A shield tunnel segment assembly axis positioning information analysis and processing method and system

Abstract

The present invention discloses a method and system for analyzing and processing information of the axis positioning of the assembly of shield tunnel segments, the method comprising: randomly selecting a first sample subset; fitting a first fitting model; constructing a first internal point set based on the first sample subset and the first fitting model; judging whether the first fitting model is valid; repeating the above steps for a predetermined number of times, selecting the model with the largest number of internal point sets as the best model estimate; refitting the first fitting model using the internal point set obtained by the best model estimate to obtain the final tunnel-specific section profile or central axis; judging whether the shield machine posture needs to be adjusted based on the final tunnel-specific section profile or central axis. The profile and central axis of the tunnel-specific section extracted by the method of the present invention can more accurately adjust the posture of the shield machine. The method has the advantages of robustness, adaptability, flexibility, accuracy and real-time performance, and can adapt to and process complex point cloud data.

Description

Technical Field

[0001] The invention belongs to the field of computer application, and in particular relates to a shield tunnel segment assembly axis positioning information analysis and processing method and system. Background Art

[0002] When shield method is used to construct urban underground tunnels, shield machines often need to pass through buildings (structures), municipal pipe networks, rivers, pile foundations, etc., which places strict requirements on the control of the tunnel axis. At the same time, the installation quality of the tunnel lining segments can also be reflected by the plane and elevation deviations of the tunnel axis. Therefore, the axis control measurement of the shield tunnel is an important task throughout the excavation construction. Good axis positioning information feedback is more conducive to controlling the extension and smooth penetration of the tunnel along the designed axis, preventing the shield machine deviation from exceeding the limit and causing engineering accidents, and ensuring that the axis of the formed tunnel meets the design requirements.

[0003] At present, shield axis control measurement mainly involves two methods: guide system measurement and segment three-dimensional coordinate measurement. Among them, the guide system uses a fully automatic total station (laser transmitter) to measure the prism (laser target) installed in the shield. Through data calculation and software processing, the shield operator can grasp the posture of the shield machine in time and correct the advancement direction at any time. The products with more applications mainly come from Germany's VMT, China's Mido, Britain's ZED, Japan's Verification Workshop and Shanghai Lixin. The segment three-dimensional coordinate measurement is coordinated with the wire control network. With the progress of construction, the tunnel segment lining is manually measured. According to the measurement data, the angle measurement and projection side length are converted and calculated to obtain the specific coordinates of each point. The more commonly used traditional methods are the horizontal ruler method, the center method and the vertical line method. At the same time, with the advancement and improvement of laser scanning technology in recent years, a new method of fitting the cross section by point cloud scanning has also begun to emerge. However, this method currently has no better measurement method and data processing means to determine whether to adjust the shield machine posture. Summary of the invention

[0004] In view of the defects existing in the above-mentioned prior art, the present invention provides a method for analyzing and processing the positioning information of the assembly axis of a shield tunnel segment, comprising the following steps:

[0005] Step S101, using a 3D laser scanner to collect original 3D point data of the tunnel segment on site;

[0006] Step S103, preprocessing the original three-dimensional point data to obtain a first point cloud data set;

[0007] Step S105: randomly selecting a first sample subset from the first point cloud data set;

[0008] Step S107, fitting a first fitting model based on the first sample subset;

[0009] Step S109, calculating the distances from all sample points to the first fitting model, and selecting sample points whose distances are less than a first preset value as a first inlier set;

[0010] Step S1011: If the number of the first interior point set reaches a second preset value, the first fitting model is considered to be valid;

[0011] Step S1013, repeating steps S105 to S1011 for a predetermined number of times, and selecting the model with the largest number of inliers as the best model estimate;

[0012] Step S1015: refitting the first fitting model using the interior point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or center axis;

[0013] Step S1017: Based on the final tunnel specific cross-section profile or central axis, determine whether the shield machine posture needs to be adjusted.

[0014] The preprocessing of the original three-dimensional point data in step S103 includes: data splicing, coordinate conversion, data denoising, data thinning and data smoothing.

[0015] Wherein, the step S105 includes:

[0016] Randomly select a point from the first point cloud data set as the first point of the first sample subset;

[0017] Randomly select the next point from the remaining points and add it to the first sample subset;

[0018] The above steps are repeated until the first sample subset reaches a desired size.

[0019] The first fitting model includes a first cross-sectional profile model and a first central axis model.

[0020] Among them, the first cross-section contour model is a polygonal model, and the first central axis model is a spline curve model.

[0021] Wherein, when the first fitting model is a first cross-sectional profile model, the step S107 includes:

[0022] Suppose we want to fit an N-gon model, where N is the number of sides of the polygon;

[0023] The parameters of the polygon are a0, a1, ..., a N-1 , represents the vertex coordinates of the polygon;

[0024] The polygonal model is represented by the following linear equation:

[0025] y(x)=a0+a1*x+a2*x 2 +...+a n *x n

[0026] Where n is the order of the polygon, a0, a1, ..., a n is the coefficient of the polygon

[0027] We need to minimize each point P i The sum of the distances to the polygonal model is to minimize the following objective function:

[0028] E=Σ(dist(P i ,a0,a1,...,a N-1 )) 2 ;

[0029] Among them, dist(P i ,a0,a1,...,a N-1 ) represents point P i Distance to the polygonal model, P i represents the points in the first sample subset;

[0030] By solving the objective function, the polygon parameter a j The partial derivatives of , we get the least squares equations;

[0031] Solve the system of equations to get an estimate of the parameter a of the polygon.

[0032] Wherein, when the first fitting model is a first central axis model, the step S107 includes:

[0033] Assume there are N central axis points, represented by (x i ,y i ), where i = 0, 1, ..., N-1;

[0034] Use N-1 polynomial functions to connect adjacent central axis points, each polynomial function represents a central axis segment within a cell;

[0035] For the i-th small interval, the parameter of the polynomial function is (a i ,b i ,c i ,d i ), representing the coefficients of the polynomial function;

[0036] In each small interval, the polynomial function is in the form of:

[0037] y(x)=a i +b i *(xx i )+ci *(xx i ) 2 +d i *(xx i ) 3 .

[0038] For each cell interval i, the following equation is set:

[0039] y'(x i )=y'(x i+1 ), so that the first-order derivative values ​​of the central axis at the connection points between adjacent cells will be equal, thus achieving first-order derivative continuity.

[0040] The three-dimensional laser scanner takes itself as the center and the horizontal direction as the reference to obtain point cloud data by rotating or scanning. At each scanning angle, the distance value measured by the three-dimensional laser scanner and the horizontal angle together constitute the position information of each data point.

[0041] The present invention also proposes a shield tunnel segment assembly axis positioning information analysis and processing system, which includes:

[0042] 3D laser scanner, which is used to collect original 3D point data of tunnel segments on site;

[0043] A preprocessing module, which is used to preprocess the original three-dimensional point data to obtain a first point cloud data set;

[0044] A sample subset generation module, which is used to randomly select a first sample subset from the first point cloud data set;

[0045] A fitting model generating module, which is used to fit a first fitting model based on the first sample subset;

[0046] An inlier point set generation module, which is used to calculate the distances from all sample points to the first fitting model, and select sample points whose distances are less than a first preset value as a first inlier point set;

[0047] a first judging module, used for judging whether the number of the first inner point set reaches a second preset value, and if so, considering that the first fitting model is valid;

[0048] An iteration module, which is used to repeat steps S105 to S1011 for a predetermined number of times, and select the model with the largest number of inliers as the best model estimate;

[0049] A refitting module, which is used to refit the first fitting model using the internal point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or a central axis;

[0050] The second judgment module is used to judge whether the shield machine posture needs to be adjusted based on the final tunnel specific section profile or central axis.

[0051] Compared with the prior art, the present invention has the following advantages:

[0052] Robustness: The present invention can provide robust fitting results in the presence of outliers or noise. It estimates the model parameters by selecting a sample subset with the minimum median error, thereby reducing the impact of outliers on the fitting results. This enables the method to handle imperfect point cloud data and improves tolerance to noise and outliers.

[0053] Adaptability: The present invention can adaptively select subset samples according to the characteristics of the data, and evaluate the quality of the fitting model according to the median error. This enables the system to adapt to different data distributions and noise levels, and improves adaptability to different tunnel sections.

[0054] Flexibility: The present invention can fit polygonal models of different shapes and complexities according to requirements. This enables the system to adapt to the geometric characteristics and complexity of different tunnel sections and has a certain degree of flexibility.

[0055] Accuracy: By extracting the profile and centerline of a specific tunnel section through the present invention, the method can provide an accurate description of the tunnel geometry. This helps in further analysis and calculation, such as the area and shape parameters of the tunnel section.

[0056] Real-time performance: The present invention can process and analyze data in real-time or near real-time conditions, meeting the demand for real-time performance in practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present disclosure will become readily understood. In the accompanying drawings, several embodiments of the present disclosure are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0058] Figure 1 It is a flow chart showing a method for analyzing and processing the axis positioning information of shield tunnel segment assembly according to an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical scheme and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] The terms used in the embodiments of the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the present invention. The singular forms "a", "said" and "the" used in the embodiments of the present invention and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings, and "multiple" generally includes at least two.

[0061] It should be understood that although the terms first, second, third, etc. may be used to describe ... in the embodiments of the present invention, these ... should not be limited to these terms. These terms are only used to distinguish .... For example, without departing from the scope of the embodiments of the present invention, the first ... may also be referred to as the second ..., and similarly, the second ... may also be referred to as the first ....

[0062] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0063] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0064] It should also be noted that the term "includes", "comprising" or any other variation thereof is intended to cover non-exclusive inclusion, so that a commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprising a ..." do not exclude the existence of other identical elements in the commodity or device including the elements.

[0065] The optional embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0066] Embodiment 1

[0067] like Figure 1 As shown, the present invention discloses a method for analyzing and processing the positioning information of the assembly axis of a shield tunnel segment, comprising the following steps:

[0068] Step S101, using a 3D laser scanner to collect original 3D point data of the tunnel segment on site;

[0069] Step S103, preprocessing the original three-dimensional point data to obtain a first point cloud data set;

[0070] Step S105: randomly selecting a first sample subset from the first point cloud data set;

[0071] Step S107, fitting a first fitting model based on the first sample subset;

[0072] Step S109, calculating the distances from all sample points to the first fitting model, and selecting sample points whose distances are less than a first preset value as a first inlier set;

[0073] Step S1011: If the number of the first interior point set reaches a second preset value, the first fitting model is considered to be valid;

[0074] Step S1013, repeating steps S105 to S1011 for a predetermined number of times, and selecting the model with the largest number of inliers as the best model estimate;

[0075] Step S1015: refitting the first fitting model using the interior point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or center axis;

[0076] Step S1017: Based on the final tunnel specific cross-section profile or central axis, determine whether the shield machine posture needs to be adjusted.

[0077] Embodiment 2

[0078] A method for analyzing and processing the positioning information of the assembly axis of a shield tunnel segment proposed in the present invention comprises the following steps:

[0079] Step S101, using a 3D laser scanner to collect original 3D point data of the tunnel segment on site;

[0080] Step S103, preprocessing the original three-dimensional point data to obtain a first point cloud data set;

[0081] Step S105: randomly selecting a first sample subset from the first point cloud data set;

[0082] Step S107, fitting a first fitting model based on the first sample subset;

[0083] Step S109, calculating the distances from all sample points to the first fitting model, and selecting sample points whose distances are less than a first preset value as a first inlier set;

[0084] Step S1011: If the number of the first interior point set reaches a second preset value, the first fitting model is considered to be valid;

[0085] Step S1013, repeating steps S105 to S1011 for a predetermined number of times, and selecting the model with the largest number of inliers as the best model estimate. The predetermined number of times is the number of iterations, which can usually be predetermined as 100 times. For each iteration: randomly select a minimum sample set, the size of which is related to the number of parameters of the selected fitting model. Use the selected fitting method to fit the minimum sample set to obtain a fitting model. Calculate the distance between all sample points and the fitting model, and select the inliers according to the distance threshold. Calculate the number of inliers, and save the fitting model and its corresponding number of inliers.

[0086] Step S1015: refitting the first fitting model using the interior point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or center axis;

[0087] Step S1017: Based on the final tunnel specific cross-section profile or central axis, determine whether the shield machine posture needs to be adjusted.

[0088] The preprocessing of the original three-dimensional point data in step S103 includes: data splicing, coordinate conversion, data denoising, data thinning and data smoothing.

[0089] Data denoising: The original point cloud data may contain some noise points, such as abnormal points introduced by scanner errors, stray light, etc. Data denoising can reduce or eliminate these noise points by applying filtering algorithms (such as Gaussian filtering, median filtering, statistical filtering, etc.).

[0090] Data thinning: Raw point cloud data is usually very dense. In order to reduce the amount of data and improve processing efficiency, the data can be thinned. Data thinning can be done by retaining a portion of important points or retaining point cloud data according to a certain sampling density. Data thinning includes: random sampling, uniform sampling, grid sampling, and maximum distance sampling. Random sampling is a simple thinning method that reduces the amount of data by randomly selecting a portion of points in the raw point cloud data. Uniform sampling is a method of thinning by uniformly selecting points in the raw point cloud data according to a certain sampling density. Grid sampling divides the point cloud data into a regular voxel grid and then selects a point from each voxel as a representative. Maximum distance sampling is an iterative thinning method that selects an initial point from the raw point cloud data and then gradually selects the point with the farthest distance from the selected point as the new selected point.

[0091] Coordinate transformation: Before coordinate transformation, the position and orientation of the laser scanner in the tunnel need to be accurately determined. The position and orientation information of the scanner is obtained through ground control points, total stations or other surveying equipment. Set ground control points at the tunnel entrance or other clear locations, and measure their coordinates and azimuths using surveying equipment.

[0092] Tunnel segments usually have a local coordinate system or other reference coordinate system to describe their geometry and position. Choose a suitable reference point or reference line as the origin of the tunnel local coordinate system and determine the coordinate axis direction. The reference point can be the tunnel entrance, tunnel coordinate marker or other known coordinate point.

[0093] The point cloud data in the scanner coordinate system is transformed into the tunnel local coordinate system or other reference coordinate system by applying the appropriate coordinate transformation matrix to the point cloud data. The coordinate transformation matrix includes transformation parameters such as translation, rotation and scaling, which are implemented using matrix operations in linear algebra. After the coordinate transformation is completed, some known points or features can be selected for verification to ensure the accuracy of the transformation. For example, the distance and azimuth between the transformed data and the ground control points or other known points can be measured and compared with the expected values.

[0094] Data stitching: Extract shared boundary points or other feature points from the data of each scanning position. Feature points can be significant geometric features, such as edge points, curvature extreme points, etc. Common feature point extraction algorithms include SIFT, SURF, Harris corner detection, etc. For the data of adjacent scanning positions, perform feature point matching operations to find corresponding feature point pairs. Use feature descriptors and feature matching algorithms, such as SIFT feature descriptors and nearest neighbor algorithms. The matching process considers some geometric constraints, such as least squares fitting or RANSAC algorithm. Based on the feature point matching results, calculate the coordinate transformation relationship between the scanning positions. Use least squares or other optimization methods to estimate the optimal coordinate transformation parameters. According to the coordinate transformation parameters, the data of adjacent scanning positions are transformed so that they are aligned in the same coordinate system. Linear interpolation or other interpolation methods are used to fill the data missing in the stitching area. Finally, the stitched data is merged into complete tunnel segment data.

[0095] Data smoothing: In order to obtain a smoother surface representation, the point cloud data is smoothed to reduce local irregularities. The smoothing operation can apply a filtering algorithm (such as smoothing filtering, surface fitting, etc.) to average or fit neighboring points.

[0096] Wherein, the step S105 comprises: randomly selecting a point from the first point cloud data set as the first point of the first sample subset;

[0097] Randomly select the next point from the remaining points and add it to the first sample subset;

[0098] The above steps are repeated until the first sample subset reaches a desired size.

[0099] The first fitting model includes a first cross-sectional profile model and a first central axis model.

[0100] Among them, the first cross-section contour model is a polygonal model, and the first central axis model is a spline curve model.

[0101] Wherein, when the first fitting model is a first cross-sectional profile model, the step S107 includes:

[0102] Suppose we want to fit an N-gon model, where N is the number of sides of the polygon;

[0103] Assume the parameters of the polygon are a0,a1,...,a N-1 , represents the vertex coordinates of the polygon;

[0104] The polygonal model is represented by the following linear equation:

[0105] y(x)=a0+a1*x+a2*x 2 +...+a n *x n

[0106] Where n is the order of the polygon, a0, a1, ..., a n is the coefficient of the polygon

[0107] We need to minimize each point P i The sum of the distances to the polygonal model is to minimize the following objective function:

[0108] E=Σ(dist(P i ,a0,a1,...,a N-1 )) 2 ;

[0109] Among them, dist(P i ,a0,a1,...,a N-1 ) represents point P i Distance to the polygonal model, P i represents the points in the first sample subset;

[0110] By solving the objective function, the polygon parameter a j The partial derivatives of , we get the least squares equations;

[0111] Solve the system of equations to get the parameter a of the polygon i The estimated value of .

[0112] Wherein, when the first fitting model is a first central axis model, the step S107 includes:

[0113] Assume there are N central axis points, represented by (x i ,y i ), where i = 0, 1, ..., N-1;

[0114] Use N-1 polynomial functions to connect adjacent central axis points, each polynomial function represents a central axis segment within a cell;

[0115] For the i-th small interval, the parameter of the polynomial function is (a i ,b i ,c i ,d i ), representing the coefficients of the polynomial function;

[0116] In each small interval, the polynomial function is in the form of:

[0117] y(x)=a i +b i *(xx i )+c i *(xx i ) 2 +d i *(xx i ) 3 .

[0118] Using this algorithm, some smoothness conditions need to be met, such as continuity and continuity of first-order or second-order derivatives.

[0119] For each cell interval i, the following equation is set:

[0120] y'(x i )=y'(x i +1), so that the first-order derivative values ​​of the central axis at the connection points between adjacent cells will be equal, thereby achieving first-order derivative continuity.

[0121] The three-dimensional laser scanner takes itself as the center and the horizontal direction as the reference to obtain point cloud data by rotating or scanning. At each scanning angle, the distance value measured by the three-dimensional laser scanner and the horizontal angle together constitute the position information of each data point.

[0122] The step 109 includes: performing model fitting on the sample points using the selected fitting method to obtain a fitting curve or a fitting path.

[0123] For each sample point, calculate its distance to the fitted model. The specific calculation method uses the distance formula or projection method to calculate the distance from the point to the model.

[0124] According to the distance threshold, a part of the sample points with the smallest distance is selected as the inlier set. The selection of the threshold can be adjusted according to the specific situation to ensure that the inlier set contains sufficiently accurate sample points. The inlier points can be determined in the following way: among the sample points with the smallest distance, their other attributes or features are further compared to select the inliers. For example, consider selecting sample points with smoother or more continuity as inliers. Alternatively, the sample points with the smallest distance are weighted, the weights are determined according to their distance values, and then the inliers are selected based on the weighted values. Sample points with smaller distances can obtain higher weights and are more likely to be selected as inliers.

[0125] If the fitting results are not satisfactory, you can consider using iterative methods to improve the fitting model and the selection of the inlier set. For example, you can restrict the fitting model to fit only using the inlier set, then recalculate the inlier set and update the fitting model until the convergence criteria are met.

[0126] The step S1017 includes: by fitting the tunnel center axis, the difference between it and the designed center axis can be analyzed. If the fitted center axis has a large deviation from the designed center axis, the attitude of the shield machine may need to be adjusted. Alternatively, by analyzing the elevation data of the tunnel section, the elevation change of the tunnel center axis can be determined. If the elevation change exceeds the design requirements, the attitude of the shield machine may need to be adjusted.

[0127] Embodiment 3

[0128] The present invention also proposes a shield tunnel segment assembly axis positioning information analysis and processing system, which includes:

[0129] 3D laser scanner, which is used to collect original 3D point data of tunnel segments on site;

[0130] A preprocessing module, which is used to preprocess the original three-dimensional point data to obtain a first point cloud data set;

[0131] A sample subset generation module, which is used to randomly select a first sample subset from the first point cloud data set;

[0132] A fitting model generating module, which is used to fit a first fitting model based on the first sample subset;

[0133] An inlier point set generation module, which is used to calculate the distances from all sample points to the first fitting model, and select sample points whose distances are less than a first preset value as a first inlier point set;

[0134] a first judging module, used for judging whether the number of the first inner point set reaches a second preset value, and if so, considering that the first fitting model is valid;

[0135] An iteration module, which is used to repeat steps S105 to S1011 for a predetermined number of times, and select the model with the largest number of inliers as the best model estimate;

[0136] A refitting module, which is used to refit the first fitting model using the internal point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or a central axis;

[0137] The second judgment module is used to judge whether the shield machine posture needs to be adjusted based on the final tunnel specific section profile or central axis.

[0138] Embodiment 4:

[0139] An embodiment of the present disclosure provides a non-volatile computer storage medium, wherein the computer storage medium stores computer executable instructions, and the computer executable instructions can execute the method steps described in the above embodiment.

[0140] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0141] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0142] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (AN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0143] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0144] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0145] The above introduces the preferred embodiments of the present invention, which is intended to make the spirit of the present invention clearer and easier to understand, but is not intended to limit the present invention. All modifications, substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection outlined by the claims attached to the present invention.

Claims

1. A method for analyzing and processing the positioning information of the axis of the shield tunnel segment assembly, comprising the following steps: Step S101, using a 3D laser scanner to collect original 3D point data of the tunnel segment on site; Step S103, preprocessing the original three-dimensional point data to obtain a first point cloud data set; Step S105: randomly selecting a first sample subset from the first point cloud data set; Step S107, fitting a first fitting model based on the first sample subset; Step S109, calculating the distances from all sample points to the first fitting model, and selecting sample points whose distances are less than a first preset value as a first inlier set; wherein the sample points with the smallest distances are weighted, the weights are determined according to their distance values, and then the inliers are selected based on the weighted values; Step S1011: If the number of the first interior point set reaches a second preset value, the first fitting model is considered to be valid; Step S1013, repeating steps S105 to S1011 for a predetermined number of times, and selecting the model with the largest number of inliers as the best model estimate; Step S1015: refitting the first fitting model using the interior point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or center axis; Step S1017: Based on the final tunnel specific cross-section profile or central axis, determine whether the shield machine posture needs to be adjusted; The preprocessing of the original three-dimensional point data in step S103 includes: data splicing, coordinate conversion, data denoising, data thinning and data smoothing; Wherein the step S105 comprises: Randomly select a point from the first point cloud data set as the first point of the first sample subset; Randomly select the next point from the remaining points and add it to the first sample subset; Repeat the above steps until the first sample subset reaches a required size; The first fitting model includes a first cross-sectional profile model and a first central axis model; The first cross-section contour model is a polygonal model, and the first central axis model is a spline curve model; When the first fitting model is a first cross-sectional profile model, the step S107 includes: Suppose you want to fit an N-gon model, where N is the number of sides of the polygon and the parameters of the polygon are a0, a1, ..., a N-1 , represents the vertex coordinates of the polygon; The polygonal model is represented by the following linear equation: y(x)=a0+a1*x+a2*x 2 +...+a n *x n Where n is the order of the polygon, a0, a1, ..., a n is the coefficient of the polygon We need to minimize each point P i The sum of the distances to the polygonal model is to minimize the following objective function: E=Σ(dist(P i ,a0,a1,...,a N-1 )) 2 ; Among them, dist(P i ,a0,a1,...,a N-1 ) represents point P i Distance to the polygonal model, P i represents the points in the first sample subset; By solving the objective function, the polygon parameter a j The partial derivatives of , we get the least squares equations; Solve the system of equations to get an estimate of the parameter a of the polygon.

2. The method according to claim 1, wherein when the first fitting model is a first central axis model, the step S107 comprises: Assume there are N central axis points, represented by (x i ,y i ), where i = 0, 1, ..., N-1; Use N-1 polynomial functions to connect adjacent central axis points, each polynomial function represents a central axis segment within a cell; For the i-th small interval, the parameter of the polynomial function is (a i ,b i ,c i ,d i ), representing the coefficients of the polynomial function; In each small interval, the polynomial function is in the form of: y(x)=a i +b i *(x-x i )+c i *(x-x i ) 2 +d i *(x-x i ) 3 。 3. The method of claim 2, wherein for each cell interval i, the following equation is set: y'(x i )=y'(x i +1), so that the first-order derivative values ​​of the central axis at the connection points between adjacent cells will be equal, thereby achieving first-order derivative continuity.

4. The method as claimed in claim 1, wherein the three-dimensional laser scanner takes itself as the center and the horizontal direction as the reference, and obtains point cloud data by rotating or scanning. At each scanning angle, the distance value measured by the three-dimensional laser scanner and the horizontal angle together constitute the position information of each data point.

5. A shield tunnel segment assembly axis positioning information analysis and processing system, used to implement the method according to any one of claims 1 to 4, comprising: 3D laser scanner, which is used to collect original 3D point data of tunnel segments on site; A preprocessing module, which is used to preprocess the original three-dimensional point data to obtain a first point cloud data set; A sample subset generation module, which is used to randomly select a first sample subset from the first point cloud data set; A fitting model generating module, which is used to fit a first fitting model based on the first sample subset; An inlier point set generation module, which is used to calculate the distances from all sample points to the first fitting model, and select sample points whose distances are less than a first preset value as a first inlier point set; a first judging module, used for judging whether the number of the first inner point set reaches a second preset value, and if so, considering that the first fitting model is valid; An iteration module, which is used to repeat to a predetermined number of times and select the model with the largest number of internal point sets as the best model estimate; A refitting module, which is used to refit the first fitting model using the internal point set estimated by the optimal model to obtain a final tunnel specific cross-section profile or a central axis; The second judgment module is used to judge whether the shield machine posture needs to be adjusted based on the final tunnel specific section profile or central axis.

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