Intelligent acceptance and verification method for fire-fighting installation BIM model and three-dimensional scanning

Abstract

The invention relates to the technical field of computer-aided engineering and building information models, and discloses a fire-fighting installation BIM model and three-dimensional scanning intelligent acceptance and verification method, which comprises the following steps: constructing a discretization rigidity constraint model reflecting pipe network mechanical response, converting the space position difference between the actually measured pipe network center track and the design model into a displacement load vector; resolving the reverse elastic equilibrium equation by using a finite element linear solver to obtain internal force distribution and node torque response in a pipe network matching state; according to the method, a reverse mapping mechanism of geometric deviation and mechanical load is established, traditional geometric comparison is upgraded to assembly stress calculation, flexible adaptation and forced assembly are distinguished, and engineering potential safety hazards caused by hidden stress concentration are avoided.

Description

Technical Field

[0001] This invention relates to an intelligent acceptance and verification method for fire protection installation BIM models and 3D scanning, belonging to the field of computer-aided engineering and building information modeling technology. Background Technology

[0002] In the current digital delivery phase of large-scale building electromechanical engineering, using 3D laser scanning technology to acquire point cloud data of the installation site and comparing it geometrically with the design BIM model is a routine technical means for the acceptance and verification of pipeline systems. Existing technologies, based on the principle of rigid body transformation, use iterative nearest-point algorithms to spatially register the measured point cloud with the theoretical model, calculate the surface Euclidean distance deviation, and generate deviation heatmaps or compliance reports based on geometric tolerance standards to determine the quality of engineering installation. However, relying solely on geometric appearance comparison for acceptance has limitations in handling large-span, high slenderness ratio fire protection pipeline systems. Metal pipes have elastic mechanical properties, and in actual construction, pipe sections undergo elastic deformation to adapt to on-site installation errors or avoid obstacles. Under the rigid geometric rule evaluation system, the project is safely elastically adapted within the material yield limit. However, if the spatial position deviates from the design axis, it is judged as unqualified.

[0003] To address these issues, the industry has seen the emergence of solutions that integrate emerging technologies to optimize the acceptance process. For example, Chinese invention patent application CN120679125A discloses an online fire protection acceptance system based on artificial intelligence. This system uses intelligent recognition of point cloud data and image data to build a linkage positioning mechanism between two-dimensional drawings, three-dimensional models, and BIM models, solving the problems of missed component inspections and automated verification of location distribution. However, the technical approach of this type of solution has not deviated from the traditional framework of geometric position compliance. It focuses on component appearance recognition and static coordinate comparison, only solving the shape matching problem and not involving force verification. Based on visual and geometric coordinate verification logic, it cannot convert discrete geometric scanning data into a continuous mechanical response state, making it difficult to penetrate the geometric appearance to assess the internal assembly stress distribution of the pipeline network and identify hidden engineering risks caused by forced assembly.

[0004] Therefore, the technical problem to be solved by this invention is how to establish a technical solution that transforms the geometric position differences captured by on-site three-dimensional scanning into the internal assembly stress distribution of the pipeline system, and verifies the inherent safety of pipeline installation quality based on physical and mechanical rules. Summary of the Invention

[0005] To address the problems mentioned in the background art, the technical solution of the present invention is as follows: An intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning, comprising the following steps: The steps for constructing a discretized stiffness constraint model are as follows: analyze the design data of the fire protection pipeline network to extract the pipeline topology, discretize the pipeline topology into a finite element skeleton composed of beam elements and nodes, base the stiffness properties of the beam elements on the elastic modulus and moment of inertia parameters of the pipe material, set six degrees of freedom full constraints for the flange connection nodes, release the rotational degrees of freedom along the normal plane of the pipe axis for the clamp connection nodes, and establish a system stiffness matrix that reflects the mechanical response characteristics of the pipeline network. The process of mapping forced displacement boundary conditions involves receiving 3D scanned point cloud data from the pipeline installation site and extracting the measured pipeline center trajectory. It also involves calculating the spatial position difference vector of each node in the system stiffness matrix relative to the measured pipeline center trajectory, and transforming the spatial position difference vector into a forced displacement load vector applied to the corresponding node according to the finite element boundary condition definition rules. The steps for solving the inverse elastic equilibrium equation are as follows: based on the matrix form of Hooke's law, the forced displacement load vector is substituted into the system stiffness matrix as a known input, and the internal force distribution and nodal moment response of each beam element are calculated by solving the linear equilibrium equation under the state of forced matching to the measured center trajectory of the pipeline network. The physical compliance adjudication step involves comparing the calculated internal force distribution of the beam element with the preset yield stress threshold of the pipe material, and comparing the moment response of the node with the allowable load threshold of the connector. When neither of them exceeds their respective thresholds, the current pipeline installation status is determined to have passed the physical consistency verification.

[0006] Preferably, the processing logic for releasing the rotational degree of freedom of the clamp connection node in the step of constructing the discretized stiffness constraint model specifically includes: identifying the connection node with the attribute of grooved clamp in the design data; locating the rotational degree of freedom element corresponding to the connection node in the system stiffness matrix, and setting the coefficient of the rotational stiffness term rotating around the tube axis section to a minimum value close to zero, simulating the physical behavior of ideal hinged or semi-rigid connection, thereby constructing a differentiated mechanical response network that can distinguish between rigid transmission and flexible energy release.

[0007] Preferably, before the step of mapping the forced displacement boundary conditions, the process further includes performing gravitational potential energy decoupling: obtaining the pipe linear density parameters of the fire protection network, applying a vertically downward standard gravitational acceleration field to the discretized stiffness constraint model; calling the finite element static solver to calculate the theoretical natural descent displacement vector of the discretized stiffness constraint model under only gravitational load; and performing vector difference operation when calculating the displacement load vector to subtract the theoretical natural descent displacement vector from the spatial position difference vector to obtain the net displacement load vector after eliminating the influence of gravitational descent.

[0008] Preferably, before the step of mapping the forced displacement boundary conditions, the process further includes performing a support and hanger floating domain masking: identifying the support nodes representing the supports and hangers in the discretized stiffness constraint model, and defining a spatial free-floating domain for the support nodes based on the physical adjustment stroke parameters of the supports and hangers; determining whether the spatial position difference vector falls within the spatial free-floating domain; if it falls within the spatial free-floating domain, it is determined that the difference can be eliminated through support and hanger adjustment, and the forced displacement load vector corresponding to the support node is set to zero; if it exceeds the spatial free-floating domain, the residual vector of the spatial position difference vector exceeding the boundary of the spatial free-floating domain is calculated, and the residual vector is used as the effective forced displacement load vector applied to the support node.

[0009] Preferably, the physical compliance adjudication step also includes a sealing verification based on node rotation angles: extracting the absolute rotation vectors of two corresponding nodes at the flange connection from the calculation results of the inverse elastic equilibrium equation solution step; calculating the relative rotation vector between the two corresponding nodes, and combining it with the flange diameter parameter to inversely calculate the theoretical opening gap value of the flange sealing surface; comparing the theoretical opening gap value with the preset gasket rebound compensation threshold, and determining that there is a hidden leakage risk at the flange connection when the theoretical opening gap value exceeds the rebound compensation threshold.

[0010] Preferably, when the physical compliance adjudication step fails verification, a virtual degree of freedom release analysis is performed: the stress concentration region in the system stiffness matrix where the strain energy density exceeds a preset threshold is locked; in the system stiffness matrix, the rotational stiffness parameters of each connection node in the stress concentration region are sequentially set to zero, and the total strain energy of the system is recalculated; the strain energy descent gradient corresponding to each connection node is calculated, and the node with the largest descent gradient is identified as the key energy release point; based on the equilibrium state of the key energy release point after the rotational stiffness parameter is set to zero, a rectification instruction indicating the physical adjustment direction and rebound amount of the node is generated.

[0011] Preferably, when the physical compliance adjudication step fails the verification, a support sensitivity optimization analysis is performed: the spatial coordinates of the support nodes in the discretized stiffness constraint model are set as design variables, and the total strain energy of the pipeline network is set as the objective function; the sensitivity gradient value of the total strain energy relative to the spatial coordinates of each support node is calculated; support nodes whose sensitivity gradient values ​​meet the preset significance conditions are selected, and based on the sensitivity gradient direction and magnitude of the node, fine-tuning parameters for the support position to reduce the total strain energy are generated.

[0012] Preferably, the step of mapping the forced displacement boundary conditions further includes performing environmental temperature difference compensation processing: acquiring the ambient temperature when collecting the three-dimensional scanning point cloud data, and determining the design reference temperature of the fire protection pipe network and the linear expansion coefficient of the pipe material; calculating the theoretical axial thermal expansion of each beam element under unconstrained state based on the difference between the ambient temperature and the design reference temperature; and further subtracting the theoretical axial thermal expansion from the axial component of the spatial position difference vector when calculating the forced displacement load vector to eliminate the interference of environmental thermal stress on the assembly error assessment.

[0013] Preferably, the calculation of the forced displacement load vector follows the principle of linear superposition, and its net load vector is determined according to the following formula: ,in, The net forced displacement load vector is the input system stiffness matrix. This is the original spatial location difference vector measured based on 3D scanned point cloud data. This is the theoretical natural vertical displacement vector. This is the displacement vector corresponding to the theoretical axial thermal expansion calculated based on ambient temperature.

[0014] Preferably, after performing the physical compliance adjudication step, the method further includes: generating a three-dimensional stress cloud map based on the calculation results of the internal force distribution of beam elements and the moment response of nodes; mapping the three-dimensional stress cloud map back to the design model of the fire protection pipe network; and overlaying augmented reality markers containing stress values ​​and suggested rectification directions at the pipe sections or nodes that have failed verification, so as to form a digital delivery model containing mechanical attribute dimensions.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. In the fire protection installation BIM model, by constructing a model that includes the elastic modulus of pipes and the stiffness matrix of cross-sectional properties, the spatial position difference between the measured geometric trajectory of the pipe network obtained by 3D scanning and the design skeleton is defined as the forced displacement load vector applied to the corresponding node. The internal force distribution and torque response of the pipe network under the forced matching to the measured state are calculated using the finite element inverse solution algorithm. The traditional geometric dimension comparison is transformed into assembly stress calculation. It distinguishes between reasonable flexible adaptation of pipes within the elastic range and forced assembly that leads to the danger of node connection failure. It solves the technical problem that the inherent safety of long-distance flexible pipe network systems cannot be determined by simply relying on geometric deviations, and avoids misjudgment or omission of hidden dangers due to confusion between elastic deformation and construction errors.

[0016] 2. Utilizing the principle of superposition of mechanics, gravitational potential energy decoupling and environmental temperature difference compensation are introduced when calculating forced displacement loads. Gravity field and thermal strain loads are pre-applied to the stiffness model. The natural sag displacement of the pipeline due to its own weight and the thermal expansion and contraction deformation due to environmental temperature differences are calculated and vectoredly deducted. Non-construction factors and physical background noise are removed from the total deviation, so that the displacement load input to the verification model only reflects the assembly error caused by the installation process itself, and the interference of natural deformation of large-span pipe sections under different support spans and different environmental temperatures on the acceptance results is eliminated.

[0017] 3. When the mechanical verification fails, initiate a virtual degree-of-freedom release analysis based on the stiffness matrix. Simulate the removal of rotational stiffness at specific connection nodes and recalculate the total strain energy of the system. Calculate the energy drop gradient after release at each node to locate key energy release points. Reuse existing finite element calculation data to mine the stress distribution sensitive areas of the system and generate physical rectification guidelines to achieve maximum stress release with minimal geometric adjustment. Eliminate accumulated internal forces by fine-tuning the position of specific supports or loosening key interface bolts, replacing the traditional blind disassembly and rectification mode, and reducing rework costs and operational risks caused by stress concentration on site. Attached Figure Description

[0018] Figure 1 This is a flowchart of the intelligent acceptance and closed-loop rectification process based on the reverse finite element analysis of this invention. Figure 2 This is a comparison chart of pipeline geometric deviation and net assembly error under multi-physics decoupling according to the present invention; Figure 3 This is a fishbone diagram showing the key technology modules for verifying the physical compliance of the fire protection pipeline network in this invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] This invention discloses an intelligent acceptance and verification method for fire protection installation BIM models and 3D scanning. It establishes a discretized stiffness constraint model reflecting the material properties and topology of the pipeline network. The geometric spatial differences between the measured pipeline network obtained from the 3D scanning and the design model are transformed into forced displacement load vectors applied to the finite element skeleton through physical field vector cleaning processing, including gravity decoupling, temperature difference compensation, and support floating correction. The inverse elastic equilibrium equation is solved using a finite element linear solver to obtain the global internal force distribution and nodal moment response of the pipeline network under forced assembly conditions. Compliance decisions are made based on multi-dimensional physical indicators such as pipe yield strength, rated load of connectors, and sealing gaps. The method involves constructing a discretized stiffness constraint model and analyzing the BIM design data of the fire protection pipeline network. According to the method, the topology of the pipeline centerline is extracted and discretized into a finite element skeleton composed of a series of beam elements and nodes. Based on the pipe material parameters defined in the design specifications, including elastic modulus, Poisson's ratio and moment of inertia of the section, and based on the corresponding stiffness properties of the beam elements, differentiated degree-of-freedom constraints are set for the connection nodes: for flange connection nodes, a rigid connection with six degrees of freedom and full constraints is set; for grooved clamp connection nodes, when establishing the system stiffness matrix, this type of node is identified and its rotational degree of freedom along the normal plane of the pipe axis is released. The release operation is specifically to locate the rotational degree-of-freedom element corresponding to the node in the system stiffness matrix, and set the coefficient of the rotational stiffness term about the pipe axis section to a minimum value close to zero, which can be one ten-thousandth of the bending stiffness of the standard pipe section.

[0021] The process of mapping forced displacement boundary conditions involves receiving 3D scanned point cloud data from the pipeline installation site, extracting the measured pipeline center trajectory, and calculating the original spatial position difference vector of each node in the system stiffness matrix relative to the measured pipeline center trajectory. The process involves decoupling the gravitational potential energy to obtain the linear density parameters of the fire protection network pipes. A vertically downward standard gravitational acceleration field is then applied to the discretized stiffness-constrained model. Finally, the finite element static solver is used to calculate the theoretical natural descent displacement vector of the model under gravity load alone. Perform environmental temperature difference compensation processing to obtain the ambient temperature during the acquisition of 3D scan point cloud data. and based on the design reference temperature and the coefficient of linear expansion of the pipe Calculate the displacement vector corresponding to the theoretical axial thermal expansion of each beam element under unconstrained conditions. The process involves performing a floating domain masking for the support and hanger, identifying the support nodes representing the support and hanger, and defining a spatial free-floating domain based on the physical adjustment stroke parameters. If the original spatial position difference vector falls within this floating domain, it is assumed that the deviation can be eliminated through support adjustment, and the corresponding displacement load component is set to zero. If it exceeds this floating domain, only the residual vector exceeding the boundary is calculated, and based on the principle of linear superposition, the formula is used to... Perform vector difference operation to obtain the net displacement load vector after eliminating gravity descent, thermal stress interference, and support adjustment margin. This is applied as a valid boundary condition to the corresponding node.

[0022] The steps to solve the inverse elastic equilibrium equations include using the matrix form of Hooke's law. Construct the linear equilibrium equations for the entire system, and then convert the net displacement load vector. Substituting the known displacement input into the system stiffness matrix, the sparse matrix linear solver is called to obtain the internal force distribution of each beam element and the moment response of each node when the pipeline is matched to the measured pipeline center trajectory. The internal force distribution includes axial force, shear force, and bending moment. The finite element inverse solver adopts the direct solution method based on Cholesky decomposition of sparse matrices. To address the problem of stiffness matrix singularity caused by the release of rotational degrees of freedom of the grooved clamp node, residual stiffness parameters are introduced at the corresponding elements on the main diagonal of the system stiffness matrix. Taking one ten-thousandth of the bending stiffness of the connected beam element, the displacement load vector obtained by mapping the three-dimensional scanning data is applied to the boundary nodes of the stiffness matrix using the large number penalty function method. The penalty factor is set to the value of the largest diagonal element of the global stiffness matrix. To ensure convergence of static solutions in complex topologies containing numerous semi-rigid connections, a closed numerically stable linear equation system is constructed. Physical compliance adjudication steps are performed, including multi-dimensional mechanical threshold comparisons: the calculated beam element internal force distribution is converted to von Mises stress and compared with a pre-set pipe yield stress threshold to assess pipe safety; the nodal moment response is compared with the allowable load threshold of the connector to assess interface safety; and a sealing verification based on nodal rotation angles is performed: the absolute rotation angle vectors of two corresponding nodes at the flange connection are extracted from the equilibrium equation calculation results, and the relative rotation angle vector between them is calculated. Combined with flange diameter parameters Using geometric relationships The theoretical opening gap value of the flange sealing surface is calculated by inversion. This theoretical opening gap value is compared with the preset gasket rebound compensation threshold. When the pipe stress, interface load and sealing surface opening gap do not exceed their respective set safety thresholds, the current pipeline installation status is determined to have passed the physical consistency verification.

[0023] The physical compliance adjudication threshold is determined according to the standardized limit state statistical procedure. The yield stress threshold for pipe materials is set as the non-proportional elongation strength specified in the stress-strain curve of the batch material as defined in GB / T228.1 Metallic Materials Tensile Test. The lower quantile value; the allowable load threshold for connectors is based on the load-displacement curve of destructive bench tests. The load value corresponding to the tangent modulus decaying to 50% of the initial elastic modulus is taken as the critical failure point. The final allowable limit value is calculated in combination with the partial factor of the engineering structure reliability design standard to eliminate the verification deviation of setting a single empirical value; virtual degree of freedom release and support sensitivity optimization steps: for the pipeline area that has not passed the verification, virtual degree of freedom release analysis is initiated: the stress concentration area in the system stiffness matrix where the strain energy density exceeds the preset threshold is locked, and the rotational stiffness parameter of each connection node in the matrix is ​​set to zero in turn, and the total strain energy of the system is recalculated; the strain energy descent gradient corresponding to each connection node is calculated, and the node with the largest descent gradient is identified as the key energy release point. Based on the equilibrium of the node after the rotational stiffness parameter is set to zero, the following steps are taken: The system generates rectification instructions indicating the physical adjustment direction and rebound amount of the node, and initiates support sensitivity optimization analysis: the spatial coordinates of the support nodes in the discretized stiffness constraint model are set as design variables, the total strain energy of the pipeline network is set as the objective function, and the sensitivity gradient value of the total strain energy relative to the spatial coordinates of each support node is calculated; support nodes whose sensitivity gradient values ​​meet the preset significance conditions are selected, and based on the sensitivity gradient direction and magnitude of the node, fine-tuning parameters for the support position to reduce the total strain energy are generated; a three-dimensional stress cloud map is generated based on the calculated beam element internal force distribution and node moment response, and mapped back to the design model of the fire protection pipeline network; at pipe sections or nodes that fail the verification, a mark containing stress values ​​and suggested rectification directions is superimposed and displayed, forming a digital delivery model containing mechanical attribute dimensions.

[0024] Example 1: In the installation of the fire sprinkler main pipeline in the underground parking level of a large commercial complex, there is a 60-meter-long straight section of DN150 galvanized steel pipe that spans multiple fire compartments and passes through several structural beams. Both ends are connected to the riser via flanges, and the middle section is connected using grooved clamps. The ambient temperature was scanned on-site. 5 Below the design reference temperature 20 Three-dimensional laser scanning point cloud data shows that the maximum vertical sag in the middle of the long straight pipe section reaches 38mm, and the total axial length is 12mm shorter than the design model. According to conventional geometric tolerance standards, the above deviations all exceed the allowable range of ±10mm. This pipe section faces the risk of being deemed unqualified and needing to be dismantled and reinstalled. This invention constructs a discretized stiffness constraint model. Based on the moment of inertia and elastic modulus parameters of the DN150 steel pipe section and the stiffness properties of the beam element, for the grooved clamp connection node in the middle section, the rotational stiffness term coefficient about the normal plane of the pipe axis is set to [value missing] in the system stiffness matrix. The system simulates a semi-rigid hinged node that allows for slight rotation, setting the flange connection nodes at both ends as six-degree-of-freedom fully constrained rigid nodes. Then, multiphysics decoupling and forced displacement mapping are performed, and the system calls the finite element static solver to calculate the theoretical natural sag displacement vector of the 60-meter span pipe segment under its own weight. The calculation results show that the theoretical natural sag at mid-span is 35mm. Simultaneously, based on the temperature difference and the linear expansion coefficient of the pipe material... Calculate the displacement vector corresponding to the theoretical axial thermal expansion. The calculation results show that the theoretical thermal shrinkage is 10.8 mm.

[0025] The system performs vector difference operations based on the principle of linear superposition, using the formula... From the original spatial location difference vector After subtracting the aforementioned natural physical field components, the net displacement load vector reflecting the actual assembly error is obtained. It has only 3mm remaining in the vertical component and only 1.2mm remaining in the axial component. The system will then physically clean the... Substituting into the inverse elastic equilibrium equation The calculation results show that, despite the original geometric deviation, after deducting natural deformation, the maximum von Mises stress of each beam element inside the pipe section is only 15% of the pipe's yield strength. The utilization rate of the rotation angle at each clamp node in the middle does not exceed 30% of its rated rotation angle, all within the safe elastic adaptation range. Furthermore, during the system's verification of the sealing performance of the end flange node, it was found that although the geometric position deviation at this location is extremely small, the relative rotation angle vector calculated from the equilibrium equations... This indicates that the flange face has a 0.3° opening tendency, which, combined with the flange diameter parameters... If the theoretical gap value calculated by the inversion exceeds the rebound compensation threshold of the pre-set sealing gasket, the system will finally issue a physical compliance judgment on the pipe body and clamp connection for the long straight pipe section, exempting the main pipe section from dismantling and modification projects, and only generating local rectification instructions for the end flange node with insufficient tightening torque or gasket failure risk, thereby realizing the inherent safety verification and hidden danger interception of the project quality.

[0026] Example 2: This example verifies the reliability and effectiveness of the intelligent verification method based on inverse finite element analysis in identifying hidden stress concentration risks. A 180-meter-long DN200 seamless steel pipe was selected as the test object. This pipe section simulates the fire-fighting riser in the core shaft of a 50-story super high-rise building. The pipe section traverses 50 floors in the simulated environment, with rigid load-bearing supports installed on each floor. Metal corrugated expansion joints are installed on the 15th, 30th, and 45th floors to absorb thermal displacement. The test environment temperature... Set to 25 With reference temperature To simulate common installation deviations and constraint conflicts in real engineering projects, a gradually increasing deviation along the horizontal X-axis was artificially introduced into the bracket installation position between the 20th and 25th floors. The deviation increased linearly from 5mm on the 20th floor to 30mm on the 25th floor, forming an artificially created bow-shaped forced displacement field.

[0027] The experiment used a conventional 3D laser scanner to acquire point cloud data of the riser, and commercial point cloud processing software was used for geometric deviation analysis. The analysis results showed that the horizontal deviations from the 20th to the 25th floors were all within the adjustable range of the support, and the overall straightness deviation of the riser did not exceed the allowable 0.2%. Based on this, the conventional method determined that the installation of this section of riser was qualified. The method of this invention was applied to process the same scan data to construct a discretized stiffness constraint model that includes pipe material properties, support stiffness, and expansion joint characteristics. When mapping the forced displacement boundary conditions, the geometric deviations measured by the scan were not only transformed into forced displacement load vectors, but also... Furthermore, specifically for the support nodes on floors 20 to 25, the horizontal constraint stiffness was set to [value missing]. To simulate the strong constraint effect of actual scaffolds, the inverse elastic equilibrium equation is solved. Subsequently, internal force distribution data were obtained; the data showed that in the pipe section between the 22nd and 23rd layers, the peak value of the von Mises stress reached 285 MPa, which is close to 90% of the yield strength of the pipe material and far exceeds the safety factor range allowed by the design. Table 1 shows the comparison results of this key area under conventional geometric judgment and mechanical judgment of the present invention.

[0028] Table 1: Comparison of Safety Assessments for Key Areas (Floors 22-23) ; Further analysis revealed that the high-stress zone was not solely caused by geometric deviations, but rather by the strong constraint of the support structure limiting the elastic release of the pipe segment under bow-shaped deviations, leading to internal force accumulation. This triggered a virtual degree-of-freedom release analysis, simulating a reduction in the horizontal constraint stiffness of the 22nd-layer support to [a specific value]. The system was changed to a sliding support. After recalculation, the maximum stress in the area dropped to 110MPa, returning to a safe range. Finally, the system output a rectification instruction to replace the rigid support on the 22nd floor with a sliding support.

[0029] Example 3: This example combines Figures 1 to 3 This document describes the intelligent acceptance and verification methods for fire protection installation BIM models and 3D scanning, such as... Figure 1As shown, the system receives two sets of input data: BIM design data containing pipeline topology, pipe material parameters, and cross-sectional moments of inertia; and 3D scanned point cloud data containing the measured pipeline center trajectory, ambient temperature, and support / hanger status. After data input, the system enters the stage of constructing a discretized stiffness constraint model. This specifically includes discretizing the topology into a finite element skeleton, defining the stiffness properties of beam elements, and releasing the rotational degrees of freedom of clamp connection nodes. The process then proceeds to the mapping of forced displacement boundary conditions, which includes calculating the spatial position difference vector, performing gravitational potential energy decoupling to eliminate sag, performing environmental temperature difference compensation to eliminate thermal expansion, and performing support / hanger floating domain masking. Based on this, the system solves the inverse elasticity... The equilibrium equation step involves inputting the net forced displacement load vector, performing matrix operations to solve the linear equilibrium equation, and obtaining the internal force distribution and nodal moments of the beam element. The process then proceeds to execute a physical compliance ruling. This module performs parallel comparisons of pipe yield stress thresholds, allowable load thresholds for connectors, and sealing verification based on nodal rotation angles. If the verification passes, a qualified digital delivery model containing a 3D stress cloud map and physical consistency certification is output. If the verification fails, the process branch enters the rectification and optimization analysis module, which performs virtual degree of freedom release analysis to identify key energy release points and generate adjustment instructions, or performs support sensitivity tuning analysis to calculate the sensitivity gradient and fine-tune the support coordinates, and finally feeds back physical rectification instructions.

[0030] like Figure 2 As shown, the graph's coordinate system uses the horizontal axis to represent pipe segment length in meters (m), with a scale range from 0m to 60m, and the vertical axis to represent deviation in millimeters (mm), with a scale range from -10 to 40. The graph clearly shows four curves: the total geometric deviation curve (indicated by a short dashed line), the gravity drop curve (indicated by a long dashed line), the thermal deformation curve (indicated by a dotted line), and the net assembly error curve (indicated by a solid line). The data points visually demonstrate that as the pipe segment length increases, the total geometric deviation and gravity drop values ​​show an upward trend and reach a peak, while the net assembly error curve, after physical field decoupling calculations, remains within a low value range or even shows a negative value. Figure 3As shown, the main branch of the fishbone diagram points to the ultimate goal: verification of the physical compliance of fire protection installation. The upper branches sequentially display the discretized stiffness constraint model module, which is subdivided into sub-items such as pipeline topology discretization, beam element stiffness attribute definition, and release of clamp rotational degrees of freedom; the inverse elastic equilibrium solution module, which is subdivided into sub-items such as net displacement input, equilibrium equation matrix solution, and internal force and moment response calculation; and the closed-loop optimization and rectification module, which is subdivided into sub-items such as virtual degree of freedom release analysis, key energy release point identification, and support sensitivity tuning. The lower branches sequentially display the forced displacement load mapping module, which is subdivided into sub-items such as support and hanger floating domain masking, environmental temperature difference compensation processing, gravitational potential energy decoupling processing, and measured center trajectory extraction; and the multi-dimensional compliance adjudication module, which is subdivided into sub-items such as flange sealing verification, allowable load comparison of connectors, and yield stress comparison of pipes.

[0031] Example 4: In the installation and verification scenario of a fire sprinkler system in the underground parking level of a large commercial complex, how to accurately remove local data loss and noise interference caused by structural beam obstruction, scanner blind spots, or surface specular reflection from point cloud scan data, thereby restoring the true topology of the pipeline network, is not only a geometric reconstruction problem, but also a fundamental prerequisite for establishing correct boundary conditions in subsequent finite element analysis. If noisy point clouds are directly used as input, it is easy to lead to topological errors such as false connections or open circuits, which in turn leads to incorrect stress calculations and misjudgments of compliance. This example addresses the aforementioned black box of data integrity by constructing a point cloud repair procedure based on topological semantic guidance, using a conventional 3D laser scanner to acquire the original point cloud data on site. For pipe segment fracture areas caused by occlusion in the data, the system does not perform blind geometric interpolation. Instead, it introduces the design BIM model as a priori semantic template. The system performs coarse registration based on the Iterative Closest Point (ICP) algorithm to transform the original point cloud. With designing the BIM framework Alignment, and based on this, the system identifies It exists in but The missing pipe segment is defined as the area with a point density lower than a preset threshold, such as 50 points / meter.

[0032] For each identified missing pipe segment, the system executes a local cylindrical fitting and axis extension algorithm to extract the effective point cloud subsets scanned at both ends of the missing pipe segment, and fits two local cylindrical surfaces and their central axis vectors respectively. The system calculates the collinearity index of the two vectors. If the deviation is less than a preset angle threshold, such as 1°, it is determined to be a continuous part of the same pipe segment, and virtual point cloud completion data connecting the two ends is generated along the axial direction. Its radius parameter is inherited from the average radius of the fitted cylinders at both ends. If the vector deviation exceeds the threshold, it is determined to be an inflection point or a change in diameter. The system automatically calls the geometric information of standard connectors such as elbows and tees at that location in the BIM model, generates the corresponding standard component virtual point cloud for filling; finally, the original point cloud is... With the generated completion data By fusing the data, an augmented point cloud model with semantic integrity and topological closure is constructed. The data is input into the aforementioned step of constructing a discretized stiffness constraint model. Through this targeted repair procedure, the present invention solves the problem of simulation model construction failure caused by incomplete field scanning data, ensuring that even under the limited working condition of only 85% scanning coverage, a 100% topologically correct finite element analysis model can still be established.

[0033] Example 5: In the development scenario of a complex fire protection pipe network verification system with highly coupled parameters, the key technical challenge faced by this invention is the setting of the rotational stiffness term coefficient of the clamp connection node in the system stiffness matrix. This parameter directly determines whether the finite element model can realistically simulate the semi-rigid physical behavior of the clamps on site. However, this parameter is usually affected by various factors such as pipe diameter, clamp type, and installation torque, and is difficult to calculate directly through theoretical formulas. To eliminate this potential parameter problem, it is particularly necessary to establish a scientific and reproducible parameter calibration procedure. This example addresses the above problem by constructing a systematic clamp rotational stiffness calibration procedure and building a physical testing environment including a standard test bench. This bench is used to fix the pipe section to be tested and apply a precisely controlled bending moment load. Grooved clamps and matching pipe sections of the same type and specification as those in actual engineering are selected as test pieces and installed according to the standard torque specified by the manufacturer. During the test, a servo motor is used to apply a gradually increasing pure bending moment to one end of the pipe section. Meanwhile, the relative rotation angle at the clamp connection is recorded in real time by an angle sensor. The data acquisition system synchronously records bending moment and rotation angle data at a frequency of 100Hz until the rotation angle reaches the maximum allowable value of the clamp design. The collected data is then analyzed... The curves are analyzed.

[0034] The system identified that the curve exhibited typical nonlinear characteristics, namely, linear elasticity in the small deformation stage, gradually entering the nonlinear hardening stage as the rotation angle increased. This is to extract the equivalent rotational stiffness suitable for the linear finite element solver of this invention. This specification uses the secant modulus method for linearization and selects... The endpoint of the linear segment of the curve is used as the calibration point, and its corresponding turning angle is... It is usually set at 50% of the rated rotation angle of the clamp, using the formula. The equivalent rotational stiffness value under this working condition was calculated. For clamps of different pipe diameters and models, the above test steps were repeated to construct a system containing multiple sets of clamps. The system uses a parameter database to automatically retrieve the corresponding rotational stiffness parameters from the database based on the clamp specifications identified in the design model. These parameters are then assigned to the corresponding elements in the stiffness matrix, transforming stiffness parameters that originally relied on experience or fuzzy estimation into deterministic inputs based on measured data and clear physical definitions.

[0035] Example 6: In the deployment scenario of a large-scale fire protection network verification system for industrial applications, this invention constructs a standardized baseline calibration procedure for the allowable load of connectors to ensure the accuracy and stability of the physical compliance adjudication steps. By collecting official technical manuals and industry-standard data from mainstream manufacturers, a standard connector load database containing thousands of specifications is constructed. For specific non-standard or outdated connectors missing from the database, equivalent load deduction based on finite element simulation or physical sampling is performed. The accurate geometric model of the connector is obtained using laser scanning, and based on general material properties, incremental loads are applied in a virtual environment until the design safety factor boundary is reached. This critical value is recorded as its allowable load baseline in the database. In addition, before each verification task is executed, the system automatically scans and identifies the model characteristics of the connectors on site. If a new model not covered by the database is encountered, a baseline adaptive interpolation algorithm is triggered. Based on the load parameters of components of the same type and similar specifications, a temporary allowable load threshold is generated through nonlinear interpolation and marked as pending review.

[0036] In addition to addressing the systematic errors in point cloud coordinates that may be caused by drift in scanning equipment accuracy or changes in ambient lighting during the mapping forced displacement boundary condition step, this invention also constructs a pre-deployment calibration procedure. Before each large-scale scanning task, several calibration spheres with known geometric features and high reflectivity are arranged at key nodes and boundary positions of the pipeline network to be tested. By scanning the calibration spheres from multiple angles and extracting the coordinates of the sphere centers, the system calculates the affine transformation matrix between the measured coordinates and the theoretical coordinates. This matrix is ​​then used to perform global correction processing on the subsequently acquired full-field point cloud data, eliminating the cumulative errors caused by equipment zero-point drift and micro-motion of the installation base.

[0037] Example 7: This example constructs a standardized process control parameter calibration procedure and a logic judgment threshold quantification procedure. For the key process parameters and logic judgment thresholds involved in the aforementioned implementation methods, it provides a systematic determination method based on statistical principles and engineering trial-and-error, eliminating fluctuations in verification results caused by arbitrary parameter settings. Addressing the uncertainty in beam element meshing accuracy during the construction of the discretized stiffness constraint model, this procedure defines an element length sensitivity analysis process. In the initial stage of system deployment, a representative complex pipe network sample is selected, containing at least three pipe diameters and five types of connectors. Meshing is performed using element lengths increasing from 0.1 meters to 2.0 meters, and the fundamental frequency and maximum static deformation under each mesh group are calculated. By plotting the element length-calculation result difference rate curve, the critical element length where the results tend to converge is identified. The convergence criterion is a difference rate of less than 1%, and... Set as the global default unit length for this type of pipe network, and set the pipe yield stress threshold involved in the physical compliance adjudication process.

[0038] This specification abandons the single theoretical yield strength value. A dynamic safety factor mechanism based on operating condition confidence is introduced to systematically evaluate the integrity index of on-site scanning data. (Values ​​range from 0 to 1) and the level of detail (LOD) of the BIM model, based on the formula Dynamically calculate the safety factor ,in To establish a basic safety factor as specified in the regulations, the final judgment threshold was set as follows: This quantitative logic ensures that in cases of poor data quality or coarse models, the system will automatically tighten compliance standards, thereby covering potential uncertainties with a more conservative strategy. Furthermore, regarding the identification of key energy release points in virtual degree-of-freedom release analysis, this procedure explicitly uses a quantitative screening logic based on strain energy release rate, with the system calculating the change in total strain energy of the system before and after releasing the rotational degree of freedom at each node. It was standardized into a dimensionless release rate indicator. When a certain node A node will be marked as a valid rectification point only if its value exceeds a preset significance threshold, such as 5%, and the corresponding geometric adjustment amount is within the feasible range of the project, such as when the bolt loosening amount is less than 5mm.

[0039] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0040] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for intelligent acceptance and verification of fire protection installation BIM model and 3D scanning, characterized in that, Includes the following steps: The steps for constructing a discretized stiffness constraint model are as follows: analyze the design data of the fire protection pipeline network to extract the pipeline topology, discretize the pipeline topology into a finite element skeleton composed of beam elements and nodes, base the stiffness properties of the beam elements on the elastic modulus and moment of inertia parameters of the pipe material, set six degrees of freedom full constraints for the flange connection nodes, release the rotational degrees of freedom along the normal plane of the pipe axis for the clamp connection nodes, and establish a system stiffness matrix that reflects the mechanical response characteristics of the pipeline network. The process of mapping forced displacement boundary conditions involves receiving 3D scanned point cloud data from the pipeline installation site and extracting the measured pipeline center trajectory. It also involves calculating the spatial position difference vector of each node in the system stiffness matrix relative to the measured pipeline center trajectory, and transforming the spatial position difference vector into a forced displacement load vector applied to the corresponding node according to the finite element boundary condition definition rules. The steps for solving the inverse elastic equilibrium equation are as follows: based on the matrix form of Hooke's law, the forced displacement load vector is substituted into the system stiffness matrix as a known input, and the internal force distribution and nodal moment response of each beam element are calculated by solving the linear equilibrium equation under the state of forced matching to the measured center trajectory of the pipeline network. The physical compliance adjudication step involves comparing the calculated internal force distribution of the beam element with the preset yield stress threshold of the pipe material, and comparing the moment response of the node with the allowable load threshold of the connector. When neither of them exceeds their respective thresholds, the current pipeline installation status is determined to have passed the physical consistency verification.

2. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, The specific processing logic for releasing the rotational degree of freedom of the clamp connection node in the steps of constructing the discretized stiffness constraint model includes: identifying the connection node with the attribute of grooved clamp in the design data; locating the rotational degree of freedom element corresponding to the connection node in the system stiffness matrix, and setting the coefficient of the rotational stiffness term rotating around the tube axis section to a minimum value close to zero, simulating the physical behavior of ideal hinged or semi-rigid connection, thereby constructing a differentiated mechanical response network that can distinguish between rigid transmission and flexible energy release.

3. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, Before mapping the forced displacement boundary conditions, the process also includes performing gravitational potential energy decoupling: obtaining the pipe linear density parameters of the fire protection network, applying a vertically downward standard gravitational acceleration field to the discretized stiffness constraint model; calling the finite element static solver to calculate the theoretical natural descent displacement vector of the discretized stiffness constraint model under only gravitational load; and performing vector difference operations when calculating the displacement load vector to subtract the theoretical natural descent displacement vector from the spatial position difference vector to obtain the net displacement load vector after eliminating the influence of gravitational descent.

4. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, Before the step of mapping the forced displacement boundary conditions, the process also includes performing a support and hanger floating domain masking: identifying the support nodes representing the supports and hangers in the discretized stiffness constraint model, and defining a spatial free floating domain for the support nodes based on the physical adjustment stroke parameters of the supports and hangers. Determine whether the spatial position difference vector falls within the spatial free-floating domain; if it falls within the spatial free-floating domain, it is determined that the difference can be eliminated by adjusting the support and hanger, and the forced displacement load vector corresponding to the support node is set to zero; if it exceeds the spatial free-floating domain, calculate the residual vector of the spatial position difference vector that exceeds the boundary of the spatial free-floating domain, and use the residual vector as the effective forced displacement load vector applied to the support node.

5. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, The physical compliance adjudication process also includes sealing verification based on node rotation angles: extracting the absolute rotation vectors of two corresponding nodes at the flange connection from the calculation results of solving the inverse elastic equilibrium equation; calculating the relative rotation vector between the two corresponding nodes, and combining it with the flange diameter parameter to inversely calculate the theoretical opening gap value of the flange sealing surface; comparing the theoretical opening gap value with the preset gasket rebound compensation threshold, and determining that there is a hidden leakage risk at the flange connection when the theoretical opening gap value exceeds the rebound compensation threshold.

6. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, When the physical compliance adjudication step fails to pass the verification, a virtual degree of freedom release analysis is performed: the stress concentration region in the system stiffness matrix where the strain energy density exceeds a preset threshold is locked; In the system stiffness matrix, the rotational stiffness parameters of each connection node in the stress concentration region are set to zero in turn, and the total strain energy of the system is recalculated. Calculate the strain energy descent gradient corresponding to each connection node, and identify the node with the largest descent gradient as the key energy release point; Based on the equilibrium state of the key energy release point after the rotational stiffness parameter is set to zero, a rectification command is generated to indicate the physical adjustment direction and springback amount of the node.

7. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, When the physical compliance adjudication step fails the verification, a support sensitivity optimization analysis is performed: the spatial coordinates of the support and hanger nodes in the discretized stiffness constraint model are set as design variables, and the total strain energy of the pipeline network is set as the objective function; the sensitivity gradient value of the total strain energy relative to the spatial coordinates of each support and hanger node is calculated. Select support nodes whose sensitivity gradient values ​​meet the preset significance conditions, and generate support position fine-tuning parameters to reduce total strain energy based on the sensitivity gradient direction and magnitude of the node.

8. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 3, characterized in that, The mapping of forced displacement boundary conditions also includes performing environmental temperature difference compensation processing: acquiring the ambient temperature when collecting 3D scanning point cloud data, and determining the design reference temperature of the fire protection pipeline network and the linear expansion coefficient of the pipe material; calculating the theoretical axial thermal expansion of each beam element under unconstrained state based on the difference between the ambient temperature and the design reference temperature; and further subtracting the theoretical axial thermal expansion from the axial component of the spatial position difference vector when calculating the forced displacement load vector.

9. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 8, characterized in that, The calculation of the forced displacement load vector follows the principle of linear superposition, and its net load vector is determined according to the following formula: ,in, The net forced displacement load vector is the input system stiffness matrix. This is the original spatial location difference vector measured based on 3D scanned point cloud data. This is the theoretical natural vertical displacement vector. This is the displacement vector corresponding to the theoretical axial thermal expansion calculated based on ambient temperature.

10. The intelligent acceptance and verification method for fire protection installation BIM model and 3D scanning according to claim 1, characterized in that, Following the physical compliance adjudication steps, the process also includes: generating a three-dimensional stress cloud map based on the calculation results of the internal force distribution of beam elements and the moment response of nodes; mapping the three-dimensional stress cloud map back to the design model of the fire protection pipe network; and overlaying augmented reality markers containing stress values ​​and suggested rectification directions at the pipe sections or nodes that have failed verification, in order to form a digital delivery model that includes the mechanical attribute dimension.

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