BIM-based digital pre-assembly method and system for glass curtain wall

Abstract

The application relates to a BIM-based digital pre-assembly method and system for a glass curtain wall, and relates to the technical field of data processing.The method comprises the following steps: determining the geometric parameters and installation position parameters of each component; obtaining point cloud data of a construction site through three-dimensional laser scanning, and performing multi-source data registration on the point cloud data to calculate a point cloud registration error; determining a feature matching degree based on the point cloud registration error; obtaining a positioning optimization target according to deviation analysis results and engineering constraint conditions; calculating an assembly precision evaluation value according to the positioning optimization target; determining a correction amount in the pre-assembly process of the glass curtain wall based on the assembly precision evaluation value; and calculating a comprehensive quality score based on the assembly precision evaluation value, the point cloud registration error, the feature matching degree and the correction amount, and determining that a corresponding pre-assembly scheme is adopted when the comprehensive quality score is greater than a preset threshold value.The application can improve the accuracy of pre-assembly of the glass curtain wall.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and particularly relates to a BIM-based digital pre-assembly method and system for a glass curtain wall, an electronic device and a non-transitory computer readable storage medium. BACKGROUND

[0002] Nowadays, as a widely used outer envelope structure in modern buildings, glass curtain walls have the advantages of beauty, transparency and light weight, and have been widely used in various high-rise buildings, commercial complexes and landmark projects. At present, curtain wall construction mainly relies on two-dimensional drawings for guidance and manual experience. In the component pre-assembly stage, traditional manual measurement and manual positioning methods are often used, and the pre-assembly accuracy is highly dependent on the operation skills of the construction personnel and the site conditions.

[0003] However, two-dimensional drawings cannot accurately express the installation relationship of curtain wall components in space, resulting in frequent errors in the pre-assembly process and high rework rate. On the other hand, traditional manual measurement and assembly methods cannot realize real-time monitoring and correction of the position, attitude and assembly accuracy of curtain wall components, affecting the construction quality and efficiency. SUMMARY

[0004] The present application provides a BIM-based digital pre-assembly method, system, electronic device and non-transitory computer readable storage medium for a glass curtain wall, which can improve the accuracy of glass curtain wall pre-assembly.

[0005] The technical solution of the present application to solve the above technical problems is as follows:

[0006] The present application provides a BIM-based digital pre-assembly method for a glass curtain wall, which comprises:

[0007] establishing a parametric BIM three-dimensional model of the glass curtain wall, determining the geometric parameters and installation position parameters of each component;

[0008] obtaining point cloud data of the construction site by three-dimensional laser scanning, and performing multi-source data registration on the point cloud data to calculate the point cloud registration error;

[0009] determining the feature matching degree based on the point cloud registration error;

[0010] obtaining a positioning optimization target according to the deviation analysis result and the engineering constraint condition;

[0011] calculating an assembly accuracy evaluation value according to the positioning optimization target;

[0012] determining the correction amount in the pre-assembly process of the glass curtain wall based on the assembly accuracy evaluation value;

[0013] The comprehensive quality score is calculated based on the assembly accuracy evaluation value, the point cloud registration error, the feature matching degree, and the correction amount, and when the comprehensive quality score is greater than a preset threshold, it is determined to adopt the corresponding pre-assembly scheme.

[0014] Optionally, the point cloud data is subjected to multi-source data registration, and a point cloud registration error is calculated, including:

[0015] Each BIM model point is transformed according to the rotation matrix and the translation vector to fit each real scene point cloud point, and a main registration item is obtained;

[0016] The rotation transformation gradient of the rotation matrix and the translation transformation gradient of the translation vector are processed according to the regularization parameter, and a regularization item is obtained;

[0017] The point cloud registration error is calculated according to the main registration item and the regularization item.

[0018] Optionally, the point cloud registration error is represented as:

[0019]

[0020] wherein E is the point cloud registration error, w i is a weight coefficient, p i and q i are matched point pairs in the BIM and the point cloud, p i is a BIM model point, q i is a real scene point cloud point, λ is a regularization parameter, R is a rotation matrix, t is a translation vector, is a rotation transformation gradient, is a translation transformation gradient.

[0021] Optionally, the feature matching degree is determined based on the point cloud registration error, including:

[0022] The point pair distance sum between the BIM model point cloud and the real scene point cloud after the iterative closest point registration algorithm is obtained;

[0023] The feature similarity sum between each of the constructions and the corresponding real scene components in local geometric features, surface normal, reflection intensity, color, and shape is obtained;

[0024] The topological consistency sum between each of the constructions and the corresponding real scene components in relative position and connection relationship consistency is obtained;

[0025] The point pair distance sum, the feature similarity sum, and the topological consistency sum are weighted and summed to obtain the feature matching degree.

[0026] Optionally, the feature matching degree is represented as:

[0027] M = a∑ICP + b∑F + g∑D;

[0028] Wherein, M is a feature matching degree, ICP is an iterative closest point distance, F is a feature similarity, D is a topological consistency, a, b, g are respectively a first weight, a second weight and a third weight.

[0029] Optionally, the positioning optimization target is obtained according to the deviation analysis result and the engineering constraint condition, and the method comprises the following steps:

[0030] A spatial offset error is determined according to an initial position of point cloud measurement and a target position corresponding to the initial position;

[0031] An attitude angle deviation is determined according to an attitude angle of point cloud measurement and a target attitude angle corresponding to the attitude angle;

[0032] A joint size error is determined according to a joint size of point cloud measurement and a target joint size corresponding to the joint size;

[0033] The spatial offset error, the attitude angle deviation and the joint size error are weighted and summed to obtain the positioning optimization target.

[0034] Optionally, the assembly precision evaluation value is calculated according to the positioning optimization target, and the method comprises the following steps:

[0035] A geometric measurement point deviation of each key measurement point is determined according to a measurement point deviation, a corresponding deviation weight and a total number of key measurement points;

[0036] A joint error exponential function is determined according to a correction coefficient, a joint error and a total number of joint measurement points;

[0037] The assembly precision evaluation value is calculated according to the geometric measurement point deviation and the joint error exponential function.

[0038] Optionally, the correction amount in the pre-assembly process of the glass curtain wall is determined based on the assembly precision evaluation value, and the method comprises the following steps:

[0039] A current position deviation representing a spatial position offset between a current measured component and the target position is obtained;

[0040] A derivative of the cumulative error is calculated to determine an error change rate compensation;

[0041] An integral of the cumulative error is calculated to determine an integral correction term;

[0042] The correction amount is determined according to the current position deviation, the error change rate compensation and the integral correction term.

[0043] Optionally, the comprehensive quality score is calculated based on the assembly accuracy evaluation value, the point cloud registration error, the feature matching degree and the correction amount, and the comprehensive quality score comprises:

[0044] According to the assembly accuracy evaluation value, an assembly accuracy contribution item is determined.

[0045] According to the point cloud registration error and the maximum value of the point cloud registration error, a point cloud error item is determined.

[0046] According to the feature matching degree, a feature matching degree item is determined.

[0047] According to the correction amount and the maximum acceptable correction threshold, a correction ability item is determined.

[0048] According to the assembly accuracy contribution item, the point cloud error item, the feature matching degree item and the correction ability item, the comprehensive quality score is determined.

[0049] The application also provides a BIM-based digital pre-assembly system for a glass curtain wall, which comprises:

[0050] A parameter determination module is configured to establish a parameterized BIM three-dimensional model of the glass curtain wall, and determine the geometric parameters and installation position parameters of each component.

[0051] A point cloud registration module is configured to obtain point cloud data of a construction site through three-dimensional laser scanning, and perform multi-source data registration on the point cloud data to calculate a point cloud registration error.

[0052] A feature matching module is configured to determine a feature matching degree based on the point cloud registration error.

[0053] A positioning optimization module is configured to obtain a positioning optimization target according to the deviation analysis result and the engineering constraint condition.

[0054] An accuracy evaluation module is configured to calculate an assembly accuracy evaluation value according to the positioning optimization target.

[0055] An assembly correction module is configured to determine a correction amount in a pre-assembly process of the glass curtain wall based on the assembly accuracy evaluation value.

[0056] A comprehensive score module is configured to calculate a comprehensive quality score based on the assembly accuracy evaluation value, the point cloud registration error, the feature matching degree and the correction amount, and determine that a corresponding pre-assembly scheme is adopted when the comprehensive quality score is greater than a preset threshold.

[0057] In addition, to achieve the above object, the application further provides an electronic device, which comprises a memory configured to store a computer software program, and a processor configured to read and execute the computer software program, thereby realizing the above-mentioned BIM-based digital pre-assembly method for a glass curtain wall.

[0058] In addition, to achieve the above object, the application further provides a non-transitory computer readable storage medium, wherein the storage medium stores a computer software program, and the computer software program is executed by a processor to implement the BIM-based digital pre-assembly method for glass curtain walls.

[0059] The application has the following beneficial effects:

[0060] (1) The application realizes accurate identification of on-site installation deviation through a high-precision registration algorithm of point cloud and BIM model, effectively improves the positioning accuracy of pre-assembly components by applying an optimization algorithm of spatial coordinate correction amount and attitude angle adjustment amount, reduces the on-site rework rate caused by misinstallation and misassembly, and saves labor and construction cost.

[0061] (2) The application can realize real-time error monitoring and feedback control of the whole assembly process based on the Internet of Things and distributed sensor network, realizes dynamic self-adaptive adjustment and deviation correction by combining assembly precision evaluation function and correction function, and ensures stable assembly quality.

[0062] (3) The application effectively solves the problems of complex on-site construction environment and difficult fusion of multi-source information by using the method of laser scanning and BIM fusion, realizes deep fusion of model and real scene data by combining feature matching degree function, geometric feature, spatial topology and ICP error, improves the coupling accuracy of construction simulation and real state, and is convenient for pre-rehearsal and problem prediction before construction. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 A flowchart of a BIM-based digital pre-assembly method for glass curtain walls provided by the application;

[0064] Figure 2 A structural schematic diagram of a BIM-based digital pre-assembly system for glass curtain walls provided by the application;

[0065] Figure 3 A hardware structure schematic diagram of a possible electronic device provided by the application;

[0066] Figure 4 A hardware structure schematic diagram of a possible computer readable storage medium provided by the application. DETAILED DESCRIPTION

[0067] With reference to the drawings and the embodiments of the present application, the technical solutions in the embodiments of the present application will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of the present application.

[0068] In the description of the present application, the terms "first", "second" are used only for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise explicitly and specifically limited.

[0069] In the description of the present application, the term "for example" is used to indicate "as an example, illustration or explanation". Any embodiment described as "for example" in the present application is not necessarily interpreted as more preferred or more advantageous than other embodiments. The following description is given in order to enable any person skilled in the art to implement and use the present application. In the following description, details are listed for the purpose of explanation. It should be understood that those skilled in the art can recognize that the present application can be implemented without using these specific details. In other examples, well-known structures and processes will not be described in detail in order to avoid unnecessary details making the description of the present application obscure. Therefore, the present application is not intended to be limited to the shown embodiments, but is consistent with the broadest scope in accordance with the principles and characteristics disclosed.

[0070] Please refer to Figure 1 , a flowchart of a BIM-based digital pre-assembly method of a glass curtain wall of the present application is provided, including the following steps:

[0071] Step 201, establishing a parametric BIM three-dimensional model of the glass curtain wall, determining the geometric parameters and installation position parameters of each component.

[0072] In the process of digital pre-assembly of the glass curtain wall, first, a BIM (Building Information Model) three-dimensional model containing all curtain wall components is constructed, and parametric design is performed. The model not only contains the geometric shape of the curtain wall, but also embeds the key parameters of the components, including:

[0073] Geometric parameters: such as the length, width, thickness, frame size of the component, etc.

[0074] Installation position parameters: such as the spatial positioning (X, Y, Z coordinates) of the component in the overall curtain wall system, installation angle, orientation, etc.

[0075] Component identification and logical relationship: such as component number, installation sequence, joint relationship with adjacent components, etc.

[0076] To realize the unity of component model and actual construction coordinate system, a component space coordinate system conversion matrix T can also be defined:

[0077]

[0078] Among them: is a rotation matrix, which represents the pose of the component relative to the global coordinate; t = [t x ,t y ,t z ] T is a translation vector, which represents the position offset of the component.

[0079] Step 202, obtain point cloud data of the construction site by three-dimensional laser scanning, and perform multi-source data registration on the point cloud data to calculate point cloud registration error.

[0080] In some embodiments, step 202 can include:

[0081] After transforming each BIM model point according to the rotation matrix and the translation vector to fit each corresponding real scene point cloud point, a main registration item is obtained;

[0082] According to the rotation transformation gradient of the rotation matrix and the translation transformation gradient of the translation vector, a regularization term is obtained;

[0083] According to the main registration item and the regularization term, the point cloud registration error is calculated.

[0084] Among them, the point cloud registration error is expressed as:

[0085]

[0086] Among them, E is the point cloud registration error, w i is a weight coefficient, p i and q i are matching point pairs in BIM and point cloud, p i is a BIM model point, q i is a real scene point cloud point, λ is a regularization parameter, R is a rotation matrix, t is a translation vector, is a rotation transformation gradient, is a translation transformation gradient.

[0087] In a specific implementation, E is the point cloud registration error, which represents the overall error evaluation function, and the smaller the value, the better the registration. w i is a weight coefficient, which represents the credibility of each point pair and is commonly used to weaken the influence of noise points. pi is the BIM model point, representing the sampled point in the BIM three-dimensional model. q i is the real scene point cloud point, representing the point in the real scene three-dimensional laser scanning point cloud matched with p i is the regularization parameter, controlling the influence of the regularization term on the total error (balancing registration accuracy and smoothness). R is the rotation matrix, representing the rotation transformation of rotating the BIM model to align with the real scene point cloud. t is the translation vector, representing the translation transformation of translating the BIM model to the position of the real scene point cloud. is the rotation transformation gradient, controlling the smoothness of the rotation transformation, inhibiting drastic rotation changes. is the translation transformation gradient, controlling the smoothness of the translation transformation, inhibiting abrupt displacement. ||Rp i +t-q i || 2 describes the Euclidean distance error between the transformed model point and the real scene point.

[0088] the main registration term (error minimization core) i w i ||Rp i +t-q i || 2 , the goal is to make the BIM point p i as close as possible to the corresponding real scene point q i after transformation R, t. i is the typical least squares point cloud matching (ICP) core term, and the weighting term w i can be calculated by the matching quality, reflection intensity or confidence of the point, improving robustness.

[0089] the regularization term (to prevent overfitting and oscillation) the goal is to control the change of R and t from being too drastic, usually measured by the first or second derivative (gradient) of the change. It can be understood as introducing a smoothness constraint on the transformation process, especially suitable for large-scale curtain wall point cloud splicing, which can avoid local registration misplacement.

[0090] In summary, the minimization of this function in the present application is usually completed by optimization algorithms (such as Levenberg-Marquardt, Gauss-Newton, ICP), which are used for: automatic alignment of BIM model and measured data, component installation deviation detection, subsequent optimization analysis (such as input of objective function L and assembly accuracy A).

[0091] Step 203, determining the feature matching degree based on the point cloud registration error.

[0092] In some embodiments, step 203 can include:

[0093] obtaining a point pair distance sum representing a distance sum between the BIM model point cloud and the real scene point cloud after an iterative closest point registration algorithm;

[0094] obtaining a feature similarity sum representing a feature similarity sum between local geometric features, surface normals, reflection intensities, colors, shapes of each of the constructions and corresponding real scene components;

[0095] obtaining a topological consistency sum representing a relative position and connection relationship consistency sum between each of the constructions and corresponding real scene components;

[0096] performing weighted summation on the point pair distance sum, the feature similarity sum and the topological consistency sum to obtain the feature matching degree.

[0097] wherein the feature matching degree is represented as:

[0098] M = a∑ICP + b∑F + g∑D;

[0099] wherein M is the feature matching degree, ICP is the iterative closest point distance, F is the feature similarity, D is the topological consistency, a, b and g are respectively a first weight, a second weight and a third weight.

[0100] In a specific implementation, the∑ICP represents a point pair distance sum between the BIM model point cloud and the real scene point cloud after an iterative closest point registration algorithm (such as ICP). The specific representation is:

[0101] ∑ICP =∑ i ||Rp i +t-q i || 2 ;

[0102] The∑ICP is used to measure the geometric fitting degree of the model and the real scene, is the most basic registration index, has high precision, but is sensitive to noise and is prone to local optimum.

[0103] The∑F represents a feature similarity sum between local geometric features, surface normals, reflection intensities, colors and shapes of the BIM components and the real scene components. Local descriptor algorithms such as FPFH, SHOT and Spin Image can be used; the Euclidean distance, correlation coefficient and cosine similarity in the feature space between the BIM point cloud and the real scene point cloud are calculated; the registration points are used to assist in judging whether the registration points are truly physically corresponding, and the deficiency of the ICP geometric error term is made up; and the non-rigid deformation and complex surface have a certain robustness.

[0104] D measures whether the spatial relationship of the components in the BIM model is consistent with the relative position and connection relationship between the components in the real scene; and ∑D is the total sum of the topological consistency terms. The quantifiable manners include the similarity of the adjacency matrix; the matching of the spatial relative direction (up, down, left, right, front, and back); the distance ratio, the angle relationship, and other topological constraints. The topological consistency terms are used to ensure the consistency of the overall structure, and are especially suitable for complex curtain wall units or special-shaped components, which can avoid the matching result of "correct geometric alignment but incorrect assembly relationship".

[0105] The sum of α, β, and γ is 1, which can be set based on experience (for example, geometry is the main factor, and features and topology are the secondary factors) or automatically learned through a data-driven method (for example, machine learning).

[0106] For example, when the structure of the glass curtain wall is simple: α=0.6, β=0.3, and γ=0.1; and when the shape of the curtain wall or the assembly relationship is complex: α=0.4, β=0.3, and γ=0.3.

[0107] In summary, the comprehensive matching degree function M can quantify the differences between the BIM and the real scene from the "geometry-feature-structure" three levels, improve the stability and semantic correctness of the registration, avoid the problem of "misregistration but minimum distance" caused by ICP, and provide key reference indexes for subsequent positioning optimization (function L), assembly accuracy evaluation (function A), and comprehensive quality scoring (function Q).

[0108] Step 204: obtaining a positioning optimization target according to the deviation analysis result and the engineering constraint condition.

[0109] In some embodiments, step 204 can include:

[0110] determining a spatial offset error according to an initial position of the point cloud measurement and a target position corresponding to the initial position;

[0111] determining a pose deviation according to a pose angle of the point cloud measurement and a target pose angle corresponding to the pose angle;

[0112] determining a joint size error according to a joint size of the point cloud measurement and a target joint size corresponding to the joint size;

[0113] performing weighted summation on the spatial offset error, the pose deviation, and the joint size error to obtain the positioning optimization target.

[0114] The positioning optimization target can be represented as:

[0115] L = min{w1∑||P i -P i ′|| 2 +w2∑||θ i -θi ||d 2 -w3∑||d i -d i ||d 2 ;

[0116] where L is the positioning optimization target, P i is the initial position of the point cloud measurement, P i ' is the target position, θ i is the attitude angle of the point cloud measurement, θ i ' is the target attitude angle, d i is the joint size of the point cloud measurement, d i ' is the target joint size, w1, w2, w3 are the fourth weight, the fifth weight and the sixth weight respectively.

[0117] In a specific implementation, the formula minimizes the sum of the weighted errors to obtain an optimal positioning scheme that makes the actual installation of the component as close as possible to the design target.

[0118] ||P i -P i ||d 2 represents the spatial offset error (translation error) between the actual component position and the target position. ||θ i -θ i ||d 2 represents the attitude deviation (such as the angle error). ||d i -d i ||d 2 represents the error or inconsistency of the joint size.

[0119] In some embodiments, w1 can be increased when the spatial accuracy requirement is high, w2 can be increased when the component assembly angle is sensitive, and w3 can be increased when the appearance / sealing requirement is high.

[0120] The goal of this formula is to minimize the total deviation between the actual measured component state and the design target in BIM. The result is a set of adjustment parameters (position, angle, spacing) that are used to guide automated assembly or manual adjustment. Multi-objective optimization algorithms (such as genetic algorithms, particle swarm optimization, gradient descent method) can be used to solve the minimum value.

[0121] This function L is directly based on the aforementioned feature matching results (function M) and point cloud deviation analysis results; its output (position, angle, joint adjustment amount) is used to control the actuators of the Internet of Things assembly system; the error term δ i , ε i in the assembly accuracy evaluation function A is calculated; and finally the quality score function Q is affected.

[0122] In summary, the function realizes high-precision bridging between a static BIM design model and on-site dynamic assembly execution, has the advantages that multi-dimensional error sources are integrated, assembly challenges are comprehensively reflected, the weight is flexibly adjusted and optimized, different engineering environments are adapted, and key data support is provided for realizing digital and high-precision glass curtain wall pre-assembly.

[0123] In step 205, an assembly precision evaluation value is calculated according to the positioning optimization target.

[0124] In some embodiments, step 205 can include:

[0125] According to the measurement point deviation, the corresponding deviation weight and the total number of key measurement points, the geometric measurement point deviation of each key measurement point is determined.

[0126] According to the correction coefficient, the joint error and the total number of joint measurement points, a joint error exponential function is determined.

[0127] According to the geometric measurement point deviation and the joint error exponential function, an assembly precision evaluation value is calculated.

[0128] The assembly precision evaluation value can be expressed as:

[0129]

[0130] Wherein, A is the assembly precision evaluation value, δ i is the measurement point deviation, ε i is the joint error, μ i is the deviation weight, k is the correction coefficient, n is the total number of key measurement points, and m is the total number of joint measurement points.

[0131] In a specific implementation, the formula is used to comprehensively evaluate the precision level in the glass curtain wall assembly process, and the precision level is evaluated from two dimensions: component measurement point deviation (geometric precision) and joint error between components (assembly coordination). Through weighted processing and combination of the two types of errors, a numerical evaluation result A of the overall assembly quality is obtained. A is the assembly precision evaluation value, which is a numerical index for evaluating the assembly effect of the entire curtain wall system. The smaller the value is, the higher the precision is. δ i is the measurement point deviation, which represents the error (such as position deviation and angle deviation) between the actual measured position of the component and the target position. ε i is the joint error, which represents the deviation between the actual joint width between adjacent components and the target joint width. μ iis the deviation weight, indicating the importance of the measuring point in the overall assembly evaluation (e.g., the corner measuring point may be more critical). k is the correction coefficient, controlling the influence of the second term on the final assembly precision score, which is usually obtained by experience or data training. n is the total number of key measuring points, used to normalize the first term, making it irrelevant to the project size. m is the total number of joint measuring points, used to normalize the second joint error term.

[0132] is the geometric measuring point deviation, indicating the weighted square deviation average of all key measuring points, reflecting the position and attitude error accumulation in the assembly process, and is suitable for monitoring the overall rigidity precision.

[0133] is the joint error exponential function, which is a negative exponential function, indicating that the smaller the joint error, the closer the exponential term to 1, and the greater the contribution to the assembly precision. If the joint error is large, the term decays faster. It is beneficial to suppress the amplification effect of small joint error on the score, reflecting the engineering "tolerance" strategy.

[0134] It can be understood that the smaller the value of A, the smaller the measuring point and joint error, and the higher the assembly precision. The geometric measuring point deviation term is large, and the joint error exponential function term is small, the geometric deviation is large, and the joint control is good, and positioning adjustment is needed. The geometric measuring point deviation term is small, and the joint error exponential function term is large, the position is basically accurate but the assembly coordination is poor, and there may be joint stretching or over-tightening. A increases, indicating that the assembly deviation is serious, and it may need to be recalibrated or reworked.

[0135] The δ i in the first term is derived from the optimization result of the positioning optimization function L; the ε i in the second term is related to the difference between the joint design size d i and the actual installation result d i ; the A output by the function will be used as an input parameter in the correction amount calculation function C; and an important part of the comprehensive quality score function Q.

[0136] In summary, the present application combines the weighted average and exponential penalty mechanism, can evaluate the rigidity deviation and flexibility error at the same time, considers the two most core curtain wall assembly quality indicators of measuring point and joint, and can be used as the core quality monitoring indicator of the intelligent assembly system.

[0137] Step 206, based on the assembly precision evaluation value, determining the correction amount in the pre-assembly process of the glass curtain wall.

[0138] In some embodiments, step 206 can include:

[0139] obtaining a current position deviation representing a spatial position offset between the measured component and the target position;

[0140] derivative of the accumulated error to determine a rate of error change compensation;

[0141] integral of the accumulated error to determine an integral correction term;

[0142] determine the correction amount according to the current position deviation, the rate of error change compensation and the integral correction term.

[0143] The correction amount can be expressed as:

[0144]

[0145] wherein C is the correction amount, ΔP is the current position deviation, E is the accumulated error, λ1 and λ2 are respectively a first coefficient and a second coefficient for controlling correction sensitivity.

[0146] In a specific implementation, the formula is used to calculate the correction amount C in the pre-assembly process in real time, and the goal is to dynamically feedback and control the installation error, so as to realize high-precision assembly. It combines the current position error, the error change rate (trend) and the historical cumulative error, and realizes an error correction mechanism similar to PID control (proportional-differential-integral). C is the correction amount, which is finally used to adjust the correction value of the actuator or operator instruction, and the unit is usually length, angle or joint displacement, etc. ΔP is the current position deviation, which is the spatial position offset between the current measured component and the target position (from the comparison of BIM and point cloud). E is the accumulated error, which is the overall error of the system at the current time, and can be the weighted or accumulated value of the deviation within a certain period of time. is the error change rate, indicating the trend (growth / reduction speed) of error change, which can be understood as the "derivative of error". ∫Edt represents the accumulated effect of error within a period of time, which is helpful for systematic adjustment of chronic deviation. λ1 and λ2 are respectively a first coefficient and a second coefficient for controlling correction sensitivity, and the first coefficient controls the influence of error change rate on correction amount (response speed), and the second coefficient controls the influence of accumulated error on correction amount (long-term adjustment ability).

[0147] ΔP is the current position deviation, indicating the immediate observation of spatial deviation, which is the most direct correction basis, and is often obtained in real time through laser scanning + BIM comparison in actual engineering.

[0148] is the error change rate compensation, which is similar to the "prediction compensation term", and judges whether the error is getting better or worse. If the error is getting worse (derivative is positive), the system will actively correct, and the key item for controlling response sensitivity.

[0149] λ2∫Edt is the integral correction term, which represents the correction of the accumulated small error that has not been eliminated for a long time, avoids the accumulation of error causing system deviation or systematic drift, and enhances the stability and anti-interference ability of the system.

[0150] ΔP is derived from the position deviation term δ in the assembly accuracy evaluation function A i E can be defined as the integrated deviation time series in the assembly process (such as the A function output sequence); the C output by this function will participate in: driving the positioning / assembly mechanical system to make real-time adjustments; at the same time, it will affect the correction term ΔC in the final integrated quality score function Q.

[0151] Application scenarios can include real-time construction assembly, intelligent control systems, and error drift monitoring. For example, C is used to guide the dynamic adjustment of component positions and postures by robots or operators, to achieve "self-adaptive calibration" based on error change trends, and the integral term helps to eliminate long-term error accumulation and prevent systematic drift.

[0152] In summary, the present application constructs an intelligent assembly closed-loop control mechanism, which has the advantages of: comprehensive consideration of current error, trend judgment and historical accumulation, stable and efficient; flexible adjustment of control strategy (through λ1, λ2) to adapt to different construction precision requirements; adaptation to Internet of Things / sensor network systems to realize automated assembly control and precision guarantee.

[0153] Step 207, based on the assembly accuracy evaluation value, the point cloud registration error, the feature matching degree, and the correction amount, the integrated quality score is calculated, and when the integrated quality score is greater than a preset threshold, the corresponding pre-assembly scheme is determined to be adopted.

[0154] In some embodiments, step 207 can include:

[0155] According to the assembly accuracy evaluation value, the assembly accuracy contribution term is determined;

[0156] According to the point cloud registration error and the maximum value of the point cloud registration error, the point cloud error term is determined;

[0157] According to the feature matching degree, the feature matching degree term is determined;

[0158] According to the correction amount and the maximum acceptable correction threshold, the correction ability term is determined;

[0159] According to the assembly accuracy contribution term, the point cloud error term, the feature matching degree term, and the correction ability term, the integrated quality score is determined.

[0160] Wherein, the integrated quality score can be expressed as:

[0161]

[0162] Wherein, Q is the integrated quality score, A is the assembly accuracy evaluation value, E is the point cloud registration error, E max is the maximum value of the point cloud registration error, M is the feature matching degree, C is the correction amount, C maxis the maximum acceptable correction threshold. ω1, ω2, ω3, ω4 are the seventh weight, the eighth weight, the ninth weight and the tenth weight respectively.

[0163] In a specific implementation, the formula is a weighted comprehensive evaluation model. By normalizing multiple key parameters, quality indicators of different dimensions are unified into the same scoring system. Q is the comprehensive quality score, which is a comprehensive index for overall evaluation of the pre-assembly effect of the glass curtain wall. It is usually standardized to 0-1. A is the assembly accuracy evaluation value. E is the point cloud registration error, which comes from the spatial registration error of the laser point cloud and the BIM model, reflecting the deviation degree of the model from the actual scene. E max is the maximum value of the point cloud registration error, which can be set as the upper limit of the engineering specification or the statistical maximum value, and is used for normalizing E. M is the feature matching degree, and C is the correction amount. ΔC represents the degree of position and attitude correction in the actual assembly process. C max is the maximum acceptable correction threshold, which indicates that the system deviation is too large and the correction effect is out of control. ω1, ω2, ω3, ω4 are used to adjust the influence of each index on the final score.

[0164] ω1A is the assembly accuracy contribution term, which directly reflects the actual measured component assembly quality, and the higher the value is, the better it is; it can be obtained according to the sensor and point cloud error calculation.

[0165] is the point cloud error term, which normalizes the point cloud registration error E to a relative error; The smaller the error is, the higher the score is.

[0166] ω3M is the feature matching degree term, which describes the geometric and topological matching of the model and the actual scene; the larger the value is, the more consistent the BIM and the actual situation are.

[0167] is the correction ability term, which indicates that if the system does not need a large correction (|ΔC| is small), it means that the initial accuracy is high and the assembly is intelligent; this term reflects the response and control ability of the automatic system to the error.

[0168] In theory, the value range of Q is from 0 (poor quality) to 1 (excellent quality); adjustability: by adjusting the weights, it can adapt to different engineering scenarios (such as paying more attention to geometric error or assembly accuracy); flexibility: it combines geometric error, structural adaptation and assembly control three types of indicators. Therefore, the Q function is the final comprehensive evaluation index of the performance of the whole pre-assembly system, which integrates the whole process data and optimization results, and is the key basis for quality acceptance, feedback control and system tuning.

[0169] The application scenarios can include construction quality score and inspection, taking Q as an automatic acceptance or supervision reference; assembly system closed-loop feedback, if the Q score is low, automatically entering secondary positioning or reorganization process; system self-learning optimization, Q value feedback can be used for training control algorithm weight adjustment or path optimization.

[0170] In conclusion, the application realizes the standardized integration of multi-dimensional quality factors, emphasizes the three standards of actual precision, error control and model consistency, and supports intelligent construction, quality closed-loop management and control and data-driven acceptance.

[0171] Please refer to Figure 2 , Figure 2 The application provides a structural schematic diagram of a BIM-based digital pre-assembly system for a glass curtain wall.

[0172] As shown in Figure 2 , the BIM-based digital pre-assembly system for a glass curtain wall comprises:

[0173] The parameter determination module 301 is configured to establish a parameterized BIM three-dimensional model of the glass curtain wall, and determine the geometric parameters and installation position parameters of each component.

[0174] The point cloud registration module 302 is configured to obtain point cloud data of a construction site through three-dimensional laser scanning, and perform multi-source data registration on the point cloud data to calculate a point cloud registration error.

[0175] The feature matching module 303 is configured to determine a feature matching degree based on the point cloud registration error.

[0176] The positioning optimization module 304 is configured to obtain a positioning optimization target according to the deviation analysis result and the engineering constraint condition.

[0177] The precision evaluation module 305 is configured to calculate an assembly precision evaluation value according to the positioning optimization target.

[0178] The assembly correction module 306 is configured to determine a correction amount in the pre-assembly process of the glass curtain wall based on the assembly precision evaluation value.

[0179] The comprehensive score module 307 is configured to calculate a comprehensive quality score based on the assembly precision evaluation value, the point cloud registration error, the feature matching degree and the correction amount, and determine to adopt a corresponding pre-assembly scheme when the comprehensive quality score is greater than a preset threshold.

[0180] It should be noted that the specific embodiments of the above modules 301-307 and the beneficial effects brought about thereby can be referred to the related description of steps 201-207 above, which will not be repeated here.

[0181] Please refer to Figure 3 ,​Figure 3 An embodiment of an electronic device provided by an embodiment of the present application is shown in the figure. Figure 3 As shown in the figure, an electronic device 400 is provided by an embodiment of the present application, which comprises a memory 410, a processor 420, and a computer program 411 stored in the memory 410 and executable on the processor 420, and the processor 420 implements the following steps when executing the computer program 411:

[0182] A parameterized BIM three-dimensional model of the glass curtain wall is established, and geometric parameters and installation position parameters of each component are determined;

[0183] Point cloud data of the construction site is obtained through three-dimensional laser scanning, and multi-source data registration is performed on the point cloud data, and a point cloud registration error is calculated;

[0184] Based on the point cloud registration error, a feature matching degree is determined;

[0185] According to the deviation analysis result and the engineering constraint condition, a positioning optimization target is obtained;

[0186] According to the positioning optimization target, an assembly precision evaluation value is calculated;

[0187] Based on the assembly precision evaluation value, a correction amount in the pre-assembly process of the glass curtain wall is determined;

[0188] Based on the assembly precision evaluation value, the point cloud registration error, the feature matching degree, and the correction amount, a comprehensive quality score is calculated, and when the comprehensive quality score is greater than a preset threshold, a corresponding pre-assembly scheme is determined to be adopted.

[0189] Please refer to Figure 4 , Figure 4 An embodiment of a computer readable storage medium provided by an embodiment of the present application is shown in the figure. Figure 4 As shown in the figure, a computer readable storage medium 500 is provided by the present embodiment, which stores a computer program 411, and the computer program 411 is executed by a processor to implement the following steps:

[0190] A parameterized BIM three-dimensional model of the glass curtain wall is established, and geometric parameters and installation position parameters of each component are determined;

[0191] Point cloud data of the construction site is obtained through three-dimensional laser scanning, and multi-source data registration is performed on the point cloud data, and a point cloud registration error is calculated;

[0192] Based on the point cloud registration error, a feature matching degree is determined;

[0193] According to the deviation analysis result and the engineering constraint condition, a positioning optimization target is obtained;

[0194] According to the positioning optimization target, an assembly precision evaluation value is calculated;

[0195] Based on the assembly precision evaluation value, a correction amount in a pre-assembly process of the glass curtain wall is determined.

[0196] Based on the assembly precision evaluation value, a point cloud registration error, a feature matching degree, and the correction amount, a comprehensive quality score is calculated, and when the comprehensive quality score is greater than a preset threshold, it is determined to adopt a corresponding pre-assembly scheme.

[0197] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0198] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing devices to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 a flow or multiple flows and / or blocks Figure 1 a system that implements the functions specified in one or more blocks or multiple blocks.

[0200] These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer-readable memory produce a manufactured product including an instruction system that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 a flow or multiple flows and / or blocks Figure 1 a system that implements the functions specified in one or more blocks or multiple blocks.

[0201] These computer program instructions can also be loaded into a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 one or more flowcharts and / or blocks

[0202] Although preferred embodiments of the application have been described herein, it will be apparent to those skilled in the art that various modifications can be made within the scope of the application and it is intended that the application should cover any and all modifications and variations of the preferred embodiments.

[0203] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A BIM-based digital pre-assembly method for glass curtain walls, characterized in that, The method includes: Establish a parametric BIM 3D model of the glass curtain wall and determine the geometric parameters and installation position parameters of each component; Point cloud data of the construction site is acquired by 3D laser scanning, and multi-source data registration is performed on the point cloud data to calculate the point cloud registration error. Based on the point cloud registration error, the feature matching degree is determined; Based on the deviation analysis results and engineering constraints, the positioning optimization objective is obtained; Based on the positioning optimization objective, the assembly accuracy evaluation value is calculated; Based on the assembly accuracy assessment value, the correction amount in the pre-assembly process of the glass curtain wall is determined; A comprehensive quality score is calculated based on the assembly accuracy evaluation value, point cloud registration error, feature matching degree and correction amount. When the comprehensive quality score is greater than the preset threshold, the corresponding pre-assembly scheme is determined to be adopted. The step of obtaining the positioning optimization objective based on the deviation analysis results and engineering constraints includes: Based on the initial position of the point cloud measurement and the target position corresponding to the initial position, the spatial offset error is determined; The attitude deviation is determined based on the attitude angle measured from the point cloud and the target attitude angle corresponding to the attitude angle. Based on the joint size measured by point cloud and the target joint size corresponding to the joint size, the error of the joint size is determined; The positioning optimization target is obtained by weighted summing of the spatial offset error, the attitude deviation, and the seam size error; Based on the positioning optimization objective, the assembly accuracy evaluation value is calculated, including: The geometric measurement point deviation of each key measurement point is determined based on the measurement point deviation, the corresponding deviation weight, and the total number of key measurement points. The joint error exponential function is determined based on the correction coefficient, joint error, and the total number of joint measurement points. The assembly accuracy assessment value is calculated based on the geometric measurement point deviation and the joint error exponential function. Based on the assembly accuracy assessment value, the correction amount during the pre-assembly process of the glass curtain wall is determined, including: Obtain the current position deviation, which represents the spatial position offset between the currently measured component and the target position; Calculate the derivative of the cumulative error to determine the error rate of change compensation; Calculate the integral of the accumulated error and determine the integral correction term; The correction amount is determined based on the current position deviation, the error change rate compensation, and the integral correction term.

2. The BIM-based digital pre-assembly method for glass curtain walls according to claim 1, characterized in that, The step of performing multi-source data registration on the point cloud data and calculating the point cloud registration error includes: After transforming each BIM model point using the rotation matrix and translation vector, it is made to fit the corresponding real point cloud point, thus obtaining the master registration term; The rotation transformation gradient of the rotation matrix and the translation transformation gradient of the translation vector are processed according to the regularization parameter to obtain the regularization term; The point cloud registration error is calculated based on the main registration term and the regularization term.

3. The BIM-based digital pre-assembly method for glass curtain walls according to claim 2, characterized in that, The point cloud registration error is expressed as: ； Where E is the point cloud registration error. These are weighting coefficients. and It is a pair of points matched between BIM and point cloud. It is a BIM model point, It is a real-world location cloud point. Here, R is the regularization parameter, R is the rotation matrix, and t is the translation vector. It is the gradient of the rotation transformation. It is the gradient of the translation transformation.

4. The BIM-based digital pre-assembly method for glass curtain walls according to claim 3, characterized in that, The determination of feature matching degree based on the point cloud registration error includes: Obtain the sum of point-to-point distances between the BIM model point cloud and the actual point cloud after iterative nearest-point registration algorithm; Obtain the sum of feature similarity between each of the aforementioned components and the corresponding real-world components in terms of local geometric features, surface normals, reflection intensity, color, and shape; Obtain the topological consistency sum that indicates the consistency of the relative positions and connection relationships between each component and its corresponding real-world component; The feature matching degree is obtained by weighted summation of the sum of the point-pair distances, the sum of the feature similarities, and the sum of the topological consistency.

5. The BIM-based digital pre-assembly method for glass curtain walls according to claim 4, characterized in that, The feature matching degree is expressed as: ； Where M is the feature matching degree, It is the distance between the nearest points in the iterative process. It represents feature similarity, and D represents topological consistency. These are the first weight, the second weight, and the third weight, respectively.

6. The BIM-based digital pre-assembly method for glass curtain walls according to claim 1, characterized in that, The comprehensive quality score is calculated based on the assembly accuracy assessment value, point cloud registration error, feature matching degree, and correction amount, including: Based on the assembly accuracy assessment values, determine the components contributing to assembly accuracy; The point cloud error term is determined based on the point cloud registration error and the maximum value of the point cloud registration error. Based on the feature matching degree, determine the feature matching degree item; The correction capability item is determined based on the correction amount and the maximum acceptable correction threshold; The comprehensive quality score is determined based on the assembly accuracy contribution item, the point cloud error item, the feature matching degree item, and the correction capability item.

7. A BIM-based digital pre-assembly system for glass curtain walls, characterized in that, The system includes: The parameter determination module is used to create a parametric BIM 3D model of the glass curtain wall and determine the geometric parameters and installation position parameters of each component. The point cloud registration module is used to acquire point cloud data of the construction site through three-dimensional laser scanning, perform multi-source data registration on the point cloud data, and calculate the point cloud registration error. The feature matching module is used to determine the feature matching degree based on the point cloud registration error; The positioning optimization module is used to obtain the positioning optimization target based on the deviation analysis results and engineering constraints. The accuracy assessment module is used to calculate the assembly accuracy assessment value based on the positioning optimization target. An assembly correction module is used to determine the correction amount during the pre-assembly process of the glass curtain wall based on the assembly accuracy evaluation value. The comprehensive scoring module is used to calculate a comprehensive quality score based on the assembly accuracy evaluation value, point cloud registration error, feature matching degree and correction amount. When the comprehensive quality score is greater than a preset threshold, the corresponding pre-assembly scheme is determined to be adopted. The step of obtaining the positioning optimization objective based on the deviation analysis results and engineering constraints includes: Based on the initial position of the point cloud measurement and the target position corresponding to the initial position, the spatial offset error is determined; The attitude deviation is determined based on the attitude angle measured from the point cloud and the target attitude angle corresponding to the attitude angle. Based on the joint size measured by point cloud and the target joint size corresponding to the joint size, the error of the joint size is determined; The positioning optimization target is obtained by weighted summing of the spatial offset error, the attitude deviation, and the seam size error; Based on the positioning optimization objective, the assembly accuracy evaluation value is calculated, including: The geometric measurement point deviation of each key measurement point is determined based on the measurement point deviation, the corresponding deviation weight, and the total number of key measurement points. The joint error exponential function is determined based on the correction coefficient, joint error, and the total number of joint measurement points. The assembly accuracy assessment value is calculated based on the geometric measurement point deviation and the joint error exponential function. Based on the assembly accuracy assessment value, the correction amount during the pre-assembly process of the glass curtain wall is determined, including: Obtain the current position deviation, which represents the spatial position offset between the currently measured component and the target position; Calculate the derivative of the cumulative error to determine the error rate of change compensation; Calculate the integral of the accumulated error and determine the integral correction term; The correction amount is determined based on the current position deviation, the error change rate compensation, and the integral correction term.

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