Bridge tower section attitude intelligent prediction method and system based on multi-point cooperative positioning

Abstract

The invention discloses a bridge tower section attitude intelligent prediction method and system based on multi-point cooperative localization, and the method comprises the steps: S100, selecting a plurality of observation points on two adjacent side surfaces of an installed section, obtaining the space coordinates of the observation points through a laser reflector, and carrying out the cooperative measurement through a plurality of total stations; s200, performing attitude change prediction on the to-be-installed segment based on the rigid body transformation mathematical model, calculating the optimal attitude of the to-be-installed segment by using a rotation matrix and a translation vector, optimizing attitude estimation by excessive-point redundant information, and controlling an accumulative error; s300, performing BIM modeling on the bridge tower structure, performing high-precision three-dimensional rendering, converting complex monitoring data into a visual graphical interface, and displaying a segment state in real time; s400, according to data comparison, analysis is carried out in combination with errors, and if the errors with design data are large, corresponding adjustment is carried out; and S500, the steps are repeated till all the bridge tower sections are accurately positioned and installed. And the erection precision and the construction efficiency of the bridge tower sections are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge tower installation control, and more specifically, relates to a method and system for intelligently predicting the posture of a bridge tower segment based on multi-point collaborative positioning. Background Art

[0002] In large-scale bridge projects, the precise positioning and installation of bridge towers are crucial to ensure the safety and functionality of the overall structure. During the on-site segment erection process, the lower port of the segment is first matched with the upper port of the previously installed segment to ensure that the misalignment of each wall panel meets the welding conditions and that the geometric shape of the erection is within the allowable error range. The usual practice is to set up measuring instruments at ground control points, place targets at the upper ports of the segments, and collect coordinate positions. However, due to interference from other construction environments such as climbing platforms, it is necessary to flexibly adjust the positions of the feature point coordinates based on the actual on-site conditions. In addition, the points to be measured are usually located in different measurement control networks, and a single instrument cannot cover all measurement requirements at the same time, so the measurement accuracy is difficult to guarantee.

[0003] A Chinese patent with publication number CN118534809A discloses a camera array-based super-high bridge tower intelligent measurement and control method and system thereof, which is provided with a camera array monitoring module, an image acquisition and processing module, a monitoring device, a bridge tower state evaluation and prediction module, and a bridge tower intelligent control and early warning module. By arranging targets vertically along the upper edge of the bridge tower and monitoring the real-time displacement of each target in the full height range of the super-high bridge tower through an array camera, the monitoring device obtains environmental parameters and stress data, establishes a bridge tower structure state model in the bridge tower state evaluation and prediction module, predicts the future linear change trend of the bridge tower, and performs real-time early warning and control of the bridge tower through the bridge tower intelligent control and early warning module, thereby realizing automatic monitoring and intelligent control of the all-weather linear shape of the bridge tower structure, effectively improving measurement efficiency, reducing labor costs, ensuring the accuracy and reliability of measurement results, and improving the quality of bridge tower construction.

[0004] However, the Chinese patent with publication number CN118534809A is not only extremely challenging in the monitoring process itself, but also the complexity of the monitoring data brings difficulties to engineers. The massive data obtained from monitoring is often highly academic, and deep professional knowledge is required to understand and interpret these data, which makes it difficult for on-site personnel to make judgments and adjustments quickly and intuitively. Therefore, a method and system for intelligent prediction of the attitude of bridge tower segments based on multi-point collaborative positioning is needed to ensure that the geometric form of the erection is within the allowable error range, and to convert complex numerical and geometric information into easy-to-understand graphical presentations to ensure the installation accuracy and structural safety of the project. Summary of the invention

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a method and system for intelligent prediction of the attitude of bridge tower segments based on multi-point collaborative positioning, which can obtain the spatial coordinates and environmental parameters of bridge tower segments in real time. Then, an intelligent prediction algorithm based on rigid body transformation is adopted to optimize the attitude estimation through multi-point redundant information, and the rotation matrix and translation vector are used to accurately predict the installation position of the next segment, and effectively control the cumulative error. Finally, 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, helping engineering personnel to promptly identify and adjust deviations in 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 an embodiment of the present invention, a method for intelligently predicting the attitude of a bridge tower segment based on multi-point collaborative positioning is provided, comprising the following steps:

[0007] S100, selecting multiple observation points on two adjacent sides of the installed segment, using a laser reflector to obtain the spatial coordinates of the observation points, and using multiple total stations to collaboratively measure and calibrate the spatial coordinates;

[0008] S200, predicting the posture change of the segment to be installed based on the rigid body transformation mathematical model, using the rotation matrix and the translation vector to calculate the optimal posture of the segment to be installed, optimizing the posture estimation with redundant information of too many points, and controlling the cumulative error;

[0009] S300, BIM modeling of the bridge tower structure and high-precision 3D rendering, converting complex monitoring data into an intuitive graphical interface to display the segment status in real time;

[0010] S400, the engineering staff compares the monitored data with the design data of the segment to be installed, and analyzes the error of the bridge tower erection with the help of a graphical interface. If the error with the design data is large, corresponding adjustments are made;

[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 a process of determining the direction reference of the instrument during the measurement process, specifically: when two observation points are known, first aim at one of the observation points and use it as the zero direction or reference direction to determine the azimuth of the total station;

[0013] The specific method of verifying the coordinates of the third point is as follows: after completing the rear-sight orientation, the engineer uses the oriented total station to aim at the third observation point and reads the coordinate information of the point, and then compares the coordinates read by the instrument with the known actual coordinates of the point to check and ensure that the measurement results are accurate.

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

[0015] S101, selecting two adjacent side surfaces of the installed segment as observation surfaces, selecting six key points on each observation surface as observation points, installing a laser reflector on each observation point, and feeding back the spatial coordinates of each observation point through the laser reflector;

[0016] S102, selecting two observation points on each observation surface as positioning points, and obtaining the coordinates of the positioning points;

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

[0018] Furthermore, in step S101, the distribution of the observation points on each observation surface is as follows: three observation points are arranged equidistantly along the upper and lower edges of each observation surface;

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

[0020] Furthermore, in step S200, the data of the segment to be installed is monitored, specifically based on a mathematical model of rigid body transformation, the posture change of the segment is accurately determined, and the error is minimized through redundant information of multiple points, the optimal posture of the segment to be installed is calculated through a rotation matrix and a translation vector, and the position of the new point is predicted, including the following steps:

[0021] S201, calculating the posture of the segment to be installed by using the rotation matrix R and the translation vector t;

[0022] S202, selecting two adjacent side surfaces on the segment to be installed that correspond to the observation surface on the installed segment as monitoring surfaces, selecting six key points on each monitoring surface as monitoring points, installing a laser reflector on each monitoring point, and feeding back the spatial coordinates of each monitoring point through the laser reflector;

[0023] S203 , taking the coordinates of four monitoring points to form a redundant information, and optimizing the posture prediction of the segment 2 to be installed by using the redundant information.

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

[0025] p′=Rp+t,

[0026] Where p′ is 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, and the optimal rotation matrix R and translation vector t are found in multiple 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] Among them, p i is the i-th arbitrary point on the segment to be installed,

[0032] p′ i is the i-th arbitrary point p on the segment to be installed i The transformed position;

[0033] The centroid of all arbitrary points p on the segment to be installed and the centroid of the transformed position of all arbitrary points p on the segment to be installed are obtained by the centroid alignment method, specifically:

[0034]

[0035]

[0036] in, is the centroid of all arbitrary points p on the segment to be installed,

[0037] It is the centroid of the transformed position of all arbitrary points p on the segment to be installed.

[0038] Furthermore, the centroid of all arbitrary points p on the segment to be installed and the centroid of the transformed position of all arbitrary points p on the segment to be installed 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] Among them, q iis the i-th arbitrary point p on the segment to be installed i The i-th decentralized arbitrary point obtained after decentralization,

[0041] q′ i is the i-th arbitrary point p on the segment to be installed i The transformed position is decentralized to obtain the i-th decentralized change position;

[0042] The covariance matrix H also needs to be decomposed into singular values, specifically:

[0043] H=U∑V T ,

[0044] Among them, U is an orthogonal matrix after the decomposition of the covariance matrix H, and its column vector is the left singular vector of the covariance matrix H.

[0045] V is another orthogonal matrix after the decomposition of 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 covariance matrix H is decomposed, and its diagonal elements are singular values.

[0047] Further, we get the optimal rotation matrix R opt for:

[0048] R opt =VU T ,

[0049] Optimal translation vector t opt for:

[0050]

[0051] By 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:

[0052] p′ new =R opt p new +t opt ,

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

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

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

[0056] Prediction module: used to refer to the centering point of the installed segment to locate the segment to be installed, and monitor and predict the data including the centering point of the steel shell, axis and height difference before pouring the concrete of the segment to be installed;

[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 graphics to engineering personnel in real time;

[0058] Adjustment module: It is used for engineering personnel to compare the monitored data with the design data of the segment to be installed, and analyze the error of the bridge tower erection with the help of the graphical interface. If the error with the design data is large, corresponding adjustments will be made.

[0059] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

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

[0061] 2. The present invention provides an intelligent prediction method for the posture of bridge tower segments 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 manner, helping engineering personnel to promptly identify and adjust deviations in segment installation, thereby improving the accuracy and construction efficiency of bridge tower segment erection, and providing innovative technical support for large-scale bridge projects.

[0062] 3. The present invention provides an intelligent prediction method for bridge tower segment posture based on multi-point collaborative positioning. Through the multi-measurement point fusion technology, it solves the problem of insufficient measurement coverage and difficulty in ensuring accuracy of traditional single instrument measurement, and realizes accurate monitoring in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a flow chart of a method for intelligently predicting the posture of a bridge tower segment based on multi-point collaborative positioning according to an embodiment of the present invention;

[0064] Figure 2A schematic diagram of observation point arrangement of a bridge tower segment posture intelligent prediction method based on multi-point collaborative positioning according to an embodiment of the present invention;

[0065] Figure 3 This is a specific flow chart of step S100 of a method for intelligently predicting the posture of a bridge tower segment based on multi-point collaborative positioning according to an embodiment of the present invention;

[0066] Figure 4 This is a specific flow chart of step S200 of a method for intelligently predicting the posture of a bridge tower segment based on multi-point collaborative positioning according to an embodiment of the present invention;

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

[0068] In all the drawings, the same reference numerals represent the same technical features, specifically: 1-installed segment, 2-segment to be installed, 3-total station. DETAILED DESCRIPTION

[0069] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present 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 geometric shape, verticality, plane position and elevation of the tower column and detailed structure meet the specifications and design requirements. The construction of bridge tower mainly involves the layout of segment section axis points and corner points, inspection and positioning of tower column steel shell and installation and positioning of embedded parts. Various positioning and layout are mainly based on the three-dimensional coordinate method of the total station 3, and the level instrument controls the relative height difference of the top surface.

[0071] Example 1

[0072] like Figure 1 As shown, an embodiment of the present invention provides a method for intelligently predicting the attitude of a bridge tower segment based on multi-point collaborative positioning, comprising the following steps:

[0073] S100, selecting a plurality of observation points on two adjacent sides of the installed segment 1, obtaining the spatial coordinates of the observation points using a laser reflector, and performing collaborative measurement and calibration of the spatial coordinates using a plurality of total stations 3;

[0074] S200, predicting the posture change of the segment 2 to be installed based on the rigid body transformation mathematical model, using the rotation matrix and the translation vector to calculate the optimal posture of the segment to be installed, optimizing the posture estimation with redundant information of too many points, and controlling the cumulative error;

[0075] S300, BIM modeling of the bridge tower structure and high-precision 3D rendering, converting complex monitoring data into an intuitive graphical interface to display the segment status in real time;

[0076] S400, the engineering personnel compare the monitored data with the design data of the segment 2 to be installed, and analyze the error of the bridge tower erection in combination with the graphical interface, and make corresponding adjustments if the error with the design data is large;

[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 axis deviation of the top surface of the installed segment 1 should be performed 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, first aim at one of the observation points (called the backsight point) and use it as the zero direction or reference direction to determine the azimuth of the total station 3.

[0080] The third point coordinate verification is specifically as follows: after completing the backsight orientation, the engineer uses the 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, the accuracy of the measurement result is checked and ensured.

[0081] Step S100 also includes the following steps:

[0082] S101, selecting two adjacent side surfaces of the installed segment 1 as observation surfaces, selecting six key points on each observation surface as observation points, installing a laser reflector on each observation point, and feeding back the spatial coordinates of each observation point through the laser reflector;

[0083] S102, selecting two observation points on each observation surface as positioning points, and obtaining the coordinates of the positioning points;

[0084] S103, a total station 3 is arranged on each observation surface, and the difference between the measurement data of the two total stations 3 is calibrated through the coordinates of the positioning point to accurately locate the spatial position and posture data of the installed segment 1.

[0085] In step S101, the distribution of the observation points on each observation surface is as follows: three observation points are set equidistantly 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, the data of the segment 2 to be installed is monitored, specifically based on a mathematical model of rigid body transformation, the posture change of the segment is accurately determined, and the error is minimized through redundant information of multiple points, the optimal posture of the segment 2 to be installed is calculated through the rotation matrix and translation vector, and the position of the new point is predicted. The following steps are included:

[0087] S201, calculating the posture of the segment 2 to be installed by using the rotation matrix R and the translation vector t;

[0088] S202, selecting two adjacent side surfaces corresponding to the observation surface on the installed segment 1 on the segment 2 to be installed as monitoring surfaces, selecting six key points on each monitoring surface as monitoring points, installing a laser reflector on each monitoring point, and feeding back the spatial coordinates of each monitoring point through the laser reflector;

[0089] S203 , taking the coordinates of four monitoring points to form a redundant information, and optimizing the posture prediction of the segment 2 to be installed by using the redundant information.

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

[0091] p′=Rp+t,

[0092] Where p′ is 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. The optimal rotation matrix R and translation vector t are found in multiple sets of corresponding points to minimize the error between each set of corresponding points before and after the change, specifically, to minimize the following function:

[0099]

[0100] Among them, p i is the i-th arbitrary point on the segment to be installed,

[0101] p′ i is the i-th arbitrary point p on the segment to be installed i The transformed position.

[0102] The centroid of all arbitrary points p on the segment 2 to be installed and the centroid of the transformed position of all arbitrary points p on the segment 2 to be installed are obtained by the centroid alignment method, specifically:

[0103]

[0104]

[0105] in, is the centroid of all arbitrary points p on the segment to be installed,

[0106] It is the centroid of the transformed position of all arbitrary points p on the segment to be installed.

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

[0108]

[0109] Among them, q i is the i-th arbitrary point p on the segment to be installed i The i-th decentralized arbitrary point obtained after decentralization,

[0110] q′ i is the i-th arbitrary point p on the segment to be installed i The transformed position is decentralized to obtain the i-th decentralized changed position.

[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 be decomposed into singular values, specifically:

[0116] H=U∑V T ,

[0117] Among them, U is an orthogonal matrix after the decomposition of the covariance matrix H, and its column vector is the left singular vector of the covariance matrix H.

[0118] V is another orthogonal matrix after the decomposition of 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 covariance matrix H is decomposed, and its diagonal elements are singular values.

[0120] Then the optimal rotation matrix is:

[0121] R opt =VU T ,

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

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

[0124]

[0125] The optimal translation vector is:

[0126]

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

[0128] By 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:

[0129] p′ new =R opt p new +t opt ,

[0130] Among them, 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 needs to be established, and the graphical interface is displayed on the three-dimensional visualization platform. The three-dimensional visualization platform converts complex monitoring data into an intuitive and easy-to-understand graphical interface by combining the BIM (Building Information Model) modeling technology of the bridge structure, real-time database access and the visualization capability of the three-dimensional graphics library Three.js, so that engineers can better control the erection process of the bridge tower segment.

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

[0133] In order to achieve real-time display, the platform is seamlessly connected to the project's monitoring database. The database stores real-time monitoring data obtained from a variety of sensors and total stations, including the coordinate position of the bridge tower segment, attitude deviation, environmental influences (such as wind speed, temperature, etc.). These data are automatically transmitted to the platform through the interface to update the information in the model in real time.

[0134] The visualization system uses Three.js for 3D rendering, which supports real-time display of bridge structures. Three.js can render high-precision 3D scenes. In conjunction with the BIM model, the platform can dynamically display the deviation between the actual posture of the current segment and the design status, helping engineers quickly identify problem areas. The deviation of each monitoring point is represented by a different color mark or geometric symbol, visually displaying the status of each segment, greatly reducing data complexity.

[0135] The platform supports animation to show geometric changes during the erection process. By combining historical data with future forecast data, the system can generate a complete construction animation to show the installation steps of the segment, the evolution of the accumulated error, and possible future error trends. This method helps engineers intuitively understand the various stages of the erection process and make pre-adjustments according to different situations.

[0136] Based on the real-time data update of the database and the dynamic integration of the BIM model, the platform can provide real-time feedback to engineers to ensure that any deviations or errors can be identified and handled in a timely manner. Engineers can instantly view the latest segment erection data on the platform, adjust construction plans and parameters, and reduce potential risks. Since a large amount of data is involved in the erection process, the system has efficient data processing capabilities and can quickly convert complex data into intuitive three-dimensional graphics. This allows on-site staff to quickly understand the current project status and make adjustment decisions without a deep professional background.

[0137] The platform is closely integrated with the prediction algorithm module, so engineers can not only view the current status, but also predict the future erection status through the visual interface. This combination helps the management team to discover potential problems in advance and adjust the construction plan according to the model to reduce the accumulation of errors.

[0138] The visualization platform is designed with open interfaces, which can be integrated with more sensors and equipment in the future to adapt to more complex construction environments. The platform's customizable interface also allows users to adjust the display content according to project needs, and even supports remote monitoring and management.

[0139] Example 2

[0140] The embodiment of the present invention provides a bridge tower segment posture intelligent prediction system based on multi-point collaborative positioning, comprising:

[0141] Calibration module: used to measure the relative height difference of the top surface of the installed segment 1 and the axis deviation after the steel shell is cast. The total station 3 uses the backsight orientation and verifies the coordinates of the third point to complete the calibration of the coordinate information;

[0142] Prediction module: used to refer to the centering point of the installed segment 1 to install the segment 2 to be installed, and monitor and predict the data including the centering point of the steel shell, axis and height difference before pouring the concrete of the segment 2 to be installed;

[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 graphics to engineering personnel in real time;

[0144] Adjustment module: It is used for engineers to compare the monitored data with the design data of the segment 2 to be installed, and analyze the error of the bridge tower erection in combination with the graphical interface. If the error with the design data is large, corresponding adjustments will be made.

[0145] It will be easily understood by those skilled in the art that the above description is only 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 in the protection scope of the present invention.

Claims

1. A method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning, characterized in that: The following steps are involved: S100, selecting a plurality of observation points on two adjacent sides of the installed segment (1), obtaining the spatial coordinates of the observation points using a laser reflector, and using a plurality of total stations (3) to collaboratively measure and calibrate the spatial coordinates; S200, predicting the posture change of the segment to be installed (2) based on the rigid body transformation mathematical model, using the rotation matrix and the translation vector to calculate the optimal posture of the segment to be installed, optimizing the posture estimation with redundant information of too many points, and controlling the cumulative error; S300, BIM modeling of the bridge tower structure and high-precision 3D rendering, converting complex monitoring data into an intuitive graphical interface to display the segment status in real time; S400, the engineering personnel compare the monitored data with the design data of the segment to be installed (2), and analyze the error of the bridge tower erection in combination with the graphical interface, and make corresponding adjustments if the error with the design data is large; S500. Repeat the above steps until all bridge tower segments are accurately positioned and installed.

2. According to the method of intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 1, it is characterized in that: In step S100, the backsight orientation is a process of determining the direction reference of the instrument during the measurement process, specifically: when two observation points are known, first aim at one of the observation points and use it as the zero direction or reference direction to determine the azimuth of the total station (3); The coordinate verification of the third point is specifically as follows: after completing the backsight orientation, the engineer uses the oriented total station (3) to aim at the third observation point and read the coordinate information of the point, and then compares the coordinates read by the instrument with the known actual coordinates of the point to check and ensure that the measurement result is accurate.

3. The method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 1 is characterized in that: Step S100 also includes the following steps: S101, selecting two adjacent side surfaces of the installed segment (1) as observation surfaces, selecting six key points on each observation surface as observation points, installing a laser reflector on each observation point, and feeding back the spatial coordinates of each observation point through the laser reflector; S102, selecting two observation points on each observation surface as positioning points, and obtaining the coordinates of the positioning points; S103, a total station (3) is arranged on each observation surface, and the difference between the measurement data of the two total stations (3) is calibrated through the coordinates of the positioning point to accurately locate the spatial position and posture data of the installed segment (1).

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

5. A method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to any one of claims 1-4, characterized in that: In step S200, the data of the segment to be installed (2) is monitored, specifically based on a mathematical model of rigid body transformation, the posture change of the segment is accurately determined, and the error is minimized through redundant information of multiple points, the optimal posture of the segment to be installed (2) is calculated through a rotation matrix and a translation vector, and the position of a new point is predicted, including the following steps: S201, calculating the posture of the segment to be installed (2) by using the rotation matrix R and the translation vector t; S202, selecting two adjacent side surfaces corresponding to the observation surface on the installed segment (1) on the segment to be installed (2) as monitoring surfaces, selecting six key points on each monitoring surface as monitoring points, installing a laser reflector on each monitoring point, and feeding back the spatial coordinates of each monitoring point through the laser reflector; S203 , taking the coordinates of four monitoring points to form a redundant information, and optimizing the posture prediction of the segment 2 to be installed by using the redundant information.

6. The method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 5 is characterized in that: In step S201, an arbitrary point p on the segment (2) to be installed is selected, and the position p′ of the point p after transformation is represented by a rotation matrix and a translation vector, specifically: p′=Rp+t, Where p′ is the transformed position of any point p on the segment to be installed, R is the rotation matrix, t is the translation vector.

7. The method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 6 is characterized in that: Each arbitrary point p on the segment to be installed (2) and its corresponding transformed position p′ form a group of corresponding points. The optimal rotation matrix R and translation vector t are found in the multiple groups of corresponding points to minimize the error between each group of corresponding points before and after the change, specifically, the following function is minimized: Among them, p i is the i-th arbitrary point on the segment to be installed, p′ i is the transformed position of the i-th arbitrary point pi on the segment to be installed; The centroids of all arbitrary points p on the segment to be installed (2) and the centroids of the transformed positions of all arbitrary points p on the segment to be installed (2) are obtained by the centroid alignment method, specifically: in, is the centroid of all arbitrary points p on the segment to be installed, It is the centroid of the transformed position of all arbitrary points p on the segment to be installed.

8. The method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 7 is characterized in that: The centroid of all arbitrary points p on the segment to be installed (2) and the centroid of the transformed position of all arbitrary points p on the segment to be installed The covariance matrix H of the decentralized point set of any point p on the segment to be installed (2) is obtained through decentralization: Among them, q i is the i-th arbitrary point p on the segment to be installed i The i-th decentralized arbitrary point obtained after decentralization, q′ i is the i-th arbitrary point p on the segment to be installed i The transformed position is decentralized to obtain the i-th decentralized change position; The covariance matrix H also needs to be decomposed into singular values, specifically: H=U∑V T , Among them, U is an orthogonal matrix after the decomposition of the covariance matrix H, and its column vector is the left singular vector of the covariance matrix H. V is another orthogonal matrix after the decomposition of the covariance matrix H, and its column vectors are the right singular vectors of the covariance matrix H. ∑ is a matrix whose diagonal elements are non-negative real numbers after the covariance matrix H is decomposed, and its diagonal elements are singular values.

9. The method for intelligent prediction of bridge tower segment posture based on multi-point collaborative positioning according to claim 8 is characterized in that: Get the optimal rotation matrix R opt for: R opt =VU T , Optimal translation vector t opt for: By 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: p′ new =R opt p new +t opt , Among them, p′ new For any new point p new The transformed position.

10. A bridge tower segment posture intelligent prediction system based on multi-point collaborative positioning, used to implement a bridge tower segment posture intelligent prediction method based on multi-point collaborative positioning as described in any one of claims 1 to 9, characterized in that: include: Calibration module: used to measure the relative height difference and axis deviation of the top surface of the installed segment (1) after the steel shell is cast, and the total station (3) uses backsight orientation and verifies the coordinates of the third point to complete the calibration of the coordinate information; Prediction module: used to refer to the centering point of the installed segment (1), to install and position the segment to be installed (2), and to monitor and predict the data including the centering point of the steel shell, the axis and the height difference before pouring the concrete 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 graphics to engineering personnel in real time; Adjustment module: used for the engineering personnel to compare the monitored data with the design data of the segment to be installed (2), and analyze the error of the bridge tower erection in combination with the graphical interface, and make corresponding adjustments if the error with the design data is large.

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