Point domain processing method for tunnel settlement detection data

Abstract

The invention discloses a point domain processing method for tunnel settlement detection data, and relates to the technical field of tunnel monitoring data processing. The invention provides a point domain processing method for tunnel settlement detection data, through two main steps of point and domain data processing, the influence of horizontal and longitudinal position deviations between different detection stations on a data comparison result is eliminated, so that the precision and reliability of data analysis are improved, and in the point data processing stage, the accuracy and reliability of data analysis are improved. According to the method, firstly, the coordinates of an unknown point are deduced through back calculation of the coordinates of a known point, and specifically, the accurate coordinates of the unknown point can be obtained through regression analysis and calculation of the point coordinates of a detection site. The method can effectively correct the data comparison error caused by the horizontal position deviation of the detection station. By means of the back calculation technology, on the premise that high precision is kept, deviation caused by measurement errors can be reduced, and comparability of data between different detection sites is ensured.

Description

Technical Field

[0001] The invention relates to the technical field of tunnel monitoring data processing, and in particular to a point domain processing method for tunnel settlement detection data. Background Art

[0002] With the continuous expansion of tunnel construction and operation, tunnel structure settlement monitoring has become an important means to ensure tunnel safety. Tunnel settlement usually refers to the displacement change of tunnel structure in the vertical direction caused by various factors generated during geological conditions, tunnel construction and operation.

[0003] In order to ensure the safety of the tunnel structure and the reliability of operation, accurate settlement monitoring and data analysis of the tunnel structure must be carried out.

[0004] However, due to the complex on-site measurement environment, certain errors in measurement equipment and methods, the horizontal and vertical positions of the same detection site may deviate. These deviations will cause errors in data comparison and analysis, affecting the accuracy and reliability of settlement monitoring results. Therefore, how to deal with these data deviations through scientific methods has become an important research topic in tunnel settlement detection.

[0005] Therefore, a new solution to the above problems needs to be proposed. Summary of the invention

[0006] The purpose of the present invention is to provide a point domain processing method for tunnel settlement detection data, which can realize the correction of detection value comparison error caused by position deviation of mobile detection robot and accurate identification of settlement blocks through coordinate point inverse calculation algorithm and block algorithm based on cubic B-spline algorithm, so as to solve the technical problems raised in the background technology.

[0007] To achieve the above object, the present invention provides the following technical solution: a point domain processing method for tunnel settlement detection data, comprising at least the following steps:

[0008] S1: Build the coordinate point inverse calculation algorithm and the Gaussian block algorithm based on the cubic B-spline algorithm;

[0009] S2: The coordinate point back-calculation algorithm and Gaussian block algorithm are used to correct the detection value comparison error caused by the position deviation of the mobile detection robot and accurately identify the settlement block.

[0010] Furthermore, the coordinate point inverse calculation algorithm in S1 is used for data point processing, and a point data processing algorithm is used for the horizontal position deviation of the Y axis;

[0011] Set P1 and P2 as the first monitoring points, P3 and P4 as the second monitoring points, and P1 and P3 are the same monitoring position points for two repeated detections of the tunnel top arc section;

[0012] The monitoring distances are divided into L1 and L3, P2 and P2 are the same monitoring position points of the tunnel vertical side wall for two repeated tests, and their monitoring distances are divided into L2 and L4, and Δ3 is the horizontal position deviation of the two detection tests;

[0013] β i is the monitoring angle value of each detection point, h0 is the height of the vertical side wall and the side arc segment, d is the distance from the detection robot to the horizontal endpoint, and R is the radius value of the top arc segment;

[0014] The point data processing algorithm is to use the first monitoring data and known parameters to inversely calculate the theoretical position of the second monitoring data. For details, please refer to formula (1) and formula (2), wherein formula (1) is applicable to the tunnel top arch ring, and formula (2) is applicable to the tunnel vertical side wall;

[0015]

[0016] in, is the angle value of the first detection point, To detect the distance from the robot to the left endpoint, Detect the distance from the robot to the right endpoint.

[0017] Furthermore, the Gaussian block algorithm is a data domain processing method, and a domain data processing algorithm is used for the vertical position deviation of the X-axis;

[0018] The domain data processing algorithm is a tunnel network construction and site block method based on a cubic B-spline interpolation algorithm;

[0019] The distance value of each point in the monitoring data and its corresponding X-axis coordinate value are combined as the type value point Zi of the cubic B-spline interpolation algorithm. Assume that there are l+1 type value points Z in a longitudinal section of the tunnel monitoring section. i (l=0,1,2...l), n+1 control points P i (i=0,1,2...n), m+1 nodes t i (i=0, 1, 2, ..., m);

[0020] Where n, m, k satisfy m = n + k + 1;

[0021] The cubic B-spline curve is given by referring to formula (3),

[0022]

[0023] Among them: B i,k (t) is the K-order B-spline basis function, also called the harmonic function;

[0024] When the first and last ends of the node list are repeated K+1 times, the first and last values ​​of the generated spline curve are the same as the type value points, thus ensuring the integrity of the spline curve. This spline curve is called clamped B-spline curves.

[0025] In the case of clamped B-spline Curves, the node t i Satisfies formula (4).

[0026]

[0027] According to the one-to-one correspondence between the type value points and the nodes in the definition domain, equation (5) is satisfied, and the matrix expression is equation (6):

[0028]

[0029] In order to solve the numerous unknowns in the matrix, the accumulated chord length parameterization method is used to obtain the interval division of each node to solve the value of each basis function. The non-kink method is used to establish the boundary condition equations at the beginning and end, and the l+3 unknowns and l+3 equations are solved to finally realize the construction of the longitudinal section of the tunnel.

[0030] Furthermore, the site block method is to achieve the conversion of different sections to equal block section comparison;

[0031] According to the block division of different cross sections at the same station of the tunnel settlement section, the station block division matrix refers to formula (7);

[0032]

[0033] Where: L i (i=1,2..l) is the longitudinal deviation value of the i-th monitoring station, Z h，c (h=1, 2; c=1, 2...l) are the plasticity point values ​​of two groups of comparative experiments at the same monitoring station, Z 1，c For the smaller value group, Z 2，c For the larger value group;

[0034] The block width D refers to formula (8):

[0035] D=(Z 2.c +L i )-(Z 1，0 -L i )=3L i (8)

[0036] After the tunnel section is segmented, four points inside the block are divided into one plastic value point and three prediction points with different weights.

[0037] The weight division is based on the central diffusion method, and the weight value decays equidistantly or geometrically from the plastic value point to both sides;

[0038] Finally, the block mean is calculated as the corresponding value at the same station and the same block of the tunnel;

[0039] Z 1，c Taking a smaller array as an example, equation (9) is the Gaussian weight f i Mathematical expression: Formula (10) is the weighted average value of each block in the cross section of site i The matrix expression of ;

[0040]

[0041] Finally, according to the structural settlement characteristics during the tunnel construction period, the simultaneous settlement threshold of continuous block points in the same or connected tunnel sections is determined, thereby achieving accurate determination of the tunnel settlement area.

[0042] Furthermore, the S2 at least includes the following steps:

[0043] By building a point data processing method of coordinate point back-calculation algorithm, that is, back-calculating unknown point coordinates from known point coordinates, the data comparison deviation caused by horizontal position deviation of the same detection site can be realized;

[0044] Then, the domain data processing method of the Gaussian block algorithm and the site block method are used to realize the data deviation caused by the longitudinal position deviation of the same detection site.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. The present invention proposes a point domain processing method for tunnel settlement detection data. Through two main steps of point and domain data processing, the influence of horizontal and longitudinal position deviations between different detection sites on data comparison results is eliminated, thereby improving the accuracy and reliability of data analysis.

[0047] 2. In the point data processing stage, the present invention first derives the coordinates of the unknown points by back-calculating the coordinates of the known points. Specifically, the precise coordinates of the unknown points can be obtained by performing regression analysis and extrapolation on the point coordinates of the detection site. This method can effectively correct the data comparison error caused by the horizontal position deviation of the detection site. Using this back-calculation technology, the deviation caused by measurement error can be reduced while maintaining high accuracy, and the comparability of data between different detection sites can be ensured.

[0048] 3. In the domain data processing stage, the present invention introduces a block processing technology based on the cubic B-spline algorithm. B-spline is a mathematical tool widely used in curve fitting and interpolation calculations, and has good smoothness and flexibility. In the present invention, the cubic B-spline algorithm is used to perform domain block processing on the data, the entire detection area is divided into several adjacent sub-domains, and curve fitting is performed on the data in each sub-domain. This processing method can effectively correct the data comparison error caused by the longitudinal position deviation of the detection site. By performing curve fitting on the data in each sub-domain, an accurate description of the data in the entire detection area can be achieved, thereby improving the accuracy of data analysis in a larger range.

[0049] 4. The method proposed in the present invention is not only applicable to data processing of tunnel settlement detection, but can also be extended to other similar engineering monitoring and data analysis fields. By effectively eliminating the position deviation problem in the measurement data, the present invention provides a reliable tool that can maintain high-precision data analysis capabilities in various complex measurement environments. This will provide strong technical support for the safe operation of tunnel structures and have a positive impact on the design, construction and maintenance of tunnel projects. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0051] Figure 1 It is the overall algorithm structure diagram of the present invention;

[0052] Figure 2 It is a plane diagram of the point tunnel section processing algorithm of the present invention;

[0053] Figure 3 This is a regional division plan of different sections of the same site of the present invention. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0055] See also Figure 1 , Figure 2 , Figure 3 , a point domain processing method for tunnel settlement detection data, comprising at least the following steps:

[0056] S1: Build the coordinate point inverse calculation algorithm and the Gaussian block algorithm based on the cubic B-spline algorithm;

[0057] S2: The coordinate point back-calculation algorithm and Gaussian block algorithm are used to correct the detection value comparison error caused by the position deviation of the mobile detection robot and accurately identify the settlement block.

[0058] The coordinate point inversion algorithm in S1 is used for data point processing, and a point data processing algorithm is used for the horizontal position deviation of the Y axis;

[0059] Set P1 and P2 as the first monitoring points, P3 and P4 as the second monitoring points, and P1 and P3 are the same monitoring position points for two repeated detections of the tunnel top arc section;

[0060] The monitoring distances are divided into L1 and L3, P2 and P2 are the same monitoring position points of the tunnel vertical side wall for two repeated tests, and their monitoring distances are divided into L2 and L4, and Δ3 is the horizontal position deviation of the two detection tests;

[0061] β i is the monitoring angle value of each detection point, h0 is the height of the vertical side wall and the side arc segment, d is the distance from the detection robot to the horizontal endpoint, and R is the radius value of the top arc segment;

[0062] The point data processing algorithm is to use the first monitoring data and known parameters to inversely calculate the theoretical position of the second monitoring data. For details, please refer to formula (1) and formula (2), wherein formula (1) is applicable to the tunnel top arch ring, and formula (2) is applicable to the tunnel vertical side wall;

[0063]

[0064] in, is the angle value of the first detection point, To detect the distance from the robot to the left endpoint, Detect the distance from the robot to the right endpoint.

[0065] The Gaussian block algorithm is a data domain processing method, which uses a domain data processing algorithm for the vertical position deviation of the X-axis;

[0066] The domain data processing algorithm is a tunnel network construction and site block method based on a cubic B-spline interpolation algorithm;

[0067] The distance value of each point in the monitoring data and its corresponding X-axis coordinate value are combined as the type value point Zi of the cubic B-spline interpolation algorithm. Assume that there are l+1 type value points Z in a longitudinal section of the tunnel monitoring section. i (l=0,1,2...l), n+1 control points P i (i=0,1,2...n), m+1 nodes ti (i=0, 1, 2, ..., m);

[0068] Where n, m, k satisfy m = n + k + 1;

[0069] The cubic B-spline curve is given by referring to formula (3),

[0070]

[0071] Among them: B i，k (t) is the K-order B-spline basis function, also called the harmonic function;

[0072] When the first and last ends of the node list are repeated K+1 times, the first and last values ​​of the generated spline curve are the same as the type value points, thus ensuring the integrity of the spline curve. This spline curve is called clamped B-spline curves.

[0073] In the case of clamped B-spline Curves, the node t i Satisfies formula (4).

[0074]

[0075] According to the one-to-one correspondence between the type value points and the nodes in the definition domain, equation (5) is satisfied, and the matrix expression is equation (6):

[0076]

[0077] In order to solve the numerous unknowns in the matrix, the accumulated chord length parameterization method is used to obtain the interval division of each node to solve the value of each basis function. The non-kink method is used to establish the boundary condition equations at the beginning and end, and the l+3 unknowns and l+3 equations are solved to finally realize the construction of the longitudinal section of the tunnel.

[0078] The site block method is to achieve the conversion of different sections to equal block section comparison;

[0079] According to the block division of different cross sections at the same station of the tunnel settlement section, the station block division matrix refers to formula (7);

[0080]

[0081] Where: L i (i=1,2..l) is the longitudinal deviation value of the i-th monitoring station, Z h，c (h=1, 2; c=1, 2...l) are the plasticity point values ​​of two groups of comparative experiments at the same monitoring station, Z 1，c For the smaller value group, Z 2，c For the larger value group;

[0082] The block width D refers to formula (8):

[0083] D=(Z 2.c +L i )-(Z 1，0 -L i )=3L i (8)

[0084] After the tunnel section is segmented, four points inside the block are divided into one plastic value point and three prediction points with different weights.

[0085] The weight division is based on the central diffusion method, and the weight value decays equidistantly or geometrically from the plastic value point to both sides;

[0086] Finally, the block mean is calculated as the corresponding value at the same station and the same block of the tunnel;

[0087] Z 1，c Taking a smaller array as an example, equation (9) is the Gaussian weight f i Mathematical expression: Formula (10) is the weighted average value of each block in the cross section of site i The matrix expression of ;

[0088]

[0089] Finally, according to the structural settlement characteristics during the tunnel construction period, the simultaneous settlement threshold of continuous block points in the same or connected tunnel sections is determined, thereby achieving accurate determination of the tunnel settlement area.

[0090] The S2 at least comprises the following steps:

[0091] By building a point data processing method of coordinate point back-calculation algorithm, that is, back-calculating unknown point coordinates from known point coordinates, the data comparison deviation caused by horizontal position deviation of the same detection site can be realized;

[0092] Then, the domain data processing method of the Gaussian block algorithm and the site block method are used to realize the data deviation caused by the longitudinal position deviation of the same detection site.

[0093] To verify the accuracy of the present invention in tunnel settlement data processing. A mobile tunnel settlement monitoring robot equipped with a high-precision rangefinder is used to collect tunnel settlement data for construction. The collected data is compared and analyzed by traditional data processing methods and the data processing method of the present invention. A test highway tunnel was selected as the tunnel. In order to achieve a benchmarking effect for settlement, a 3mm thick foam board was attached to the tunnel wall during the second test as the settlement area. First, the traditional data processing method was used for accuracy comparison. The test steps are as follows:

[0094] Step 1: Place the mobile monitoring robot at the starting point and start testing.

[0095] Step 2: The detection robot uses a rangefinder to perform point detection on the tunnel section, and the detection data is the distance value.

[0096] Step 3: After the detection is completed, the three-dimensional coordinates of the tunnel detection data are calculated through trigonometric function relationships.

[0097] Step 4: Conduct a second test along the same testing route and at the same testing site.

[0098] Step 5: After the second detection is completed, the processed two-dimensional coordinate point data are compared to check whether there are points exceeding the threshold and areas exceeding the threshold.

[0099] The results show that there are many points in the detection data that exceed the threshold in the non-settlement area. At the stations with large horizontal and vertical deviations, the station comparison values ​​show obvious deviations.

[0100] Next, the accuracy of the settlement data processing method of the present invention is compared. The test steps are as follows:

[0101] Step 1: Place the mobile monitoring robot at the starting point and start testing.

[0102] Step 2: The detection robot uses a rangefinder to perform point detection on the tunnel section, and the detection data is the distance value.

[0103] Step 3: After the detection is completed, the detection data point processing algorithm performs data processing on the horizontal position deviation, and then the domain processing algorithm performs data processing on the vertical position deviation.

[0104] Step 4: Conduct a second test along the same testing route and at the same testing site.

[0105] Step 5: After the second detection is completed, the processed two-dimensional coordinate point data are compared to check whether there are points exceeding the threshold and areas exceeding the threshold.

[0106] The results show that the points exceeding the threshold in the non-subsidence area in the detection data are few and discontinuous. At the sites with large horizontal and vertical deviations, the deviation problem is greatly reduced after point domain algorithm processing, achieving accurate identification and screening of target subsidence blocks.

[0107] By comparing and analyzing the above-mentioned traditional data processing method with the data processing method of the present invention, it can be seen that the present invention has better environmental adaptability and accuracy in tunnel settlement detection data processing than the traditional processing method.

[0108] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A point domain processing method for tunnel settlement detection data, characterized in that: At least the following steps are included: S1: Build the coordinate point inverse calculation algorithm and the Gaussian block algorithm based on the cubic B-spline algorithm; S2: The coordinate point back-calculation algorithm and Gaussian block algorithm are used to correct the detection value comparison error caused by the position deviation of the mobile detection robot and accurately identify the settlement block.

2. A point domain processing method for tunnel settlement detection data according to claim 1, characterized in that: The coordinate point inversion algorithm in S1 is used for data point processing, and a point data processing algorithm is used for the horizontal position deviation of the Y axis; Set P1 and P2 as the first monitoring points, P3 and P4 as the second monitoring points, and P1 and P3 are the same monitoring position points for two repeated detections of the tunnel top arc section; The monitoring distances are divided into L1 and L3, P2 and P2 are the same monitoring position points of the tunnel vertical side wall for two repeated tests, and their monitoring distances are divided into L2 and L4, and Δ3 is the horizontal position deviation of the two detection tests; β i is the monitoring angle value of each detection point, h0 is the height of the vertical side wall and the side arc segment, d is the distance from the detection robot to the horizontal endpoint, and R is the radius value of the top arc segment; The point data processing algorithm is to use the first monitoring data and known parameters to inversely calculate the theoretical position of the second monitoring data. For details, please refer to formula (1) and formula (2), wherein formula (1) is applicable to the tunnel top arch ring, and formula (2) is applicable to the tunnel vertical side wall; in, is the angle value of the first detection point, To detect the distance from the robot to the left endpoint, Detect the distance from the robot to the right endpoint.

3. A point domain processing method for tunnel settlement detection data according to claim 2, characterized in that: The Gaussian block algorithm is a data domain processing method, which uses a domain data processing algorithm for the vertical position deviation of the X-axis; The domain data processing algorithm is a tunnel network construction and site block method based on a cubic B-spline interpolation algorithm; The distance value of each point in the monitoring data and its corresponding X-axis coordinate value are combined as the type value point Z of the cubic B-spline interpolation algorithm. i , assuming that there are l+1 type value points Z in a longitudinal section of the tunnel monitoring section i (l=0,1,2…l),n+1 control points P i (i=0,1,2…n), m+1 nodes t i (i=0,1,2…m); Where n, m, k satisfy m = n + k + 1; The cubic B-spline curve is given by referring to formula (3), Among them: B i,k (t) is the K-order B-spline basis function, also called the harmonic function; When the first and last ends of the node list are repeated K+1 times, the first and last values ​​of the generated spline curve are the same as the type value points, thus ensuring the integrity of the spline curve. This spline curve is called clamped B-spline curves. In the case of clamped B-spline curves, the node t i Satisfies formula (4). According to the one-to-one correspondence between the type value points and the nodes in the definition domain, equation (5) is satisfied, and the matrix expression is equation (6): In order to solve the numerous unknowns in the matrix, the accumulated chord length parameterization method is used to obtain the interval division of each node, so as to solve the value of each basis function, realize the solution of l+3 unknowns and l+3 equations, and finally realize the construction of the tunnel longitudinal section.

4. A point domain processing method for tunnel settlement detection data according to claim 3, characterized in that: The site block method is to achieve the conversion of different sections to equal block sections for comparison; According to the block division of different cross sections at the same station of the tunnel settlement section, the station block division matrix refers to formula (7); Where: L i (i=1,2..l) is the longitudinal deviation value of the i-th monitoring station, Z h,c (h=1, 2; c=1, 2…l) are the plasticity point values ​​of two groups of comparative experiments at the same monitoring station, Z 1,c For the smaller value group, Z 2,c For the larger value group; The block width D refers to formula (8): D=(Z 2.c +L i )-(Z 1,0 -L i )=3L i (8) After the tunnel section is segmented, four points inside the block are divided into one plastic value point and three prediction points with different weights. The weight division is based on the central diffusion method, and the weight value decays equidistantly or geometrically from the plastic value point to both sides; Finally, the block mean is calculated as the corresponding value at the same station and the same block of the tunnel; Z 1,c Taking a smaller array as an example, equation (9) is the Gaussian weight f i Mathematical expression: Formula (10) is the weighted average value of each block in the cross section of site i The matrix expression of ; Finally, according to the structural settlement characteristics during the tunnel construction period, the simultaneous settlement threshold of continuous block points in the same or connected tunnel sections is determined, thereby achieving accurate determination of the tunnel settlement area.

5. A point domain processing method for tunnel settlement detection data according to claim 4, characterized in that: The S2 at least comprises the following steps: By building a point data processing method of coordinate point back-calculation algorithm, that is, back-calculating unknown point coordinates from known point coordinates, the data comparison deviation caused by horizontal position deviation of the same detection site can be realized; Then, the domain data processing method of the Gaussian block algorithm and the site block method are used to realize the data deviation caused by the longitudinal position deviation of the same detection site.

Images