Error closed-loop control method for superimposed assembly station construction, medium and equipment

Abstract

The present application relates to the field of building construction technology, and particularly relates to a kind of superimposed assembly type station construction error closed loop control method, medium and equipment, method includes steps: fusion instant positioning and map construction dynamic scanning and static precision scanning data acquisition;Point cloud data preprocessing and measured model construction;Construction parameterized, adaptable BIM design model;Double model data unification and format alignment;Model registration and space alignment;Construction error quantitative analysis and visualization;Based on parameterized adaptive BIM intelligent correction scheme generation;Field correction and effect verification.The present application breaks the static limitation of traditional fixed station scanning, realizes continuous real-time tracking to component hoisting, assembly whole process, simultaneously with dynamic data and subsequent steps depth fusion, can capture dynamic deviation that traditional method cannot find, solves the existing superimposed assembly type subway station construction error detection dependent on static scanning, model application passive technical problem.

Description

Technical Field

[0001] This invention relates to the field of building construction technology, and in particular to a method, medium, and equipment for closed-loop control of construction errors in composite prefabricated railway stations. Background Technology

[0002] As a key node in urban rail transit systems, the construction quality of subway stations directly affects operational safety and the lifespan of the project. Prefabricated stations, which utilize precast components assembled on-site, offer advantages such as rapid construction and minimal environmental impact. However, during assembly, factors like component positioning deviations and installation errors can easily lead to discrepancies between structural dimensions and the design. If these construction errors are not identified in a timely manner, they can cause problems such as component interference and stress concentration, increasing rework costs and even creating safety hazards.

[0003] Traditional methods for detecting construction errors in subway stations primarily rely on point-based measurement techniques such as total stations and levels. These methods suffer from low data acquisition efficiency, limited coverage, and an inability to comprehensively reflect overall deviations in complex structures, thus failing to meet the quality control requirements of high-precision assembly in prefabricated stations. Currently, existing technologies for detecting construction errors in subway stations mainly fall into the following categories: Traditional manual surveying techniques involve measuring key points one by one using instruments such as total stations and levels. This method relies on manual operation and data recording, resulting in low data acquisition efficiency. It can only obtain discrete point deviations and is difficult to fully cover the continuous error distribution of complex structures (such as curved surfaces and assembly joints). The measurement results are greatly affected by subjective factors and cannot be directly linked to the design model, leading to weak error tracing capabilities and difficulty in supporting fine-grained corrections.

[0004] BIM-based digital monitoring methods integrate design information using Building Information Modeling (BIM) and achieve partially automated monitoring through sensors or manual input of on-site data. However, this method relies on preset monitoring points and cannot fully reflect the spatial deviations of assembled components. The systems are mostly limited to specific construction stages (such as foundation pit monitoring) and lack specialized accuracy analysis modules. The fusion of BIM and measured data is mostly limited to the visualization level, failing to achieve quantitative comparison and automatic early warning of millimeter-level errors, and lacks dynamic data support for the construction process, making it impossible to trace the process causes of deviations.

[0005] 3D laser scanning point cloud technology: This method rapidly acquires massive point cloud data of structural surfaces through non-contact scanning, making it suitable for large-scale spatial measurements. However, it lacks an automatic registration and difference analysis process between the point cloud data and the design model, resulting in low detection efficiency and a tendency to miss local errors. Processing relies on specialized software, which has a high operational threshold and is difficult to deploy quickly on-site. More importantly, these technologies are primarily static and post-construction detection, unable to track dynamic deviations during construction in real time. They lack dynamic data to support error tracing, and the BIM model serves only as a passive comparison benchmark, lacking the ability to intelligently generate correction schemes based on measured data.

[0006] Therefore, there is an urgent need for a closed-loop control method for construction errors in composite prefabricated railway stations to solve the above-mentioned technical problems. Summary of the Invention

[0007] The main objective of this invention is to provide a closed-loop control method, medium, and equipment for construction errors in composite prefabricated subway stations, aiming to solve the technical problems of existing composite prefabricated subway station construction error detection relying on static scanning and passive model application.

[0008] To achieve the above objectives, this invention proposes a closed-loop control method for construction errors in composite prefabricated railway stations, comprising the following steps: S1. Data acquisition that integrates real-time positioning and map construction with dynamic and static scanning; S2. Point cloud data preprocessing and measured model construction, specifically: preprocessing the data collected in S1 and generating a measured point cloud model; S3. Construct a parametric, adaptable BIM design model; S4. Data unification and format alignment of the two models: Specifically, the measured point cloud model of S2 and the BIM design model of S3 are imported into the 3D comparison and analysis software to unify the data foundation. S5. Model registration and spatial alignment, specifically: performing model registration and spatial alignment on the two models after the data foundation is unified in S4; S6. Construction error quantification analysis and visualization: Specifically, point-by-point distance calculation is performed between the two models that have completed spatial registration in S5. Combined with the dynamic scanning data of real-time positioning and map construction, the spatial deviation and dynamic change law between the construction entity and the design model are quantified. S7. Intelligent correction scheme generation based on parametric adaptive BIM: Based on the spatial deviation and dynamic change law of S6, combined with the BIM design model constructed by S3 and the real-time positioning and map construction dynamic process data collected by S1, error source tracing and correction scheme generation are carried out to realize the fusion of real-time positioning and map construction dynamic data and correction decisions. S8. On-site correction and effect verification: Based on the correction plan generated in S7, combined with real-time positioning and map construction, the root cause of deviation is traced through dynamic data, and corresponding on-site correction measures are implemented based on the root cause of deviation.

[0009] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S2 specifically includes the following steps: S201. Denoise the data collected by S1 and remove invalid points from the data; S202. The data after denoising in S201 is stitched together. Specifically, the target is used as the control point, and a registration algorithm based on target matching is used to stitch together the point clouds of each station into a complete three-dimensional point cloud model of the station. S203. Thinning is performed on the stitched data from S202. Specifically, while preserving the main structural features and key nodes of the dynamic trajectory for real-time positioning and map construction, the point cloud is randomly sampled and thinned to generate a measured point cloud model. The point cloud thinning sampling uses secondary random sampling, and the specific formula is as follows: ; in: The number of points after thinning; The original number of points; The sampling rate.

[0010] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S4 specifically includes the following steps: S401, Unit unification: Reduce the BIM design model by 1000 times and unify it with the measured point cloud model to the same unit system; S402. Format unification: Convert the BIM design model into point cloud format through uniform sampling to make it consistent with the measured point cloud in terms of data structure, which facilitates subsequent comparison and analysis. At the same time, ensure that the BIM model is compatible with the format of dynamic trajectory data of real-time positioning and map construction.

[0011] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S5 specifically includes the following steps: S501, Coarse Registration: Multiple feature points are manually selected in the measured point cloud model and the BIM design model, and the initial rotation and translation matrices are calculated using the least squares method to achieve the initial alignment of the models; S502, Fine Registration: The Iterative Closest Point (ICP) algorithm is used for fine registration. Through multiple iterations, the transformation parameters are optimized to minimize the overall distance error between the two models, ultimately achieving model registration and spatial alignment.

[0012] The method for closed-loop control of construction errors in composite prefabricated stations of the present invention is further improved in that the ICP algorithm in S502 uses ICP iterative optimization of the objective function for calculation, assuming the measured point cloud is... , The measured 3D coordinates are used; the point cloud after model transformation is... , The design point has three-dimensional coordinates; the ICP algorithm uses iterative optimization of the rotation matrix. Translation matrix ICP iterative optimization objective function The expression is as follows: ; Where: ||•|| is the Euclidean distance. The square of the Euclidean distance. The total number of points in the point cloud data. The index of the point cloud data; the iteration termination condition is when two consecutive iterations... The process stops when the difference is less than the convergence threshold or when the number of iterations reaches a preset value, ultimately minimizing the error.

[0013] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S6 specifically includes the following steps: S601, Deviation Calculation: Calculate the shortest distance from each point in the measured point cloud to the corresponding surface of the BIM design model, and use it as the deviation value of that point; at the same time, combine the dynamic data of real-time positioning and map construction to determine the construction process caused by the positioning deviation; S602, Visualization Output: Color mapping is used to generate a deviation distribution cloud map, which is then overlaid with the dynamic trajectory of real-time positioning and map construction to show the spatial location, size and distribution characteristics of the error; S603, Statistical Report: Outputs an analysis report including statistical indicators such as maximum deviation, average deviation, standard deviation, and the proportion of points exceeding limits. Based on real-time positioning and map construction, dynamic data is used to trace and synchronously mark the construction stages caused by deviations, providing a targeted basis for subsequent correction plans.

[0014] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S7 specifically includes the following steps: S701 Error Source Tracing: Based on the dynamic trajectory data constructed by real-time positioning and map, trace the specific construction links and causes of deviations. If the deviation occurs during hoisting and continues to increase, it is determined to be a hoisting angle deviation or a hoisting tool positioning deviation. If the deviation occurs after positioning and adjustment, it is determined to be an adjustment operation deviation. If the deviation exists only in a local area and is not related to the dynamic trajectory, it is determined to be a data acquisition error. S702, Intelligent Adaptation Calculation: For physical construction errors, the parametric BIM model automatically calculates and generates an adaptation adjustment scheme based on measured deviation data, the dynamic deviation change law of real-time positioning and map construction, and the built-in component connection logic. S703, Solution Output: Output the generated adaptation adjustment solution to the on-site construction personnel in the form of standardized instructions.

[0015] The method for closed-loop control of construction errors in composite prefabricated railway stations of the present invention is further improved in that S8 specifically includes the following steps: S801. For the side wall positioning deviation, a jacking device is used to jack and correct it. At the same time, the angle of the lifting device is adjusted, and the dynamic trajectory data of real-time positioning and map construction are referenced to ensure that the posture of the component after correction is consistent with the design model. S802. For hoisting angle deviation, a rotating hoisting system is used for rotation correction, and dynamic scanning of real-time positioning and map building is started simultaneously to track the component posture adjustment process in real time and ensure that the adjustment angle meets the requirements of the correction plan. S803. Deviations other than side wall positioning deviation and hoisting angle deviation are corrected by means of component fine-tuning, local repair, repositioning of embedded parts, and surface treatment. During the correction process, deviation changes are monitored in real time through dynamic scanning of real-time positioning and map construction.

[0016] In addition, the present invention provides a readable storage medium storing a computer program adapted to be loaded by a processor and executed as described above for closed-loop control of construction errors in composite prefabricated stations.

[0017] In addition, the present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, running the construction error closed-loop control method for composite prefabricated stations as described above.

[0018] The application of the technical solution of the present invention has the following beneficial effects: The present invention introduces real-time localization and mapping (SLAM) dynamic perception technology into the construction error closed-loop control method of composite prefabricated station, breaking through the static limitations of traditional fixed-station scanning. It realizes continuous real-time tracking of the entire process of component hoisting and assembly, and deeply integrates dynamic data with subsequent steps, providing core support for deviation tracing, intelligent correction and effect verification. It can capture dynamic deviations that traditional methods cannot detect, providing a data foundation for process early warning and active control, and solving the technical problems of existing composite prefabricated subway station construction error detection relying on static scanning and passive model application.

[0019] This invention constructs a parameterized and adaptable BIM design model, reserves a dynamic data interface, and can import real-time positioning and mapping (SLAM) dynamic trajectory data. It upgrades the traditional static BIM into an intelligent model with built-in component connection logic, geometric constraints, and the ability to associate with dynamic processes. This enables it to automatically generate quantitative and executable correction schemes by combining dynamic data and measured deviation data, truly transforming error analysis into an effective tool to guide construction and significantly reducing reliance on human experience.

[0020] This invention proposes a complete data preprocessing, format unification, and spatial registration process for dual-model (BIM design model and point cloud measured model) construction scenarios for composite prefabricated railway stations. It simultaneously achieves accurate alignment of dynamic and static data and BIM models in real-time positioning and mapping (SLAM). Through unit normalization, data thinning optimization, and a two-stage registration strategy of "coarse registration of feature points + fine registration of ICP", it achieves millimeter-level accurate alignment of two types of heterogeneous models and dynamic data in a unified coordinate system.

[0021] This invention organically integrates multiple processes such as real-time positioning and mapping (SLAM) dynamic scanning, 3D laser scanning, BIM model comparison and analysis, error correction decision-making and retesting verification, etc., and incorporates dynamic data throughout the closed-loop process to form a complete and cyclical digital management and control loop for construction quality, realizing full-process empowerment from dynamic perception to intelligent correction.

[0022] This invention represents a leap from "static detection" to "dynamic perception + full-process traceability": it introduces SLAM mobile dynamic scanning technology, breaking through the limitations of traditional fixed-station scanning which can only obtain "snapshot" information. This enables continuous real-time tracking of the entire process of component hoisting and assembly. At the same time, it deeply integrates dynamic data with subsequent deviation analysis, traceability, and correction steps. It can not only capture dynamic deviations that traditional methods cannot detect, but also accurately trace the root cause of deviations. This provides a core data foundation for process early warning, proactive control, and optimization of correction schemes, avoiding blind corrections.

[0023] This invention upgrades from "passive comparison" to "active adaptation + precise correction": it transforms the traditional static BIM model into a parameterized, adaptable intelligent BIM model. Combined with SLAM dynamic data, it not only serves as a comparison benchmark but also automatically traces the root cause of deviations and generates quantifiable and actionable correction plans based on measured deviation data and dynamic deviation change patterns. Simultaneously, it provides prevention and control suggestions during construction, truly transforming error analysis into an effective tool for guiding construction. This significantly reduces reliance on manual experience, improves the accuracy and efficiency of correction plans, and prevents deviation recurrence.

[0024] This invention achieves a detection coverage of over 98% through SLAM dynamic perception and static scanning. Dynamic traceability and intelligent adaptation improve the accuracy and efficiency of the correction scheme. The pass rate of re-inspection after correction increases from about 85% to 98%, the detection time is shortened by more than 50%, the overall cost is reduced by more than 30%, and the number of reworks is reduced, ensuring the construction progress. This invention realizes the full-process empowerment of construction quality by digital technology. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0026] Figure 1 This is a flowchart of the construction error closed-loop control method for composite prefabricated railway stations according to the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0028] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0029] Furthermore, in this invention, descriptions involving "first," "second," etc., are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0030] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0031] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are feasible for those skilled in the art. If the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection claimed by the present invention.

[0032] like Figure 1 As shown, this invention proposes a closed-loop control method for construction errors in composite prefabricated railway stations, comprising the following steps: S1. Data acquisition that integrates dynamic scanning and static precision scanning using Simultaneous Localization and Mapping (SLAM); S101, Mobile SLAM Laser Scanner Dynamic Scanning: During component hoisting and assembly, construction personnel use handheld or mounted SLAM laser scanners to move across the work surface, continuously collecting point cloud data of the construction site environment and components in real time. This simultaneously records the three-dimensional position and attitude change trajectory of the components from hoisting, transportation, adjustment to initial positioning (collection frequency no less than 15Hz), forming a complete dynamic database of the construction process. This dynamic data not only provides process evidence for subsequent deviation tracing but also captures dynamic component offsets in real time (such as hoisting angle deviations and instantaneous displacement during positioning), triggering timely dynamic deviation warnings to prevent the deviation from escalating. Simultaneously, it provides core process data support for the error tracing and intelligent correction scheme generation of S7.

[0033] S102. Stationary Static Precision Scanning: At key nodes (such as after component assembly), a high-precision stationary 3D laser scanner is used to perform a comprehensive, high-density scan of the completed structure to obtain precise point cloud data, which serves as the basis for final quality acceptance. Before scanning, targets are set up in the survey area as control points, with a target spacing of approximately 15m. Multiple scanning stations are planned, with each station spaced approximately 20 meters apart. The scanning time for each station is controlled within 10 minutes to avoid interference from personnel and debris. Static scan data and SLAM dynamic scan data complement each other. Static data is used to accurately quantify the final deviation, while dynamic data is used to trace the causes of deviations during the construction process.

[0034] S2. Point cloud data preprocessing and measured model construction, specifically: preprocessing the data collected in S1 and generating a measured point cloud model; S201. Denoise the data collected by S1 and remove invalid points caused by on-site obstructions, personnel movement, instrument noise, etc. S202. The data after denoising in S201 is stitched together. Specifically, using the target as the control point, a registration algorithm based on target matching is used to stitch together the point clouds of each station (including the continuous frame point clouds of dynamic scanning) into a complete three-dimensional point cloud model of the station, ensuring that the dynamic scanning data and the static scanning data are aligned in the same coordinate system, and preserving the complete association between the dynamic change trajectory of the component and the final static state. S203. The data stitched together in S202 is thinned out. Specifically, to improve subsequent processing efficiency, while preserving the main structural features and key nodes of the dynamic trajectory for real-time localization and map construction, the point cloud is randomly sampled and thinned (e.g., reduced to the million-point level) to generate a measured point cloud model. This ensures that key feature points of dynamic deviation are still fully preserved after thinning. Point cloud thinning sampling uses secondary random sampling, controlling the number of point clouds to the million-point level while maintaining structural features and key nodes of the SLAM dynamic trajectory, thus balancing data processing efficiency with the preservation of geometric details and dynamic features. The specific formula is as follows: ; in: The number of points after thinning; The original number of points; The sampling rate.

[0035] S3. Construct a parametric, adaptable BIM design model; Based on the construction drawings and relevant design specifications of the prefabricated railway station, a parametric BIM design model is constructed in the existing software platform. This BIM design model not only includes design information such as the geometric dimensions, spatial positioning, and material properties of structural components, but more importantly, it incorporates the geometric constraints and connection logic between components (such as "the edge of the side wall panel should be aligned with the centerline of the bottom slab" and "the distance between the beam end and the column edge should not be less than the design value of 50mm"). It also reserves a dynamic data interface to import SLAM dynamic trajectory data collected by S101, enabling real-time comparison between the dynamic process and the BIM design model. During the modeling process, the consistency of parameter associations for digital objects such as walls, columns, beams, and prefabricated components in the model is ensured. Adjustment of any component parameter can trigger automatic updates of related components, avoiding information contradictions. When subsequent steps identify construction deviations, this parametric model can automatically trace the stage where the deviation occurred (such as hoisting angle deviation or positioning adjustment deviation) by combining SLAM dynamic process data and deviation data, and automatically calculate and generate an adaptive adjustment scheme according to preset constraint rules.

[0036] S4. Data unification and format alignment of the two models: Specifically, the measured point cloud model of S2 and the BIM design model of S3 are imported into the existing 3D comparison and analysis software to unify the data foundation. S401, Unit unification: Reduce the BIM design model (usually in millimeters) by 1000 times and unify it with the measured point cloud model (usually in meters) to the same unit system; S402. Format unification: Convert the BIM design model (usually a grid model) into a point cloud format through uniform sampling to make it consistent with the measured point cloud (including dynamic and static point clouds) in terms of data structure, so as to facilitate subsequent comparison and analysis. At the same time, ensure that the BIM model is compatible with the format of dynamic trajectory data of real-time positioning and map construction.

[0037] S5. Model registration and spatial alignment, specifically: performing model registration and spatial alignment on the two models after the data foundation is unified in S4; S501. Coarse Registration: In the measured point cloud model (prioritizing key frame point clouds after component positioning in SLAM dynamic scanning) and the BIM design model, several pairs of significantly corresponding feature points (such as column corners and wall intersections) are manually selected. The initial rotation and translation matrices are calculated using the least squares method to achieve preliminary alignment of the models. S502, Fine Registration: The Iterative Closest Point (ICP) algorithm is used for fine registration. Through multiple iterations and optimization of transformation parameters, the overall distance error between the two models is minimized, ultimately achieving model registration and spatial alignment. Preferably, the number of iterations is set to 40, the convergence threshold is set to 1e-6, and the overlap is preset to above 98% to ensure that the registration accuracy meets the requirements for millimeter-level error analysis. During the registration process, SLAM dynamic trajectory data is synchronously associated to ensure accurate comparison between the component positions of each node and the BIM design model during the dynamic process, laying the foundation for subsequent dynamic deviation analysis.

[0038] Let the measured point cloud be , The measured 3D coordinates are used; the point cloud after model transformation is... , The design point has three-dimensional coordinates; the ICP algorithm uses iterative optimization of the rotation matrix. Translation matrix ICP iterative optimization objective function The expression is as follows: ; Where: ||•|| is the Euclidean distance. The square of the Euclidean distance. The total number of points in the point cloud data. The index of the point cloud data; the iteration termination condition is when two consecutive iterations... The process stops when the difference is less than the convergence threshold or when the number of iterations reaches a preset value, ultimately minimizing the error.

[0039] S6. Construction error quantification analysis and visualization: Specifically, point-by-point distance calculation is performed between the two models that have completed spatial registration in S5. Combined with the dynamic scanning data of real-time positioning and map construction, the spatial deviation and dynamic change law between the construction entity and the design model are quantified. S601, Deviation Calculation: Calculate the shortest distance from each point in the measured point cloud to the corresponding surface of the BIM design model, which is used as the deviation value for that point; simultaneously, combine the dynamic data of real-time positioning and map construction to pinpoint key construction stages where deviations occur. This can be performed according to station structure layers (e.g., basement level 1, basement level 2), outputting deviation distribution maps and statistical reports for each layer, facilitating targeted construction management.

[0040] Let the coordinates of a point in the measured point cloud be... The coordinates of the nearest point on the BIM model surface to this point are: Then the deviation value at that point The expression is shown below: ; like ( If the deviation is within the design tolerance range, then the deviation at that point is acceptable; otherwise, it is considered an out-of-range point.

[0041] S602. Visualization output: A color mapping (such as blue-green-yellow-red to represent deviation from small to large) is used to generate a deviation distribution cloud map, and the dynamic trajectory of real-time positioning and map construction is superimposed to show the spatial location, size and distribution characteristics of the error. S603, Statistical Report: Outputs an analysis report including statistical indicators such as maximum deviation, average deviation, standard deviation, and the proportion of points exceeding limits. Based on real-time positioning and mapping (SLAM) dynamic data, it traces and marks the construction process caused by deviations, providing a targeted basis for subsequent correction plans.

[0042] S7. Intelligent correction scheme generation based on parametric adaptive BIM: Based on the spatial deviation and dynamic change law of S6, combined with the BIM design model constructed by S3 and the real-time positioning and map construction dynamic process data collected by S1, error source tracing and correction scheme generation are carried out to realize the fusion of real-time positioning and map construction dynamic data and correction decisions. S701 Error Source Tracing: Based on the dynamic trajectory data constructed by real-time positioning and map, trace the specific construction links and causes of deviations. If the deviation occurs during hoisting and continues to increase, it is determined to be a hoisting angle deviation or a hoisting tool positioning deviation. If the deviation occurs after positioning and adjustment, it is determined to be an adjustment operation deviation. If the deviation exists only in a local area and is not related to the dynamic trajectory, it is determined to be a data acquisition error (such as scanning occlusion). S702, Intelligent Adaptation Calculation: For physical construction errors, the parametric BIM model automatically calculates and generates adaptation adjustment schemes based on measured deviation data, dynamic deviation changes in real-time positioning and map construction, and built-in component connection logic, ensuring that the correction schemes align with the root causes of the deviations. For example: If the SLAM dynamic data traceability reveals that the verticality deviation of the side wall gradually increases with the offset of the hoisting angle during the hoisting process, the system automatically calculates the required shim thickness and jacking displacement according to the constraint rules of the "side wall-bottom plate connection node" and the dynamic deviation change trend, and outputs: "X mm shims need to be added to the bottom of the side wall, and Y mm need to be pushed outward at the top. At the same time, the hoisting angle should be adjusted to Z° to avoid deviations in subsequent hoisting." If the SLAM dynamic data shows that the component's hoisting angle deviation causes it to shift after being positioned, the system directly outputs the angle and direction that need to be rotated. At the same time, it associates the hoisting trajectory data and provides angle control suggestions during the hoisting process to prevent the deviation from recurring.

[0043] S703, Solution Output: Output the generated adaptation adjustment solution to the on-site construction personnel in the form of standardized instructions.

[0044] S8. On-site correction and effect verification: Based on the correction plan generated in S7, combined with real-time positioning and map construction, the root cause of deviation is traced through dynamic data, and corresponding on-site correction measures are implemented based on the root cause of deviation.

[0045] S801. For the side wall positioning deviation (traced back to the hoisting angle deviation), a jacking device (such as using jacks for correction) is used to jack and correct it. At the same time, the hoisting angle is adjusted, and the dynamic trajectory data of real-time positioning and map construction are referenced to ensure that the posture of the corrected component is consistent with the design model. S802. For hoisting angle deviation, a rotating hoisting system is used for rotation correction, and dynamic scanning of real-time positioning and map building is started simultaneously to track the component posture adjustment process in real time and ensure that the adjustment angle meets the requirements of the correction plan. S803. Other deviations (such as planar position deviation, elevation deviation, embedded part deviation, and appearance / material deviation, etc.) are corrected by means of component fine-tuning (such as using jacks to correct deviation), local repair, repositioning of embedded parts, and surface treatment. During the correction process, deviation changes are monitored in real time through dynamic scanning of real-time positioning and map construction.

[0046] For data acquisition errors, after clearing away the interference objects on site, a local re-measurement is performed on the affected area, the point cloud data is updated, and then the analysis is repeated.

[0047] After the corrections are completed, a new 3D laser scan (static scan) is performed on the corrected area. Simultaneously, SLAM dynamic scanning is initiated to record the corrected component posture, acquiring updated point cloud data. This data is then compared and analyzed with the BIM model to verify whether the correction effect meets the design accuracy requirements. Furthermore, the corrected dynamic data is compared with the original dynamic data to evaluate the effectiveness of the corrective measures. This forms a digital closed-loop management process for construction quality, encompassing "dynamic perception - static detection - quantitative analysis - intelligent adaptation - precise correction - effect verification." SLAM dynamic data is integrated throughout the entire closed loop, enabling full-process tracking and control of deviations.

[0048] Through the above steps, this invention integrates SLAM dynamic scanning data throughout the entire process of data acquisition, preprocessing, registration, deviation analysis, error tracing, correction implementation, and effect verification. This enables digital and refined management of the entire process of composite prefabricated subway stations, from the acquisition of the construction entity status, high-precision comparison with the design model, intelligent error identification and classification, to targeted on-site correction and effect verification, significantly improving construction quality, efficiency, and safety.

[0049] Figure 1 This is a flowchart of the closed-loop control method for construction errors in prefabricated railway stations according to the present invention. The specific process is as follows: The design and construction drawings are constructed into a BIM model (parametric and adaptable). SLAM dynamic scanning (real-time process tracking) and station static precision scanning (node ​​quality acceptance) are performed on the construction site. Then, the data of the two models are unified and aligned in format (unit unification, BIM model to point cloud format conversion). Next, spatial alignment and model registration are performed (coarse registration of feature points + fine registration of ICP, millimeter-level alignment). Subsequently, quantitative analysis and visualization of construction errors are performed (deviation calculation, color mapping cloud map, statistical report). An automatic error source tracing (entity error / data error) is generated through a parametric adaptive BIM intelligent correction scheme. Based on the adaptation calculation of component connection constraints, quantitative correction instructions (shim thickness / jacking displacement / angle) are output, and precise on-site corrections are performed (prefabricated sidewall jacking device, rotating hoist system, other fine-tuning measures), thereby achieving closed-loop control (BIM model status update, data archiving).

[0050] In addition, the present invention provides a readable storage medium storing a computer program adapted to be loaded by a processor and executed as described above for closed-loop control of construction errors in composite prefabricated stations.

[0051] In addition, the present invention also provides a computer device, the computer device including a memory and a processor, the memory storing a computer program, and when the computer program is executed by the processor, running the construction error closed-loop control method for composite prefabricated stations as described above.

[0052] The invention will be further illustrated below with examples: S1. Data acquisition integrating SLAM dynamic scanning and static precision scanning: S101. Mobile SLAM Dynamic Scanning: During the hoisting of side wall components, on-site technicians use a handheld SLAM laser scanner to move around the work surface, collecting point cloud data in real time. The system continuously records the complete trajectory and attitude changes of the component from lifting to positioning at a frequency of 20Hz, forming a dynamic database. Simultaneously, it monitors for a 3° angular deviation of the component during hoisting, triggering a dynamic early warning in real time to remind operators to pause and adjust. This dynamic data will be used for subsequent deviation tracing and correction scheme generation, identifying the deviation as occurring at the hoisting angle.

[0053] S102. Stationary Static Precision Scanning: After the components are initially positioned, a FARO Focus S350 high-precision stationary scanner is used to perform a comprehensive scan of the side walls and surrounding completed structures according to a scheme of 20 meters between stations and 15 meters between targets. Precise point cloud data is obtained and stitched with SLAM dynamic scanning data to form a complete measured point cloud model.

[0054] S2. Point cloud data preprocessing and experimental model construction: Preprocessing is performed on the collected raw point cloud data from multiple stations (including SLAM dynamic scan data and static precision scan data), including: S201. Noise Reduction: Eliminate invalid points caused by on-site obstructions, personnel movement, instrument noise, etc. S202. Stitching: Using the target as the control point, a registration algorithm based on target matching is used to stitch the point clouds of each station (including the continuous frame point clouds of dynamic scanning) into a complete three-dimensional point cloud model of the station, ensuring that the hoisting trajectory recorded by dynamic scanning is accurately aligned with the final state of static scanning. S203. Thinning: To improve the efficiency of subsequent processing, while retaining the main structural features and key nodes of the SLAM dynamic trajectory (such as the point cloud of the frame with the largest hoisting angle offset), the massive point cloud is randomly sampled and thinned to generate a measured point cloud model that can be used for comparative analysis. The point cloud thinning adopts secondary random sampling.

[0055] S3. Construct a parametric, adaptable BIM design model: Based on the construction drawings of the prefabricated railway station, a high-precision BIM design model was built in Revit software. In addition to geometric dimensions and spatial positioning, the model also includes the constraint logic of "side wall-base plate connection node": the bottom of the side wall and the top surface of the base plate should fit tightly, with an allowable deviation of ±5mm; the allowable deviation of the side wall verticality is ±5mm. At the same time, a dynamic data interface is reserved to import SLAM dynamic trajectory data collected by S101, so as to realize the real-time comparison between the hoisting process and the design model, and clearly show the correlation between the hoisting angle offset and the final deviation.

[0056] S4. Data unification and format alignment between the two models: The measured point cloud model (including dynamic scan stitching data and static scan data) and the BIM design model were imported into the 3D comparison and analysis software CloudCompare to unify the data foundation: S401, Unit unification: Reduce the BIM model (usually in millimeters) by 1000 times and unify it with the point cloud model (usually in meters) to the same unit system; S402. Format unification: Convert the BIM model (usually a grid model) into a point cloud format through uniform sampling to make it consistent with the measured point cloud (including dynamic and static point clouds) in terms of data structure, so as to facilitate subsequent comparison and analysis and ensure that the BIM model and SLAM dynamic trajectory data are compatible in format.

[0057] S5, Model Registration and Spatial Alignment High-precision spatial registration of the two models after unifying the format, including: S501, Coarse Registration: Several pairs of significantly corresponding feature points (such as column corners and wall intersections) are manually selected in the measured point cloud (selecting key frame point clouds after the component is in place in SLAM dynamic scanning) and the BIM model. The initial rotation and translation matrices are calculated by the least squares method to achieve the initial alignment of the model. S502, Fine Registration: The Iterative Closest Point (ICP) algorithm is used for fine registration. Through multiple iterations to optimize the transformation parameters, the overall distance error between the two models is minimized, and finally, spatial alignment with millimeter-level accuracy is achieved.

[0058] S6. Quantitative Analysis and Visualization of Construction Errors: Point-by-point distance calculations are performed between the registered dual models, and combined with SLAM dynamic trajectory data, the spatial deviation between the construction entity and the design model is quantified. Deviation calculation: Calculate the shortest distance from each point in the measured point cloud to the corresponding surface of the BIM model, and use it as the deviation value for that point.

[0059] Visualization output: A color map (e.g., blue-green-yellow-red to represent deviations from small to large) is used to generate a deviation distribution cloud map, which intuitively displays the spatial location, size and distribution characteristics of the error; Statistical Report: Outputs an analytical report including statistical indicators such as maximum deviation, average deviation, standard deviation, and the proportion of points exceeding the limit.

[0060] S7. Intelligent correction scheme generation based on parametric adaptive BIM: The deviation data is input into the parametric BIM model, and the root cause of the deviation (lifting angle deviation) is traced by SLAM dynamic trajectory data. The model automatically calculates the adjustment plan based on the built-in "side wall-bottom plate connection node" constraint logic: "A 2mm thick shim needs to be added to the inner side of the bottom of the side wall to compensate for the bottom deviation; the top needs to be pushed outward by 5mm using a jacking device to restore the verticality to the allowable range; at the same time, the lifting angle is adjusted by 3°, referring to SLAM dynamic trajectory data, to ensure that the posture of the component meets the design requirements during the subsequent lifting process and to avoid the recurrence of deviation."

[0061] S8. On-site correction and effect verification: Following instructions, on-site personnel inserted 2mm shims into the bottom of the sidewall, then used a prefabricated sidewall jacking device, setting the jacking displacement to 5mm, for precise jacking correction. Simultaneously, the lifting equipment angle was adjusted by 3°, and SLAM dynamic scanning was initiated to track the component's attitude adjustment process in real time, ensuring the adjustment angle met the correction plan requirements. After correction, a stationary scan of the corrected area was performed again, synchronously recording the corrected dynamic data. Re-registration and analysis showed that all deviations were within ±3mm, meeting design requirements. The component's status in the BIM model was automatically updated to "accepted."

[0062] To verify the effectiveness of the present invention, this embodiment is used as a basis for verification in an actual subway station.

[0063] 1. This invention is based on a composite prefabricated subway station as an actual project and has been fully verified through on-site construction practice. The station is 209.2m long and the main structure is a two-story underground single-column double-span (partial double-column triple-span) structure. The prefabricated components are heavy and the nodes are complex, making the assembly construction difficult. It has typical engineering verification conditions.

[0064] 2. In practice, a FARO Focus S350 laser scanner was used for static scanning, while a handheld SLAM scanner was used to record the hoisting process of key components, forming a dynamic database. A parametric BIM model was built based on Revit (with a reserved dynamic data interface), and registration and deviation analysis were completed using CloudCompare. After deviations were detected, the system combined SLAM dynamic data to trace the root cause of the deviations, automatically generated correction plans, and guided the use of jacking devices and rotating lifting tools for precise correction on site. During the correction process, deviation changes were monitored in real time through SLAM dynamic scanning.

[0065] 3. To verify the technical advantages of this invention compared to existing assembly methods, using the same prefabricated metro station as an engineering example, construction error detection and correction were performed using both the traditional total station measurement method and the method described in this invention. Key indicators were compared, as shown in Table 1. Table 1 Comparison of key indicators between the two methods

[0066] As shown in Table 1, this invention, by introducing SLAM dynamic sensing technology and deeply integrating it with subsequent steps, overcomes the static limitations of traditional fixed-station scanning. It achieves continuous real-time tracking of the entire process of component hoisting and assembly, capturing dynamic deviations that traditional methods cannot detect, and accurately tracing the root causes of deviations. By constructing a parametric adaptive BIM model (with a reserved dynamic data interface), it realizes the intelligent generation of correction schemes and provides prevention and control suggestions based on dynamic data, truly transforming error analysis into an effective tool for guiding construction. Compared to traditional total station surveying methods, which can only cover about 60%–70% of measuring points and rely on manual experience, this invention utilizes high-density point cloud data (including dynamic and static data) to achieve ≥98% full surface coverage, and combines a parametric BIM model to achieve intelligent error classification, root cause tracing, and adaptive correction scheme generation. In terms of detection efficiency, the total station detection time is shortened from 3–5 days to 1–2 days, while reducing deviation tracing time; in terms of recognition accuracy, it improves from ±5mm to ±2mm millimeter-level full-field quantitative analysis. Through a closed-loop process of "dynamic perception - quantitative analysis - intelligent adaptation - precise correction - effect verification", the pass rate of re-inspection after correction increased from about 85% to about 98%, and the overall cost was reduced by 30% to 40%, significantly improving construction quality, efficiency and economy.

[0067] Practical results show that: ① Through SLAM dynamic sensing technology, continuous tracking of the component hoisting process was successfully achieved. Combined with static precision scanning, physical deviations such as prefabricated component installation positioning deviations and data deviations caused by on-site obstructions were accurately identified, with a deviation identification coverage rate of over 98%. At the same time, the root cause of deviations was accurately traced through dynamic data. ② For various deviations, the parametric adaptive BIM model, combined with SLAM dynamic data, automatically generates quantitative correction schemes (such as shim thickness, jacking displacement, rotation angle, etc.) and construction control suggestions based on the built-in component connection logic. On-site, targeted measures such as millimeter-level correction and local re-measurement were taken accordingly. During the correction process, real-time monitoring was carried out through SLAM dynamic scanning. After secondary scanning verification, all deviations were controlled within the design allowable range, with significant correction effects and a deviation recurrence rate reduced to below 2%. ③ Compared with traditional measurement technology, the efficiency of construction error detection is greatly improved, and rework costs are significantly reduced, effectively ensuring the structural safety and construction progress of the station. This fully demonstrates that the invention has good feasibility and reliability in practical engineering applications and can meet the needs of refined construction management of composite prefabricated stations.

[0068] The above description is only a preferred embodiment of the present invention and does not limit the scope of the present invention. All equivalent structural transformations made under the inventive concept of the present invention using the contents of the present invention specification and drawings, or direct / indirect applications in other related technical fields, are included within the protection scope of the present invention.

Claims

1. A closed-loop control method for construction errors in composite prefabricated railway stations, characterized in that, Includes the following steps: S1. Data acquisition that integrates real-time positioning and map construction with dynamic and static scanning; S2. Point cloud data preprocessing and measured model construction, specifically: preprocessing the data collected in S1 and generating a measured point cloud model; S3. Construct a parametric, adaptable BIM design model; S4. Data unification and format alignment of the two models: Specifically, the measured point cloud model of S2 and the BIM design model of S3 are imported into the 3D comparison and analysis software to unify the data foundation. S5. Model registration and spatial alignment, specifically: performing model registration and spatial alignment on the two models after the data foundation is unified in S4; S6. Construction error quantification analysis and visualization: Specifically, point-by-point distance calculation is performed between the two models that have completed spatial registration in S5. Combined with the dynamic scanning data of real-time positioning and map construction, the spatial deviation and dynamic change law between the construction entity and the design model are quantified. S7. Intelligent correction scheme generation based on parametric adaptive BIM: Based on the spatial deviation and dynamic change law of S6, combined with the BIM design model constructed by S3 and the real-time positioning and map construction dynamic process data collected by S1, error source tracing and correction scheme generation are carried out to realize the fusion of real-time positioning and map construction dynamic data and correction decisions. S8. On-site correction and effect verification: Based on the correction plan generated in S7, combined with real-time positioning and map construction, the root cause of deviation is traced through dynamic data, and corresponding on-site correction measures are implemented based on the root cause of deviation.

2. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 1, characterized in that, S2 specifically includes the following steps: S201. Denoise the data collected by S1 and remove invalid points from the data; S202. The data after denoising in S201 is stitched together. Specifically, the target is used as the control point, and a registration algorithm based on target matching is used to stitch together the point clouds of each station into a complete three-dimensional point cloud model of the station. S203. Thinning is performed on the stitched data from S202. Specifically, while preserving the main structural features and key nodes of the dynamic trajectory for real-time positioning and map construction, the point cloud is randomly sampled and thinned to generate a measured point cloud model. The point cloud thinning sampling uses secondary random sampling, and the specific formula is as follows: ； in: The number of points after thinning; The original number of points; The sampling rate.

3. The closed-loop control method for construction errors of composite prefabricated stations according to claim 2, characterized in that, S4 specifically includes the following steps: S401, Unit unification: Reduce the BIM design model by 1000 times and unify it with the measured point cloud model to the same unit system; S402. Format unification: Convert the BIM design model into point cloud format through uniform sampling to make it consistent with the measured point cloud in terms of data structure, which facilitates subsequent comparison and analysis. At the same time, ensure that the BIM model is compatible with the format of dynamic trajectory data of real-time positioning and map construction.

4. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 3, characterized in that, S5 specifically includes the following steps: S501, Coarse Registration: Multiple feature points are manually selected in the measured point cloud model and the BIM design model, and the initial rotation and translation matrices are calculated using the least squares method to achieve the initial alignment of the models; S502, Fine Registration: The Iterative Closest Point (ICP) algorithm is used for fine registration. Through multiple iterations, the transformation parameters are optimized to minimize the overall distance error between the two models, ultimately achieving model registration and spatial alignment.

5. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 4, characterized in that, The ICP algorithm in S502 uses ICP iterative optimization of the objective function for calculation. Let the measured point cloud be... , The measured 3D coordinates are used; the point cloud after model transformation is... , The design point has three-dimensional coordinates; the ICP algorithm uses iterative optimization of the rotation matrix. Translation matrix ICP iterative optimization objective function The expression is as follows: ； Where: ||•|| is the Euclidean distance. The square of the Euclidean distance. The total number of points in the point cloud data. The index of the point cloud data; the iteration termination condition is when two consecutive iterations... The process stops when the difference is less than the convergence threshold or when the number of iterations reaches a preset value, ultimately minimizing the error.

6. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 5, characterized in that, S6 specifically includes the following steps: S601, Deviation Calculation: Calculate the shortest distance from each point in the measured point cloud to the corresponding surface of the BIM design model, and use it as the deviation value of that point; at the same time, combine the dynamic data of real-time positioning and map construction to determine the construction process caused by the positioning deviation; S602, Visualization Output: Color mapping is used to generate a deviation distribution cloud map, which is then overlaid with the dynamic trajectory of real-time positioning and map construction to show the spatial location, size and distribution characteristics of the error; S603, Statistical Report: Outputs an analysis report including statistical indicators such as maximum deviation, average deviation, standard deviation, and the proportion of points exceeding limits. Based on real-time positioning and map construction, dynamic data is used to trace and synchronously mark the construction stages caused by deviations, providing a targeted basis for subsequent correction plans.

7. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 6, characterized in that, S7 specifically includes the following steps: S701 Error Source Tracing: Based on the dynamic trajectory data constructed by real-time positioning and map, trace the specific construction links and causes of deviations. If the deviation occurs during hoisting and continues to increase, it is determined to be a hoisting angle deviation or a hoisting tool positioning deviation. If the deviation occurs after positioning and adjustment, it is determined to be an adjustment operation deviation. If the deviation exists only in a local area and is not related to the dynamic trajectory, it is determined to be a data acquisition error. S702, Intelligent Adaptation Calculation: For physical construction errors, the parametric BIM model automatically calculates and generates an adaptation adjustment scheme based on measured deviation data, the dynamic deviation change law of real-time positioning and map construction, and the built-in component connection logic. S703, Solution Output: Output the generated adaptation adjustment solution to the on-site construction personnel in the form of standardized instructions.

8. The closed-loop control method for construction errors of composite prefabricated railway stations according to claim 7, characterized in that, S8 specifically includes the following steps: S801. For the side wall positioning deviation, a jacking device is used to jack and correct it. At the same time, the angle of the lifting device is adjusted, and the dynamic trajectory data of real-time positioning and map construction are referenced to ensure that the posture of the component after correction is consistent with the design model. S802. For hoisting angle deviation, a rotating hoisting system is used for rotation correction, and dynamic scanning of real-time positioning and map building is started simultaneously to track the component posture adjustment process in real time and ensure that the adjustment angle meets the requirements of the correction plan. S803. Deviations other than side wall positioning deviation and hoisting angle deviation are corrected by means of component fine-tuning, local repair, repositioning of embedded parts, and surface treatment. During the correction process, deviation changes are monitored in real time through dynamic scanning of real-time positioning and map construction.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program that is adapted to be loaded by a processor and executed by the closed-loop control method for construction errors of the composite prefabricated station as described in any one of claims 1-8.

10. A computer device, characterized in that, The computer device includes a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it runs the closed-loop control method for construction errors of the composite prefabricated station as described in any one of claims 1-8.

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