A BIM and AR-based electromechanical pipeline installation monitoring system

Abstract

The present invention discloses a BIM and AR-based electromechanical pipeline installation monitoring system, which relates to the technical field of pipeline installation. The BIM and AR-based electromechanical pipeline installation monitoring system includes: a three-dimensional scanning and data preprocessing module, a drawing three-dimensional modeling module, an AR difference display and error identification module, a correction suggestion and real-time guidance module, and a recording and feedback improvement module; through three-dimensional scanning and data preprocessing, high-precision actual pipeline data collection is achieved, providing a solid foundation for subsequent analysis. The introduction of the drawing three-dimensional modeling module makes the comparison between the design and the actual installation more intuitive. The application of the AR difference display and error identification module not only improves the accuracy of error identification, but also effectively evaluates the severity of the error by predicting the error threshold through the regression model. The correction suggestion and real-time guidance module can provide correction suggestions in real time, guide the construction personnel's operation, and improve construction efficiency and quality.

Description

Technical Field

[0001] The present invention relates to the technical field of pipeline installation, and in particular to a BIM and AR-based electromechanical pipeline installation monitoring system. Background Art

[0002] The mechanical and electrical systems of high-rise and super-high-rise buildings typically include water supply, hot water, drainage, fire protection, air conditioning, heating, smoke and exhaust, lighting, power, lightning protection, and intelligent systems. These systems require a vast number of equipment and pipelines, which are intertwined and interconnected like a spider web, making their comprehensive layout and construction quite challenging.

[0003] At present, the construction of electromechanical pipelines requires strict construction in accordance with the drawings. However, errors are still prone to occur during the actual construction process, and due to the influence of actual construction conditions, construction cannot sometimes be carried out completely according to the drawings. The accumulation of errors will eventually cause large construction errors. Publication No. CN116050040A discloses an intelligent layout method and system based on the spatial characteristics of pipeline layout, which includes the following steps: S1. Measuring the architectural parameters of the construction building where the pipeline is to be arranged and constructing a digital twin scene; S2. Importing the original pipeline layout drawings into the digital twin scene to construct a pipeline layout model; S3. Obtaining the process parameters of the pipeline layout to evaluate and optimize the pipeline layout model; S4. Obtaining the pipeline layout spatial characteristics and construction sequence of the optimized pipeline layout model; S5. Performing pipeline layout construction according to the pipeline layout spatial characteristics and construction sequence; S6. Measuring the actual spatial characteristics of the pipeline during construction to monitor the construction process errors; S7. Dynamically adjusting and correcting the pipeline layout construction process based on the monitoring results.

[0004] As shown in the above technology, there are currently more professional systems to replace drawings for viewing, so as to carry out more accurate pipeline construction. However, how to intuitively check the differences between actual construction and drawings, and how to intuitively identify error values ​​for correction still need to be improved. Failure to identify errors and correct them in a timely manner will affect the subsequent construction accuracy. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a BIM and AR-based electromechanical pipeline installation monitoring system, which solves the problem that the existing technology cannot intuitively view the differences between actual construction and drawings, and cannot intuitively identify error values ​​for correction.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A BIM and AR-based electromechanical pipeline installation monitoring system, comprising:

[0007] Drawing 3D modeling module, used to create 3D drawing models of electromechanical pipelines based on design drawings;

[0008] The actual pipeline 3D modeling module uses the AR device's shooting function to capture images of the actual pipeline during the actual installation process, processes the images to extract point cloud data of the actual pipeline, generates 3D scan data through pre-processing steps such as denoising, registration, and stitching, and creates a model of the actually installed pipeline based on the 3D scan data;

[0009] The AR differentiation display and error identification module is used to import 3D drawing models and actual models into AR devices, distinguish the two models, calculate spatial distance to identify errors, optimize error calculation methods to improve accuracy, and establish a regression model trained based on historical data to predict threshold comparison errors and determine the severity of errors. It also provides error marking and correction suggestion display functions.

[0010] The correction suggestion and real-time guidance module classifies and quantifies identified errors, defines the size and importance of each error, generates correction suggestions, and displays them in real time on the AR device to guide construction personnel in making adjustments.

[0011] The recording and feedback improvement module records the pipeline spatial feature data before and after correction and the error data identified during the correction process, and records the correction suggestion data as historical data to train and improve the regression model.

[0012] Preferably, the working content of the drawing three-dimensional modeling module specifically includes: using BIM software to create a three-dimensional drawing model of the electromechanical pipeline according to the design drawing, wherein the parameters include defining the type, size, position and connection relationship of the pipeline.

[0013] Preferably, the working content of the actual pipeline 3D modeling module specifically includes:

[0014] Point cloud data extraction: Select an AR device with a depth sensor to obtain depth information through other structured light or stereo vision methods, extract depth data from the image, and generate point cloud data;

[0015] Data preprocessing:

[0016] Denoising: removing unwanted noise from scan data;

[0017] Registration: Use the ICP algorithm to align images from multiple perspectives into a common coordinate system;

[0018] Stitching: Use the point cloud stitching algorithm to merge the registered multi-view point cloud data into a complete 3D model data;

[0019] Actual modeling: Import the pre-processed 3D model data into the modeling software, generate polygonal meshes or surfaces from the point cloud data, and create pipeline models based on the meshes or surfaces.

[0020] Preferably, the specific steps of the actual modeling include:

[0021] A1. Point cloud data import and preparation:

[0022] Import point cloud data: Use modeling software to import pre-processed point cloud data, denoted as P={p1, p2, ..., p n}, where p i Represents the three-dimensional coordinates of the i-th point;

[0023] Data preparation: Clean the point cloud data and remove duplicate points and isolated points, which are recorded as P c =C(P); and according to the project requirements, the point cloud data is segmented and recorded as P s =S(P c );

[0024] A2. Automatic or semi-automatic mesh generation:

[0025] Automatic mesh generation: Use the built-in meshing tool of the software to convert the point cloud data into a polygonal mesh, denoted as M=Mesh(P s ), set the grid parameters including resolution r and smoothness s;

[0026] Semi-automatic adjustment: Use editing tools to manually adjust the mesh shape of the automatically generated mesh, denoted as M a =Adjust(M);

[0027] A3. Pipeline Information Extraction and Identification:

[0028] Pipeline recognition based on machine learning: Apply the pipeline recognition algorithm based on machine learning to process the grid data, denoted as I=Recognize(M a ), where I represents the extracted pipeline information;

[0029] When training the model, the labeled pipeline data is used as the training set, which is recorded as D={(a1, b1), (a2, b2), ..., (a m , b m )}, where a k represents the input feature, b k Indicates the marked pipeline information;

[0030] Manual verification and adjustment: Manual verification of the identified pipeline information, denoted as I v =Validate(I); Manually adjust the pipeline with identification error, recorded as I f =Adjust(I v );

[0031] A4. 3D model construction and optimization:

[0032] Construct a 3D model: Construct a 3D model based on the extracted pipeline information, denoted as M 3D =Construct(I f ) to ensure that the geometry, size and position of the model are consistent with the actual pipeline;

[0033] Model optimization: simplify the model structure and reduce the number of polygons, recorded as M o =Optimize(M 3D ) to improve the rendering speed and interactive performance of the model;

[0034] A5. Export and Integration:

[0035] Export model: Export the constructed 3D model to a format suitable for subsequent analysis and application, denoted as F=Export(M o );

[0036] Integration and verification: Integrate the exported 3D model into the BIM platform or AR device to verify the accuracy, completeness and consistency of the model, denoted as V(F).

[0037] Preferably, the work contents of the AR difference display and error identification module specifically include:

[0038] Importing models into AR devices: Importing both the drawing model and the actual model into AR devices or AR-enabled applications in appropriate formats;

[0039] Differentiated display: Display two models simultaneously on an AR device using different colors, transparencies, or line styles. This can be achieved by setting the material properties of the models.

[0040] Error identification: Calculate the spatial distance between the corresponding points or line segments in the drawing modeling and the actual modeling. Assume that the position offsets of the spatial distance on the X, Y, and Z coordinate axes are Δx, Δy, and Δz respectively:

[0041] Δx=x2-x1;

[0042] Δy=y2-y1;

[0043] Δz=z2-z1;

[0044] Calculate the spatial error distance d using the Euclidean distance formula:

[0045] ;

[0046] Among them, (x1, y1, z1) and (x2, y2, z2) are the coordinates of the corresponding points in the drawing modeling and the actual modeling respectively;

[0047] Error threshold setting: Define an error threshold to determine whether the difference between two models constitutes an error. The error threshold is based on the prediction of the regression model trained with historical data and is automatically adjusted according to project requirements and accuracy standards.

[0048] Error marking: Use visual markers and voice or text prompts on the AR device to mark errors that exceed the threshold;

[0049] Error marking: For points or line segments that exceed the error threshold, they are marked using visual markers and voice or text prompts on the AR device.

[0050] Preferably, the step of predicting the error threshold of the regression model based on historical data training specifically includes:

[0051] B1. Data collection and preprocessing:

[0052] Data collection: Collect data on error thresholds, installation accuracy, pipeline types, materials, and environmental conditions from multiple completed projects, and record the actual error thresholds used in each project and the corresponding installation results;

[0053] Data preprocessing: Clean the collected data, remove incomplete or abnormal data points, and normalize the data to ensure that different features have the same weight in model training;

[0054] B2. Feature selection and model training:

[0055] Feature selection: Select features related to error threshold setting from the collected data, including pipeline type, material, environmental conditions, and installation accuracy requirements. These features will be used as input variables for the regression model.

[0056] Model training: Select linear regression as the model and train the regression model using the preprocessed data. The goal is to predict the appropriate error threshold for a given feature. Then use the mean squared error, root mean squared error, or R² score to evaluate the model's performance.

[0057] B3. The regression model formula is expressed as:

[0058] T=β0+β1·F1+β2·F2+...+β p ·F p ;

[0059] Among them, T is the prediction error threshold, β0 is the intercept term, (β1, β2, ..., β p ) are the regression coefficients, (F1, F2, ..., F p ) are the selected features;

[0060] During the model training phase, the regression coefficients (β0, β1, ..., β p );

[0061] B4. Real-time application and feedback:

[0062] In new projects, we use the trained regression model to predict the appropriate error threshold based on the project characteristics and accuracy requirements. We then apply the predicted error threshold to the error identification process on the AR device to guide construction workers during pipeline installation.

[0063] After the project is completed, data on the actual installation accuracy and the error threshold used are collected and used to update the regression model.

[0064] Preferably, after the spatial error distance d is calculated and obtained, an error estimation method based on the least squares method is further introduced to improve the error identification accuracy, and the formula is expressed as:

[0065] ;

[0066] Among them, E represents the sum of squares of errors, that is, the objective function;

[0067] p is a parameter vector, representing the transformation parameters to be solved;

[0068] d j represents the jth actual data point, i.e., the three-dimensional coordinates of the pipeline point obtained during the actual scanning and modeling process;

[0069] M is a transformation matrix, which represents the transformation relationship from the drawing model to the actual model, including rotation, scaling and translation transformations, and is used to transform points in the drawing model into the actual coordinate system;

[0070] p j It represents the position that the j-th point in the drawing model should reach after a certain transformation. The transformation is defined by the matrix M and the vector t. The optimal transformation parameters M and t are found to minimize the error between the transformed point and the actual point.

[0071] t is a translation vector used to adjust the overall position of the drawing model to align with the actual model;

[0072] n represents the number of data points, that is, the total number of pipeline points obtained during the actual scanning and modeling process;

[0073] ‖·‖ represents the Euclidean norm of the vector, that is, the length or modulus of the vector, which is used to calculate the error between the actual point and the transformed point.

[0074] Preferably, the specific work contents of the correction suggestion and real-time guidance module include:

[0075] Generate correction suggestions: Based on the identified errors, use algorithms or manual methods to generate correction suggestions, including adjusting the position, height, angle and connection relationship of the pipeline;

[0076] Real-time correction guidance: Correction suggestions are displayed on the AR device, and the display is updated in real time to reflect the corrected pipeline status; construction workers refer to the instructions on the AR device to make adjustments.

[0077] Preferably, the step of generating correction suggestions specifically includes:

[0078] C1 Error Statistics and Analysis: After analyzing the 3D error of each point cloud data on the 3D coordinates, we further calculate the height error and angle error of a certain section of pipeline installation, determine the connection relationship error, classify and quantify the errors, and define the size and importance of each error;

[0079] C2 Select the correction method: Select the appropriate correction method based on the type and size of the error; for smaller errors, use manual adjustment; for complex or larger errors, use genetic algorithms or particle swarm optimization algorithms for correction;

[0080] C3 parameter setting: according to the actual situation of the pipeline, set the correction parameters, including position offset Δx, Δy, Δz, height adjustment Δh, angle adjustment Δθ;

[0081] C4 generates correction suggestions: Generates specific correction suggestions based on parameter values, including adjusting the position, height, angle or connection relationship of the pipeline; and displays the correction suggestions to the user in graphical or textual form on the AR device.

[0082] Preferably, the step of adjusting the pipeline in C4 specifically includes:

[0083] Position adjustment: Use Δx, Δy, and Δz to represent the position offset of the pipeline in three-dimensional space. Based on the results of the error analysis, calculate the position offset of each pipeline and apply it to the actual position adjustment of the pipeline:

[0084] (x′, y′, z′)=(x+Δx, y+Δy, z+Δz);

[0085] Among them, (x, y, z) is the original position coordinate, (x', y', z') is the adjusted position coordinate;

[0086] Height adjustment: Use Δh to represent the height adjustment of the pipeline; calculate the height adjustment amount based on the size and direction of the height error and apply it to the actual height adjustment of the pipeline:

[0087] h′=h+Δh;

[0088] Where h is the original height and h' is the adjusted height;

[0089] Angle adjustment: Δθ is used to represent the pipeline angle adjustment, where θ is the pipeline's tilt angle or rotation angle. Based on the size and direction of the angle error, the angle adjustment is calculated and applied to the actual angle adjustment of the pipeline:

[0090] θ′=θ+Δθ;

[0091] Among them, θ is the original angle, and θ' is the adjusted angle;

[0092] Use trigonometric functions to calculate the angle between the coordinates of the two points of the adjusted pipeline;

[0093] Connection relationship adjustment: Redesign the connection method and sequence of pipelines based on connection relationship errors.

[0094] The present invention provides a BIM and AR-based electromechanical pipeline installation monitoring system. Compared with the existing technology, it has the following advantages:

[0095] 1. This BIM- and AR-based electromechanical pipeline installation monitoring system achieves high-precision data collection of actual pipelines through 3D scanning and data preprocessing, providing a solid foundation for subsequent analysis. The introduction of the 3D modeling module for drawings makes the comparison between the design and the actual installation more intuitive. The application of the AR difference display and error identification module not only improves the accuracy of error identification, but also effectively assesses the severity of errors by predicting error thresholds through regression models. The correction suggestion and real-time guidance module can provide instant correction suggestions, guide construction personnel's operations, and improve construction efficiency and quality. Finally, the recording and feedback improvement module continuously accumulates data, optimizes the regression model, forms a virtuous cycle, and continuously improves the system's monitoring and guidance capabilities.

[0096] 2. This BIM- and AR-based electromechanical pipeline installation monitoring system uses 3D scanning technology to accurately capture the actual spatial characteristics of electromechanical pipelines. Through pre-processing steps such as denoising, registration, and splicing, it generates accurate and reliable 3D scan data, providing a solid foundation for subsequent modeling and analysis. Secondly, the drawing 3D modeling module uses BIM software and point cloud data generation technology to achieve accurate comparison between design drawings and actual installation conditions, improving modeling efficiency and accuracy. In particular, the application of technologies such as automated or semi-automated mesh generation and machine learning-based pipeline identification further enhances the automation and accuracy of modeling. Finally, the export and integration steps ensure the availability and accuracy of the 3D model in subsequent analysis and applications, providing strong support for construction monitoring and guidance. These improvements have jointly promoted the advancement of electromechanical pipeline installation monitoring technology and improved construction quality and efficiency.

[0097] 3. This BIM- and AR-based electromechanical pipeline installation monitoring system uses AR devices to simultaneously display drawing modeling and actual modeling, achieving an intuitive and differentiated display, making it easier for construction personnel to quickly identify differences. Secondly, the introduction of spatial error distance calculation and a regression model trained based on historical data can accurately identify errors and predict appropriate error thresholds, improving the accuracy and intelligence of error identification. In addition, the error estimation method based on the least squares method further improves the accuracy of error identification and ensures the accuracy of pipeline installation. These improvements not only improve construction efficiency and quality, but also reduce rework costs caused by errors. At the same time, by continuously collecting actual installation data and updating the regression model, the system can continuously learn and optimize to adapt to more types of projects and higher precision requirements, promoting the continuous advancement of electromechanical pipeline installation technology.

[0098] 4. This BIM- and AR-based electromechanical pipeline installation monitoring system accurately classifies and quantifies errors through error statistics and analysis, providing a reliable basis for generating correction suggestions. Secondly, it selects the appropriate correction method based on the type and size of the error, sets specific correction parameters, and generates targeted correction suggestions, improving the accuracy and efficiency of corrections. In addition, the real-time guidance correction function enables construction personnel to instantly obtain correction suggestions and make adjustments based on the instructions on the AR device, realizing the visualization and intelligentization of the construction process. These improvements not only improve the accuracy and quality of electromechanical pipeline installation, but also reduce the difficulty and cost of construction, bringing more efficient and accurate solutions to the construction industry and promoting the innovation and development of construction technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0099] Figure 1 This is a system principle block diagram of the present invention;

[0100] Figure 2 A schematic flow chart of the steps for actually modeling the present invention;

[0101] Figure 3 This is a schematic flow chart of the steps for predicting the error threshold of the regression model of the present invention;

[0102] Figure 4 Schematic flow chart of the steps for generating correction suggestions for the present invention. DETAILED DESCRIPTION

[0103] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0104] The present invention provides the following four technical solutions:

[0105] Figure 1 The first embodiment is shown: a BIM and AR-based electromechanical pipeline installation monitoring system, comprising:

[0106] Drawing 3D modeling module, used to create 3D drawing models of electromechanical pipelines based on design drawings;

[0107] The actual pipeline 3D modeling module uses the AR device's shooting function to capture images of the actual pipeline during the actual installation process, processes the images to extract point cloud data of the actual pipeline, generates 3D scan data through pre-processing steps such as denoising, registration, and stitching, and creates a model of the actually installed pipeline based on the 3D scan data;

[0108] The AR differentiation display and error identification module is used to import 3D drawing models and actual models into AR devices, distinguish the two models, calculate spatial distance to identify errors, optimize error calculation methods to improve accuracy, and establish a regression model trained based on historical data to predict threshold comparison errors and determine the severity of errors. It also provides error marking and correction suggestion display functions.

[0109] The correction suggestion and real-time guidance module classifies and quantifies identified errors, defines the size and importance of each error, generates correction suggestions, and displays them in real time on the AR device to guide construction personnel in making adjustments.

[0110] The recording and feedback improvement module records the pipeline spatial feature data before and after correction and the error data identified during the correction process, and records the correction suggestion data as historical data to train and improve the regression model.

[0111] Through 3D scanning and data preprocessing, high-precision data collection of actual pipelines was achieved, providing a solid foundation for subsequent analysis. The introduction of the 3D modeling module for drawings makes the comparison between the design and the actual installation more intuitive. The application of the AR difference display and error identification module not only improves the accuracy of error identification, but also effectively assesses the severity of errors by predicting error thresholds through regression models. The correction suggestion and real-time guidance module can provide instant correction suggestions, guide construction personnel's operations, and improve construction efficiency and quality. Finally, the recording and feedback improvement module continuously accumulates data, optimizes the regression model, forms a virtuous cycle, and continuously improves the system's monitoring and guidance capabilities.

[0112] Figure 2A second embodiment is shown, which differs from the first embodiment mainly in that: the work content of the drawing 3D modeling module specifically includes: using BIM software to create a 3D drawing model of the electromechanical pipeline based on the design drawing, where the parameters include defining the type, size, location and connection relationship of the pipeline;

[0113] The actual pipeline 3D modeling module's work specifically includes:

[0114] Point cloud data extraction: Select an AR device with a depth sensor to obtain depth information through other structured light or stereo vision methods, extract depth data from the image, and generate point cloud data;

[0115] Data preprocessing:

[0116] Denoising: removing unwanted noise from scan data;

[0117] Registration: Use the ICP algorithm to align images from multiple perspectives into a common coordinate system;

[0118] Stitching: Use the point cloud stitching algorithm to merge the registered multi-view point cloud data into a complete 3D model data;

[0119] Actual modeling: Import the pre-processed 3D model data into the modeling software, generate polygonal meshes or surfaces from the point cloud data, and create pipeline models based on the meshes or surfaces.

[0120] The specific steps of actual modeling include:

[0121] A1. Point cloud data import and preparation:

[0122] Import point cloud data: Use modeling software (such as AutoCAD Civil 3D, SketchUp) to import pre-processed point cloud data, denoted as P = {p1, p2, ..., p n}, where p i Represents the three-dimensional coordinates of the i-th point;

[0123] Data preparation: Clean the point cloud data and remove duplicate points and isolated points, which are recorded as P c =C(P); and according to the project requirements, the point cloud data is segmented and recorded as P s =S(P c );

[0124] A2. Automatic or semi-automatic mesh generation:

[0125] Automatic mesh generation: Use the built-in meshing tool of the software to convert the point cloud data into a polygonal mesh, denoted as M=Mesh(P s), set the mesh parameters including resolution r and smoothness s to ensure that the generated mesh is both accurate and meets the project requirements;

[0126] Semi-automatic adjustment: Use editing tools to manually adjust the mesh shape of the automatically generated mesh, denoted as M a =Adjust(M); make it more accurately reflect the shape and position of the actual pipeline;

[0127] A3. Pipeline Information Extraction and Identification:

[0128] Pipeline recognition based on machine learning: Apply the pipeline recognition algorithm based on machine learning to process the grid data, denoted as I=Recognize(M a ), where I represents the extracted pipeline information;

[0129] When training the model, the labeled pipeline data is used as the training set, which is recorded as D={(a1, b1), (a2, b2), ..., (a m , b m )}, where a k represents the input feature, b k Indicates the marked pipeline information;

[0130] Manual verification and adjustment: Manual verification of the identified pipeline information, denoted as I v =Validate(I); Manually adjust the pipeline with identification error, recorded as I f =Adjust(I v );

[0131] A4. 3D model construction and optimization:

[0132] Construct a 3D model: Construct a 3D model based on the extracted pipeline information, denoted as M 3D =Construct(I f ) to ensure that the geometry, size and position of the model are consistent with the actual pipeline;

[0133] Model optimization: simplify the model structure and reduce the number of polygons, recorded as M o =Optimize(M 3D ) to improve the rendering speed and interactive performance of the model;

[0134] A5. Export and Integration:

[0135] Export model: Export the constructed 3D model to a format suitable for subsequent analysis and application, denoted as F=Export(M o ), where F represents the exported file format (such as OBJ, FBX, DWG, etc.);

[0136] Integration and verification: Integrate the exported 3D model into the BIM platform or AR device to verify the accuracy, completeness and consistency of the model, denoted as V(F); ensure that it is consistent with the actual pipeline installation situation.

[0137] 3D scanning technology can capture the actual spatial characteristics of electromechanical pipelines with high precision. Through pre-processing steps such as denoising, registration, and splicing, it generates accurate and reliable 3D scanning data, providing a solid foundation for subsequent modeling and analysis. Secondly, the drawing 3D modeling module uses BIM software and point cloud data generation technology to achieve an accurate comparison between design drawings and actual installation conditions, improving modeling efficiency and accuracy. In particular, the application of technologies such as automated or semi-automated mesh generation and machine learning-based pipeline identification has further improved the degree of automation and accuracy of modeling. Finally, the export and integration steps ensure the availability and accuracy of the 3D model in subsequent analysis and application, providing strong support for construction monitoring and guidance. These improvements have jointly promoted the advancement of electromechanical pipeline installation monitoring technology and improved construction quality and efficiency.

[0138] Figure 3 The third embodiment is shown, which differs from the first embodiment mainly in that the working contents of the AR difference display and error recognition module specifically include:

[0139] Importing models into AR devices: Import both the drawing model and the actual model into AR devices or AR-supported applications in appropriate formats (such as OBJ, FBX, etc.);

[0140] Differentiated display: Display two models simultaneously on an AR device using different colors, transparencies, or line styles. This can be achieved by setting the material properties of the models.

[0141] Error identification: Calculate the spatial distance between the corresponding points or line segments in the drawing modeling and the actual modeling. Assume that the position offsets of the spatial distance on the X, Y, and Z coordinate axes are Δx, Δy, and Δz respectively:

[0142] Δx=x2-x1;

[0143] Δy=y2-y1;

[0144] Δz=z2-z1;

[0145] Calculate the spatial error distance d using the Euclidean distance formula:

[0146] ;

[0147] Among them, (x1, y1, z1) and (x2, y2, z2) are the coordinates of the corresponding points in the drawing modeling and the actual modeling respectively;

[0148] Error threshold setting: Define an error threshold to determine whether the difference between two models constitutes an error. The error threshold is based on the prediction of the regression model trained with historical data and is automatically adjusted according to project requirements and accuracy standards.

[0149] Error marking: Use visual markers and voice or text prompts on the AR device to mark errors that exceed the threshold;

[0150] Error marking: For points or line segments that exceed the error threshold, they are marked using visual markers (such as highlighting, arrow pointing, etc.) and voice or text prompts on the AR device.

[0151] The steps for predicting the error threshold of the regression model trained based on historical data include:

[0152] B1. Data collection and preprocessing:

[0153] Data Collection: Collect data on error thresholds, installation accuracy, pipeline types, materials, and environmental conditions from multiple completed projects. Record the actual error thresholds used in each project and the corresponding installation results (e.g., whether accuracy requirements were met, whether rework was required, etc.);

[0154] Data preprocessing: Clean the collected data, remove incomplete or abnormal data points, and normalize the data to ensure that different features have the same weight in model training;

[0155] B2. Feature selection and model training:

[0156] Feature selection: Select features related to error threshold setting from the collected data, including pipeline type, material, environmental conditions (such as temperature and humidity), and installation accuracy requirements. These features will be used as input variables for the regression model.

[0157] Model training: Select linear regression as the model and train the regression model using the preprocessed data. The goal is to predict the appropriate error threshold for a given feature. Then use the mean squared error (MSE), root mean square error (RMSE), or R² score to evaluate the model performance.

[0158] B3. The regression model formula is expressed as:

[0159] T=β0+β1·F1+β2·F2+...+β p ·F p ;

[0160] Among them, T is the prediction error threshold, β0 is the intercept term, (β1, β2, ..., β p ) are the regression coefficients, (F1, F2, ..., F p) are the selected features;

[0161] During the model training phase, the regression coefficients (β0, β1, ..., β p );

[0162] Techniques such as cross-validation can be used to optimize model parameters, such as choosing the best polynomial degree, the depth of the decision tree, or the number of trees in the random forest;

[0163] B4. Real-time application and feedback:

[0164] In new projects, we use the trained regression model to predict the appropriate error threshold based on the project characteristics and accuracy requirements. We then apply the predicted error threshold to the error identification process on the AR device to guide construction workers during pipeline installation.

[0165] After the project is completed, data collected on the actual installation accuracy and the error threshold used is used to update the regression model to improve the model's predictive accuracy for future projects.

[0166] After the spatial error distance d is calculated and obtained, the error estimation method based on the least squares method is further introduced to improve the error identification accuracy. The formula is expressed as:

[0167] ;

[0168] Among them, E represents the sum of squares of errors, that is, the objective function;

[0169] p is a parameter vector, representing the transformation parameters to be solved;

[0170] d j represents the jth actual data point, i.e., the three-dimensional coordinates of the pipeline point obtained during the actual scanning and modeling process;

[0171] M is a transformation matrix, which represents the transformation relationship from the drawing model to the actual model, including rotation, scaling and translation transformations, and is used to transform points in the drawing model into the actual coordinate system;

[0172] p j It represents the position that the j-th point in the drawing model should reach after a certain transformation. The transformation is defined by the matrix M and the vector t. The optimal transformation parameters M and t are found to minimize the error between the transformed point and the actual point.

[0173] t is a translation vector used to adjust the overall position of the drawing model to align with the actual model;

[0174] n represents the number of data points, that is, the total number of pipeline points obtained during the actual scanning and modeling process;

[0175] ‖·‖ represents the Euclidean norm of the vector, that is, the length or modulus of the vector, which is used to calculate the error between the actual point and the transformed point.

[0176] The goal of the least squares method is to minimize the sum of the squares of the distances between all actual points and the transformed points by adjusting the transformation parameters M and t. This usually involves taking the derivative of the objective function E and setting the derivative equal to zero to find the optimal transformation parameters.

[0177] By simultaneously displaying the drawing model and the actual model on an AR device, an intuitive distinction is achieved, making it easier for construction workers to quickly identify differences. Secondly, the introduction of spatial error distance calculation and a regression model trained based on historical data can accurately identify errors and predict appropriate error thresholds, improving the accuracy and intelligence of error identification. In addition, the error estimation method based on the least squares method further improves the accuracy of error identification and ensures the accuracy of pipeline installation. These improvements not only improve construction efficiency and quality, but also reduce the rework costs caused by errors. At the same time, by continuously collecting actual installation data and updating the regression model, the system can continuously learn and optimize to adapt to more types of projects and higher precision requirements, promoting the continuous advancement of electromechanical pipeline installation technology.

[0178] Figure 4 The fourth embodiment is shown, which differs from the first embodiment mainly in that the specific working contents of the correction suggestion and real-time guidance module include:

[0179] Generate correction suggestions: Based on the identified errors, use algorithms or manual methods to generate correction suggestions, including adjusting the position, height, angle and connection relationship of the pipeline;

[0180] Real-time correction guidance: Correction suggestions are displayed on the AR device, and the display is updated in real time to reflect the corrected pipeline status; construction workers refer to the instructions on the AR device to make adjustments.

[0181] The steps to generate correction suggestions include:

[0182] C1 Error Statistics and Analysis: After analyzing the 3D error of each point cloud data on the 3D coordinates, we further calculate the height error and angle error of a certain section of pipeline installation, determine the connection relationship error, classify and quantify the errors, and define the size and importance of each error;

[0183] C2 Select the correction method: Select the appropriate correction method based on the type and size of the error; for smaller errors, use manual adjustment; for complex or larger errors, use algorithms (such as genetic algorithms, particle swarm optimization) for correction;

[0184] C3 parameter setting: according to the actual situation of the pipeline, set the correction parameters, including position offset Δx, Δy, Δz, height adjustment Δh, angle adjustment Δθ;

[0185] C4 generates correction suggestions: Generates specific correction suggestions based on parameter values, including adjusting the position, height, angle or connection relationship of the pipeline; and displays the correction suggestions to the user in graphical or textual form on the AR device for user understanding and implementation.

[0186] The steps for adjusting the pipeline in C4 include:

[0187] Position adjustment: Use Δx, Δy, and Δz to represent the position offset of the pipeline in three-dimensional space. Based on the results of the error analysis, calculate the position offset of each pipeline and apply it to the actual position adjustment of the pipeline:

[0188] (x′, y′, z′)=(x+Δx, y+Δy, z+Δz);

[0189] Among them, (x, y, z) is the original position coordinate, (x', y', z') is the adjusted position coordinate;

[0190] Height adjustment: Use Δh to represent the height adjustment of the pipeline; calculate the height adjustment amount based on the size and direction of the height error and apply it to the actual height adjustment of the pipeline:

[0191] h′=h+Δh;

[0192] Where h is the original height and h' is the adjusted height;

[0193] Angle adjustment: Δθ is used to represent the pipeline angle adjustment, where θ is the pipeline's tilt angle or rotation angle. Based on the size and direction of the angle error, the angle adjustment is calculated and applied to the actual angle adjustment of the pipeline:

[0194] θ′=θ+Δθ;

[0195] Among them, θ is the original angle, and θ' is the adjusted angle;

[0196] Use trigonometric functions (such as sin and cos) to calculate the angle between the coordinates of the two points of the adjusted pipeline;

[0197] Connection relationship adjustment: Redesign the connection method and sequence of pipelines based on connection relationship errors; ensure that the adjusted pipeline connection relationship meets the design requirements while maintaining the fluidity and functionality of the system.

[0198] Through error statistics and analysis, errors are accurately classified and quantified, providing a reliable basis for generating correction suggestions. Secondly, appropriate correction methods are selected based on the error type and size, and specific correction parameters are set to generate targeted correction suggestions, improving the accuracy and efficiency of corrections. Furthermore, the real-time guided correction function enables construction workers to instantly receive correction suggestions and make adjustments based on the instructions on the AR device, achieving visualization and intelligent construction processes. These improvements not only enhance the accuracy and quality of electromechanical pipeline installation, but also reduce construction difficulty and costs, bringing more efficient and accurate solutions to the construction industry and promoting innovation and development in construction technology.

[0199] Meanwhile, the contents not described in detail in this specification belong to the prior art known to those skilled in the art.

[0200] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0201] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A BIM and AR-based electromechanical pipeline installation monitoring system, characterized by: include: The 3D scanning and data preprocessing module uses 3D scanning technology to capture the actual spatial characteristics of electromechanical pipelines and generates 3D scanning data through preprocessing steps such as denoising, registration, and stitching; The drawing and actual 3D modeling module is used to create 3D drawing models of electromechanical pipelines based on design drawings, and to import pre-processed pipeline scanning data during the actual installation process to create actual installed pipeline models; The AR differentiation display and error identification module is used to import the 3D drawing model and the actual model into the AR device, distinguish the two models, calculate the spatial distance to identify errors, optimize the error calculation method to improve accuracy, and establish a regression model trained based on historical data to predict the threshold comparison error and determine the severity of the error; It also provides error marking and correction suggestion display functions; The correction suggestion and real-time guidance module classifies and quantifies identified errors, defines the size and importance of each error, generates correction suggestions, and displays them in real time on the AR device to guide construction personnel in making adjustments. The recording and feedback improvement module records the pipeline spatial feature data before and after correction, the error data identified during the correction process, and the correction suggestion data, which are used as historical data to train and improve the regression model; The AR difference display and error recognition module specifically includes: Importing models into AR devices: Importing both the drawing model and the actual model into AR devices or AR-enabled applications in appropriate formats; Differentiated display: Display two models simultaneously on AR devices using different colors, transparency, or line styles; Error identification: Calculate the spatial distance between the corresponding points or line segments in the drawing modeling and the actual modeling. Assume that the position offsets of the spatial distance on the X, Y, and Z coordinate axes are Δx, Δy, and Δz respectively: Δx=x2-x1; Δy=y2-y1; Δz=z2-z1; Calculate the spatial error distance d using the Euclidean distance formula: ； Among them, (x1, y1, z1) and (x2, y2, z2) are the coordinates of the corresponding points in the drawing modeling and the actual modeling respectively; Error threshold setting: Define an error threshold to determine whether the difference between two models constitutes an error. The error threshold is based on the prediction of the regression model trained with historical data and is automatically adjusted according to project requirements and accuracy standards. Error marking: Use visual markers and voice or text prompts on the AR device to mark errors that exceed the threshold; Error marking: For points or line segments that exceed the error threshold, visual markers and voice or text prompts on the AR device are used to mark them; The steps for predicting the error threshold of the regression model trained based on historical data include: B1. Data collection and preprocessing: Data collection: Collect data on error thresholds, installation accuracy, pipeline types, materials, and environmental conditions from multiple completed projects, and record the actual error thresholds used in each project and the corresponding installation results; Data preprocessing: Clean the collected data, remove incomplete or abnormal data points, and normalize the data to ensure that different features have the same weight in model training; B2. Feature selection and model training: Feature selection: Select features related to error threshold setting from the collected data, including pipeline type, material, environmental conditions, and installation accuracy requirements. These features will be used as input variables for the regression model. Model training: Select linear regression as the model and train the regression model using the preprocessed data. The goal is to predict the appropriate error threshold for a given feature. Then use the mean squared error, root mean squared error, or R² score to evaluate the model's performance. B3. The regression model formula is expressed as: T=β0+β1·F1+β2·F2+...+β p ·F p ; Among them, T is the prediction error threshold, β0 is the intercept term, (β1, β2, ..., β p ) are the regression coefficients, (F1, F2, ..., F p ) are the selected features; During the model training phase, the regression coefficients (β0, β1, ..., β p ); B4. Real-time application and feedback: In new projects, we use the trained regression model to predict the appropriate error threshold based on the project characteristics and accuracy requirements. We then apply the predicted error threshold to the error identification process on the AR device to guide construction workers during pipeline installation. After the project is completed, data on the actual installation accuracy and the error threshold used are collected and used to update the regression model.

2. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 1, characterized in that: The work content of the 3D scanning and data preprocessing module specifically includes: Use a 3D scanner to scan and acquire data: Select a handheld laser scanner or structured light scanner to capture the 3D coordinates of an object by emitting laser or structured light and receiving reflected light, thereby obtaining surface data of electromechanical pipelines; Data preprocessing: Denoising: removing unwanted noise from scan data; Registration: Use the ICP algorithm to align multiple scan views into a common coordinate system; Stitching: Use point cloud stitching algorithms to merge the registered scan data into a complete 3D model.

3. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 1, characterized in that: The work content of the drawings and actual 3D modeling module specifically includes: Drawing modeling: Based on the design drawings, use BIM software to create a 3D drawing model of the electromechanical pipelines, where the parameters include defining the type, size, location, and connection relationship of the pipelines; Actual modeling: Import the pre-processed scan data into the modeling software, or directly use the modeling software that supports the scan data to generate polygon meshes or surfaces from the point cloud data, and create pipeline models based on the meshes or surfaces.

4. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 3 is characterized by: The specific steps of the actual modeling include: A1. Point cloud data import and preparation: Import point cloud data: Use modeling software to import pre-processed point cloud data, denoted as P={p1, p2, ..., p n }, where p i Represents the three-dimensional coordinates of the i-th point; Data preparation: Clean the point cloud data and remove duplicate points and isolated points, which are recorded as P c =C(P); and according to the project requirements, the point cloud data is segmented and recorded as P s =S(P c ); A2. Automatic or semi-automatic mesh generation: Automatic mesh generation: Use the built-in meshing tool of the software to convert the point cloud data into a polygonal mesh, denoted as M=Mesh(P s ), set the grid parameters including resolution r and smoothness s; Semi-automatic adjustment: Use editing tools to manually adjust the mesh shape of the automatically generated mesh, denoted as M a =Adjust(M); A3. Pipeline Information Extraction and Identification: Pipeline recognition based on machine learning: Apply the pipeline recognition algorithm based on machine learning to process the grid data, denoted as I=Recognize(M a ), where I represents the extracted pipeline information; When training the model, the labeled pipeline data is used as the training set, which is recorded as D={(a1, b1), (a2, b2), ..., (a m , b m )}, where a k represents the input feature, b k Indicates the marked pipeline information; Manual verification and adjustment: Manual verification of the identified pipeline information, denoted as I v =Validate(I); Manually adjust the pipeline with identification error, recorded as I f =Adjust(I v ); A4. 3D model construction and optimization: Construct a 3D model: Construct a 3D model based on the extracted pipeline information, denoted as M 3D =Construct(I f ) to ensure that the geometry, size and position of the model are consistent with the actual pipeline; Model optimization: simplify the model structure and reduce the number of polygons, recorded as M o =Optimize(M 3D ) to improve the rendering speed and interactive performance of the model; A5. Export and Integration: Export model: Export the constructed 3D model to a format suitable for subsequent analysis and application, denoted as F=Export(M o ); Integration and verification: Integrate the exported 3D model into the BIM platform or AR device to verify the accuracy, completeness and consistency of the model, denoted as V(F).

5. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 1 is characterized by: After the spatial error distance d is calculated and obtained, an error estimation method based on the least squares method is further introduced to improve the error identification accuracy. The formula is expressed as: ； Among them, E represents the sum of squares of errors, that is, the objective function; p is a parameter vector, representing the transformation parameters to be solved; d j represents the jth actual data point, i.e., the three-dimensional coordinates of the pipeline point obtained during the actual scanning and modeling process; M is a transformation matrix, which represents the transformation relationship from the drawing model to the actual model, including rotation, scaling and translation transformations, and is used to transform points in the drawing model into the actual coordinate system; p j It represents the position that the j-th point in the drawing model should reach after a certain transformation. The transformation is defined by the matrix M and the vector t. The optimal transformation parameters M and t are found to minimize the error between the transformed point and the actual point. t is a translation vector used to adjust the overall position of the drawing model to align with the actual model; n represents the number of data points, i.e., the total number of pipeline points obtained during the actual scanning and modeling process; ‖·‖ represents the Euclidean norm of the vector, that is, the length or modulus of the vector, which is used to calculate the error between the actual point and the transformed point.

6. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 1, characterized in that: The specific work content of the correction suggestion and real-time guidance module includes: Generate correction suggestions: Based on the identified errors, use algorithms or manual methods to generate correction suggestions, including adjusting the position, height, angle and connection relationship of the pipeline; Real-time correction guidance: Correction suggestions are displayed on the AR device, and the display is updated in real time to reflect the corrected pipeline status; construction workers refer to the instructions on the AR device to make adjustments.

7. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 6, characterized in that: The step of generating correction suggestions specifically includes: C1 Error Statistics and Analysis: After analyzing the 3D error of each point cloud data on the 3D coordinates, we further calculate the height error and angle error of a certain section of pipeline installation, determine the connection relationship error, classify and quantify the errors, and define the size and importance of each error; C2 Select the correction method: Select the appropriate correction method based on the type and size of the error; for smaller errors, use manual adjustment; for complex or larger errors, use genetic algorithms or particle swarm optimization algorithms for correction; C3 parameter setting: according to the actual situation of the pipeline, set the correction parameters, including position offset Δx, Δy, Δz, height adjustment Δh, angle adjustment Δθ; C4 generates correction suggestions: Generates specific correction suggestions based on parameter values, including adjusting the position, height, angle or connection relationship of the pipeline; and displays the correction suggestions to the user in graphical or textual form on the AR device.

8. The BIM and AR-based electromechanical pipeline installation monitoring system according to claim 7, characterized in that: The steps for adjusting the pipeline in C4 include: Position adjustment: Use Δx, Δy, and Δz to represent the position offset of the pipeline in three-dimensional space. Based on the results of the error analysis, calculate the position offset of each pipeline and apply it to the actual position adjustment of the pipeline: (x′, y′, z′)=(x+Δx, y+Δy, z+Δz); Among them, (x, y, z) is the original position coordinate, (x', y', z') is the adjusted position coordinate; Height adjustment: Use Δh to represent the height adjustment of the pipeline; calculate the height adjustment amount based on the size and direction of the height error and apply it to the actual height adjustment of the pipeline: h′=h+Δh; Where h is the original height and h' is the adjusted height; Angle adjustment: Δθ is used to represent the pipeline angle adjustment, where θ is the pipeline's tilt angle or rotation angle. Based on the size and direction of the angle error, the angle adjustment is calculated and applied to the actual angle adjustment of the pipeline: θ′=θ+Δθ; Among them, θ is the original angle, and θ' is the adjusted angle; Use trigonometric functions to calculate the angle between the coordinates of the two points of the adjusted pipeline; Connection relationship adjustment: Redesign the connection method and sequence of pipelines based on connection relationship errors.

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