Real-time mapping-based building construction scheme dynamic collaborative design system and method

Abstract

The application provides a real-time survey-based construction scheme dynamic collaborative design system and method, belonging to the technical field of building construction management and intelligent construction, comprising: a data acquisition module for acquiring survey data of a construction site through a multi-source sensor to generate real-time survey data; a data analysis module for comparing the real-time survey data with a preset BIM model to identify a construction deviation area and obtain key deviation parameters; a deviation acquisition module for determining a dynamic deviation coefficient based on the key deviation parameters and the construction deviation area; a strategy generation module for dividing deviation levels according to the numerical range of the dynamic deviation coefficient and generating corresponding construction response strategies; and a deviation correction module for executing the construction response strategies, verifying the deviation correction results based on the executed survey data, and updating the dynamic deviation coefficient until the deviation correction results meet the preset requirements. The accuracy and efficiency of the construction process are improved.

Description

Technical Field

[0001] This invention relates to the fields of building construction management and intelligent construction technology, and in particular to a dynamic collaborative design system and method for building construction schemes based on real-time mapping. Background Technology

[0002] With the development of the construction industry, deviation control during construction has become a crucial factor in ensuring building quality and construction progress. Traditional construction deviation monitoring relies on manual inspection and static data, making it difficult to promptly identify and correct problems during construction, leading to delays and increased costs. In recent years, the combination of BIM (Building Information Modeling) and real-time mapping technology has become a key means to improve construction accuracy and efficiency.

[0003] Currently, many construction management systems use a combination of BIM models and sensor data for deviation detection. However, these solutions often suffer from poor real-time performance and delayed deviation correction. For example, some systems can only perform static comparisons of deviations, failing to achieve dynamic adjustments and real-time feedback. Existing technologies largely rely on manual intervention; after a construction deviation is detected, manual judgment and adjustments to the construction plan are required, resulting in low efficiency in correcting construction deviations. The main drawbacks of existing technologies are: deviation monitoring and correction cannot be coordinated in real time, a dynamic deviation correction mechanism is lacking, and the system's response speed to deviations is slow. Furthermore, existing technologies rely heavily on static analysis, making it difficult to achieve continuous optimization and automated adjustment of construction plans. They also fail to fully utilize real-time surveying data in collaboration with BIM models, resulting in insufficient accuracy and efficiency in construction deviation correction.

[0004] Therefore, this invention provides a dynamic collaborative design system and method for building construction schemes based on real-time mapping. Summary of the Invention

[0005] This invention provides a dynamic collaborative design system and method for building construction schemes based on real-time mapping. Through the dynamic collaboration of real-time mapping data and BIM models, it achieves real-time monitoring and automatic correction of construction deviations. Compared with existing technologies, it can acquire real-time mapping data from the construction site and optimize deviation correction strategies through spatiotemporal registration algorithms and dynamic deviation coefficients. This avoids the problems of delayed deviation correction and reliance on manual adjustments in traditional schemes, improving the accuracy and efficiency of the construction process and ensuring precise control of construction quality and schedule.

[0006] The dynamic collaborative design system for building construction schemes based on real-time mapping provided by the present invention includes:

[0007] Data acquisition module: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data;

[0008] Data analysis module: used to compare real-time survey data with the preset BIM model, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data;

[0009] Deviation acquisition module: Determines dynamic deviation coefficients based on key deviation parameters and construction deviation areas;

[0010] Strategy generation module: Classifies deviation levels based on the numerical range of dynamic deviation coefficients and generates corresponding construction response strategies;

[0011] Deviation Correction Module: Executes the construction response strategy and acquires the survey data after execution. Verifies the deviation correction results based on the executed survey data and updates the dynamic deviation coefficients based on the deviation correction results until the deviation correction results meet the preset requirements.

[0012] Preferably, the data acquisition module includes:

[0013] Multi-source data acquisition unit: Acquires overall 3D point cloud data of the construction area through a ground-based fixed laser scanner, acquires detailed local mapping data through mobile surveying equipment, and acquires real-time pose data of the machinery's working face through positioning sensors mounted on the construction machinery; Spatiotemporal registration unit: Performs time synchronization processing on the data collected by the multi-source sensors, establishes the transformation relationship between the coordinate systems corresponding to all acquisition devices, and unifies the mapping data from all sources into the global construction coordinate system through a spatiotemporal registration algorithm; Data fusion unit: Performs fusion processing on all mapping data to generate real-time mapping data.

[0014] Preferably, the data analysis module includes:

[0015] Data extraction unit: Extracts the set of salient feature points from real-time surveying data, and at the same time, identifies the corresponding set of theoretical feature points in the preset BIM model;

[0016] Initial registration unit: The salient feature point set and the theoretical feature point set are initially registered using a feature matching algorithm, and the initial registration error is determined. The registration quality is then determined based on the initial registration error.

[0017] Mesh building unit: If the registration quality meets the preset requirements, a three-dimensional spatial mesh is built based on the initial registration;

[0018] Candidate region: Local geometric features are compared within the three-dimensional spatial grid to obtain the geometric difference degree within each grid cell. Grid cells with geometric difference degrees exceeding a preset difference threshold are selected as candidate deviation regions.

[0019] Region determination unit: Merge spatially adjacent candidate deviation regions to generate deviation regions with continuous boundaries, and determine the deviation regions with continuous boundaries as construction deviation regions;

[0020] Parameter extraction unit: Based on real-time mapping data, determine the volumetric characteristics, spatial distribution characteristics, and temporal duration characteristics of the construction deviation area as key deviation parameters.

[0021] Preferably, the parameter extraction unit includes:

[0022] Volume feature extraction sub-unit: The volume percentage of the construction deviation area relative to the corresponding area of ​​the preset BIM model is determined using the voxel mesh generation method;

[0023] Spatial distribution feature extraction subunit: Performs spatial distribution analysis on real-time mapping data, determines the main direction of spatial distribution of deviation areas, and determines the deviation angle between the main direction of spatial distribution and the preset reference axis.

[0024] Time-duration feature extraction subunit: Based on the time of the first occurrence of the deviation, the current construction progress, and the criticality of the current process, the influence coefficient of the deviation duration is determined.

[0025] ;

[0026] in, The duration of the deviation is the influence coefficient, and the first detection time of the deviation is recorded as follows: , This refers to the time corresponding to the current construction progress. Set a planned completion time for the current construction procedure. This is the attenuation coefficient, and its value is determined based on the criticality of the process.

[0027] Preferably, the deviation acquisition module includes:

[0028] Weight determination unit: Dynamic weights are determined based on the construction deviation area.

[0029] ;

[0030] ;

[0031] ;

[0032] in, For volume feature dynamic weights, For spatial distribution characteristics, dynamic weights, The dynamic weights for the time-duration feature. The benchmark weighting coefficient for volume characteristics. The baseline weighting coefficients for spatial distribution characteristics. The baseline weighting coefficients for the time-duration feature. This is the amplification factor of the load-bearing structure's weight on the volumetric characteristics, and , For the time-duration characteristic weighting factor of non-load-bearing structures, and , This is the spatial range weighting factor, and A is the actual area of ​​the deviation region. The preset threshold for the area of ​​the deviation region. It is a linear rectifier function with the following function: This is used to determine whether the area of ​​deviation exceeds a threshold. If it exceeds the limit, it will participate in the weight adjustment. This is the amplification coefficient for the spatial distribution characteristics weight during the structural construction phase, and , This is the amplification coefficient for the weighting of volumetric features during the decoration construction stage, and d is the distance from the deviation region to the critical structure. This is a preset threshold for the critical distance;

[0033] Coefficient Determination Unit: Determining dynamic deviation coefficients based on dynamic weights and key deviation parameters.

[0034] ;

[0035] in, DC is the dynamic deviation coefficient.

[0036] Preferably, the strategy generation module includes:

[0037] The grade classification unit divides construction deviations into several deviation grades based on the numerical range of the dynamic deviation coefficient; the strategy matching unit matches a corresponding strategy from a preset response strategy library for each deviation grade, generating a construction response strategy containing specific operation instructions. Preferably, the deviation correction module includes:

[0038] Command issuing unit: Converts construction response strategies into executable commands and sends them to the corresponding construction terminal equipment;

[0039] Effect verification unit: Acquires the survey data after execution to verify the effectiveness of the deviation correction measures;

[0040] Iterative optimization unit: Adjusts the dynamic weights corresponding to the dynamic deviation coefficients based on the verification results until the deviation correction results meet the preset requirements.

[0041] The dynamic collaborative design method for building construction schemes based on real-time mapping provided by the present invention includes:

[0042] Step 1: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data;

[0043] Step 2: Used to compare real-time survey data with the preset BIM model, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data;

[0044] Step 3: Determine the dynamic deviation coefficient based on key deviation parameters and construction deviation areas;

[0045] Step 4: Classify the deviation levels according to the numerical range of the dynamic deviation coefficient and generate the corresponding construction response strategies;

[0046] Step 5: Execute the construction response strategy and obtain the post-execution survey data. Verify the deviation correction results based on the post-execution survey data. Update the dynamic deviation coefficient based on the deviation correction results until the deviation correction results meet the preset requirements.

[0047] Compared with the prior art, the beneficial effects of this application are as follows:

[0048] By dynamically coordinating real-time surveying data with the BIM model, real-time monitoring and automatic correction of construction deviations are achieved. Compared with existing technologies, this method can acquire surveying data from the construction site in real time and optimize deviation correction strategies through spatiotemporal registration algorithms and dynamic deviation coefficients. This avoids the problems of delayed deviation correction and reliance on manual adjustments in traditional solutions, improving the accuracy and efficiency of the construction process and ensuring precise control of construction quality and schedule. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0050] Figure 1 This is a schematic diagram of the structure of the dynamic collaborative design system for building construction schemes based on real-time mapping provided in an embodiment of the present invention.

[0051] Figure 2 This is a flowchart illustrating the dynamic collaborative design method for building construction schemes based on real-time mapping provided in this embodiment of the invention. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0053] Example 1:

[0054] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, such as... Figure 1 As shown, it includes:

[0055] Data acquisition module: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data;

[0056] Data analysis module: used to compare real-time survey data with the preset BIM model, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data;

[0057] Deviation acquisition module: Determines dynamic deviation coefficients based on key deviation parameters and construction deviation areas;

[0058] Strategy generation module: Classifies deviation levels based on the numerical range of dynamic deviation coefficients and generates corresponding construction response strategies;

[0059] Deviation Correction Module: Executes the construction response strategy and acquires the survey data after execution. Verifies the deviation correction results based on the executed survey data and updates the dynamic deviation coefficients based on the deviation correction results until the deviation correction results meet the preset requirements.

[0060] In this embodiment, the construction global coordinate system refers to a unified coordinate system used to provide a consistent reference framework for various data and models during the construction process. This coordinate system is typically based on the geographical location of the construction site, the specific requirements of the building structure, and the standards of surveying tools, ensuring seamless integration of data from different sensors or measurement sources. Through spatiotemporal registration algorithms, data from different sensors are unified into this global coordinate system, ensuring that the spatial position of each data point correctly corresponds to the actual position on the construction site. For example, if GPS sensors and laser scanners are used to measure building components at different locations during construction, they need to be unified into a common coordinate system to ensure that all data represent the same location on the construction site.

[0061] In this embodiment, the construction deviation area refers to the region where there are differences between the measured data and the preset design (such as a BIM model) during the actual construction process. Construction deviations can be caused by factors such as construction errors, material problems, or equipment inaccuracies. When real-time survey data is compared with the BIM model, deviation areas are identified. These areas may have differences in size, location, or shape, requiring further correction or adjustment. For example, during the construction of a wall, the design drawings specify a wall length of 10 meters, but real-time surveying reveals that the actual wall length is only 9.8 meters. This 0.2-meter difference is the construction deviation, and the affected area constitutes the "construction deviation area."

[0062] The beneficial effects of the above technical solution are as follows: Through the dynamic collaboration between real-time surveying data and the BIM model, real-time monitoring and automatic correction of construction deviations are achieved. Compared with existing technologies, it can acquire surveying data from the construction site in real time, and optimize the deviation correction strategy through spatiotemporal registration algorithms and dynamic deviation coefficients. This avoids the problems of lagging deviation correction and reliance on manual adjustments in traditional solutions, improves the accuracy and efficiency of the construction process, and ensures precise control of construction quality and schedule.

[0063] Example 2:

[0064] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a data acquisition module comprising:

[0065] Multi-source data acquisition unit: Acquires overall 3D point cloud data of the construction area through a ground-based fixed laser scanner, acquires detailed local mapping data through mobile surveying equipment, and acquires real-time pose data of the machinery's working face through positioning sensors mounted on the construction machinery; Spatiotemporal registration unit: Performs time synchronization processing on the data collected by the multi-source sensors, establishes the transformation relationship between the coordinate systems corresponding to all acquisition devices, and unifies the mapping data from all sources into the global construction coordinate system through a spatiotemporal registration algorithm; Data fusion unit: Performs fusion processing on all mapping data to generate real-time mapping data.

[0066] In this embodiment, the transformation relationship refers to the mathematical relationship between different coordinate systems, used to transform data from one coordinate system to another. During multi-source sensor data acquisition, each sensor may have its own independent coordinate system. The transformation relationship determines how to transform data points from one coordinate system to other coordinate systems, ultimately unifying all data into a global coordinate system. This is crucial to ensuring that data from different sources can work in a coordinated manner. For example, suppose the coordinate system of the 3D point cloud data measured using a terrestrial laser scanner is different from the coordinate system of the positioning sensors on the construction machinery. The transformation relationship is used to transform the coordinates of the laser scanner data to the same coordinate system as the machinery, ensuring that the measurement results of both can be compared and analyzed within the same reference frame.

[0067] In this embodiment, the spatiotemporal registration algorithm is an algorithm that aligns multi-source data in time and space. It considers not only the temporal differences at different data acquisition points but also the spatial differences between different devices. This algorithm ensures that mapping data from different sensors can be accurately merged and synchronized to the same unified global coordinate system. For example, if a ground laser scanner and a positioning sensor on construction machinery acquire data at different times and in different coordinate systems, the spatiotemporal registration algorithm can precisely align the measurement results from these two data sources to the same time point and unified coordinate system based on timestamps and spatial locations, making data fusion and analysis more accurate.

[0068] In this embodiment, data fusion processing refers to integrating and processing data from different sensors or data sources, so that the final dataset can provide more comprehensive and accurate information about the construction site. Fusion processing not only addresses temporal and spatial differences but also integrates the characteristics of data from different sources, removes redundant information, fills data gaps, and ensures data consistency and efficiency. For example, during construction, a laser scanner obtains overall 3D point cloud data, while mobile surveying equipment obtains detailed mapping data for local areas, and construction machinery provides real-time operational pose data. The fusion processing process unifies and integrates this data from different devices to generate a complete and seamless real-time mapping dataset, facilitating subsequent analysis and decision-making.

[0069] The beneficial effects of the above technical solution are as follows: through spatiotemporal registration and fusion processing of multi-source sensor data, efficient integration of real-time mapping data from different devices can be achieved. This method ensures accurate synchronization and uniformity of various types of data, providing a reliable basis for deviation monitoring and correction during construction, avoiding data inconsistencies and manual intervention in traditional technologies, improving construction accuracy and efficiency, and optimizing construction management.

[0070] Example 3:

[0071] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a data analysis module:

[0072] Data extraction unit: Extracts the set of salient feature points from real-time surveying data, and at the same time, identifies the corresponding set of theoretical feature points in the preset BIM model;

[0073] Initial registration unit: The salient feature point set and the theoretical feature point set are initially registered using a feature matching algorithm, and the initial registration error is determined. The registration quality is then determined based on the initial registration error.

[0074] Mesh building unit: If the registration quality meets the preset requirements, a three-dimensional spatial mesh is built based on the initial registration;

[0075] Candidate region: Local geometric features are compared within the three-dimensional spatial grid to obtain the geometric difference degree within each grid cell. Grid cells with geometric difference degrees exceeding a preset difference threshold are selected as candidate deviation regions.

[0076] Region determination unit: Merge spatially adjacent candidate deviation regions to generate deviation regions with continuous boundaries, and determine the deviation regions with continuous boundaries as construction deviation regions;

[0077] Parameter extraction unit: Based on real-time mapping data, determine the volumetric characteristics, spatial distribution characteristics, and temporal duration characteristics of the construction deviation area as key deviation parameters.

[0078] In this embodiment, the salient feature point set refers to representative and easily identifiable points extracted from real-time mapping data. These points are typically spatially located with geometric features, such as corners, edges, or intersections. They can effectively describe the shape and structure of the construction site. For example, in building construction, the four corners of a wall can be used as salient feature points, which are easily identifiable and located in three-dimensional space.

[0079] In this embodiment, the theoretical feature point set refers to the corresponding feature points defined in the pre-defined BIM (Building Information Model) based on the design model. Theoretical feature points are usually model features determined according to architectural design drawings, such as column centers and wall intersections. For example, in the BIM model, the intersection of the center of a building column and a wall corner can be used as a theoretical feature point.

[0080] In this embodiment, initial registration refers to the process of initially aligning and matching salient feature points in real-time mapping data with theoretical feature points in the BIM model. The goal of this step is to determine the relationship between the two through feature matching algorithms and find the initial geometric alignment. For example, at a construction site, there may be positional discrepancies between the 3D point cloud data collected by a laser scanner and the design data in the BIM model. Through initial registration, the system will align the feature points (such as wall corners, column center points, etc.) of both to find their relative positions.

[0081] In this embodiment, the preliminary registration error refers to the geometric differences or positional deviations between matched feature points during the initial registration process. These errors reflect the alignment accuracy between the survey data and the BIM model. A large preliminary registration error may indicate problems with data matching or the need for further adjustments. For example, after preliminary registration, if there is a 1-centimeter deviation between a theoretical feature point in the BIM model and a significant feature point in the survey data, this 1-centimeter deviation is the preliminary registration error, and further optimization of the registration method may be required.

[0082] In this embodiment, the preset requirement refers to a standard or condition defined in advance in the system to measure whether the data registration has achieved the expected accuracy or quality. Only when the registration error meets the preset requirement can subsequent mesh construction or other processing steps be performed. For example, the system's preset requirement may be that the registration error is less than 5 mm. This means that if the initial registration error is greater than 5 mm, the system will consider the registration quality to be unqualified and further adjustments are required.

[0083] In this embodiment, geometric variability refers to the degree of difference between measured geometric features when comparing different spatial regions (such as grid cells). Geometric variability is typically used to assess the discrepancy between the shape, size, and other characteristics of a region and the theoretical design. For example, if the measured wall thickness within a grid cell differs from the wall thickness in the BIM model, this difference constitutes geometric variability. If the difference is too large, it means that there may be a deviation in that region.

[0084] In this embodiment, the preset difference threshold refers to a difference standard set in advance in the system. Only when the geometric difference of a certain area exceeds this threshold will it be considered a deviation area. This threshold is used to determine which areas need further processing or correction. When the difference exceeds 2 cm, the unit will be marked as a candidate deviation area and enter the subsequent correction process.

[0085] The beneficial effects of the above technical solution are as follows: Through multi-step processing including data extraction, registration, grid construction, and region identification, it can accurately identify construction deviation areas and extract key deviation parameters. Compared with existing technologies, the dynamic comparison between real-time survey data and the BIM model not only improves the accuracy of deviation identification but also enables real-time analysis of the volume, spatial distribution, and duration characteristics of deviations. This ensures meticulous management and timely correction of the construction process, improves construction accuracy and efficiency, and reduces the risk of human error and delays.

[0086] Example 4:

[0087] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a parameter extraction unit comprising:

[0088] Volume feature extraction sub-unit: The volume percentage of the construction deviation area relative to the corresponding area of ​​the preset BIM model is determined using the voxel mesh generation method;

[0089] Spatial distribution feature extraction subunit: Performs spatial distribution analysis on real-time mapping data, determines the main direction of spatial distribution of deviation areas, and determines the deviation angle between the main direction of spatial distribution and the preset reference axis.

[0090] Time-duration feature extraction subunit: Based on the time of the first occurrence of the deviation, the current construction progress, and the criticality of the current process, the influence coefficient of the deviation duration is determined.

[0091] ;

[0092] in, The duration of the deviation is the influence coefficient, and the first detection time of the deviation is recorded as follows: , This refers to the time corresponding to the current construction progress. Set a planned completion time for the current construction procedure. This is the attenuation coefficient, and its value is determined based on the criticality of the process.

[0093] In this embodiment, the spatial distribution analysis includes: spatial distribution feature analysis, determining the covariance matrix of the point cloud in the deviation region, performing eigenvalue decomposition on the covariance matrix, extracting eigenvectors to form a local coordinate system, determining the direction of the eigenvector corresponding to the largest eigenvalue, determining the principal direction, taking the direction of the largest eigenvector as the preliminary principal direction, optimizing the principal direction estimation through the RANSAC algorithm, verifying the spatial consistency of the principal direction, and determining the final spatial distribution principal direction.

[0094] In this embodiment, the criticality of a process is determined based on the process relationships extracted from the construction schedule plan (PERT / CPM network), the total float of each process is determined, and the core process set on the critical path is identified. The criticality assessment dimensions are: path criticality: the positional characteristics of the process in the project network; resource exclusivity: the availability of special resources required by the process; quality sensitivity: the degree of impact of process quality on the overall project; and safety risk: the level of safety risk in the implementation of the process. The classification criteria are determined as follows: k=3 (critical path process): processes with a total float of zero, processes that directly affect the total project duration, and processes that require special equipment or processes; k=2 (general critical process): non-critical path processes with a total float less than a threshold (e.g., 3 days), processes with quality acceptance standards as critical control points, and processes involving high-risk operations; k=1 (non-critical process): ordinary processes with a total float greater than a threshold, processes with high resource substitutability, and processes with little impact on overall quality.

[0095] In this embodiment, the value is determined based on the criticality of the process: when k=1, =1.0 (non-critical process), k=2, =1.5 (generally critical processes), k=3, =2.0 (critical path process).

[0096] The beneficial effects of the above technical solution are as follows: By extracting deviation features from multiple dimensions such as volume, spatial distribution, and temporal duration, it can comprehensively and accurately analyze construction deviations and provide dynamic collaborative design support based on real-time mapping. Compared with existing technologies, it can not only monitor construction deviations in real time but also analyze the impact of deviations on construction progress. Furthermore, by calculating the influence coefficient of deviation duration, it can dynamically adjust construction strategies, thereby optimizing construction management, improving construction accuracy, reducing costs, and timely adjusting construction plans to ensure project quality and schedule control.

[0097] Example 5:

[0098] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a deviation acquisition module:

[0099] Weight determination unit: Dynamic weights are determined based on the construction deviation area.

[0100] ;

[0101] ;

[0102] ;

[0103] in, For volume feature dynamic weights, Dynamic weights for spatial distribution characteristics. The dynamic weights for the time-duration feature. The benchmark weighting coefficient for volume characteristics. The baseline weighting coefficients for spatial distribution characteristics. The baseline weighting coefficients for the time-duration feature. This is the amplification factor of the load-bearing structure's weight on the volumetric characteristics, and , For the time-duration characteristic weighting factor of non-load-bearing structures, and , This is the spatial range weighting factor, and A is the actual area of ​​the deviation region. The preset threshold for the area of ​​the deviation region. It is a linear rectifier function with the following function: This is used to determine whether the area of ​​deviation exceeds a threshold. If it exceeds the limit, it will participate in the weight adjustment. This is the amplification coefficient for the spatial distribution characteristics weight during the structural construction phase, and , This is the amplification coefficient for the weighting of volumetric features during the decoration construction stage, and d is the distance from the deviation region to the critical structure. This is a preset threshold for the critical distance;

[0104] Coefficient Determination Unit: Determining dynamic deviation coefficients based on dynamic weights and key deviation parameters.

[0105] ;

[0106] in, DC is the dynamic deviation coefficient.

[0107] In this embodiment, the dynamic weights determined based on the construction deviation area are based on the following logic: when the deviation area exceeds a preset threshold area, the weight coefficient of the spatial distribution feature is increased; the weight value of the volume feature is dynamically adjusted according to the distance relationship between the deviation area and the key structural parts; structural importance weight adjustment: identify the type of structural part where the deviation area is located; for deviations in load-bearing structural parts, increase the weight coefficient of the volume feature; for deviations in non-load-bearing parts, increase the weight coefficient of the time duration feature; construction stage weight adjustment: obtain the current construction progress stage information; during the structural construction stage, increase the weight of the spatial distribution feature; during the decoration construction stage, increase the weight of the volume feature.

[0108] In this embodiment, the baseline weight coefficient of the volume feature represents the basic weight ratio of the volume feature when it is not adjusted;

[0109] In this embodiment, the amplification coefficient of the load-bearing structure on the volume feature weight increases the volume feature weight when the deviation is within the load-bearing structure, and the amplification coefficient reflects the specific amplification ratio.

[0110] In this embodiment, the weighting coefficient for the time duration feature of non-load-bearing structures is used to increase the proportion of the weighting of the time duration feature when the deviation region is a non-load-bearing structure.

[0111] In this embodiment, the spatial range weight amplification coefficient is used to increase the weight of spatial distribution features when the area of ​​the deviation region exceeds a threshold, and represents the specific amplification ratio.

[0112] In this embodiment, the preset threshold of the area of ​​the deviation region is the critical area that triggers the spatial range weight adjustment;

[0113] In this embodiment, the distance from the deviation area to the critical structure is the straight-line spatial distance from the geometric center of the construction deviation area to the nearest critical structural component (such as a load-bearing wall, core tube, etc.). This distance is calculated using three-dimensional spatial coordinates, with structural positioning data from the BIM model as the benchmark. The distance value reflects the degree of impact of the deviation on the safety of the main structure; the closer the distance, the higher the structural risk. The calculation takes into account the thickness of the structural protective layer during actual construction and automatically excludes non-permanent structures such as temporary supports. This parameter is used to dynamically adjust the weight allocation to ensure that deviations in critical areas receive greater attention.

[0114] In this embodiment, the preset threshold for critical distance refers to the minimum safe distance value for determining whether a deviation area affects the critical structure. This threshold is determined based on structural design specifications, material properties, and engineering experience. When the actual distance between the deviation area and the critical structure is less than this threshold, the system will automatically increase the risk level. For example, for reinforced concrete frame structures: the critical threshold for the main beam area can be set to 500mm, for the core tube area to 300mm, and for the ordinary floor slab area to 800mm. Setting different thresholds for different structural parts ensures safety while avoiding excessive warnings. The threshold data is stored in the BIM model attributes and can be dynamically adjusted as construction progresses.

[0115] In this embodiment, the value range of α is: [0.4, 0.7] Lower limit 0.4: ensures that volumetric features still have a basic influence in non-critical parts (such as decorative layers), upper limit 0.7: strengthens the sensitivity to large volume deviations in load-bearing structures (such as concrete defects at beam-column joints); the value range of spatial distribution feature β is: [0.2, 0.5] Lower limit 0.2: ensures basic monitoring of minor spatial deviations (such as curtain wall installation errors), upper limit 0.5: focuses on responding to axial offsets on critical paths (such as core tube verticality deviations); the value range of γ is: [0.1, 0.4] Lower limit 0.1: maintains baseline tracking for long-term untreated deviations (such as masonry misalignment lasting a week), upper limit 0.4: highlights the time pressure of urgent process delays (such as delayed steel reinforcement acceptance before the initial setting of concrete).

[0116] The beneficial effects of the above technical solution are as follows: By introducing dynamic weight calculation, the impact of construction deviations on different construction stages and structural types can be accurately assessed. Compared with existing technologies, this solution not only considers volume, spatial distribution, and temporal duration characteristics, but also incorporates factors such as load-bearing structure, construction stage, deviation area, and distance to critical structures for weight adjustment. This achieves intelligent quantification of the impact of deviations, improves the accuracy of deviation identification, provides a scientific basis for construction adjustments, optimizes construction quality management, reduces rework costs, and improves the overall efficiency and safety of the project.

[0117] Example 6:

[0118] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a strategy generation module:

[0119] Level Classification Unit: Based on the numerical range of the dynamic deviation coefficient, the construction deviation is divided into several deviation levels; Strategy Matching Unit: For each deviation level, a corresponding strategy is matched in the preset response strategy library to generate a construction response strategy containing specific operation instructions.

[0120] In this embodiment, construction deviations are divided into several deviation levels based on the numerical range of the dynamic deviation coefficient, including: establishing an adaptive threshold model based on historical data, classifying deviations as Level 1 (minor) when DDC∈[0,μ-σ], Level 2 (moderate) when DDC∈(μ-σ,μ+σ], and Level 3 (severe) when DDC>μ+σ, where μ and σ are the mean and standard deviation dynamically calculated during system operation, respectively.

[0121] In this embodiment, the construction response strategy includes: Level 1 deviation strategy, which includes AR visualization annotation specifications, manual correction operation guidelines, and a list of local reinforcement materials; Level 2 deviation strategy, which includes mechanical path replanning algorithm, resource scheduling optimization scheme, and suggestions for process connection adjustment; and Level 3 deviation strategy, which includes structural safety verification process, design change impact assessment, and emergency resource allocation plan.

[0122] The beneficial effects of the above technical solution are as follows: By classifying and matching dynamic deviation coefficients, corresponding construction response strategies can be accurately generated for different deviation levels. Compared with existing technologies, this solution can automatically generate construction response strategies with specific operational instructions based on real-time surveying data and deviation levels, improving the real-time nature and accuracy of construction adjustments. This not only enhances the adaptability during construction but also optimizes resource allocation, reduces unnecessary waste caused by deviations, and ensures the project proceeds smoothly as planned. Example 7:

[0123] This invention provides a dynamic collaborative design system for building construction schemes based on real-time mapping, including a deviation correction module:

[0124] Command issuing unit: Converts construction response strategies into executable commands and sends them to the corresponding construction terminal equipment;

[0125] Effect verification unit: Acquires the survey data after execution to verify the effectiveness of the deviation correction measures;

[0126] Iterative optimization unit: Adjusts the dynamic weights corresponding to the dynamic deviation coefficients based on the verification results until the deviation correction results meet the preset requirements.

[0127] In this embodiment, verifying the effectiveness of the deviation correction measures involves determining the deviation improvement rate of the deviation region, and the effectiveness is determined based on a preset improvement rate threshold.

[0128] The beneficial effects of the above technical solution are as follows: Through closed-loop control of command issuance, effect verification, and iterative optimization, construction deviations can be adjusted in real time and the correction effect can be ensured. Compared with existing technologies, it not only automatically transforms construction response strategies into execution commands, but also continuously optimizes deviation correction strategies by verifying execution results, improving correction accuracy and construction efficiency. It can effectively reduce human intervention, improve construction accuracy, ensure that construction quality meets preset requirements, and enhance the flexibility and precision of the construction process by adjusting and optimizing based on real-time feedback.

[0129] Example 8:

[0130] This invention provides a dynamic collaborative design method for building construction schemes based on real-time mapping, such as... Figure 2 As shown, it includes:

[0131] Step 1: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data;

[0132] Step 2: Used to compare real-time survey data with the preset BIM model, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data;

[0133] Step 3: Determine the dynamic deviation coefficient based on key deviation parameters and construction deviation areas;

[0134] Step 4: Classify the deviation levels according to the numerical range of the dynamic deviation coefficient and generate the corresponding construction response strategies;

[0135] Step 5: Execute the construction response strategy and obtain the post-execution survey data. Verify the deviation correction results based on the post-execution survey data. Update the dynamic deviation coefficient based on the deviation correction results until the deviation correction results meet the preset requirements.

[0136] The beneficial effects of the above technical solution are as follows: Through the dynamic collaboration between real-time surveying data and the BIM model, real-time monitoring and automatic correction of construction deviations are achieved. Compared with existing technologies, it can acquire surveying data from the construction site in real time, and optimize the deviation correction strategy through spatiotemporal registration algorithms and dynamic deviation coefficients. This avoids the problems of lagging deviation correction and reliance on manual adjustments in traditional solutions, improves the accuracy and efficiency of the construction process, and ensures precise control of construction quality and schedule.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A dynamic collaborative design system for building construction schemes based on real-time mapping, characterized in that, include: Data acquisition module: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data; Data analysis module: used to compare real-time survey data with preset BIM models, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data; Deviation acquisition module: Determines dynamic deviation coefficients based on key deviation parameters and construction deviation areas; Strategy generation module: Classifies deviation levels based on the numerical range of dynamic deviation coefficients and generates corresponding construction response strategies; Deviation Correction Module: Executes the construction response strategy and acquires the survey data after execution, verifies the deviation correction results based on the survey data after execution, and updates the dynamic deviation coefficient based on the deviation correction results until the deviation correction results meet the preset requirements; The data analysis module includes: Data extraction unit: Extracts the set of salient feature points from real-time surveying data, and at the same time, identifies the corresponding set of theoretical feature points in the preset BIM model; Initial registration unit: The salient feature point set and the theoretical feature point set are initially registered using a feature matching algorithm, and the initial registration error is determined. The registration quality is then determined based on the initial registration error. Mesh building unit: If the registration quality meets the preset requirements, a three-dimensional spatial mesh is built based on the initial registration; Candidate region: Local geometric features are compared within the three-dimensional spatial grid to obtain the geometric difference degree within each grid cell. Grid cells with geometric difference degrees exceeding a preset difference threshold are selected as candidate deviation regions. Region determination unit: Merge spatially adjacent candidate deviation regions to generate deviation regions with continuous boundaries, and determine the deviation regions with continuous boundaries as construction deviation regions; Parameter extraction unit: Based on real-time mapping data, determine the volumetric characteristics, spatial distribution characteristics, and temporal duration characteristics of the construction deviation area as key deviation parameters; The parameter extraction unit includes: Volume feature extraction sub-unit: The volume percentage of the construction deviation area relative to the corresponding area of ​​the preset BIM model is determined using the voxel mesh generation method; Spatial distribution feature extraction subunit: Performs spatial distribution analysis on real-time mapping data, determines the main direction of spatial distribution of deviation areas, and determines the deviation angle between the main direction of spatial distribution and the preset reference axis. Time-duration feature extraction subunit: Based on the time of the first occurrence of the deviation, the current construction progress, and the criticality of the current process, the influence coefficient of the deviation duration is determined. ； in, The duration of the deviation is the influence coefficient, and the first detection time of the deviation is recorded as follows: , This refers to the time corresponding to the current construction progress. Set a planned completion time for the current construction procedure. This is the attenuation coefficient, and its value is determined based on the criticality of the process. The deviation acquisition module includes: Weight determination unit: Dynamic weights are determined based on the construction deviation area. ； ； ； in, For volume feature dynamic weights, For spatial distribution characteristics, dynamic weights, The dynamic weights for the time-duration feature. The benchmark weighting coefficient for volume characteristics. The baseline weighting coefficients for spatial distribution characteristics. The baseline weighting coefficients for the time-duration feature. This is the amplification factor of the load-bearing structure's weight on the volumetric characteristics, and , For the time-duration characteristic weighting factor of non-load-bearing structures, and , This is the spatial range weighting factor, and A is the actual area of ​​the deviation region. The preset threshold for the area of ​​the deviation region. It is a linear rectifier function with the following function: This is used to determine whether the area of ​​deviation exceeds a threshold; if it does, it participates in weight adjustment. This is the amplification coefficient for the spatial distribution characteristics weight during the structural construction phase, and , This is the amplification coefficient for the weighting of volumetric features during the decoration construction stage, and d is the distance from the deviation region to the critical structure. This is a preset threshold for the critical distance; Coefficient Determination Unit: Determining dynamic deviation coefficients based on dynamic weights and key deviation parameters. ； in, DC is the dynamic deviation coefficient.

2. The dynamic collaborative design system for building construction schemes based on real-time mapping as described in claim 1, characterized in that, The data acquisition module includes: Multi-source data acquisition unit: Acquires overall 3D point cloud data of the construction area through a ground-based fixed laser scanner, acquires detailed local mapping data through mobile surveying equipment, and acquires real-time pose data of the machinery's working face through positioning sensors mounted on the construction machinery; Spatiotemporal registration unit: Performs time synchronization processing on the data collected by the multi-source sensors, establishes the transformation relationship between the coordinate systems corresponding to all acquisition devices, and unifies the mapping data from all sources into the global construction coordinate system through a spatiotemporal registration algorithm; Data fusion unit: Performs fusion processing on all mapping data to generate real-time mapping data.

3. The dynamic collaborative design system for building construction schemes based on real-time mapping as described in claim 1, characterized in that, The strategy generation module includes: Grading Unit: Based on the numerical range of the dynamic deviation coefficient, construction deviations are divided into several deviation levels; Strategy matching unit: Matches the corresponding strategy in the preset response strategy library for each deviation level and generates a construction response strategy containing specific operation instructions.

4. The dynamic collaborative design system for building construction schemes based on real-time mapping as described in claim 1, characterized in that, Deviation correction module, including: Command issuing unit: Converts construction response strategies into executable commands and sends them to the corresponding construction terminal equipment; Effect verification unit: Acquires the survey data after execution to verify the effectiveness of the deviation correction measures; Iterative optimization unit: Adjusts the dynamic weights corresponding to the dynamic deviation coefficients based on the verification results until the deviation correction results meet the preset requirements.

5. A dynamic collaborative design method for building construction schemes based on real-time mapping, characterized in that, The system for dynamic collaborative design of building construction schemes based on real-time mapping, as described in any one of claims 1 to 4, comprises: Step 1: Used to collect surveying data from the construction site through multi-source sensors, and use a spatiotemporal registration algorithm to unify the surveying data from several sources into the global construction coordinate system to generate real-time surveying data; Step 2: Used to compare real-time survey data with the preset BIM model, thereby identifying construction deviation areas and obtaining key deviation parameters by combining real-time survey data; Step 3: Determine the dynamic deviation coefficient based on key deviation parameters and construction deviation areas; Step 4: Classify the deviation levels according to the numerical range of the dynamic deviation coefficient and generate the corresponding construction response strategies; Step 5: Execute the construction response strategy and obtain the post-execution survey data. Verify the deviation correction results based on the post-execution survey data. Update the dynamic deviation coefficient based on the deviation correction results until the deviation correction results meet the preset requirements.

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