Bridge girder segment point cloud data model acquisition method

Abstract

The invention relates to a bridge girder segment point cloud data model acquisition method, which comprises the following steps of: based on initial point cloud data xA of a bridge girder segment in an initial state, acquiring an initial sampling point set # imgabs0 # and an initial covariance matrix PA corresponding to the initial point cloud data; based on initial environment data eA in an initial state of an initial sampling point set # imgabs1 # and actually measured environment data eB in a bridge construction state, according to a physical model, obtaining a predicted sampling point set # imgabs2 # in the bridge construction state, and based on the predicted sampling point set # imgabs3 #, obtaining a predicted point cloud data model of a bridge truss section in the bridge construction state, the predicted point cloud data model comprises predicted point cloud data # imgabs4 # and a predicted covariance matrix PB; and based on the error between the predicted point cloud data # imgabs5 # and the actual point cloud data yB of the bridge truss section in the bridge construction state and the initial covariance matrix PA, correcting the predicted covariance matrix PB to obtain an actual point cloud data model of the bridge truss section. According to the method, measurement errors caused by environmental factors are overcome, and high precision and reliability of truss reverse modeling are ensured.

Description

Technical Field

[0001] This application relates to the field of bridge construction, and particularly to a method for obtaining a point cloud data model of a bridge truss segment. Background Art

[0002] Traditional bridge truss segment modeling methods mainly rely on manual measurement and empirical operation, using traditional measurement tools such as total stations and levels for positioning and modeling.

[0003] Existing methods for scanning truss segments with laser scanners have problems with insufficient accuracy. Although laser scanners can cover a large measurement range, when capturing small details such as the ends of members or complex geometries, the limitations of their spatial resolution and the accumulation of long-distance measurement errors may lead to the loss of key information and deviations in measurement results. Especially on bridge truss segments with complex structures and multiple mutually occluding components, the poor adaptability of laser scanners further affects the overall measurement quality. Therefore, although laser scanners are convenient in some scenarios, in projects aiming for high-precision bridge truss segment modeling, their disadvantage of insufficient accuracy is obvious, and a higher-precision measurement scheme is needed to meet the engineering requirements.

[0004] In addition, the influence of environmental factors such as temperature, humidity, and wind on measurement results is often ignored in traditional modeling methods. Changes in these factors may lead to inaccurate measurement data, further affecting the modeling accuracy. Temperature changes can cause thermal expansion or contraction of materials, and changes in humidity and wind may affect the stability of measurement equipment and data accuracy, resulting in the accumulation of errors between the measurement equipment and the object being measured, and affecting the accuracy and consistency of the overall model. Summary of the Invention

[0005] This application provides a method for obtaining a point cloud data model of a bridge truss segment, which can solve the problem in related technologies that the change of environmental factors is not considered during modeling, affecting the modeling accuracy.

[0006] An embodiment of this application provides a method for obtaining a point cloud data model of a bridge truss segment, which includes:

[0007] Based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A ;

[0008] Based on the initial sampling point set the initial environmental data e in the initial state A and the measured environmental data e in the bridge construction state B , and according to the physical model, obtain the predicted sampling point set in the bridge construction state

[0009] Based on the predicted sampling point set Obtain the predicted point cloud data model of the bridge truss segment under the bridge construction state. The predicted point cloud data model includes predicted point cloud data and the predicted covariance matrix P B ;

[0010] Based on the predicted point cloud data and the actual point cloud data y of the bridge truss segment under the bridge construction state B between the errors and the initial covariance matrix P A , correct the predicted covariance matrix P B , and obtain the actual point cloud data model of the bridge truss segment.

[0011] In some embodiments, based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A , specifically including:

[0012] Based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial covariance matrix P A ;

[0013] Through unscented transformation, and based on the initial point cloud data x A and the initial covariance matrix P A , generate the initial sampling point set

[0014] In some embodiments, based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial covariance matrix P A , specifically including:

[0015] Based on the initial point cloud data x of the bridge truss segment in the initial state A , calculate the mean vector μ A ;

[0016] Based on the initial point cloud data x A and the mean vector μ A , calculate the initial covariance matrix P A .

[0017] In some embodiments, x A =(x i ,y i ,z i );

[0018]

[0019] Among them, n is the dimension of the initial point cloud data.

[0020] In some embodiments, the initial sampling point set includes:

[0021] Center point X 0 and extended point X i 、X i+n ;

[0022] Among them, X 0 = x A ,

[0023] Among them, i = 1, 2,..., n, L is the square root matrix of the initial covariance matrix P A , L i is the i-th column of the matrix L, n is the dimension of the initial point cloud data x A , and λ is an adjustment parameter.

[0024] In some embodiments, the initial environmental data e A includes the average initial temperature average initial humidity average initial wind speed

[0025] The measured environmental data e B includes the average measured temperature average measured humidity average measured wind speed

[0026]

[0027] In some embodiments, based on the predicted sampling point set obtain the predicted point cloud data model of the bridge truss segment under the bridge construction state, specifically including:

[0028] Based on the predicted sampling point set and the weight W of the predicted sampling point set i , obtain the predicted point cloud data

[0029] Based on the predicted sampling point set predicted point cloud data predicted sampling point set weight W i and the process noise covariance Q, obtain the predicted covariance matrix P B .

[0030] In some embodiments, based on the predicted point cloud data and the actual point cloud data y of the bridge truss segment in the bridge construction state B the error between them and the initial covariance matrix P A correct the predicted covariance matrix P B The specific steps are as follows:

[0031] Based on the predicted point cloud data and the actual point cloud data y of the bridge truss segment in the bridge construction state B obtain the error vector δ B ;

[0032] Based on the error vector δ B obtain the covariance matrix of the error according to the expectation operation

[0033] Based on the covariance matrix of the error the initial covariance matrix P A and the predicted covariance matrix P B correct the predicted covariance matrix P B to obtain the corrected actual covariance matrix

[0034] In some embodiments,

[0035] wherein, the actual point cloud data model of the bridge truss segment includes the actual point cloud data x B and the actual covariance matrix

[0036] In some embodiments, based on the initial point cloud data x of the bridge truss segment in the initial state A obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A Before that, the method further includes:

[0037] Filter and denoise the collected initial point cloud data x A ;

[0038] The beneficial effects brought by the technical solutions provided by the embodiments of the present application include:

[0039] The embodiment of the present application provides a method for obtaining a point cloud data model of a bridge truss segment, which fuses the initial environmental data and the initial point cloud data of the bridge truss segment, and obtains a predicted point cloud data model based on the measured environmental data of the bridge truss segment in the bridge construction state. Then, the predicted point cloud data model is corrected based on the actual point cloud data to obtain the actual point cloud data model of the bridge truss segment, eliminating the influence of environmental factors on the modeling of the bridge truss segment. The actual point cloud data model of the bridge truss segment is used for subsequent reverse modeling and structural analysis, overcoming the measurement error caused by environmental factors and ensuring the high precision and reliability of the reverse modeling of the truss segment. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0041] Figure 1 Structural schematic diagram of a bridge truss segment and a base station scanner provided by an embodiment of the present application;

[0042] Figure 2 Flowchart of the method for obtaining the point cloud data model of the bridge truss segment provided by the embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] In order to enable those skilled in the art to better understand the solution of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0044] The embodiment of the present application provides a method for obtaining a point cloud data model of a bridge truss segment, which can solve the problem that the change of environmental factors is not considered during modeling in the related art, affecting the modeling accuracy.

[0045] See Figures 1 to 2 , the embodiment of the present application provides a method for obtaining a point cloud data model of a bridge truss segment, which includes:

[0046] 101: Based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A ;

[0047] 102: Based on the initial sampling point set Initial environmental data e in the initial state A and the measured environmental data e in the bridge construction state B , and according to the physical model, obtain the predicted sampling point set in the bridge construction state

[0048] 103: Based on the predicted sampling point set Obtain the predicted point cloud data model of the bridge truss segment in the bridge construction state. The predicted point cloud data model includes predicted point cloud data and the predicted covariance matrix P B ;

[0049] 104: Based on the predicted point cloud data and the actual point cloud data y of the bridge truss segment in the bridge construction state B The error between them and the initial covariance matrix P A , correct the predicted covariance matrix P B , and obtain the actual point cloud data model of the bridge truss segment

[0050] In this application, the initial environmental data and the initial point cloud data of the bridge truss segment are fused, and the predicted point cloud data model is obtained based on the measured environmental data of the bridge truss segment in the bridge construction state. Then, the predicted point cloud data model is corrected based on the actual point cloud data to obtain the actual point cloud data model of the bridge truss segment, eliminating the influence of environmental factors on the modeling of the bridge truss segment. The actual point cloud data model of the bridge truss segment is used for subsequent reverse modeling and structural analysis, overcoming the measurement errors caused by environmental factors and ensuring the high precision and reliability of the reverse modeling of the truss segment

[0051] In this application, the initial point cloud data x of the bridge truss segment in the initial state A refers to using a scanner to perform three-dimensional scanning on the bridge truss segment in the steel beam processing factory to ensure complete coverage of all key structural parts of the truss segment, so as to obtain detailed initial point cloud data x A . During acquisition, a base station scanner is used to scan and collect the overall information of the bridge truss segment. In this embodiment, a total of 9 stations are set for scanning during scanning ( Figure 1 at B in Figure 1 ), and the scanned content includes the overall information of the bridge truss segment and four targets around the bridge truss segment ( Figure 1 at A in Figure 1 ). Each time, the horizontal angle is set to 90° (at the corner) and 210° (at the front elevation) according to the location. The scanning quality is 4 times, the resolution is set to 1 / 2, it takes 180 minutes to complete the scanning work, and the total number of points in the obtained model is 120 million. This model can accurately reflect the appearance contour of the bridge truss segment

[0052] Initial environmental data e in the initial state ARefers to the environmental data collected in real time by temperature sensors, humidity sensors, and wind speed sensors at the steel beam processing plant, and ensures that at the initial point cloud data x A During the acquisition process, these data are continuously recorded to accurately reflect the changes in environmental conditions; the measured environmental data e under the bridge construction state B Refers to the environmental data collected in real time by temperature sensors, humidity sensors, and wind speed sensors at the bridge construction site. During the acquisition, high-precision temperature sensors (±0.1°C), high-precision humidity sensors (±2% RH), and high-precision wind speed sensors (±0.5 m / s) are installed around the bridge truss segments and calibrated, and the environmental data during the three-dimensional laser scanning are recorded, and the average value is taken to participate in the following algorithm calculation.

[0053] In order to improve the accuracy of the point cloud data model, based on the above embodiments, in this embodiment, based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A Before that, the method further includes: Step 100: Filter and denoise the collected initial point cloud data x A Perform filtering and denoising.

[0054] Specifically, that is, after the initial point cloud data x A is collected, perform filtering and denoising processing on the initial point cloud data x A using methods such as mean filtering, median filtering, or radius filtering to remove noise and outliers and improve the data quality. During the processing, first import the collected initial point cloud data x A into the processing software, perform preliminary smoothing through mean filtering to reduce noise; then use median filtering to remove sharp noise points, and then use radius filtering to clean up isolated points and outliers; subsequently, apply the ICP algorithm to accurately align the multiple scan data to form a complete three-dimensional model; finally, save and back up the processed initial point cloud data x A to ensure the integrity and security of the initial point cloud data x A and provide high-quality basic data for subsequent reverse modeling and analysis.

[0055] After performing filtering and denoising processing on the initial point cloud data x A , use the adaptive fading unscented Kalman filter algorithm (AFUKF) for the subsequent steps until the actual point cloud data model of the bridge truss segment is obtained:

[0056] Based on the above embodiments, in this embodiment, based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A , specifically including steps 1011 to 1012:

[0057] Step 1011: Based on the initial point cloud data x of the bridge truss segment in the initial state A , obtain the initial covariance matrix P A .

[0058] Among them, based on the initial point cloud data of the bridge truss segment in the initial state, obtaining the initial covariance matrix PA specifically includes: First, based on the initial point cloud data x of the bridge truss segment in the initial state A , calculate the mean vector μ A ; then based on the initial point cloud data x A and the mean vector μ A , calculate the initial covariance matrix P A .

[0059] Specifically, the point cloud data scanned in the initial state is denoted as the initial point cloud data x A , x A represents the geometric shape and position of the bridge truss segment in the initial state, and x A =(x i , y i , z i ).

[0060] Then determine the initial covariance matrix P A , the initial covariance matrix P A is the covariance matrix describing the uncertainty and measurement error of the initial point cloud data x A , where the algorithm of the covariance matrix P A is:

[0061] First, calculate the mean vector μ A of the initial point cloud data x A , the mean vector μ A represents the average value of each coordinate axis (x, y, z) in the initial point cloud data x A :

[0062]

[0063] Among them, refers to the mean value in the x direction of all initial point cloud data; refers to the mean value in the y direction of all initial point cloud data; refers to the mean value in the z direction of all initial point cloud data.

[0064] Then based on the initial point cloud data x A and the mean vector μ A, calculate the initial covariance matrix P A :

[0065] Among them, the initial covariance matrix P A is a 3×3 matrix, and its elements represent the variances and covariances between the dimensions in the initial point cloud data x A . The calculation formula is as follows:

[0066]

[0067] Among them, n is the dimension of the initial point cloud data.

[0068] Step 1012: Through unscented transformation, and based on the initial point cloud data x A and the initial covariance matrix P A , generate the initial sampling point set

[0069] Specifically, through unscented transformation, from the initial point cloud data x A and the initial covariance matrix P A generate a set of Sigma points, and this set of Sigma points is also the initial sampling point set

[0070] The said initial sampling point set includes: the center point X 0 and the extended points X i , X i+n ;

[0071] Among them, X 0 = x A ;

[0072]

[0073] Among them, i = 1, 2,..., n, L is the square root matrix of the initial covariance matrix P A , L i is the i-th column of the matrix L, n is the dimension of the initial point cloud data x A , and λ is the adjustment parameter.

[0074] On the basis of the above embodiments, in this embodiment, in step 102: Based on the initial sampling point set the initial environmental data e A in the initial state and the measured environmental data e B in the bridge construction state, and according to the physical model, obtain the predicted sampling point set in the bridge construction state,

[0075] In the steel beam processing factory (in the initial state) and at the bridge construction site (in the bridge construction state), corresponding sensors (such as temperature sensors, humidity sensors, wind speed sensors) are used to continuously measure the temperature, humidity, and wind speed in the area where the bridge truss segments are located, obtaining a series of measurement data:

[0076] Temperature parameters in the initial state: Temperature parameters in the bridge construction state:

[0077] Humidity parameters in the initial state: Humidity parameters in the bridge construction state:

[0078] Wind speed parameters in the initial state: Wind speed parameters in the bridge construction state:

[0079] Then, the measurement data is processed to calculate the average values of the environmental parameters:

[0080] Average temperature in the initial state: Average temperature in the bridge construction state:

[0081] Average humidity in the initial state: Average humidity in the bridge construction state:

[0082] Average wind speed in the initial state: , Average wind speed in the bridge construction state:

[0083] Among them, the initial environmental data e A includes the average initial temperature the average initial humidity the average initial wind speed

[0084] The measured environmental data e B includes the measured average temperature the measured average humidity the measured average wind speed

[0085] Then, according to the physical models (such as thermal expansion, stress-strain relationship) and the measured environmental data e B , the initial sampling point set is propagated from the initial state to the bridge construction state:

[0086] Among them, f() is the state propagation function, which takes into account the influence of environmental changes on the point cloud data of the steel beam.

[0087] For ease of understanding, an example is given: In the initial state, the initial point cloud data x of a bridge truss segment is obtained through a three-dimensional laser scanner under the conditions of 20°C, a wind speed of 4 m / s, and a humidity of 50 in the steel beam processing plant. A After transporting the bridge truss segment to the construction site, the environment at the construction site becomes 10°C, a wind speed of 6 m / s, and a humidity of 80. At this time, the geometric data of the bridge truss segment will change. Therefore, the bridge truss segment is transformed from the initial state to the bridge construction state through the above algorithm.

[0088] Based on the above embodiments, in this embodiment, based on the predicted sampling point set A predicted point cloud data model of the bridge truss segment in the bridge construction state is obtained, specifically including steps 1031 to 1032:

[0089] Step 1031: Based on the predicted sampling point set And the weight W of the predicted sampling point set Obtain the predicted point cloud data i Specifically,

[0090] Specifically, Among them, W i Is the weight of the predicted sampling point set

[0091] Step 1032: Based on the predicted sampling point set The predicted point cloud data The predicted sampling point set The weight W of i And the process noise covariance Q, obtain the predicted covariance matrix P B .

[0092] Specifically, Among them, Q is the process noise covariance.

[0093] Based on the above embodiments, in this embodiment, based on the predicted point cloud data And the error between the actual point cloud data y of the bridge truss segment in the bridge construction state B And the initial covariance matrix P A , correct the predicted covariance matrix P B , the specific steps include steps 1041 to 1043:

[0094] Step 1041: Based on the predicted point cloud data And the actual point cloud data y of the bridge truss segment in the bridge construction state B ​, obtain the error vector δ B .

[0095] Specifically, first, use a 3D laser scanner to scan and collect the actual point cloud data at the bridge construction site (under the bridge construction state), denoted as y B ; then calculate the predicted point cloud data and the error vector δ B between the actual point cloud data y B :

[0096] It should be noted that: is the state value predicted through the filter.

[0097] Step 1042: Based on the error vector δ B , obtain the covariance matrix of the error according to the expectation operation

[0098] Specifically, use the error vector δ B to calculate the covariance matrix of the error

[0099] where E[] represents the expectation operation.

[0100] Step 1043: Based on the covariance matrix of the error the initial covariance matrix P A and the predicted covariance matrix P B , correct the predicted covariance matrix P B , to obtain the corrected actual covariance matrix

[0101] Specifically, according to the covariance matrix of the error the initial covariance matrix P A and the predicted covariance matrix P B to update the predicted covariance matrix P B . Adjust the update of the predicted covariance matrix P B through the adaptive fading factor ρ:

[0102]

[0103] where, when ρ is close to 1, specifically when ρ = 1 ± 0.2: The filter believes that the current predicted point cloud data model is relatively accurate, has more confidence in the prediction result, and the predicted covariance matrix P B will not be adjusted significantly.

[0104] When ρ is small, that is, not within 1±0.2: The filter believes that there may be a large uncertainty in the current predicted point cloud data model. Therefore, the trust in the predicted point cloud data model is reduced (i.e., the predicted covariance matrix P B ) is increased, and it is prepared to rely more on new measurement data in subsequent observations to correct the predicted point cloud data model.

[0105] The final corrected result is: The actual point cloud data model of the bridge truss segment includes the actual point cloud data x B and the actual covariance matrix

[0106] Through this process, you can directly start from the point cloud data in area A and the environmental parameter e A and use the AFUKF algorithm to predict the point cloud data under the environmental conditions in area B. Through the measured data, the true state information of the system can be obtained, so as to correct the bias in the model and make the prediction result closer to the actual situation. After the model correction is completed, the point cloud data in the B state can be directly predicted from the point cloud data of the truss segment in the A state through this model, without the need for further correction.

[0107] Through the above steps, the point cloud data is corrected from the initial state to the bridge construction state. This process takes into account the impact of environmental changes on the point cloud data and improves the accuracy and consistency of the point cloud data by dynamically adjusting the covariance matrix. This method is applicable to the high-precision reverse modeling of bridge truss segments and maintains the high quality of point cloud data under different environmental conditions.

[0108] At the same time, temperature sensors, humidity sensors, and wind speed sensors are used to collect environmental data under different states of the bridge truss segment, and the environmental data is fused with the point cloud data obtained by the scanner to accurately reflect the changes in environmental conditions and ensure the high precision and consistency of the data; The adaptive fading unscented Kalman filter algorithm (AFUKF) is used to correct the point cloud data in real time. This algorithm dynamically adjusts the state and covariance of the point cloud data by combining the real-time collected temperature, humidity, and wind speed data, eliminates the errors caused by environmental factors, and ensures the high precision of the data. And an adaptive fading factor ρ is introduced to dynamically adjust the predicted covariance matrix P B , enhancing the adaptability of the filter in dealing with non-stationary noise or sudden changes; During the state transition process, the impact of environmental factors such as temperature, humidity, and wind speed on the point cloud data is considered to ensure that the point cloud model can adapt to the data changes under different environmental conditions, overcome the deficiencies of traditional modeling methods in the face of complex environmental conditions, and significantly improve the accuracy and reliability of the reverse modeling of bridge truss segments.

[0109] In the description of the present application, it should be noted that the orientation or positional relationship indicated by terms such as "upper" and "lower" is based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation to the present application. Unless otherwise clearly specified and defined, the terms "installed", "connected", and "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to specific circumstances.

[0110] It should be noted that in the present application, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element.

[0111] The above are only specific embodiments of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for acquiring a bridge truss segment point cloud data model, characterized in that: It includes: Initial point cloud data x based on the bridge truss segment in the initial state A , get the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A ; Based on the initial sampling point set Initial environment data in the initial state A and measured environmental data under bridge construction B , and based on the physical model, obtain the predicted sampling point set under the bridge construction state Based on the prediction sampling point set Obtain the predicted point cloud data model of the bridge truss section under the bridge construction state. The predicted point cloud data model includes the predicted point cloud data and the prediction covariance matrix P B ; Based on predicted point cloud data The actual point cloud data of the bridge truss under construction B The error between the initial covariance matrix P A , the modified prediction covariance matrix P B , and obtain the actual point cloud data model of the bridge truss segment.

2. The method for obtaining a bridge truss segment point cloud data model according to claim 1, characterized in that: Initial point cloud data x based on the bridge truss segment in the initial state A , get the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A , specifically including: Initial point cloud data x based on the bridge truss segment in the initial state A , get the initial covariance matrix P A ; Through unscented transformation, and based on the initial point cloud data x A and the initial covariance matrix P A , generate the initial sampling point set 3. The method for obtaining a bridge truss segment point cloud data model according to claim 2, characterized in that: Initial point cloud data x based on the bridge truss segment in the initial state A , get the initial covariance matrix P A , specifically including: Initial point cloud data x based on the bridge truss segment in the initial state A , calculate the mean vector μ A ; Based on the initial point cloud data x A and the mean vector μ A , calculate the initial covariance matrix P A .

4. The method for obtaining a bridge truss segment point cloud data model according to claim 3, characterized in that: x A =(x i ,y i ,z i ); Where n is the dimension of the initial point cloud data.

5. The method for obtaining a bridge truss segment point cloud data model according to claim 1, characterized in that: The initial sampling point set include: Center point X0 and extension point X i , X i+n ; Where X0 = x A , Where i = 1, 2, ..., n, L is the initial covariance matrix P A The square root matrix, L i is the i-th column of matrix L, n is the initial point cloud data x A dimension, and λ is a tuning parameter.

6. The method for obtaining a bridge truss segment point cloud data model according to claim 1, characterized in that: Initial environment data A Including the initial temperature average Initial humidity average Initial wind speed average Measured environmental data B Including measured temperature average Measured humidity average Average wind speed 7. The method for acquiring a bridge truss point cloud data model according to claim 5, characterized in that: Based on the prediction sampling point set Obtain the predicted point cloud data model of the bridge truss section under the bridge construction state, including: Based on the prediction sampling point set and the predicted sampling point set The weight W i , get the predicted point cloud data Based on the prediction sampling point set Predicting point cloud data Prediction sampling point set The weight W i And process noise covariance Q, get the prediction covariance matrix P B .

8. The method for acquiring a bridge truss segment point cloud data model according to claim 1, characterized in that: Based on predicted point cloud data The actual point cloud data of the bridge truss under construction B The error between the initial covariance matrix P A , the modified prediction covariance matrix P B , the specific steps include: Based on predicted point cloud data and the actual point cloud data of the bridge truss segment under construction B , get the error vector δ B ; Based on the error vector δ B , according to the expectation operation, the covariance matrix of the error is obtained Error-based covariance matrix Initial covariance matrix P A and the prediction covariance matrix P B , the modified prediction covariance matrix P B , and obtain the corrected actual covariance matrix 9. The method for obtaining a bridge truss segment point cloud data model according to claim 8, characterized in that: The actual point cloud data model of the bridge truss segment includes the actual point cloud data x B and the actual covariance matrix 10. The method for acquiring a bridge truss point cloud data model according to claim 1, characterized in that: Initial point cloud data x based on the bridge truss segment in the initial state A , get the initial sampling point set corresponding to the initial point cloud data and the initial covariance matrix P A Previously, the method also included: For the collected initial point cloud data x A Filtering and denoising.

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