Prestress vector height intelligent measuring method

Abstract

The invention discloses a pre-stress vector height intelligent measurement method, which comprises the following steps: based on obtained standardized design parameter data, constructing a calibratable control space model including preset coordinate system calibratable parameters and pre-stress curve shape calibratable parameters; collecting multi-point observation data at a preset position of a construction site, wherein the multi-point observation data comprises space coordinate information of each measurement point; combining the multi-point observation data with the calibratable control space model, and inversely solving the coordinate system calibratable parameters and the prestress curve shape calibratable parameters by minimizing the residual error between the theoretical vector height and the actually measured vector height to generate an updated self-calibration control space model; and calculating self-calibration rise deviation data corresponding to the multi-point observation data based on the self-calibration control space model. Through bidirectional driving closed loop of the model and data, conversion from static control to adaptive control is realized, systematic deviation interference is effectively eliminated, and the measurement precision and risk control capability of prestress construction are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of engineering surveying, and particularly relates to a prestressed height intelligent measuring method. BACKGROUND

[0002] In the construction of prestressed concrete structures, the linear precision of prestressed corrugated pipes directly determines the stress state and structural safety after the bridge is completed, so the accurate measurement and control of prestressed height have important engineering research significance. Accurate height control can not only ensure that the prestressed reinforcement produces equivalent load in line with the design, but also avoid the risk of insufficient concrete cover or structural cracking caused by pipe positioning deviation, which is a key link to ensure the durability of the bridge.

[0003] Currently, the measurement and control of prestressed height mainly rely on the static lofting method based on design drawings. Construction personnel usually establish a fixed engineering coordinate system according to design documents, use a total station or a level, and perform point-by-point lofting and verification according to the given C4 curve equation or discrete coordinate points. In the acceptance stage, the vertical distance of the pipe to the formwork is directly measured and compared with the theoretical design value to determine whether the construction quality is qualified. This method assumes that the geometric shape of the beam body on site is completely consistent with the design model, without considering the structural deformation factors in the construction process.

[0004] The existing technology mainly has the following technical problems to be solved: the core problem is the mismatch between the rigid design model and the time-varying physical state, which causes the coupling of systematic structural deviation and local construction error, and cannot truly evaluate the construction quality. Specifically, in actual construction, the actual spatial posture of the beam body is often systematically deviated from the design coordinate system due to the influence of support settlement, formwork installation error or overall rotation of the beam body. The existing method lacks the ability to correct the reference model based on the measured data, which leads to misjudgment of the large-area positioning error of the corrugated pipe as a systematic overall deviation, causing unnecessary rework or adjustment. In addition, the existing method only focuses on the deterministic numerical deviation and ignores the cumulative propagation effect of error sources such as plate thickness, beam height and measurement noise, resulting in a lack of sufficient observation density in error-sensitive high-risk areas, making it difficult to effectively identify the substantial quality defects hidden by uncertainty. SUMMARY

[0005] The application aims to provide a prestressed height intelligent measuring method to solve the above problems in the prior art.

[0006] The technical scheme is a prestressed height intelligent measuring method, comprising:

[0007] Based on the obtained standardized design parameter data, a calibratable control space model is constructed, including preset coordinate system calibratable parameters and prestressed curve shape calibratable parameters;

[0008] Collecting multi-point observation data at predetermined positions on the construction site, including spatial coordinate information of each measuring point;

[0009] Jointing the multi-point observation data and the calibratable control space model, and inversely solving the calibratable parameters of the coordinate system and the calibratable parameters of the prestress curve shape by minimizing the residual error between the theoretical free height and the measured free height, to generate an updated self-calibration control space model;

[0010] Based on the self-calibration control space model, calculating the self-calibration free height deviation data corresponding to the multi-point observation data.

[0011] Beneficial effects, the present application realizes the transformation from static control to adaptive control through the bidirectional driving closed loop of model and data, effectively eliminates the systematic deviation interference, and improves the measurement accuracy and risk control ability of prestress construction. BRIEF DESCRIPTION OF DRAWINGS

[0012] Figure 1 A prestress free height intelligent measurement method step flowchart provided by the embodiment of the present application.

[0013] Figure 2 A step flowchart of constructing a calibratable control space model provided by the embodiment of the present application.

[0014] Figure 3 A step flowchart of collecting multi-point observation data provided by the embodiment of the present application.

[0015] Figure 4 A step flowchart of generating an updated self-calibration control space model provided by the embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to make the personnel in the technical field better understand the present application scheme, the technical scheme in the embodiment of the present application will be described clearly and completely in combination with the drawings in the embodiment of the present application. Obviously, the described embodiment is only a part of the embodiment of the present application, not all. Based on the embodiment in the present application, all other embodiments obtained by the person skilled in the art without creative labor should belong to the protection scope of the present application.

[0017] It should be noted that the terms include and have and any variants thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to the clearly listed steps or units, but can include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] As Figure 1As shown, a prestressed vector height intelligent measurement method comprises the following steps:

[0019] Based on the obtained standardized design parameter data, a calibratable control space model is constructed, which contains preset coordinate system calibratable parameters and prestressed curve shape calibratable parameters.

[0020] In other words, standardized design parameter data is obtained, and based on the standardized design parameter data, a calibratable control space model containing preset coordinate system calibratable parameters and prestressed curve shape calibratable parameters is constructed.

[0021] In this embodiment, the standardized design parameter data refers to the basic data extracted from design drawings, structural calculation books or engineering specifications, which has been processed by format unification. Specifically, these data can include beam span geometric dimension information such as span length, beam height, plate thickness, etc., prestressed curve type information such as parabolic parameter, circular curve radius, etc., and prestressed design vector height information. The calibratable control space model is a dynamic parameterized model established on the basis of these static design data. Unlike traditional fixed models, this model introduces specific variable parameters, enabling it to adjust according to actual measurement results.

[0022] Specifically, the coordinate system calibratable parameters can include variables describing the rotation angle and translation offset of the local coordinate system relative to the global engineering coordinate system. The prestressed curve shape calibratable parameters can include deviation factors describing the actual linear shape of the prestressed tendon relative to the design linear shape, such as control point vector height correction amount within curve segmentation, end slope correction amount, etc. By presetting these parameters, the system can subsequently find the parameter combination that best fits the actual physical state through numerical optimization algorithms, overcoming systematic deviations caused by construction errors and structural deformations.

[0023] Multi-point observation data is collected at predetermined positions on the construction site, which contains spatial coordinate information of each measurement point.

[0024] In other words, multi-point observation data containing spatial coordinate information of each measurement point is collected at predetermined positions on the construction site.

[0025] In this embodiment, unlike the traditional single-point measurement, multiple representative positions along the beam length are required to be selected for measurement at the construction site. The selection of predetermined positions should take into account the ability to fully reflect structural deformation and curve characteristics, such as key sections at midspan, quarter point, beam end, and prestressed curve inflection point. The collection process can be performed using high-precision total station, scanner or photogrammetry equipment. For each measurement point, the device will obtain its three-dimensional spatial coordinates in the unified engineering coordinate system, i.e. X, Y, Z coordinate values. In order to ensure the effectiveness of the data, auxiliary information such as instrument height and prism height should also be recorded during the collection process, in order to make necessary geometric corrections. These discrete spatial coordinate point sets constitute the observation basis for subsequent parameter inversion, and the number and distribution quality directly affect the accuracy of model calibration.

[0026] The multi-point observation data is combined with the calibratable control space model, and the calibratable parameters of the coordinate system and the calibratable parameters of the prestressed curve shape are solved by minimizing the residual error between the theoretical height and the measured height, to generate an updated self-calibration control space model.

[0027] Specifically, the system calculates the theoretical height corresponding to the position of each measurement point based on the current model parameters, and calculates the measured height according to the observation data. An optimization objective function is constructed, which takes the sum of squares of the differences between the theoretical height and the measured height of all measurement points as an index to quantify the degree of agreement between the model and reality. Optionally, by applying numerical optimization methods such as nonlinear least squares, Gauss-Newton method or Levenberg-Marquardt algorithm, the optimal parameter value is iteratively searched to make the objective function reach a minimum value. This set of optimal parameters represents the most real state of the coordinate system and curve shape. Using this set of parameters to update the original calibratable control space model, the self-calibration control space model can be obtained. This model no longer relies solely on design theoretical values, but integrates field measurement information, and can more accurately describe the current construction state.

[0028] Based on the self-calibration control space model, the self-calibration height deviation data corresponding to the multi-point observation data is calculated as the basis for evaluating the construction quality.

[0029] In this embodiment, the self-calibration height deviation data is the basis for the final decision of quality control. Specifically, the system uses the corrected coordinate system in the updated self-calibration control space model to remap the coordinates of the original observation points to the local reference frame, and calculates the corrected measured height. At the same time, the target design height at the corresponding position is calculated by using the corrected prestressed curve equation. The difference between the two is the self-calibration height deviation. The false deviation caused by the error of the coordinate system establishment or the systematic structural deformation is eliminated, and the local construction error of the corrugated pipe or the prestressed tendon relative to the ideal linearity is truly reflected. Construction personnel can determine whether to adjust the support height or the position of the corrugated pipe according to the deviation data, or perform engineering acceptance according to the deviation data. Compared with directly using the original design value for comparison, the system error and the random construction error can be effectively distinguished, false judgment and unnecessary rework can be avoided, and the scientificity and efficiency of construction control can be improved.

[0030] In this embodiment, a parameterized control model is constructed, and model parameters are corrected in reverse using multi-point observation data on site, so as to realize closed-loop control from static design to dynamic adaptation.

[0031] As shown in Figure 2 , in an exemplary embodiment, a calibratable control space model is constructed, including:

[0032] Based on the standardized design parameter data, the beam span geometric information and the structural size information are analyzed, and the local plane coordinate system data and the initial beam bottom reference surface data describing the basic geometric relationship of the beam body are constructed.

[0033] In this embodiment, constructing the local plane coordinate system data is the first step to realize spatial positioning. Specifically, the system extracts the key physical feature points from the standardized design parameter data. For example, the corner position at the intersection of the floor and the beam can be selected as the station reference point, denoted as P corner . At the same time, the design starting point position of the prestressed tendon is identified, denoted as P start . In order to establish a coordinate system in accordance with engineering habits, the prestressed starting point P start is defined as the origin O of the local coordinate system. The design position of the prestressed end point P end is extracted, and a unit vector pointing from the starting point to the end point is constructed, which is defined as the first axial direction of the local plane coordinate system, i.e. the X-axis. According to the right-hand rule or engineering convention, the direction perpendicular to the first axial direction and parallel to the floor plane is defined as the second axial direction, i.e. the Y-axis. The planar position relationship of the beam body is established. On this basis, the system determines the vertical position of the beam bottom reference surface in combination with the structural size information such as the design beam height H beam and the design plate thickness T slab , constructs the initial beam bottom reference surface data, and forms a complete initial geometric framework.

[0034] A local plane coordinate system data is constructed, and a plane rotation angle and an original point translation offset of the local plane coordinate system data relative to a preset engineering survey coordinate system are determined. The plane rotation angle and the original point translation offset are marked as coordinate system calibratable parameter data.

[0035] In the present embodiment, in order to capture the actual deviation of the coordinate system in the field construction, specific geometric transformation parameters are introduced. Specifically, an angle variable describing the rotation of the local coordinate system about the Z axis relative to the global engineering coordinate system is defined, denoted as Δθ, which reflects the actual deflection of the beam axis in the horizontal plane. At the same time, the translation offsets of the original point in the three coordinate axis directions are defined, respectively denoted as ΔX, ΔY and ΔZ. Among them, ΔZ can absorb the elevation system deviation caused by the support settlement or the measurement reference point error. These variables together constitute the coordinate system calibratable parameter data. In mathematical expression, it can be expressed as part of the state vector containing geometric degrees of freedom. For example, the first four components of the state vector can be defined as [Δθ, ΔX, ΔY, ΔZ]. By setting these parameters as unknowns to be solved, the model has the ability to automatically align the actual structure according to the measured data in the subsequent steps, without forcibly assuming that the design coordinate system and the actual coordinate system are completely coincident.

[0036] A key shape control factor of the prestressed design curve is constructed, and the key shape control factor is marked as calibratable curve parameter data. The key shape control factor at least includes a control point height correction or an end slope correction in a curve segment.

[0037] Specifically, the mathematical description of the prestressed C4 curve is parameterized and expanded. The original C4 curve is usually composed of segmented parabolas or circular curves, and its shape is uniquely determined by design parameters such as tangent slope k, segment point position, etc. In order to adapt to the linear change in actual construction, the present embodiment introduces a correction factor. Exemplarily, a vertical position correction at the control point can be defined, denoted as Δ k . For example, for a typical four-segment C4 curve, its equation can be expressed as y=f(x, k1, k2, k3), where x is the position variable, and k1, k2, k3 are design coefficients. In the present embodiment, these fixed shape coefficients are converted into variable parameters, for example, the actual coefficient k1' is defined as k1+Δ k1 ; wherein Δ k1 is the correction amount. These correction amounts Δ k , i.e. constitute the calibratable curve parameter data, reflect the real shape change of the prestressed pipe under the action of self-weight, support deformation or construction error. Adding these parameters to the state vector enables the model to describe a flexible and deformable theoretical curve.

[0038] The coordinate system calibratable parameter data and the calibratable curve parameter data are connected to the local plane coordinate system data and the initial beam bottom reference surface data to generate a calibratable control space model with fixed geometric properties and parameterized adjustment properties.

[0039] In the embodiment, the final model is constructed by mathematical encapsulation. The system integrates the geometric coordinate system, the initial reference surface, and all the calibratable parameters defined above into a unified mathematical object. Specifically, the mathematical object contains a parameter vector X, the structure of which can be represented as X = [Δθ, ΔX, ΔY, ΔZ, Δ k1 , Δ k2 , Δ k3 ]. At the same time, the model contains a function relationship for mapping these parameters to the three-dimensional coordinates of any point.

[0040] Further, in order to ensure the physical reasonableness of the subsequent inversion process, a prior constraint mechanism is introduced. In an optional implementation, before constructing the parameter identification optimization objective function, historical experience constraints are introduced, specifically:

[0041] Obtain the historical engineering alignment adjustment records of the same type of beam span, extract the experience distribution range of the coordinate system adjustment amount and the curve shape adjustment amount, and generate prior constraint data; add the prior constraint data as boundary conditions or regularization terms to the parameter identification optimization objective function to constrain the solution space of the self-calibration parameter data and prevent abnormal solutions that violate engineering physical common sense from appearing in the inversion results.

[0042] That is, according to the historical engineering alignment adjustment records of the same type of beam span, the experience distribution range of each parameter is extracted. For example, based on experience, it is known that the rotation angle deviation is usually very small, and the value range of Δθ can be set to between -1 degree and 1 degree. The system generates prior constraint data and adds it to the model as boundary conditions. The model generated in this way not only has self-adaptive adjustment capability, but is also effectively constrained by engineering experience, preventing overfitting or divergence phenomena in the mathematical solution process.

[0043] As shown in Figure 3 , according to one aspect of the present application, multi-point observation data is collected, including:

[0044] Perform parameter sensitivity analysis on the calibratable control space model to calculate the degree of response of the theoretical cant of each position along the beam length to the changes in the coordinate system calibratable parameters and the prestressed curve shape calibratable parameters, and obtain sensitivity distribution data.

[0045] In the present embodiment, sensitivity analysis can quantify the impact of the variation of each parameter on the final calculation result of the final height. Specifically, the system defines the theoretical height H as a function of the beam length position L and the parameter vector X, denoted as H = f(L, X). The sensitivity is specifically represented as the partial derivative of this function with respect to each component of the parameter vector X. For the i-th position and the j-th parameter, its sensitivity coefficient S ij = ΨH i / ΨX j , where Ψ is the partial derivative. In actual calculation, the finite difference method is preferably used to approximate the solution of this partial derivative. For example, for a certain parameter X j , give it a small perturbation amount Δ, calculate the height change before and after the perturbation, and the ratio of the two is the approximate value of the sensitivity. The system traverses all positions along the length of the beam, for example every 0.1 meters, to calculate the sensitivity of all parameters at all positions, forming a sensitivity matrix or curve atlas, which intuitively shows which areas of the beam are most sensitive to the rotation angle and which areas are most sensitive to the vertical deformation of the curve, constituting the sensitivity distribution data.

[0046] Based on the sensitivity distribution data, select the positions with a response degree to the parameter variation higher than a preset threshold as the self-calibration measuring points, and generate extended measuring point planning data containing the position information of the self-calibration measuring points.

[0047] Specifically, the sensitivity data is used for measuring point screening. According to the information theory principle, measuring at a high sensitivity place can most effectively constrain the solution of the corresponding parameter. Exemplarily, the system sets a preferred rule, for example, for each parameter to be identified, find the position L max with the largest absolute value of sensitivity. For example, the analysis may find that the positions at the ends of the beam have the highest sensitivity to the rotation angle Δθ of the coordinate system, and the positions at the midspan have the highest sensitivity to the overall settlement ΔZ of the curve or the shape factor Δ k . Therefore, the system will mark the beam ends, the midspan, and the wave peak positions of the sensitivity curve as the must-measure points. Preferably, in order to ensure comprehensive coverage, the zero-crossing points of the sensitivity curve can also be selected as auxiliary measuring points. Integrate the coordinates of these screened key positions and their corresponding measurement priorities to generate extended measuring point planning data, which indicates where the field measurement personnel should measure, thereby avoiding the low efficiency and information redundancy caused by blind uniform point distribution.

[0048] According to the extended measuring point planning data, drive the measuring equipment to capture the target at the construction site to obtain the spatial coordinates of the self-calibration measuring points, which constitute the multi-point observation data used to drive the parameter inversion.

[0049] In this embodiment, the digital planning is converted into physical actions. After receiving the planning data, the measuring personnel or the fully automatic measuring robot moves to the designated position to perform the work. The device can capture the prism center by using the automatic target recognition technology to obtain high-precision three-dimensional coordinate data. Further, in order to ensure the data quality, the collection process can also include multiple repeated observations to eliminate random noise. The collected raw data includes the coordinates of the measuring station, the coordinates of the back sight point, and the slant distance, horizontal angle and zenith distance of each target measuring point, or directly outputs the three-dimensional rectangular coordinates. All these data are collected and attached with position labels to form the multi-point observation data. Since this set of data is collected based on the sensitivity planning, it contains the key geometric information required for analyzing the model parameters, and provides a solid data support for the subsequent high-precision parameter inversion.

[0050] In this embodiment, the sensitivity of the model parameters to the observation values is calculated, and the measuring point positions are planned scientifically to obtain observation data containing the most information with the least measuring points.

[0051] As shown in Figure 4 , in one possible implementation, an updated self-calibration control space model is generated, including:

[0052] Based on the calibratable control space model, the theoretical height data of the corresponding positions of the multi-point observation data in the parameterized state are calculated, and the measured height data in the multi-point observation data are extracted.

[0053] In this embodiment, the mapping relationship between the observation space and the parameter space is established. Specifically, for each observation point i, the system calculates the theoretical height according to its position x i in the local coordinate system. theory_i , using the current parameterized C4 curve equation. Assuming that the parameterized curve equation is y=f(x, P), where P is a state vector containing coordinate parameters and shape factors, the theoretical height H i =f(x prism , P), where f is the function mapping of the curve equation. For the extraction of the measured height data, the system needs to perform physical geometric conversion. Specifically, according to the prism center elevation Z actual_i measured by the total station, combined with the auxiliary measurement parameters, the actual elevation of the beam bottom or the corrugated pipe bottom is calculated. The calculation formula can be expressed as:

[0054] H prism = Z rod - H slab - T beam + H actual_i ;

[0055] where H prismH represents the absolute height of the prism center measured by the total station rod H represents the prism rod height used during measurement slab H represents the floor thickness beam H represents the beam height. In some embodiments, if the measurement point is directly on the corrugated pipe, the floor thickness and beam height terms in the above formula can be adjusted or omitted according to the actual measurement position. By conversion, the photoelectric measurement value is converted into a structural physical quantity to obtain the measured vector height data.

[0056] A parameter identification optimization objective function is constructed with the coordinate system calibrable parameters and the pre-stressed curve shape calibrable parameters as unknown quantities, and the minimum deviation between the theoretical vector height data and the measured vector height data as the target.

[0057] Specifically, in order to recover the true parameters from noisy observation data, a mathematical optimization model needs to be constructed. The system defines the residual r i H represents the theoretical vector height of the i-th measurement point theory_i H represents the measured vector height actual_i The difference between the two. The parameter identification optimization objective function J(P) can be constructed in the form of the sum of squares of all measurement point residuals, i.e. J(P)=∑ i=1 n (r i 2 ), where n is the total number of observation points.

[0058] In a preferred implementation, in order to improve the algorithm's resistance to gross errors, the parameter identification optimization objective function is constructed to include:

[0059] The single-point residual data between the theoretical vector height data and the measured vector height data of each measurement point position is calculated.

[0060] Specifically, the system can perform preliminary fitting or use median estimation to obtain the initial residual of each point.

[0061] According to the numerical value of the single-point residual data, the corresponding residual weight coefficient data is assigned to each measurement point; wherein, a lower weight value is assigned to the measurement point whose single-point residual data exceeds the preset threshold value, so as to reduce the influence of abnormal observation on the inversion result.

[0062] For example, Huber weight function or IGG weight function can be used. When the absolute value of the residual of a certain measurement point is less than the preset threshold value k, its weight w i is set to 1; when the absolute value of the residual is greater than k, the weight w i can be set to k divided by the absolute value of the residual. The bad points that deviate too far from the normal trend are automatically down-weighted or even removed.

[0063] Based on the residual weight coefficient data, a weighted parameter identification optimization objective function in the form of weighted least squares is constructed, and a robust self-calibration parameter data with noise resistance is obtained by solving.

[0064] In this embodiment, the objective function is changed to J(P)=∑ i=1 n (w i ·r i 2 ). So even if some abnormal data caused by prism rod skew or alignment deviation is mixed in the field measurement, the finally calculated model parameters are still reliable.

[0065] Solving the parameter identification optimization objective function, the optimal parameter value that minimizes the deviation is obtained, and the optimal parameter value is recorded as the self-calibration parameter data.

[0066] Preferably, the parameter identification optimization objective function is solved by using a numerical optimization algorithm, that is, a computer algorithm is used to find the parameter solution that minimizes the objective function. Since the C4 curve equation and coordinate transformation usually contain nonlinear terms, this problem belongs to the nonlinear least squares problem. The system can use Gauss-Newton method or Levenberg-Marquardt algorithm for iterative solution.

[0067] In a specific numerical case, it is assumed that the state vector P to be solved contains the coordinate system rotation angle α and the Z-axis offset ΔZ. In the initial state, α is 0 and ΔZ is 0. Five measurement points are collected in the field, and the initial calculation deviations are [12mm, 15mm, 18mm, 14mm, 10mm] respectively. It can be seen that the deviations are generally large and show a systematic trend. After iterative solution by the algorithm, the optimal parameter values α and ΔZ are identified as 0.05 rad and-10mm respectively. After substituting these parameters into the model, the calculated residual is reduced to [2mm, -1mm, 2mm, -2mm, 0mm]. It shows that the systematic error has been absorbed by the parameters, and the remaining is only random error. This set of optimal solution is recorded as the self-calibration parameter data.

[0068] The coordinate system and curve shape in the calibratable control space model are corrected using the self-calibration parameter data, and a self-calibration control space model containing the corrected coordinate system and the corrected prestressed curve is generated.

[0069] In this embodiment, the solved mathematical results are converted into engineering models. The system updates the coordinate conversion matrix and the curve equation coefficients in the model. For example, the rotation matrix of the local coordinate system is updated to the matrix containing the calculated rotation angle α, and the shape parameter k value of the C4 curve is updated to k+Δ kThe self-calibration control space model generated at this time represents the digital twin state of the current beam body, and all subsequent measurements and lofting will be based on this.

[0070] In further embodiments, the self-calibration freeboard deviation data is calculated, including:

[0071] Using the corrected coordinate system in the self-calibration control space model, the spatial coordinates in the multi-point observation data are remapped to the local space, and the self-calibration measured freeboard data is calculated.

[0072] In this embodiment, the observation data is traced back and remapped. The original observation data is absolute in the engineering coordinate system, but the relative position in the local coordinate system will change with the correction of the coordinate system. The system uses the updated coordinate transformation parameters to recalculate the local coordinates (x new , y new , z new ) of each measuring point relative to the new coordinate axes, and recalculates the freeboard according to this to obtain the self-calibration measured freeboard data. The consistency of the evaluation reference is ensured.

[0073] Using the corrected prestress curve in the self-calibration control space model, the self-calibration design freeboard data corresponding to the position of each measuring point is calculated.

[0074] Specifically, using the C4 curve equation containing the correction parameters, substituting the new local transverse coordinate x new of each measuring point, the theoretical height that the position should reach is calculated, i.e. the self-calibration design freeboard data, which represents the ideal target after adapting to the structural deformation.

[0075] The difference between the self-calibration measured freeboard data and the self-calibration design freeboard data is calculated to generate the self-calibration freeboard deviation data reflecting the deviation of the construction alignment from the self-adaptive target alignment.

[0076] In this embodiment, the final quality evaluation index is output. By subtracting the measured value from the design value, the final deviation is obtained. This deviation eliminates the influence of coordinate system establishment error and structural systematic deformation, and purely reflects the local quality of the corrugated pipe installation. In some optional implementations, the system can also calculate the root mean square error of this deviation data as a comprehensive index for evaluating the construction quality of the entire beam.

[0077] This embodiment solves the real structure parameters by constructing a numerical optimization problem and processing the observation data using the weighted least squares method, thereby eliminating systematic errors and recalculating high-precision construction deviations based on the corrected model.

[0078] The engineering tolerance specification corresponding to the standardized design parameter data and the measurement equipment technical index information are acquired, and the statistical distribution characteristics of the plate thickness deviation, beam height deviation and measurement noise are extracted to generate the uncertainty prior data.

[0079] In this embodiment, the system quantifies the uncertainty of the input data. Specifically, the relevant engineering construction quality acceptance specification is consulted to acquire the allowed deviation range of the plate thickness and beam height, for example, the plate thickness allowed deviation is plus or minus 5 mm. According to the statistical principle, it can be assumed that these structure sizes obey the normal distribution, and the allowed deviation range is converted into the standard deviation. For example, it is assumed that the plate thickness standard deviation σ t is equal to the allowed deviation divided by 3. At the same time, according to the technical specification of the measurement equipment such as the total station, the ranging accuracy and the angle measuring accuracy are acquired, and the standard deviation σ noise of the height measurement is calculated. These statistical parameters jointly constitute the uncertainty prior data. In some optional embodiments, the statistical distribution of the measured data accumulated by the history engineering can also be used to replace the specification value to obtain the prior data more conforming to the level of the specific construction team.

[0080] The mapping relationship between the uncertainty prior data and the standardized design parameter data is established, the corresponding standard deviation or confidence interval attribute is given to the geometric size in the calibratable control space model, and the uncertainty structure size data is generated.

[0081] Specifically, the statistical attribute is connected to the specific physical parameter. The system adds the variance or standard deviation field to each key size parameter in the data structure. For example, the plate thickness parameter is represented as T slab =T nominal +N(0, σ t 2 ), wherein T nominal is the nominal value or design value of the plate thickness, and N( ) is a normal distribution random variable. This data set with probability distribution attribute is the uncertainty structure size data, which provides the input source for the subsequent error propagation analysis.

[0082] The uncertainty structure size data is encapsulated into the calibratable control space model to generate the uncertainty enhanced control space model with error statistical attribute.

[0083] In this embodiment, the attribute upgrade of the model is completed. The uncertainty enhanced control space model not only contains the geometric equation for calculating the nominal value, but also integrates the covariance matrix for calculating the variance. Specifically, the model internally maintains the parameter covariance matrix ∑ P , the diagonal elements of which correspond to the variances of the input parameters, and the non-diagonal elements correspond to the correlations between the parameters.

[0084] In further embodiments, a probabilistic risk assessment of the construction quality is also included, specifically:

[0085] Based on the error statistical properties defined by the uncertainty-enhanced control space model, combined with the geometric transmission path determined by the self-calibration control space model, the variance or confidence interval of the height deviation at each position along the beam length is calculated using the pre-configured error propagation model, and the height uncertainty distribution data is generated.

[0086] In the present embodiment, the variance of the output quantity can be calculated using the error propagation law. The system calculates the sensitivity matrix J of the height calculation function with respect to each input parameter, which is composed of partial derivatives. According to the linear error propagation formula, the variance Var H of the output height is H Var P = J·∑ T ·J T , where H is the transpose; Var P represents the variance of the height deviation at a certain position calculated; ∑ total represents the covariance matrix of the input parameters (such as plate thickness, beam height), and the diagonal elements are the variances of each parameter. Preferably, the influence of measurement noise can also be superimposed, i.e. the total variance σ 2 H = Var noise + σ 2 noise , where σ mean represents the random noise standard deviation of the measurement equipment itself. The system performs this calculation point by point along the beam length to obtain the height standard deviation at each position. Based on this, the confidence interval of the height can be constructed, for example, the 95% confidence interval is [H total -1.96·σ mean , H total +1.96·σ mean ], where H total is the expected value or mean value of the theoretical height, and σ limit is the total standard deviation. This series of statistical quantities distributed with position constitutes the height uncertainty distribution data.

[0087] A preset engineering allowable deviation threshold is introduced, and the probability value of the height deviation exceeding the limit at each measurement point position is calculated based on the height uncertainty distribution data, and the risk assessment data indexed by position is generated.

[0088] Specifically, the statistical distribution is converted into an intuitive risk probability. Let the maximum allowable height deviation of the project be Δ x . For any position x, its deviation is subject to a mean of μ total (the current measured deviation) and a standard deviation of σ _ xof a normal distribution. The system calculates the probability P limit that the absolute value of the deviation is greater than Δ fail . Exemplarily, this can be achieved by the cumulative distribution function CDF of the standard normal distribution:

[0089] P fail = 1 - (CDF((Δ limit - μ x ) / σ total_x ) - CDF((-Δ limit - μ x ) / σ total_x ));

[0090] where P fail represents the probability that the height deviation of a certain point exceeds the allowed range; CDF represents the cumulative distribution function of the standard normal distribution; Δ limit represents the maximum deviation threshold allowed by the engineering specification, for example 5mm; μ x represents the current height deviation mean of the position, i.e. the measured deviation; σ total_x represents the total standard deviation of the height deviation of the position, i.e. the square root of Var H . The calculation results are organized into risk assessment data indexed by position. For example, the data structure can be represented as: {position: L = 15m, current deviation: 4mm, uncertainty: 2mm, over-limit probability: 15%}.

[0091] In yet further embodiments, adaptive control is also performed based on the risk assessment data, specifically:

[0092] Retrieving the risk assessment data, filtering out the beam segment positions with over-limit probability higher than a preset safety level, and marking the filtered beam segment positions as a high-risk position set.

[0093] In this embodiment, risk classification and filtering are performed. Specifically, the system sets a safety level threshold, for example 10%. All calculation points are traversed, and positions with over-limit probability P fail greater than 10% are identified and classified into the high-risk position set. These positions are often areas with complex structural deformation, high sensitivity, or current deviation close to the critical value.

[0094] Control instructions for encrypted observation or increased number of repeated measurements are generated for the high-risk position set, forming risk control point data to guide the construction site to perform intensive measurement and adjustment in the corresponding risk areas.

[0095] Specifically, for each point in the high-risk set, the system generates a corresponding control strategy. For example, for points with an extremely high probability of exceeding limits, it generates an encrypted observation instruction, requiring the addition of auxiliary measurement points before and after that point; or it generates a re-measurement instruction, requiring at least three independent measurements of that point to reduce random noise σ. noise The impact of these instructions constitutes risk control point data, which, as specific action plans, are directly issued to surveying equipment or construction teams, achieving a closed loop from data analysis to on-site actions.

[0096] This embodiment extends deterministic geometric calculation to statistical inference of random variables, realizing risk-driven intelligent control.

[0097] In one possible embodiment, the method further includes performing on-site stakeout control based on the self-calibration results, specifically:

[0098] During the bellows positioning or acceptance phase, the self-calibrating control space model is invoked in real time to calculate the target layout coordinate data of the current layout position under the corrected target line shape, and drive the fully automatic measuring equipment to indicate the target position.

[0099] In this embodiment, the digital model is transformed into physical guidance. When construction workers need to locate a specific corrugated pipe, they input the mileage value or select the point to be laid out via a handheld terminal. The system backend then calls the latest self-calibrating control space model in real time and calculates the theoretical coordinates (x, y, x) of the point in the local coordinate system based on the corrected curve equation. target y target , z target Using the corrected coordinate transformation parameters, the local coordinates are inversely transformed into absolute coordinates (X, Y) in the engineering survey coordinate system. global Y global Z global The system transmits the absolute coordinates to a fully automatic total station via a wireless communication interface. Upon receiving the command, the total station automatically rotates the alidade and telescope, emitting a laser to indicate the physical location of the three-dimensional coordinates in space, or automatically tracks the prism and guides the worker to the target location. This achieves intelligent lofting based on adaptive target alignment.

[0100] The self-calibrated sag deviation data is displayed in real time on the display interface. When the deviation data exceeds the preset range, a construction adjustment instruction is generated to guide the construction personnel to adjust the height of the corrugated pipe support until the deviation meets the requirements.

[0101] Specifically, the display interface of the handheld terminal is divided into a graphic area and a data area. The graphic area displays the relative position relationship between the design line of the prestressed curve and the current measured point; and the data area displays the self-calibration height deviation data in real time. Preferably, in order to improve the efficiency of on-site operation, the system adopts a color coding mechanism: when the deviation is less than a preset qualified threshold value, for example, 5 mm, the numerical value is displayed in green, accompanied by a qualified prompt sound; when the deviation is greater than the threshold value, the numerical value is displayed in red, accompanied by an alarm sound. At the same time, the system generates specific textual or voice construction adjustment instructions according to the sign and size of the deviation, for example, the support is 12 mm lower, please adjust upward. After the worker adjusts the corrugated pipe support according to the instructions, the system drives the total station instrument to measure again, and refreshes the deviation value in real time. The process is repeated until the deviation value turns green, so that the construction quality of each control point strictly meets the standard. Intuitive visual feedback and closed-loop control are provided.

[0102] The embodiment constructs a complete closed-loop technical system from parameterized modeling, sensitivity planning, robust self-calibration inversion, uncertainty risk assessment to intelligent visualized construction, and effectively solves the problem of insufficient construction control precision caused by model solidification and neglect of uncertainty.

[0103] In an exemplary embodiment, local plane coordinate system data is constructed, including:

[0104] The position information of the intersection angle point of the floor and the beam is extracted from the standardized design parameter data as a station setting reference point, and the position of the prestressed starting point along the layout direction is defined as the coordinate origin; based on the station setting reference point and the coordinate origin, the direction of the prestressed starting point pointing to the prestressed ending point is defined as the first axis direction of the local plane coordinate system, and the direction perpendicular to the first axis direction and parallel to the floor plane is defined as the second axis direction of the local plane coordinate system, to generate local plane coordinate system data strictly corresponding to the physical position of the engineering entity.

[0105] In the embodiment, the floor corner point coordinates in the engineering coordinate system (global coordinate system) are P corner (X c , Y c , Z c ), the prestressed starting point coordinates are P start (X s , Y s , Z s ), and the prestressed ending point coordinates are P end (X e , Y e , Z e ). The X-axis unit vector u x of the local coordinate system is calculated:

[0106] u x =(P end -Pstart ) / ∣∣P end -P start ∣∣;

[0107] where P end -P start is the vector difference, and ∣∣P end -P start ∣∣ is the length of the vector difference. In most construction projects, the Z-axis is by default perpendicular to the horizontal plane upward, i.e. the unit vector u z is (0, 0, 1). However, considering that there can be a design slope of the floor slab, preferably, the normal vector of the floor slab plane is utilized as the local Z-axis direction. If the floor slab is assumed to be horizontal, u z takes (0, 0, 1). The local Y-axis unit vector u y is calculated by vector cross product: u y = u z x u x . At this time, a rotation matrix R is constructed: R = [u x , u y , u z ] T . The conversion formula from the global coordinates P global to the local coordinates P local is established:

[0108] P local = R * (P global - P start );

[0109] where P local represents the coordinate vector (x local , y local , z local ) of the measuring point in the local plane coordinate system; R represents the rotation matrix from the engineering global coordinate system to the local plane coordinate system, which is composed of the three axis direction unit vectors u x , u y , u z of the local coordinate system; P global represents the measured coordinate vector (X, Y, Z) of the measuring point in the engineering global coordinate system; P start represents the coordinate vector of the prestressed starting point in the engineering global coordinate system, which serves as the origin of the local coordinate system. Through linear algebraic transformation, the coordinates of any measuring point on site are mapped into the local space with the prestressed starting point as the origin and the prestressed tendon direction as the X-axis, providing a unified geometric reference for subsequent curve equation calculation.

[0110] Further, the key shape control factors for constructing the prestressed design curve are marked as calibratable curve parameter data, and the key shape control factors at least include a control point height correction or an end slope correction in a curve segment. The specific process can also be: the initial mathematical equation of the prestressed C4 curve in the local coordinate system is a basic form before the model is parameterized and corrected. The C4 curve is divided into four segments, and the function relationship of the design height y of the C4 curve with respect to the local horizontal coordinate x is as follows:

[0111] First segment (0≤x≤K1·L): ;

[0112] Second segment (K1·L≤x≤K2·L): ;

[0113] Third segment (K2·L≤x≤L-K3·L): ;

[0114] Fourth segment (L-K3·L≤x≤L): ;

[0115] Wherein y represents the design height of the prestressed tendon in the local coordinate system, that is, the vertical height from the beam bottom or the reference surface; x represents the horizontal position of the prestressed tendon in the local coordinate system, that is, the horizontal distance from the prestressed starting point; L is the beam span length; h is the beam height; e1, e2 and e3 are respectively the protective layer thickness or control distance of the prestressed tendon at the starting point, the midspan and the terminal point from the beam bottom or the beam top; k1, k2 and k3 are dimensionless curve segment proportionality coefficients. In the parameterized model of the embodiment, the parameters h, L, e1, e2, e3, k1, k2 and k3 in the above formula are no longer regarded as absolute constants, but are superimposed with correction terms to be identified, such as h actual =h design +Δ h , so that the static equation is converted into a dynamic calibratable curve model, wherein h actual is the actual value, h design is the nominal value or theoretical value determined in the design stage, and Δ h is the correction.

[0116] According to an aspect of the present application, the construction of the calibratable control space model data can also be: reading design parameter data from design drawings and structural calculation books, the design parameter data including beam span geometric information, prestressed curve type information, prestressed design vector height information, and control section position marking information; at the same time, extracting structural size data from structural construction drawings, the structural size data including beam height, plate thickness, relative position relationship of beam end and building site, and elevation reference information of related components. Convert the design parameter data and the structural size data into a unified engineering coordinate system expression form, check the unit system and coordinate direction convention, and obtain standardized design parameter data and standardized structural size data that are consistent in format and can be called for subsequent modeling.

[0117] Based on the standardized structural size data, the corner points at the intersection of the floor and the beam are selected as the local building sites, the coordinate values of the building sites in the engineering coordinate system and the relative position relationship between the prestressed starting point and the building site are read, the prestressed starting point is defined as the origin of the local plane coordinate system along the prestressed tendon layout direction, the prestressed end point direction is defined as the first axis direction of the local plane coordinate system, and the direction perpendicular to the prestressed end point direction and parallel to the floor plane is defined as the second axis direction of the local plane coordinate system, thereby obtaining the local plane coordinate system data for describing the position relationship of the beam span plane. Any plane point coordinate in the engineering coordinate system can be converted into the expression form in the local plane coordinate system data by using the preset coordinate translation and rotation relationship, so as to ensure that the subsequent prestressed curve and measurement point can be uniformly described in the same local plane system. Further, while constructing the local plane coordinate system data, adjustable parameters for self-calibration can also be introduced, the plane rotation angle and the origin translation offset of the local plane coordinate system are recorded as coordinate system calibratable parameter data in the form of parameters, and the calibratable local plane coordinate system data containing fixed parts and adjustable parts are obtained.

[0118] Based on the calibratable local plane coordinate system data and the standardized structure size data, according to the geometric relationship of the design slab thickness, the design beam height and the station site elevation, the vertical coordinate value of the prestressed starting beam bottom position relative to the local plane coordinate system is determined, the position is defined as the vertical zero point of the local space coordinate system, and the local space coordinate system data including the plane and the vertical three dimensions are constructed. The beam bottom reference surface, the upper surface of the floor and the space where the prestressed pipe design line is located are uniformly expressed under the local space coordinate system data, and the initial beam bottom reference surface data for subsequent prestressed vector height calculation are obtained. Further, in order to support uncertainty analysis, while constructing the local space coordinate system data and the initial beam bottom reference surface data, the uncertainty prior data is mapped with the structure size such as the slab thickness, the beam height and the station site elevation, the corresponding standard deviation or interval range is given to each structure size, and the uncertainty structure size data with statistical attributes is formed. On this basis, the uncertainty structure size data is connected to the local space coordinate system data and the initial beam bottom reference surface data, and the uncertainty enhanced local space coordinate system data with geometric coordinate attributes and uncertainty attributes is constructed.

[0119] Based on the standardized design parameter data, when generating the initial curve model data, the key shape parameters of the curve, such as the sectional inner control point vector height, the end slope, the midspan vector height correction amount and the like are recorded in the form of adjustable parameters to form the calibratable curve parameter data with the adjustable shape parameter set; at the same time, the calibratable curve parameter data is combined with the calibratable local plane coordinate system data and the initial beam bottom reference surface data to generate the calibratable control space model data with the calibratable characteristics. In some preferred embodiments, the prior constraint data can also be introduced into the calibratable curve parameter data, the reasonable value range and the initial estimated value of each shape adjustable parameter are set, and in the subsequent self-calibration process, the abnormal shape which does not conform to the engineering experience is avoided through the constraint condition, and the stability and convergence of the self-calibration solution are improved.

[0120] At the same time, the calibratable local plane coordinate system data, the uncertainty enhanced local space coordinate system data and the calibratable control space model data are uniformly packaged to form the enhanced control space model data containing the fixed parameters, the adjustable parameters and the uncertainty attributes, and the model version number, the parameter initial state and the uncertainty configuration are recorded in the data for calling when executing the self-calibration algorithm and the risk assessment.

[0121] According to another aspect of the present application, the calculation of the self-calibration height deviation data can also be: based on the self-calibration measurement observation data and the enhanced control space model data, using the coordinate transformation relationship in the control space model, converting the measurement point engineering coordinates and instrument elevation in the measurement observation data into the measurement point coordinates under the local space coordinate system data; using the vertical position of the beam bottom reference surface in the local space coordinate system, calculating the difference between the measurement point elevation and the beam bottom reference surface elevation as the measured height data of the measurement point. The measured height data of all measurement points will be sorted according to the corresponding beam length position index, so as to be compared with the design height point by point in the subsequent. Further, in order to facilitate subsequent uncertainty analysis, while calculating the measured height data, according to the uncertainty structure size information in the enhanced local space coordinate system data, an error transfer coefficient caused by the structure size and the measurement noise is added to each measured height, and enhanced measured height data containing the mean value and the error coefficient are generated.

[0122] Based on the enhanced measured height data and the enhanced control space model data, at the beam length position where each measurement point is located, the design height data of the corresponding position is calculated through the initial curve model, and the measured height data and the design height data are subtracted point by point to obtain the initial height deviation data reflecting the degree of construction deviation. The initial height deviation data is sorted and summarized according to the measurement point number or the beam length position, which is used to guide the construction adjustment and acceptance evaluation. Further, the sensitivity coefficient of the corresponding design height to the curve parameter is recorded for each position, and the enhanced design height data containing the design value and the sensitivity information are generated, which are used to build the objective function and the Jacobian matrix of the self-calibration parameter identification, so as to improve the solving efficiency and stability.

[0123] Based on the self-calibration measurement observation data, the calibratable control space model data, and the enhanced design height data and the enhanced measured height data, a parameter identification model is constructed with the coordinate system calibratable parameters and the curve shape calibratable parameters as unknown quantities, the difference between the measured height at each self-calibration point and the theoretical height calculated by the calibratable control space model is taken as the residual term, an optimization problem with the sum of squares of residuals as the target is established, and a set of optimal self-calibration parameter data is solved. The system uses the self-calibration parameter data to update the calibratable local plane coordinate system data and the calibratable curve parameter data synchronously, generates the self-calibration control space model data containing the corrected coordinate system and the corrected prestressed curve, which is used for subsequent recalculation of height deviation and construction control. Further, in order to improve the robustness of the self-calibration result to abnormal observation, when constructing the parameter identification model, different weights are given to the residual terms of each self-calibration point, the weight of the measurement point with larger residual is reduced, the optimization target of weighted least squares or robust regression is constructed, so as to obtain the robust self-calibration parameter data which is not sensitive to abnormal points, and the self-calibration control space model data is updated based on the robust parameter, which enhances the applicability of the model in complex construction environment.

[0124] Based on the self-calibration control space model data and the self-calibration measurement observation data, the coordinate transformation and the height calculation are re-executed, the spatial coordinates of all measurement points are transformed in the coordinate system after self-calibration, the self-calibration measured height data is calculated, and the self-calibration design height data corresponding to the position is calculated by using the self-calibration prestressed curve, and the self-calibration height deviation data reflecting the construction quality under the corrected target linear is obtained by subtracting the two. The system compares the difference between the initial height deviation data and the self-calibration height deviation data, evaluates the improvement effect of the self-calibration process on the overall deviation distribution, and provides a basis for subsequent construction adjustment strategy.

[0125] Based on the uncertainty prior data, the uncertainty enhanced control space model data, the enhanced measured height data and the self-calibration control space model data, a height error propagation model is constructed, the variance or confidence interval of the height value at each position in the beam length range is calculated by using the linear error propagation formula or the numerical simulation method, and the height uncertainty distribution data reflecting the uncertainty level of the height changing with the position is generated. On this basis, combined with the engineering allowable deviation threshold, the probability of each position exceeding the limit of the height is calculated as risk assessment data, and the position set that needs to be controlled is selected according to the risk probability, and the risk control point data for subsequent lofting and acceptance is formed. In some preferred implementation manners, in order to realize the comprehensive optimization of risk and cost, the construction cost of increasing measurement points, increasing supports or increasing the number of re-measurements can be considered when constructing the risk control point set, the exceeding risk and the control cost are jointly included in the evaluation function, and the heuristic algorithm is used to generate the optimized risk control scheme data with the lowest risk under the cost constraint, guiding the lofting and adjustment priority.

[0126] In summary, the intelligent measurement method of prestressed height includes: constructing a calibrable control space model containing coordinate system parameters and curve shape parameters; collecting multi-point observation data on site, inversely solving the model parameters by minimizing the objective function of the theoretical and measured height residual, and generating a self-calibration control space model that fits the actual physical state; evaluating the height exceeding risk by using the error propagation model, and optimizing the measurement point layout based on the risk distribution.

[0127] The application adopts parameterized modeling and multi-point inversion self-calibration, constructs a calibratable model containing rotation, translation and shape factor, and constructs a least square optimization problem by using field multi-point observation data, so that the actual deflection of the coordinate system and the actual deformation of the curve can be accurately identified and calculated. The systematic structural deviation, such as support settlement and overall rotation, is separated from the total deviation, and the evaluation reference is updated, so that the finally output self-calibration height deviation only reflects the local installation quality of the corrugated pipe, and the misjudgment problem caused by the error of the reference is solved. Uncertainty modeling and risk adaptive control means are introduced, the plate thickness, beam height and equipment noise are modeled as random variables, and the linear error propagation calculation is performed by using the Jacobian matrix, the height uncertainty distribution along the beam length direction is quantified, and the risk evaluation data is generated accordingly. Based on the risk evaluation, the system can automatically generate densification observation instructions in the area with high probability of exceeding the limit, solve the problem that the traditional uniform distribution strategy may miss the key defects in the error sensitive area such as the inflection point and the midspan, and realize the scientization and refinement of quality control.

[0128] The preferred embodiments of the application are described in detail above, but the application is not limited to the specific details in the above-described embodiments, and various equivalent transformations can be made to the technical solutions of the application within the technical concept of the application, and these equivalent transformations all belong to the protection scope of the application.

Claims

1. A method for intelligent measurement of prestressed height, characterized in that, The method comprises the following steps: Based on the obtained standardized design parameter data, a calibratable control space model is constructed, including preset coordinate system calibratable parameters and prestressed curve shape calibratable parameters; Multi-point observation data is collected at predetermined positions on the construction site, including the spatial coordinate information of each measuring point; The multi-point observation data is combined with the calibratable control space model, and the coordinate system calibratable parameters and the prestressed curve shape calibratable parameters are inversely solved by minimizing the residual error between the theoretical height and the measured height, to generate an updated self-calibration control space model; Based on the self-calibration control space model, self-calibration height deviation data corresponding to the multi-point observation data is calculated.

2. The method of claim 1, wherein, The method for constructing the calibratable control space model comprises the following steps: Based on the standardized design parameter data, the beam span geometric information and the structural size information are analyzed, and the local plane coordinate system data and the initial beam bottom reference surface data describing the basic geometric relationship of the beam body are constructed; The plane rotation angle and the original point translation offset of the local plane coordinate system data relative to the preset engineering survey coordinate system are constructed, which are marked as coordinate system calibratable parameter data; The key shape control factors of the prestressed design curve are constructed, which are marked as calibratable curve parameter data, and the key shape control factors at least include the control point height correction or the end slope correction in the curve segment; The coordinate system calibratable parameter data and the calibratable curve parameter data are connected to the local plane coordinate system data and the initial beam bottom reference surface data, to generate a calibratable control space model with fixed geometric properties and parameterized adjustment properties.

3. The method of claim 1, wherein, The method for collecting multi-point observation data comprises the following steps: Performing parameter sensitivity analysis on the calibratable control space model, calculating the response degree of the theoretical height at each position along the beam length to the change of the coordinate system calibratable parameters and the prestressed curve shape calibratable parameters, and obtaining sensitivity distribution data; Based on the sensitivity distribution data, selecting positions with a response degree higher than a preset threshold to parameter change as self-calibration measuring points, and generating extended measuring point planning data containing the position information of the self-calibration measuring points; According to the extended measuring point planning data, driving the measuring equipment to capture targets on the construction site, obtaining the spatial coordinates of the self-calibration measuring points, and composing multi-point observation data for driving parameter inversion.

4. The method of claim 1, wherein, The method for generating the updated self-calibration control space model comprises the following steps: Based on the calibratable control space model, calculating the theoretical height data of the positions corresponding to the multi-point observation data in the parameterized state, and extracting the measured height data in the multi-point observation data; Constructing a parameter identification optimization objective function taking the coordinate system calibratable parameters and the prestressed curve shape calibratable parameters as unknown quantities and minimizing the deviation between the theoretical and measured height data as the target; Solving the parameter identification optimization objective function to obtain the optimal parameter value that minimizes the deviation, and recording it as self-calibration parameter data; Using the self-calibration parameter data to correct the coordinate system and the curve shape in the calibratable control space model, to generate a self-calibration control space model containing the corrected coordinate system and the corrected prestressed curve.

5. The method of claim 4, wherein, The method for constructing the parameter identification optimization objective function comprises the following steps: Calculating the single-point residual error data between the theoretical height data and the measured height data of each measuring point position; According to the numerical size of the single-point residual data, a corresponding residual weight coefficient data is assigned to each measuring point; wherein, a lower weight value is given to the measuring point whose single-point residual data exceeds the preset threshold value; Based on the residual weight coefficient data, a weighted parameter identification optimization objective function in the form of weighted least squares is constructed, and the robust self-calibration parameter data with noise resistance is obtained by solving.

6. The method of claim 4, wherein, The self-calibration height deviation data is calculated, including: Using the modified coordinate system in the self-calibration control space model, the spatial coordinates in the multi-point observation data are remapped to the local space, and the self-calibration measured height data is calculated; Using the modified prestressed curve in the self-calibration control space model, the self-calibration design height data corresponding to the position of each measuring point is calculated; The difference between the self-calibration measured height data and the self-calibration design height data is calculated, and the self-calibration height deviation data reflecting the deviation degree of the construction alignment from the adaptive target alignment is generated.

7. The method of claim 1, wherein, It also includes constructing a statistical model for risk analysis, specifically: Obtain the engineering tolerance specification corresponding to the standardized design parameter data and the measurement equipment technical index information, extract the statistical distribution characteristics of the plate thickness deviation, beam height deviation and measurement noise, and generate the uncertainty prior data; Establish the mapping relationship between the uncertainty prior data and the standardized design parameter data, give the corresponding standard deviation or confidence interval attribute to the geometric size in the calibrable control space model, and generate the uncertainty structural size data; Encapsulate the uncertainty structural size data into the calibrable control space model to generate the uncertainty enhanced control space model with error statistical properties.

8. The method of claim 7, wherein, Further including probability risk assessment of construction quality, specifically: Based on the error statistical properties defined by the uncertainty enhanced control space model, combined with the geometric transmission path determined by the self-calibration control space model, the variance or confidence interval of the height deviation at each position along the beam length direction is calculated using the pre-configured error propagation model, and the height uncertainty distribution data is generated; Introduce the preset engineering allowable deviation threshold, calculate the probability value of the height deviation exceeding the limit at each measuring point position based on the height uncertainty distribution data, and generate the risk assessment data indexed by position.

9. The method of claim 8, wherein, Further including adaptive control based on risk assessment data, specifically: Retrieve the risk assessment data, filter out the beam segment positions with exceeding probability higher than the preset safety level, and mark them as a high-risk position set; Generate control instructions for encrypted observation or increased number of repeated measurements for the high-risk position set, and form risk control point data.

10. The method of claim 4, wherein, Before constructing the parameter identification optimization objective function, it also includes introducing historical experience constraints, specifically: Obtain the historical engineering alignment adjustment records of the same type of beam span, extract the experience distribution range of the coordinate system adjustment amount and the curve shape adjustment amount, and generate the prior constraint data; Add the prior constraint data as boundary conditions or regularization terms to the parameter identification optimization objective function to constrain the solution space of the self-calibration parameter data.

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