A method, medium and device for fine-tuning and optimizing the spatial alignment of an operating high-speed railway

Abstract

The present invention relates to the technical field of high-speed railway tracks, and specifically provides a method, medium and equipment for fine-tuning and optimizing the spatial line position of an operating high-speed railway. In the method provided by the present invention, by constructing a spatial linear model of the operating high-speed railway, the construction of a horizontal and vertical combined spatial line element array is realized, laying a foundation for the construction and solution of the subsequent spatial line position optimization model of the operating high-speed railway; by constructing the spatial line position optimization model, and further generating a three-dimensional line adjustable perimeter pipe gallery according to the horizontal and vertical surface linear shape, structure distribution, track adjustable amount, structure limit requirements and spatial linear model of the operating high-speed railway, the spatial line position optimization model is optimized in the three-dimensional line adjustable perimeter pipe gallery, and finally an optimized line plan is generated, so as to realize the fine-tuning optimization of the spatial line position of the operating high-speed railway, improve the optimization quality and enhance the railway travel efficiency.

Description

Technical Field

[0001] The present invention relates to the field of high-speed railway track technology, and in particular to a method, medium and equipment for fine-tuning and optimizing the spatial line position of an operating high-speed railway. Background Art

[0002] As the first batch of high-speed railways have been in service for more than 10 to 15 years, the aging of the lines and the increasing demand for speed increases have become major technical issues that must be addressed in the development of high-speed railways. Since the spatial alignment has a significant impact on railway driving safety and operating speed, optimizing the spatial alignment of the line, such as increasing the radius of curves, extending the length of straight lines or gentle curves, reducing the slope, and increasing the radius of vertical curves, is an important means to increase the speed of trains. However, the implementation conditions for the speed-up reconstruction of high-speed railways are relatively harsh. The operating railways need to minimize interference with daily operations, and speed-up construction can only be carried out during the "skylight" at night or during the interval between trains. When fine-tuning the spatial alignment, limited by the fixed characteristics of the track structure, the workload can only be concentrated on the subtle adjustments of the track system, such as the adjustment of fasteners, gaskets, screws and other fastening components, but the track cannot be moved significantly to achieve large-scale reconstruction of structures such as roadbeds, bridges, and tunnels.

[0003] Due to this limitation, the existing spatial line adjustment tasks that rely on manual decision-making require a lot of human and material resources, and it is difficult to ensure the optimal adjustment of the entire railway line. In this regard, the line intelligent optimization method provides an effective solution to improve its line adjustment efficiency and line plan quality. However, the existing railway intelligent optimization methods are often for wide-area spatial line adjustment of newly built lines. The spatial line adjustment of the existing operating high-speed railway speed increase is limited by many factors such as the allowable adjustment amount of the track and the limit of the structure. In addition, the optimization goals of the newly built line are mainly cost, disaster reduction, environmental protection, etc., while the optimization goals of the operating high-speed railway are mainly running time, track shifting amount, etc. There are significant differences in the optimization goals of the two, which makes the existing line optimization technology cannot be directly applied to such fine adjustments.

[0004] Therefore, it is urgent to develop a method, medium and equipment for fine-tuning and optimizing the spatial line position of operating high-speed railways, so as to achieve fine-tuning and optimization of the spatial line position of operating high-speed railways under harsh conditions with limited adjustable range, and ensure the efficient implementation of speed-up and reconstruction projects. Summary of the invention

[0005] The purpose of the present invention is to provide a method, medium and equipment for fine-tuning and optimizing the spatial alignment of an operating high-speed railway. The specific technical scheme is as follows:

[0006] A method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway comprises the following steps:

[0007] Step S1, constructing a spatial linear model of an operating high-speed railway, generating a plane line element array and a longitudinal section line element array based on the model, establishing a mapping function from mileage to coordinates, azimuth, and elevation, combining the plane line element array and the longitudinal section line element array to form a three-dimensional space line element array, and calculating its added slope;

[0008] Step S2: constructing a spatial alignment optimization model for operating high-speed railways, determining decision variables of the optimization model, constructing a travel time objective function, and proposing relevant constraints for spatial alignment design;

[0009] Step S3, generating the adjustable range of the line centerline pile by pile according to the horizontal and vertical alignment of the operating high-speed railway, the distribution of structures, the adjustable amount of the track, and the limit requirements of the structures, applying the spatial linear model generated in step S1 to calculate the spatial coordinates of the boundary points of the adjustable range, and further generating a three-dimensional line adjustable perimeter pipe gallery;

[0010] Step S4: randomly generate a series of initial route plans within the adjustable perimeter pipeline corridor, use the route plans as particles of the particle swarm algorithm, and use the particle swarm algorithm to generate the optimal route plan.

[0011] Furthermore, step S1 specifically includes:

[0012] Step S1.1, construct a plane linear model, realize the conversion of the intersection array to the plane line element array, and establish a mapping from mileage to coordinates and azimuths; construct a longitudinal section linear model, realize the conversion of the slope change point array to the longitudinal section line element array, and establish a mapping from mileage to elevation;

[0013] Step S1.2, perform a union operation on the plane line element array generated by the plane linear model and the longitudinal section line element array generated by the longitudinal section linear model, construct a horizontal and vertical combined space line element array and calculate the added slope.

[0014] Furthermore, step S2 specifically includes:

[0015] Step S2.1, determine the decision variables of the optimization model, and the three-dimensional spatial position of the line is represented by the following vectors:

[0016] The X-coordinate column vector of the intersection point: ;

[0017] Intersection point Y coordinate column vector: ;

[0018] Plane circular curve radius column vector: ;

[0019] Plane transition curve long column vector: ;

[0020] Column vector of slope change point mileage: ;

[0021] Column vector of design elevation of slope change point: ;

[0022] Vertical curve radius column vector: ;

[0023] in, is the number of plane intersections, is the number of slope change points in the longitudinal section;

[0024] The constraints related to the spatial linear design include geometric specification constraints, maximum travel time constraints and adjustable perimeter constraints;

[0025] Geometric specification constraints: including minimum circular curve radius, minimum circular curve length, minimum clip line length, minimum slope section length, and maximum slope;

[0026] Maximum running time constraint: The maximum running time of the route plan generated during the optimization process Should be less than or equal to the running time of the initial given plan , in order to achieve the speed-up goal, that is ;

[0027] Adjustable perimeter constraint: Any point on the centerline of the route generated during optimization Adjustable perimeter required Within the constraints, that is ;

[0028] Step S2.2: Construct the objective function of decision variables and train running time:

[0029] ;

[0030] in, Add time for the start of the vehicle. Add time for parking. is the length of a slope section, It is the running minutes per kilometer on a certain slope; It means that the route plan with smaller train running time is more optimal.

[0031] Furthermore, in the geometric specification constraints, the minimum circular curve radius The following constraints are met:

[0032] ;

[0033] in, is the minimum circular curve radius, For the The radius of the circular curve, is the number of plane intersections;

[0034] Minimum circular curve length The following constraints are met:

[0035] ;

[0036] in, is the minimum circular curve length, For the The corner of the curve, Indicates The transition curve of each curve is long;

[0037] Minimum clamping line length The following constraints are met:

[0038] ;

[0039] in, is the minimum straight line length between adjacent curves, , For the The coordinates of the plane intersection points, , For the The coordinates of the plane intersection points, Indicates The length of the back tangent of a circular curve is Indicates The length of the front tangent of the circular curve;

[0040] Minimum slope length The following constraints are met:

[0041] ;

[0042] in, For the Mileage of the vertical change point of the longitudinal section, For the Mileage of the vertical change point of the longitudinal section, is the minimum slope length; is the number of slope change points in the longitudinal section;

[0043] Maximum slope The following constraints are met:

[0044] ;

[0045] in, For the The design elevation of each slope change point of the longitudinal section, For the The design elevation of each slope change point of the longitudinal section, For the The first vertical section slope change point and the The distance between the slope change points of the longitudinal section, is the maximum slope value.

[0046] Furthermore, step S3 specifically includes:

[0047] Step S3.1, input the line plane intersection point, radius, and slow length data of the operating high-speed railway, generate a plane line element array according to the plane linear model of step S1.1, and realize the mileage conversion to coordinates; input the line longitudinal section slope change point mileage, elevation and vertical curve radius data of the operating high-speed railway, generate a longitudinal section line element array according to the longitudinal section linear model of step S1.1, and realize the mileage conversion to elevation;

[0048] Step S3.2: Generate a series of number sequences at certain intervals along the starting and ending points of the line, calculate the three-dimensional spatial point coordinates and tangent azimuth of the line centerline for each pile, obtain the track adjustable range data for each pile from the engineering department, and calculate the three-dimensional spatial point coordinates for each pile. , tangent azimuth With the adjustable range data, the three-dimensional spatial coordinates of the horizontal adjustable range boundary points and the vertical adjustable range boundary points of the line center of each pile are calculated. Assuming that The horizontal adjustable range of the pile number is the left offset distance , , right offset distance , , then the corresponding three-dimensional coordinates of the horizontal boundary point are:

[0049] ;

[0050] Assume that the vertical adjustable range is up and down offset , , then the corresponding vertical boundary point is:

[0051] ;

[0052] Fit the four points of the perimeter with an ellipse-like curve to generate the adjustable area of ​​the single pile section;

[0053] Step S3.3, connecting each elliptical cross section in sequence along the pile number sequence to form a complete three-dimensional line adjustable perimeter pipeline corridor.

[0054] Furthermore, step S4 specifically includes:

[0055] Step S4.1, in the three-dimensional adjustable perimeter pipe gallery generated in step S3, an initial particle group is generated based on the spatial line position of the existing operating high-speed railway; specifically, a step-by-step initialization strategy is adopted, the plane is initialized first, and after the plane plan is generated, the longitudinal section is initialized along the line plane, so as to generate the initial line plane and longitudinal plan;

[0056] Step S4.2: Particle iterative optimization to obtain an optimized line plan.

[0057] Furthermore, in step S4.1, the line plane initialization is specifically:

[0058] First, the adjustable perimeter corridor of the three-dimensional line is cut along the center line of the line with a horizontal plane to obtain a strip plane projection polygon of the three-dimensional corridor; then, the concept of butterfly area template is applied inside the polygon to initialize the line plane; finally, new intersection coordinates are randomly generated in the butterfly area, and new radius and delay length values ​​are randomly selected within the threshold range of the curve radius and delay length. The spatial point coordinates of each pile are solved with the help of the spatial linear model in step S1, and it is detected whether they are within the adjustable range of the spatial corridor, and the line plane schemes in which all point sets are within the adjustable range of the spatial corridor are retained to form an initial line plane scheme set;

[0059] The specific initialization of the line profile is:

[0060] First, the initial plan set of the route plane is traversed, and the three-dimensional route adjustable perimeter corridor is vertically cut with a longitudinal section along the center line of the route to obtain the strip longitudinal section projection polygon of the three-dimensional corridor; then, according to the maximum and minimum longitudinal slopes, an initial envelope area is generated between the starting and ending points, and the intersection area between the envelope area and the strip longitudinal section projection polygon of the three-dimensional corridor is calculated to form an adjustable range of the longitudinal section design; finally, the total number of slope change points is kept unchanged, and at each slope change point, the adjustable range of mileage is set based on the initial slope change point mileage, and a new slope change point mileage is randomly generated within the range. Within the adjustable range of the longitudinal section design, the elevation of the slope change point at the corresponding mileage is randomly generated, and within the vertical curve radius threshold range, a new vertical curve radius is randomly selected, and the spatial point elevation of each pile is solved with the help of the spatial linear model in step S1, and it is detected whether it is within the adjustable range of the spatial corridor, and the route longitudinal section schemes of all point sets within the adjustable range of the spatial corridor are retained to form the initial plan set of the route plane and longitudinal plane, that is, the initial particle group.

[0061] Furthermore, step S4.2 specifically includes:

[0062] Step S4.2.1: Construct the position and velocity update operator of the particle swarm. The calculation formula is as follows:

[0063] ;

[0064] ;

[0065] in, Indicates the current iteration number, is the inertia weight, Indicates the population The current velocity characteristics of each particle, Indicates the population The updated velocity characteristics of the particles, For the population The current position of a particle, For the population The updated position of the particle, and are social constants and cognitive constants, and is a random number distributed in the range [0, 1], is the number of all particles in the entire group up to the current iteration The optimal position of , that is, the global optimal position, It is Particles up to the current iteration number The optimal position of , that is, the individual optimal position;

[0066] Step S4.2.2, plane scheme perturbation:

[0067] On the basis of the initialized plane solution set, the decision variable set of each particle in the current particle swarm is disturbed and updated according to the position of the plane intersection point corresponding to the current global optimal position and the individual optimal position, the curve radius, and the length of the transition curve. The updated line plane solution set is subjected to constraint detection and judgment according to the relevant constraints of the spatial linear design. If it fails, the particle plane decision variable is adjusted in the direction of satisfying the constraints until the detection passes.

[0068] Synchronous disturbance update of longitudinal section scheme:

[0069] On the current longitudinal section scheme group, according to the current global optimal position and individual optimal position corresponding to the longitudinal slope change point position and vertical curve radius, the mileage, elevation, and vertical curve radius of the slope change point of each particle in the current particle group are disturbed and updated, and the updated line longitudinal section scheme is subjected to constraint detection and judgment according to the relevant constraints of the spatial linear design. If it fails, the mileage, elevation, and vertical curve radius are adjusted in the direction of satisfying the constraints until the detection passes;

[0070] Step S4.2.3, generate a new circuit particle swarm solution:

[0071] According to the horizontal and vertical parameters of the line updated in step S4.2.2, the spatial linear model in step S1 is used to recalculate the spatial point coordinates of each pile; check whether the new line plan group is within the adjustable range of the three-dimensional pipeline corridor to ensure that all horizontal and vertical points are within the allowable range; then evaluate the newly generated line plan particle group based on the objective function of the decision variable and the train running time. The smaller the running time, the better the corresponding line plan;

[0072] Step S4.2.4, update the optimal solution:

[0073] According to the evaluation results, the positions of the individual optimal solutions and the global optimal solutions of the particles are updated;

[0074] Step S4.2.5: Determine the current number of iterations Is it the maximum number of iterations? If so, select the route plan with the smallest travel time in the last algebraic particle swarm as the optimal route plan; if not, take , return to step S4.2.1.

[0075] A device comprises a memory and a processor, wherein the memory stores a computer program; the processor executes the computer program to implement the method for fine-tuning and optimizing the spatial line position of an operating high-speed railway as described above.

[0076] A medium, which is a readable storage medium, on which a computer program is stored, and a processor executes the computer program to implement the method for fine-tuning and optimizing the spatial line position of an operating high-speed railway as described above.

[0077] The application of the technical solution of the present invention has the following beneficial effects:

[0078] The present invention provides a method for fine-tuning and optimizing the spatial line position of an operating high-speed railway. By constructing a spatial linear model of the operating high-speed railway, the construction of a horizontal and vertical combined spatial line element array is realized, laying a foundation for the subsequent construction and solution of the spatial line position optimization model of the operating high-speed railway; by constructing the spatial line position optimization model, and further generating a three-dimensional line adjustable perimeter pipeline corridor according to the horizontal and vertical surface linear shape, structure distribution, track adjustable amount, structure limit requirements and spatial linear model of the operating high-speed railway, the spatial line position optimization model is optimized in the three-dimensional line adjustable perimeter pipeline corridor, and finally an optimized line plan is generated, so as to realize the fine-tuning optimization of the spatial line position of the operating high-speed railway, improve the optimization quality, and enhance the railway travel efficiency.

[0079] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0080] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0081] Figure 1 It is a flow chart of the method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway in the present invention;

[0082] Figure 2 It is a three-dimensional line adjustable perimeter pipeline corridor diagram, where A is the adjustable perimeter pipeline corridor and B is the adjustable perimeter section at any point. DETAILED DESCRIPTION

[0083] The embodiments of the present invention are described in detail below with reference to the accompanying drawings. However, the present invention can be implemented in many different ways as defined and covered.

[0084] In the description of the present invention, it should be noted that the terms "up", "down", "left", "right", "vertical", "horizontal", "inside", "outside", "front", "back", "lateral", "longitudinal" and the like indicate directions or positional relationships based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as a limitation on the present invention.

[0085] In addition, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0086] Example:

[0087] See also Figure 1 This embodiment provides a method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway, comprising the following steps:

[0088] Step S1, constructing a spatial linear model of an operating high-speed railway, generating a plane line element array and a longitudinal section line element array based on the model, establishing a mapping function from mileage to coordinates, azimuth, and elevation, combining the plane line element array and the longitudinal section line element array to form a three-dimensional space line element array, and calculating its added slope;

[0089] Step S2: constructing a spatial alignment optimization model for operating high-speed railways, determining decision variables of the optimization model, constructing a travel time objective function, and proposing relevant constraints for spatial alignment design;

[0090] Step S3, generating the adjustable range of the line centerline pile by pile according to the horizontal and vertical alignment of the operating high-speed railway, the distribution of structures, the adjustable amount of the track, and the limit requirements of the structures, applying the spatial linear model generated in step S1 to calculate the spatial coordinates of the boundary points of the adjustable range, and further generating a three-dimensional line adjustable perimeter pipe gallery;

[0091] Step S4: randomly generate a series of initial route plans within the adjustable perimeter pipeline corridor, use the route plans as particles of the particle swarm algorithm, and use the particle swarm algorithm to generate the optimal route plan.

[0092] Step S1 specifically includes:

[0093] Step S1.1, construct a plane linear model, realize the conversion of intersection array to plane line element array, and establish a mapping from mileage to coordinates and azimuth; construct a longitudinal section linear model, realize the conversion of slope change point array to longitudinal section line element array, and establish a mapping from mileage to elevation.

[0094] Plane linear model construction:

[0095] Firstly, the coordinates of the plane intersection points on the center line of the railway line, the curve radius, and the length of the transition curve are collected to form a plane intersection information array as the initial input data of the plane linear model;

[0096] Then, the intersection information array is further processed and converted into a line element array. The specific process is as follows: (1) Calculate the curve elements corresponding to each intersection, including the curve deflection, tangent length, curve length, and the mileage and coordinates of the curve feature points (i.e., line element segmentation points: straight-slow point ZH, slow-round point HY, round-slow point YH, slow-straight point HZ); (2) Calculate the line element array corresponding to each intersection, and customize the storage information of each line element as shown in Table 1-1-1;

[0097] Table 1-1-1 Line element storage information format

[0098]

[0099] Finally, a mapping function from the mileage of any point on the line to the coordinates and tangent azimuth is established based on the line element array. The solution idea is as follows: (1) Input the mileage of any point on the line, traverse the line element array, and determine the line element where the given point is located based on the relationship between the given mileage and the line element segment mileage; (2) Extract the line element information data, and use the corresponding line shape calculation formula according to the line element type (straight line, transition curve, and circular curve) to solve the coordinates and tangent azimuth of the given point.

[0100] By constructing a plane linear model, the conversion from intersection array to line element array can be realized, and the mapping from mileage to coordinates can be established, laying the foundation for the construction and solution of the subsequent high-speed railway spatial line optimization model.

[0101] Construction of longitudinal section alignment model:

[0102] For the longitudinal section of the railway line, first collect the data such as the mileage, elevation, vertical curve radius, etc. of the slope change point of the longitudinal section of the line centerline to form a slope change point information array as the initial input data of the longitudinal section linear model;

[0103] Then, the slope change point information array is further processed and converted into a longitudinal section line element array. The specific process is as follows: (1) Calculate the vertical curve elements corresponding to each slope change point, including slope, slope length, tangent length of the vertical curve, curve length, characteristic point mileage and elevation (straight circle point ZY, circle straight point YZ); (2) Calculate the longitudinal section line element array corresponding to each slope change point. The storage information of each line element is similar to that of the plane line element.

[0104] Finally, based on the longitudinal section line element array, a mapping function is established from the mileage of any point on the line to the design elevation and slope. The solution idea is similar to the solution of fixed-point coordinates based on the plane line element array.

[0105] By constructing the longitudinal section alignment model, the conversion of the slope change point array to the longitudinal section line element array can be realized, and the mapping of mileage to elevation can be established, laying the foundation for the construction and solution of the subsequent high-speed railway spatial line position optimization model.

[0106] Step S1.2, perform a union operation on the plane line element array generated by the plane linear model and the longitudinal section line element array generated by the longitudinal section linear model, construct a horizontal and vertical combined space line element array and calculate the added slope.

[0107] The specific operations are as follows: (1) Since both the plane line element array and the longitudinal section line element array are ordered arrays with mileage arranged in ascending order, the plane line element array and the longitudinal section line element array are mixed and rearranged in ascending order of mileage to finally form an ordered array with mileage arranged in ascending order after the horizontal and vertical combination; (2) According to the types of horizontal and vertical line elements, six types of horizontal and vertical combined spatial line elements are defined: straight + vertical straight, flat + vertical straight, flat circle + vertical straight, straight + vertical circle, flat + vertical circle, flat circle + vertical circle; (3) Traverse the spatial line element array, solve the line element length and line element type, and calculate the added slope based on the line element type. , that is, the original slope Conversion slope of additional resistance to curve sum:

[0108] Formula 1);

[0109] In formula 1), is the curve turning angle within the current slope section. is the length of the slope section; the accurate calculation of the added slope lays the foundation for the subsequent solution of the optimization objective function of the train running time.

[0110] Step S2 specifically includes:

[0111] Step S2.1, determine the decision variables of the optimization model, and the three-dimensional spatial position of the line is represented by the following vectors:

[0112] The X-coordinate column vector of the intersection point: ;

[0113] Intersection point Y coordinate column vector: ;

[0114] Plane circular curve radius column vector: ;

[0115] Plane transition curve long column vector: ;

[0116] Column vector of slope change point mileage: ;

[0117] Column vector of design elevation of slope change point: ;

[0118] Vertical curve radius column vector: ;

[0119] in, is the number of plane intersections, is the number of slope change points in the longitudinal section; under the condition of given decision variables, the horizontal and vertical alignment models in step S1 can be used to generate a spatial line element array, establish a mapping from any mileage to spatial coordinates, and calculate the type and added slope value of each line element.

[0120] The constraints related to the spatial linear design include geometric specification constraints, maximum travel time constraints and adjustable perimeter constraints;

[0121] Geometric specification constraints: including minimum circular curve radius, minimum circular curve length, minimum clip line length, minimum slope section length, and maximum slope;

[0122] Minimum circular curve radius The following constraints are met:

[0123] Formula 2);

[0124] In formula 2), is the minimum circular curve radius, For the The radius of the circular curve, is the number of plane intersections;

[0125] Minimum circular curve length The following constraints are met:

[0126] Formula 3);

[0127] In formula 3), is the minimum circular curve length, For the The corner of the curve, Indicates The length of the transition curve of each curve (generally, the length of the transition curves before and after each intersection is equal);

[0128] Minimum clamping line length The following constraints are met:

[0129] Formula 4);

[0130] In formula 4), is the minimum straight line length between adjacent curves, , For the The coordinates of the plane intersection points, , For the The coordinates of the plane intersection points, Indicates The length of the back tangent of a circular curve is Indicates The front tangent length of each circular curve (the front and rear tangent lengths at the start and end points are both set to 0);

[0131] Minimum slope length The following constraints are met:

[0132] Formula 5);

[0133] In formula 5), For the Mileage of the vertical change point of the longitudinal section, For the Mileage of the vertical change point of the longitudinal section, is the minimum slope length; is the number of slope change points in the longitudinal section;

[0134] Maximum slope The following constraints are met:

[0135] Formula 6);

[0136] In formula 6), For the The design elevation of each slope change point of the longitudinal section, For the The design elevation of each slope change point of the longitudinal section, For the The first vertical section slope change point and the The distance between the slope change points of the longitudinal section, is the maximum slope value.

[0137] Maximum running time constraint: The maximum running time of the route plan generated during the optimization process Should be less than or equal to the running time of the initial given plan , in order to achieve the speed-up goal, that is ;

[0138] Adjustable perimeter constraints: To ensure that existing high-speed rail line subgrades, bridges, tunnels and other structures along the track do not undergo major reconstruction projects, it is limited to track fine-tuning. Any point on the line centerline generated during the optimization process Adjustable perimeter required Within the constraints, that is ;

[0139] Step S2.2: Construct the objective function of decision variables and train running time:

[0140] Formula 7);

[0141] In formula 7), Add time for the start of the vehicle. Additional time for parking, usually taken for electric and internal combustion traction =1~3 min, =1~2min; is the length of a slope section in km. It is the running time per kilometer on a certain slope, in min / km; It means that the route plan with smaller train running time is more optimal.

[0142] Since the optimization process involves a large number of iterative calculations and a large computational load, the calculation of the running time should adopt a simple equilibrium speed method: it is assumed that when the train runs on each slope section, it runs at a constant speed according to the equilibrium speed (or speed limit) of the slope. Each spatial line element of the slope section corresponds to a speed limit. If the length of the spatial line element is ,use ( The running time of each section is calculated by the method of equalizing speed or limiting speed, and then the running time of the entire line is accumulated to obtain the running time.

[0143] Step S3 specifically includes:

[0144] Step S3.1, input the line plane intersection point, radius, and slow length data of the operating high-speed railway, generate a plane line element array according to the plane linear model of step S1.1, and realize the mileage conversion to coordinates; input the line longitudinal section slope change point mileage, elevation and vertical curve radius data of the operating high-speed railway, generate a longitudinal section line element array according to the longitudinal section linear model of step S1.1, and realize the mileage conversion to elevation;

[0145] Step S3.2: Generate a series of number sequences at certain intervals along the starting and ending points of the line, calculate the three-dimensional spatial point coordinates and tangent azimuth of the line centerline for each pile, obtain the track adjustable range data for each pile from the engineering department, and calculate the three-dimensional spatial point coordinates for each pile. , tangent azimuth With the adjustable range data, the three-dimensional spatial coordinates of the horizontal adjustable range boundary points and the vertical adjustable range boundary points of the line center of each pile are calculated. Assuming that The horizontal adjustable range of the pile number is the left offset distance , , right offset distance , , then the corresponding three-dimensional coordinates of the horizontal boundary point are:

[0146] Formula 8);

[0147] Assume that the vertical adjustable range is up and down offset , , then the corresponding vertical boundary point is:

[0148] Formula 9);

[0149] Fit the four points of the perimeter with an ellipse-like curve to generate the adjustable area of ​​the single pile section;

[0150] Step S3.3: Connect each elliptical section in sequence along the pile number sequence to form a complete three-dimensional adjustable perimeter pipeline corridor. Figure 2 First, the point set should be encrypted on the basis of the initial four perimeter points, and the number of points should be consistent before the corresponding points can be connected to ensure the accuracy and continuity of the connection, and finally form a complete three-dimensional line adjustable perimeter corridor A. In the subsequent optimization process, the function of extracting the adjustable perimeter section B at any point on the line centerline is provided, and the adjustable range of the track corresponding to the point is further determined based on this.

[0151] Step S4 specifically includes:

[0152] Step S4.1, in the three-dimensional adjustable perimeter pipe gallery generated in step S3, an initial particle group is generated based on the spatial line position of the existing operating high-speed railway; specifically, a step-by-step initialization strategy is adopted, the plane is initialized first, and after the plane plan is generated, the longitudinal section is initialized along the line plane, so as to generate the initial line plane and longitudinal plan;

[0153] The line plane initialization is specifically:

[0154] First, the adjustable perimeter corridor of the three-dimensional line is cut with a horizontal plane along the center line of the line to obtain the strip plane projection polygon of the three-dimensional corridor; then, the concept of butterfly area template is applied inside the polygon to initialize the line plane: the intersection array is traversed, the direction of the angle bisector of the angle between the lines of adjacent intersections is taken, and then it is rotated 45 degrees left and right. The scanning area is the butterfly area, that is, the variable range of the plane intersection; finally, new intersection coordinates are randomly generated in the butterfly area, and new radius and delay length values ​​are randomly selected within the threshold range of curve radius and delay length specified in the "Railway Line Design Specification" (TB 10098-2017) issued by the National Railway Administration, hereinafter referred to as the "Line Specification", and the spatial linear model in step S1 is used to solve the spatial point coordinates of each pile, and detect whether it is within the adjustable range of the spatial corridor. The line plane schemes in which all point sets are within the adjustable range of the spatial corridor are retained to form an initial line plane scheme set.

[0155] The specific initialization of the line profile is:

[0156] First, the initial plan set of the route plane is traversed, and the three-dimensional route adjustable perimeter corridor is vertically cut along the route centerline with a longitudinal section to obtain the strip longitudinal section projection polygon of the three-dimensional corridor; then, based on the maximum and minimum longitudinal slopes, an initial envelope area is generated between the starting and ending points (that is, the area enclosed by four rays extending outward from the starting and ending points with the maximum and minimum slopes, respectively), and the intersection area of ​​the envelope area and the strip longitudinal section projection polygon of the three-dimensional corridor is calculated to form an adjustable range of the longitudinal section design; finally, the total number of slope change points is kept unchanged, and at each slope change point, the mileage of the initial slope change point is set. The adjustable range of the spatial pipeline corridor is determined, and a new slope change point mileage is randomly generated within the range. The elevation of the slope change point at the corresponding mileage is randomly generated within the adjustable range of the longitudinal section design. At the same time, according to the vertical curve radius threshold specified in the "Line Specification", a new vertical curve radius is randomly selected. The spatial point elevation of each pile is solved with the help of the spatial linear model in step S1, and it is detected whether it is within the adjustable range of the spatial pipeline corridor. The longitudinal section scheme of the line in which all point sets are within the adjustable range of the spatial pipeline corridor is retained to form an initial plan set for the horizontal and vertical planes of the line, that is, an initial particle group, in which one particle represents a horizontal and vertical plane line scheme.

[0157] Step S4.2: Particle iterative optimization to obtain an optimized line solution. Specifically:

[0158] Step S4.2.1: Construct the position and velocity update operator of the particle swarm. The calculation formula is as follows:

[0159] Formula 10);

[0160] Formula 11);

[0161] in, Indicates the current iteration number, is the inertia weight, Indicates the population The current velocity characteristics of each particle, Indicates the population The updated velocity characteristics of the particles, For the population The current position of a particle, For the population The updated position of the particle, and are social constants and cognitive constants, Controls the speed at which individual particles move toward the global optimal position (social experience), and Controls the speed at which individual particles move toward their historical optimal positions (individual experience), usually taken as = =2; and is a random number distributed in the range [0, 1], is the number of all particles in the entire group up to the current iteration The optimal position of , that is, the global optimal position, It is Particles up to the current iteration number The optimal position of , that is, the individual optimal position;

[0162] Step S4.2.2, plane scheme perturbation:

[0163] Based on the initialized plane solution set, according to the formula in step S4.2.1, the decision variable set of each particle (i.e., line solution) in the current particle swarm is disturbed and updated respectively based on the position of the plane intersection corresponding to the current global optimal position and the individual optimal position, the curve radius, and the length of the transition curve. The updated line plane solution set is subjected to constraint detection and judgment based on the relevant constraints of the spatial linear design. If it fails, the particle plane decision variable is adjusted in the direction of satisfying the constraint (for example, if the current clamping line is less than the minimum clamping line, the curve radius is reduced or the distance between the two intersections is increased) until the detection passes;

[0164] Synchronous disturbance update of longitudinal section scheme:

[0165] On the current longitudinal section scheme group, according to the formula in step S4.2.1, based on the current global optimal position and the individual optimal position corresponding to the longitudinal slope change point position and the vertical curve radius, the mileage, elevation, and vertical curve radius of the slope change point of each particle (i.e., line scheme) in the current particle group are disturbed and updated, and the updated line longitudinal section scheme is subjected to constraint detection and judgment according to the relevant constraints of the spatial linear design. If it fails, the mileage, elevation, and vertical curve radius are adjusted in the direction that satisfies the constraints (for example, if the current slope length is less than the minimum slope length, the mileage of the slope change point is increased) until the detection passes;

[0166] Step S4.2.3, generate a new circuit particle swarm solution:

[0167] According to the horizontal and vertical parameters of the line updated in step S4.2.2, the spatial linear model in step S1 is used to recalculate the spatial point coordinates of each pile; check whether the new line plan group is within the adjustable range of the three-dimensional pipeline corridor to ensure that all horizontal and vertical points are within the allowable range; then evaluate the newly generated line plan particle group based on the objective function of the decision variable and the train running time. The smaller the running time, the better the corresponding line plan;

[0168] Step S4.2.4, update the optimal solution:

[0169] According to the evaluation results, the positions of the individual optimal solutions and the global optimal solutions of the particles are updated;

[0170] Step S4.2.5: Determine the current number of iterations Is it the maximum number of iterations? If so, select the route plan with the smallest travel time in the last algebraic particle swarm as the optimal route plan; if not, take , return to step S4.2.1.

[0171] The technical solution of the present invention is further described below by taking a high-speed railway speed-up reconstruction section as an example. The aviation distance between the starting and ending points of the section is 45.3 km.

[0172] Step S1: Construct a spatial linear model of the operating high-speed railway, generate a plane line element array and a longitudinal section line element array based on it, establish a mapping function from mileage to coordinates, azimuth, and elevation, combine the plane line element array and the longitudinal section line element array to form a three-dimensional space line element array, and calculate its added slope.

[0173] Step S1.1, plane linear model construction: The coordinates of the 8 plane intersection points, curve radius, and transition curve length of this section of the line are used to form an intersection array, and then an array of 33 plane line elements is formed. According to the mapping relationship function from the mileage of any point on the line to the coordinates and tangent azimuth, the coordinates and tangent azimuth of the given point are solved.

[0174] Construction of longitudinal line shape model: The mileage, elevation and vertical curve radius of the 13 slope change points of the section line constitute a slope change point information array, which then forms an array of 27 longitudinal line elements. Based on the longitudinal line element array, a mapping function from the mileage of any point on the line to the design elevation and slope is established to solve the design elevation and slope of a given point.

[0175] Step S1.2: construct an array of 64 spatial line elements by combining the horizontal and vertical spatial line elements, and perform the added slope calculation according to the added slope calculation formula.

[0176] Step S2: construct a spatial alignment optimization model for operating high-speed railways.

[0177] Step S2.1, according to 8 plane intersection points and 13 longitudinal slope change points, determine 7 columns of decision variables of the line optimization model;

[0178] Among the relevant constraints of the spatial linear design, the minimum plane curve radius is 4500m, the minimum vertical curve radius is 25000m, the minimum circular curve length is 240m, the minimum straight line length is 240m, the minimum slope section length is 900m, the maximum slope is 20‰, the maximum running time is 0.15h, and the specific values ​​of the adjustable perimeter constraints are obtained from the design data.

[0179] Step S2.2: Construct the objective function of decision variables and train running time, where =3 min, =2min.

[0180] Step S3: Generate the adjustable range of the centerline pile by pile according to the horizontal and vertical alignment of the high-speed railway, the distribution of structures, the adjustable amount of the track, and the limit requirements of the structures, and further generate the adjustable perimeter pipeline corridor of the centerline of the line.

[0181] Step S3.1, input the data of the plane intersection, radius, slow length, slope change point, etc. of the operating high-speed railway line, and use the plane linear model and longitudinal section linear model proposed in step S1.1 to solve the coordinates, azimuth and elevation of any point on the line along the direction of increasing mileage.

[0182] Step S3.2: Generate a series of numbering sequences at intervals of 20 m along the starting and ending points of the route, and substitute their coordinates, azimuths and elevations into formulas 8 and 9 to solve the horizontal boundary point coordinates and vertical boundary point coordinates of each pile point.

[0183] Step S3.3, interpolate and encrypt 30 points based on the four perimeter points of each pile to form a quasi-elliptical pile number perimeter interface, connect each section in sequence along the pile number sequence, and form a three-dimensional line centerline adjustable pipeline corridor.

[0184] Step S4: Using the particle swarm algorithm to iteratively generate the optimal route plan within the adjustable pipeline corridor.

[0185] Step S4.1: Generate 100 initial route horizontal and vertical plan particle groups based on the three-dimensional route adjustable perimeter pipeline corridor generated in step S3.

[0186] Step S4.2, according to formula 10), formula 11) and the current global optimal position and individual optimal position corresponding to the plane intersection, horizontal curve radius, transition curve length, longitudinal slope change point position and vertical curve radius, the particle swarm of the initial line horizontal and vertical plane schemes is disturbed, and the particle swarm algorithm continuously iterates and optimizes until the optimal line scheme is found, where the maximum number of iterations is defined as 5000 times.

[0187] In this embodiment, the optimized linear scheme formed after the above optimization process has 8 plane intersections and 12 longitudinal section slope change points. The travel time of this section is reduced from 12.3 minutes to 8.5 minutes, and the railway travel efficiency is greatly improved.

[0188] The present invention also provides a device corresponding to the above embodiment, which may be a processing device for a client, such as a mobile phone, a laptop computer, a tablet computer, a desktop computer, etc., to execute the method of the above embodiment.

[0189] The device of this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method of the above embodiment.

[0190] In some implementations, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0191] In some other implementations, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0192] The present invention also provides a medium corresponding to the above embodiment, which is a readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the method of the above embodiment are implemented.

[0193] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0194] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0195] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0197] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway, characterized in that: The following steps are involved: Step S1, constructing a spatial linear model of an operating high-speed railway, generating a plane line element array and a longitudinal section line element array based on the model, establishing a mapping function from mileage to coordinates, azimuth, and elevation, combining the plane line element array and the longitudinal section line element array to form a three-dimensional space line element array, and calculating its added slope; Step S2: constructing a spatial alignment optimization model for operating high-speed railways, determining decision variables of the optimization model, constructing a travel time objective function, and proposing relevant constraints for spatial alignment design; Step S3, generating the adjustable range of the line centerline pile by pile according to the horizontal and vertical alignment of the operating high-speed railway, the distribution of structures, the adjustable amount of the track, and the limit requirements of the structures, applying the spatial linear model generated in step S1 to calculate the spatial coordinates of the boundary points of the adjustable range, and further generating a three-dimensional line adjustable perimeter pipe gallery; Step S4, randomly generate a series of initial route plans within the adjustable perimeter pipe gallery, use the route plans as particles of the particle swarm algorithm, and use the particle swarm algorithm to generate the optimal route plan; Step S4 specifically includes: Step S4.1, in the three-dimensional adjustable perimeter pipe gallery generated in step S3, an initial particle group is generated based on the spatial line position of the existing operating high-speed railway; specifically, a step-by-step initialization strategy is adopted, the plane is initialized first, and after the plane plan is generated, the longitudinal section is initialized along the line plane, so as to generate the initial line plane and longitudinal plan; Step S4.2, iterative particle optimization to obtain an optimized line plan; In step S4.1, the line plane initialization is specifically: First, the adjustable perimeter corridor of the three-dimensional line is cut along the center line of the line with a horizontal plane to obtain a strip plane projection polygon of the three-dimensional corridor; then, the concept of butterfly area template is applied inside the polygon to initialize the line plane; finally, new intersection coordinates are randomly generated in the butterfly area, and new radius and delay length values ​​are randomly selected within the threshold range of the curve radius and delay length. The spatial point coordinates of each pile are solved with the help of the spatial linear model in step S1, and it is detected whether they are within the adjustable range of the spatial corridor, and the line plane schemes in which all point sets are within the adjustable range of the spatial corridor are retained to form an initial line plane scheme set; The specific initialization of the line profile is: First, the initial plan set of the route plane is traversed, and the three-dimensional route adjustable perimeter corridor is vertically cut with a longitudinal section along the center line of the route to obtain the strip longitudinal section projection polygon of the three-dimensional corridor; then, according to the maximum and minimum longitudinal slopes, an initial envelope area is generated between the starting and ending points, and the intersection area between the envelope area and the strip longitudinal section projection polygon of the three-dimensional corridor is calculated to form an adjustable range of the longitudinal section design; finally, the total number of slope change points is kept unchanged, and at each slope change point, the adjustable range of mileage is set based on the initial slope change point mileage, and a new slope change point mileage is randomly generated within the range. Within the adjustable range of the longitudinal section design, the elevation of the slope change point at the corresponding mileage is randomly generated, and within the vertical curve radius threshold range, a new vertical curve radius is randomly selected, and the spatial point elevation of each pile is solved with the help of the spatial linear model in step S1, and it is detected whether it is within the adjustable range of the spatial corridor, and the route longitudinal section schemes of all point sets within the adjustable range of the spatial corridor are retained to form the initial plan set of the route plane and longitudinal plane, that is, the initial particle group.

2. The method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway according to claim 1, characterized in that: Step S1 specifically includes: Step S1.1, construct a plane linear model, realize the conversion of the intersection array to the plane line element array, and establish a mapping from mileage to coordinates and azimuths; construct a longitudinal section linear model, realize the conversion of the slope change point array to the longitudinal section line element array, and establish a mapping from mileage to elevation; Step S1.2, perform a union operation on the plane line element array generated by the plane linear model and the longitudinal section line element array generated by the longitudinal section linear model, construct a horizontal and vertical combined space line element array and calculate the added slope.

3. The method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway according to claim 2, characterized in that: Step S2 specifically includes: Step S2.1, determine the decision variables of the optimization model, and the three-dimensional spatial position of the line is represented by the following vectors: Intersection point X coordinate column vector: X = [X1, X2, ..., X M ] T ; Intersection point Y coordinate column vector: Y = [Y1, Y2, ..., Y M ] T ; Plane circular curve radius column vector: R = [R1, R2, ..., R M ] T ; Plane transition curve long column vector: LH = [LH1, LH2, ..., LH M ] T ; Slope change point mileage column vector: K = [K1, K2, ..., K N ] T ; Slope change point design elevation column vector: H = [H1, H2, ..., H N ] T ; Vertical curve radius column vector: RS = [RS1, RS2, …, RS N ] T ; Among them, M is the number of plane intersection points, and N is the number of slope change points in the longitudinal section; The constraints related to the spatial linear design include geometric specification constraints, maximum travel time constraints and adjustable perimeter constraints; Geometric specification constraints: including minimum circular curve radius, minimum circular curve length, minimum clip line length, minimum slope section length, and maximum slope; Maximum running time constraint: The maximum running time T of the route plan generated during the optimization process max It should be less than or equal to the running time T0 of the initial given plan in order to achieve the speed-up target, that is, T max ≤T0; Adjustable perimeter constraint: Any point (x i ,y i , z i ) must be within the adjustable perimeter Ω constraint, that is, x i ,y i ,z i ∈Ω; Step S2.2: Construct the objective function of decision variables and train running time: T(X,Y,R,LH,K,H,RS)=Σ(t i ×L i )+t q +t t (min); Among them, t q Additional time for starting the vehicle, t t Additional time for parking, L i is the length of a slope section, t i It is the running time per kilometer on a certain slope; (min) means that the smaller the train running time, the better the line plan.

4. The method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway according to claim 3, characterized in that: In the geometric specification constraint, the minimum circular curve radius R min The following constraints are met: R min -R i ≤0,i=1,2,...,M; Among them, R min is the minimum circular curve radius, R i is the radius of the i-th circular curve, M is the number of plane intersection points; Minimum circular curve length RK min The following constraints are met: RK min -(α i R i -l 0,i )≤0,i=1,2,...,M; Among them, RK min is the minimum circular curve length, α i is the turning angle of the i-th curve, l 0,i represents the length of the transition curve of the i-th curve; Minimum clamping straight line length D min The following constraints are met: Among them, D min is the minimum straight line length between adjacent curves, x i ,y i is the coordinate of the i-th plane intersection point, x i-1 ,y i-1 is the coordinate of the i-1th plane intersection point, represents the length of the back tangent line of the i-1th circular curve, represents the length of the front tangent line of the i-th circular curve; Minimum slope length L min The following constraints are met: K i+1 -K i ≥L min ,i=0,1,2,...,N-1; Among them, K i+1 is the mileage of the i+1th longitudinal section slope change point, K i is the mileage of the i-th longitudinal section slope change point, L min is the minimum slope section length; N is the number of slope change points in the longitudinal section; Maximum slope g max The following constraints are met: Among them, H i is the design elevation of the i-th longitudinal section slope change point, H i-1 is the design elevation of the i-1th longitudinal section slope change point, d i is the distance between the i-1th longitudinal section slope change point and the i-th longitudinal section slope change point, g max is the maximum slope value.

5. The method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway according to claim 4, characterized in that: Step S3 specifically includes: Step S3.1, input the line plane intersection point, radius, and slow length data of the operating high-speed railway, generate a plane line element array according to the plane linear model of step S1.1, and realize the mileage conversion to coordinates; input the line longitudinal section slope change point mileage, elevation and vertical curve radius data of the operating high-speed railway, generate a longitudinal section line element array according to the longitudinal section linear model of step S1.1, and realize the mileage conversion to elevation; Step S3.2: Generate a series of number sequences at certain intervals along the starting and ending points of the line, calculate the three-dimensional spatial point coordinates and tangent azimuth of the line centerline for each pile, obtain the track adjustable range data for each pile from the engineering department, and calculate the three-dimensional spatial point coordinates (x i ,y i , z i ), tangent azimuth θ i The three-dimensional coordinates of the horizontal adjustable range boundary points and the vertical adjustable range boundary points of the line center of each pile are calculated with the adjustable range data. Assuming that the horizontal adjustable range of the i-th pile number is the left offset distance Δx l , Δy l , right offset distance Δx r , Δy r , then the corresponding three-dimensional coordinates of the horizontal boundary point are: Assume that the vertical adjustable range is the upper and lower offset Δz u , Δz d , then the corresponding vertical boundary point is: Fit the four points of the perimeter with an ellipse-like curve to generate the adjustable area of ​​the single pile section; Step S3.3, connecting each elliptical cross section in sequence along the pile number sequence to form a complete three-dimensional line adjustable perimeter pipeline corridor.

6. The method for fine-tuning and optimizing the spatial alignment of an operating high-speed railway according to claim 5, characterized in that: Step S4.2 specifically includes: Step S4.2.1: Construct the position and velocity update operator of the particle swarm. The calculation formula is as follows: Pos i (p+1)=Pos i (p)+Vel i (p+1); Among them, p represents the current iteration number, w is the inertia weight, Vel i (p) represents the current velocity characteristic of the i-th particle in the population, Vel i (p+1) represents the updated velocity characteristic of the i-th particle in the population, Pos i (p) is the current position of the i-th particle in the population, Pos i (p+1) is the updated position of the ith particle in the population, C1 and C2 are social constants and cognitive constants respectively, r1 and r2 are random numbers distributed in the range [0,1], gBest(p) is the best position of all particles in the entire population up to the current iteration number p, that is, the global optimal position, pBest i (p) is the optimal position of the i-th particle up to the current iteration number p, that is, the individual optimal position; Step S4.2.2, plane scheme perturbation: On the basis of the initialized plane solution set, the decision variable set of each particle in the current particle swarm is disturbed and updated according to the position of the plane intersection point corresponding to the current global optimal position and the individual optimal position, the curve radius, and the length of the transition curve. The updated line plane solution set is subjected to constraint detection and judgment according to the relevant constraints of the spatial linear design. If it fails, the particle plane decision variable is adjusted in the direction of satisfying the constraints until the detection passes. Synchronous disturbance update of longitudinal section scheme: On the current longitudinal section scheme group, according to the current global optimal position and individual optimal position corresponding to the longitudinal slope change point position and vertical curve radius, the mileage, elevation, and vertical curve radius of the slope change point of each particle in the current particle group are disturbed and updated, and the updated line longitudinal section scheme is subjected to constraint detection and judgment according to the relevant constraints of the spatial linear design. If it fails, the mileage, elevation, and vertical curve radius are adjusted in the direction of satisfying the constraints until the detection passes; Step S4.2.3, generate a new circuit particle swarm solution: According to the horizontal and vertical parameters of the line updated in step S4.2.2, the spatial linear model in step S1 is used to recalculate the spatial point coordinates of each pile; check whether the new line plan group is within the adjustable range of the three-dimensional pipeline corridor to ensure that all horizontal and vertical points are within the allowable range; then evaluate the newly generated line plan particle group based on the objective function of the decision variable and the train running time. The smaller the running time, the better the corresponding line plan; Step S4.2.4, update the optimal solution: According to the evaluation results, the positions of the individual optimal solutions and the global optimal solutions of the particles are updated; Step S4.2.5, determine whether the current number of iterations p is the maximum number of iterations. If so, select the route plan with the smallest travel time in the last algebraic particle swarm as the optimal route plan; if not, take p=p+1 and return to step S4.2.

1.

7. A device, characterized in that: The device includes a memory and a processor, wherein the memory stores a computer program; the processor executes the computer program to implement the method for fine-tuning and optimizing the spatial line position of an operating high-speed railway as described in any one of claims 1-6.

8. A medium, characterized in that The medium is a readable storage medium having a computer program stored thereon. The processor executes the computer program to implement the method for fine-tuning and optimizing the spatial line position of an operating high-speed railway as described in any one of claims 1 to 6.

Images