Pipe ring flatness measuring method based on total station

Abstract

A pipe ring flatness measurement method based on a total station specifically relates to the field of pipe ring flatness measurement, and the scheme comprises the following steps: collecting a three-dimensional coordinate point cloud of a pipe ring end face through the total station, dividing the pipe ring end face into measurement areas according to segment partitions, carrying out iterative sampling on the three-dimensional coordinate point cloud of a single observation station by adopting an RANSAC algorithm, screening effective measurement points, and obtaining the flatness of the pipe ring. Calculating a reference surface distance and a residual standard deviation, taking the number of the effective points of each observation station as a weight to obtain a global reference surface distance and a fusion residual standard deviation, dynamically adjusting an initial screening threshold, re-screening the effective measurement points according to the adjusted screening threshold and an RANSAC algorithm to obtain the number of the adjusted effective points, and calculating the total reference surface distance and the fusion residual standard deviation. According to the method, a complete closed loop is formed through data screening, reference fitting, multi-station fusion, quality evaluation and threshold optimization, so that the stability, interference resistance and working condition adaptation accuracy of flatness measurement are improved.

Description

Technical Field

[0001] This invention relates to the field of pipe ring flatness measurement technology, specifically a pipe ring flatness measurement method based on a total station. Background Technology

[0002] The tunnel ring of a shield tunnel is assembled from multiple precast concrete segments. The flatness of the ring is a core indicator for measuring construction quality, which directly affects the uniformity of stress, waterproofing, and long-term durability of the tunnel structure. Currently, total stations have become the mainstream tool for measuring the flatness of tunnel rings due to their high precision and non-contact measurement advantages. Their basic principle is to collect a three-dimensional coordinate point cloud of the tunnel ring end face, fit it to a reference plane, and then calculate the distance deviation of each point from the reference plane.

[0003] However, existing flatness measurement methods rely heavily on manual point selection for datum plane fitting. Differences in point selection by different operators can lead to datum plane tilting. Furthermore, they fail to consider local anomalies on the pipe ring end face, such as a 5mm protrusion caused by construction bumps. When set as a static threshold for screening, these anomalies can be misjudged as valid points, causing the datum plane to "shift toward the anomaly point." In addition, the number of valid points and residual dispersion vary between different stations. Simple arithmetic averaging can lower the overall accuracy due to low-quality data. Finally, existing technologies often use "maximum distance from point to datum plane" as the evaluation index, and the screening threshold is fixed. This makes it difficult to adjust the screening strategy according to the actual condition of the pipe ring (flatness, surface complexity), resulting in weak engineering guidance for the measurement results.

[0004] Therefore, those skilled in the art have provided a method for measuring the flatness of a pipe ring based on a total station to solve the problems mentioned in the background art. Summary of the Invention

[0005] The technical problem solved by the present invention is to provide a method for measuring the flatness of a pipe loop based on a total station, so as to improve the stability of the reference surface, the accuracy of the global reference surface, and the adaptability to working conditions.

[0006] To address the above problems, the present invention provides the following technical solution: A method for measuring the flatness of a pipe loop based on a total station includes the following steps: The three-dimensional coordinate point cloud of the pipe ring end face was collected by a total station, and the pipe ring end face was divided into measurement areas according to the pipe segments, and each station was numbered. The RANSAC algorithm is used to iteratively sample the three-dimensional coordinate point cloud of each station, screen the valid measurement points, and calculate the datum distance and residual standard deviation. Using the number of valid points at each station as the weight, a weighted average of the datum distance and residual standard deviation of multiple stations is calculated to obtain the global datum distance and fused residual standard deviation. The comprehensive evaluation index is calculated based on the global reference plane distance, the standard deviation of the fusion residual and the number of valid points. The initial screening threshold is dynamically adjusted to obtain the adjusted screening threshold. Then, the valid measurement points are re-screened according to the adjusted screening threshold and the RANSAC algorithm to obtain the adjusted number of valid points. This process continues until the adjusted screening threshold and the adjusted number of valid points reach the preset convergence conditions. Output the global reference plane distance, fusion residual standard deviation, and comprehensive evaluation index that meet the convergence conditions to complete the flatness measurement.

[0007] Further: The method for selecting valid measurement points based on the RANSAC algorithm includes: An initial plane is constructed by randomly selecting 3 points from the initial measurement point set and calculating the residuals from all points to the initial plane. The number of interior points whose absolute residual value is less than the initial screening threshold; After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the effective point, and the number is recorded as the effective measurement point. X is set to 100-200 times.

[0008] Further: The specific methods for calculating the reference plane distance and the residual standard deviation include: The inner product of the valid point coordinates and the plane normal vector is summed, the negative is taken, and then divided by the number of valid points to obtain the datum plane distance, which reflects the spatial position of the datum plane. The effective point residual is obtained by adding the sum of the inner product of the effective point coordinates and the plane normal vector to the distance from the reference plane. The standard deviation of the residuals, which reflects the dispersion of the data, is obtained by taking the square root of the sum of squares of the effective point residuals divided by the degrees of freedom. The degree of freedom is the number of valid points minus the number of constraints.

[0009] Further: The specific methods for calculating the global reference plane distance and the standard deviation of the fused residual include: The reference distance of a single station is weighted by the number of valid points at each station. The sum of the products of the reference distance of a single station and the number of valid points at the corresponding single station is divided by the sum of the number of valid points at all stations to obtain the global reference distance. The weighted sum of squares of the residual standard deviations of each station is calculated, and then the sum is divided by the total number of valid points and the square root is taken to obtain the fused residual standard deviation.

[0010] Further: The specific method for calculating the comprehensive evaluation index includes: The local deviation term is obtained by calculating the ratio of the absolute value of the global reference plane distance to the maximum local deviation threshold. The distribution uniformity term is obtained by calculating the ratio of the fused residual standard deviation to the maximum residual standard deviation threshold. The data sufficiency term is calculated based on the ratio of the number of standard valid points to the total number of valid points. A comprehensive evaluation index is obtained by weighted summation of the local deviation term, the distribution uniformity term, and the data sufficiency term; Among them, the maximum local deviation threshold, the maximum residual standard deviation threshold, and the number of standard valid points are preset standard values.

[0011] Further: The specific method for calculating the adjusted screening threshold includes: pass Calculate the adjusted screening threshold; Where: S1 is the adjusted screening threshold, S0 is the initial screening threshold, and P is the comprehensive evaluation index; The adjusted screening threshold is reintroduced into the RANSAC algorithm, and the number of interior points whose absolute residual value is less than the adjusted screening threshold is counted based on the adjusted screening threshold. After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the adjusted effective points, with the number denoted as the number of adjusted effective points.

[0012] Furthermore: the convergence conditions specifically include: Calculate the relative changes in the adjusted screening threshold and the number of adjusted valid points for adjacent iterations, and stop iterating when both relative changes are less than 5%. The specific calculation formula used for the convergence judgment is as follows: ; ; Where: ΔS1 j To filter the relative change of the threshold, Δn1 j S1 represents the relative change in the number of valid points. j S1 is the threshold for the j-th iteration. j-1 Let n1 be the threshold for the (j-1)th iteration. j Let n1 be the number of valid points in the j-th iteration. j-1 Let be the number of valid points in the j-th iteration, where j is the iteration number.

[0013] Further: When the local deviation term, the distribution uniformity term, and the data sufficiency term are weighted and summed, the weight of the local deviation term is set to 60%, the weight of the distribution uniformity term is set to 30%, and the weight of the data sufficiency term is set to 10%.

[0014] The effects of the above solution are as follows: 1. This invention uses the RANSAC algorithm to iteratively sample three-point combinations, calculates the residuals of each initial plane, and then automatically removes outliers whose residuals exceed the initial screening threshold. The plane with the smallest sum of squared residuals is selected as the reference plane, thereby obtaining the number of valid points for calculating the distance to the reference plane and the standard deviation of the residuals. The number of valid points is determined by data statistics, avoiding manual intervention. Secondly, the measurement method of dividing the measurement area and performing local fitting and global weighted fusion according to the pipe segment can take into account both the "local flatness" and "overall trend" of the pipe ring flatness measurement.

[0015] 2. This invention reduces the impact of low-quality data, including stations with large residuals, by using the number of effective points at each station as the weight to calculate the weighted average of the distance to the reference surface at each station. It also simultaneously calculates the standard deviation of the fusion residuals, quantifies the overall dispersion of multi-station data, and provides error boundaries for subsequent evaluation of the pipe ring smoothness measurement.

[0016] 3. This invention constructs and calculates a comprehensive evaluation index that includes "local deviation", "distribution uniformity" and "data sufficiency" to fully align with construction standards. The RANSAC screening threshold is adjusted in real time through the comprehensive evaluation index to form a closed loop of "threshold → effective point → reference surface → comprehensive evaluation index → ​​threshold". When the comprehensive evaluation index is large, the threshold is relaxed to retain more points, and when the comprehensive evaluation index is small, the threshold is tightened to eliminate noise, thereby ensuring that the measurement results remain reliable under complex working conditions. Attached Figure Description

[0017] Figure 1 This is a schematic diagram illustrating the method steps for measuring the flatness of a pipe ring according to the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0019] Example 1, please refer to Figure 1 A method for measuring the flatness of a pipe loop based on a total station includes the following steps: Step 1: Collect the three-dimensional coordinate point cloud of the pipe ring end face using a total station, divide the pipe ring end face into measurement areas according to the pipe segments, and number each station; Specifically, a high-precision total station is used to establish a three-dimensional coordinate system based on the tunnel design axis. The X-axis is the longitudinal direction of the tunnel, the Y-axis is the transverse direction, and the Z-axis is the vertical direction. The origin of the coordinate system is the center of the starting end face of the pipe ring assembly. When the diameter of the pipe ring end face is 6m, 300 points are collected at each station and evenly distributed on the pipe ring end face with a point spacing of ≤50mm. The pipe ring end face is divided into multiple fan-shaped areas according to the number of pipe segments, and each area is marked as AF to facilitate subsequent partition fitting of the reference surface. Then, m stations are set up along the tunnel axis direction. The station numbering rule is: G + station number (e.g., G1 is the first station).

[0020] At this point, the three-dimensional coordinate point cloud of each station has been obtained.

[0021] Step 2: Use the RANSAC algorithm to iteratively sample the three-dimensional coordinate point cloud of each station, screen the valid measurement points, and calculate the datum distance and residual standard deviation; The specific methods for calculating the datum distance and residual standard deviation include: The inner product of the valid point coordinates and the plane normal vector is summed, the negative is taken, and then divided by the number of valid points to obtain the datum plane distance, which reflects the spatial position of the datum plane. Add the sum of the inner product of the effective point coordinates and the plane normal vector to the distance from the reference plane. This yields the effective point residual. The standard deviation of the residuals, which reflects the dispersion of the data, is obtained by taking the square root of the sum of squares of the effective point residuals divided by the degrees of freedom. The degree of freedom is the number of valid points minus the number of constraints. It is understandable that the datum distance is a datum parameter obtained by fitting multiple points, and its definition is: ; Right now: ; Substitute into the residual formula: ; Summing over all valid points: ; Therefore, we can conclude that the sum of the residuals is 0, indicating that c i It is the fluctuation of a point around a reference surface; In the formula: D1 is the distance to the reference plane of the first station, n1 is the number of valid points of the first station, and A, B, and C are the reference plane normal vectors. xi , yi and zi Let be the effective coordinates of the i-th measurement point, σ1 be the standard deviation of the residuals, and c be the standard deviation of the residuals. i The effective point residual of the i-th measurement point; also, ; In the formula: σ1 is the standard deviation of the residuals, c iThe effective point residual of the i-th measurement point; The residual standard deviation σ1 is an index of the dispersion of effective points around the datum plane. A smaller residual standard deviation σ1 indicates that the effective points are more concentrated near the datum plane, resulting in higher datum plane fitting accuracy. This reflects the "noise level" of single-station data, including total station measurement errors and minor unevenness on the pipe loop surface. Middle denominator Instead of n1, the reason for using n1 is to achieve unbiased estimation. Specifically, when estimating the population standard deviation using sample data (effective point residuals), directly using n1 as the denominator will underestimate the population standard deviation. (Degrees of freedom = number of valid points at the first station n1 - number of constraints) can be used as the denominator to correct the bias, making the residual standard deviation σ1 an unbiased estimator of the population residual standard deviation.

[0022] The RANSAC algorithm's methods for selecting valid measurement points include: An initial plane is constructed by randomly selecting 3 points from the initial measurement point set and calculating the residuals from all points to the initial plane. The number of interior points whose absolute residual value is less than the initial screening threshold; After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the effective point, and the number is recorded as the effective measurement point. X is set to 100-200 times; In summary, the initial plane is fitted by iterative sampling of 3 points using the RANSAC algorithm, and the residual |c is statistically analyzed. i The number of interior points ≤ S0 (S0 is the initial screening threshold) is used to select the plane with the most interior points as the optimal model, thus obtaining the number of effective points n1 for the first station; then through... Calculate the distance D1 from the first station's reference plane to ensure that the distance D1 is determined by "effective points that reflect the overall trend". This avoids misjudgment by manual point selection (such as mistakenly selecting a dent or bump on the pipe segment as the reference point), automatically eliminates local abnormal points, and ensures that the reference plane fitting is not affected by extreme values.

[0023] At this point, the distance D1 from the first station datum plane and the residual standard deviation σ1 of each station are obtained.

[0024] Step 3: Using the number of valid points at each station as the weight, perform a weighted average of the datum distance and residual standard deviation of multiple stations to obtain the global datum distance and fused residual standard deviation; The specific methods for calculating the global reference plane distance and the standard deviation of the fused residual include: The reference distance of a single station is weighted by the number of valid points at each station. The sum of the products of the reference distance of a single station and the number of valid points at the corresponding single station is divided by the sum of the number of valid points at all stations to obtain the global reference distance. The weighted sum of squares of the residual standard deviations of each station is calculated, and then the sum is divided by the total number of valid points and the square root is taken to obtain the fused residual standard deviation. Understandably, fusing the datum parameters from multiple stations eliminates single-station measurement errors, thereby obtaining the globally optimal datum and improving data reliability. The formulas for calculating the global datum distance and the standard deviation of the fused residuals are as follows: ; ; In the formula: D fused The distance to the global reference surface is m, where m is the number of stations and n is n. k D represents the number of valid points at the k-th station. k σ is the distance to the reference surface of the k-th station. fused To integrate the residual standard deviation, σ k Let k be the standard deviation of the residual at the k-th station, where k is the number of stations. The number of valid points at the k-th station of each station is n. k As the weight (the more points, the stronger the data representativeness), the distance D from the reference surface of the k-th station in a single station. k Weighted average: This can eliminate the limitations of a single station's field of view (such as uneven point distribution caused by a station being close to the edge of the pipe ring), making the distance D of the global reference plane after fusion... fused Combining multiple station perspectives, the distance from the reference plane at the first station (d1) is closer to the actual location of the pipe loop than that from a single station; subsequently, through... Calculate the global residual dispersion to quantify the overall stability of multi-station data, and obtain the standard deviation σ of the fused residual. fused Directly correlated with the uniformity of stress on the surface of the pipe ring—its fusion residual standard deviation σ fused The smaller the value, the more uniform the deviation distribution at each point on the pipe ring end face, and the more evenly the bolt preload can be transmitted, thus avoiding segment cracking caused by local stress concentration.

[0025] At this point, the global reference plane distance D has been obtained. fused and the standard deviation of the fused residuals σ fused .

[0026] Step 4.1: Calculate the comprehensive evaluation index based on the global reference plane distance, the standard deviation of the fused residual, and the number of valid points, and dynamically adjust the initial screening threshold to obtain the adjusted screening threshold; The specific methods for calculating the comprehensive evaluation indicators include: The local deviation term is obtained by calculating the ratio of the absolute value of the global reference plane distance to the maximum local deviation threshold. The distribution uniformity term is obtained by calculating the ratio of the fused residual standard deviation to the maximum residual standard deviation threshold. The data sufficiency term is calculated based on the ratio of the number of standard valid points to the total number of valid points. A comprehensive evaluation index is obtained by weighted summation of the local deviation term, the distribution uniformity term, and the data sufficiency term; Among them, the maximum local deviation threshold, the maximum residual standard deviation threshold, and the number of standard valid points are preset standard values; The formula for calculating the comprehensive evaluation index is as follows: ; Where: P is the comprehensive evaluation index, D0 is the maximum local deviation threshold, σ0 is the maximum residual standard deviation threshold, and n0 is the number of standard valid points; The specific methods for calculating the adjusted screening threshold include: pass Calculate the adjusted screening threshold; Where: S1 is the adjusted screening threshold, S0 is the initial screening threshold, and P is the comprehensive evaluation index; The adjusted screening threshold is reintroduced into the RANSAC algorithm, and the number of interior points whose absolute residual value is less than the adjusted screening threshold is counted based on the adjusted screening threshold. After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the adjusted effective points, with the number denoted as the number of adjusted effective points.

[0027] w1, w2, and w3 are all weighting coefficients, and w1 + w2 + w3 = 1; Furthermore, when weighting the local deviation term, the distribution uniformity term, and the data sufficiency term, the weight of the local deviation term is set to 60%, the weight of the distribution uniformity term is set to 30%, and the weight of the data sufficiency term is set to 10%. Therefore... It incorporates "local deviation" "Distribution uniformity" "Data sufficiency" "These three dimensions, with their corresponding weights, correspond to the priorities of construction work, thus avoiding the one-sidedness of existing technologies that only use 'maximum distance deviation' for evaluation." Subsequently, through Dynamically adjust the RANSAC screening threshold—when the comprehensive evaluation index P is large (poor flatness, many outliers), the adjusted screening threshold S1 is relaxed (retaining more points); when the comprehensive evaluation index P is small (good flatness, less noise), the adjusted screening threshold S1 is tightened (eliminating noise), thereby adapting to different construction scenarios and improving the accuracy of the reference surface.

[0028] Step 4.2: Then, based on the adjusted screening threshold and the RANSAC algorithm, re-screen the valid measurement points to obtain the adjusted number of valid points, until the adjusted screening threshold and the adjusted number of valid points reach the preset convergence condition. Understandably, the convergence conditions specifically include: Calculate the relative changes in the adjusted screening threshold and the number of adjusted valid points for adjacent iterations, and stop iterating when both relative changes are less than 5%. The specific calculation formula used for the convergence judgment is as follows: ; ; Where: ΔS1 j To filter the relative change of the threshold, Δn1 j S1 represents the relative change in the number of valid points. j S1 is the threshold for the j-th iteration. j-1 Let n1 be the threshold for the (j-1)th iteration. j Let n1 be the number of valid points in the j-th iteration. j-1 Let j be the number of valid points in the j-th iteration, where j is the iteration number. In summary, steps 4.1-4.2 adjust the selection of valid points in the initial plane by adjusting the screening threshold S1, forming a closed-loop optimization of "threshold → valid points → reference plane → evaluation → threshold". Its core effect is to adaptively balance "outlier removal" and "data retention", ensuring that the reference plane fitting remains robust and reliable under complex working conditions. The specific mechanism is as follows: Adjusted screening threshold S1 → RANSAC screening → Number of valid points n1 → Distance to the first station's datum plane D1 → Distance to the fused global datum plane D fused →Comprehensive evaluation index P→Adjusted screening threshold S1; When there are a large number of abnormal points in the pipe loop, the initial number of valid points n1 is small → the comprehensive evaluation index P is increased → the screening threshold S1 is relaxed after adjustment (e.g., from 2mm to 4mm) → RANSAC re-screens and retains more points (the number of valid points increases after adjustment) → the distance of the first station's datum plane from D1 is closer to the overall trend of the pipe loop, avoiding datum plane "distortion" caused by insufficient valid points.

[0029] By stopping the iteration through the convergence condition, the threshold and effective points can be stabilized. Specifically, when the surface of the pipe ring is extremely irregular (such as the coexistence of local protrusions and depressions), the iterative optimization makes the adjusted screening threshold S1 and the number of adjusted effective points converge quickly (usually 3 iterations), the fluctuation of the reference surface deviation is reduced, the stability is improved, and reliable single-station reference data is provided for subsequent multi-station fusion and quality evaluation.

[0030] Step 5: Output the global reference plane distance that meets the convergence condition, the standard deviation of the fused residual, and the comprehensive evaluation index to complete the flatness measurement; It is understandable that this implementation method achieves high-precision measurement of pipe ring flatness through a complete process of "data acquisition → robust fitting → multi-station fusion → dynamic optimization → quality evaluation". Among them, the RANSAC algorithm effectively filters outliers, multi-station weighted fusion improves global accuracy, and dynamic threshold iteration ensures robustness under complex working conditions. The final output global reference plane distance, fusion residual standard deviation and comprehensive evaluation index can be used to guide on-site construction.

[0031] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can make various modifications and alterations without departing from the spirit and scope of the invention; therefore, the scope of protection of the present invention should be determined by the scope defined in the claims.

Claims

1. A method for measuring the flatness of a pipe loop based on a total station, characterized in that, Includes the following steps: The three-dimensional coordinate point cloud of the pipe ring end face was collected by a total station, and the pipe ring end face was divided into measurement areas according to the pipe segments, and each station was numbered. The RANSAC algorithm is used to iteratively sample the three-dimensional coordinate point cloud of each station, screen the valid measurement points, and calculate the datum distance and residual standard deviation. Using the number of valid points at each station as the weight, a weighted average of the datum distance and residual standard deviation of multiple stations is calculated to obtain the global datum distance and fused residual standard deviation. The comprehensive evaluation index is calculated based on the global reference plane distance, the standard deviation of the fusion residual and the number of valid points. The initial screening threshold is dynamically adjusted to obtain the adjusted screening threshold. Then, the valid measurement points are re-screened according to the adjusted screening threshold and the RANSAC algorithm to obtain the adjusted number of valid points. This process continues until the adjusted screening threshold and the adjusted number of valid points reach the preset convergence conditions. Output the global reference plane distance, fusion residual standard deviation, and comprehensive evaluation index that meet the convergence conditions to complete the flatness measurement.

2. The method for measuring the flatness of a pipe loop based on a total station according to claim 1, characterized in that, The method for selecting valid measurement points based on the RANSAC algorithm includes: An initial plane is constructed by randomly selecting 3 points from the initial measurement point set and calculating the residuals from all points to the initial plane. The number of interior points whose absolute residual value is less than the initial screening threshold; After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the effective point, and the number is recorded as the effective measurement point. X is set to 100-200 times.

3. The method for measuring the flatness of a pipe loop based on a total station according to claim 2, characterized in that, The specific methods for calculating the datum distance and the residual standard deviation include: The distance to the reference plane is obtained by summing the inner product of the valid point coordinates and the plane normal vector, taking the negative, and then dividing by the number of valid points. The effective point residual is obtained by adding the sum of the inner product of the effective point coordinates and the plane normal vector to the distance from the reference plane. The standard deviation of the residuals is obtained by taking the square root of the sum of squares of the effective point residuals divided by the degrees of freedom; The degree of freedom is the number of valid points minus the number of constraints.

4. The method for measuring the flatness of a pipe loop based on a total station according to claim 1, characterized in that, The specific methods for calculating the global reference plane distance and the standard deviation of the fused residual include: The reference distance of a single station is weighted by the number of valid points at each station. The sum of the products of the reference distance of a single station and the number of valid points at the corresponding single station is divided by the sum of the number of valid points at all stations to obtain the global reference distance. The weighted sum of squares of the residual standard deviations of each station is calculated, and then the sum is divided by the total number of valid points and the square root is taken to obtain the fused residual standard deviation.

5. The method for measuring the flatness of a pipe loop based on a total station according to claim 2, characterized in that, The specific methods for calculating the comprehensive evaluation index include: The local deviation term is obtained by calculating the ratio of the absolute value of the global reference plane distance to the maximum local deviation threshold. The distribution uniformity term is obtained by calculating the ratio of the fused residual standard deviation to the maximum residual standard deviation threshold. The data sufficiency term is calculated based on the ratio of the number of standard valid points to the total number of valid points. A comprehensive evaluation index is obtained by weighted summation of the local deviation term, the distribution uniformity term, and the data sufficiency term; Among them, the maximum local deviation threshold, the maximum residual standard deviation threshold, and the number of standard valid points are preset standard values.

6. The method for measuring the flatness of a pipe loop based on a total station according to claim 5, characterized in that, The specific method for calculating the adjusted screening threshold includes: pass Calculate the adjusted screening threshold; Where: S1 is the adjusted screening threshold, S0 is the initial screening threshold, and P is the comprehensive evaluation index; The adjusted screening threshold is reintroduced into the RANSAC algorithm, and the number of interior points whose absolute residual value is less than the adjusted screening threshold is counted based on the adjusted screening threshold. After X iterations, the plane model with the most interior points is selected as the optimal reference plane, and the corresponding set of interior points is the adjusted effective points, with the number denoted as the number of adjusted effective points.

7. The method for measuring the flatness of a pipe loop based on a total station according to claim 1, characterized in that, The convergence conditions specifically include: Calculate the relative changes in the adjusted screening threshold and the number of adjusted valid points for adjacent iterations, and stop iterating when both relative changes are less than 5%. The specific calculation formula used for the convergence judgment is as follows: ； ； Where: ΔS1 j To filter the relative change of the threshold, Δn1 j S1 represents the relative change in the number of valid points. j S1 is the threshold for the j-th iteration. j-1 Let n1 be the threshold for the (j-1)th iteration. j Let n1 be the number of valid points in the j-th iteration. j-1 Let be the number of valid points in the j-th iteration, where j is the iteration number.

8. The method for measuring the flatness of a pipe loop based on a total station according to claim 5, characterized in that, When the local deviation term, the distribution uniformity term, and the data sufficiency term are weighted and summed, the weight of the local deviation term is set to 60%, the weight of the distribution uniformity term is set to 30%, and the weight of the data sufficiency term is set to 10%.

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