High-speed railway fine tuning optimization method suitable for multiple working conditions

Abstract

The invention discloses a high-speed railway fine tuning optimization method suitable for multiple working conditions. The method comprises the steps that S1, high-speed railway ballastless track fine tuning basic data are obtained; s2, establishing a high-speed rail ballastless track double-track multi-target optimization model; s3, solving a double-track adjustment amount based on an interior point method; and S4, obtaining a fastener adjustment grade. According to the method, the adjustment amount of the fastener is used as a decision variable, irregularity indexes such as the adjustable allowance of the fastener, the long-medium-short wave irregularity of double rails, the track gauge and the level are used as constraints, and on the basis, linear weighting is carried out through three targets of minimum adjustment amount, linear regression and track smoothness to establish a target function; the track adjustment amount optimization suitable for various working conditions is achieved, the use amount of fastener accessories in the fine adjustment process is reduced, track double-track fine adjustment is achieved in place at a time, and the track fine adjustment process is simplified.

Description

Technical Field

[0001] The present invention relates to the field of high-speed railway fine-tuning, and in particular to a high-speed railway fine-tuning optimization method adaptable to multiple working conditions. Background Art

[0002] High-speed rail ballastless track fine-tuning is a crucial step in the opening of new lines and the routine maintenance of existing lines, directly impacting train safety and comfort. Currently, ballastless track fine-tuning relies primarily on manual design based on experience. Based on the difference between the measured track coordinates and the designed track alignment, the lateral and elevation deviations are calculated. Fine-tuning begins with a first round of adjustments aimed at eliminating the 30m and 300m long-wave irregularities of the reference rail. Subsequently, at least three rounds of adjustments are conducted to control irregularities of the reference and non-reference rails, as well as track gauge and horizontal parameters. During the fine-tuning process, it's common for the adjustment to exceed the fastener's adjustable allowance or for the static TQI to fail to meet acceptance criteria, necessitating rework. This results in significant waste of fastener components and delays in both construction schedules and window time.

[0003] During the track fine-tuning process, it is necessary to give priority to controlling the static irregularity indicators of the track according to the specifications. However, for different working conditions and different track structures, the fine-tuning strategies are different. In the track fine-tuning work after the stress relief of the long rails of the newly built line, since the fasteners used are all standard parts, the adjustable margin of each fastener is the same, which is conducive to the optimization of the line shape and smoothness. Therefore, the main consideration is to control the overall adjustment amount of the fasteners to reduce the types of fastener accessories adjusted and the use and consumption of fasteners; the operating lines mainly carry out track fine-tuning during the overhaul process. Due to years of track fine-tuning maintenance, the adjustable margin range of each fastener is inconsistent and fluctuates greatly. Therefore, the adjustment is mainly based on line shape and smoothness control, and the control of the fastener adjustment amount is relaxed. The main problems with track fine-tuning at this stage are as follows:

[0004] (1) Manual fine-tuning scheme design cannot provide the fine-tuning amount of double tracks at one time, resulting in more overall steps for fine-tuning high-speed rail ballastless tracks and a complicated process.

[0005] (2) During the design of the fine-tuning scheme, only the track smoothness requirements were considered, and the constraints of the adjustable margin of the fasteners were not considered.

[0006] (3) The design of the fine-tuning scheme only considers the principle of minimum adjustable amount, and does not consider the differences in design objectives under different working conditions.

[0007] Track fine-tuning requires comprehensive consideration of multiple objective factors, so an automated track fine-tuning algorithm that can adapt to various working conditions is needed. Summary of the Invention

[0008] In order to solve the problems existing in the prior art, the present invention provides a high-speed railway fine-tuning optimization method that is adaptable to multiple working conditions. Through this optimization method, the high-speed railway ballastless track fine-tuning solution can be implemented in one go, thereby improving the fine-tuning efficiency.

[0009] To this end, the present invention adopts the following technical solutions:

[0010] A high-speed railway fine-tuning optimization method adapted to multiple working conditions specifically comprises the following steps:

[0011] S1, obtain the basic data for fine-tuning the high-speed rail ballastless track:

[0012] S11, obtain the deviation value of the segment to be fine-tuned Wherein, k = L represents the left rail, k = R represents the right rail, w = x represents the horizontal direction, and w = y represents the elevation. i is the sleeper number, 0≤i≤n, and n is the total number of sleepers in the section to be fine-tuned.

[0013] S12, determine the decision variable vector

[0014] First, obtain the fastener adjustment amount and adjustable margin; the fastener adjustment amount on sleeper i is The upper and lower limits of the adjustable allowance of the fastener are l represents the lower limit of the adjustable margin, and u represents the upper limit of the adjustable margin;

[0015] Then, construct the decision variable vector There is the following formula:

[0016]

[0017] S2, establish a multi-objective optimization model for high-speed railway ballastless double-track:

[0018] S21, constructing constraint conditions for the adjustable margin of the fastener;

[0019] S22, construct the constraints of the track acceptance criteria:

[0020] When fine-tuning the lateral direction of the section to be fine-tuned, the constraints of the track acceptance standard are specifically track direction, 30m sag difference, 300m sag difference and track gauge;

[0021] When fine-tuning the elevation of the section to be fine-tuned, the constraints of the track acceptance standard are specifically height, 30m sag difference, 300m sag difference, and level;

[0022] The track direction and height belong to the 10m midpoint chord;

[0023] S23, determine the objective function

[0024] S3, solve the dual-track adjustment based on the interior point method:

[0025] S31, use the interior point method to transform all the constraints and objective functions constructed in S2 To unconstrained repair

[0026] Positive objective function

[0027] S32, for the Construct optimization constraints and optimization objective function

[0028] S33, using the interior point method to transform the optimization constraints and optimization objective function in S32 into an unconstrained modified optimization objective function

[0029] S34, using Newton's method to solve the The obtained results are taken as The initial solution of

[0030] S35, using the initial solution obtained in S34 and Newton's method to solve the The optimal solution

[0031] S4, obtain the fastener adjustment level:

[0032] Calculate the fastener grade by the following formula

[0033]

[0034] Where μ is the level difference; after obtaining the value of the fastener level, it is used to fine-tune the railway in the section to be fine-tuned.

[0035] Constraints on the adjustable allowance of fasteners in S21 As shown in the following formula:

[0036]

[0037] The constraint condition of the 10m midpoint chord in S22 is as follows:

[0038]

[0039] Among them, i∈[8,n-8], is the value of the 10m midpoint chord corresponding to sleeper i, σ 10 is the limit value of the 10m midpoint chord of sleeper i in the design document;

[0040] The constraint condition of the 30m vector difference in S22 is as follows:

[0041]

[0042] in, The deviation value of the starting point of the 30m chord (0≤m <i<i+8<m+48≤n), is the 30m vector difference corresponding to sleeper node i, σ 30 This is the 30m vector difference limit in the design document;

[0043] The constraint condition of 300m vector difference in S22 is as follows:

[0044]

[0045] in, is the deviation value of the starting point of the 300m chord (0≤j <i<i+240<j+480≤n), is the 300m vector difference corresponding to sleeper i, σ 300 This is the 300m vector difference limit in the design document;

[0046] The track gauge or level constraint in S22 is as follows:

[0047]

[0048] in, is the gauge or level constraint function of sleeper i, σ val is the gauge or level limit in the design document; The result has no effect, so no matter whether the left or right track is calculated, The values ​​are the same.

[0049] Objective function in S23 As shown in the following formula:

[0050]

[0051] in, is a linear regression subfunction, which is used to control the fine-tuned linear shape to not deviate from the original design linear shape; for

[0052] The fastener adjustment amount sum sub-function is used to control the fastener adjustment amount; is the linear smoothness subfunction, which is used to control the smoothness of the trajectory line after fine-tuning; α c is the weight coefficient, which is set according to the working conditions of the section to be fine-tuned.

[0053] Unconstrained modified objective function in S31 As shown in the following formula:

[0054]

[0055] in, is the value of the 10m midpoint chord corresponding to sleeper i, is the 30m vector distance difference corresponding to sleeper node i, is the 300m vector difference corresponding to sleeper i, λ is the first barrier multiplier; the λ is used to

[0056] Correct the above mentioned solution

[0057] The optimization objective function is The z is a construction optimization variable; the optimization constraint condition is as follows:

[0058]

[0059] Unconstrained modified optimization objective function in S33 As shown in the following formula:

[0060]

[0061] Where γ is the second barrier multiplier; the γ is used to correct the

[0062] S34 specifically includes the following steps:

[0063] S341, construct solution conditions:

[0064] First, set S33 The solution is Where t is the second iteration number. When t = 0, it is the initial solution without iteration and is set to:

[0065]

[0066] Then, set the barrier multiplier γ0 = 10, and the second iteration error threshold and the second barrier multiplier error threshold τ2=10 -3 ;

[0067] S342, calculate and obtain the second search direction vector

[0068]

[0069] in, is the Laplace operator, is the Hamiltonian operator;

[0070] S343, yes The norm of is used to judge:

[0071] like Then enter S344, otherwise update the second barrier multiplier so that γ t+1 =γ t / 10, at this time, if γ t+1 When ≤τ2, the solution ends, indicating that there is no solution that satisfies the constraints. The constraints in S2 are adjusted and the method is executed again; otherwise, the method returns to S342;

[0072] S344. Calculate by the following formula

[0073]

[0074] If z t+1 ≤0, then the solution ends, and z t+1 Corresponding Then it is the The initial solution

[0075] Otherwise, use Return to step S342 and continue iterative calculation until the initial solution is obtained.

[0076] S35 includes the following steps:

[0077] S351, construct solution conditions:

[0078] Newton's method is used to iteratively calculate the Solution The value obtained using S34 As of

[0079] Initial value, s is the first iteration number, is the second search direction vector; set the first obstacle multiplier λ = 10, the first iteration error threshold is The first barrier multiplier error threshold θ2 = 10 -3 ;

[0080] S352, calculate and obtain the second search direction vector

[0081]

[0082] S353, for the The norm of is used to judge:

[0083] if Then calculate and use Return to S352 to continue iteration, otherwise update the first barrier multiplier so that λ s+1 =λ s / 10, if λ s+1 >θ2, then use λ s+1 Return to S352 to continue iteration; if λ s+1 ≤θ2, then at this time is the optimal solution and is marked as

[0084] Preferably, the The optimal solution For the The minimum value in the solution.

[0085] Preferably, the step difference μ in S4 is 0.5 mm or 1 mm.

[0086] Compared with the prior art, the present invention has the following beneficial effects:

[0087] 1. The optimization method of the present invention takes into account the correlation between the two rails during the calculation process. The optimization result can give the optimal adjustment amount of the reference rail and the non-reference rail at one time, simplifying the existing fine-tuning process of "reference rail first, then non-reference rail", and can achieve fine-tuning in one time, effectively shortening the project construction period and skylight time.

[0088] 2. The optimization method of the present invention takes into account the three optimization goals of minimum adjustment, linear regression, and track smoothness. It can be applied to track fine-tuning work under conditions such as the construction of new high-speed railway lines and overhaul of existing lines. It has strong adaptability and provides a high degree of flexibility for the design of fine-tuning schemes.

[0089] 3. The optimization method of the present invention comprehensively considers constraints such as the adjustable margin of fasteners and the acceptance conditions for track smoothness. While effectively improving the smoothness of the track, it avoids rework of the design solution due to the adjustment amount exceeding the adjustable margin of fasteners. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] Figure 1 is a flow chart of the method of the present invention;

[0091] Figure 2 is a flow chart of step S34 in the method of the present invention;

[0092] Figure 3 is a flow chart of step S35 in the method of the present invention;

[0093] Figure 4 Graph showing the relationship between the left and right adjustment amounts and the fastener constraint amounts after optimization in an embodiment of the present invention;

[0094] Figure 5 This is a diagram comparing the height and low values ​​of the front and rear left and right rails optimized in an embodiment of the present invention;

[0095] Figure 6 This is a comparison diagram of the 30m sag difference between the front and rear left and right tracks optimized in an embodiment of the present invention;

[0096] Figure 7 This is a comparison diagram of the optimized 300m distance difference between the front and rear left and right tracks in an embodiment of the present invention. DETAILED DESCRIPTION

[0097] S1, obtain the basic data for fine-tuning the high-speed rail ballastless track:

[0098] S11, obtain the track deviation value:

[0099] High-speed rail ballastless track measurement mainly uses the track inspection instrument to conduct joint measurement on the CPIII point of the track section to be fine-tuned to obtain the three-dimensional coordinates of the track section to be fine-tuned, and then obtain the deviation value of the sleeper i by comparing the coordinates with the center line of the designed line k = L indicates the left rail, k = R indicates the right rail, w = x indicates the horizontal direction, and w = y indicates the elevation; i is the sleeper number, 0 ≤ i ≤ n, and n is the total number of sleepers in the section to be fine-tuned; the design line centerline is the line centerline in the design file;

[0100] For the deviation value The total lateral deviation of the left rail of sleeper i is Right rail lateral deviation Left track elevation deviation and right track elevation deviation Four situations.

[0101] S12, determine the decision variable vector

[0102] Since the fine-tuning of high-speed railway ballastless tracks is mainly achieved by adjusting fasteners, the adjustable range of fasteners is limited. In particular, for high-speed railway ballastless tracks that have been in operation for many years, the fasteners have undergone repeated adjustments over the years, and the adjustable range of each fastener is different. With the development of hardware technology related to track inspection instruments, by integrating structured light scanning modules on track inspection instruments, it is possible to measure the relative position relationship between the rails and the rail supports or sleepers, thereby indirectly obtaining the adjustable margin of the fasteners. By establishing a "one sleeper, one file" fastener accessory file, the remaining adjustable margin of the fasteners can also be obtained.

[0103] Get the fastener adjustment v and adjustable margin σ; the fastener adjustment on sleeper i is The upper and lower limits of the adjustable allowance of each fastener are l represents the lower limit of the adjustable margin, and u represents the upper limit of the adjustable margin.

[0104] Constructing a decision variable vector There is the following formula:

[0105]

[0106] Therefore, the horizontal decision variable vector is The decision variable vector for elevation is

[0107] S2, establish a multi-objective optimization model for high-speed railway ballastless double-track:

[0108] Since the lateral and elevation fine-tuning models of the ballastless track are consistent, then:

[0109] When w=x, the transverse fine adjustment mainly considers the track direction (10m midpoint chord), 30m sag difference, 300m sag difference and track gauge;

[0110] When w=y, the elevation fine-tuning mainly considers the height (10m midpoint chord), 30m sag difference, 300m sag difference, and level. Moreover, the calculation method of the level is consistent with the calculation method of the track gauge. Since there is no correlation between the lateral and elevation fine-tuning, they can be decoupled and the lateral and elevation fine-tuning can be performed separately, reducing the number of decision variables and improving the speed of calculation convergence. Specifically, the following steps are included:

[0111] S21, construct the constraint conditions for the adjustable allowance of fasteners

[0112] Through S12 Adjustment of fasteners on sleeper i To constrain, we have the following formula:

[0113]

[0114] S22, construct the constraints of the track acceptance criteria:

[0115] The track acceptance standard constraints are specifically for the 10m midpoint chord (track direction or height), 30m sag difference, 300m sag difference, and track gauge (horizontal), including the following steps:

[0116] S221, construct the constraint condition of the 10m midpoint chord, for the adjustment of the fastener on sleeper i There is the following formula:

[0117]

[0118] Among them, i∈[8,n-8], the 10m midpoint chord is an indicator of track direction or height during static detection, is the value of the 10m midpoint chord corresponding to sleeper i, σ10 It is the limit value of the 10m midpoint chord of sleeper i in the design document.

[0119] S222, construct the constraint condition of 30m vector difference, for the adjustment of the fastener on sleeper i There is the following formula:

[0120]

[0121] in, The deviation value of the starting point of the 30m chord (0≤m <i<i+8<m+48≤n), is the 30m vector difference corresponding to sleeper node i, σ 30 This is the limit value of the 30m vector distance difference in the design document.

[0122] S223, construct the constraint condition of 300m vector difference, for the adjustment of the fastener on sleeper i There is the following formula:

[0123]

[0124] in, is the deviation value of the starting point of the 300m chord (0≤j <i<i+240<j+480≤n), is the 300m vector difference corresponding to sleeper i, σ 300 This is the limit value of the 300m vector distance difference in the design document.

[0125] S224, construct the track gauge (horizontal) constraint condition, which is as follows:

[0126]

[0127] in, is the gauge or level constraint function of sleeper i, σ val is the gauge or level limit in the design document; The result has no effect, so no matter whether the left track or the right track is calculated, The values ​​are the same.

[0128] S23, determine the objective function:

[0129] The goals of track fine-tuning are different under different working conditions, so different optimization objective sub-functions The multi-objective value optimization is realized by linear weighting. Considering the requirements for the optimal solution under different working conditions, the objective function is obtained. There is the following formula:

[0130]

[0131] in:

[0132] f1( (w) v0 is the linear regression subfunction, which represents the degree of convergence between the fine-tuned track shape and the track shape in the design file, and is used to control the fine-tuned shape to not deviate from the original design shape;

[0133] is the total amount of fastener adjustment sub-function, which represents the total amount of fastener adjustment after fine-tuning and is used to control the amount of fastener adjustment;

[0134] It is a linear smoothness sub-function that characterizes the curvature change of the track line after fine-tuning. It is used to control the smoothness of the track line after fine-tuning to avoid the distortion of the track line.

[0135] α c is the weight coefficient, which can be set according to different working conditions.

[0136] In actual situations, when the lateral adjustment of the railway line is made, the corresponding objective function is When the elevation of the railway line is adjusted, the corresponding objective function is

[0137] S3, solve the dual-track adjustment based on the interior point method:

[0138] S31, constructing an unconstrained modified objective function based on the interior point method

[0139] Use the interior point method to convert the constraints and objective function constructed in S2 into an unconstrained modified objective function And introduce the first barrier multiplier λ, we have the following formula:

[0140]

[0141] S32, construct optimization constraints and optimization objective function:

[0142] Due to too many constraints, it is impossible to directly solve the initial solution of formula (8) that satisfies all the constraints. Therefore, it is necessary to construct new optimization constraints and optimization objective functions to solve:

[0143] Optimization objective function:

[0144] Optimization constraints:

[0145] Among them, z is the construction optimization variable;

[0146] S33, using the interior point method to transform equations (9) and (10) in S32 into unconstrained modified optimization objective functions And introduce the second barrier multiplier γ:

[0147]

[0148] Among them, since the value of k is The result has no effect, so there is no need to use the left and right rails in equations (8) and (11). The results are summed up;

[0149] S34, such as Figure 2 As shown, Newton's method is used to solve The initial solution The following steps are involved:

[0150] S341, construct solution conditions:

[0151] First, assume that the solution of equation (11) is Where t is the second iteration number. When t = 0, it is the initial solution without iteration.

[0152] Then, set the barrier multiplier γ0 = 10, and the second iteration error threshold and the second barrier multiplier error threshold τ2=10 -3 ;

[0153] S342, calculate and obtain the second search direction vector

[0154]

[0155] in, Used for iterative solution; is the Laplace operator, is the Hamiltonian operator.

[0156] S343, yes Make a judgment:

[0157] like Then go to S344; otherwise update the second barrier multiplier so that γ t+1 =γ t / 10, at this time, if γ t+1 ≤τ2, then the solution ends, indicating that there is no solution that meets the constraints. The constraints in S2 need to be adjusted. Otherwise, return to S342 until Enter S344;

[0158] S344 is calculated by the following formula

[0159]

[0160] After getting the result, if z t+1 ≤0, then the solution ends, and z t+1 Corresponding Then it is the initial solution of the modified objective function

[0161] Otherwise, use Return to step S342 and continue iterative calculation until the initial solution is obtained.

[0162] S35, using Newton's method The minimum value of the solution includes the following steps:

[0163] S351, construct solution conditions:

[0164] Newton's method is also used to iteratively calculate the modified objective function Solution The value obtained using S34 As The initial value of , s is the first iteration number, is the first search direction vector.

[0165] like Figure 3 As shown, considering that the deviation and fastener adjustment are both in mm, integer optimization needs to be performed later, that is, the optimal result can be retained to one decimal place, and the first obstacle multiplier λ is set to 10, and the first iteration error threshold is The first barrier multiplier error threshold θ2 = 10 -3 ;

[0166] S352, calculate and obtain the first search direction vector

[0167]

[0168] S353, yes The norm of is used to judge:

[0169] calculate norm, if Then calculate and use Return to S352 to continue iteration, otherwise update the first barrier multiplier so that λ s+1 =λ s / 10, if λ s+1 >θ2, then use λ s+1 Return to S352 to continue iteration; if λ s+1 ≤θ2, then at this time is the optimal solution and is marked as The optimal solution at this time for The minimum value of the solution.

[0170] From the above steps, we can see that the horizontal optimal solution is The optimal solution for elevation is

[0171] S4, obtain the fastener adjustment level:

[0172] Fine-tuning of railways is achieved by adjusting fasteners, which are mainly adjusted through accessories such as the gauge block and height adjustment pad. These adjustment parts can only be adjusted according to the step difference, which is generally 0.5mm or 1mm. The final fastener adjustment level is obtained by rounding off the optimal solution obtained by S353. Specifically, the horizontal fastener adjustment level is The elevation fastener adjustment level is

[0173] Example 1

[0174] This embodiment performs fine adjustment on the horizontal direction of the section to be fine-tuned, specifically as follows:

[0175] The section of a high-speed railway ballastless track to be fine-tuned is about 1.9 km long, with a total of 3,000 sleepers. The track elevation adjustment is optimized. In the objective function of S23, the linear regression weight coefficient α1 is 1×10 -4 , the weight coefficient α2 of the adjustment function is 1×10 -5 , the weight coefficient of the smoothing subfunction α3 is 1. The lateral adjustable margin of the fastener is measured by the structured light track detector, and the track irregularity parameter is σ 10 =0.3,σ 30 =0.3,σ 300 =5,σ val =1, their units are all mm; the step difference μ is selected as 0.5mm.

[0176] After optimization, the adjustment amounts of the left and right rails are within the adjustable margin range, such as Figure 4 shown.

[0177] After using the method of the present invention, the maximum and minimum values ​​of the height of the left and right rails before optimization are [-4.7mm, 3.6mm]. After optimization with a step difference of 0.5mm, the maximum and minimum values ​​of the height converge to [-0.48mm, 0.49mm]. Figure 5 shown.

[0178] Before optimization, the maximum and minimum values ​​of the 30m chord-segment difference between the left and right rails are [-5.0mm.4.6mm]. After optimization with 0.5mm step difference, the maximum and minimum values ​​of the 30m chord-segment difference converge to [-0.58mm, 0.62mm]. Figure 6 shown.

[0179] Before optimization, the maximum and minimum values ​​of the 300m chord-segment difference between the left and right rails are [-6.0mm, 4.8mm]. After optimization with 0.5mm step difference, the maximum and minimum values ​​of the 300m chord-segment difference converge to [-1.5mm, 1.5mm]. Figure 7 shown.

[0180] By comparison with the above embodiments, it can be seen that the optimization calculation method of the present invention has a very obvious effect on improving the track smoothness within the adjustable range of the fasteners.

Claims

1. A high-speed railway fine-tuning optimization method adapted to multiple working conditions, characterized in that: The specific steps include: S1, obtain the basic data for fine-tuning the high-speed rail ballastless track: S11, obtaining the deviation value of the segment to be fine-tuned Wherein, k = L indicates the left rail, k = R indicates the right rail, w = x indicates the horizontal direction, and w = y indicates the elevation; i is the sleeper number, 0≤i≤n, and n is the total number of sleepers in the section to be fine-tuned; S12, determine the decision variable vector First, obtain the fastener adjustment amount and adjustable margin; the fastener adjustment amount on sleeper i is The upper and lower limits of the adjustable allowance of the fastener are l represents the lower limit of the adjustable margin, and u represents the upper limit of the adjustable margin; Then, construct the decision variable vector There is the following formula: S2, establish a multi-objective optimization model for high-speed railway ballastless double-track: S21, constructing constraint conditions for the adjustable margin of the fastener; S22, construct the constraints of the track acceptance criteria: When fine-tuning the transverse direction of the section to be fine-tuned, the constraints of the track acceptance standard are specifically the track direction, 30m sag difference, 300m sag difference and track gauge; When fine-tuning the elevation of the section to be fine-tuned, the constraints of the track acceptance standard are specifically height, 30m sag difference, 300m sag difference and level; The track direction and height belong to the 10m midpoint chord; S23, determine the objective function S3, solve the dual-track adjustment based on the interior point method: S31, using the interior point method to transform all the constraints and objective functions constructed in S2 Convert to unconstrained modified objective function S32, for the Construct optimization constraints and optimization objective function S33, using the interior point method to transform the optimization constraints and optimization objective function in S32 into an unconstrained modified optimization objective function S34, using Newton's method to solve the The obtained results are taken as The initial solution of S35, using the initial solution obtained in S34 and Newton's method to solve the The optimal solution S4, obtain the fastener adjustment level: The fastener grade is calculated by the following formula Among them, μ is the level difference; after the value of the fastener level is obtained, it is used to fine-tune the railway in the section to be fine-tuned.

2. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 1 is characterized in that: Constraints on the adjustable allowance of fasteners in S21 As shown below: The constraint condition of the 10m midpoint chord in S22 is as follows: Among them, i∈[8,n-8], is the value of the 10m midpoint chord corresponding to sleeper i, σ 10 is the limit value of the 10m midpoint chord of sleeper i in the design document; The constraint condition of the 30m vector difference in S22 is as follows: in, is the deviation value of the starting point of the 30m chord (0≤m <i<i+8<m+48≤n), is the 30m vector difference corresponding to sleeper node i, σ 30 It is the limit value of 30m vector difference in the design document; The constraint condition of 300m vector difference in S22 is as follows: in, is the deviation value of the starting point of the 300m chord (0≤j <i<i+240<j+480≤n), is the 300m vector difference corresponding to sleeper i, σ 300 It is the limit value of 300m vector difference in the design document; The track gauge or level constraint in S22 is as follows: in, is the gauge or horizontal constraint function of sleeper i, σ val is the limit value of track gauge or level in the design document; The result has no effect, so no matter whether the left or right track is calculated, The values ​​are the same.

3. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 1 is characterized in that: Objective function in S23 As shown below: in, is a linear regression subfunction, which is used to control the fine-tuned linear shape not to deviate from the original designed linear shape; It is the sub-function of summing the fastener adjustment amount, which is used to control the size of the fastener adjustment amount; is the linear smoothness subfunction, which is used to control the smoothness of the trajectory line after fine-tuning; α c is the weight coefficient, which is set according to the working conditions of the section to be fine-tuned.

4. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 1 is characterized in that: Unconstrained modified objective function in S31 As shown below: in, is the value of the 10m midpoint chord corresponding to sleeper i, is the 30m vector difference corresponding to sleeper node i, is the 300m vector difference corresponding to sleeper i, λ is the first obstacle multiplier; the λ is used to correct the 5. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 4 is characterized in that: The optimization objective function is The z is a construction optimization variable; the optimization constraint condition is as follows: Unconstrained Modified Optimization Objective Function in S33 As shown below: Where γ is the second obstacle multiplier; the γ is used to correct the 6. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 5 is characterized in that: S34 specifically includes the following steps: S341, construct solution conditions: First, set S33 The solution is Where t is the second iteration number. When t = 0, it is the initial solution without iteration and is set to: Then, set the barrier multiplier γ0 = 10 and the second iteration error threshold and the second barrier multiplier error threshold τ2 = 10 -3 ; S342, calculate and obtain the second search direction vector in, is the Laplace operator, is the Hamiltonian operator; S343, yes The norm of is used to judge: like Then proceed to S344, otherwise update the second barrier multiplier so that γ t+1 =γ t / 10, at this time, if γ t+1 When ≤τ2, the solution is terminated, indicating that there is no solution that satisfies the constraint conditions. The constraint conditions in S2 are adjusted and the method is executed again; otherwise, the method returns to S342; S344. Calculate by the following formula If t+1 ≤0, then the solution ends, and z t+1 Corresponding Then it is the The initial solution Otherwise, use Return to step S342 and continue iterative calculation until the initial solution is obtained.

7. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 5 is characterized in that: S35 includes the following steps: S351, construct solution conditions: Newton's method is used to iteratively calculate the Solution The value of S34 is obtained As The initial value of , s is the first iteration number, is the second search direction vector; the first obstacle multiplier λ is set to 10, and the first iteration error threshold is The first barrier multiplier error threshold θ2 = 10 -3 ; S352, calculate and obtain the second search direction vector S353, for the The norm of is used to judge: if Then calculate and use Return to S352 to continue iteration, otherwise update the first barrier multiplier so that λ s+1 =λ s / 10, if λ s+1 >θ2, then use λ s+1 Return to S352 to continue iteration; if λ s+1 ≤θ2, then is the optimal solution and is marked as 8. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 1 is characterized in that: Said The optimal solution For the The minimum value in the solution.

9. The high-speed railway fine-tuning optimization method adapted to multiple working conditions according to claim 1 is characterized in that: The step difference μ in S4 is 0.5mm or 1mm.

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