Intelligent prediction method and system for posture of bridge tower segment based on multi-point cooperative positioning

Abstract

The application discloses a kind of bridge tower segment attitude intelligent prediction method and system based on multi-point cooperative positioning, method includes: S100, in the two adjacent sides of the segment that has been installed, select multiple observation points, using laser reflector obtains the space coordinates of observation point, using multiple total station instruments cooperates measurement;S200, based on rigid body transformation mathematical model, the attitude change prediction of segment to be installed, using rotation matrix and translation vector calculates the optimal attitude of segment to be installed, too much point redundant information optimizes attitude estimation, controls cumulative error;S300, BIM modeling is carried out to bridge tower structure, and high-precision three-dimensional rendering is carried out, complex monitoring data is converted into intuitive graphical interface, and segment state is shown in real time;S400, according to data comparison, combined with error analysis, if the error is larger than design data, then corresponding adjustment is carried out;S500, repeat the above steps, until all bridge tower segments are accurately positioned and installed. The precision and construction efficiency of bridge tower segment erection are improved.

Description

Technical Field

[0001] This invention belongs to the field of bridge tower installation control technology, and more specifically, relates to a method and system for intelligent prediction of bridge tower segment attitude based on multi-point collaborative positioning. Background Technology

[0002] In large-scale bridge engineering, the precise positioning and installation of bridge towers are crucial to ensuring the safety and functionality of the overall structure. During the on-site segmental erection process, the lower end of each segment is first matched with the upper end of the previously installed segment to ensure that the misalignment of each panel meets welding requirements and that the geometric shape of the erection is within the allowable error range. The common practice is to set up measuring instruments at ground control points and place targets at the upper ends of the segments to collect coordinate positions. However, due to interference from climbing platforms and other construction environmental factors, the positions of characteristic point coordinates need to be flexibly adjusted according to the actual site conditions. Furthermore, the points to be measured are often located in different measurement control networks, and a single instrument cannot simultaneously cover all measurement needs, thus making it difficult to guarantee measurement accuracy.

[0003] Chinese Patent CN118534809A discloses a method and system for intelligent measurement and control of ultra-high bridge towers based on a camera array. The system includes a camera array monitoring module, an image acquisition and processing module, a monitoring device, a bridge tower status assessment and prediction module, and a bridge tower intelligent control and early warning module. By vertically arranging targets on the bridge tower and monitoring the real-time displacement of each target across the entire height range of the ultra-high bridge tower using an array camera, the monitoring device acquires environmental parameters and stress data. The bridge tower status assessment and prediction module establishes a structural status model of the bridge tower, predicts the future trend of the bridge tower's alignment, and provides real-time early warning and control of the bridge tower through the intelligent control and early warning module. This achieves automatic monitoring and intelligent control of the bridge tower structure's alignment around the clock, effectively improving measurement efficiency, reducing labor costs, ensuring the accuracy and reliability of measurement results, and enhancing the construction quality of the bridge tower.

[0004] However, the Chinese patent with publication number CN118534809A presents challenges not only in the inherently difficult monitoring process but also in the complexity of the monitoring data, which poses difficulties for engineers. The massive amounts of data obtained from monitoring are often highly academic, requiring deep expertise to understand and interpret, making it difficult for on-site personnel to make quick and intuitive judgments and adjustments. Therefore, a method and system for intelligent prediction of bridge tower segment attitude based on multi-point collaborative positioning is needed to ensure that the erected geometry is within the allowable error range and to transform complex numerical and geometric information into easily understandable graphical representations, thereby ensuring the installation accuracy and structural safety of the project. Summary of the Invention

[0005] To address the aforementioned deficiencies or improvement needs of existing technologies, this invention provides a method and system for intelligent prediction of bridge tower segment attitude based on multi-point collaborative positioning, which acquires the spatial coordinates and environmental parameters of bridge tower segments in real time. Then, an intelligent prediction algorithm based on rigid body transformation is employed to optimize attitude estimation through multi-point redundant information, accurately predicting the installation position of the next segment using rotation matrices and translation vectors, and effectively controlling accumulated errors. Finally, the system integrates a high-fidelity 3D visualization platform, combining BIM modeling technology and real-time data display, enabling complex monitoring data to be presented in an intuitive and easy-to-understand manner. This helps engineers promptly identify and adjust deviations during segment installation, improving the accuracy and construction efficiency of bridge tower segment erection, and providing technical support for large-scale bridge projects.

[0006] To achieve the above objectives, according to a first aspect of the present invention, a method for intelligent prediction of bridge tower segment attitude based on multi-point cooperative localization is provided, comprising the following steps:

[0007] S100. Select multiple observation points on two adjacent sides of the installed segment, use a laser reflector to obtain the spatial coordinates of the observation points, and use multiple total stations to perform collaborative measurement and calibration of the spatial coordinates.

[0008] S200: Based on the rigid body transformation mathematical model, the attitude change of the segment to be installed is predicted. The rotation matrix and translation vector are used to calculate the optimal attitude of the segment to be installed. The attitude estimation is optimized by using redundant information at too many points and the cumulative error is controlled.

[0009] S300: Perform BIM modeling on the bridge tower structure and high-precision 3D rendering to transform complex monitoring data into an intuitive graphical interface that displays the segment status in real time.

[0010] S400: Engineers compare the monitored data with the design data of the segments to be installed, and analyze the error of the bridge tower erection using a graphical interface. If the error is large, adjustments are made accordingly.

[0011] S500. Repeat the above steps until all bridge tower segments are accurately positioned and installed.

[0012] Further, in step S100, the backsight orientation is the process of determining the direction reference of the instrument during the measurement process. Specifically, when two observation points are known, one of the observation points is first aimed at and used as the zero direction or reference direction to determine the azimuth of the total station.

[0013] The verification of the third point coordinates specifically involves: after completing the backsight orientation, the engineers use the already oriented total station to aim at the third observation point and read the coordinate information of that point. Then, by comparing the coordinates read by the instrument with the known actual coordinates of that point, they check to ensure that the measurement results are accurate.

[0014] Furthermore, step S100 also includes the following steps:

[0015] S101. Select two adjacent sides of the installed segment as observation surfaces, select six key points on each observation surface as observation points, install a laser reflector at each observation point, and use the laser reflector to feed back the spatial coordinates of each observation point.

[0016] S102. Select two observation points on each observation surface as positioning points, and obtain the coordinates of the positioning points;

[0017] S103. A total station is set up on each observation surface. The difference between the measurement data of the two total stations is calibrated by the coordinates of the positioning point to accurately locate the spatial position and attitude data of the installed segment.

[0018] Furthermore, in step S101, the distribution of the observation points on each observation surface is as follows: three points are set at equal intervals along the upper and lower edges of each observation surface.

[0019] The observation surface includes a first observation surface and a second observation surface. The positioning points on the first observation surface are point S1 and point X1, and the positioning points on the second observation surface are point S2 and point X2.

[0020] Further, in step S200, the monitoring of the data of the segment to be installed specifically involves using a mathematical model based on rigid body transformation to accurately determine the attitude change of the segment, minimizing errors through redundant information from multiple points, calculating the optimal attitude of the segment to be installed using a rotation matrix and translation vector, and predicting the position of new points. This includes the following steps:

[0021] S201. The attitude of the segment to be installed is calculated using the rotation matrix R and the translation vector t.

[0022] S202. Select two adjacent sides on the segment to be installed that correspond to the observation surface on the installed segment as monitoring surfaces. Select six key points on each monitoring surface as monitoring points. Install a laser reflector at each monitoring point and use the laser reflector to feed back the spatial coordinates of each monitoring point.

[0023] S203. Take the coordinates of four monitoring points to form a redundant information, and use this redundant information to optimize the attitude prediction of the segment 2 to be installed.

[0024] Furthermore, in step S201, an arbitrary point p on the segment to be installed should be selected, and the transformed position p′ of point p should be represented by a rotation matrix and a translation vector, specifically as follows:

[0025] p′=Rp+t,

[0026] Where p′ represents the transformed position of any point p on the segment to be installed.

[0027] R is the rotation matrix.

[0028] t is the translation vector.

[0029] Furthermore, each arbitrary point p on the segment to be installed and its corresponding transformed position p′ form a set of corresponding points. The optimal rotation matrix R and translation vector t are found among these sets of corresponding points to minimize the error between each set of corresponding points before and after the change. Specifically, the following function is minimized:

[0030]

[0031] Where, p i Let i be any point on the segment to be installed.

[0032] p′ i Let p be any point i on the segment to be installed. i The transformed position;

[0033] The centroids of all arbitrary points p on the segment to be installed, as well as the centroids of the transformed positions of all arbitrary points p on the segment to be installed, are obtained through centroid alignment. Specifically:

[0034]

[0035]

[0036] in, Let p be the centroid of any point p on the segment to be installed.

[0037] Let p be the centroid of the transformed position of any point p on the segment to be installed.

[0038] Furthermore, the centroid of all arbitrary points p on the segment to be installed is... The centroids of all arbitrary points p on the segment to be installed after transformation The covariance matrix H of the decentralized point set of any point p on the segment to be installed is obtained through decentralization:

[0039]

[0040] Where, q iLet p be any point i on the segment to be installed. i The i-th decentralized arbitrary point obtained after decentralization

[0041] q′ i Let p be any point i on the segment to be installed. i The i-th decentralized transformed position is obtained after the transformed position is decentralized;

[0042] The covariance matrix H also needs to undergo singular value decomposition, specifically:

[0043] H=U∑V T ,

[0044] Where U is an orthogonal matrix obtained by decomposing the covariance matrix H, and its column vectors are the left singular vectors of the covariance matrix H.

[0045] V is another orthogonal matrix obtained by decomposing the covariance matrix H, and its column vectors are the right singular vectors of the covariance matrix H.

[0046] ∑ is a matrix whose diagonal elements are non-negative real numbers after the decomposition of the covariance matrix H, and its diagonal elements are singular values.

[0047] Furthermore, the optimal rotation matrix R is obtained. opt for:

[0048] R opt =VU T ,

[0049] Optimal translation vector t opt for:

[0050]

[0051] Using the optimal rotation matrix R opt and the optimal translation vector t opt For any new point p new The transformed position is predicted, specifically as follows:

[0052] p′ new =R opt p new +t opt ,

[0053] Where, p′ new For any new point p new The transformed position.

[0054] According to another aspect of the present invention, a bridge tower segment attitude intelligent prediction system based on multi-point cooperative positioning is provided, comprising:

[0055] Calibration module: After the installed steel shell segment is poured, it is used to measure the relative height difference of the top surface of the segment and the axis deviation. The total station uses backsight orientation and verifies the coordinates of the third point to complete the calibration of coordinate information.

[0056] Prediction module: Used to locate the segment to be installed by referring to the center point of the installed segment, and to monitor and predict the data of the segment to be installed, including the center point of the steel shell, the axis and the height difference, before the concrete is poured.

[0057] Visualization module: Used for BIM modeling of bridge structures, converting monitored data into an intuitive and easy-to-understand graphical interface, simulating the erection process of bridge tower segments, and presenting the graphics to engineers in real time;

[0058] Adjustment module: This module allows engineers to compare the monitored data with the design data of the segment to be installed, and analyze the error in the bridge tower erection using a graphical interface. If the error is large, corresponding adjustments are made.

[0059] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0060] 1. The present invention provides an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning, which acquires the spatial coordinates and environmental parameters of bridge tower segments in real time, adopts an intelligent prediction algorithm based on rigid body transformation, optimizes attitude estimation through multi-point redundant information, accurately predicts the installation position of the next segment using rotation matrix and translation vector, and effectively controls cumulative error.

[0061] 2. The present invention provides an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning. The system integrates a high-fidelity three-dimensional visualization platform, combined with BIM modeling technology and real-time data display, so that complex monitoring data can be presented in an intuitive and easy-to-understand way. This helps engineers to identify and adjust deviations in segment installation in a timely manner, improves the accuracy and construction efficiency of bridge tower segment erection, and provides innovative technical support for large-scale bridge projects.

[0062] 3. The present invention provides an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning. By using multi-measurement point fusion technology, it solves the problems of insufficient measurement coverage and difficulty in ensuring accuracy of traditional single instruments, and realizes accurate monitoring in complex environments. Attached Figure Description

[0063] Figure 1 This is a flowchart illustrating an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative localization, according to an embodiment of the present invention.

[0064] Figure 2This is a schematic diagram of the observation point arrangement for an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning according to an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram of the specific process of step S100 of a bridge tower segment attitude intelligent prediction method based on multi-point collaborative positioning according to an embodiment of the present invention.

[0066] Figure 4 This is a schematic diagram of step S200 of a bridge tower segment attitude intelligent prediction method based on multi-point collaborative positioning according to an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram of the graphical interface of a visualization platform for an intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning, according to an embodiment of the present invention.

[0068] In all the accompanying drawings, the same reference numerals indicate the same technical features, specifically: 1-Installed segment, 2-Segment to be installed, 3-Total station. Detailed Implementation

[0069] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0070] The purpose of bridge tower monitoring is to ensure that the geometry, verticality, planar position, and elevation of the tower columns and detailed structures meet the specifications and design requirements. Bridge tower construction mainly involves setting out the axis points and corner points of the segment sections, checking and positioning the steel shell of the tower columns, and installing and positioning the embedded parts. Various positioning and setting out are mainly carried out using the three-dimensional coordinate method of a total station 3, and the level is used to control the relative height difference of the top surface.

[0071] Example 1

[0072] like Figure 1 As shown in the figure, this embodiment of the invention provides a method for intelligent prediction of bridge tower segment attitude based on multi-point cooperative localization, including the following steps:

[0073] S100. Select multiple observation points on two adjacent sides of the installed segment 1, use a laser reflector to obtain the spatial coordinates of the observation points, and use multiple total stations 3 to perform collaborative measurement and calibration of the spatial coordinates.

[0074] S200. Based on the rigid body transformation mathematical model, the attitude change of the segment to be installed 2 is predicted. The optimal attitude of the segment to be installed is calculated using the rotation matrix and translation vector. The attitude estimation is optimized by using redundant information at too many points to control the cumulative error.

[0075] S300: Perform BIM modeling on the bridge tower structure and high-precision 3D rendering to transform complex monitoring data into an intuitive graphical interface that displays the segment status in real time.

[0076] S400: The engineers compare the monitored data with the design data of the section 2 to be installed, and analyze the error of the bridge tower erection using the graphical interface. If the error is large, the corresponding adjustments are made.

[0077] S500. Repeat the above steps until all bridge tower segments are accurately positioned and installed.

[0078] like Figure 2 , 3 As shown, in step S100, the measurement of the relative height difference and axial misalignment of the top surface of the installed segment 1 should be carried out during a period of stable temperature.

[0079] The backsight orientation is the process of determining the direction reference of the instrument during the measurement process. Specifically, when two observation points are known, one of the observation points (called the backsight point) is first aimed at and used as the zero direction or reference direction to determine the azimuth of the total station 3.

[0080] The verification of the third point's coordinates specifically involves the following steps: After completing the backsight orientation, engineers use the already oriented total station 3 to aim at the third observation point and read its coordinate information. Then, by comparing the coordinates read by the instrument with the known actual coordinates of the point, the accuracy of the measurement results is checked and ensured.

[0081] Step S100 also includes the following steps:

[0082] S101. Select two adjacent sides of the installed segment 1 as observation surfaces, select six key points on each observation surface as observation points, install a laser reflector at each observation point, and use the laser reflector to feed back the spatial coordinates of each observation point.

[0083] S102. Select two observation points on each observation surface as positioning points, and obtain the coordinates of the positioning points;

[0084] S103. A total station 3 is set up on each observation surface. The difference between the measurement data of the two total stations 3 is calibrated by the coordinates of the positioning point, so as to accurately locate the spatial position and attitude data of the installed segment 1.

[0085] In step S101, the distribution of the observation points on each observation surface is as follows: three points are equidistantly set along the upper and lower edges of each observation surface. The observation surface includes a first observation surface and a second observation surface. The positioning points on the first observation surface are point S1 and point X1, and the positioning points on the second observation surface are point S2 and point X2.

[0086] like Figure 4 As shown, in step S200, monitoring the data of the segment 2 to be installed specifically involves using a mathematical model based on rigid body transformation to accurately determine the segment's attitude change, minimizing errors through redundant information from multiple points, calculating the optimal attitude of the segment 2 to be installed using a rotation matrix and translation vector, and predicting the position of new points. This includes the following steps:

[0087] S201. The attitude of the segment 2 to be installed is calculated using the rotation matrix R and the translation vector t.

[0088] S202. Select two adjacent sides on the segment to be installed 2 that correspond to the observation surface on the installed segment 1 as monitoring surfaces. Select six key points on each monitoring surface as monitoring points. Install a laser reflector at each monitoring point and use the laser reflector to feed back the spatial coordinates of each monitoring point.

[0089] S203. Take the coordinates of four monitoring points to form a redundant information, and use this redundant information to optimize the attitude prediction of the segment 2 to be installed.

[0090] In step S201, an arbitrary point p on the segment 2 to be installed should also be selected, and the transformed position p′ of point p should be represented by a rotation matrix and a translation vector, specifically:

[0091] p′=Rp+t,

[0092] Where p′ represents the transformed position of any point p on the segment to be installed.

[0093] R is the rotation matrix.

[0094] t is the translation vector.

[0095] The rotation matrix R is a 3×3 orthogonal matrix, that is:

[0096] R T R = I, det(R) = 1,

[0097] The translation vector t is a 3×3 vector.

[0098] Each arbitrary point p on the segment 2 to be installed and its corresponding transformed position p′ form a set of corresponding points. Among multiple sets of corresponding points, the optimal rotation matrix R and translation vector t are found to minimize the error between each set of corresponding points before and after the change. Specifically, the following function is minimized:

[0099]

[0100] Where, p i Let i be any point on the segment to be installed.

[0101] p′ i Let p be any point i on the segment to be installed. i The transformed position.

[0102] The centroids of all arbitrary points p on the segment 2 to be installed, as well as the centroids of the transformed positions of all arbitrary points p on the segment 2 to be installed, are obtained by centroid alignment. Specifically:

[0103]

[0104]

[0105] in, Let p be the centroid of any point p on the segment to be installed.

[0106] Let p be the centroid of the transformed position of any point p on the segment to be installed.

[0107] The centroid of all arbitrary points p on the segment 2 to be installed is... The centroids of all arbitrary points p on the segment to be installed after transformation The covariance matrix H of the decentralized point set of any point p on the segment to be installed 2 is obtained through decentralization:

[0108]

[0109] Where, q i Let p be any point i on the segment to be installed. i The i-th decentralized arbitrary point obtained after decentralization

[0110] q′ i Let p be any point i on the segment to be installed. i The i-th decentralized position obtained after the transformed position is decentralized.

[0111] The i-th decentralized arbitrary point qi is specifically:

[0112]

[0113] The i-th decentralized change position q′ i Specifically:

[0114]

[0115] The covariance matrix H also needs to undergo singular value decomposition, specifically:

[0116] H=U∑V T ,

[0117] Where U is an orthogonal matrix obtained by decomposing the covariance matrix H, and its column vectors are the left singular vectors of the covariance matrix H.

[0118] V is another orthogonal matrix obtained by decomposing the covariance matrix H, and its column vectors are the right singular vectors of the covariance matrix H.

[0119] ∑ is a matrix whose diagonal elements are non-negative real numbers after the decomposition of the covariance matrix H, and its diagonal elements are singular values.

[0120] The optimal rotation matrix is:

[0121] R opt =VU T ,

[0122] Among them, R opt The optimal rotation matrix;

[0123] If the optimal rotation matrix R obtained by applying the above formula is... opt The determinant det(R) opt If ) < 0, then a correction matrix needs to be introduced, that is, the optimal rotation matrix R at this time. opt for:

[0124]

[0125] The optimal translation vector is:

[0126]

[0127] Among them, t opt This is the optimal translation vector.

[0128] Using the optimal rotation matrix R opt and the optimal translation vector t opt For any new point p new The transformed position is predicted, specifically as follows:

[0129] p′ new =R opt p new +t opt ,

[0130] Where, p′ new For any new point p new The transformed position.

[0131] like Figure 5 As shown, in step S300, a three-dimensional visualization platform also needs to be established, and the graphical interface is displayed on this platform. The three-dimensional visualization platform, by combining BIM (Building Information Modeling) modeling technology for bridge structures, real-time database access, and the visualization capabilities of the Three.js three-dimensional graphics library, transforms complex monitoring data into an intuitive and easy-to-understand graphical interface, enabling engineers to better control the erection process of bridge tower segments.

[0132] The visualization platform is based on the bridge's BIM model, ensuring that all erected segments and monitoring points are highly consistent with the actual structure. The BIM model not only contains the geometric information of the bridge tower segments but also integrates material properties, construction sequence, and relevant construction environment information. This integration process allows the platform to not only display the current state of the structure but also dynamically update the model according to the design and construction progress, reflecting the actual situation of the project in real time.

[0133] To enable real-time visualization, the platform is seamlessly connected to the project's monitoring database. The database stores real-time monitoring data acquired from various sensors and total stations, including the coordinate positions, attitude deviations, and environmental influences (such as wind speed and temperature) of bridge tower segments. This data is automatically transmitted to the platform via an interface, updating the information in the model in real time.

[0134] The visualization system uses Three.js for 3D rendering, supporting real-time display of the bridge structure. Three.js can render high-precision 3D scenes, and combined with the BIM model, the platform can dynamically display the deviation between the actual posture of the current segment and its design state, helping engineers quickly identify problem areas. The deviation of each monitoring point is represented by different colored markers or geometric symbols, visually displaying the status of each segment and greatly reducing data complexity.

[0135] The platform supports animated demonstrations of geometric changes during the erection process. By combining historical data with future predictions, the system can generate complete construction animations, showcasing the installation steps of each segment, the evolution of cumulative errors, and potential future error trends. This approach helps engineers intuitively understand each stage of the erection process and make pre-adjustments based on different situations.

[0136] Based on real-time data updates from the database and dynamic integration with the BIM model, the platform provides engineers with real-time feedback, ensuring that any deviations or errors are promptly identified and addressed. Engineers can instantly view the latest segment erection data on the platform, adjust construction plans and parameters, and mitigate potential risks. Because the erection process involves a large amount of data, the system possesses efficient data processing capabilities, quickly transforming complex data into intuitive 3D graphics. This allows on-site staff, without requiring extensive professional background, to quickly understand the current project status and make adjustment decisions.

[0137] The platform is tightly integrated with the predictive algorithm module, allowing engineers not only to view the current status but also to predict future installation progress through a visual interface. This integration helps management teams identify potential problems in advance and adjust construction plans based on the model, reducing the accumulation of errors.

[0138] This visualization platform features an open interface, allowing for future integration with more sensors and devices to adapt to more complex construction environments. Its customizable interface also allows users to adjust the displayed content according to project needs, and even supports remote monitoring and management.

[0139] Example 2

[0140] This invention provides a bridge tower segment attitude intelligent prediction system based on multi-point cooperative localization, comprising:

[0141] Calibration module: After the steel shell of installed segment 1 is poured, the relative height difference of the top surface of the segment and the axis deviation are measured. The total station 3 uses backsight orientation and verifies the coordinates of the third point to complete the calibration of coordinate information.

[0142] Prediction module: Used to locate the installation of the segment to be installed 2 by referring to the center point of the installed segment 1, and to monitor and predict its data, including the center point of the steel shell, axis and height difference, before the concrete pouring of the segment to be installed 2.

[0143] Visualization module: Used for BIM modeling of bridge structures, converting monitored data into an intuitive and easy-to-understand graphical interface, simulating the erection process of bridge tower segments, and presenting the graphics to engineers in real time;

[0144] Adjustment module: This module allows engineers to compare the monitored data with the design data of the section 2 to be installed, and analyze the error in the bridge tower erection using a graphical interface. If the error is large, corresponding adjustments are made.

[0145] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent prediction of bridge tower segment attitude based on multi-point cooperative localization, characterized in that, Includes the following steps: S100. Select multiple observation points on two adjacent sides of the installed segment (1), use a laser reflector to obtain the spatial coordinates of the observation points, and use multiple total stations (3) to perform collaborative measurement and calibration of the spatial coordinates. S200. Based on the rigid body transformation mathematical model, the attitude change of the segment to be installed (2) is predicted. The optimal attitude of the segment to be installed is calculated using the rotation matrix and translation vector. The attitude estimation is optimized by multi-point redundant information to control the cumulative error. S300: Perform BIM modeling on the bridge tower structure and high-precision 3D rendering to transform complex monitoring data into an intuitive graphical interface that displays the segment status in real time. S400. The engineers compare the monitored data with the design data of the section to be installed (2), and analyze the error of the bridge tower erection in conjunction with the graphical interface. If the error with the design data is large, the corresponding adjustment is made. S500. Repeat the above steps until all bridge tower segments are accurately positioned and installed. In step S300, a three-dimensional visualization platform also needs to be established. The graphical interface is displayed on this three-dimensional visualization platform. By combining the BIM modeling technology of the bridge structure, real-time database access and the visualization capabilities of the three-dimensional graphics library Three.js, the three-dimensional visualization platform transforms complex monitoring data into an intuitive and easy-to-understand graphical interface. The visualization system performs three-dimensional rendering through Three.js to display the bridge structure in real time. The deviation of each monitoring point is represented by different colored markers or geometric symbols, visually showing the status of each segment. In step S200, the data of the segment to be installed (2) is monitored. Specifically, based on the mathematical model of rigid body transformation, the attitude change of the segment is accurately determined, and the error is minimized by using redundant information from multiple points. The optimal attitude of the segment to be installed (2) is calculated by the rotation matrix and translation vector, and the position of the new point is predicted. This includes the following steps: S201, via rotation matrix Translation vector The attitude of the segment (2) to be installed is calculated; S202. Select two adjacent sides on the segment to be installed (2) that correspond to the observation surface on the installed segment (1) as monitoring surfaces. Select six key points on each monitoring surface as monitoring points. Install a laser reflector on each monitoring point and use the laser reflector to feed back the spatial coordinates of each monitoring point. S203. Take the coordinates of four monitoring points to form a redundant information, and use this redundant information to optimize the attitude prediction of the segment (2) to be installed.

2. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative localization according to claim 1, characterized in that, In step S100, backsight orientation is the process of determining the direction reference of the instrument during the measurement process. Specifically, when two observation points are known, one of the observation points is first aimed at and used as the zero direction or reference direction to determine the azimuth of the total station (3). The specific steps for verifying the coordinates of the third point are as follows: After completing the backsight orientation, the engineers use the already oriented total station (3) to aim at the third observation point and read the coordinate information of the point. Then, by comparing the coordinates read by the instrument with the known actual coordinates of the point, they check to ensure that the measurement results are accurate.

3. The intelligent prediction method for bridge tower segment attitude based on multi-point collaborative positioning according to claim 1, characterized in that, Step S100 also includes the following steps: S101. Select two adjacent sides of the installed segment (1) as observation surfaces, select six key points on each observation surface as observation points, install a laser reflector on each observation point, and use the laser reflector to feed back the spatial coordinates of each observation point. S102. Select two observation points on each observation surface as positioning points, and obtain the coordinates of the positioning points; S103. A total station (3) is set up on each observation surface. The difference between the measurement data of the two total stations (3) is calibrated by the coordinates of the positioning point, and the spatial position and attitude data of the installed segment (1) are accurately located.

4. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative positioning according to claim 3, characterized in that, In step S101, the distribution of the observation points on each observation surface is as follows: three points are set at equal intervals along the upper and lower edges of each observation surface. The observation surface includes a first observation surface and a second observation surface. The positioning points on the first observation surface are point S1 and point X1, and the positioning points on the second observation surface are point S2 and point X2.

5. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative positioning according to claim 1, characterized in that, In step S201, any point on the segment (2) to be installed should also be selected. Points are represented by rotation matrices and translation vectors. Transformed position Specifically: ， in, Any point on the segment to be installed The transformed position For rotation matrix, It is a translation vector.

6. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative positioning according to claim 5, characterized in that, Each arbitrary point on the segment (2) to be installed Its corresponding transformed position Each pair of corresponding points forms a set of points. The optimal rotation matrix is ​​then found among multiple sets of corresponding points. Translation vector To minimize the error between corresponding points before and after the change, specifically by minimizing the following function: ， in, For the first segment to be installed any point, For the first segment to be installed any point The transformed position; All arbitrary points on the segment (2) to be installed are obtained by centroid alignment. The centroid of the segment to be installed, and all arbitrary points on the segment (2). The centroid of the transformed position is specifically: ； ， in, For any point on the segment to be installed The center of mass, For any point on the segment to be installed The centroid of the transformed position.

7. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative positioning according to claim 6, characterized in that, All arbitrary points on the segment (2) to be installed center of mass and all arbitrary points on the segment to be installed centroid of the transformed position By decentralization, any point on the segment to be installed (2) is obtained. The covariance matrix of the decentralized point set : ， in, For the first section to be installed any point The result obtained after decentralization A decentralized arbitrary point, For the first segment to be installed any point The transformed position is obtained by decentralization. A decentralized change location; The covariance matrix Singular value decomposition is also required, specifically: ， in, Covariance matrix The decomposed orthogonal matrix has column vectors that are covariance matrices. The left singular vector, Covariance matrix The other orthogonal matrix after decomposition has column vectors that are covariance matrices. The right singular vector, Covariance matrix The resulting matrix has diagonal elements that are non-negative real numbers, and these diagonal elements are singular values.

8. The intelligent prediction method for bridge tower segment attitude based on multi-point cooperative localization according to claim 7, characterized in that, Obtain the optimal rotation matrix for: ， Optimal translation vector for: ， Using the optimal rotation matrix and optimal translation vector For any new point The transformed position is predicted, specifically as follows: ， in, For any new point The transformed position.

9. A bridge tower segment attitude intelligent prediction system based on multi-point cooperative localization, used to implement the bridge tower segment attitude intelligent prediction method based on multi-point cooperative localization as described in any one of claims 1-8, characterized in that, include: Calibration module: After the steel shell of the installed segment (1) is poured, measure the relative height difference of the top surface of the segment and the axis deviation. The total station (3) uses backsight orientation and verifies the coordinates of the third point to complete the calibration of coordinate information. Prediction module: Used to locate the installation of the segment to be installed (2) by referring to the center point of the installed segment (1), and to monitor and predict its data, including the center point of the steel shell, axis and height difference, before the concrete pouring of the segment to be installed (2). Visualization module: Used for BIM modeling of bridge structures, converting monitored data into an intuitive and easy-to-understand graphical interface, simulating the erection process of bridge tower segments, and presenting the graphics to engineers in real time; Adjustment module: It is used to enable engineers to compare the monitored data with the design data of the section to be installed (2), and to analyze the error of the bridge tower erection in conjunction with the graphical interface. If the error with the design data is large, the corresponding adjustment will be made.

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