A high-rise building steel skeleton inclined column BIM modeling and digital construction control method

Abstract

The application discloses a kind of digital construction control methods of high-rise building steel skeleton inclined column, to solve the precision control problem caused by structural dynamic deformation and error accumulation in construction process.The method uses extended Kalman filter to estimate the real-time global deviation state of structure online, and combines recursive least squares method to identify and adaptively update the structure state space model online;Further, the theoretical design pose, the environment-induced displacement based on environment data prediction, and the active compensation displacement for active suppression of deviation are synthesized to determine the optimal installation pose of the inclined column to be installed;Finally, digital instructions are generated to guide construction, and the actual installation pose is measured to form a closed-loop feedback for correcting the state estimation of the next cycle.The application can actively suppress error accumulation through closed-loop control combining model adaptation and predictive compensation, improving the construction accuracy and robustness of high-rise building.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, specifically to a BIM modeling and digital construction control method for steel-framed inclined columns in high-rise buildings. Background Technology

[0002] High-rise and super high-rise buildings, especially structures containing complex components such as steel-framed inclined columns, require extremely high construction precision. Current construction methods typically rely on theoretical coordinates provided by Building Information Modeling (BIM) or design drawings, and use surveying equipment such as total stations to guide the on-site installation of components.

[0003] However, this approach treats the BIM model as a static baseline, while high-rise building structures are dynamic systems affected by various factors during construction. For example, temperature differences caused by sunlight can lead to uneven thermal expansion and contraction, resulting in slow tilting and torsion of the entire structure; wind loads can also cause real-time swaying of the structure. Furthermore, construction deviations of installed components are transmitted layer by layer and coupled with subsequent structural deformations.

[0004] Therefore, at any given moment, the actual state of the structure deviates from its theoretical design state, rendering the theoretical installation location no longer the optimal choice under the current circumstances. Continuing to install based on static theoretical coordinates not only fails to eliminate existing accumulated deviations but also forces components into unnatural positions, introducing initial stress and solidifying new errors into the structure. These deviations accumulate layer by layer as construction progresses, posing a threat to the final accuracy and structural safety of the building.

[0005] Existing construction control methods are mostly open-loop or simple local closed-loop, lacking real-time perception of the overall dynamic state of the structure, the ability to predict future deformation, and a decision-making mechanism for proactive compensation based on this information. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a BIM modeling and digital construction control method for steel-framed inclined columns in high-rise buildings, which solves the problem that existing technologies are unable to effectively cope with the aforementioned dynamic changes and error accumulation, thus limiting the construction accuracy of super high-rise buildings.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The first aspect of this invention provides a digital construction control method for inclined steel columns in high-rise buildings. This method guides the installation process of the inclined columns by constructing a mechanism combining closed-loop feedback and feedforward control. The method includes the following steps:

[0009] First, a structural state-space model is established to describe the dynamic characteristics of the structure to be built. This model forms the basis for subsequent state estimation and prediction.

[0010] During construction, a multi-source heterogeneous sensor network, such as automated total stations, tilt sensors, and temperature sensors, is deployed at key locations on the structure to collect real-time data on deformation, displacement, and other status parameters of the existing structure. This raw data undergoes preprocessing, including synchronization and noise reduction, to form effective observational data for state estimation.

[0011] This method includes key steps of online state estimation and model adaptation. This step employs the Extended Kalman Filter (EKF) algorithm, fusing the predicted values ​​from the structural state-space model with the actual sensor observations to obtain the optimal estimate of the current real-time global deviation state of the constructed structure. To address the potential discrepancy between the state-space model and the actual physical properties of the structure, this method further introduces the Recursive Least Squares (RLS) algorithm. This RLS algorithm utilizes the prediction residuals output by the EKF to identify and continuously update the state transition matrix in the state-space model online. This mechanism of EKF and RLS working together enables the model on which the control system relies to adaptively approximate the true dynamic characteristics of the structure, improving the accuracy of state estimation.

[0012] After obtaining the real-time global deviation state, the system makes an optimal installation pose decision for the inclined column to be installed. This optimal installation pose is not a fixed theoretical design pose, but is dynamically synthesized from three components:

[0013] Theoretical design pose ( ): The design position and orientation of components extracted from the BIM model under ideal conditions.

[0014] Environmentally induced displacement ( ): By analyzing real-time environmental data (such as temperature and solar radiation intensity) and using finite element models or data-driven models, we can predict the structural deformation and displacement caused by environmental factors during a future construction cycle.

[0015] Active displacement compensation ( This is an active control variable designed to counteract the existing real-time global deviation. This displacement is obtained by establishing an optimization equation aimed at minimizing the future global structural deviation and solving it using mathematical methods such as the pseudo-inverse method.

[0016] The optimal installation pose is synthesized using the following formula:

[0017] ;

[0018] in:

[0019] This represents the optimal installation pose calculated at the end.

[0020] Represents the theoretical design pose;

[0021] Represents the displacement vector caused by environmental changes;

[0022] This represents the active compensation displacement vector.

[0023] Subsequently, the system generates digital guidance commands based on the calculated optimal installation pose. By measuring the current pose of the inclined column to be installed in real time and comparing it with the optimal installation pose, the system can calculate the deviation vectors for six degrees of freedom. This information is then translated into visual instructions on a graphical user interface (GUI) or augmented reality (AR) device to guide on-site personnel in their actions.

[0024] Finally, after the inclined column is installed and fixed, the system measures its final actual installation pose and calculates the installation deviation between the actual installation pose and the optimal installation pose. This installation deviation will serve as a known control input for the extended Kalman filter prediction stage in the next construction cycle, thus incorporating the errors in the construction execution process into the entire control loop, forming a feedback closed loop.

[0025] A second aspect of this invention provides a digital construction control system for steel-framed inclined columns in high-rise buildings. This system serves as the physical carrier for implementing the aforementioned method. The system includes:

[0026] Data acquisition module: Composed of a multi-source heterogeneous sensor network consisting of automated total station, tilt sensor, etc., responsible for collecting structural status data and measuring component pose.

[0027] Data processing module: As the computing core of the system, it includes:

[0028] Model Adaptation and State Estimation Engine: Integrates and runs the Extended Kalman Filter algorithm and the Recursive Least Squares algorithm, responsible for online estimation of the real-time global deviation state of the structure and adaptively updating the state space model.

[0029] Optimal pose decision module: Based on the state estimation results, the module integrates the theoretical design pose, environmentally induced displacement, and active compensation displacement to calculate the optimal installation pose of the inclined column to be installed.

[0030] Digital guidance module: Receives the optimal installation position and generates visual instructions, which are then issued to on-site operators via mobile terminals or AR devices.

[0031] In this system, the modules work together, and the data flow and control flow form a closed loop. The actual installation results obtained by the data acquisition module are fed back to the data processing module to update the state estimation for the next round, ensuring the continuity and convergence of system control.

[0032] This invention provides a BIM modeling and digital construction control method for steel-framed inclined columns in high-rise buildings. It has the following beneficial effects:

[0033] 1. This invention improves construction accuracy and suppresses error accumulation through real-time state estimation and closed-loop feedback mechanisms. The system not only guides components to the target pose but also measures the actual installation pose and feeds back its deviation to the state estimation of the next cycle. This method of incorporating actual construction results into the control loop can effectively correct and compensate for residual errors introduced in the previous stage, solving the technical problem of error propagation and amplification layer by layer in traditional construction.

[0034] 2. This invention achieves model adaptation through online identification of model parameters, enhancing the robustness and environmental adaptability of the control system. By employing the recursive least squares algorithm, the state transition matrix in the state-space model is continuously updated using the predicted residuals, enabling the model to approximate the true physical characteristics of the structure. Therefore, the system can better cope with model mismatch problems caused by factors such as changes in material properties and unmodeled external disturbances, and its control effect is more stable and reliable compared to control methods using fixed models.

[0035] 3. This invention achieves prediction- and compensation-based optimization decision-making through pose synthesis. The final output optimal installation pose is not a fixed theoretical design pose, but rather integrates proactive compensation for existing deviations and feedforward prediction of future environmental impacts. This decision-making mechanism transforms construction control from passive deviation correction to proactive pose optimization, comprehensively handling current errors and anticipated deformations to achieve better global control performance. Attached Figure Description

[0036] Figure 1 This is a schematic diagram of the overall system architecture according to an embodiment of the present invention;

[0037] Figure 2 This is a flowchart illustrating the overall closed-loop control method according to an embodiment of the present invention.

[0038] Figure 3 This is a schematic diagram of the collaborative working mechanism of state estimation and model parameter identification in an embodiment of the present invention;

[0039] Figure 4 This is a schematic diagram illustrating the optimal installation pose synthesis principle of an embodiment of the present invention;

[0040] Figure 5 This is a schematic diagram of the visual guide interface according to an embodiment of the present invention;

[0041] Figure 6 This is a schematic diagram of the sensor network deployment according to an embodiment of the present invention. Detailed Implementation

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0043] Example:

[0044] Please see the appendix Figure 1 -Appendix Figure 6 This invention provides a BIM modeling and digital construction control method for steel-framed inclined columns in high-rise buildings, including:

[0045] During the system initialization phase, it is necessary to import the BIM design model and establish the geometric benchmark. This step is the foundation for all subsequent calculations and controls.

[0046] S111 imports the BIM design model data of high-rise buildings into the database module via standard BIM software interfaces, such as Industry Foundation Classes (IFC) file format or software development kits (SDKs). This BIM model contains the geometric information, material properties, connection relationships, and theoretical design coordinates of all structural components, including steel-reinforced inclined columns, core tubes, floor slabs, and beams. This model provides the theoretical design pose for all subsequent geometric calculations. The theoretically designed pose is described. Includes the three-dimensional spatial coordinates of the component or its key points. And attitude information (such as rotation information represented by Euler angles or quaternions).

[0047] S112, Extract the theoretical design poses of all steel-reinforced inclined column members requiring construction control from the imported BIM design model. ,in This indicates the component number. Simultaneously, it identifies key points in the structure requiring close monitoring. These key points typically include the upper and lower connection nodes of steel-reinforced inclined columns, the corner points of the core tube, and critical support points of large-span beams. For each key point, the system assigns its theoretical design 3D coordinates in the BIM model. Stored as geometric datum data, where These indicate the key point numbers. These theoretical coordinates serve as the reference zero points for all subsequent deviation calculations.

[0048] S113. Establish a unified global coordinate system. Typically, the local coordinate system used in a building BIM model needs to be transformed into a global geodetic coordinate system compatible with the construction site surveying system (such as the observation coordinate system of an automated total station). This transformation is achieved through precise measurements using a pre-established network of control points on site, and by employing methods such as Helmert transformation or affine transformation, the BIM model coordinate system is registered with the site coordinate system. This ensures that the theoretical design pose and the site measurement data are compared and calculated within the same spatial reference system. Once established and calibrated, this global coordinate system serves as the geometric benchmark for the entire construction process.

[0049] After importing the BIM design model and establishing the geometric datum, it is necessary to construct a structural state-space model to describe the evolution of structural deviation states. This model serves as the mathematical foundation for subsequent state estimation and predictive control.

[0050] S121 defines a system state vector, abstracting the deviation states of a high-rise building during construction into a multi-dimensional state vector. ,in These are discrete construction steps or time series numbers. This represents the total number of structural key points determined in step S112. This state vector specifically represents the number of key points determined in step S112. At that time, all The deviation of each keypoint from its theoretical design coordinates. One implementation is that this vector consists of the deviation components of all keypoints in the X, Y, and Z directions in the global coordinate system:

[0051] ;

[0052] in, , , The first Key points in the steps The deviation values ​​along the X, Y, and Z axes.

[0053] S122, Construct an initial state-space model, which consists of a state equation and an observation equation. The state equation describes the evolution of the system's state vector with each construction step, and its form is:

[0054] ;

[0055] in, For steps The state vector at that time. This is the state transition matrix, which quantifies the state transition in the steps... How do existing structural deviations affect the process through the structure's own stiffness? The deviation state. For steps The known control input vector contains the actual installation deviation of the newly installed component in this step. The input matrix describes how the installation deviation affects the overall deviation state of the structure. Let be the process noise vector, representing the random deviation caused by unmodeled factors (such as temperature changes and construction load disturbances), assumed to have a mean of zero and a covariance matrix of . The Gaussian distribution.

[0056] The observation equation describes the mathematical relationship between sensor measurements and the system state vector, and its form is:

[0057] ;

[0058] in, In the steps At that time, the sensor network collects and preprocesses the observation vector. The observation matrix is ​​used to represent the abstract state vector containing all keypoint deviations. Mapped to physical quantities that can be directly measured by sensors. Let the measurement noise vector represent the sensor's own measurement error and environmental interference, and assume it follows a mean of zero and a covariance matrix of... The Gaussian distribution.

[0059] S123, Determine the specific values ​​of the initial model matrix, for the initial state transition matrix. and input matrix To determine the appropriate parameters, those skilled in the art can establish a finite element analysis (FEM) model of the structure based on the imported BIM model. By applying a unit displacement or unit load, the corresponding mechanical response can be calculated, thereby obtaining the influence coefficient matrix between various deviations. This method is well-known in the field and will not be elaborated further here. One specific implementation is to calculate the parameters in step [unclear - possibly "step 1"]. After applying unit displacement to each key point at the time, in step The displacements generated at each key point constitute the state transition matrix. .

[0060] For the observation matrix The determination of this matrix is ​​directly determined by the type and location of the sensors. If the sensor (such as an automated total station) directly measures the first... The three-dimensional coordinates of the key points are then The row in the matrix corresponding to the sensor measurement value, in the row corresponding to the first... The column containing the deviation components of each key point is set to 1, while the rest are 0. If the sensor (such as an inclinometer) measures the attitude angle, then the elements in the corresponding row are conversion coefficients derived from the structural geometry, which convert the three-dimensional displacement of the relevant points into attitude angles.

[0061] For the process noise covariance matrix and measurement noise covariance matrix The determination of the matrix The diagonal elements are typically set according to the measurement accuracy (square of the standard deviation) specified in the sensor device manual. Matrix The values ​​are set based on empirical estimates of uncertainties in the construction site environment, and are used to characterize the confidence level of the model's predictions. These initial setting matrices, especially the state transition matrix... This will be corrected online in subsequent steps.

[0062] In the real-time data acquisition and preprocessing step, the system needs to acquire data from multi-source heterogeneous sensors deployed throughout the structure and perform time synchronization processing on these data to form a unified observation dataset that can be used for subsequent state estimation.

[0063] S211, initiate periodic data acquisition from all sensors in the data acquisition layer. The multi-source heterogeneous data may specifically include: three-dimensional coordinate data of the monitoring prism acquired by an automated total station; tilt angle data of the structural surface acquired by a MEMS tilt sensor; strain data of the component surface acquired by a strain sensor; and temperature data of the structural surface acquired by a temperature sensor. Each sensor converts the acquired raw physical quantities into digital signals through its associated data acquisition unit and transmits them to the data processing layer via a wireless data transmission unit.

[0064] S212, Time synchronization processing is performed on the multi-source heterogeneous data. Since the multi-source heterogeneous data comes from sensors with independent clocks and different sampling frequencies, there is a problem of inconsistent timestamps when the data arrives at the data processing layer. To ensure the effectiveness of data fusion, time synchronization processing is required for all collected data.

[0065] S213, a specific time synchronization implementation method is to set up a master time server within the local area network of the construction site. All data acquisition units support Network Time Protocol (NTP) or a higher-precision Precision Time Protocol (PTP, IEEE 1588) at the hardware or software level. During system operation, each data acquisition unit periodically synchronizes with the master time server to obtain a unified high-precision time reference.

[0066] S214, upon acquiring each frame of data, each data acquisition unit immediately appends a precise timestamp to that frame using its synchronized local clock. This timestamp is sent to the data processing layer along with the data. After receiving the data with the unified reference timestamp, the data access and preprocessing module can, according to a preset time step (e.g., 1 second), align the measurements from different sensors at the same time using algorithms such as interpolation or nearest-neighbor sampling, thereby generating data at discrete time points. Time-synchronized observation vectors .

[0067] After completing the time synchronization of multi-source heterogeneous data, it is necessary to perform a unified transformation of the spatial coordinate system of the observation data and implement noise reduction processing to improve data quality and provide reliable input for subsequent state estimation algorithms.

[0068] S221, establish the first coordinate system for the observation data. Since different sensors (such as multiple automated total stations) may operate in their own independent measurement coordinate systems, while sensors such as tilt and strain output physical quantities in a local coordinate system based on their own installation orientation, it is necessary to transform all these measurement data into the global coordinate system established in step S113.

[0069] S222, For 3D coordinate data acquired by an automated total station, the transformation parameters from each instrument's coordinate system to the global coordinate system can be calculated using the coordinates of pre-measured common control points. These transformation parameters typically include a rotation matrix. Translation vector Coordinates of any instrument The coordinates can be converted to global coordinates using the following formula. :

[0070] ;

[0071] For the calculation of the coordinate transformation parameters, those skilled in the art can use standard methods such as the least squares method, which are well-known techniques in the field and will not be elaborated here.

[0072] S223. For locally measured physical quantities such as tilt angle and strain, the measured values ​​need to be converted into constraints on structural deviations in the global coordinate system based on the precise installation position and orientation information recorded by the sensor in the BIM model. For example, the tilt angle reading of a tilt sensor installed in the YZ plane around the X-axis... It can be converted into the relative displacement of a specific monitoring point in the Z direction through geometric relationships, so that it can be uniformly incorporated into the observation equation based on the global coordinate system.

[0073] S224 describes the denoising process for the observation data after unifying the coordinate system. The raw data acquired by the sensors inevitably contains high-frequency noise, which may originate from instrument electrical noise, minor vibrations, or atmospheric disturbances. To extract the true structural deformation signal, the time-series data needs to be filtered and denoised.

[0074] S225, a specific noise reduction implementation method is to use digital filtering algorithms. For example, Kalman filtering (here, Kalman filtering is used as a signal processor, distinct from the system-level Kalman filtering used for state estimation later), moving average filtering, or low-pass filtering (such as Butterworth filtering) can be applied to the time series data of each monitoring point. Taking moving average filtering as an example, at time... Filtered observations It can be calculated using the following formula:

[0075] ;

[0076] in, It is a moment The original observations, This refers to the size of the filtering window. The choice of filter and its parameter settings depend on the noise characteristics of the site and the real-time requirements of the signal. After coordinate unification and denoising, the final observation vector is formed. It will be used as input for the state estimation step.

[0077] In the state estimation and model adaptive correction steps, the system first fuses noisy real-time observation data with an uncertain state-space model based on the Extended Kalman Filter (EKF) algorithm to obtain the most likely true global deviation state of the structure. When the state-space model is linear, the Extended Kalman Filter algorithm can degenerate into the standard Kalman Filter algorithm.

[0078] The algorithm is executed at each time step. Each stage includes a prediction phase and an update phase.

[0079] S311, Execute the prediction phase. This phase is based on steps... Optimal posterior state estimation and known control inputs Using state equations to predict steps Prior state :

[0080] ;

[0081] in, For steps The time-optimal posterior state estimate vector. To start from steps To the steps The state transition matrix may have been corrected online in the previous cycle.

[0082] S312, while predicting the state, the system also needs to predict the error covariance of the prior state estimate. The covariance characterizes the degree of uncertainty in the prediction results:

[0083] ;

[0084] in, For steps The posterior error covariance matrix at that time, Let be the covariance matrix of the process noise.

[0085] S313, Perform the update phase. When in step... Obtain the new observation vector after preprocessing. Then, the system first calculates the Kalman gain. This gain is used to balance the confidence of prior state predictions with the confidence of new observation data:

[0086]

[0087] in, For steps The observation matrix at that time, This is the covariance matrix for measuring noise.

[0088] S314, using the calculated Kalman gain and observation vector Compared with predicted observations The difference between the two (i.e., the prediction residual) is used to correct the prior state estimate, thus obtaining the current step. Optimal posterior state estimation :

[0089] ;

[0090] S315, Finally, the system updates the posterior error covariance matrix. This reflects the reduction in state estimation uncertainty after incorporating new observational data:

[0091] ;

[0092] in, It is an identity matrix.

[0093] After the above steps, the system obtains the optimal posterior state estimation vector. This represents the best estimate of the current global structural deviation state, and this result will serve as the basis for subsequent optimal installation pose decisions. Simultaneously, the prediction residuals generated during the calculation process ( This will be passed to the online model parameter identification module to drive the adaptive correction of the model.

[0094] To address potential discrepancies between the initially established state-space model and the actual physical properties of the structure, this invention further includes a step of online identification and correction of model parameters. This step utilizes the prediction residuals generated during the extended Kalman filtering process to estimate and update key unknown parameters in the state transition matrix online using the recursive least squares (RLS) algorithm.

[0095] S321, regarding the state transition matrix Perform parameterization. Transform the state transition matrix. Elements that characterize the physical properties of the structure and contain uncertainties (such as coefficients related to node connection stiffness and material elastic modulus) are extracted to form a parameter vector to be identified. ,in This represents the total number of parameters to be identified. This operation rewrites the prediction part of the state equation into a linear regression model containing this parameter vector, in the form:

[0096] ;

[0097] in, It can be done by steps Actual observed values The calculated target observations; It is a regression vector whose elements are derived from the steps Optimal state estimation It consists of other known information; This represents the error term of the regression model.

[0098] S322 employs the Recursive Least Squares (RLS) algorithm with a forgetting factor at each time step. Update the parameter vector recursively The estimated value The algorithm specifically includes the following calculation process:

[0099] S323, Calculate the gain vector :

[0100] ;

[0101] in, This is the covariance matrix of the parameters estimated in the previous step; The forgetting factor, with values ​​ranging from (0,1), is used to adjust the weights of old and new data in parameter estimation. A smaller value indicates better performance. The new data will be given greater weight, thereby enhancing the algorithm's ability to track the time-varying nature of parameters.

[0102] S324, Calculate the estimation error This error reflects the difference between the predicted values ​​based on the old parameters and the current observations:

[0103] ;

[0104] in, This is the optimal estimate of the parameter vector obtained in the previous step.

[0105] S325, Update the estimated values ​​of the parameter vector. :

[0106] ;

[0107] S326, Update the covariance matrix of the parameter estimates For use in the next step:

[0108] ;

[0109] in, It is an identity matrix.

[0110] S327, the latest estimated parameter vector Substituting into its parameterized expression, the updated state transition matrix is ​​obtained by reconstruction. The updated matrix It will be used for the next time step. The prediction stage of the extended Kalman filter is used to achieve online adaptive correction of the state-space model.

[0111] This invention achieves close collaboration between the Extended Kalman Filter (EKF) state estimation algorithm and the Recursive Least Squares (RLS) model parameter identification algorithm within the model adaptation and state estimation engine. This mechanism forms a two-way feedback loop, enabling the system to simultaneously estimate the current deviation state of the structure and dynamically correct the internal model parameters used to describe the structural behavior, thereby enhancing the system's adaptability to changes in the structural mechanical properties under complex construction environments.

[0112] S331, EKF provides prediction residual information for RLS at each time step. During the EKF update phase (refer to S314), the Kalman filter algorithm calculates a prediction residual term. This residual term represents the difference between the current actual observations and the predictions based on the existing model of the system. This difference includes not only random measurement noise but also reflects the systematic bias that arises when the existing model parameters do not match the actual physical properties of the structure. This residual information is transmitted to the RLS parameter identification module in real time.

[0113] S332, RLS updates model parameters using the prediction residuals. After receiving the prediction residuals from EKF, the RLS module uses them as the estimation error in its own identification algorithm. (Refer to S324). By calculating this error, the RLS algorithm adjusts the state transition matrix according to its recursive formula (refer to S323 to S326). The unknown physical parameters, represented in a parameterized form, are updated. This update causes the state transition matrix to... It can more accurately reflect the actual mechanical behavior of the structure at the current stage, such as stiffness and connection characteristics.

[0114] S333, RLS feeds the updated model parameters back to EKF. The RLS module updates the parameter vectors. Subsequently, a new state transition matrix was reconstructed. (Refer to S327). This is the corrected state transition matrix. Immediately used for the next time step The EKF prediction phase (see S311 and S312).

[0115] S334, through the aforementioned two-way interaction mechanism, the prediction accuracy of EKF is improved. When the state transition matrix... After being more accurately corrected by RLS, EKF is able to generate more accurate prior state predictions. This more accurate prediction improved the EKF's estimation results. It more closely approximates the true deviation state of the structure and can better distinguish between random noise and systematic biases in the observations. Conversely, a more accurate EKF state estimate can provide a more reliable regression vector for RLS. This further improves the accuracy and convergence speed of parameter identification. This collaborative working mechanism ensures that the system can accurately estimate the structural state and continuously optimize the model parameters throughout the construction process, thereby improving the robustness and adaptability of the entire control system.

[0116] In the optimal installation pose decision-making step, it is necessary to first predict the structural displacement caused by changes in environmental factors. The purpose of this step is to pre-compensate for the position drift of the structural installation reference point caused by environmental factors such as temperature changes during the period from the completion of the previous state estimation to the imminent installation and fixing of the current component.

[0117] S411, the prediction process utilizes real-time temperature data of the structure collected by the temperature sensor network in the data acquisition layer, and can combine it with short-term (e.g., 1-2 hour) weather forecast data provided by external meteorological services, such as forecast temperature and solar irradiance.

[0118] S412, a specific implementation of predicting environmentally induced displacement, involves calculation based on a thermo-mechanically coupled finite element model (FEM). A predicted temperature field is input into the established structural finite element model, generated based on the real-time and predicted temperature data obtained in step S411. The model calculates the structural deformation caused by thermal expansion and contraction based on the thermophysical properties of the material. The core of the calculation is solving for the deformation caused by thermal strain. The resulting displacement, the thermal strain is:

[0119] ;

[0120] in, It is the linear thermal expansion coefficient of the material, which can be obtained from the material property library in the BIM model; It is the predicted temperature applied to the model; This is the reference temperature for the structural stress free state. The finite element solver solves the static equilibrium equations that include this thermal strain term, and finally outputs the predicted vector of the environmentally induced displacement of the installation reference point of the component to be installed at the predicted time point. .

[0121] S413, another approach to predicting environmentally induced displacement is to use a data-driven statistical regression model. This method establishes a mathematical relationship between temperature changes in key structural components and displacement at the installation reference point by analyzing historical monitoring data stored in a database module. For example, a multivariate multinomial regression model can be constructed:

[0122] ;

[0123] in, It is the first Displacement of a certain direction at an installation reference point; It comes from Temperature readings at key temperature measurement points; These are regression coefficients. These coefficients are determined by least-squares fitting of historical data. When making predictions, the predicted temperature value obtained in step S411 is substituted into this regression model to directly calculate the environmentally induced displacement prediction vector. .

[0124] S414, regardless of the implementation method, the final output of this step is the displacement prediction vector. This will serve as an important compensation factor in the final synthesis calculation of the optimal installation pose.

[0125] After predicting the environmentally induced displacement, the optimal pose decision module calculates an active compensation displacement. This step is the core of the active closed-loop control of this invention. Its purpose is not simply to correct the current position deviation of the installation reference point, but to calculate a specific installation offset applied to the component to be installed, in order to actively offset and suppress the transmission and amplification of the historical cumulative deviations already existing in the entire structure to subsequent construction stages.

[0126] S421, this solution process calls upon two key inputs: the current-time optimal posterior state estimate vector output by the extended Kalman filter algorithm. This vector comprehensively represents the current global bias distribution of the structure; and the latest state-space model after online correction by the recursive least squares algorithm, especially the updated state transition matrix. and input matrix .

[0127] S422, the theoretical basis for the solution is the predictive power of the state-space model. According to the state equations, if no active control is applied (i.e., the installation deviation of the new component is zero), then the existing deviation... The mechanical transmission characteristics of the structure itself will evolve into deviations in the next construction step. The goal of this step is to introduce a specific installation deviation into the component to be installed. This causes the overall structural deviation increase due to the installation deviation. It can offset the effects transmitted by historical biases to the greatest extent possible.

[0128] S423, this objective can be formalized as an optimization problem, namely, finding an optimal control input (installation deviation). This makes the predicted state vector for the next construction step... Minimize the norm of, i.e. According to the state equation The ideal control objective is to make:

[0129] ;

[0130] S424, Solving the above equation will yield the required active compensation displacement vector. This vector is the optimal control input. Due to the input matrix Since the matrix is ​​usually not square, it cannot be directly inverted. Therefore, the pseudo-inverse method is used to solve the least-squares solution of the equation:

[0131] ;

[0132] in:

[0133] This is the first one to be installed. The active compensation displacement vector calculated for each component.

[0134] It is the input matrix The Moore-Penrose pseudoinverse. One way to calculate it is... .

[0135] It is the currently updated state transition matrix.

[0136] It is the optimal estimation vector of the global structural deviation at the current moment.

[0137] S425, the calculated active compensation displacement vector This vector represents the three-dimensional translation and attitude adjustment of the installation position of the component to be installed relative to its theoretical design pose, in order to achieve optimal control. This vector is a key decision quantity for achieving active error suppression and will be used in the final synthesis of the optimal installation pose in the next step.

[0138] After obtaining the predicted environmentally induced displacement and the active compensation displacement calculation results, the optimal pose decision module needs to synthesize these calculations with the theoretical design pose of the component to be installed, thereby generating the best installation target pose to guide on-site construction.

[0139] S431, Extract the theoretical design pose of the component to be installed from the BIM design model. The pose includes the component's theoretical spatial position (e.g., barycentric coordinates) and theoretical orientation (e.g., Euler angles or quaternions) in the global coordinate system. This data has been imported and stored in step S111.

[0140] S432, predicting the environmentally induced displacement vector and active compensation displacement vector This is superimposed onto the theoretically designed pose. The final synthesized optimal installation pose is obtained. It can be represented as:

[0141] ;

[0142] in:

[0143] It is the calculated optimal installation pose of the component to be installed, which includes its final target position and orientation.

[0144] It is the theoretical design orientation of the component to be installed.

[0145] This is the environmentally induced displacement prediction vector calculated in step S414. This vector is an estimate of the displacement that the structure may undergo due to environmental factors (such as temperature) at the time of installation. Its function is to ensure that the final stable position of the component under environmental influence can return to the theoretical design position.

[0146] This is the active compensation displacement vector calculated in step S424. This vector is a key control quantity used to offset and suppress the current historical cumulative deviation of the structure, and its function is to minimize the global deviation of the entire structure after the installation of this component.

[0147] S433, it should be noted that the superposition of displacement vectors should take into account their physical meaning. If If it includes position and attitude, then and It should also include the corresponding position offset and attitude adjustment. Position components can be directly vector-added, while the superposition of attitude components may require methods such as quaternion multiplication or Euler angle transformation to ensure the correctness of attitude superposition. The specific implementation method can be selected by those skilled in the art based on the pose representation method used; this is well-known technology in the field and will not be elaborated upon here.

[0148] S434, the final synthesized It contains the precise target position and attitude information of the component to be installed in the global coordinate system. This information will be output to the digital guidance module to generate visual installation instructions, thereby guiding the on-site installation equipment to accurately place the component in this optimal pose. This optimal installation pose is derived after considering the current actual deviation of the structure, environmental influences, and active error suppression strategies, ensuring the accuracy of the entire construction process and the final quality of the structure.

[0149] In the digital guidance, execution, and closed-loop feedback steps, the system first needs to synthesize the optimal installation pose from the previous step. Transform them into visual instructions that are easy for on-site construction workers to understand and execute.

[0150] S511, the digital guidance and execution module receives the optimal installation pose output from the optimal pose decision module. Simultaneously, this module acquires the current three-dimensional coordinates of the monitoring prism on the component to be installed, tracked in real time by measuring equipment such as automated total stations, through the data acquisition layer, thereby calculating the current pose of the component to be installed in real time. .

[0151] The S512 module continuously calculates the current pose in a high-frequency loop. With optimal installation position Deviation vector between The deviation vector contains information for six degrees of freedom: three translational components ( ) and three rotational components ( (This section provides a comprehensive description of the range and direction of adjustments required for the components to be installed.)

[0152] S513, the system is based on the calculated deviation vector Generate visualization instructions. One specific implementation involves simultaneously rendering the current pose and optimal installation pose of the component to be installed within a 3D graphical user interface. The optimal installation pose can be displayed as a semi-transparent target model, while the current pose is displayed as a solid model. The interface also clearly displays the translational deviations along the X, Y, and Z axes and the rotational deviations around each axis in numerical form.

[0153] To further enhance the intuitiveness of the guidance, the S514 interface can also generate dynamic indicator arrows to visually indicate the direction of movement or rotation. When the deviation value is large, the numbers and arrows can be displayed in red; as the component approaches the target position, the color gradually turns yellow; when the deviation enters the preset installation tolerance range (e.g., translation less than 5mm, rotation less than 0.1°), the color turns green, thus providing clear feedback to the operator.

[0154] Another implementation of the S515 is through augmented reality (AR) technology. Operators wearing AR glasses can see a 3D virtual image of the optimal installation pose of the component to be installed, superimposed on the actual construction scene within their real-world view. The operator's task is to manipulate the lifting equipment to perfectly align the real component with this virtual image. Simultaneously, deviation data and indicator arrows can be displayed in real-time within the AR glasses' field of view.

[0155] S516 These generated visual instructions are transmitted in real time via a wireless network to mobile terminal devices (such as industrial tablets) or AR glasses held by on-site operators. Based on the real-time updated graphics and data on the screen, operators perform precise operations on the lifting equipment until the deviation between the current pose of the component to be installed and the optimal installation pose meets the engineering accuracy requirements.

[0156] After the component to be installed is precisely positioned and fixed, the system needs to measure and verify its final installation result. This measurement data is not only used to record the actual installation posture of the component, but also a key link in forming a closed-loop feedback for the entire control system.

[0157] S521: Before the final welding or fastening of the component to be installed, but after the initial fixing of the component is completed, immediately start the high-precision measuring equipment in the data acquisition layer, such as an automated total station, to accurately measure the preset monitoring points (such as prisms) on the component to be installed. Obtain the three-dimensional coordinate data of these monitoring points in the global coordinate system.

[0158] S522, based on the measured coordinates of the monitoring points and their geometric relationship with the component body in the BIM model, the actual installation posture of the component to be installed is accurately calculated using methods such as least squares fitting. This pose includes the actual position and actual orientation of the component.

[0159] S523, the actual installation position of the component The optimal mounting pose synthesized in step S432 By comparing the results, the final installation deviation can be calculated. This installation deviation reflects the precision of on-site construction operations, as well as random errors that could not be completely eliminated in the measurement, prediction, and control processes.

[0160] S524, the installation deviation As the "control input" for this construction step Part of this is fed back to the data access and preprocessing module in real time. This actual installation deviation... This data will be stored and, at the start of the next construction cycle, used along with overall structural deviation data collected from sensors, as input to an extended Kalman filter to update the structural state estimate.

[0161] S525, by comparing the actual installation results with the optimal installation pose and using the actual installation deviation as the known control input for the next round of state estimation, forms a complete closed-loop feedback mechanism. This mechanism ensures that even if unavoidable installation deviations exist during construction, these deviations can be accurately perceived and quantified by the system and incorporated into subsequent state estimation and optimal pose decisions. In this way, the system can continuously adjust and optimize the control strategy, thereby suppressing the accumulation and propagation of errors and ultimately ensuring the overall construction accuracy of high-rise building structures.

[0162] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings, characterized in that, Includes the following steps: a. Construct a structural state space model of the structure to be built based on Building Information Modeling (BIM); b. Collect multi-source heterogeneous data of the existing structure in real time and perform preprocessing; c. Based on the structural state space model and the multi-source heterogeneous data, estimate the real-time global deviation state of the constructed structural part online; d. Determine the optimal installation pose of the inclined column to be installed based on the real-time global deviation status; e. Generate and issue digital guidance instructions corresponding to the optimal installation pose to guide on-site construction; f. Measure the actual installation posture of the inclined column to be installed, and use the deviation between the actual installation posture and the optimal installation posture as feedback for estimating the real-time global deviation state in the next construction cycle, thus forming a closed-loop control.

2. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 1, characterized in that, Step c, which involves online estimation of the real-time global deviation state of the established structural portion, specifically includes: Using the extended Kalman filter algorithm, combined with the state estimation results of the previous construction cycle and the currently collected multi-source heterogeneous data, the real-time global deviation state of the constructed structure in the current construction cycle is calculated.

3. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 2, characterized in that, The method further includes: The recursive least squares algorithm is used to identify and update the state transition matrix in the structural state space model online based on the prediction residuals output by the extended Kalman filter algorithm, so as to achieve adaptive correction of the model.

4. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 1, characterized in that, Step d, determining the optimal installation position of the inclined column to be installed, specifically includes: The optimal installation pose is obtained by synthesizing the theoretical design pose of the inclined column to be installed, the environmentally induced displacement predicted based on environmental data, and the active compensation displacement used to actively suppress the real-time global deviation state.

5. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 4, characterized in that, The calculation of the active compensation displacement is obtained by establishing an optimization equation with the goal of minimizing the real-time global deviation state and solving the equation; the active compensation displacement aims to minimize the global deviation of the existing structure after the inclined column to be installed is installed.

6. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 4, characterized in that, The prediction of environmentally induced displacement is achieved by acquiring real-time environmental data from the construction site and using a finite element model or a data-driven model to predict structural displacement caused by changes in environmental factors.

7. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 2, characterized in that, Step f uses the deviation as feedback, specifically including: The installation deviation between the actual installation pose and the optimal installation pose is calculated, and the installation deviation is used as a known control input and substituted into the prediction stage of the extended Kalman filter algorithm in the next construction cycle.

8. The method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 1, characterized in that, Step e, which generates and issues digital guidance instructions, specifically includes: The current pose of the inclined column to be installed is acquired in real time, the deviation vector between it and the optimal installation pose is calculated, and a visual instruction containing numerical values ​​and translation / rotation directions is generated based on the deviation vector.

9. A method for BIM modeling and digital construction control of steel-framed inclined columns in high-rise buildings according to claim 8, characterized in that, The visualization instructions are presented through the graphical user interface of a mobile terminal or an augmented reality device.

10. A system applied to the control method of claim 1, characterized in that, include: The data acquisition module is used to collect multi-source heterogeneous data from the existing structure in real time. A data processing module, connected to the data acquisition module, includes: The model adaptation and state estimation engine is used to estimate the real-time global deviation state of the built structure part online based on the structure state space model and the multi-source heterogeneous data, using the extended Kalman filter algorithm and the recursive least squares algorithm, and adaptively update the structure state space model. The optimal pose decision module is used to synthesize the theoretical design pose, environmentally induced displacement, and active compensation displacement based on the real-time global deviation state to determine the optimal installation pose of the inclined column to be installed. A digital guidance module, connected to the data processing module, is used to generate and issue digital guidance commands corresponding to the optimal installation posture to the site; The data acquisition module is also used to measure the actual installation position of the inclined column to be installed, and feed the measurement result back to the data processing module for state estimation in the next construction cycle, thereby forming a closed-loop control system.

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