Virtual pre-assembling method for bridge tower reinforcing steel bar component

Abstract

The invention provides a bridge tower reinforcing steel bar component virtual pre-assembling method, which comprises the following steps of: performing data analysis and modeling on contour point coordinates of upper, middle and lower sections of each section of a reinforcing steel bar component by adopting a BIM tool to form theoretical coordinate point data, and considering influence factors of a monitoring pre-bias value and concrete pouring deformation in a current actual construction stage to obtain a pre-assembling result of the reinforcing steel bar component; forming a segment theoretical data model; building an actually measured data model, acquiring forming precision information of the reinforcing steel bar component through various technologies of a total station, three-dimensional laser and image vision, and importing the forming precision information into a digital system to build a real-time data model; real-time comparative analysis is conducted, deviation visualization is achieved through comparative analysis of a theoretical model and a real-time model, size data of the next section of steel bar component is predicted through a special algorithm, the forming process of the next section of steel bar component is guided, and dynamic adjustment of production data is achieved; a set of tower column steel bar component virtual pre-assembly software is researched and developed, all the functions are integrated, and digital virtual construction is achieved.

Description

Technical Field

[0001] The invention relates to the field of virtual pre-assembly of steel bar components, and in particular to a virtual pre-assembly method for steel bar components of a bridge tower. Background Art

[0002] For complex steel components, in order to ensure that on-site installation can proceed accurately and smoothly, it is often necessary to pre-assemble the physical components so that when deviations occur in the components, timely rectification can be carried out and the cumulative error can be reduced. The pre-assembly of physical components not only consumes a lot of manpower, material resources, transportation, site and other resources, but also puts higher requirements on the project progress management. Pre-assembly operations not only require a large site, the detection process is cumbersome, the measurement time is long, the detection cost is high, and the detection accuracy is relatively low. There are also certain safety hazards during the operation. With the development of measurement technology and computer technology, three-dimensional pre-assembly technology represented by digitization and virtualization has become the development direction. Summary of the invention

[0003] The main purpose of the present invention is to provide a virtual pre-assembly method for bridge tower steel bar components, which realizes quality digitization, deviation visualization and construction standardization by applying digital construction and virtual pre-assembly technology of steel bar components.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a virtual pre-assembly method of bridge tower steel bar components, the method comprising: S1. Use BIM tools to analyze and model the contour point coordinates of the upper, middle and lower sections of each segment of the steel bar component to form theoretical coordinate point data, and consider the monitoring pre-bias value and concrete pouring deformation in the current actual construction stage to form a segment theoretical data model; S2. Build a measured data model, collect the forming accuracy information of steel bar parts through various technologies such as total station, 3D laser and image vision, and import it into the digital system to build a real-time data model; S3, real-time comparative analysis, through comparative analysis of theoretical models and real-time models, deviation visualization is achieved, and the size data of the next section of steel bar parts is predicted through a special algorithm to guide the forming process of the next section of steel bar parts and realize dynamic adjustment of production data; S4. Develop a set of virtual pre-assembly software for tower column reinforcement components, integrating all the above functions to realize digital virtual construction.

[0005] In the preferred embodiment, the theoretical point data acquisition includes: Monitor the pre-bias value: Consider the tower limb line shape, and obtain the pre-bias value of the control point through simulation analysis through MIDAS / CIVIL; Concrete pouring deformation: The deformation value is obtained by measuring the coordinates of the control points before and after pouring with a total station; Deformation of steel bar components after hoisting: The deformation value of steel bar components after hoisting is obtained by simulation analysis using MIDAS / CIVIL; Adjustment value: used to guide the construction of the next segment reinforcement components.

[0006] In the preferred solution, the measured data includes: The measured coordinates of the eight control points at the top and bottom of the steel bar components to control the overall linear shape and posture are obtained through the total station; The coordinates of the main reinforcement used to control the jointing of steel bar parts are obtained through scanners or drones; After the total station, scanner and drone acquire data, the data is processed, and the measured coordinates of the eight control points that control the steel bar components and the coordinates of the main bars connected to the steel bar components are extracted to obtain the measured data.

[0007] In the preferred solution, the specific steps of collecting measured data are: A1. Standardize the measured coordinate data of the eight control points obtained by the total station and the main reinforcement coordinate data obtained by the scanner or drone. For the data obtained by the total station, the coordinates of the eight control points are , calculate the mean of each coordinate component , , , and then each coordinate is standardized. The standardized coordinates are , , , .

[0008] For the main reinforcement coordinate data obtained by scanner or drone, the coordinates of a single main reinforcement are , calculate its distance to the origin of the coordinate system , and then normalize it. The normalized coordinates are ; A2. Calculate the weights of total station data and scanner / drone data in fusion. The weight of total station data is , the weight of scanner / drone data is ,and ; The formula for calculating weight is , ,in , , To adjust the parameters, adjust them based on experience or actual data; A3. Fuse the standardized total station data with the scanner / drone data. The coordinate data of a single main reinforcement after fusion is: , perform fusion operation on each main reinforcement coordinate separately; A4. Restore the fused data to obtain the measured data used to control the steel bar components. For the data of the eight control points, the restored coordinates are: , , , , for the main reinforcement coordinates, the restored coordinates are .

[0009] These restored coordinate data are extracted as the final measured data for subsequent analysis and processing.

[0010] In the preferred embodiment, the specific steps in step S3 are: S31, Theoretical Model The coordinates of the control points are , the coordinates of the corresponding control points in the real-time model are , , is the number of control points; Calculate the spatial deviation vector for each control point ; Extract the magnitude of the deviation vector As a deviation size feature; Calculate the cosine value of the angle between the deviation vector and the coordinate axis as the direction feature; Take the axis as an example, , and similarly Cosine of axis angle and with Cosine of axis angle ; Among them, the deviation vector formula It directly reflects the position difference between the theoretical point and the real-time point in space, and is the basis for subsequent analysis of deviations; Deviation modulus formula Based on the Euclidean distance formula, it is used to quantify the deviation size; The direction cosine formula is Used to describe the direction of the deviation vector relative to the coordinate axis, providing a basis for subsequent analysis considering directional factors; S32. Define deviation visualization function ;set up ,in , , , To adjust the parameters; according to The value of maps the deviation to a visual color or brightness range; S33. Collect deviation feature data of multiple groups of historical segments , , is the number of historical segments; The prediction vector of the next segment steel bar component size is , the size vector of the next segment in the theoretical model is ; Building a predictive model: First, the deviation feature data is normalized; let the normalized deviation modulus be , the normalized direction cosine values ​​are , , ; The prediction model then takes the following form: ; ; ; in , , is the weight coefficient, , , is the bias term; these weight coefficients and bias terms are learned by minimizing the prediction error function; The deviation feature data normalization formula maps the deviation modulus and direction cosine value to the [0, 1] interval, which is convenient for subsequent model training and weight calculation; The prediction model formula predicts the next segment size by considering the size and direction of the historical deviation feature data; Among them, the weight coefficient , , and the bias term , , Adjust by learning from historical data to adapt to different deviation patterns and characteristics of rebar parts.

[0011] In the optimal solution, after comparative analysis of the theoretical model and the real-time model, the measured point and theoretical point matching algorithm EOPA was first used, and then the virtual assembly alignment algorithm GPA was used to develop virtual pre-assembly software, and single-component and multi-component virtual matching analysis was carried out.

[0012] In the preferred embodiment, the steps of the virtual assembly enhanced matching algorithm are: B1. Establishment of optimization objective function, i.e. EOPA algorithm optimization: Assume the theoretical point is The measured point is , , is the number of control points; Define a new distance metric function , this function comprehensively considers the relationship between the coordinate difference and the sum of the squares of the coordinates themselves; Establishing the optimization objective function ,in To adjust the parameters; Distance metric function By performing a special normalization on the coordinate difference, the size information of the coordinate itself is taken into account, making the distance measurement more reasonable and comprehensive; this special form helps to more accurately measure the difference between theoretical points and measured points under coordinates of different scales; Optimizing the objective function Based on the exponential function and distance metric function, when The smaller it is, the larger the value of the exponential function is. The smaller the value, the better the matching effect. The sensitivity of this matching level can be controlled; B2. Virtual assembly alignment GPA algorithm optimization: The original control point matrix is ( ), the target point matrix is ( ); Compute the weighted covariance matrix ,in , and They are and The fundamental mean matrix of is a point calculated based on the above distance metric function To the mean The distance To adjust the parameters; The weighted covariance matrix Perform singular value decomposition , get the optimal rotation matrix ; Calculate the optimal translation vector ; Weighted covariance matrix formula By introducing weight , adjust the contribution of the control point in the covariance calculation according to its distance from the mean, so that the algorithm pays more attention to the control points far from the mean, improving the accuracy of position calculation; Singular value decomposition formula Used to find the optimal rotation matrix , the matrix decomposition method is used to obtain the optimal rotation relationship; Optimal translation vector formula Used to calculate the translation of the control point from the original position to the target position; B3. Obtain the optimal rotation matrix in the above calculation and translation vectors On the basis of , we further consider the consistency of the directions of the measured points and theoretical points; let the direction vectors of the measured points and theoretical points be and ,in , , , and similarly for the theoretical point; Calculate the directional consistency index , which indicates the similarity between the directions of the measured points and the theoretical points at adjacent points; Translation vector Make adjustments, and the adjusted translation vector ,in is the weight coefficient, is the mean of the directional consistency index; Finally, the translation matrix is ​​obtained and translation matrix ,in is the adjusted translation vector The translation matrix composed of homogeneous coordinate matrix form, expanding the three-dimensional translation vector to the homogeneous coordinate space); Directional consistency index formula By calculating the ratio of the vector dot product to the vector modulus, the similarity between the directions of the measured points and the theoretical points at adjacent points is obtained, providing a basis for adjusting the translation vector. Translation vector adjustment formula The translation vector is adjusted according to the difference in the directional consistency index, so that the translation control point is more consistent with the requirements of the theoretical point in direction; by introducing the weight coefficient and mean , the degree and direction of adjustment can be flexibly controlled.

[0013] In the preferred solution, based on the above results, multi-working condition comparative analysis, component precision analysis, and virtual assembly precision analysis are carried out.

[0014] In the preferred solution, C1, multi-condition comparative analysis: Define the working condition similarity function , used to measure two working conditions and The degree of similarity between ,in To adjust the parameters; The working conditions are classified according to the value of the working condition similarity function; When and similar; when When and There is a certain similarity; when When and The difference is large; C2. Calculate the accuracy of parts , ,in To adjust the parameters; Evaluate component accuracy based on the value of component accuracy index; when When the part is considered to have high precision; when When the part accuracy is medium, When the part is considered to have low precision; The component accuracy index formula is to perform an exponential weighted summation of the deviation size of each control point under each working condition, and then average it to obtain a comprehensive accuracy index; adjust the parameters The weight of the deviation size in the accuracy index calculation can be controlled; C3. The coordinates of the control points after virtual assembly are , calculate the virtual assembly deviation vector ; Extract the modulus length of the virtual assembly deviation vector As a virtual assembly deviation size feature; Calculate the virtual assembly accuracy index ,in To adjust the parameters; The virtual assembly accuracy is evaluated according to the value of the virtual assembly accuracy index; When , it is considered that the virtual assembly accuracy is high; when When , the virtual assembly accuracy is considered medium; when When , it is considered that the virtual assembly accuracy is low; Virtual assembly deviation vector formula It reflects the deviation between the control point and the original coordinates after virtual assembly, which is the basis for the subsequent analysis of virtual assembly accuracy; Virtual assembly deviation module length formula Based on the Euclidean distance formula, it is used to quantify the size of virtual assembly deviation.

[0015] The present invention provides a virtual pre-assembly method for bridge tower steel bar parts. Compared with the pre-assembly of physical components, it avoids the consumption of a large amount of manpower, material resources, transportation, site and other resources, and reduces the requirements for project progress management. It does not require a large site, simplifies the detection process, and may shorten the measurement time and reduce the detection cost.

[0016] By adopting a variety of advanced technologies and algorithms, using BIM tools for modeling, total stations and other data collection, and considering a variety of influencing factors, higher accuracy can be achieved. It can form accurate theoretical and measured data models and conduct real-time comparative analysis, visualize deviations, and help to discover and correct problems in a timely manner.

[0017] It realizes various functions on the tower column reinforcement structure, including automatic calculation of virtual assembly data, automatic assembly of measured data, automatic correction of various related factors of virtual assembly, display of error exceeding limit, data report output, free measurement and display of virtual assembly data, etc.

[0018] The special algorithm is used to predict the size data of the next segment of steel bar parts, guide the forming process, realize dynamic adjustment of production data, and ensure construction quality and installation accuracy. On-site assembly problems are discovered in advance, deviations are corrected in time, and smooth installation is ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 This is a technical flow chart of the digital pre-assembly of steel bar parts of the present invention; Figure 2 It is the Midas / civil simulation analysis result diagram of the present invention; Figure 3 It is a diagram of the Midas / civil simulation analysis result of the present invention; Figure 4 This is a single component accuracy analysis diagram of the present invention; Figure 5 It is a multi-component virtual assembly analysis diagram of the present invention; Figure 6 It is a statistical analysis diagram of error data of the present invention; DETAILED DESCRIPTION Example 1 like Figures 1 to 6 As shown, a virtual pre-assembly method for bridge tower steel bar components comprises: S1. Use BIM tools to analyze and model the contour point coordinates of the upper, middle and lower sections of each segment of the steel bar component to form theoretical coordinate point data, and consider the monitoring pre-bias value and concrete pouring deformation in the current actual construction stage to form a segment theoretical data model; S2. Build a measured data model, collect the forming accuracy information of steel bar parts through various technologies such as total station, 3D laser and image vision, and import it into the digital system to build a real-time data model; S3, real-time comparative analysis, through comparative analysis of theoretical models and real-time models, deviation visualization is achieved, and the size data of the next section of steel bar parts is predicted through a special algorithm to guide the forming process of the next section of steel bar parts and realize dynamic adjustment of production data; S4. Develop a set of virtual pre-assembly software for tower column reinforcement components, integrating all the above functions to realize digital virtual construction.

[0020] In the preferred embodiment, the theoretical point data acquisition includes: Monitor the pre-bias value: Consider the tower limb line shape, and use MIDAS / CIVIL to perform simulation analysis to obtain the pre-bias value of the control point; Figure 2 shown.

[0021] Concrete pouring deformation: The deformation value is obtained by measuring the coordinates of the control points before and after pouring with a total station; Deformation of steel bar components after hoisting: The deformation value of steel bar components after hoisting is obtained by simulation analysis using MIDAS / CIVIL; Figure 3 shown.

[0022] Adjustment value: used to guide the construction of the next segment reinforcement components.

[0023] Example 2 Further illustrate with reference to Example 1, Figure 1 The structure shown, the measured data includes: The measured coordinates of the eight control points at the top and bottom of the steel bar components to control the overall linear shape and posture are obtained through the total station; The coordinates of the main reinforcement used to control the jointing of steel bar parts are obtained through scanners or drones; After the total station, scanner and drone acquire data, the data is processed, and the measured coordinates of the eight control points that control the steel bar components and the coordinates of the main bars connected to the steel bar components are extracted to obtain the measured data.

[0024] In the preferred solution, the specific steps of collecting measured data are: A1. Standardize the measured coordinate data of the eight control points obtained by the total station and the main reinforcement coordinate data obtained by the scanner or drone. For the data obtained by the total station, the coordinates of the eight control points are , calculate the mean of each coordinate component , , , and then each coordinate is standardized. The standardized coordinates are , , , .

[0025] For the main reinforcement coordinate data obtained by scanner or drone, the coordinates of a single main reinforcement are , calculate its distance to the origin of the coordinate system , and then normalize it. The normalized coordinates are ; Mean formula The coordinate data are centrally located to make them more comparable and stable. This can eliminate the systematic deviation caused by different measurement locations.

[0026] Standardization formula It is based on statistical principles to convert data into a relatively uniform scale to facilitate subsequent calculations and analysis.

[0027] A2. Calculate the weights of total station data and scanner / drone data in fusion. The weight of total station data is , the weight of scanner / drone data is ,and ; The formula for calculating weight is , ,in , , To adjust the parameters, adjust them based on experience or actual data; The weight formula is based on a nonlinear mapping relationship, and uses an exponential function to dynamically assign weights based on a certain "information content" of the total station data (here measured by the sum of the absolute values ​​of the coordinates). When the coordinate changes of the total station data are large (i.e., the amount of information is large), will be larger, giving more weight to the total station data; conversely, Will be relatively large. Adjustment parameters , , The sensitivity of this mapping can be controlled.

[0028] A3. Fuse the standardized total station data with the scanner / drone data. The coordinate data of a single main reinforcement after fusion is: , perform fusion operation on each main reinforcement coordinate separately; A4. Restore the fused data to obtain the measured data used to control the steel bar components. For the data of the eight control points, the restored coordinates are: , , , , for the main reinforcement coordinates, the restored coordinates are .

[0029] These restored coordinate data are extracted as the final measured data for subsequent analysis and processing.

[0030] Example 3 Further illustrate with reference to Example 1, Figure 1 The structure shown in FIG. 1 , the specific steps in step S3 are: S31, Theoretical Model The coordinates of the control points are , the coordinates of the corresponding control points in the real-time model are , , is the number of control points; Calculate the spatial deviation vector for each control point ; Extract the magnitude of the deviation vector As a deviation size feature; Calculate the cosine value of the angle between the deviation vector and the coordinate axis as the direction feature; Take the axis as an example, , and similarly Cosine of axis angle and with Cosine of axis angle ; Among them, the deviation vector formula It directly reflects the position difference between the theoretical point and the real-time point in space, and is the basis for subsequent analysis of deviations; Deviation modulus formula Based on the Euclidean distance formula, it is used to quantify the deviation size; The direction cosine formula is Used to describe the direction of the deviation vector relative to the coordinate axis, providing a basis for subsequent analysis considering directional factors; S32. Define deviation visualization function ;set up ,in , , , To adjust the parameters; according to The value of maps the deviation to a visualized color or brightness range; when When , green is used to indicate a smaller deviation; when When , yellow is used to indicate that the deviation is moderate; when , red is used to indicate a large deviation.

[0031] The deviation visualization function formula maps the deviation vector to a value in the interval [0, 1] through a nonlinear function that includes the deviation size and direction factors, so as to facilitate visual classification according to the set threshold. Adjustment parameters , , , You can control how the size and direction of the deviations are weighted in the visualization map.

[0032] S33. Collect deviation feature data of multiple groups of historical segments , , is the number of historical segments; The prediction vector of the next segment steel bar component size is , the size vector of the next segment in the theoretical model is ; Building a predictive model: First, the deviation feature data is normalized; let the normalized deviation modulus be , the normalized direction cosine values ​​are , , ; The prediction model then takes the following form: ; ; ; in , , is the weight coefficient, , , is the bias term; these weight coefficients and bias terms are learned by minimizing the prediction error function; The deviation feature data normalization formula maps the deviation modulus and direction cosine value to the [0, 1] interval, which is convenient for subsequent model training and weight calculation; The prediction model formula predicts the next segment size by considering the size and direction of the historical deviation feature data; Among them, the weight coefficient , , and the bias term , , Adjust by learning from historical data to adapt to different deviation patterns and characteristics of rebar parts.

[0033] Example 3 Further illustrate with reference to Example 1, Figure 1 For the structure shown in the figure, after comparing and analyzing the theoretical model and the real-time model, the measured point and theoretical point matching algorithm EOPA was first adopted, and then the virtual assembly alignment algorithm GPA was adopted to develop the virtual pre-assembly software, and carry out single-component and multi-component virtual matching analysis.

[0034] In the preferred embodiment, the steps of the virtual assembly enhanced matching algorithm are: B1. Establishment of optimization objective function, i.e. EOPA algorithm optimization: Assume the theoretical point is The measured point is , , is the number of control points; Define a new distance metric function , this function comprehensively considers the relationship between the coordinate difference and the sum of the squares of the coordinates themselves; Establishing the optimization objective function ,in To adjust the parameters; Distance metric function By performing a special normalization on the coordinate difference, the size information of the coordinate itself is taken into account, making the distance measurement more reasonable and comprehensive; this special form helps to more accurately measure the difference between theoretical points and measured points under coordinates of different scales; Optimizing the objective function Based on the exponential function and distance metric function, when The smaller it is, the larger the value of the exponential function is. The smaller the value, the better the matching effect. The sensitivity of this matching level can be controlled; B2. Virtual assembly alignment GPA algorithm optimization: The original control point matrix is ( ), the target point matrix is ( ); Compute the weighted covariance matrix ,in , and They are and The fundamental mean matrix of is a point calculated based on the above distance metric function To the mean The distance To adjust the parameters; The weighted covariance matrix Perform singular value decomposition , get the optimal rotation matrix ; Calculate the optimal translation vector ; Weighted covariance matrix formula By introducing weight , adjust the contribution of the control point in the covariance calculation according to its distance from the mean, so that the algorithm pays more attention to the control points far from the mean, improving the accuracy of position calculation; Singular value decomposition formula Used to find the optimal rotation matrix , the matrix decomposition method is used to obtain the optimal rotation relationship; Optimal translation vector formula Used to calculate the translation of the control point from the original position to the target position; B3. Obtain the optimal rotation matrix in the above calculation and translation vectors On the basis of , we further consider the consistency of the directions of the measured points and theoretical points; let the direction vectors of the measured points and theoretical points be and ,in , , , and similarly for the theoretical point; Calculate the directional consistency index , which indicates the similarity between the directions of the measured points and the theoretical points at adjacent points; Translation vector Make adjustments, and the adjusted translation vector ,in is the weight coefficient, is the mean of the directional consistency index; Finally, the translation matrix is ​​obtained and translation matrix ,in is the adjusted translation vector The translation matrix composed of homogeneous coordinate matrix form, expanding the three-dimensional translation vector to the homogeneous coordinate space); Directional consistency index formula By calculating the ratio of the vector dot product to the vector modulus, the similarity between the directions of the measured points and the theoretical points at adjacent points is obtained, providing a basis for adjusting the translation vector. Translation vector adjustment formula The translation vector is adjusted according to the difference in the directional consistency index, so that the translation control point is more consistent with the requirements of the theoretical point in direction; by introducing the weight coefficient and mean , the degree and direction of adjustment can be flexibly controlled.

[0035] In the preferred solution, based on the above results, multi-working condition comparative analysis, component precision analysis, and virtual assembly precision analysis are carried out.

[0036] C1. Comparative analysis of multiple working conditions: Define the working condition similarity function , used to measure two working conditions and The degree of similarity between ,in To adjust the parameters; The working conditions are classified according to the value of the working condition similarity function; When and similar; when When and There is a certain similarity; when When and The difference is large; C2. Calculate the accuracy of parts , ,in To adjust the parameters; Evaluate component accuracy based on the value of component accuracy index; when When the part is considered to have high precision; when When the part accuracy is medium, When the part is considered to have low precision; The component accuracy index formula is to perform an exponential weighted summation of the deviation size of each control point under each working condition, and then average it to obtain a comprehensive accuracy index; adjust the parameters The weight of the deviation size in the accuracy index calculation can be controlled; C3. The coordinates of the control points after virtual assembly are , calculate the virtual assembly deviation vector ; Extract the modulus length of the virtual assembly deviation vector As a virtual assembly deviation size feature; Calculate the virtual assembly accuracy index ,in To adjust the parameters; The virtual assembly accuracy is evaluated according to the value of the virtual assembly accuracy index; When , it is considered that the virtual assembly accuracy is high; when When , the virtual assembly accuracy is considered medium; when When , it is considered that the virtual assembly accuracy is low; Virtual assembly deviation vector formula It reflects the deviation between the control point and the original coordinates after virtual assembly, which is the basis for the subsequent analysis of virtual assembly accuracy; Virtual assembly deviation module length formula Based on the Euclidean distance formula, it is used to quantify the size of virtual assembly deviation.

[0037] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A virtual pre-assembly method for bridge tower steel bar components, characterized by: The method includes: S1. Use BIM tools to analyze and model the contour point coordinates of the upper, middle and lower sections of each segment of the steel bar component to form theoretical coordinate point data, and consider the monitoring pre-bias value and concrete pouring deformation in the current actual construction stage to form a segment theoretical data model; S2. Build a measured data model, collect the forming accuracy information of steel bar parts through various technologies such as total station, 3D laser and image vision, and import it into the digital system to build a real-time data model; S3, real-time comparative analysis, through comparative analysis of theoretical models and real-time models, deviation visualization is achieved, and the size data of the next section of steel bar parts is predicted through a special algorithm to guide the forming process of the next section of steel bar parts and realize dynamic adjustment of production data; S4. Develop a set of virtual pre-assembly software for tower column reinforcement components, integrating all the above functions to realize digital virtual construction.

2. A virtual pre-assembly method for bridge tower steel bar components according to claim 1, characterized in that: Theoretical point data acquisition includes: Monitor the pre-bias value: Consider the tower limb line shape, and obtain the pre-bias value of the control point through simulation analysis through MIDAS / CIVIL; Concrete pouring deformation: The deformation value is obtained by measuring the coordinates of the control points before and after pouring with a total station; Deformation of steel bar components after hoisting: The deformation value of steel bar components after hoisting is obtained by simulation analysis using MIDAS / CIVIL; Adjustment value: used to guide the construction of the next segment reinforcement components.

3. According to claim 1, a virtual pre-assembly method for bridge tower steel bar components is characterized by: The data includes: The measured coordinates of the eight control points at the top and bottom of the steel bar components to control the overall linear shape and posture are obtained through the total station; The coordinates of the main reinforcement used to control the jointing of steel bar parts are obtained through scanners or drones; After the total station, scanner and drone acquire data, the data is processed, and the measured coordinates of the eight control points that control the steel bar components and the coordinates of the main bars connected to the steel bar components are extracted to obtain the measured data.

4. According to claim 3, a virtual pre-assembly method for bridge tower steel bar components is characterized by: The specific steps of collection are: A1. Standardize the measured coordinate data of the eight control points obtained by the total station and the main reinforcement coordinate data obtained by the scanner or drone. For the data obtained by the total station, the coordinates of the eight control points are , calculate the mean of each coordinate component , , , and then each coordinate is standardized. The standardized coordinates are , , , ; For the main reinforcement coordinate data obtained by scanner or drone, the coordinates of a single main reinforcement are , calculate its distance to the origin of the coordinate system , and then normalize it. The normalized coordinates are ; A2. Calculate the weights of total station data and scanner / drone data in fusion. The weight of total station data is , the weight of scanner / drone data is ,and ; The formula for calculating weight is , ,in , , To adjust the parameters, adjust them based on experience or actual data; A3. Fuse the standardized total station data with the scanner / drone data. The coordinate data of a single main reinforcement after fusion is: , perform fusion operation on each main reinforcement coordinate separately; A4. Restore the fused data to obtain the measured data used to control the steel bar components. For the data of the eight control points, the restored coordinates are: , , , , for the main reinforcement coordinates, the restored coordinates are ; These restored coordinate data are extracted as the final measured data for subsequent analysis and processing.

5. According to claim 1, a virtual pre-assembly method for bridge tower steel bar components is characterized by: The specific steps in step S3 are: S31, Theoretical Model The coordinates of the control points are , the coordinates of the corresponding control points in the real-time model are , , is the number of control points; Calculate the spatial deviation vector for each control point ; Extract the magnitude of the deviation vector As a deviation size feature; Calculate the cosine value of the angle between the deviation vector and the coordinate axis as the direction feature; by Take the axis as an example, , and similarly Cosine of axis angle and with Cosine of axis angle ; Among them, the deviation vector formula It directly reflects the position difference between the theoretical point and the real-time point in space, and is the basis for subsequent analysis of deviations; Deviation modulus formula Based on the Euclidean distance formula, it is used to quantify the deviation size; The direction cosine formula is Used to describe the direction of the deviation vector relative to the coordinate axis, providing a basis for subsequent analysis considering directional factors; S32. Define deviation visualization function ;set up ,in , , , To adjust the parameters; according to The value of maps the deviation to a visual color or brightness range; S33. Collect deviation feature data of multiple groups of historical segments , , is the number of historical segments; The prediction vector of the next segment steel bar component size is , the size vector of the next segment in the theoretical model is ; Building a predictive model: First, the deviation feature data is normalized; let the normalized deviation modulus be , the normalized direction cosine values ​​are , , ; The prediction model then takes the following form: ； ； ； in , , is the weight coefficient, , , is the bias term; these weight coefficients and bias terms are learned by minimizing the prediction error function; The deviation feature data normalization formula maps the deviation modulus and direction cosine value to the [0, 1] interval, which is convenient for subsequent model training and weight calculation; The prediction model formula predicts the next segment size by considering the size and direction of the historical deviation feature data; Among them, the weight coefficient , , and the bias term , , Adjust by learning from historical data to adapt to different deviation patterns and characteristics of rebar parts.

6. A virtual pre-assembly method for bridge tower reinforcement components according to claim 5, characterized in that: After comparing and analyzing the theoretical model and the real-time model, the measured point and theoretical point matching algorithm EOPA was first used, and then the virtual assembly alignment algorithm GPA was used to develop virtual pre-assembly software, and single-component and multi-component virtual matching analysis was carried out.

7. A virtual pre-assembly method for bridge tower reinforcement components according to claim 6, characterized in that: The steps of virtual assembly enhanced matching algorithm are as follows: B1. Establishment of optimization objective function, i.e. EOPA algorithm optimization: Assume the theoretical point is The measured point is , , is the number of control points; Define a new distance metric function , this function comprehensively considers the relationship between the coordinate difference and the sum of the squares of the coordinates themselves; Establishing the optimization objective function ,in To adjust the parameters; Distance metric function By performing a special normalization on the coordinate difference, the size information of the coordinate itself is taken into account, making the distance measurement more reasonable and comprehensive; this special form helps to more accurately measure the difference between theoretical points and measured points under coordinates of different scales; Optimizing the objective function Based on the exponential function and distance metric function, when The smaller it is, the larger the value of the exponential function is. The smaller the value, the better the matching effect. The sensitivity of this matching level can be controlled; B2. Virtual assembly alignment GPA algorithm optimization: The original control point matrix is ( ), the target point matrix is ( ); Compute the weighted covariance matrix ,in , and They are and The fundamental mean matrix of is a point calculated based on the above distance metric function To the mean The distance To adjust the parameters; The weighted covariance matrix Perform singular value decomposition , get the optimal rotation matrix ; Calculate the optimal translation vector ; Weighted covariance matrix formula By introducing weight , adjust the contribution of the control point in the covariance calculation according to its distance from the mean, so that the algorithm pays more attention to the control points far from the mean, improving the accuracy of position calculation; Singular value decomposition formula Used to find the optimal rotation matrix , the matrix decomposition method is used to obtain the optimal rotation relationship; Optimal translation vector formula Used to calculate the translation of the control point from the original position to the target position; B3. Obtain the optimal rotation matrix in the above calculation and translation vectors On the basis of , we further consider the consistency of the directions of the measured points and theoretical points; let the direction vectors of the measured points and theoretical points be and ,in , , , and similarly for the theoretical point; Calculate the directional consistency index , which indicates the similarity between the directions of the measured points and the theoretical points at adjacent points; Translation vector Make adjustments, and the adjusted translation vector ,in is the weight coefficient, is the mean of the directional consistency index; Finally, the translation matrix is ​​obtained and translation matrix ,in is the adjusted translation vector The translation matrix composed of homogeneous coordinate matrix form, expanding the three-dimensional translation vector to the homogeneous coordinate space); Directional consistency index formula By calculating the ratio of the vector dot product to the vector modulus, the similarity between the directions of the measured points and the theoretical points at adjacent points is obtained, providing a basis for adjusting the translation vector. Translation vector adjustment formula The translation vector is adjusted according to the difference in the directional consistency index, so that the translation control point is more consistent with the requirements of the theoretical point in direction; by introducing the weight coefficient and mean , the degree and direction of adjustment can be flexibly controlled.

8. A virtual pre-assembly method for bridge tower reinforcement components according to claim 7, characterized in that: Based on the above results, multi-working condition comparative analysis, component precision analysis, and virtual assembly precision analysis were carried out.

9. A virtual pre-assembly method for bridge tower reinforcement components according to claim 8, characterized in that: C1. Comparative analysis of multiple working conditions: Define the working condition similarity function , used to measure two working conditions and The degree of similarity between ,in To adjust the parameters; The working conditions are classified according to the value of the working condition similarity function; When and similar; when When and There is a certain similarity; when When and The difference is large; C2. Calculate the accuracy of parts , ,in To adjust the parameters; Evaluate component accuracy based on the value of component accuracy index; when When the part is considered to have high precision; when When the part accuracy is medium, When the part is considered to have low precision; The component accuracy index formula is to perform an exponential weighted summation of the deviation size of each control point under each working condition, and then average it to obtain a comprehensive accuracy index; adjust the parameters The weight of the deviation size in the accuracy index calculation can be controlled; C3. The coordinates of the control points after virtual assembly are , calculate the virtual assembly deviation vector ; Extract the modulus length of the virtual assembly deviation vector As a virtual assembly deviation size feature; Calculate the virtual assembly accuracy index ,in To adjust the parameters; The virtual assembly accuracy is evaluated according to the value of the virtual assembly accuracy index; When , it is considered that the virtual assembly accuracy is high; when When , the virtual assembly accuracy is considered medium; when When , it is considered that the virtual assembly accuracy is low; Virtual assembly deviation vector formula It reflects the deviation between the control point and the original coordinates after virtual assembly, which is the basis for the subsequent analysis of virtual assembly accuracy; Virtual assembly deviation module length formula Based on the Euclidean distance formula, it is used to quantify the size of virtual assembly deviation.

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