Intelligent slope and line adjustment method and device

Abstract

The application discloses a kind of intelligent slope adjustment line method and equipment, comprising: obtaining the measured section offset of each measuring point of the line to be adjusted and the elevation of top and bottom plate, based on the measured section offset and the elevation of top and bottom plate, measured line chart is drawn;The measured line chart includes: plan and longitudinal profile;Obtain the design line chart of the line to be adjusted, with the minimum comprehensive deviation function between design line chart and measured line chart as optimization goal, optimization adjustment is carried out to design line chart based on optimization algorithm.The application abstracts mass measurement data into a virtual "measured line", and abstracts the slope adjustment line process into an intelligent optimization process of approximating the "design line" to the "measured line", which realizes the dimension reduction of the problem and greatly improves the calculation efficiency;At the same time, by constructing the corresponding comprehensive deviation function, the overall and local encroachment is considered, so that the optimization process meets the actual engineering demand.

Description

Technical Field

[0001] This invention relates to the field of intelligent power line engineering technology, and in particular to an intelligent slope adjustment and alignment method and equipment. Background Technology

[0002] Gradient and alignment adjustments are essential steps before subway track laying. During subway tunnel construction, errors in civil engineering, foundation settlement and deformation, and abnormal tunneling posture of the tunnel boring machine can easily cause the tunnel alignment to deviate from the original design, making it difficult to meet the requirements for track smoothness and the installation of equipment along the line. Therefore, after tunnel completion and before track laying, cross-sectional measurements are required. By comparing the measured bridge and tunnel cross-sections with the design cross-sections, encroachment sections are identified. For these encroachment sections, gradient and alignment adjustments are necessary, i.e., adjusting the horizontal and vertical alignment to reduce the relative deviation between the line and the existing bridges and tunnels, thereby reducing or eliminating encroachment. Subsequent track laying and installation of various equipment along the line are based on the new alignment map after gradient and alignment adjustments. Traditional gradient and alignment adjustments are mainly manual, requiring a large amount of manual analysis, comparison, and trial calculations, which is inefficient, generally taking 7-10 working days. Human error is highly likely during this process, and the resulting alignment scheme is not guaranteed to be optimal.

[0003] With the development of modern technology, there are already studies on intelligent line engineering adjustment methods in existing technologies. For example, patent CN110363298A discloses an intelligent line adjustment and slope adjustment method based on evolutionary algorithm. By perturbing the horizontal and vertical profile parameters of the line, multiple initial lines are formed. Then, by using genetic operators similar to natural genetics for selection, crossover and mutation, a population representing a new solution set is generated. This process will make the subsequent generation of the population more adapted to the environment than the previous generation. The optimal individual in the last generation of the population is decoded to finally obtain an optimal line adjustment and slope adjustment scheme. However, this method has the following shortcomings: (1) The search speed of the algorithm is relatively slow, and more training time is required to obtain a more accurate solution; (2) The programming implementation of the algorithm is relatively complex. First, the problem needs to be binary encoded, and after finding the optimal solution, the problem needs to be decoded; (3) Binary encoding has mapping errors when discretizing continuous functions. When the length of the binary string is short, the solution may not reach the required accuracy; when the binary string is long enough, the search space for the solution will expand dramatically; (4) the local search capability of the algorithm is poor, the search efficiency is low in the later stages of evolution, and the calculation is time-consuming; (5) simply using the total number of encroachment points as a measure of the degree of tunnel encroachment ignores the important factor of the local maximum encroachment value; in actual engineering, shield tunnels use prefabricated assembled segments, and U-shaped beams use prefabricated thin-walled prestressed structures, neither of which can remove excessively large encroachment points locally. Therefore, for this type of structure, the primary task of slope adjustment and alignment adjustment is to remove the excessively large encroachment points. The goal is to reduce the local maximum encroachment limit value, rather than simply reducing the number of encroachment limit points; (6) Each iteration calculation requires the encroachment limit point statistics for each encroachment limit section. Generally, at least 8 to 10 encroachment limit points need to be analyzed and compared on each section, which is inefficient and takes a long time to calculate; (7) In each iteration calculation, the encroachment limit points need to be found by comparing the "theoretical circle" of the designed tunnel with the "actual circle" excavated by the actual shield machine. Therefore, it is only suitable for circular cross-section tunnels with regular shapes and relatively simple mathematical analytical expressions, and is not suitable for rectangular and horseshoe-shaped tunnels with more variable cross-section dimensions.

[0004] Furthermore, in the article "Research on Intelligent Alignment and Gradient Adjustment of Urban Rail Transit Based on Particle Swarm Optimization Algorithm", it was also disclosed that: based on the particle swarm optimization algorithm, the three-dimensional spatial geometric parameters of the line are used as design variables, and a general mathematical model of multivariate functions is established with the goal of reducing the number of key encroachment points, thus constructing an intelligent alignment and gradient adjustment model. However, this method has the following shortcomings: (1) The global optimization ability of the particle swarm optimization algorithm is poor, and it is prone to premature convergence and getting stuck in local optima, making it difficult to guarantee the optimal quality of the gradient and alignment adjustment results; (2) Simply using the total number of encroachment points as a measure of the degree of tunnel encroachment ignores the important factor of the local maximum encroachment value. In actual engineering, shield tunnels use prefabricated segments, and U-beams use prefabricated thin-walled prestressed structures, making it impossible to remove excessively large encroachment points locally. Therefore, for this type of structure, the primary goal of slope and alignment adjustment is to reduce the maximum local encroachment value, rather than simply reducing the number of encroachment points; (3) Each iteration of calculation requires counting encroachment points for each encroachment section, which is inefficient and time-consuming; (4) It is necessary to manually compare the tunnel section with the boundary map and set the weight of each encroachment point. Considering the diverse types of subway tunnels, even tunnels of the same type may have multiple boundary maps in different sections due to different equipment installation requirements and track types. Therefore, the process of setting the weight of encroachment points is very complex and relies entirely on the experience of designers, making it highly susceptible to human interference. Summary of the Invention

[0005] The purpose of this invention is to overcome the problems of complex algorithms, low computational efficiency, single optimization target, and insufficient accuracy in existing intelligent line adjustment methods, and to provide an intelligent slope adjustment and line adjustment method and equipment. This invention converts measured data into measured lines, thereby enabling the overall deviation between the designed line and the measured line as the optimization target, and realizing the automatic adjustment of the line through optimization algorithms.

[0006] To achieve the above-mentioned objectives, the present invention provides the following technical solution:

[0007] A smart slope and alignment adjustment method includes:

[0008] Obtain the measured cross-sectional offset and top and bottom plate elevations of each measuring point on the line to be adjusted, and draw a measured line diagram based on the measured cross-sectional offset and top and bottom plate elevations; the measured line diagram includes: a plan view and a longitudinal profile view.

[0009] Obtain the design route diagram of the route to be adjusted. With the minimum comprehensive deviation function between the design route diagram and the measured route diagram as the optimization objective, optimize and adjust the design route diagram based on the optimization algorithm so that the design route diagram approximates the measured route diagram. The comprehensive deviation function is constructed based on the maximum intrusion limit value and the total number of intrusion limit points.

[0010] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, the step of drawing a measured alignment map based on the measured cross-sectional offset and the top and bottom plate elevations includes:

[0011] S101: Calculate the cross-sectional offset deviation of each measuring point based on the measured cross-sectional offset and the design cross-sectional offset of each measuring point;

[0012] S102: Calculate the horizontal deviation of the measured line center point corresponding to each measuring point based on the cross-sectional offset deviation;

[0013] S103: Using the center point of the designed line as a reference, the plane coordinates of the measured line center point are obtained based on the horizontal deviation of the measured line center point at each measuring point.

[0014] S104: Connect the plane coordinates of the center points of each measured line in sequence according to the cross-sectional mileage to obtain the plane diagram of the measured line;

[0015] S105: Calculate the elevation deviation of the top and bottom plates based on the measured top plate elevation, measured bottom plate elevation, and designed top plate elevation and designed bottom plate elevation at each measuring point;

[0016] S106: Calculate the measured elevation deviation of the center point of the line based on the elevation deviation of the top and bottom plates in each section;

[0017] S107: Using the center point of the designed line as a reference, the elevation of the measured center point of the line is obtained based on the elevation deviation of the measured center point of the line.

[0018] S108: Connect the elevations of the center points of each measured line in sequence according to the cross-sectional mileage to obtain the longitudinal profile of the measured line.

[0019] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, in step S103, the plane coordinates of the measured line center point are obtained by the following formula:

[0020]

[0021] Where x0 is the x-coordinate of the designed route plan at mileage m0; y0 is the y-coordinate of the designed route plan at mileage m0; x'(m0) is the derivative of the parametric equation x(m) of the designed route plan at mileage m0; and y'(m0) is the derivative of the parametric equation y(m) of the designed route plan at mileage m0.

[0022] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, in step S102, the measured horizontal deviation of the center point of the line is obtained by the following formula:

[0023]

[0024] Where, ΔLi右 —The cross-sectional offset deviation of the i-th measuring point on the right side wall of the tunnel / bridge; ΔL i左 — Cross-sectional offset deviation of the i-th measuring point on the left side wall of the tunnel / bridge; n — Number of measuring points on a single side wall (left or right side wall) of the tunnel / bridge.

[0025] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, in step S106, the measured elevation deviation of the center point of the line is obtained by the following formula:

[0026]

[0027] Where: ΔH 顶 —Tunnel roof elevation deviation; ΔH 底 —Elevation deviation of tunnel floor or bridge beam surface.

[0028] According to one specific implementation method, the optimization algorithm in the above-mentioned intelligent slope adjustment and alignment method is the Bald Eagle algorithm.

[0029] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, the comprehensive deviation function is:

[0030]

[0031] Among them, E max —Maximum intrusion limit; E 0max —Initial maximum intrusion limit; n sum —Total number of intrusion points; n 0sum —Initial number of intrusion limits; λ1—Weighting coefficient for maximum intrusion limit value, ranging from 0 to 1; λ2—Weighting coefficient for total number of intrusion limits, ranging from 0 to 1.

[0032] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, for the subgrade foundation (such as horseshoe-shaped tunnel, rectangular tunnel, cast-in-place concrete box girder) using cast-in-place reinforced concrete structure, the comprehensive deviation function satisfies: λ1 < λ2.

[0033] According to a specific implementation method, in the above-mentioned intelligent slope adjustment and alignment method, for the subgrade foundation with prefabricated structure (such as shield tunnel, U-shaped beam), the comprehensive deviation function satisfies: λ2<λ1.

[0034] In another aspect, the present invention provides an electronic device comprising: a processor, a network interface, and a memory, wherein the processor, the network interface, and the memory are interconnected, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is configured to invoke the program instructions to execute the above-described intelligent slope adjustment and line adjustment method.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. The method provided in this embodiment of the invention abstracts massive measurement data into a virtual "measured line" and abstracts the slope adjustment and alignment process into an intelligent optimization process that approximates the "measured line" with the "designed line". Thus, in the optimization process, there is no need to perform algebraic comparisons of the "measured cross-section data" and the "designed cross-section data" one by one, realizing dimensionality reduction of the intelligent optimization and adjustment model of the line and greatly improving the computational efficiency. On this basis, a comprehensive deviation function is constructed based on the maximum encroachment limit value and the total number of encroachment limit points. The constructed comprehensive deviation function takes into account both the overall encroachment limit and the local encroachment limit factors, which can more comprehensively and accurately describe the encroachment limit situation and make it easier to find an optimized line scheme that meets the actual needs of the project.

[0037] 2. In the iterative calculation process, this invention only compares the "measured route" and the "designed route", instead of comparing the "measured cross section" and the "designed cross section". Therefore, there is no need to perform algebraic processing on the cross-sectional shape of the tunnel (or bridge). This solves the problem that the algebraic processing of horseshoe-shaped tunnels and rectangular tunnels with variable cross-sectional dimensions is difficult and cross-sectional comparison is difficult. It has strong engineering applicability.

[0038] 3. The comprehensive deviation function formula based on "maximum encroachment limit value" and "total number of encroachment limit points" provided by this invention can adapt to the slope adjustment and alignment requirements of different subgrade foundations by adjusting the weights of "maximum encroachment limit value" and "total number of encroachment limit points"; using the comprehensive deviation function as the objective function, it is easier to find an optimized route scheme that meets the actual needs of the project.

[0039] 4. This invention is the first to apply the Bald Eagle algorithm to the intelligent optimization process of slope and alignment adjustment. It has strong global and local search capabilities, and its optimization effect on "maximum encroachment limit value" and "total number of encroachment limit points" is better than that of existing intelligent slope and alignment adjustment algorithms. Attached Figure Description

[0040] Figure 1 A flowchart illustrating an exemplary embodiment of the present invention;

[0041] Figure 2 This is a flowchart illustrating an exemplary embodiment of the present invention for obtaining the measured line plane deviation and the measured line plan view based on the processing of measured cross-sectional offset.

[0042] Figure 3 This is a flowchart illustrating an exemplary embodiment of the present invention for obtaining the measured elevation deviation and longitudinal profile of the measured line based on the processing of measured top and bottom plate elevations;

[0043] Figure 4This is a flowchart illustrating the optimization and adjustment of a design circuit diagram based on the Bald Eagle algorithm, an exemplary embodiment of the present invention.

[0044] Figure 5 This is an exemplary embodiment of the present invention, showing the "Cross-section Measurement Results Table" of a section of the Qingdao Metro, as well as the calculation results of cross-section offset deviation and horizontal deviation of the line centerline;

[0045] Figure 6 This is an exemplary embodiment of the present invention, in which the measured line coordinates are calculated based on the designed line coordinates and the horizontal deviation of the line centerline.

[0046] Figure 7 These are design circuit diagrams, measured circuit diagrams, and schematic diagrams of the horizontal deviation between them, representing exemplary embodiments of the present invention.

[0047] Figure 8 This is an exemplary embodiment of the present invention, showing the optimized and adjusted circuit diagram, the measured circuit diagram, and the residual deviation between the two based on the Bald Eagle algorithm provided by the present invention.

[0048] Figure 9 This is a comparison diagram of the effects of the slope adjustment and alignment method based on the vulture algorithm according to an exemplary embodiment of the present invention, and the slope adjustment and alignment method based on the evolutionary algorithm and the particle swarm algorithm.

[0049] Figure 10 This is a block diagram of an electronic device architecture, which is an exemplary embodiment of the present invention. Detailed Implementation

[0050] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0051] Example 1

[0052] Figure 1 An exemplary embodiment of the present invention illustrates an intelligent slope adjustment and alignment method, comprising:

[0053] S100. Obtain the measured cross-sectional offset and top and bottom plate elevations of each measuring point of the line to be adjusted, and draw a measured line diagram based on the measured cross-sectional offset and top and bottom plate elevations; the measured line diagram includes: a plan view and a longitudinal profile view.

[0054] S200. Obtain the design route diagram of the route to be adjusted. With the minimum comprehensive deviation function between the design route diagram and the measured route diagram as the optimization objective, optimize and adjust the design route diagram based on the optimization algorithm so that the design route diagram approximates the measured route diagram. The comprehensive deviation function is constructed based on the maximum intrusion limit value and the total number of intrusion limit points.

[0055] This invention abstracts massive amounts of measurement data into a virtual "measured route," and abstracts the slope adjustment and alignment process into an intelligent optimization process that approximates the "measured route" with a "designed route," achieving dimensionality reduction and significantly improving computational efficiency. Existing intelligent slope adjustment and alignment methods take the positional relationship of the spatial route in a three-dimensional tunnel as the research object. In each iterative calculation, the distance between the new route and all measuring points (at least 8-10 measuring points on each measuring section) needs to be calculated, and then the total number of all encroachment points on all measuring sections is accumulated. This calculation process is complex and has low computational efficiency, taking several minutes to several hours to complete the adjustment of a plane curve. In contrast, this invention only needs to calculate the distance between the new route and the "center point of the measured route" on each measuring section in each iteration. Therefore, the computational workload is only 1 / 10 to 1 / 8 of that of existing methods, and it does not involve the distance calculation of the spatial curve in the three-dimensional tunnel. Therefore, it significantly saves computational costs and improves computational efficiency, completing the adjustment of a plane curve in only 6-10 seconds.

[0056] Example 2

[0057] In one possible implementation, step S100 includes: (1) obtaining the measured line plane deviation and the measured line plan view based on the measured section offset of each measuring point; (2) obtaining the measured line elevation deviation and the measured line longitudinal profile view based on the measured top and bottom plate elevations of each measuring point.

[0058] like Figure 2 As shown, in step S100, the process of obtaining the measured line plane deviation and the measured line plane diagram based on the measured cross-sectional offset of each measuring point includes the following steps:

[0059] S101: Calculate the cross-sectional offset deviation based on the measured cross-sectional offset and the design cross-sectional offset at each measuring point. The specific procedure is as follows:

[0060] Within the measurement section at mileage m0, the section offset deviation is obtained by subtracting the design value from the measured value of the section offset (i.e., the horizontal distance from the measurement point to the centerline of the design line).

[0061] The cross-sectional offset deviation ΔL at the i-th measuring point on the sidewall of a tunnel / bridge i for:

[0062] ΔL i =L i '-L i

[0063] in:

[0064] L i '——Measured value of cross-sectional offset at the i-th measuring point;

[0065] L i —Design value of cross-sectional offset at the i-th measuring point.

[0066] S102: Calculate the horizontal deviation of the measured line center point based on the cross-sectional offset deviation within each section. The specific procedure is as follows:

[0067] Measuring points on the sidewalls of tunnels / bridges are generally set in pairs, and the cross-sectional offset deviation ΔL of the i-th measuring point on the right sidewall is used. i右 Subtract the cross-sectional offset deviation ΔL at the i-th measuring point on the left wall i左 Then divide by 2 to get the line centerline deviation at that location; calculate the average of the line centerline deviations at the 1st to nth pairs of measuring points to get the horizontal deviation of the measured line center point relative to the designed line center point on that cross section.

[0068] The measured horizontal deviation e of the center point of the line relative to the center point of the designed line p for:

[0069]

[0070] in:

[0071] ΔL i右 — Cross-sectional offset deviation of the i-th measuring point on the right side wall of the tunnel / bridge;

[0072] ΔL i左 — Cross-sectional offset deviation at the i-th measuring point on the left side wall of the tunnel / bridge;

[0073] n — the number of measuring points on one side wall (left or right side wall) of a tunnel / bridge.

[0074] S103: Using the designed line center point as a reference, the plane coordinates of the measured line center point are obtained based on the horizontal deviation of the measured line center point. The specific procedure is as follows:

[0075] In the world coordinate system, the coordinates (x, y) of the designed route plan are represented by a parametric equation with respect to mileage m:

[0076]

[0077] in:

[0078] x, y — coordinates of the design route plan in the world coordinate system;

[0079] m—the continuous mileage of the line, obtained by eliminating chain breaks from the designed line mileage.

[0080] Based on the above line parameter equations and the horizontal deviation e obtained in step S102 pThe measured plane coordinates (x, y) of the line at section mileage m0 can be obtained. s ,y s )for:

[0081]

[0082] in:

[0083] x0 — The x-coordinate of the designed route plan at mileage m0;

[0084] y0 — The y-coordinate of the designed route at mileage m0;

[0085] x'(m0) — The derivative of the parametric equation x(m) of the design route plan at mileage m0;

[0086] y'(m0) — The derivative of the parametric equation y(m) of the design route plan at mileage m0.

[0087] S104: Connect the plane coordinates of the center points of each measured route in sequence according to the cross-sectional mileage to obtain the plan view of the measured route. The specific procedure is as follows:

[0088] The measured line coordinates (x, y) on all measurement sections are obtained according to steps S101 to S103. s1 ,y s1 ),(x s2 ,y s2 ),(x s3 ,y s3 ),…,(x sk ,y sk ), and according to the cross-sectional mileage m1, m2, m3, ..., m k By sorting the data in ascending order and connecting them in series, we can obtain the actual measured route plan.

[0089] like Figure 3 As shown, in step S100, the process of obtaining the measured elevation deviation and longitudinal profile of the measured line based on the measured top and bottom elevations of each measuring point includes the following steps:

[0090] S105: Calculate the elevation deviation of the top and bottom slabs based on the measured elevations of each measuring point and the designed elevations of the top and bottom slabs. The specific procedure is as follows:

[0091] Within the measurement section at mileage m0, the elevation deviation ΔH of the top and bottom slabs is obtained by subtracting the design values ​​from the measured values ​​of the top and bottom slab elevations. 顶 ΔH 底 .

[0092] S106: The elevation deviation of the measured line center point is obtained based on the elevation deviations of the top and bottom plates in each section. The specific method is as follows:

[0093] Within the measurement section at mileage m0, the measured elevation deviation e of the center point of the line is... h for:

[0094]

[0095] in:

[0096] ΔH 顶 —Tunnel roof elevation deviation;

[0097] ΔH 底 —Elevation deviation of tunnel floor (or elevation deviation of bridge beam surface).

[0098] S107: Using the designed center point of the line as a reference, the elevation of the measured center point of the line is obtained based on the elevation deviation of the measured center point. The specific procedure is as follows:

[0099] The measured elevation h at section mileage m0 s for:

[0100] h s =h0+e h

[0101] in:

[0102] h0 — Elevation of the longitudinal profile of the designed route at kilometer m0.

[0103] e h —Measured elevation deviation of the center point of the line

[0104] S108: By sequentially connecting the elevations of the center points of each measured route according to the cross-sectional mileage, a longitudinal profile of the measured route is obtained. The specific procedure is as follows:

[0105] The measured line elevation h on all measurement sections is obtained according to steps S105 to S107. s1 ,h s2 ,h s3 ,…,h sk And according to the cross-sectional mileage m1, m2, m3, ..., m k By sorting the data in ascending order and connecting them in series, we can obtain the measured longitudinal profile of the railway line.

[0106] like Figure 4 As shown, step S200 includes:

[0107] S201: Select the curve to be adjusted and represent the curve parameters as an initial vector. First, based on the line deviation obtained in step S100, determine the curve to be adjusted and represent the curve parameters as an initial vector P. The specific values ​​of the initial vector P are:

[0108] (1) When adjusting the track plane:

[0109] P = [r, l1, l2, x, y]

[0110] in:

[0111] r—radius of the circular curve;

[0112] l1—Length of the initial transition curve;

[0113] l2 — Length of the post-gradualization curve;

[0114] x — the coordinates of the intersection point of the curves;

[0115] y — the coordinates of the intersection point of the curves.

[0116] (2) When adjusting the longitudinal profile of the line:

[0117] P = [r, m, h]

[0118] in:

[0119] r—radius of the vertical curve;

[0120] m — continuous mileage of the slope change point;

[0121] h — Elevation of the slope change point.

[0122] S202: Randomly generate N curve parameter vectors around the initial vector as the initial population. The elements of the initial vector P can be randomly adjusted within a certain range by multiplying them by random numbers to generate the initial population. For example, to randomly adjust the elements of P within a range of ±15%, simply multiply each element of P by a random number between 0.85 and 1.15, repeating this N times to obtain new vectors P1, P2, P3, ... P N This forms an initial population with a sample size of N.

[0123] S203: Generate N lines based on N curve parameter vectors. For each curve parameter vector P in the initial population of step S201... i , with P i The elements in the curve are used as curve parameters to generate corresponding line diagrams Line-i, and a total of N new line diagrams Line-1, Line-2, Line-3...Line-N are generated.

[0124] S204: Calculate the "comprehensive deviation function" between each line and the measured line. The "comprehensive deviation function" is obtained by weighted combination of the maximum encroachment limit value and the total number of encroachment limit points. Among them, the maximum encroachment limit value reflects the most unfavorable degree of encroachment limit in a local area, and the total number of encroachment limit points reflects the size of the overall encroachment limit range.

[0125] The formula for calculating the "comprehensive deviation function" δ is:

[0126]

[0127] in:

[0128] E max —Maximum invasion limit;

[0129] E 0max —Initial maximum intrusion limit;

[0130] n sum —Total number of intrusion points;

[0131] n 0sum —Initial number of intrusion points;

[0132] λ1 — Weighting coefficient for the maximum intrusion limit, generally ranging from 0 to 1;

[0133] λ2 — Weighting coefficient for the total number of encroachment points, generally ranging from 0 to 1.

[0134] The weights of the maximum intrusion limit and the total number of intrusion points can be adjusted by changing the values ​​of λ1 and λ2, depending on the actual situation. The specific method is as follows:

[0135] (1) For horseshoe-shaped tunnels, rectangular tunnels, and cast-in-place concrete box girders, since the tunnel lining (or beam surface) is a cast-in-place reinforced concrete structure, it is permissible to appropriately remove surface laitance. Therefore, larger local encroachment points are relatively easy to handle, and the number of encroachment points should be the main optimization objective for slope adjustment and alignment adjustment. Therefore, a smaller value (e.g., 0.1 to 0.3) can be assigned to the weighting coefficient λ1 of the maximum encroachment value, and a larger value (e.g., 0.7 to 0.9) can be assigned to the weighting coefficient λ2 of the total number of encroachment points.

[0136] (2) For shield tunnels and U-shaped beams, since the shield segments and U-shaped beams are prefabricated structures, there is generally no surface laitance, and surface removal is not allowed. Their encroachment is generally caused by the overall installation deviation of the shield segments (or U-shaped beams). Therefore, it is difficult to deal with large local encroachment points. The maximum encroachment value should be the main optimization target. Therefore, a larger value (e.g., 0.7 to 0.9) can be assigned to the weighting coefficient λ1 of the maximum encroachment value, and a smaller value (e.g., 0.1 to 0.3) can be assigned to the weighting coefficient λ2 of the total number of encroachment points.

[0137] S205: Update N curve parameter vectors using the bald eagle calculation selection phase formula. The specific calculation formula is as follows:

[0138] P new,i =P best +α·r·(P mean -P i )

[0139] in:

[0140] P new,i —The new vector obtained after updating the i-th curve parameter vector in the current population;

[0141] P best —The curve parameter vector corresponding to the line with the smallest comprehensive deviation function δ in the current population;

[0142] α — a constant, ranging from 1.5 to 2;

[0143] r—a random number uniformly distributed between 0 and 1;

[0144] P mean —The average value of all curve parameter vectors in the current population;

[0145] P i —The parameter vector of the i-th curve in the current population.

[0146] S206: Generate N new lines based on the updated N curve parameter vectors. The specific method is the same as S203.

[0147] S207: Calculate the "comprehensive deviation function" between each new line and the measured line. The specific method is the same as S204.

[0148] S208: If the new "comprehensive deviation function" is less than the original "comprehensive deviation function", then the new line will replace the original line; otherwise, the original line will still be used.

[0149] S209: Update the N curve parameter vectors using the Bald Eagle algorithm search phase formula. The specific calculation formula is as follows:

[0150] P new,i =P i +x(i)·(P i -P mean )+y(i)·(P i -P i+1 )

[0151] In the formula:

[0152]

[0153]

[0154] xr(i)=r(i)·sin(θ(i))

[0155] yr(i)=r(i)·cos(θ(i))

[0156] θ(i) = a·π·rand

[0157] r(i)=θ(i)+R·rand

[0158] in:

[0159] P new,i —The new vector obtained after updating the i-th curve parameter vector in the current population;

[0160] P i —The i-th curve parameter vector in the current population;

[0161] P i+1 —The (i+1)th curve parameter vector in the current population;

[0162] P mean —The average value of all curve parameter vectors in the current population;

[0163] a — a constant, ranging from 5 to 10;

[0164] R—a constant, ranging from 0.5 to 2;

[0165] rand is a random number that is uniformly distributed between 0 and 1.

[0166] After updating the N curve parameter vectors in the population according to the above formula, N new routes are generated based on the updated N curve parameter vectors. Then, the "comprehensive deviation function" between each new route and the measured route is calculated. If the new "comprehensive deviation function" is less than the original "comprehensive deviation function", the new route is used to replace the original route; otherwise, the original route is used.

[0167] S210: Update N curve parameter vectors using the dive phase formula of the Bald Eagle algorithm. The specific calculation formula is as follows:

[0168] P new,i =rand·P best +xl(i)·(P i -c1·P mean )+yl(i)·(P i -c2·P best )

[0169] In the formula:

[0170]

[0171]

[0172] xr(i)=r(i)·sinh(θ(i))

[0173] yr(i)=r(i)·cosh(θ(i))

[0174] θ(i) = a·π·rand

[0175] r(i)=θ(i)

[0176] in:

[0177] P new,i —The new vector obtained after updating the i-th curve parameter vector in the current population;

[0178] P i —The i-th curve parameter vector in the current population;

[0179] P mean —The average value of all curve parameter vectors in the current population;

[0180] P best —The curve parameter vector corresponding to the line with the smallest comprehensive deviation function δ in the current population;

[0181] c1, c2 — constants, usually taken as 2;

[0182] a — a constant, ranging from 5 to 10;

[0183] rand is a random number that is uniformly distributed between 0 and 1.

[0184] After updating the N curve parameter vectors in the population according to the above formula, N new routes are generated based on the updated N curve parameter vectors. Then, the "comprehensive deviation function" between each new route and the measured route is calculated. If the new "comprehensive deviation function" is less than the original "comprehensive deviation function", the new route is used to replace the original route; otherwise, the original route is used.

[0185] S211: Determine if the stopping condition is met. Generally, the stopping condition is the total number of iterations or the change in the "comprehensive deviation function" before and after each iteration being less than a certain threshold. If the stopping condition is not met, continue iterating through the selection phase, search phase, and dive phase in sequence; if the stopping condition is met, output the result.

[0186] S212: End. Output the optimized route plan and residual deviation. If the stopping condition in S211 is met, stop the iteration and output the optimal curve parameter vector P. best Then P best The elements in the curve are used as curve parameters, and the optimized curve diagram is output, along with the corresponding maximum intrusion limit value and the total number of intrusion points.

[0187] In summary, the method provided by the embodiments of the present invention has the following advantages:

[0188] 1. This invention is applicable to different tunnel (or bridge) cross-sections, and is not limited to shield tunnels (circular cross-section tunnels). Existing intelligent slope adjustment and alignment methods require comparing the designed tunnel's "theoretical circle" with the actual circle excavated by the shield machine in each iterative calculation to find the encroachment point. For shield tunnels, since the tunnel cross-section is circular with uniform dimensions and the mathematical analytical expression of the cross-sectional shape is simple, digital comparison is easy to achieve. However, for rectangular tunnels using the cut-and-cover method and horseshoe-shaped tunnels using the mined-and-cover method, the cross-sectional width and height are complex and varied, and the cross-sectional shape is difficult to express with a simple non-segmented mathematical analytical expression, making digital comparison difficult. Therefore, existing intelligent slope adjustment and alignment methods are often only applicable to shield tunnels, deliberately avoiding rectangular cut-and-cover tunnels and horseshoe-shaped mined-and-cover tunnels. This invention generates a "measured line" based on the cross-sectional offset deviation and the elevation of the top and bottom plates. Then, it compares the "measured line" with the "designed line" instead of directly comparing the tunnel cross-section. Therefore, it is not restricted by the shape of the cross-section and can be applied to tunnels (or bridges) of any cross-sectional form.

[0189] 2. This invention proposes the concept of a "comprehensive deviation function" and its corresponding calculation formula. The calculation of the "comprehensive deviation function" considers both overall and local encroachment factors, allowing for a more accurate description of encroachment conditions. Existing slope adjustment and alignment algorithms only aim to reduce the total number of encroachment points, neglecting the crucial factor of the local maximum encroachment value. In actual engineering, shield tunnels use prefabricated segments, and U-beams use prefabricated thin-walled prestressed structures, making it impossible to remove excessively large local encroachment points. Therefore, for these types of structures, the primary goal of slope adjustment and alignment is to reduce the local maximum encroachment value, not simply to reduce the number of encroachment points. Thus, existing intelligent slope adjustment and alignment algorithms have certain irrationalities in their selection of optimization objectives. This invention aims to reduce the "comprehensive deviation function," which is obtained by weighted combination of the "maximum encroachment limit value" and the "total number of encroachment limit points." The "maximum encroachment limit value" reflects local encroachment, while the "total number of encroachment limit points" reflects overall encroachment. By comprehensively considering these two factors, a more reasonable slope adjustment and alignment scheme can be found.

[0190] 3. In the combined calculation of the "comprehensive deviation function", the weights of "maximum encroachment limit value" and "total number of encroachment limit points" can be adjusted by adjusting the weighting coefficients to adapt to the slope adjustment and alignment requirements of different types of tunnel (or bridge) sections: (1) For horseshoe-shaped tunnels, rectangular tunnels, and cast-in-place concrete box girders, since the tunnel lining (or beam surface) is a cast-in-place reinforced concrete structure, it is permissible to remove surface laitance appropriately. Therefore, larger encroachment limit points are relatively easy to handle. Based on the consideration of processing cost, the total number of encroachment limit points should be the main optimization target. In this case, the weight of "total number of encroachment limit points" can be appropriately increased and the weight of "maximum encroachment limit value" can be decreased; (2) For shield tunnels and U-shaped beams, since the shield tunnel segments and U-shaped beam bodies are prefabricated structures, there is generally no surface laitance, and surface removal is not allowed. Their encroachment limit is generally caused by the overall installation deviation of the shield tunnel segments (or U-shaped beam bodies). Therefore, larger encroachment limit points are more difficult to handle. The maximum encroachment limit value should be the main optimization target. In this case, the weight of "maximum intrusion limit value" can be appropriately increased, while the weight of "total number of intrusion points" can be decreased.

[0191] 4. This invention is the first to apply the Bald Eagle algorithm to the intelligent optimization process of slope and alignment adjustment, demonstrating superior computational efficiency and optimization performance compared to existing intelligent slope and alignment adjustment algorithms. The Bald Eagle algorithm is a novel metaheuristic algorithm with a unique three-stage (selection, search, and dive) search method, exhibiting strong global and local search capabilities. During computation, the Bald Eagle algorithm eliminates the need for binary encoding and decoding of variables, avoiding mapping errors. Furthermore, the algorithm requires fewer manually set parameters, minimizing errors caused by subjective factors. Experimental results show that using the Bald Eagle algorithm for intelligent slope and alignment adjustment optimization yields superior computational efficiency and optimization performance compared to existing intelligent slope and alignment adjustment algorithms such as evolutionary algorithms and particle swarm optimization.

[0192] Example 3

[0193] In a further embodiment of the present invention, the "Cross-section Measurement Results Table" provided by the surveying unit is used as input for the slope adjustment and alignment work of a certain section of the Qingdao Metro. The "Cross-section Measurement Results Table" provided by the surveying unit, and the cross-section offset deviation and the horizontal deviation of the line centerline calculated through steps S101 and S102, are as follows: Figure 5 As shown. Figure 5In the table shown: (1) The first column is the mileage m of the measurement section. Generally, measurement sections are selected at intervals of 3 to 5 m along the line. (2) The second and sixth columns are the numbers of the measuring points on the left and right sides of the tunnel, respectively. It can be seen that in each measurement section of this project, 4 measuring points are set on the left and right sides (set in pairs). (3) The third and seventh columns are the measured values ​​L' of the section offset of each measuring point on the left and right sides, respectively. (4) The fourth and eighth columns are the design values ​​L of the section offset of each measuring point on the left and right sides, respectively. (5) The fifth and ninth columns are the section offset deviations ΔL of each measuring point on the left and right sides calculated according to step S101 of this invention. (6) The tenth column is the horizontal deviation e of the line center point in each section calculated according to step S102 of this invention. p .

[0194] The plane coordinates of the measured line center point in each section are calculated according to step S103, as follows: Figure 6 As shown. Figure 6 In the table shown: (1) The first column is the mileage m of the measurement section. Generally, measurement sections are selected at intervals of 3 to 5 m along the line. (2) The second column is the horizontal deviation e of the line center point in each section calculated according to step S102 of the present invention. p (3) Columns 3 and 4 are the plane coordinates x and y of the center point of the designed line. (4) Columns 5 and 6 are the plane coordinates x and y of the center point of the measured line obtained according to step S103.

[0195] The plan view of the measured line obtained by connecting the plane coordinates of the center points of each measured line in step S104 is as follows: Figure 7 As shown. Figure 7 The diagram also shows the design route diagram and the horizontal deviation e between the design route and the measured route diagram. p According to statistics, the maximum intrusion limit in this section is 396 mm, and the total number of intrusion points is 92.

[0196] According to step S201, a curve in the route diagram is selected as the object for planar adjustment. The radius of the circular curve of this curve is 450m, the length of the preceding transition curve is 70m, the length of the following transition curve is 70m, and the coordinates of the curve intersection point are (241201.1860, 110517.6510). The curve parameters are represented as an initial vector as P = [450, 70, 70, 241201.1860, 110517.6510].

[0197] According to steps S201-S212, the Bald Eagle algorithm was used for intelligent slope and alignment adjustment. The number of encroachment points after adjustment was 44, and the maximum encroachment limit was 268 mm. The corresponding curve parameter vector is P = [451.1273, 67.9410, 71.1282, 241201.2343, 110517.6124]. The alignment diagram optimized and adjusted by the Bald Eagle algorithm, the measured alignment diagram, and the residual deviation between them are shown below. Figure 8 As shown.

[0198] In a further embodiment of the present invention, taking the adjustment of the horizontal encroachment limit in a certain section of the Qingdao Metro as an example, the slope adjustment and alignment method based on the Bald Eagle algorithm of the present invention is compared with the slope adjustment and alignment method based on evolutionary algorithm and particle swarm algorithm. The results are as follows: Figure 9 As shown.

[0199] from Figure 9 As can be seen, the initial maximum encroachment limit for the interval was 396 mm, with a total of 92 encroachment points. After optimization using the slope adjustment and alignment method based on the evolutionary algorithm, the maximum encroachment limit was reduced to 274 mm, and the number of encroachment points was reduced to 52. After optimization using the slope adjustment and alignment method based on the particle swarm optimization algorithm, the maximum encroachment limit was reduced to 287 mm, and the number of encroachment points was reduced to 45. After optimization using the slope adjustment and alignment method based on the bald eagle algorithm of this invention, the maximum encroachment limit was reduced to 268 mm, and the number of encroachment points was reduced to 44. It is evident that the slope adjustment and alignment method based on the bald eagle algorithm of this invention is significantly superior to the slope adjustment and alignment methods based on the evolutionary algorithm and the particle swarm optimization algorithm.

[0200] Another aspect of the present invention, such as Figure 10 As shown, an electronic device is also provided, which includes a processor, a network interface, and a memory. The processor, the network interface, and the memory are interconnected. The memory is used to store a computer program, which includes program instructions. The processor is configured to call the program instructions to execute the above-described intelligent slope adjustment and line adjustment method.

[0201] In another aspect, the present invention provides a computer storage medium storing program instructions, which, when executed by at least one processor, are used to implement the above-described intelligent slope and alignment adjustment method.

[0202] In embodiments of the present invention, the processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0203] The various methods, steps, and logic diagrams disclosed in the embodiments of this invention can be implemented or executed. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules can reside in random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, or other mature storage media in the art. The processor reads information from the storage medium and, in conjunction with its hardware, completes the steps of the above methods.

[0204] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0205] Among them, non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory.

[0206] Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous linked dynamic random access memory (Synchlink DRAM, SLDRAM), and direct memory bus RAM (DRRAM).

[0207] The storage media described in the embodiments of the present invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0208] It should be understood that the system disclosed in this invention can be implemented in other ways. For example, the division of modules is merely a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the communication connection between modules can be through some interfaces, indirect coupling or communication connections between servers or units, and can be electrical or other forms.

[0209] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0210] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0211] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart slope and alignment adjustment method, characterized in that, include: Obtain the measured cross-sectional offset and top and bottom plate elevations of each measuring point on the line to be adjusted, and draw the measured line diagram based on the measured cross-sectional offset and top and bottom plate elevations; The measured route diagram includes: a plan view and a longitudinal profile view; Obtain the design route diagram of the route to be adjusted. With the minimum comprehensive deviation function between the design route diagram and the measured route diagram as the optimization objective, optimize and adjust the design route diagram based on the Bald Eagle algorithm so that the design route diagram approximates the measured route diagram. The comprehensive deviation function is constructed based on the maximum intrusion limit value and the total number of intrusion points. The process of drawing the measured line diagram based on the measured cross-sectional offset and the elevations of the top and bottom plates includes: S101: Calculate the cross-sectional offset deviation of each measuring point based on the measured cross-sectional offset and the design cross-sectional offset of each measuring point; S102: Calculate the horizontal deviation of the measured line center point corresponding to each measuring point based on the cross-sectional offset deviation; S103: Using the center point of the designed line as a reference, the plane coordinates of the measured line center point are obtained based on the horizontal deviation of the measured line center point at each measuring point. S104: Connect the plane coordinates of the center points of each measured line in sequence according to the cross-sectional mileage to obtain the plane diagram of the measured line; S105: Calculate the elevation deviation of the top and bottom plates based on the measured top plate elevation, measured bottom plate elevation, and designed top plate elevation and designed bottom plate elevation at each measuring point; S106: Calculate the measured elevation deviation of the center point of the line based on the elevation deviation of the top and bottom plates in each section; S107: Using the center point of the designed line as a reference, the elevation of the measured center point of the line is obtained based on the elevation deviation of the measured center point of the line. S108: Connect the elevations of the center points of each measured line in sequence according to the cross-sectional mileage to obtain the longitudinal profile of the measured line. The comprehensive deviation function is: in, —Maximum infringement limit; —Initial maximum intrusion limit; —Total number of intrusion points; —Number of initial intrusion points; —The weighting coefficient for the maximum infringement limit, taking a value between 0 and 1; —The weighting coefficient for the total number of intrusion points, taking a value between 0 and 1.

2. The intelligent slope adjustment and alignment method according to claim 1, characterized in that, In step S103, the plane coordinates of the measured line center point are obtained using the following formula: in, —Design the x-coordinate of the route plan at mileage m0; —The y-coordinate of the design route plan at mileage m0; —The derivative of the parametric equation x(m) of the route plan at mileage m0; —The derivative of the parametric equation y(m) of the route plan at mileage m0, e p This represents the measured horizontal deviation of the center point of the line.

3. The intelligent slope adjustment and alignment method according to claim 1, characterized in that, In step S102, the measured horizontal deviation of the line center point is obtained using the following formula: in, — Cross-sectional offset deviation of the i-th measuring point on the right side wall of the tunnel / bridge; — Cross-sectional offset deviation of the i-th measuring point on the left side wall of the tunnel / bridge; —Number of measuring points on one side wall of a tunnel / bridge.

4. The intelligent slope adjustment and alignment method according to claim 1, characterized in that, In S106, the measured elevation deviation of the center point of the line is obtained using the following formula: in: —Tunnel roof elevation deviation; —Elevation deviation of tunnel floor or bridge beam surface.

5. The intelligent slope and alignment adjustment method according to claim 1, characterized in that, For sub-foundations using cast-in-place reinforced concrete structures, the comprehensive deviation function satisfies: < .

6. The intelligent slope adjustment and alignment method according to claim 1, characterized in that, For subgrade foundations using prefabricated structures, the comprehensive deviation function satisfies: < .

7. An electronic device, characterized in that, include: The system includes a processor, a network interface, and a memory, which are interconnected. The memory stores a computer program, which includes program instructions. The processor is configured to invoke the program instructions to execute the intelligent slope adjustment and alignment method as described in any one of claims 1 to 6.

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