A method and apparatus for grading

Abstract

The application discloses a slope adjusting method and device, comprising the following steps: drawing a deviation curve according to the mileage and elevation deviation of each measuring point of a line to be adjusted, performing curve fitting on the deviation curve to obtain a fitting curve, identifying the position of an additional slope change point based on the fitting curve and the deviation curve, and setting the additional slope change point as a first slope change point; selecting a second slope change point from the first slope change point based on a predetermined slope length, inserting the second slope change point into existing slope change points according to the mileage, and regenerating a line to be optimized; selecting a slope change point to be adjusted based on a preset adjustment rule, and optimizing and adjusting the slope change point to be adjusted by using an optimization algorithm to obtain an optimized line with the minimum comprehensive elevation deviation between the line to be optimized and a measured line; and the application can effectively eliminate the invasion limit, greatly improve the operability and flexibility of slope adjusting work, and avoid the adverse effects of slope adjusting of the invasion limit section on other non-invasion limit sections.

Description

Technical Field

[0001] This invention relates to the field of railway line slope adjustment technology, and in particular to a slope adjustment method and equipment. Background Technology

[0002] The slope change point is the intersection of two adjacent slope lines on the longitudinal profile of the line. Adjusting the longitudinal profile of the line is called slope adjustment. After the tunnel is completed and before the track is laid, slope adjustment is required to minimize the elevation deviation between the line and the existing bridges and tunnels, thereby reducing encroachment.

[0003] CN116432288A describes an intelligent slope adjustment and alignment method and device. This method uses an optimization algorithm to adjust the vertical curve radius and the coordinates of existing slope change points to obtain an alignment adjustment scheme. CN110363298A discloses an intelligent slope adjustment method based on an evolutionary algorithm, and CN110363298A discloses another intelligent slope adjustment method based on a particle swarm optimization algorithm in the paper "Research on Intelligent Alignment and Slope Adjustment of Urban Rail Transit Based on Particle Swarm Optimization Algorithm." These slope adjustment methods essentially optimize and adjust the design line parameters through intelligent optimization algorithms to reduce encroachment. However, in reality, the maximum encroachment location is not limited to a few slope change points, but is likely located in the middle of the slope segment. In this case, only by adding slope change points in the middle of the slope segment can the actual alignment be better approximated. However, the algorithm optimization process cannot add slope change points, and adjusting the parameters of a single slope change point will affect at least two adjacent slope segments, with an impact range typically hundreds of meters or even several kilometers. Furthermore, slope adjustments within the encroachment area can affect the longitudinal profile of the line in other non-encroachment areas, causing new encroachments to appear in sections that were not previously affected. The above methods are often ineffective in eliminating encroachments.

[0004] The slope adjustment methods described in "Design of Alignment and Gradient Adjustment for Urban Rail Transit Lines" involve manual slope adjustment design, including adjusting the radius of vertical curves, the location and elevation of slope change points, gradient and slope length, and adding slope change points by splitting long slope sections to adjust the longitudinal profile of the line to eliminate encroachment. However, this method relies on the experience and judgment of designers, and misjudgments are easily made regarding the location of added slope change points, often failing to effectively eliminate encroachment. Manual comparison and calculation are inefficient, labor-intensive, and prone to human error, and the adjusted line scheme is difficult to guarantee as optimal, which greatly restricts slope adjustment work.

[0005] Therefore, there is an urgent need for a highly automated and intelligent slope adjustment method that can add slope adjustment points while avoiding impact on non-encroaching areas. Summary of the Invention

[0006] To address the problems of existing slope adjustment methods, such as the inability to add slope change points and the high difficulty and error of manual adjustment, this invention provides a slope adjustment method and equipment, the technical solution of which is as follows:

[0007] Firstly, a slope adjustment method is provided, the method comprising:

[0008] Based on the mileage and elevation deviation of each measuring point on the line to be adjusted, a deviation curve is plotted. The deviation curve is then fitted to obtain a fitted curve. Based on the fitted curve and the deviation curve, the location of the added slope change point is identified and set as the first slope change point.

[0009] Based on the predetermined slope length, the second slope point is selected from the first slope point. According to the mileage, the second slope point is inserted into the existing slope points, and the route to be optimized is regenerated.

[0010] Based on preset adjustment rules, a second slope change point and an existing slope change point are selected to form a slope change point to be adjusted. An optimization algorithm is used to optimize and adjust the slope change point to be adjusted, and the optimized route with the smallest comprehensive elevation deviation between the route to be optimized and the measured route is obtained.

[0011] The comprehensive elevation deviation is composed of the weighted encroachment limit deviation of the top plate elevation and the weighted encroachment limit deviation of the bottom plate elevation at each measuring point.

[0012] According to a specific implementation, in the above-mentioned slope adjustment method, the deviation curve is composed of mileage x and elevation deviation y, wherein the elevation deviation y is:

[0013] y = (E top +E bot ) / 2,

[0014] Among them, E top E represents the elevation deviation of the roof at mileage x, calculated by subtracting the design elevation from the measured roof elevation. bot The elevation deviation of the tunnel floor at mileage x is calculated by subtracting the design elevation from the measured elevation of the tunnel floor.

[0015] According to a specific implementation, in the above-mentioned slope adjustment method, the step of identifying the location of the added slope change point based on the fitted curve and the deviation curve, and designating it as the first slope change point, includes:

[0016] The zero-point mileage corresponding to the zero point of the curve is obtained based on the fitted curve, and multiple intrusion zones are divided by combining the deviation curve and the zero-point mileage.

[0017] Obtain the mileage of the encroachment point corresponding to the maximum elevation deviation of each encroachment area, and select the zero-point mileage adjacent to the encroachment point mileage as the encroachment start mileage and encroachment end mileage.

[0018] The mileage of the intrusion point, the mileage of the intrusion start point, and the mileage of the intrusion end point are set as the first slope change point.

[0019] According to a specific implementation, in the above-mentioned slope adjustment method, the step of selecting a second slope change point from the first slope change point based on a predetermined slope length includes:

[0020] Step 1: Based on the mileage difference between the first slope change point and the existing slope change points, select the first slope change point whose mileage difference is greater than the predetermined slope length.

[0021] Step 2: Based on the mileage difference between the first slope change points obtained in Step 1, filter out the first slope change points whose mileage difference is greater than the predetermined slope length.

[0022] Step 3: Set the first slope change point obtained in Step 2 as the second slope change point.

[0023] According to one specific implementation, in the above-mentioned slope adjustment method, the step of regenerating the line to be optimized includes:

[0024] A new vertical curve table is generated based on the mileage, elevation, and vertical curve radius of the second slope change point;

[0025] The newly added vertical curve table is inserted into the vertical curve table of the line database, the line database is updated, and the line to be optimized is regenerated.

[0026] According to a specific implementation method, in the above-mentioned slope adjustment method, the newly added vertical curve table has an elevation increment for the elevation of the second slope change point, and the elevation increment can prevent the line generation from failing.

[0027] According to one specific implementation, in the above slope adjustment method, the preset adjustment rules include:

[0028] When the second slope change point is not adjacent, select the second slope change point and the existing slope change points adjacent to each second slope change point to form the slope change point to be adjusted. For duplicate existing slope change points, only one is retained.

[0029] When the second slope change point is adjacent, the second slope change point and the existing slope change points before and after the second slope change point are selected to form the slope change point to be adjusted.

[0030] According to a specific implementation method, in the above-mentioned slope adjustment method, the comprehensive elevation deviation is expressed as:

[0031]

[0032] Where δ is the comprehensive elevation deviation, G(E) top，i G(E) represents the weighted deviation of the top elevation of the i-th measuring point. bot，i ) represents the weighted deviation of the base elevation of the i-th measuring point, and n is the number of measuring points.

[0033] According to a specific implementation method, in the above-mentioned slope adjustment method, the weighted deviation of the top slab elevation and the weighted deviation of the bottom slab elevation are expressed as follows:

[0034]

[0035]

[0036] Among them, E top E represents the elevation deviation of the top plate at the i-th measuring point. bot Let E0 be the elevation deviation of the base plate at the i-th measuring point, E0 be the intrusion threshold, and k be the sensitivity coefficient.

[0037] In a second aspect, an electronic device is provided, characterized in that it includes: 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 includes program instructions, and the processor is configured to invoke the program instructions to execute the above-described slope adjustment method.

[0038] The beneficial effects of the technical solution of this invention are:

[0039] This invention identifies the locations of additional slope adjustment points by fitting and analyzing the elevation deviation curve of the railway line. It then selects slope adjustment points that meet the line requirements, solving the problem of misjudgment of slope adjustment point locations when manually adding them. This achieves automatic identification and selection of additional slope adjustment points, overcoming the limitation of existing intelligent slope adjustment algorithms that cannot add new slope adjustment points. Furthermore, by combining multiple slope adjustment points and synchronous optimization, it compensates for the problem of existing intelligent slope adjustment algorithms being unable to automatically capture the slope adjustment points to be adjusted. This avoids the large workload and errors associated with selecting a single slope adjustment point for parameter optimization, significantly improving the efficiency of slope adjustment work. This invention improves the operability and flexibility of the operation, as well as the efficiency and quality of the slope adjustment algorithm. Furthermore, based on the goal of route optimization, this invention creates an elevation-weighted encroachment function that integrates threshold control, exponential weighting, and sensitivity adjustment. By accumulating the elevation-weighted encroachments of each measuring point, the minimum comprehensive elevation deviation is obtained as the objective function of the optimization algorithm. This results in an exponential amplification of encroachment amplitudes exceeding the threshold, aligning with the engineering understanding and slope adjustment design principle that "the larger the encroachment value exceeds the allowable value, the more unacceptable it becomes," facilitating the acquisition of optimized route schemes that better meet engineering expectations. Simultaneously, a sensitivity coefficient is used in the exponent to adjust the sensitivity of the weighted encroachment function to the encroachment amplitude, indirectly controlling the relative weights of "encroachment amplitude" and "number of encroachment points" in the comprehensive elevation deviation, thus adapting to the slope adjustment needs of different types of tunnels. Attached Figure Description

[0040] Figure 1 This is a schematic diagram of a slope adjustment method provided as an example of the present invention;

[0041] Figure 2 The image of the weighted function G(E) for the top elevation deviation provided in this embodiment of the invention;

[0042] Figure 3 The image of the weighted function G(E) for the elevation deviation of the base plate provided in the embodiment of the present invention;

[0043] Figure 4 The image shows the elevation deviation weighting function corresponding to different sensitivity coefficient values ​​provided in this embodiment of the invention (taking the bottom plate elevation deviation weighting function as an example);

[0044] Figure 5 A flowchart of a method for obtaining a first slope change point provided in another embodiment of the present invention;

[0045] Figure 6 A flowchart of a method for processing a second slope change point provided in another embodiment of the present invention;

[0046] Figure 7 A flowchart of an optimization method based on particle swarm optimization algorithm provided in another embodiment of the present invention;

[0047] Figure 8 This invention provides measured elevation data of a subway tunnel section and a calculation table of line elevation deviation, as part of another embodiment of the invention.

[0048] Figure 9 This invention provides a measured curve of elevation deviation of a subway section and polynomial fitting results, as part of another embodiment of the invention.

[0049] Figure 10 A schematic diagram illustrating the relationship between the first slope change point mileage and the maximum encroachment area, provided for another embodiment of the present invention;

[0050] Figure 11 A comparative diagram of the vertical curves of the line before and after the addition of the second gradient point, provided as another embodiment of the present invention;

[0051] Figure 12 This invention provides a longitudinal profile of a subway section and measured elevation deviations of the top and bottom slabs, as well as another embodiment of the invention.

[0052] Figure 13 This invention provides a longitudinal profile of the railway line optimized using the method of the present invention, as well as residual elevation deviations of the top and bottom slabs;

[0053] Figure 14 This is a longitudinal profile of the line and residual elevation deviations of the top and bottom plates provided by an existing intelligent optimization method, as another embodiment of the present invention. Detailed Implementation

[0054] The present invention will now be described in further detail with reference to embodiments and specific implementations. However, this should not be construed as limiting the scope of the 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. It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0055] Example 1

[0056] Figure 1 A flowchart of a slope adjustment method provided by an exemplary embodiment of the present invention is shown, including:

[0057] S100. Draw a deviation curve based on the mileage and elevation deviation of each measuring point on the line to be adjusted, perform curve fitting on the deviation curve, obtain the fitted curve, and identify the location of the added slope change point based on the fitted curve and the deviation curve, and set it as the first slope change point.

[0058] S200: Based on the predetermined slope length, a second slope point is selected from the first slope point. The second slope point is inserted into the existing slope points according to the mileage, and the route to be optimized is regenerated.

[0059] S300. Select the slope change points to be adjusted based on the preset adjustment rules, and use the optimization algorithm to optimize and adjust the slope change points to obtain the optimized route with the smallest comprehensive elevation deviation between the route to be optimized and the measured route.

[0060] Specifically, S100 includes:

[0061] S101. Plot the deviation curve based on mileage x and elevation deviation y, where the elevation deviation y is:

[0062] y = (E top +E bot ) / 2,

[0063] Among them, E top E represents the elevation deviation of the top plate at mileage x. bot The elevation deviation of the base plate at mileage x.

[0064] S102. Perform curve fitting on the deviation curve to obtain the fitted curve;

[0065] S103. Obtain the zero-point mileage corresponding to the zero point of the curve according to the fitted curve, and divide multiple intrusion zones by combining the deviation curve and the zero-point mileage.

[0066] S104. Obtain the mileage of the encroachment point corresponding to the maximum elevation deviation of each encroachment area, select the zero point mileage adjacent to the encroachment point mileage, and set it as the encroachment start mileage and encroachment end mileage; set the encroachment point mileage, encroachment start mileage and encroachment end mileage as the first slope change point.

[0067] Furthermore, the S200 specifically includes:

[0068] S201, Step 1: Based on the mileage difference between the first slope change point and the existing slope change points, select the first slope change point whose mileage difference is greater than the predetermined slope length.

[0069] Step 2: Based on the mileage difference between the first slope change points obtained in Step 1, filter out the first slope change points whose mileage difference is greater than the predetermined slope length.

[0070] Step 3: Set the first slope change point obtained in Step 2 as the second slope change point.

[0071] S202. Generate a new vertical curve table based on the mileage, elevation, and vertical curve radius of the second slope change point;

[0072] S203. Insert the newly added vertical curve table into the vertical curve table of the route database, update the route database, and regenerate the route to be optimized. The newly added vertical curve table has an elevation increment for the elevation of the second slope change point, and the elevation increment can prevent route generation failure.

[0073] Furthermore, the S300 specifically includes:

[0074] S301. Select the second slope change point and the existing slope change point to form the slope change point to be adjusted according to the preset adjustment rules. The preset adjustment rules are as follows: when the second slope change point is not adjacent, select the second slope change point and the existing slope change points adjacent to each second slope change point to form the slope change point to be adjusted. Only one of the duplicate existing slope change points is retained.

[0075] When the second slope change point is adjacent, the second slope change point and the existing slope change points before and after the second slope change point are selected to form the slope change point to be adjusted.

[0076] S302. Use optimization algorithms (such as genetic algorithm, particle swarm algorithm, vulture algorithm, etc.) to optimize and adjust the slope change points to be adjusted, and obtain the optimized route with the smallest comprehensive elevation deviation between the route to be optimized and the measured route.

[0077] Wherein, the comprehensive elevation deviation is the objective function for optimization, expressed as:

[0078]

[0079] Where δ is the comprehensive elevation deviation, G(E) top，iG(E) represents the weighted deviation of the top elevation of the i-th measuring point. bot，i Let be the weighted deviation of the bottom elevation at the i-th measuring point, and n be the number of measuring points. The weighted deviations of the top elevation and bottom elevation are expressed as follows:

[0080]

[0081]

[0082] Among them, E top E represents the elevation deviation of the top plate at the i-th measuring point. bot Let be the elevation deviation of the base plate at the i-th measuring point.

[0083] E0 is the encroachment threshold. For the tunnel roof slab, the value of E0 varies depending on the power supply method: For subways using overhead contact line power supply, the value of E0 should be determined based on the minimum installation space of the contact line, generally E0 = 0.1m; for subways using trackside contact rail power supply, the value of E0 should be determined based on the top height difference between the building clearance and the equipment clearance, generally E0 = 0.3m. For the tunnel floor slab, the value of E0 should be determined in conjunction with the building clearance margin and the track structure type. For steel spring floating slabs, E0 is generally taken as 0.05m, and for other types of tracks, E0 is generally taken as 0.1m.

[0084] k is the sensitivity coefficient. It takes a value in the range of 0.5 to 1.5. The larger the value of k, the more sensitive it is to the magnitude of the encroachment limit. For prefabricated structures such as shield tunnels, k should take a larger value, such as 1 to 1.5; for cast-in-place structures such as horseshoe tunnels and rectangular tunnels, k should take a smaller value, such as 0.5 to 1.

[0085] For top elevation deviation, encroachment will only occur when the measured elevation is less than the design elevation (i.e., E < 0). Therefore, the sign before E is "-", and the corresponding G(E) function graph is as follows. Figure 2 As shown; for the elevation deviation of the base plate, encroachment will only occur when the measured elevation is greater than the design elevation (i.e., E>0). Therefore, the sign before E is "+", and the corresponding graph of the G(E) function is as follows. Figure 3 As shown.

[0086] from Figure 2 , Figure 3 As can be seen, when the amplitude of the elevation deviation E does not exceed the allowable value E0, G(E) = 0; when the amplitude of the elevation deviation E exceeds the allowable value E0, G(E) increases exponentially. This characteristic of G(E) conforms to the basic principle in practical engineering that "the greater the deviation of the limit value from the allowable value, the more unacceptable it is." Taking the reduction of the G(E) value at each measuring point as the optimization objective makes it easier to obtain the optimization effect that meets the expectations of practical engineering.

[0087] from Figure 4As can be seen from this, the larger the value of k, the more sensitive G(E) is to the magnitude of the encroachment limit, and vice versa. Meanwhile, comparing with equation (1), it can be seen that the comprehensive elevation deviation δ is the sum of the weighted encroachment limit values ​​G(E) for all measuring points. Therefore:

[0088] When the value of k is large, G(E) is highly sensitive to the magnitude of the encroachment limit, resulting in significant differences in the weighted encroachment limit values ​​G(E) between different measuring points. This is especially true for the measuring point with the maximum encroachment limit, where the weighted encroachment limit value often exceeds the sum of the weighted encroachment limit values ​​of other measuring points, significantly affecting the magnitude of δ. Therefore, in this case, the comprehensive elevation deviation δ is greatly influenced by the maximum encroachment limit value at the measuring point. Using the comprehensive elevation deviation δ as the optimization function in this situation makes it easier to find a route scheme that effectively reduces the maximum encroachment limit value.

[0089] When the value of k is small, since G(E) is less sensitive to the magnitude of the encroachment limit value, the difference between the weighted encroachment limit values ​​G(E) of different measuring points is small. Therefore, the overall elevation deviation δ is less affected by the individual encroachment limit values ​​G(E), but more affected by the total number of encroachment limit points. In this case, using the overall elevation deviation δ as the optimization function makes it easier to find a route scheme that can effectively reduce the total number of encroachment limit points.

[0090] In practical engineering, for shield tunnels, since the shield segments are prefabricated structures, large local encroachment points are difficult to handle. Therefore, reducing the maximum encroachment value of the measuring points should be the main optimization objective. Thus, a larger k value, such as 1 to 1.5, should be selected. For horseshoe-shaped and rectangular tunnels, since the secondary lining is a cast-in-place reinforced concrete structure, appropriate removal of surface laitance is permissible. Therefore, large local encroachment points are relatively easy to handle. Considering the processing cost, reducing the total number of encroachment points should be the main optimization objective. In this case, k should be a smaller value, such as 0.5 to 1.

[0091] In summary, the slope adjustment method provided by this invention achieves automatic identification and selection of slope change points on a railway line through elevation deviation curve fitting analysis and slope change point screening based on a predetermined slope length, solving the problem that existing intelligent slope adjustment algorithms cannot add new slope change points; based on the identified new slope change points, it automatically generates a corresponding dimension of railway parameter vectors to be optimized, realizing joint optimization of parameters of multiple slope change points on the railway line, and significantly improving the quality of results from intelligent slope adjustment algorithms.

[0092] Example 2

[0093] According to an embodiment of the present invention, a method for adjusting slope is provided. In one possible implementation, step S100 is as follows: Figure 5 As shown, obtaining the first slope change point in this step specifically includes:

[0094] S101: Using the mileage of each measuring point as the x-coordinate and the corresponding line elevation deviation as the y-coordinate, plot the "line elevation deviation - mileage" curve, i.e., the deviation curve.

[0095] S102: Curve fitting is performed on the "line elevation deviation - mileage" curve. In one possible implementation, this embodiment provides a polynomial fitting method to fit the curve. Specifically, the least squares method can be used to perform polynomial fitting on the "line elevation deviation - mileage" curve to obtain the polynomial y = f(x). In this step, the polynomial order should preferably be 6-8. This process can be implemented using the polyfit() function in Matlab.

[0096] S103: Solve for the real roots x1, x2, x3, ..., x of f(x) = 0 within the mileage range of the measuring points. n This refers to the zero-point mileage. Multiple intrusion limits are then defined using the deviation curve and the real root. The specific procedure is as follows:

[0097] (1) Find the roots of f(x) = 0 in the complex field. Specifically, we can construct the adjoint matrix of the polynomial f(x) and then calculate the eigenvalues ​​of the adjoint matrix, which are the roots of f(x) = 0 in the complex field. This process can be achieved using the roots() function in Matlab.

[0098] (2) Select the real roots x1, x2, x3, ..., x that are within the mileage range of the measuring points. n This process can be achieved using the isreal() function in Matlab.

[0099] (3) Divide the encroachment areas upward and downward according to every two adjacent real roots.

[0100] S104: Obtain the first slope change point. The specific steps are as follows:

[0101] (1) Find the maximum elevation deviation y within the encroachment area. p The corresponding mileage x p This process can be achieved using the max() function in Matlab, and the mileage of the intrusion limit point can be set.

[0102] (2) For real roots x1, x2, x3, ..., x n Sort in ascending order; then compare x. p With each of its real roots x i The size of x, if it satisfies i <x p <x i+1 Then take x a =x i x b =x i+1 .

[0103] (3) Output the mileage x of the first grade change point a and x p and x b . Among them, x a represents the starting mileage of the intrusion area, and x p represents the mileage of the intrusion point, and x b represents the ending mileage of the intrusion area.

[0104] In a possible implementation manner, the above step S200 is as Figure 6 shown. The S200 includes screening the first grade change point based on the control principle, determining the position of the second grade change point, and regenerating an optimized route. Specifically as follows:

[0105] S201: First, establish an array X of the mileage of the first grade change point X = [x a , x p , x b . According to the calculation in step S104, it can be known that: x a < x p < x b , that is, the elements in X are arranged in ascending order. Therefore, X' and X" obtained in the following through this step, and the elements in them are all arranged in ascending order.

[0106] In this step, the screening process is as follows:

[0107] Delete the elements in the array X whose distance from the existing grade change point is less than the predetermined slope length L0, and obtain the array X'. Calculate the minimum distance L i between each element in the array X and the mileage of the existing grade change point; delete the elements in the array X where L i < L0, where L0 is the minimum slope section length. Obtain the array X'. Delete the elements in the array X' whose distance from each other is less than the predetermined slope length L0, and obtain the array X" of the mileage of the second grade change point. The principle is: when the distance between two elements is less than L0, remove the element that is closer to the existing grade change point among the two. The specific method is:

[0108] (1) When the number of elements in X' is 0, no new grade change point is added, that is

[0109] (2) When the number of elements in X' is 1, that is X' = [x'1]. Then add 1 grade change point, and the mileage of the second grade change point is x'1, that is X" = [x'1].

[0110] (3) When the number of elements in X' is 2, that is X' = [x'1, x'2]. The following processing is carried out:

[0111] a) If x′2-x′1≥L0, two new slope change points are added, and the mileages of the second slope change points are x′1 and x′2, respectively, i.e., X″=[x′1,x′2];

[0112] b) If x′2 - x′1 < L0, calculate the minimum distances L′1 and L′2 between x′1, x′2 and the existing gradient change point respectively: If L′1 ≥ L′2, keep x′1 and remove x′2. Then the mileage of the second gradient change point is x′1, i.e., X″ = [x′1]; if L′1 < L′2, keep x′2 and remove x′1. Then the mileage of the second gradient change point is x′2, i.e., X″ = [x′2];

[0113] (4) When the number of elements in X′ is 3, i.e., X′=[x′1,x′2,x′3], the following processing is performed:

[0114] a) If x′2-x′1≥L0 and x′3-x′2≥L0, add 3 new slope change points, and the mileages of the second slope change points are x′1, x′2, and x′3, respectively, i.e., X″=[x′1,x′2,x′3];

[0115] b) If x′2-x′1<L0 and x′3-x′2≥L0, calculate the minimum distances L′1 and L′2 between x′1 and x′2 and the existing gradient change points respectively: If L′1≥L′2, remove x′2, then the mileage of the second gradient change point is x′1 and x′3, that is, X″=[x′1,x′3]; If L′1<L′2, remove x′1, then the mileage of the second gradient change point is x′2 and x′3, that is, X″=[x′2,x′3];

[0116] c) If x′2-x′1≥L0 and x′3-x′2<L0, calculate the minimum distances L′2 and L′3 between x′2 and x′3 and the existing gradient change points respectively: If L′2≥L′3, remove x′3, then the mileage of the second gradient change point is x′1 and x′2, that is, X″=[x′1,x′2]; If L′2<L′3, remove x′2, then the mileage of the second gradient change point is x′1 and x′3, that is, X″=[x′1,x′3];

[0117] d) If x′2-x′1<L0 and x′3-x′2<L0, remove x′2 and then perform step (3) above.

[0118] Furthermore, steps S202 and S203 involve adding new gradient change points to the designed route. Specifically, this includes:

[0119] S202: Expand the second gradient change point mileage array X″ into a new vertical curve table addSQX. The specific steps are as follows:

[0120] (1) Calculate the elevation of the corresponding slope change point based on the elements in the second slope change point mileage array X″.

[0121] (2) Create a new vertical curve table addSQX with the elements in the second gradient change point mileage array X″ as the first column, "corresponding gradient change point elevation + Δh" as the second column, and "initial value of vertical curve radius" as the third column. Among them, Δh is a small elevation increment, which can generally be taken as 0.001m. Its function is to avoid the second gradient change point from being collinear with the three points before and after the gradient change point, which would cause the line regeneration to fail. The "initial value of vertical curve radius" can be taken as 5000m according to Table 6.3.3 of the "Metro Design Code (GB50157-2013)".

[0122] S203: Insert the newly added vertical curve table (addSQX) into the vertical curve table in the route database and update the route database. Currently, commonly used route design software in China is based on database technology, using vertical curve tables to store the longitudinal profile information of the route. By performing row insertion processing on the vertical curve table in the route database and then updating the route database, the route after the insertion of the gradient change point can be regenerated.

[0123] In one possible implementation, step S300 optimizes the parameters of multiple gradient change points on the route to find the route scheme with the smallest "comprehensive elevation deviation". Specifically, the process is as follows: First, the second gradient change point identified in S200 and its adjacent gradient change points are selected as the gradient change points to be adjusted; then, based on the elevation, mileage, vertical curve radius, and other parameters of the gradient change points to be adjusted, corresponding samples to be optimized are generated; subsequently, with the minimum "comprehensive elevation deviation" as the optimization objective, intelligent optimization algorithms (such as genetic algorithms, particle swarm optimization, and eagle algorithms) are used to optimize the samples. In one possible implementation, this embodiment provides an intelligent optimization algorithm based on particle swarm optimization to perform optimization adjustments, and finally outputs an optimized route map based on the optimized samples. Specifically, this includes:

[0124] S301: Select the second slope change point and its adjacent slope change points as the slope change points to be adjusted. This step can be automatically selected by the computer based on the inserted row number recorded in step S203 above and the preset adjustment rules.

[0125] S302: Optimized based on particle swarm optimization algorithm, such as Figure 7 As shown, it specifically includes:

[0126] S3021: The mileage, elevation, and vertical curve radius of the slope change point to be adjusted are represented as vectors, and after normalization, they are used as the initial position vector of the particles. The specific procedure is as follows:

[0127] (1) The mileage, elevation, and vertical curve radius of the point to be adjusted are represented as the initial vector P0:

[0128] P0=[M1,H1,R1,M2,H2,R2,M3,H3,R3,…,M n Hn ,R n (4)

[0129] in:

[0130] M—The continuous mileage of the gradient change points to be adjusted, with different gradient change points distinguished by subscripts;

[0131] H — Elevation of the slope change point to be adjusted, with different slope change points distinguished by subscripts;

[0132] R—The vertical curve radius at the slope change point to be adjusted, with different slope change points distinguished by subscripts;

[0133] n — the total number of slope change points to be adjusted.

[0134] It can be seen that since each slope change point has three elements, namely mileage, elevation, and vertical curve radius, the dimension of the initial vector P0 is 3n.

[0135] (2) Normalize the initial vector P0 to obtain the initial position vector normP0 of the particle. Generally, the Min-Max normalization method can be used. The specific steps are as follows:

[0136] Let vector P be the new vector formed after perturbing the initial vector P0. Let p be any element of the new vector P, p0 be any element of the initial vector P0, and Δp be the allowable variation of p0 during the perturbation. Normalizing p using the Min-Max normalization method yields the normalmp:

[0137]

[0138] in:

[0139] p0 — Any element in the initial vector P0;

[0140] p — any element in the new vector P formed after perturbing the initial vector P0;

[0141] Δp—The maximum disturbance amplitude of any element p0 in the initial vector P0. In actual line gradient adjustment, the adjustment amplitude for the mileage of the gradient change point generally does not exceed ±50m, the adjustment amplitude for the elevation of the gradient change point generally does not exceed ±0.5m, and the adjustment amplitude for the radius of the vertical curve generally does not exceed ±1 / 3 of the initial value. Therefore, for the mileage elements M1, M2, ..., M in P0... n The maximum disturbance amplitude Δp can generally be taken as 50m; for the elevation elements H1, H2, ..., H in P0 n The maximum disturbance amplitude Δp can generally be taken as 0.5m; for the vertical curve radius elements R1, R2, ..., R in P0 nIts maximum disturbance amplitude Δp can generally be taken as 1 / 3 of the initial value, i.e., R1 / 3, R2 / 3..., R n / 3.

[0142] p min —The minimum value of any element p0 in the initial vector P0 after the perturbation, i.e., p min =p-Δ p ;

[0143] p max —The maximum value of any element p0 in the initial vector P0 after the perturbation, i.e., p max =p + Δp;

[0144] normp — The value obtained by normalizing any element p in the new vector P.

[0145] As can be seen from equation (5), due to p min ≤p≤p max Therefore, normp ∈ [0, 1].

[0146] Specifically, in the initial state, vector P equals the initial vector P0, therefore the element p = p0. Substituting this into equation (5) yields: normp = 0.5. Therefore, the normalized vector normP0 after normalizing the initial vector P0 is:

[0147] normP0=[0.5, 0.5, 0.5,…] (6)

[0148] The dimension of normP0 is the same as that of the initial vector P0, both being 3n, where n is the number of slope change points to be adjusted. normP0 is used as the initial position vector of the particle.

[0149] S3022: Randomly generate N particles around the initial position of the particle, where each particle has a position vector and a velocity vector. The specific steps are as follows:

[0150] (1) Generate the position vector normP for each random particle:

[0151] normP=[normp1, normp2, normp3,…] (7)

[0152] where normp1, normp2, normp3, ... are all random numbers in the range [0, 1]. The dimension of normP is 3n, where n is the number of slope points to be adjusted.

[0153] (2) Generate the velocity vector V for each random particle:

[0154] V = [v1, v2, v3, ...] (8)

[0155] Where v1, v2, v3, ... are all random numbers within a certain range. Based on calculation experience, they can generally be random numbers within the range of [-0.01, 0.01]. The dimension of V is equal to normP, which is 3n, where n is the number of slope change points to be adjusted.

[0156] (3) The N particles with position vectors and velocity vectors formed by the above steps (1) and (2) constitute the initial population.

[0157] S3023: Generate N lines based on the position vectors of N particles. The specific steps are as follows:

[0158] (1) Perform inverse normalization on the position vector normP of each particle to obtain the corresponding slope change point mileage, slope change point elevation, and vertical curve radius.

[0159] The inverse normalization calculation formula is the inverse operation of equation (5):

[0160] p=(2normp-1)Δp+p0 (8)

[0161] The definitions of p0, p, Δp, and normmp are the same as those in equation (5).

[0162] After inverse normalization, the vector P = [p1, p2, p3, p4, p5, ...] is obtained, where p1, p2, and p3 are the mileage, elevation, and vertical curve radius of the first slope change point to be adjusted, respectively; p4, p5, and p6 are the mileage, elevation, and vertical curve radius of the second slope change point to be adjusted, respectively; p7, p8, and p9 are the mileage, elevation, and vertical curve radius of the third slope change point to be adjusted, respectively; ...; and so on.

[0163] (2) Generate the corresponding longitudinal profile of the line based on the mileage, elevation, and vertical curve radius of the slope change points. Based on the mileage, elevation, and vertical curve radius of each slope change point calculated in step (1) of S3023, modify the vertical curve table, update the line database, and regenerate the line.

[0164] S3024: Calculate the "comprehensive elevation deviation" corresponding to each new route, as the fitness value of the particle. The specific steps are as follows:

[0165] (1) Weight the elevation deviation of the top slab and the elevation deviation of the bottom slab at each measuring point. The elevation-weighted encroachment deviation is expressed as:

[0166]

[0167] in:

[0168] E is the elevation deviation of the top (or bottom) plate of the measuring point, which is calculated by subtracting the design elevation from the measured elevation of the top (or bottom) plate; E0 is the encroachment threshold; k is the sensitivity coefficient;

[0169] (2) Sum the weighted encroachment deviations of the top and bottom elevations of all measuring points to obtain the comprehensive elevation deviation δ. The formula for calculating the comprehensive elevation deviation δ is:

[0170]

[0171] Among them: E top，i E represents the elevation deviation of the top plate at the i-th measuring point; bot，i Let be the elevation deviation of the base plate at the i-th measuring point; n is the number of measuring points.

[0172] S3025: Calculate the individual optimal position and global optimal position of each particle based on its fitness value. After t iterations, the position corresponding to the i-th particle with the best fitness (i.e., the smallest "overall elevation deviation") in t iterations is the individual optimal position of that particle. The position corresponding to the optimal fitness (i.e., minimum "overall elevation deviation") of all particles in t iterations is the globally optimal position among all N particles.

[0173] S3026: Update the velocity vectors of N particles based on their individual optimal positions and global optimal positions. The specific steps are as follows:

[0174] For the i-th particle, its velocity update formula is:

[0175]

[0176] in:

[0177] —The velocity vector of the i-th particle at the (t+1)-th iteration;

[0178] —The velocity vector of the i-th particle at the t-th iteration;

[0179] —The position vector of the i-th particle at the t-th iteration;

[0180] —The optimal position vector of the i-th particle in the first t iterations;

[0181] —The optimal position vector of all particles in the first t iterations;

[0182] ω—the coefficient of inertia, which is generally between 0.5 and 0.8;

[0183] r1 — a random number uniformly distributed in (0, 1);

[0184] r2 — a random number uniformly distributed in (0, 1);

[0185] c1—Self-learning factor, generally ranging from 0.1 to 2;

[0186] c2 — global learning factor, typically ranging from 0.1 to 2.

[0187] S3027: Update the position vectors of N particles based on their velocity vectors. The specific steps are as follows:

[0188] For the i-th particle, its position update formula is:

[0189]

[0190] in:

[0191] —The position vector of the i-th particle at the (t+1)-th iteration;

[0192] —The position vector of the i-th particle at the t-th iteration;

[0193] —The velocity vector of the i-th particle at the (t+1)-th iteration.

[0194] S3028: Determine if the stopping condition is met. Generally, the stopping condition is set at the total number of iterations or the change in the "comprehensive elevation deviation" before and after the iteration being less than a certain threshold. If the stopping condition is not met, the iteration continues; if the stopping condition is met, the result is output.

[0195] S3029: End, output the optimized circuit scheme and residual deviation.

[0196] It should be noted that the above-mentioned optimization algorithm based on particle swarm optimization aims to minimize the comprehensive elevation deviation proposed in this invention. This embodiment only provides an example of the use of one optimization algorithm. The optimization process involved can also be implemented by other optimization algorithms, and should not impose any limitations on the scope of use of the optimization algorithms in this application embodiment.

[0197] In summary, the slope adjustment method provided in this embodiment achieves automated identification of slope change points and joint parameter optimization of multiple slope change points, significantly improving the quality of intelligent slope adjustment algorithms. Firstly, this embodiment proposes a method for automatically identifying and screening new slope change points during line slope adjustment, overcoming the limitation of existing intelligent slope adjustment algorithms that cannot add new slope change points. In this embodiment, a method is provided that uses polynomial fitting technology to analyze the trend of line elevation deviation; then, by finding the root of the polynomial, the "zero point" of the encroachment trend is found; subsequently, the encroachment area is divided; then, the mileage of the encroachment point is determined based on the peak elevation deviation; next, the adjacent "zero point" of the encroachment point's mileage is selected as the first slope change point; finally, the first slope change point is screened based on the minimum slope length requirement to determine the newly added slope change points. This embodiment provides a slope adjustment method that, for the first time, introduces the technology of adding new slope change points during the slope adjustment process. A computer program automatically identifies the mileage of the maximum encroachment point in the encroachment section and then obtains the starting and ending mileages to determine the location of the slope change point. Its significant advantages are: ① When the encroachment point is located in the middle of a certain slope section of the line, simply optimizing the existing slope change point parameters cannot effectively eliminate the encroachment. This method adds a new slope change point near the encroachment point, which can effectively split the line slope section, allowing the line to better approximate the actual tunnel alignment and effectively eliminate the encroachment; ② When line parameters are optimized and adjusted, it will not only affect the encroachment section but also change the alignment of the non-encroachment section. By adding a new slope change point at the starting and ending points of the encroachment section as a dividing point, the impact of slope adjustment in the encroachment section on the alignment of other non-encroachment sections can be reduced.

[0198] Secondly, this embodiment proposes an intelligent slope adjustment method that automatically selects slope change points to be adjusted and performs joint optimization of multiple slope change points. A computer program automatically selects newly added slope change points and their adjacent points as slope change points to be adjusted. Based on the number of slope change points to be adjusted and the line parameters, a sample vector of corresponding dimensions is automatically generated. Then, an intelligent optimization algorithm optimizes the generated sample vector. This method achieves automatic selection and synchronous optimization of multiple slope change points, overcoming the shortcomings of existing intelligent slope adjustment algorithms that cannot automatically identify slope change points to be adjusted and can only manually select a single slope change point for parameter optimization at a time. This significantly improves the efficiency and quality of intelligent slope adjustment algorithms.

[0199] Furthermore, this embodiment creates a weighted encroachment function that integrates threshold control, exponential weighting, and sensitivity adjustment. The comprehensive elevation deviation is obtained by summing the weighted encroachments of each measuring point, serving as the objective function of the intelligent optimization algorithm. Specifically, the weighted encroachment function uses an activation function to control the encroachment threshold, ensuring that the weighted encroachment value of measuring points not exceeding the threshold is calculated as 0, effectively eliminating these measuring points. Simultaneously, the threshold value can be chosen to consider different power supply methods and track structure types, more closely reflecting engineering realities. The exponential function exponentially amplifies encroachment amplitudes exceeding the threshold, aligning with the engineering understanding and slope adjustment design principle that "the larger the encroachment value exceeds the allowable value, the more unacceptable it becomes," facilitating the search for an optimized route scheme that better meets engineering expectations. A sensitivity coefficient is used in the exponent to adjust the weighted encroachment function's sensitivity to encroachment amplitudes, indirectly controlling the relative weights of "encroachment amplitude" and "number of encroachment points" in the comprehensive elevation deviation, thus adapting to the slope adjustment requirements of different types of tunnels.

[0200] Example 3

[0201] The following section uses a subway line in Qingdao as an example to introduce the implementation process of a slope adjustment method in one possible implementation method.

[0202] First, a fitting analysis is performed on the elevation deviation curve of the railway line to identify potential slope change points. The specific procedure is as follows:

[0203] Figure 8 This table presents the measured elevation data of a subway tunnel section and the calculation of the elevation deviation. Column 1 shows the mileage of the measuring points; columns 2 and 5 show the measured elevations of the tunnel floor and roof, respectively; columns 3 and 6 show the design elevations of the tunnel floor and roof, respectively; columns 4 and 7 show the elevation deviations of the tunnel floor and roof, calculated by subtracting the design values ​​from the corresponding measured elevations; and column 8 shows the calculated elevation deviation of the line. Figure 9 The “measured curve of line elevation deviation” is the same as the “line elevation deviation - mileage” curve.

[0204] Secondly, using the polyfit() function in Matlab, an 8th-order polynomial is applied to... Figure 9 The "measured curve of line elevation deviation" was fitted, and the result is as follows: Figure 9 The “polynomial fitting curve” shown in the figure corresponds to the polynomial analytical expression as follows:

[0205] f(x) = -1.359 × 10 -21 x 8 +1.438×10 -16 x 7 -6.048×10 -12 x 6 +1.178×10-7 x 5 -5.846×10 -4 x 4 -18.867x 3 +3.945

[0206] ×10 5 x 2 -3.082×10 9 x + 9.094 × 10 12 .

[0207] The roots of the equation f(x) were solved using the `roots()` function in Matlab, yielding the following roots: -14832.550, (17887.737+81.138i), (17887.7372-81.138i), 17562.864, 17236.121, 16965.668, 16875.415, 16273.995. Then, the `isreal()` function in Matlab was used to filter the roots, resulting in the following real roots: -14832.550, 17562.864, 17236.121, 16965.668, 16875.415, 16273.995. Simultaneously, according to... Figure 8 As shown in the first column, the mileage range of the measuring points is YDK16+807.5~YDK17+867.8, which translates to a continuous mileage of 16834~17894. The real roots within this mileage range are: 17562.864, 17236.121, 16965.668, 16875.415, i.e. Figure 9 The x-coordinate of the "zero point of the polynomial fitting curve".

[0208] In this embodiment, optimization is only performed on the maximum encroachment area in the deviation curve. For the optimization of other encroachment areas, the selection and optimization process in this step is repeated, and will not be elaborated here. After the above steps are completed, the max() function in Matlab is used to find the maximum value of the "line elevation deviation - mileage" data, obtaining the maximum elevation deviation y. p =179mm, corresponding mileage x p =17426.009. Then, using the sort() function in Matlab, the real roots obtained in step S103 are sorted from smallest to largest as follows: 16875.415, 16965.668, 17236.121, 17562.864, respectively, and compared with x p A size comparison revealed 17236.121. <x p <17562.864, therefore we take x. a =17236.121, x b=17562.864. Output the mileage x at the first gradient change point. a x p x b The figures are 17236.121, 17426.009, and 17562.864, respectively.

[0209] To facilitate understanding x a x p x b The geometric meaning it represents Figure 10 The first gradient change point mileage x is shown. a x p x b The relationship between the maximum encroachment area and the maximum encroachment zone. The shaded area in the diagram represents the maximum encroachment zone, i.e., the area where the line elevation deviates the most. From Figure 10 It can be seen from x a x p x b These are the starting mileage, the maximum intrusion point mileage, and the ending mileage of the maximum intrusion area, respectively.

[0210] In this embodiment, the processing of the maximum encroachment area is done by adjusting the mileage x at the maximum encroachment point. p Adding slope change points can eliminate the maximum encroachment to the greatest extent. By adding slope change points at the beginning and end of the maximum encroachment area as the boundary between the maximum encroachment section and other sections of the line, the impact of slope adjustment on other sections of the line can be reduced.

[0211] Next, establish the first gradient change point mileage array X = [17236.121, 17426.009, 17562.864], and calculate: the first element of X, 17236.121, is found to be the closest gradient change point, SJD53, with a mileage of 17336.509. Therefore, the distance L1 between them is:

[0212] L1=|17336.509-17236.121|=100.388(m)

[0213] Similarly, the distance L2 = 89.500m between the second element 17426.009 in X and its nearest slope change point; the distance L3 = 226.355m between the third element 17562.864 in X and its nearest slope change point.

[0214] According to Article 6.3.3 of the "Code for Design of Metro (GB50157-2013)", the length of the gradient section of the line should not be less than the future train length. In this example, the metro project uses 6-car B-type trains, with a train length of approximately 120m. Therefore, based on the predetermined gradient length L0 = 120m, the selection is made as follows: L1 = 100.388m. <L0;L2=89.500m<L0;L3=226.355m> L0. Therefore, by deleting the first and second elements of X, we obtain the array X′ = [17562.864].

[0215] Since X′ contains only one element, the mileage of the newly added gradient change point is 17562.864, which is the element in X′. That is, the second gradient change point mileage array X″ = [17562.864]. Subsequently, the designed route is processed to add gradient change points. The specific procedure is as follows:

[0216] Based on element 17562.864 in the mileage array X″ of the second gradient change point, the design elevation of the line at mileage 17562.864 is -0.077m. Considering an elevation increment of 0.001m, the elevation of the gradient change point is taken as -0.076m. Furthermore, according to engineering design experience and Table 6.3.3 of the "Metro Design Code (GB50157-2013)," the vertical curve radius is taken as the commonly used value of 5000m. The generated new vertical curve table addSQX is: addSQX = [17562.864, -0.076, 5000]. The new vertical curve table addSQX is inserted into the vertical curve table in the line database. A comparison of the line vertical curve tables before and after the second gradient change point is shown below. Figure 11 As shown in the figure, the shaded area represents the newly inserted vertical curve table addSQX.

[0217] Finally, an intelligent optimization algorithm is used to optimize the parameters of the route at various slope points, identifying the route scheme with the smallest "overall elevation deviation". The specific steps are as follows:

[0218] Figure 11 As can be seen, the second gradient change point is gradient change point SJD54, corresponding to row 55 of the vertical curve table (row 1 of the vertical curve table is the starting point of the line, and row 2 is gradient change point SJD1, so the gradient change point number differs from the row number of the vertical curve table by 1). The selected gradient change points to be adjusted are the second gradient change point and its adjacent gradient change points before and after it, namely SJD53, SJD54, and SJD55, corresponding to rows 54, 55, and 56 of the vertical curve table.

[0219] Will Figure 11 The mileage, elevation, and vertical curve radius of the slope change points (SJD53, SJD54, SJD55) to be adjusted are represented as vectors:

[0220] P0=[17336.509, -6.075, 5000, 17562.864, -0.076, 5000, 17826.509, 6.910, 3000] (13)

[0221] After normalizing P0, the vector normP0 is:

[0222] normP0=[0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5] (14)

[0223] Intelligent optimization based on particle swarm optimization algorithm, the optimized vector value is normP. best for:

[0224] normP best = [0.43394, 0.53483, 0.51445, 0.47921, 0.43306, 0.49386, 0.50946, 0.40449, 0.46152] (15)

[0225] According to equation (8), the vector normP best Perform inverse normalization to obtain vector P. best for:

[0226] P best = [17329.903, -6.0402, 5048.1614, 17560.7855, -0.14254, 4979.5177, 17827.4553, 6.8145, 2923.0397] (16)

[0227] The corresponding line parameters are:

[0228]

[0229] Based on the optimized line parameters described above, the optimized line scheme and residual deviation are output as follows: Figure 13 As shown.

[0230] For ease of comparison, in Figure 12 The image shows the original longitudinal profile of the route before optimization, as well as the deviations of the top and bottom slabs. From... Figure 12 As can be seen, the maximum encroachment limit of the top slab (negative value) is 181 mm, the maximum encroachment limit of the bottom slab (positive value) is 200 mm, and there are 55 encroachment points that exceed the encroachment limit threshold (the encroachment limit threshold for this project is 100 mm).

[0231] In contrast, Figure 13 The diagram shows the optimized longitudinal profile of the railway line and the deviations of the top and bottom plates using the method of this invention. Figure 13As can be seen, the maximum encroachment limit of the top slab (negative value) is 101mm, and the maximum encroachment limit of the bottom slab (positive value) is 96mm. There is only one encroachment point that exceeds the threshold (the threshold for this project is 100mm), and the encroachment limit is only 1mm. This basically eliminates the encroachment limit and meets the actual engineering requirements.

[0232] Example 4

[0233] The following section uses a subway line in Qingdao as an example to illustrate the advantages of the slope adjustment method provided by this invention compared to existing intelligent slope adjustment methods in one possible implementation.

[0234] For the case in Example 3, an existing intelligent slope adjustment method was used for optimization. However, existing intelligent slope adjustment methods cannot automatically find and add new slope change points; they can only optimize the parameters of existing slope change points. Figure 12 As can be seen, the maximum encroachment point is concentrated near the slope change point SJD53; therefore, slope change point SJD5 is selected as the optimization target. Figure 11 The original design showed that the mileage, elevation, and vertical curve radius of the SJD53 gradient change point were 17336.509, -6.075, and 5000, respectively. Using a conventional particle swarm optimization algorithm, the parameters of the SJD53 gradient change point were optimized. The optimized mileage, elevation, and vertical curve radius are now 17334.4065, -6.0367, and 5167.1433, respectively. The optimized route is shown below. Figure 14 As shown. From Figure 14 As can be seen from the data, the maximum value of the top slab encroachment limit (negative value) for the optimized line is 194mm, and the maximum value of the bottom slab encroachment limit (positive value) is 123mm. There are 46 encroachment points that exceed the encroachment limit threshold (the encroachment limit threshold for this project is 100mm).

[0235] contrast Figure 13 and Figure 14 It can be seen that the optimized line using the slope adjustment method provided by this invention ( Figure 13 ), compared with the route optimized using existing methods ( Figure 14 Compared to the previous method, the maximum intrusion limit of the top plate can be reduced by 48%, the maximum intrusion limit of the bottom plate can be reduced by 22%, and the intrusion point exceeding the threshold can be reduced by 98%. Therefore, the slope adjustment method of this invention has significant technical advantages.

[0236] Example 5

[0237] In another aspect, the present invention provides an electronic device including 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 slope adjustment method.

[0238] In another aspect, the present invention provides a computer storage medium storing program instructions that, when executed by at least one processor, implement the above-described slope adjustment method.

[0239] 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.

[0240] 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.

[0241] The storage medium can be memory, such as volatile memory or non-volatile memory, or may include both volatile and non-volatile memory.

[0242] 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.

[0243] 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).

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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.

[0248] 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 slope adjustment method, characterized in that, The method includes: S100. Draw a deviation curve based on the mileage and elevation deviation of each measuring point on the line to be adjusted, perform curve fitting on the deviation curve, obtain the fitted curve, and identify the location of the added slope change point based on the fitted curve and the deviation curve, and set it as the first slope change point. S200: Based on the predetermined slope length, a second slope point is selected from the first slope point. The second slope point is inserted into the existing slope points according to the mileage, and the route to be optimized is regenerated. S300. Select the slope change points to be adjusted based on the preset adjustment rules, and use the Bald Eagle algorithm to optimize and adjust the slope change points to be adjusted, so as to obtain the optimized route with the smallest comprehensive elevation deviation between the route to be optimized and the measured route. The comprehensive elevation deviation is composed of the weighted deviations of the top elevation encroachment limit and the weighted deviations of the bottom elevation encroachment limit at each measuring point. The step of identifying the location of the added slope change point based on the fitted curve and the deviation curve, and designating it as the first slope change point, includes: The zero-point mileage corresponding to the zero point of the curve is obtained based on the fitted curve, and multiple intrusion zones are divided by combining the deviation curve and the zero-point mileage. Obtain the mileage of the encroachment point corresponding to the maximum elevation deviation of each encroachment area, and select the zero-point mileage adjacent to the encroachment point mileage as the encroachment start mileage and encroachment end mileage. The mileage of the intrusion point, the mileage of the intrusion start point, and the mileage of the intrusion end point are set as the first slope change point; The step of selecting the second slope change point from the first slope change point based on a predetermined slope length includes: Step 1: Based on the mileage difference between the first slope change point and the existing slope change points, select the first slope change point whose mileage difference is greater than the predetermined slope length. Step 2: Based on the mileage difference between the first slope change points obtained in Step 1, filter out the first slope change points whose mileage difference is greater than the predetermined slope length. Step 3: Set the first slope change point obtained in Step 2 as the second slope change point; The process of regenerating the route to be optimized includes: A new vertical curve table is generated based on the mileage, elevation, and vertical curve radius of the second slope change point; Insert the newly added vertical curve table into the vertical curve table of the line database, update the line database, and regenerate the line to be optimized; The selection of the slope change point to be adjusted based on preset adjustment rules includes: When the second slope change point is not adjacent, select the second slope change point and the existing slope change points adjacent to each second slope change point to form the slope change point to be adjusted. For duplicate existing slope change points, only one is retained. When the second slope change point is adjacent, the second slope change point and the existing slope change points before and after the second slope change point are selected to form the slope change point to be adjusted.

2. The slope adjustment method according to claim 1, characterized in that, The deviation curve consists of mileage x and elevation deviation y, where the elevation deviation y is: ， in, Let x be the elevation deviation of the top plate at mileage x. The elevation deviation of the base plate at mileage x.

3. The slope adjustment method according to claim 1, characterized in that, The newly added vertical curve table provides an elevation increment for the elevation of the second slope change point.

4. The slope adjustment method according to claim 1, characterized in that, The comprehensive elevation deviation is expressed as follows: ， in, To take into account the elevation deviation, For the first Weighted deviation of the top elevation of each measuring point For the first Weighted deviation of the base elevation at each measuring point This represents the number of measurement points.

5. The slope adjustment method according to claim 4, characterized in that, The weighted deviation of the top plate elevation and the weighted deviation of the bottom plate elevation are expressed as follows: ， ， in, For the first The top elevation deviation of each measuring point For the first Elevation deviation of the base plate at each measuring point This is the intrusion threshold. The sensitivity coefficient is denoted as .

6. An electronic device, characterized in that, include: The device 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 a slope adjustment method as described in any one of claims 1 to 5.

Images