A bridge automatic erection whole process monitoring method and system based on unmanned aerial vehicle three-dimensional scanning and digital twinning

Abstract

The application discloses a bridge automatic erection whole-process monitoring method and system based on unmanned aerial vehicle three-dimensional scanning and digital twinning, and belongs to the technical field of bridge intelligent construction and monitoring. The application aims to solve the problems of incomplete component factory detection, poor real-time bridge erection monitoring and inability to form automatic closed-loop control in the existing assembly type bridge erection. The application generates a component-level digital twinning model with a unique identifier through two-stage registration by scanning the prefabricated component by means of an unmanned aerial vehicle, and constructs a bridge erection digital twinning model containing a target linear form. In the bridge erection process, the unmanned aerial vehicle is periodically scanned, the bridge space linear form is reconstructed based on point cloud data, the component installation deviation and the bridge linear form deviation are calculated, and the correction instruction is automatically generated when the deviation exceeds the threshold value, so that the closed-loop control is completed by driving the bridge erection equipment. The application realizes the integrated digital twinning control of the bridge component factory detection and the bridge erection process monitoring, and significantly improves the bridge automatic erection precision and the construction safety.

Description

Technical Field

[0001] This invention relates to a method and system for monitoring the entire process of automated bridge erection based on UAV 3D scanning and digital twins. Background Technology

[0002] With the development of prefabricated bridges and intelligent construction technologies, precast components and automated bridge erection equipment are increasingly widely used in bridge construction. Currently, in the bridge erection process, component quality inspection and monitoring mainly rely on manual measurement, total station measurement, or fixed sensor equipment, which suffers from limited detection range, insufficient real-time performance, and low coupling between data and the construction control system.

[0003] During the component manufacturing stage, sampling inspection or local dimensional inspection is usually used, which makes it difficult to perform high-precision inspection on the entire surface of the component. As a result, some geometric errors are only discovered during the bridge construction process, which affects construction efficiency and structural alignment accuracy.

[0004] During the bridge construction phase, traditional monitoring methods mostly rely on manual measurement or a limited number of sensor points, making it difficult to achieve real-time three-dimensional monitoring of the overall spatial alignment of the bridge. Furthermore, monitoring results typically require manual analysis before adjustments to the bridge-building equipment can be made, preventing the formation of automated closed-loop control.

[0005] In recent years, UAV 3D scanning technology and digital twin technology have been gradually applied to the field of bridge inspection and construction management. However, existing technologies are mostly focused on monitoring during the bridge service phase or construction information management, and a full-process monitoring system covering component factory inspection and real-time control of bridge erection has not yet been formed.

[0006] Therefore, there is an urgent need for a whole-process monitoring method and system that can realize the quality inspection of bridge components at the factory and the real-time three-dimensional monitoring of the bridge erection process, and can form a closed-loop control with automatic bridge erection equipment. Summary of the Invention

[0007] The main objective of this invention is to provide a method and system for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twins, aiming to solve the aforementioned technical problems.

[0008] To achieve the above objectives, the present invention proposes a method for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twins, comprising:

[0009] Step S1: During the prefabricated bridge component manufacturing stage, a drone equipped with a 3D scanner is used to perform a surround scan of the prefabricated component to obtain point cloud data of the entire surface of the component. The point cloud data is registered and compared with the design model to obtain the geometric dimension deviation of the component. A 3D digital model corresponding to the actual component is generated based on the point cloud data, a unique identification information is assigned to the component, a one-to-one mapping relationship between the actual component and the digital model is established, and a component-level digital twin model is formed.

[0010] Step S2: Import the component-level digital twin models into the bridge construction digital twin system to construct a bridge erection digital twin model that includes the erection sequence and target alignment.

[0011] Step S3: During the automatic bridge erection process, a drone is used to periodically scan the installed components along the bridge axis at a preset frequency to obtain real-time point cloud data of the bridge construction site; the current spatial geometric shape of the bridge is reconstructed based on the real-time point cloud data, and compared with the target shape in the bridge erection digital twin model to calculate the component installation deviation and bridge shape deviation during the bridge erection process.

[0012] Step S4: When the deviation exceeds the preset threshold, the system generates a bridge erecting equipment posture or path correction command directly without manual intervention and sends it to the automatic bridge erecting equipment control system to drive the bridge erecting equipment to adjust automatically.

[0013] In one embodiment, the step of registering and comparing the point cloud data with the design model to obtain the geometric dimension deviation of the component specifically includes:

[0014] Extract point cloud data and corresponding geometric feature points from the design model. The corresponding geometric feature points include at least one of component corner points, edge inflection points, center points of pre-embedded bolt holes, and center points of support pads.

[0015] Feature descriptor matching is performed on the extracted feature points, and the RANSAC algorithm is used to remove mismatched point pairs. The initial rigid body transformation matrix is ​​calculated based on the remaining valid matching point pairs to complete the initial alignment of the point cloud data with the design model.

[0016] Using the initial transformation matrix obtained from coarse registration as the starting point of the iteration, the iterative nearest point algorithm is used to search for the nearest point pair between the point cloud data and the design model, and the optimal rigid body transformation matrix that minimizes the mean square error of the two point sets is calculated. When the number of iterations reaches the preset maximum number or the mean square error is less than 0.5 mm, the iteration is terminated and the final registration is completed.

[0017] In one embodiment, the steps of assigning unique identification information to the component, establishing a one-to-one mapping relationship between the actual component and the digital model, and forming a component-level digital twin model are as follows:

[0018] Based on the registered and preprocessed component point cloud data, an initial 3D mesh model of the component is generated; the initial mesh model is then subjected to hole filling, smoothing optimization and topology repair to generate a geometric model.

[0019] Following the coding rule of "prefabrication plant code + component type code + production batch number + serial number", a globally unique digital identifier ID is generated for each component; at the same time, an RFID electronic tag and a QR code tag bound to the digital identifier ID are generated and affixed to the designated location on the physical component.

[0020] All data on the geometric dimensional deviations of the components, material performance parameters, production process parameters, factory inspection report numbers, hoisting point locations, installation sequence information, and quality acceptance records are linked to the corresponding digital identifier ID.

[0021] In the digital twin system for bridge construction, a one-to-one mapping relationship is established between "physical components - RFID / QR code identification - digital identification ID - geometric model - multi-source attribute data".

[0022] In one embodiment, the steps of comparing the bridge's alignment with the target alignment in the bridge erection digital twin model to calculate the component installation deviations and bridge alignment deviations during the bridge erection process are as follows:

[0023] Background removal and noise filtering are performed on the acquired real-time point cloud data to segment an independent point cloud subset of each installed component and match the corresponding digital twin model of the component.

[0024] Every 0.5-1m along the bridge axis, a section perpendicular to the bridge axis is taken, and the point cloud data of the corresponding section is extracted from the point cloud subset of each component.

[0025] Calculate the centroid coordinates of the point cloud at each cross section to obtain the discrete point set of the bridge centerline. Perform piecewise fitting on the discrete point set of the centerline. For straight segments, use the least squares method to fit straight lines, and for curved segments, use cubic B-spline curves to fit curves. After fitting, perform smoothing to obtain the actual spatial geometric shape of the bridge.

[0026] The independent point cloud subset of each component is precisely registered with the corresponding component-level digital twin model. The deviations of the component in six degrees of freedom are calculated, including the position deviations in the X / Y / Z directions and the attitude deviations around the X / Y / Z axes. At the same time, the docking gap deviation, misalignment deviation, and elevation and planar position deviations of the support top surface at the ends of adjacent components are calculated.

[0027] A measuring point is taken every 1m along the actual spatial geometric alignment of the bridge. The three-dimensional coordinates of the measuring point are compared with the target alignment coordinates of the corresponding position in the digital twin model of the bridge erection. The longitudinal elevation deviation, lateral offset deviation and torsional angle deviation are calculated respectively.

[0028] All component installation deviations and bridge alignment deviations are weighted and fused, and outliers are eliminated using the adjacent measuring point deviation continuity verification method to obtain the final deviation calculation results.

[0029] In one embodiment, the adjustment command for the automatic adjustment of the bridge erecting equipment includes at least one of the following:

[0030] Lifting attitude adjustment instructions;

[0031] Component translation correction command;

[0032] Component rotation correction command;

[0033] Bridge erecting machine movement path adjustment command.

[0034] In addition, the present invention also provides a bridge automatic erection full-process monitoring system based on UAV 3D scanning and digital twin, the bridge automatic erection full-process monitoring system based on UAV 3D scanning and digital twin includes:

[0035] The UAV 3D scanning unit is used to acquire 3D point cloud data of bridge components during the component manufacturing stage and the bridge erection process;

[0036] The point cloud processing and deviation analysis unit is used to: register component point clouds with the design model, generate component-level digital twin models, and calculate component installation deviations and bridge alignment deviations during bridge construction.

[0037] The digital twin management unit is used to establish a digital twin model of the entire bridge erection process and store component-level digital twin models;

[0038] The bridge erection control interface unit is used to generate correction commands for the bridge erection equipment based on the deviation analysis results and send them to the automatic bridge erection equipment control system.

[0039] The visualization and early warning unit is used to display the bridge construction status and output deviation early warning information.

[0040] In one embodiment, the UAV 3D scanning unit includes a multi-rotor UAV and a laser 3D scanner.

[0041] In one embodiment, the bridge erection control interface unit is connected to the automatic bridge erection equipment control system via wired or wireless communication.

[0042] The technical solution of this invention realizes an integrated digital twin system for factory inspection of bridge components and monitoring of the bridge erection process. Simultaneously, it can establish component-level digital twin models based on the scanning results of actual components, eliminating geometric errors between the design model and the actual components and improving the accuracy of subsequent deviation calculations. Furthermore, it utilizes UAV point cloud data to invert the bridge's spatial alignment in real time, achieving full-process three-dimensional dynamic monitoring. Attached Figure Description

[0043] 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.

[0044] Figure 1 This is a schematic diagram of the overall system structure of the present invention;

[0045] Figure 2 A schematic diagram of the 3D scanning process for components before they leave the factory;

[0046] Figure 3 This is a schematic diagram illustrating real-time monitoring of the bridge construction process.

[0047] Figure 4 This is a schematic diagram of digital twin closed-loop control.

[0048] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0049] 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.

[0050] Furthermore, the technical solutions of the various embodiments of the present invention can be combined with each other, but only if they are based on the ability of those skilled in the art to implement them. When 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.

[0051] The method for monitoring the entire process of automated bridge erection based on UAV 3D scanning and digital twins includes:

[0052] Step S1: During the prefabricated bridge component manufacturing stage, a drone equipped with a 3D scanner is used to perform a surround scan of the prefabricated component to obtain point cloud data of the entire surface of the component. The point cloud data is registered and compared with the design model to obtain the geometric dimension deviation of the component. A 3D digital model corresponding to the actual component is generated based on the point cloud data, a unique identification information is assigned to the component, a one-to-one mapping relationship between the actual component and the digital model is established, and a component-level digital twin model is formed.

[0053] Step S2: Import the component-level digital twin models into the bridge construction digital twin system to construct a bridge erection digital twin model that includes the erection sequence and target alignment.

[0054] Step S3: During the automatic bridge erection process, a drone is used to periodically scan the installed components along the bridge axis at a preset frequency to obtain real-time point cloud data of the bridge construction site; the current spatial geometric shape of the bridge is reconstructed based on the real-time point cloud data, and compared with the target shape in the bridge erection digital twin model to calculate the component installation deviation and bridge shape deviation during the bridge erection process.

[0055] Step S4: When the deviation exceeds the preset threshold, the system generates a bridge erecting equipment posture or path correction command directly without manual intervention and sends it to the automatic bridge erecting equipment control system to drive the bridge erecting equipment to adjust automatically.

[0056] The bridge automatic erection monitoring method based on UAV 3D scanning and digital twins provided in this application realizes an integrated digital twin system for bridge component factory inspection and bridge erection process monitoring. Simultaneously, it can establish component-level digital twin models based on the actual component scanning results, eliminating geometric errors between the design model and the actual components and improving the accuracy of subsequent deviation calculations. Furthermore, it utilizes UAV point cloud data to invert the bridge's spatial alignment in real time, achieving full-process 3D dynamic monitoring.

[0057] like Figure 1 As shown in the figure, the bridge automatic erection process monitoring method based on UAV 3D scanning and digital twin provided in this embodiment of the invention is applied to the automatic bridge erection process monitoring based on UAV 3D scanning and digital twin. The bridge automatic erection process monitoring system based on UAV 3D scanning and digital twin includes a UAV 3D scanning unit, a point cloud processing and deviation analysis unit, a digital twin management unit, a bridge erection control interface unit, and a visualization and early warning unit. The units are connected through wired or wireless communication to realize real-time data transmission and interaction.

[0058] The UAV 3D scanning unit includes a multi-rotor UAV and a laser 3D scanner, preferably a hexacopter UAV equipped with a 1500kHz line laser scanner, a ranging accuracy of ±2mm, and a maximum scanning distance of 100m. The UAV is also equipped with an RTK high-precision positioning system with a positioning accuracy of ≤2cm, ensuring the spatial accuracy of the scanned data.

[0059] The point cloud processing and deviation analysis unit is deployed on a cloud server or local workstation. It has point cloud data processing capabilities and mainly performs functions such as point cloud registration, digital twin generation of components, bridge alignment inversion, and deviation calculation.

[0060] The digital twin management unit is used to store and manage all component-level digital twin models and the overall digital twin model of the bridge, supporting import, export, update, and query operations of the models.

[0061] The bridge erection control interface unit is connected to the automatic bridge erection equipment control system via industrial Ethernet or 5G wireless communication, with a communication delay of ≤100ms, ensuring the real-time issuance of correction commands.

[0062] The visualization and early warning unit supports simultaneous display on multiple terminals, including computers, mobile devices, and large on-site screens, and can display the bridge's 3D model, deviation data, early warning information, and the operating status of bridge erection equipment in real time.

[0063] The following describes a specific embodiment of this application based on a highway bridge with a prefabricated box girder structure, a single span length of 30m, and each bridge consisting of 5 box girders, erected using a fully automated integrated bridge erection machine.

[0064] Please refer to Figure 2 Within the precast beam yard, the precast components to be inspected (such as box girders and T-beams) are placed in an open and flat scanning area. A drone is planned to circle the component surface, maintaining a distance of 5-10 meters, a flight speed of 3-5 m / s, and a scan overlap of ≥70%. The drone, equipped with a laser 3D scanner, performs a 360° circumferential scan of the component, acquiring high-density point cloud data of the entire surface, with a point cloud density ≥100 points / m². 2

[0065] The preprocessing steps for the collected raw point cloud data include:

[0066] Denoising: Statistical filtering algorithms are used to remove outliers and noise points;

[0067] Downsampling: A voxel grid downsampling method is used to reduce the amount of data while preserving point cloud features;

[0068] Void filling: The moving least squares method is used to fill voids generated during the scanning process;

[0069] Coordinate transformation: Converting point cloud data from the UAV coordinate system to the engineering coordinate system.

[0070] Furthermore, the step of registering and comparing the point cloud data with the design model to obtain the component's geometric dimension deviation specifically involves:

[0071] Geometric feature points with the same names from the point cloud data and the design BIM model are extracted, including feature points unique to precast bridge components such as component corners, edge inflection points, center points of embedded bolt holes, and center points of bearing pads. SHOT feature descriptors are generated from the extracted feature points, and feature descriptor matching is performed. The RANSAC algorithm is used to eliminate mismatched point pairs, and the initial rigid body transformation matrix (including rotation matrix and translation vector) is calculated based on the remaining valid matching point pairs to complete the initial alignment between the point cloud data and the design model.

[0072] Using the initial transformation matrix obtained from coarse registration as the starting point for iteration, the iterative nearest point algorithm is employed to search for the closest point pairs between the point cloud data and the design model. The optimal rigid body transformation matrix that minimizes the mean square error between the two point sets is calculated using the least squares method. The iteration terminates when the number of iterations reaches a preset maximum (100-500 times) or the mean square error is less than 0.5 mm, completing the final registration. After registration, the overall registration error is verified to ensure that the registration error is ≤5 mm.

[0073] The registered point cloud data is compared point by point with the design model to calculate the geometric dimensional deviations of each part of the component, including length, width, height, web inclination, flange thickness, and support position deviations. When the deviation exceeds the design allowable value, the system automatically generates a non-conformance report, prompting the component to be reworked or scrapped.

[0074] In the above embodiments, the steps of generating a three-dimensional digital model corresponding to the actual component based on the point cloud data, assigning unique identification information to the component, establishing a one-to-one mapping relationship between the actual component and the digital model, and forming a component-level digital twin model are as follows:

[0075] Based on the registered and preprocessed component point cloud data, a Poisson reconstruction algorithm is used to generate a triangular mesh, resulting in an initial 3D mesh model of the component. The initial mesh model is then subjected to hole filling, smoothing optimization, and topology repair to generate a high-precision geometric model with a geometric accuracy ≤2mm.

[0076] Following the coding rule of "prefabrication plant code (2 digits) + component type code (2 digits) + production batch number (4 digits) + serial number (4 digits)," a globally unique 12-digit digital identifier ID is generated for each component. Simultaneously, an RFID electronic tag and QR code are generated and bound to this digital identifier ID, which are then affixed to a designated location on the side of the physical component's end for easy subsequent scanning and identification.

[0077] All data related to the component's geometric dimensional deviations, material performance parameters (such as concrete strength and steel grade), production process parameters (such as pouring time and curing time), factory inspection report number, hoisting point location, installation sequence information, and quality acceptance records are linked to the corresponding digital identifier ID.

[0078] In the digital twin system for bridge construction, a one-to-one mapping relationship is established between "physical component - RFID / QR code identification - digital ID - high-precision geometric model - multi-source attribute data". By scanning the RFID or QR code of a physical component, all digital twin information of that component can be quickly retrieved and accessed in the system.

[0079] The generated component-level digital twin model undergoes key dimension sampling verification to ensure that the key dimension error is ≤3mm. After successful verification, the model and associated data are encrypted and stored in the component library of the digital twin system, completing the construction of the component-level digital twin model.

[0080] Subsequently, the component-level digital twin models of all qualified components were imported into the bridge construction digital twin system. Based on the bridge design drawings and construction organization design, the erection sequence, installation location, and target alignment of each component were set in the system. The component-level digital twin models were then assembled according to the erection sequence and target alignment to construct a complete bridge erection digital twin model. This model not only contains the bridge's geometric information but also dynamic information such as construction progress, equipment status, and environmental parameters, realistically reflecting the entire bridge erection process.

[0081] like Figure 3 As shown, the specific steps for reconstructing the current spatial geometric alignment of the bridge based on the real-time point cloud data, comparing it with the target alignment in the bridge erection digital twin model, and calculating the component installation deviation and bridge alignment deviation during the bridge erection process are as follows:

[0082] During the automated bridge erection process, drones can fly along the bridge axis according to a preset route, periodically performing 3D scans of the installed components. The scanning interval is set to 5-30 minutes depending on the construction progress and accuracy requirements. The drones fly at an altitude of 3-5 meters above the bridge surface and at a speed of 2-4 m / s to ensure clear acquisition of surface point cloud data of the installed components.

[0083] The acquired real-time point cloud data undergoes background culling and noise filtering to remove irrelevant point clouds such as those representing the ground, bridge erecting machine, and construction workers. A region growing algorithm, combined with the identification information of the component-level digital twin model, is used to segment an independent point cloud subset for each installed component. By identifying RFID or QR code markings on the components, the corresponding component's digital twin model is matched.

[0084] Every 0.5-1m along the bridge axis, a section perpendicular to the bridge axis is taken, and the point cloud data of the corresponding section is extracted from the point cloud subset of each component.

[0085] The centroid coordinates of the point cloud at each cross-section are calculated to obtain a discrete point set for the bridge centerline. The discrete point set is then piecewise fitted: straight segments are fitted with straight lines using the least squares method, and curved segments are fitted with cubic B-spline curves. After fitting, Gaussian filtering is used for smoothing to eliminate the influence of point cloud noise on the fitting results, thus obtaining the actual spatial geometry of the bridge.

[0086] The independent point cloud subset of each component is precisely registered with the corresponding component-level digital twin model. The deviations of the component in six degrees of freedom are calculated, including the positional deviations in the X (longitudinal), Y (lateral), and Z (vertical) directions and the attitude deviations around the X, Y, and Z axes. At the same time, the docking gap deviation, misalignment deviation, and elevation and planar position deviations of the support top surface at the ends of adjacent components are calculated.

[0087] A measuring point is taken every 1m along the actual bridge's spatial geometry. The 3D coordinates of the measuring point are compared with the target alignment coordinates of the corresponding position in the bridge's digital twin model. The longitudinal elevation deviation, lateral offset deviation, and torsional angle deviation are calculated respectively. All component installation deviations and bridge alignment deviations are weighted and fused, with component installation deviations having a weight of 0.6 and bridge alignment deviations having a weight of 0.4. An outlier is eliminated using a continuity check method for adjacent measuring point deviations: if the difference between the deviation of a certain measuring point and the deviation of adjacent measuring points exceeds a preset threshold (e.g., 5mm), the data of that measuring point is determined to be an outlier and is eliminated, ensuring that the accuracy of the final deviation calculation result is ≤1mm.

[0088] Finally, the calculated component installation deviations and bridge alignment deviations are compared with preset thresholds. These preset thresholds are determined based on bridge design specifications and construction requirements; for example, the longitudinal elevation deviation threshold is ±10mm, the lateral offset deviation threshold is ±5mm, and the torsion angle deviation threshold is ±0.05°.

[0089] When the deviation exceeds a preset threshold, the system automatically generates corresponding bridge erecting equipment attitude or path correction commands based on the type and magnitude of the deviation, without manual intervention. Correction commands include:

[0090] Lifting attitude adjustment instructions: Adjust the pitch angle, roll angle and yaw angle of the bridge erecting machine's spreader;

[0091] Component translation correction command: Controls the bridge erecting machine to translate the component a specified distance along the X, Y, and Z directions;

[0092] Component rotation correction command: Controls the bridge erecting machine to rotate the component around the X, Y, and Z axes by a specified angle;

[0093] Bridge erecting machine movement path adjustment command: Adjust the walking path and speed of the bridge erecting machine.

[0094] The bridge erection control interface unit sends the generated correction commands to the automatic bridge erection equipment control system via industrial Ethernet or 5G wireless communication. Upon receiving the commands, the automatic bridge erection equipment control system drives the bridge erecting machine to perform the corresponding adjustment actions. After the bridge erection equipment completes the adjustment, the UAV immediately performs a supplementary scan to recalculate the component installation deviations and bridge alignment deviations, verifying the adjustment effect. If the deviations still exceed the threshold, correction commands are generated again until the deviations meet the requirements.

[0095] In this application, when the deviation exceeds a preset threshold, the system simultaneously generates an early warning signal and displays the deviation distribution status in the visualization and early warning unit using a color-coded method (e.g., green indicates normal, yellow indicates early warning, and red indicates severe exceedance). The system performs full-process encrypted storage and traceability of component factory inspection data, bridge erection process deviation data, correction instructions, and equipment action data, forming an unalterable digital archive of the entire bridge erection process.

[0096] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. All equivalent structural transformations made under the 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 patent protection scope of the present invention.

Claims

1. A method for monitoring the entire process of automated bridge erection based on UAV 3D scanning and digital twin, characterized in that, The method for monitoring the entire process of automated bridge erection based on UAV 3D scanning and digital twins includes: Step S1: During the prefabricated bridge component manufacturing stage, a drone equipped with a 3D scanner is used to perform a surround scan of the prefabricated component to obtain point cloud data of the entire surface of the component. The point cloud data is registered and compared with the design model to obtain the geometric dimension deviation of the component. A 3D digital model corresponding to the actual component is generated based on the point cloud data, a unique identification information is assigned to the component, a one-to-one mapping relationship between the actual component and the digital model is established, and a component-level digital twin model is formed. Step S2: Import the component-level digital twin models into the bridge construction digital twin system to construct a bridge erection digital twin model that includes the erection sequence and target alignment. Step S3: During the automatic bridge erection process, a drone is used to periodically scan the installed components along the bridge axis at a preset frequency to obtain real-time point cloud data of the bridge construction site; the current spatial geometric shape of the bridge is reconstructed based on the real-time point cloud data, and compared with the target shape in the bridge erection digital twin model to calculate the component installation deviation and bridge shape deviation during the bridge erection process. Step S4: When the deviation exceeds the preset threshold, the system generates a bridge erecting equipment posture or path correction command directly without manual intervention and sends it to the automatic bridge erecting equipment control system to drive the bridge erecting equipment to adjust automatically.

2. The method for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twin as described in claim 1, characterized in that, The specific steps for registering and comparing the point cloud data with the design model to obtain the component geometric dimension deviation are as follows: Extract point cloud data and corresponding geometric feature points from the design model. The corresponding geometric feature points include at least one of component corner points, edge inflection points, center points of pre-embedded bolt holes, and center points of support pads. Feature descriptor matching is performed on the extracted feature points, and the RANSAC algorithm is used to remove mismatched point pairs. The initial rigid body transformation matrix is ​​calculated based on the remaining valid matching point pairs to complete the initial alignment of the point cloud data with the design model. Using the initial transformation matrix obtained from coarse registration as the starting point of the iteration, the iterative nearest point algorithm is used to search for the nearest point pair between the point cloud data and the design model, and the optimal rigid body transformation matrix that minimizes the mean square error of the two point sets is calculated. When the number of iterations reaches the preset maximum number or the mean square error is less than 0.5 mm, the iteration is terminated and the final registration is completed.

3. The method for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twin as described in claim 1, characterized in that, The steps of generating a 3D digital model corresponding to the actual component based on the point cloud data, assigning unique identification information to the component, establishing a one-to-one mapping relationship between the actual component and the digital model, and forming a component-level digital twin model are as follows: Based on the registered and preprocessed component point cloud data, an initial 3D mesh model of the component is generated; the initial mesh model is then subjected to hole filling, smoothing optimization and topology repair to generate a geometric model. Following the coding rule of "prefabrication plant code + component type code + production batch number + serial number", a globally unique digital identifier ID is generated for each component; at the same time, an RFID electronic tag and a QR code are generated and bound to the digital identifier ID, which are then affixed to the designated location on the physical component. All data on the geometric dimensional deviations of the components, material performance parameters, production process parameters, factory inspection report numbers, hoisting point locations, installation sequence information, and quality acceptance records are linked to the corresponding digital identifier ID. In the digital twin system for bridge construction, a one-to-one mapping relationship is established between "physical components - RFID / QR code identification - digital identification ID - geometric model - multi-source attribute data".

4. The method for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twin as described in claim 1, characterized in that, The specific steps for reconstructing the current spatial geometric alignment of the bridge based on the real-time point cloud data, comparing it with the target alignment in the bridge erection digital twin model, and calculating the component installation deviation and bridge alignment deviation during the bridge erection process are as follows: Background removal and noise filtering are performed on the acquired real-time point cloud data to segment an independent point cloud subset of each installed component and match the corresponding digital twin model of the component. Every 0.5-1m along the bridge axis, a section perpendicular to the bridge axis is taken, and the point cloud data of the corresponding section is extracted from the point cloud subset of each component. Calculate the centroid coordinates of the point cloud at each cross section to obtain the discrete point set of the bridge centerline. Perform piecewise fitting on the discrete point set of the centerline. For straight segments, use the least squares method to fit straight lines, and for curved segments, use cubic B-spline curves to fit curves. After fitting, perform smoothing to obtain the actual spatial geometric shape of the bridge. The independent point cloud subset of each component is precisely registered with the corresponding component-level digital twin model. The deviations of the component in six degrees of freedom are calculated, including the position deviations in the X / Y / Z directions and the attitude deviations around the X / Y / Z axes. At the same time, the docking gap deviation, misalignment deviation, and elevation and planar position deviations of the support top surface at the ends of adjacent components are calculated. A measuring point is taken every 1m along the actual spatial geometric alignment of the bridge. The three-dimensional coordinates of the measuring point are compared with the target alignment coordinates of the corresponding position in the digital twin model of the bridge erection. The longitudinal elevation deviation, lateral offset deviation and torsional angle deviation are calculated respectively. All component installation deviations and bridge alignment deviations are weighted and fused, and outliers are eliminated using the adjacent measuring point deviation continuity verification method to obtain the final deviation calculation results.

5. The method for monitoring the entire process of automatic bridge erection based on UAV 3D scanning and digital twin as described in claim 1, characterized in that, The automatic adjustment commands of the bridge erecting equipment include at least one of the following: Lifting attitude adjustment instructions; Component translation correction command; Component rotation correction command; Bridge erecting machine movement path adjustment command.

6. A bridge automatic erection full-process monitoring system based on UAV 3D scanning and digital twin, characterized in that, The bridge automatic erection monitoring system based on UAV 3D scanning and digital twin includes: The UAV 3D scanning unit is used to acquire 3D point cloud data of bridge components during the component manufacturing stage and the bridge erection process; The point cloud processing and deviation analysis unit is used to: register component point clouds with the design model, generate component-level digital twin models, and calculate component installation deviations and bridge alignment deviations during bridge construction. The digital twin management unit is used to establish a digital twin model of the entire bridge erection process and store component-level digital twin models; The bridge erection control interface unit is used to generate correction commands for the bridge erection equipment based on the deviation analysis results and send them to the automatic bridge erection equipment control system. The visualization and early warning unit is used to display the bridge construction status and output deviation early warning information.

7. The bridge automatic erection full-process monitoring system based on UAV 3D scanning and digital twin as described in claim 6, characterized in that, The UAV 3D scanning unit includes a multi-rotor UAV and a laser 3D scanner.

8. The bridge automatic erection full-process monitoring system based on UAV 3D scanning and digital twin as described in claim 1, characterized in that, The bridge erection control interface unit is connected to the automatic bridge erection equipment control system via wired or wireless communication.

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