; the component matching unit matches components from a component database and constructs an initial assembly model; the optimization unit outputs the optimal assembly scheme through a multi-objective optimization algorithm; and the assembly execution unit executes the assembly operation through an automated assembly module and an AR guidance module, with the installation quality monitored in real time by a precision detection module. All units work together seamlessly and collaboratively to complete the entire process from data acquisition to final assembly.

[0160] The system of this invention realizes an integrated and modular design for the construction of irregularly shaped scaffolding. Each unit has a clear function and works together efficiently, reducing the coupling between different stages and facilitating maintenance and upgrades. By combining hardware modules and software algorithms, the steps of data acquisition, modeling, planning, optimization, and construction are integrated into a unified solution, eliminating the need for multiple independent devices or systems. This improves the continuity and convenience of the construction process, while ensuring the technical compatibility of each stage, further guaranteeing the accuracy, safety, and efficiency of the construction solution.

[0161] Reference Figure 3 The third aspect of the present invention provides a computer device, the computer device including a memory and a processor, the memory storing code, characterized in that the processor is configured to acquire the code and execute the adaptive multi-scene irregular structure scaffolding construction method as described above.

[0162] It should be noted that the computer device's memory stores the code for implementing the adaptive multi-scene irregular structure scaffolding construction method, including the program logic for each step such as parameter acquisition, modeling, node generation, component matching, optimization, and construction execution. The processor retrieves and executes this code from memory, utilizing hardware resources (such as computing units and communication interfaces) to perform functions such as multi-source data processing, Bézier surface modeling, multi-objective optimization algorithm solving, and construction control command output, supporting the implementation of the entire construction method. This provides a stable and efficient hardware operating environment for the adaptive multi-scene irregular structure scaffolding construction method, and the processor's computing power ensures the smooth execution of complex algorithms (such as Kalman filtering, etc.). The optimization algorithm enables rapid solution, and the storage capacity of the memory ensures the secure storage and rapid retrieval of large-scale data (such as 3D point cloud data and component database information). Through the collaboration of hardware and software, the construction method can be run efficiently and stably, adapting to the construction needs of irregular structures of different scales and complexities, thereby improving the practicality and operability of the method.

[0163] In some embodiments, the adaptive multi-scaffolding construction method for irregular structures in the above embodiments can be implemented by a computer device, which includes at least one processor, a communication bus, a memory, and at least one communication interface.

[0164] A processor can be a general-purpose central processing unit (CPU) or an application-specific integrated circuit (ASIC).

[0165] A communication bus can be used to transmit information between the aforementioned components.

[0166] The memory can be read-only memory (ROM) or other types of static storage devices capable of storing static information and instructions, random access memory (RAM) or other types of dynamic storage devices capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, universal optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited to these. The memory can exist independently and be connected to the processor via a communication bus. The memory can also be integrated with the processor.

[0167] The memory stores program code for executing the solution of this application, and its execution is controlled by a processor. The processor executes the program code stored in the memory. The program code may include one or more software modules. In the above embodiments, the adaptive multi-scene irregular structure scaffolding construction method can be implemented by a processor and one or more software modules in the program code in the memory.

[0168] A communication interface is a device that uses any transceiver or similar device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0169] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0170] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device can be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0171] A fourth aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adaptive multi-scene irregular structure scaffolding construction method described above.

[0172] It should be noted that the computer-readable storage medium stores a computer program that implements the adaptive multi-scene irregular structure scaffolding construction method. This program includes execution logic for each step, such as parameter acquisition, 3D modeling, node generation, component matching, multi-objective optimization, and construction execution. When the computer program is called and executed by the processor, the processor sequentially completes operations such as data acquisition and fusion, model building, node planning, component matching, model optimization, and construction control according to the instructions in the program, ultimately achieving adaptive construction of the irregular structure scaffolding. This facilitates the storage, transmission, and deployment of the adaptive multi-scene irregular structure scaffolding construction method, allowing for reuse on different computer devices and improving the method's versatility and scalability. The stability of the storage medium ensures the secure preservation of the computer program, preventing program loss or damage. Through standardized storage and execution of the program, the consistency of the construction method's execution on different devices is ensured, facilitating technology promotion and application and reducing the cost of implementing the method.

[0173] To provide a clearer understanding of the invention, the invention is further described below:

[0174] Reference Figure 1 In a first aspect, this application provides an adaptive multi-scene irregular structure scaffolding erection method, comprising the following steps:

[0175] Collect the irregular structure parameters and environmental constraint parameters of the target scene:

[0176] By employing multi-source sensor fusion technology, the geometric and mechanical properties of irregularly shaped structures, as well as environmental constraints, are comprehensively collected, providing accurate data support for subsequent modeling and solution design. Specifically, this includes:

[0177] LiDAR data acquisition: A Velodyne VLP-16 LiDAR sensor was used to acquire 3D point cloud data of the irregular structure at a frequency of 10Hz. Point cloud density... 100 points Extract geometric parameters such as contour dimensions, surface curvature, and key control point coordinates;

[0178] Visual sensor acquisition: An Intel RealSense D455 RGB-D camera is used to acquire color and depth images of the target scene. Environmental constraint parameters such as the location of spatial obstacles, the available building space, and lighting conditions are extracted through image segmentation algorithms.

[0179] Mechanical parameter acquisition: A BX120-3AA strain gauge sensor was used, which was attached to key locations on the irregularly shaped substrate to collect mechanical parameters of the substrate, such as load-bearing limit and vibration frequency. The measurement accuracy was high. FS;

[0180] Data fusion: The Kalman filter algorithm is used to fuse multi-source data. The state update equation of the Kalman filter is:

[0181] ;

[0182] The measurement update equation is:

[0183] ;

[0184] ;

[0185] ;

[0186] in Here, A is the state estimate, A is the state transition matrix, and B is the control input matrix. To control the input, K is the Kalman gain, P is the covariance matrix, H is the observation matrix, Z is the observation value, R is the observation noise covariance, and I is the identity matrix. This algorithm removes data noise and obtains a standardized parameter set.

[0187] Construct a three-dimensional model of the irregular structure based on the aforementioned irregular structure parameters:

[0188] The Bézier surface equation is used to achieve accurate modeling of irregular structures, enabling flexible fitting of arbitrarily complex surfaces. Specific steps:

[0189] Control point extraction: Feature control points of irregular structures are extracted from 3D point cloud data in a standardized parameter set using the RANSAC algorithm. ( ), m and n are taken as values ​​from 5 to 20 according to the complexity of the irregular structure;

[0190] Bézier surface modeling: construction The equation for a sub-Bézier surface is:

[0191] ,

[0192] Where Bessel basis functions satisfy:

[0193] ;

[0194] Combinations satisfy:

[0195] ;

[0196] For example, when , hour, This describes the weights of the control points' influence on the surface.

[0197] Surface fitting optimization: Minimize the fitting error function using the least squares method.

[0198] ;

[0199] in, For sampling points in point cloud data, iteratively optimize feature control points. The coordinates of the model reduce the geometric error between the 3D model and the actual irregular structure. 0.5mm, to ensure modeling accuracy.

[0200] Generate an adaptive scaffold node distribution scheme for the irregular structure:

[0201] Based on the geometric properties and mechanical constraints of the 3D model, intelligent node distribution is achieved, ensuring the fit and stability of the scaffolding with irregular structures. Specific steps:

[0202] Node density is dynamically determined based on the surface curvature k of the 3D model (unit: The node density is determined using a dynamic density equation. (Unit: individuals) ):

[0203] ;

[0204] in The baseline node density ranges from 0.5 to 1. , The curvature coefficient has a value of [value missing]. The greater the surface curvature, the higher the node density, ensuring the support strength in complex areas;

[0205] Solving for nodal coordinates: Establish a system of force equilibrium equations for the nodal forces, considering gravity, wind loads, construction loads, etc. The system of equations is as follows:

[0206] , , ;

[0207] , , ;

[0208] in , , These represent the net external forces at the nodes in the x, y, and z directions (unit: N). , , These are the net external moments of the nodes about the x, y, and z axes, respectively (unit: The optimal spatial coordinates of each node are obtained by solving the system of equations using the Newton-Raphson method. ;

[0209] Connection relationship construction: Constructing a node connection relationship matrix n is the total number of nodes. Represents a node With nodes There is a connection. This indicates no connection relationship; any connection relationship must satisfy the overall stability constraint of the scaffolding: overturning resistance coefficient. 1.5, bearing capacity safety factor 2.0, ensuring structural safety.

[0210] Based on the node distribution scheme, match the corresponding scaffolding components and construct an initial scaffolding erection model:

[0211] The optimal component is selected through an intelligent matching algorithm to achieve precise adaptation between components and nodes. Specific steps:

[0212] Component Database Setup: Construct a scaffolding component database, containing three core component categories:

[0213] Telescopic support rod: Length adjustment range 1-3m, adjustment precision 0.5mm thick, load-bearing capacity 1-5t, made of high-strength aluminum alloy;

[0214] Adaptive rotary connector: rotation angle Torsional stiffness It supports multi-directional angle adjustment;

[0215] Adjustable base: Height adjustment range 0-20cm, horizontal adjustment accuracy 0.1mm thick, with anti-slip and shock-absorbing functions;

[0216] Each component is associated with parameters including dimensions, load-bearing capacity, stiffness, and compatible node type;

[0217] Intelligent component matching: based on node type (corner node, load-bearing node, ordinary node) and node stress. The K-nearest neighbor algorithm (K=3) is used for component matching, and the matching criteria are as follows:

[0218] ;

[0219] in (Weight-bearing adaptation weight). (Size adaptation weight) (Stiffness adaptation weight) For the k-th parameter of the component, For the k-th requirement parameter of the node, The smallest component is the optimal matching component;

[0220] Initial Model Construction: Based on the matching results, BIM technology was used to construct the initial scaffolding model, clarifying the installation positions, connection methods, and fastening torques of components (for ordinary nodes). load-bearing nodes ).

[0221] The initial construction model is dynamically optimized using a multi-objective optimization algorithm to obtain the optimal construction scheme:

[0222] A multi-objective optimization algorithm is used to balance security, stability, economy, and construction efficiency to obtain a solution with optimal overall performance. Specific steps:

[0223] Optimization target definition:

[0224] Safety S: Based on the ratio of the maximum force on a node to the rated load capacity of the component. The value ranges from 0 to 1, and the closer it is to 1, the higher the security.

[0225] Stability T: Based on the offset of the scaffold's center of gravity. d is the centroid offset. To allow the maximum offset ( The value ranges from 0 to 1, and the closer it is to 1, the higher its stability.

[0226] Economic efficiency C: Total component cost , Let $\frac{k}{k}$ be the unit price of the component of type k. For quantities used, the smaller the value, the better the economic efficiency;

[0227] Efficiency E: Total setup time (in hours), the smaller the value, the higher the efficiency;

[0228] Fitness function construction:

[0229] ;

[0230] in , , , ,and A larger F-value indicates better overall performance of the solution;

[0231] Optimized solution: using A multi-objective optimization algorithm is used, with a population size of 100, 50 iterations, a crossover probability of 0.8, and a mutation probability of 0.05. The Pareto optimal solution is found, and the scheme with the largest F-value is selected as the optimal construction scheme.

[0232] Automated or semi-automated scaffolding construction will be performed according to the optimal construction plan:

[0233] By combining automated equipment with AR technology, efficient and accurate construction can be achieved. Specific steps:

[0234] Automated assembly: The ABB IRB 1200 robotic arm actuator assembly was installed. Based on the coordinate information of the optimal assembly scheme, the attitude parameters of the robotic arm end effector were calculated through inverse kinematics. The inverse kinematics equations are as follows:

[0235] ;

[0236] in This is the joint angle vector of the robotic arm (6-dimensional). for Jacobian matrix for The pseudo-inverse matrix, where x is the spatial position vector (3D) of the end effector, and the repeatability of the robotic arm. 0.02mm, ensuring precise component installation;

[0237] Semi-automated assembly: Using HoloLens2 AR devices, the 3D model of the optimal assembly solution is overlaid onto the real scene, and the component installation sequence, position marks and operation instructions are displayed in real time to guide workers to install step by step;

[0238] Accuracy Inspection: A Basler acA2500-14gm vision sensor is used to detect the component installation accuracy in real time. When installation deviations occur... When the deviation is 1mm, an audible and visual warning is issued, and deviation correction suggestions are output through AR devices to ensure the quality of the construction.

[0239] Reference Figure 2 Secondly, this application provides an adaptive multi-scene irregular structure scaffolding erection system, the system comprising:

[0240] Parameter acquisition unit: used to acquire the irregular structure parameters and environmental constraint parameters of the target scene, including the lidar module (Velodyne VLP-16), the vision sensor module (Intel RealSense D455), the strain sensor module (BX120-3AA), and the data fusion module (based on Kalman filter algorithm).

[0241] Modeling Unit: Used to construct 3D models based on the parameters of irregular structures. Its core is the Bézier surface modeling algorithm, which enables accurate fitting of irregular structures.

[0242] Node generation unit: used to generate adaptive node distribution schemes, including a node density dynamic calculation module, a force balance solution module, and a connection relationship construction module;

[0243] Component matching unit: used to match scaffolding components and build an initial assembly model, including a component database, a K-nearest neighbor matching module, and a BIM modeling module;

[0244] Optimization Unit: Used for dynamic optimization of the initial model; its core is... Multi-objective optimization algorithm module;

[0245] Assembly execution unit: used to perform assembly operations, including automated assembly module (ABB IRB 1200 robotic arm), AR guidance module (HoloLens2) and accuracy detection module (Basler acA2500-14gm).

[0246] To better understand the technical solution of this application, the application will be described in detail below with reference to specific embodiments.

[0247] Example: Scaffolding erection for curved building facades

[0248] Scene parameter acquisition:

[0249] The Velodyne VLP-16 LiDAR was used to acquire 3D point cloud data of the facade of a curved building. The facade is a hyperboloid structure with an outline dimension of 50m in length and 30m in height, and the curvature of the surface is [not specified]. ;

[0250] The scene environment data was collected using an Intel RealSense D455 camera, which identified that there were no obstacles within 3m around the building, there was ample space available for construction, and the lighting conditions were good.

[0251] The load-bearing limit of the substrate was collected using a BX120-3AA strain gauge sensor. The vibration frequency is 5Hz;

[0252] By fusing multi-source data using the Kalman filter algorithm, a standardized parameter set is obtained, achieving a data noise removal rate of 95%. .

[0253] 3D model construction:

[0254] Extract 20 feature control points (m=5, n=4) and construct The sub-Bézier surface model uses the Bézier basis functions. and ;

[0255] By fitting the point cloud data using the least squares method and iterating 10 times, the geometric error between the model and the actual building is 0.3 mm, which meets the construction accuracy requirements.

[0256] Node distribution scheme generation:

[0257] Reference node density curvature coefficient Through the dynamic density equation The total number of nodes is determined to be indivual;

[0258] Establish a set of force equilibrium equations for the nodes, considering gravity (component self-weight 20 N / m) and wind load ( The spatial coordinates of each node are obtained by solving the problem, and the node density at the corner is... It is higher than the planar area;

[0259] Construct a connection matrix to ensure that after all nodes are connected, the scaffolding's overturning resistance coefficient is 1.8 and its load-bearing capacity safety factor is 2.2, thus meeting the stability requirements.

[0260] Component matching and initial model building:

[0261] Match components from the component database: match the corner node with a retractable support rod with a load capacity of 3t and... Rotary connectors, standard nodes are matched with support rods and fixed connectors with a load capacity of 1t;

[0262] The initial construction model was built using BIM technology, specifying the adjustment range of the support rod length as 1.2-2.5m and the fastening torque of the connectors for ordinary nodes. load-bearing nodes .

[0263] Dynamic optimization:

[0264] use The algorithm was optimized with a population size of 100 and 50 iterations to obtain the Pareto optimal solution.

[0265] The fitness function of the optimal solution:

[0266] (Note: The economic and efficiency terms are calculated after normalization and need to be standardized in actual optimization.) The solution has a safety S=0.92, stability T=0.95, total component cost of 12,000 yuan, construction time of 8 hours, and the best overall performance.

[0267] Setup and execution:

[0268] The ABB IRB 1200 robotic arm was used for automated assembly. Inverse kinematics control was employed to install the components, achieving high installation accuracy. 0.08mm;

[0269] In some complex areas, a semi-automated setup was adopted. Workers received installation instructions through HoloLens 2 AR devices, and visual sensors detected installation deviations in real time. 0.8mm, no warning generated;

[0270] After the scaffolding was erected, an inspection revealed an overall verticality deviation. 3mm, load-bearing capacity meets design requirements ( It successfully adapts to the support requirements of curved building facades.

[0271] This embodiment verifies the feasibility and superiority of the method of this application, which can efficiently and accurately complete the construction of irregular structure scaffolding, and is significantly better than the traditional construction scheme.

[0272] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0273] Highly adaptable: By combining LiDAR with Bézier surface modeling, it achieves precise adaptation to arbitrary irregular structures without the need for customized dedicated components, increasing component reusability by 60%. The above-mentioned applicable scenarios cover building construction, stage construction, temporary exhibitions, etc.

[0274] Construction efficiency is significantly improved: automated node planning and component matching replace manual measurement and planning, shortening the construction cycle. And the installation accuracy has been improved to 1mm, reducing the amount of subsequent adjustments required;

[0275] Safety and stability assurance: Through nodal force balance analysis and multi-objective optimization, the overturning resistance coefficient of the scaffolding is ensured. 1.5, bearing capacity safety factor Version 2.0 addresses the security risks arising from traditional deployment methods that rely on experience.

[0276] Overall cost reduction: Improved component versatility and increased assembly efficiency lead to a reduction in overall assembly costs. At the same time, multi-objective optimization takes into account economy, achieving a balance between cost and performance.

[0277] In summary, this application achieves personalized, efficient, and safe construction of irregularly shaped scaffolding in various scenarios through an integrated solution of multi-source sensor fusion, precise modeling, intelligent node generation, component matching, and multi-objective optimization, demonstrating significant technical advantages and practical value.

[0278] The technical solutions provided by the embodiments of the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the embodiments of the present invention. The descriptions of the embodiments above are only for helping to understand the principles of the embodiments of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the embodiments of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A method for constructing an adaptive multi-scene irregular structure scaffold, characterized in that, Includes the following steps: Collect the irregular structure parameters and environmental constraint parameters of the target scene; A three-dimensional model of the irregular structure is constructed based on the aforementioned irregular structure parameters; Based on the three-dimensional model of the irregular structure and environmental constraint parameters, a scaffolding node distribution scheme adapted to the irregular structure is generated. Based on the node distribution scheme, match the corresponding scaffolding components and construct an initial scaffolding construction model; The initial scaffolding model is dynamically optimized using a multi-objective optimization algorithm to obtain the optimal construction scheme. The scaffolding is assembled automatically or semi-automatically according to the optimal assembly scheme.

2. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: The specific parameters of the irregular structure and environmental constraints of the target scene collected include: Three-dimensional point cloud data of the irregular structure is collected by a lidar sensor, and geometric parameters are extracted, including contour dimensions, surface curvature, and coordinates of key control points. Environmental image data of the target scene is collected by a visual sensor, and environmental constraint parameters are extracted. The environmental constraint parameters include the location of spatial obstacles, the available building space range, and the lighting conditions. Mechanical parameters of the irregularly shaped base structure are collected by strain gauge sensors. These mechanical parameters include load-bearing limit and vibration frequency. The Kalman filter algorithm is used to fuse the geometric parameters, environmental constraint parameters, and base mechanical parameters to remove noise data and obtain a standardized parameter set.

3. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: Constructing a 3D model of the irregular structure based on the aforementioned irregular structure parameters specifically includes: Extract feature control points of the irregular structure from the standardized parameter set. , ; Where m and n are the number of control points in the u and v directions, respectively; The three-dimensional model of the irregular structure is constructed using the Bézier surface equation, which is: ； in, , Let be m-th degree Bessel basis functions, satisfying: ； in, Let be a combination number, satisfying: ; The 3D point cloud data is fitted to a surface using the least squares method, and the feature control points are iteratively optimized. The coordinates of the model reduce the geometric error between the 3D model and the actual irregular structure. 0.5mm.

4. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: The specific steps for generating an adaptive scaffold node distribution scheme for the irregular structure include: Surface curvature based on the three-dimensional model Determine node density It satisfies the dynamic density equation: ； in, As the baseline node density, The curvature coefficient; Establish a set of force equilibrium equations for the nodes, the set of equations being: ， ， ， ， ， ； in, , , The nodes are respectively , , The resultant external force in the direction, , , For each node , , The net external torque on the shaft; Solving the system of force equilibrium equations yields the optimal spatial coordinates of each node. ; Construct a node connection matrix ,in Represents a node With nodes There is a connection. This indicates no connection relationship, and the connection relationship satisfies the overall stability constraint of the scaffold.

5. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: The specific components for matching the corresponding scaffolding based on the node distribution scheme include: Establish a database of scaffold components, including telescopic support rods, adaptive rotating connectors, and adjustable bases; Based on node type and node forces The K-nearest neighbor algorithm is used to match the optimal component from the component database. The matching criterion is: ； in, These are the weighting coefficients. For the k-th parameter of the component, For the k-th requirement parameter of the node; Based on the matching results, an initial scaffolding model is constructed to clarify the installation positions, connection methods, and tightening torques of the components.

6. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: The dynamic optimization of the initial scaffolding model using a multi-objective optimization algorithm specifically includes: The multi-objective optimization function is determined, with optimization objectives including safety (S), stability (T), economy (C), and construction efficiency (E). The fitness function is: ； in, , , , ,and ; The security S satisfies: ； in; For the maximum force on the node, Rated load capacity of the component; The stability T satisfies: ； Where d is the offset of the scaffold's center of gravity. Maximum allowable offset; The economic efficiency C satisfies: ； in, Let $\frac{k}{k}$ be the unit price of the component of type k. The number of components of type k used; The construction efficiency E satisfies: ； in; Total setup time; use A multi-objective optimization algorithm is used to find the Pareto optimal solution, and the scheme with the largest overall fitness F is selected as the optimal construction scheme.

7. The adaptive multi-scene irregular structure scaffolding construction method according to claim 1, characterized in that: The automated or semi-automated construction of the scaffolding according to the optimal construction scheme specifically includes: During automated assembly, the robotic arm performs component installation. Based on the coordinate information of the optimal assembly scheme, the attitude parameters of the robotic arm's end effector are calculated using inverse kinematics. The inverse kinematics equations are as follows: ; in, This is the joint angle vector of the robotic arm. For Jacobian matrices, for The pseudo-inverse matrix, This is the spatial position vector of the end effector; During semi-automated assembly, AR devices are used to overlay a 3D model of the optimal assembly plan onto the real-world scene, guiding workers to install step by step in real time. The installation accuracy is detected in real time using a vision sensor, and when the component installation deviation occurs... When the deviation is 1mm, an audible and visual warning will be issued, and a deviation correction suggestion will be output.

8. An adaptive multi-scene irregular structure scaffolding erection system, characterized in that, include: The parameter acquisition unit is used to acquire the irregular structure parameters and environmental constraint parameters of the target scene. The parameter acquisition unit includes a lidar module, a vision sensor module, a strain sensor module and a data fusion module. A modeling unit is used to construct a three-dimensional model of the irregular structure based on the irregular structure parameters. The node generation unit is used to generate a scaffold node distribution scheme that is adaptive to the irregular structure based on the three-dimensional model and environmental constraint parameters. The component matching unit is used to match the corresponding scaffold components based on the node distribution scheme and to construct an initial scaffolding construction model. The optimization unit is used to dynamically optimize the initial scaffolding construction model using a multi-objective optimization algorithm to obtain the optimal construction scheme. An execution unit is set up to perform automated or semi-automated scaffolding construction according to the optimal construction scheme. The execution unit includes an automated construction module, an AR guidance module, and an accuracy detection module.

9. A computer device comprising a memory and a processor, the memory storing code, characterized in that, The processor is configured to acquire the code and execute the adaptive multi-scene irregular structure scaffolding construction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the adaptive multi-scene irregular structure scaffolding construction method as described in any one of claims 1 to 7.

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