Variable wavelength and fixed wavelength combined steel rail high smoothness optimization method

Abstract

The invention discloses a variable wavelength and fixed wavelength combined steel rail high-smoothness optimization method, and belongs to the application category of a precision engineering measurement technology in the field of rail transit. The method comprises the following steps: segmenting a steel rail to be optimized, and establishing an optimization model containing typical sensitive wavelength irregularity; adding the initial tolerance of each evaluation index for the optimization model, adding the adjustable limitation of the sleeper fastener, and establishing an inequality constraint model; solving the inequality constraint model in the segmentation unit segment by segment, and evaluating the linear state of the obtained steel rail; iteratively correcting the inequality constraint model tolerance value, and repeating the process until the state evaluation value judges that iteration is terminated; and based on the smooth information, determining an optimal adjustment steel rail line shape and obtaining a sleeper fastener adjustment amount. Variable wavelength irregularity causing train excitation is considered, the riding comfort of passengers is further improved, automatic and efficient control over the steel rail state is achieved, and the problems that a long rail fine adjustment scheme is time-consuming and labor-consuming to formulate, and the adjustment effect and quality are uneven are solved.

Description

Technical Field

[0001] The invention relates to the technical field of railway track precision engineering measurement, and in particular to a rail high smoothness optimization method combining a variable wavelength and a fixed wavelength. Background Art

[0002] High track smoothness is the key to high-speed, safe and smooth running of trains, and also the guarantee of passenger riding comfort. The optimization and adjustment of rail shape and position is a necessary way to ensure that the track state is highly smooth. From the fine-tuning of double-block ballastless track rails or plate ballastless track track plates in the construction phase, to the fine-tuning of long rails and track fine-tuning in the operation and maintenance phase, the static adjustment acceptance indicators include lateral / vertical deviations, height / track unevenness of fixed wavelengths such as 10m, 20m, 30m and 300m chords, and several types of horizontal / gauge and twist / gauge change rates. With the transition from the construction phase to the operation and maintenance phase, the smoothness requirements of the track are higher and more complex. The formulation of the existing track fine-tuning plan relies on random software, which has deficiencies in theoretical models, processing methods, and adjustment effects, which are manifested in low automation, time-consuming and labor-intensive, and the smoothness of the track state cannot be optimally controlled. "A Method for Optimizing Rail Smoothness" (Application No. 202210394546.4) proposes to optimize the adjustment amount of the rail fasteners in the segmented planning unit, gradually realize the smoothness control of the overall rail state, and obtain the optimized solution. This optimization method can obtain a feasible high-smoothness rail shape and position under the requirements of the fixed-wavelength unevenness index specified in the existing specifications. In order to further improve the comfort of passengers, reduce TQI, and reduce the shaking phenomenon, it is necessary to consider the influence of variable-wavelength unevenness that causes train excitation, and incorporate it into the optimization model for effective control. Summary of the invention

[0003] In view of the above problems, the present invention aims to provide a high smoothness optimization method for rails that combines variable wavelength and fixed wavelength, taking the objective function, typical sensitive wavelength unevenness function and rail state evaluation function as the optimization model, the overall rail to be adjusted as the evaluation object, adjusting the constraint boundary value according to the overall evaluation result, and using it as the condition for determining the termination of the iteration, and then evaluating the rail line shape obtained each time, to obtain the best rail line shape set that can significantly improve the passenger riding comfort, and finally obtaining the optimal adjustment line shape based on the smoothness condition. The present invention effectively controls the unevenness of variable wavelength and fixed wavelength, and in the smoothness optimization control process of solving, evaluating, iteratively correcting and re-evaluating, formulates the optimal adjustment amount plan that takes into account the passenger riding comfort, further effectively improves the unevenness of the rail state, significantly weakens the shaking and shaking phenomenon, and provides a preferred solution for rail fine adjustment.

[0004] The technical solution of the present invention is as follows:

[0005] A rail high smoothness optimization method combining variable wavelength and fixed wavelength comprises the following steps:

[0006] S1: Divide the length of the rail to be optimized into segments according to preset lengths;

[0007] S2: Establishing a rail state optimization model within a segmented length interval, wherein the length interval is a basic planning unit;

[0008] S3: Based on the typical sensitive wavelength irregularity function and the rail state evaluation function, additional adjustment amount limits for the sleeper fasteners are set, constraint boundaries corresponding to the adjustment amount, the typical sensitive wavelength irregularity and the state evaluation are set, and the constraint boundaries are the limit difference values, and an inequality constraint model for rail state optimization is established;

[0009] S4: Optimize and solve the objective function of the amount to be adjusted and the inequality constraint model in the rail state optimization model, calculate the amount to be adjusted, and if there is no solution in the current optimization calculation, set the corresponding adjustment amount to zero;

[0010] S5: Sliding segment length interval, the rails in the interval are processed according to S2-S4 until the processing of the rails to be optimized is completed;

[0011] S6: Evaluate the state of the adjusted rail obtained by S1-S5, and adjust the constraint boundary value according to the preset correction step size, use the adjusted constraint boundary value as the constraint boundary value of the inequality constraint model in the next solution, and perform iteration termination condition judgment, the execution of iteration termination condition includes: if the evaluation value of the state of the rail obtained by the current solution is worse than the evaluation value of the state of the rail obtained by the previous solution and is less than a threshold, then terminate the iteration;

[0012] S7: Repeat S1-S6 until the iterative correction is terminated;

[0013] S8: After the iteration is terminated, the state of the rail obtained from each solution is judged, a set of rail states represented by a better state evaluation value is selected, and the optimal rail shape and position is selected according to the smoothing information to obtain the optimal solution for the rail smoothness state.

[0014] Preferably, in step S1, the preset length is greater than or equal to the longest reference chord length used in the rail state optimization model.

[0015] Preferably, in step S2, the rail state optimization model includes an objective function of the amount to be adjusted of all sleeper fasteners in the interval, a typical sensitive wavelength irregularity function and a rail state evaluation function.

[0016] Preferably, in step S3, the sensitive wavelength is determined by the natural frequency of the vehicle body and the running speed of the train, and the typical sensitive wavelength is one or more wavelengths with the most serious unevenness among the sensitive wavelengths.

[0017] Preferably, in step S3, the rail state evaluation function is one or more of internal and external geometric parameters of the track, poor deviation, and deviation change rate.

[0018] Preferably, the adjustment object of the track is a reference rail and / or a non-reference rail.

[0019] Preferably, the inequality constraint model comprises:

[0020] When the adjustment object of the track is a reference track, the type of the inequality constraint model includes a height / track irregularity constraint of a typical sensitive wavelength, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, and deviation change rate;

[0021] When the adjustment object of the track is a non-reference track, and the reference track state cannot be used as an adjustment reference, the type of the inequality constraint model includes the height / track irregularity constraints of typical sensitive wavelengths, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, and deviation change rate;

[0022] When the adjustment object of the track is a non-reference track, and the reference track state can be used as an adjustment reference, the type of the inequality constraint model includes the height / track irregularity constraints of typical sensitive wavelengths, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, deviation change rate, level / gauge, distortion / gauge change rate;

[0023] When the adjustment object of the track is a double track, the reference track can be used as the adjustment object first according to the above method, and then the non-reference track can be used as the adjustment object to establish a constraint model; or the non-reference track can be used as the adjustment object first, and then the reference track can be used as the adjustment object to establish a constraint model; or both can be adjusted simultaneously;

[0024] When adjusting simultaneously, the types of the inequality constraint models include one or more constraints of height / track irregularity of a typical sensitive wavelength of the left track, and vertical / lateral deviation of the left track, height / track irregularity of the conventional 30m chord check / 300m chord check / 10m chord serpentine / 20m chord serpentine of the left track, amount of left rail fasteners to be adjusted, poor left rail deviation, and rate of change of left rail deviation, and height / track irregularity of a typical sensitive wavelength of the right track, and vertical / lateral deviation of the right track, height / track irregularity of the conventional 30m chord check / 300m chord check / 10m chord serpentine / 20m chord serpentine of the right track, amount of right rail fasteners to be adjusted, poor right rail deviation, and rate of change of right rail deviation, and one or more constraints of level / gauge, distortion / gauge change rate.

[0025] Preferably, when the inequality constraint model is a height / track constraint, the positron difference constraint is incorporated into the height / track constraint.

[0026] Preferably, in step S3, when the inequality constraint model is first established, the inequality constraint boundary value does not exceed the limit difference value required by the corresponding current specification.

[0027] Preferably, in step S5, the sliding segment length interval is a segment interval moved according to a preset overlapping interval.

[0028] Preferably, the preset overlapping interval is the overlapping length of adjacent basic planning units.

[0029] Preferably, the overlapping length is greater than or equal to the detection chord length of the longest reference chord used in the rail condition optimization model.

[0030] Preferably, in step S6, the preset correction step length to adjust the constraint boundary value calculation process includes: if the solution of the rail to be optimized is completed for the previous i times, the correction step length of the constraint boundary value used in the i+1th solution is 0.5 times the constraint boundary value used in the i-th solution, that is, the constraint boundary value of each type of inequality constraint model used in the i+1th solution is 0.5 of the constraint boundary value used when it is first solvable. i times; and so on, determine the constraint boundary value of the inequality constraint model in the next iterative calculation.

[0031] Preferably, in step S6, the constraint boundary value of the certain type of constraint boundary value, the constraint boundary value used for the i-th solution is:

[0032]

[0033] Where: θ' is the constraint limit value of the i-th time; θ is the constraint limit value when it is solvable for the first time.

[0034] Preferably, in step S6, the evaluation value of the rail state is a function value of mean statistics of statistical results of typical sensitive wavelength irregularities and track geometric state parameters.

[0035] Preferably, the track geometry state parameters include one or more of deviation, height / track direction, deviation difference, deviation change rate, level / track gauge, and distortion / track gauge change rate.

[0036] Preferably, the statistical results include one or more of the maximum value, minimum value, mean value, and mean error of each parameter.

[0037] Preferably, the mean statistics of the statistical results include one or more of the mean of the maximum values, the mean of the minimum values, the mean of the means, and the mean of the mean errors of each parameter.

[0038] Preferably, in step S6, the threshold is 0.1.

[0039] Preferably, in step S8, the set of rail states is an adjusted rail shape and position represented by a state evaluation value of a combination of optimal, optimal and suboptimal, optimal and suboptimal, and suboptimal.

[0040] Preferably, in step S8, the smoothing information includes one or more of a deviation difference and a deviation change rate of the track geometry state parameters.

[0041] Compared with the prior art, the present invention has the following advantages:

[0042] The present invention not only optimizes and adjusts the track state through the general state index requirements of the current track fine-tuning related specifications on track state unevenness control, but more importantly, considers the vibration of the running train and the comfort of the passengers, and controls the sensitive wavelength that causes the resonance of the natural frequency of the car body at different speeds as a key factor, so as to more accurately ensure the stability of high-speed operation of trains at different speeds, achieve high smoothness of the track state under complex train operating conditions, and obtain the optimal adjustment amount to achieve this state, thereby providing a preferred solution for optimizing the track unevenness state. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 It is a schematic diagram of the overall solution flow of an embodiment of the present invention;

[0045] Figure 2 A schematic diagram of the contents included in the rail state optimization model in an embodiment of the present invention;

[0046] Figure 3 Schematic diagram of the process of determining the optimal rail in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] The present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the absence of conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. Unless otherwise defined, the technical terms or scientific terms used in the disclosure of the present invention should be the usual meanings understood by people with ordinary skills in the field to which the disclosure belongs. The words "including" or "comprising" and the like used in the disclosure of the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0049] like Figure 1-3 As shown, a method for optimizing rail high smoothness by combining variable wavelength and fixed wavelength is provided, wherein the adjustment object can be directed to the reference rail alone or the non-reference rail alone, or the reference rail first and then the non-reference rail, or the non-reference rail first and then the reference rail, or both rails at the same time. The method comprises the following steps:

[0050] S1: The length of the rail to be optimized is segmented according to a preset length, wherein the preset length is greater than or equal to the longest reference chord length used in the rail state optimization model.

[0051] S2: Establish a rail state optimization model within a segmented length interval, wherein the length interval is a basic planning unit, and the rail state optimization model includes an objective function of the amount to be adjusted of all sleeper fasteners within the interval, a typical sensitive wavelength unevenness function, and a rail state evaluation function. The objective function may be to minimize the total amount to be adjusted of all sleeper fasteners in the segmented planning unit, or minimize the sum of the absolute values ​​of the amounts to be adjusted, or minimize the sum of the squares of the amounts to be adjusted. The sensitive wavelength is determined by the natural frequency of the vehicle body and the running speed of the train. The typical sensitive wavelength is one or more wavelengths with the most serious unevenness among the sensitive wavelengths. The state evaluation function is one or more of internal and external geometric parameters of the track, poor deviation, and deviation change rate.

[0052] The irregularity of the sensitive wavelength is the track / height irregularity of the variable wavelength. The function of the sensitive wavelength irregularity is the track / height irregularity function of the variable wavelength.

[0053] In a specific embodiment, a typical sensitive wavelength determination method is as follows:

[0054] (1) Determine the sensitive wavelength by the natural frequency of the vehicle body and the train running speed:

[0055] (2)

[0056] In the formula, Sensitive wavelengths determined for different speeds, ; is the running speed of the train, ; is the natural frequency of the vehicle body.

[0057] (2) Evaluate the uneven state of the sensitive wavelengths and select one or several sensitive wavelengths with the most serious state as typical sensitive wavelengths.

[0058] (3) Incorporate the track orientation and height irregularity of the determined typical sensitive wavelength into the track state optimization model.

[0059] The internal and external geometric parameters of the track include one or more of vertical deviation, lateral deviation, height, track direction, level, track gauge, twist, and track gauge change rate.

[0060] The height / track irregularity in the track internal and external geometric parameters refers to the track irregularity / height irregularity of a certain wavelength.

[0061] In a specific embodiment, the rail state evaluation function includes the internal and external geometric parameters of the track, the deviation difference and the deviation change rate. The internal and external geometric parameters of the track include vertical deviation, lateral deviation, height unevenness of 10m chord, 20m chord, 5m / 30m chord, 150m / 300m chord, track irregularity of 10m chord, 20m chord, 5m / 30m chord, 150m / 300m chord, level, gauge, twist and gauge change rate. Among them, the evaluation function corresponding to each parameter is as follows:

[0062] 1) Vertical (or lateral) deviation

[0063] (3)

[0064] Where: is the original deviation; The amount to be adjusted.

[0065] 2) Uneven height (or track direction)

[0066] (4)

[0067] Where: and They are the deviations after adjustment of the first and last points of the reference chord respectively; and They are the deviations after adjustment of the first and last points of the detection string respectively; ,in, , are the serial numbers of the first and last points of the reference chord respectively; ,in, , are the serial numbers of the first and last points of the detection chord respectively; w is the number of sleepers included in the detection chord length; .

[0068] When using 5m / 30m chord calibration, ; When using 150m / 300m chord calibration, .

[0069] Incorporating the verse difference constraint into the height / track irregularity constraint, when using 10m chord or 20m chord verse, the above formula is simplified to:

[0070] (5)

[0071] 3) Level (or track gauge)

[0072] (6)

[0073] Where: and are the adjusted deviations of the two tracks, where For the left (or right) track Point adjusted deviation, For the right (or left) track The deviation after point adjustment can be calculated by formula (3).

[0074] 4) Distortion (or track gauge change rate)

[0075] (7)

[0076] Where: n is the number of sleepers corresponding to the action distance. The static laying requirements of ballastless track stipulate that the distortion action distance is 3m and the gauge change rate action distance is 0.625m. r represents the calculation parameter of distortion or gauge change rate, r=1 (or r=625).

[0077] The evaluation function with poor deviation is as follows:

[0078] (8)

[0079] In the formula, .

[0080] The evaluation function of the deviation change rate is as follows:

[0081] (9)

[0082] Where: l is the mileage difference between points i and j.

[0083] S3: Based on the typical sensitive wavelength irregularity function and the rail state evaluation function, additional adjustment amount limits for the sleeper fasteners are set, and constraint limits corresponding to the adjustment amount, typical sensitive wavelength irregularity and state evaluation are set. The constraint limits are limit differences, and an inequality constraint model for rail state optimization is established. The sleeper fastener adjustment amount function is as follows:

[0084] (10)

[0085] When setting the constraint limit value for the first time, the value assigned should not exceed the tolerance value required by the current specification. For example, the height (or track direction) inequality constraint of a 10m chord can be described as:

[0086] (11)

[0087] Where: and It is the limit difference value of height (or track direction) irregularity, that is, the constraint boundary value of the state inequality constraint model.

[0088] In a specific embodiment, if the reference rail is the only adjustment object, the type of the inequality constraint model includes the height (or track direction) unevenness constraint of the typical sensitive wavelength, and ① vertical (or lateral) deviation and / or ② 30m chord calibration and / or 300m chord calibration and / or other fixed chord length (wavelength) calibration and / or 10m chord verse and / or 20m chord verse and / or other fixed chord length (wavelength) verse height (or track direction) unevenness and / or ③ fastener adjustment amount and / or ④ deviation difference and / or ⑤ deviation change rate constraint.

[0089] If the non-reference rail is used as the adjustment object alone, when the reference rail state cannot be used as an adjustment reference, the types of inequality constraint models include the height (or track direction) unevenness constraint of the typical sensitive wavelength, and ① vertical (or lateral) deviation and / or ② 30m chord check and / or 300m chord check and / or other fixed chord length (wavelength) check and / or 10m chord verse and / or 20m chord verse and / or other fixed chord length (wavelength) verses height (or track direction) unevenness and / or ③ fastener adjustment amount and / or ④ poor deviation and / or ⑤ deviation change rate constraint; when the reference rail state is not suitable for adjustment, the inequality constraint model shall be used as an adjustment reference. The state can be used as a reference for adjustment, and the types of inequality constraint models include height (or track direction) unevenness constraints of typical sensitive wavelengths, and ① vertical (or lateral) deviation and / or ② 30m chord check and / or 300m chord check and / or other fixed chord length (wavelength) check and / or 10m chord verse and / or 20m chord verse and / or other fixed chord length (wavelength) verse height (or track direction) unevenness and / or ③ fastener adjustment amount and / or ④ poor deviation and / or ⑤ deviation change rate and / or ⑥ level (or gauge) and / or ⑦ distortion (or gauge change rate) constraints.

[0090] If two tracks (one of which is a reference track and the other is a non-reference track) are used as the adjustment object, the reference track can be used as the adjustment object first according to the above method, and then the non-reference track can be used as the adjustment object; or the non-reference track can be used as the adjustment object, and then the reference track can be used as the adjustment object; or both the reference track and the non-reference track can be used as the adjustment object. In this case, the type of the inequality constraint model includes reference track: height (or track direction) unevenness constraint of typical sensitive wavelength, and ① vertical (or lateral) deviation and / or ② 30m chord calibration and / or 300m chord calibration and / or other fixed chord length (wavelength) calibration and / or 10m chord verse and / or 20m chord verse and / or other fixed Height (or track direction) unevenness of chord length (wavelength) positron and / or ③ amount of fasteners to be adjusted and / or ④ poor deviation and / or ⑤ rate of deviation change; Non-reference rail: height (or track direction) unevenness constraints of typical sensitive wavelengths, and ① vertical (or lateral) deviation and / or ② 30m chord calibration and / or 300m chord calibration and / or other fixed (wavelength) chord length calibration and / or 10m chord positron and / or 20m chord positron and / or height (or track direction) of other fixed (wavelength) chord length positrons and / or ③ amount of fasteners to be adjusted and / or ④ poor deviation and / or ⑤ rate of deviation change and / or ⑥ level (or track gauge) and / or ⑦ distortion (or track gauge change rate) constraints.

[0091] S4: Using a mathematical programming method to optimize and solve the objective function and the inequality constraint model, the mathematical programming method may use a simplex method, a dual simplex method, or an interior point method.

[0092] If there is no solution in the current optimization calculation, the corresponding adjustment amount will be set to zero.

[0093] S5: Sliding segment length interval, the rails in the interval are processed according to S2-S4 until the processing of the rails to be optimized is completed. The sliding segment length interval is a segment interval moved according to a preset overlapping interval, the preset overlapping interval is the overlapping length of adjacent basic planning units, and the overlapping length is greater than or equal to the detection chord length of the longest reference chord used in the rail state optimization model.

[0094] S6: Evaluate the state of the adjusted rail obtained by S1-S5, adjust the constraint boundary value according to the preset correction step size, use the adjusted constraint boundary value as the constraint boundary value of the inequality constraint model in the next solution, and perform iteration termination condition judgment.

[0095] The preset correction step length adjustment constraint boundary value calculation process includes: if the solution of the rail to be optimized is completed for the previous i times, the correction step length of the constraint boundary value used in the i+1th solution is the constraint boundary value used in the i-th solution. times, that is, the constraint limits of various inequality constraint models used in the i+1th solution are the constraint limits used when the solution is first solved. times; and so on, determine the constraint boundary value of the inequality constraint model in the next iterative calculation.

[0096] In a specific embodiment, the track irregularity constraint limit value of the 30m chord check, the constraint limit value used in the i-th solution is:

[0097] (12)

[0098] Where: θ' is the constraint limit value of the i-th time; θ is the constraint limit value when it is solvable for the first time.

[0099] The initial constraint boundary values ​​of different types of inequality constraint models, such as the height irregularity of 30m chord calibration, the track irregularity of 300m chord calibration, and the height irregularity of 300m chord calibration, may be the same or different, and the values ​​are determined based on actual conditions.

[0100] The execution iteration termination condition includes: if the evaluation value of the state of the rail obtained by the current solution is less than the evaluation value of the state of the rail obtained by the previous solution and is less than a threshold, the iteration is terminated. The evaluation value of the state of the rail is a function value of the mean value statistics of the statistical results of the typical sensitive wavelength irregularity and the track geometric state parameters.

[0101] In a specific embodiment, the evaluation value of the rail state includes the height (or track direction) unevenness of the typical sensitive wavelength, and ① vertical (or lateral) deviation and / or ② 30m chord check and / or 300m chord check and / or other fixed chord length (wavelength) check and / or 10m chord verse and / or 20m chord verse and / or other fixed chord length (wavelength) verse height (or track direction) unevenness and / or ③ poor deviation and / or ④ deviation change rate and / or ⑤ level (or track gauge) and / or ⑥ distortion (or track gauge change rate). The statistical results include ① maximum value and / or ② minimum value and / or ③ mean value and / or ④ mean error of the parameters used above. The mean statistics of the statistical results include ① mean value of maximum value and / or ② mean value of minimum value and / or ③ mean value and / or ④ mean error of the statistical results used above. The function value of the mean statistics is a single mean statistic, or one or more of the mean, maximum, minimum, and root mean square error of multiple mean statistics, or a poor mean statistic, or a product of poor mean statistics. The poor evaluation value is the absolute value of the poor.

[0102] In a specific embodiment, the state of the overall rail to be adjusted obtained after the i-th iteration solution is evaluated, such as the typical sensitive wavelength, 30m chord check, 300m chord check, 10m chord sine, 20m chord sine height (or track direction) unevenness and deviation change rate, a total of 6 parameters, and the maximum value, minimum value and mean error of the evaluation results of each parameter are counted, and then the average statistics of the maximum value, minimum value and mean error are taken to obtain the evaluation value of the state. :

[0103] (13)

[0104] In the formula, y a ,y b ,y c ,y d ,y e and f It is the evaluation result vector of each of the six parameters in the segment planning unit, namely, typical sensitive wavelength, 30m chord calibration, 300m chord calibration, 10m chord verse, 20m chord verse, height (or track) irregularity and deviation change rate; max, min and rmse are the maximum, minimum and mean error calculation functions respectively.

[0105] In other specific embodiments, Can be:

[0106] ,or , or . Among them, mean is the mean calculation function.

[0107] Similarly, the rail status obtained by the iterative solution of the i+1th iteration is evaluated similarly to obtain the evaluation value If the absolute value of the difference between the two evaluation values ​​is less than the threshold value, which is 0.1, the iteration is stopped, otherwise the iterative calculation is continued.

[0108] In another specific embodiment, other thresholds may be set according to optimization requirements.

[0109] S7: Repeat S1-S6 until the iterative correction is terminated.

[0110] S8: After the iteration is terminated, the state of the rail obtained from each solution is judged, a set of rail states represented by a better state evaluation value is selected, and the optimal rail shape and position is selected according to the smoothing information to obtain the optimal solution for the rail smoothness state.

[0111] In a specific embodiment, the determination is the mean of the statistical results of the track geometry state parameters. The set of rail states is the adjusted rail shape and position represented by the state evaluation value of ① optimal or ② optimal and suboptimal or ③ optimal and suboptimal and suboptimal. The smoothing information includes ① poor deviation and / or ② deviation change rate, and the method is the statistical result of the smoothing information, and the statistical result is one or more of the maximum value, minimum value, mean value, and mean error.

[0112] In a specific embodiment, if the iteration is terminated at the i-th time, the evaluation value of the rail state obtained in each iteration is , select the optimal and suboptimal evaluation values ​​from the 1-i rail positions and , and then calculate the deviation difference and deviation change rate index of the adjusted rail shape and position obtained by the pth and qth iterations. The maximum, minimum and mean errors of the deviation difference and deviation change rate index of the adjusted rail shape and position obtained by the pth and qth iterations are counted and compared, and the rail shape and position represented by the smaller one is taken as the optimal rail shape and position, and the optimal solution is obtained.

[0113] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A rail high smoothness optimization method combining variable wavelength and fixed wavelength, characterized in that: The following steps are involved: S1: Divide the length of the rail to be optimized into segments according to preset lengths; S2: Establishing a rail state optimization model within a segmented length interval, wherein the length interval is a basic planning unit, and the rail state optimization model includes an objective function of the amount to be adjusted of all sleeper fasteners within the interval, a typical sensitive wavelength irregularity function, and a rail state evaluation function; S3: Based on the typical sensitive wavelength irregularity function, the state evaluation function, the limit of the amount of adjustment of the sleeper fasteners and the constraint boundary value, an inequality constraint model for rail state optimization is established; S4: Optimizing and solving the objective function and the inequality constraint model to obtain the adjustment amount of the sleeper fastener; S5: Process the rails in each segment length interval according to S2-S4 until the processing of the rails to be optimized is completed, and evaluate the state of the adjusted rails to obtain an evaluation value of the rail state; S6: Adjust the constraint boundary value, repeat S1-S5, and determine whether to terminate the iteration based on the evaluation value; S7: If it is not terminated, repeat S6 until the iteration is terminated; S8: After the iteration is terminated, the state of the rail obtained by each iteration is judged, a set of rail states represented by a better state evaluation value is selected, and the optimal rail shape and position is selected according to the smoothing information to obtain the optimal solution for the rail smoothness state.

2. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S1, the preset length is greater than or equal to the longest reference chord length used in the rail state optimization model.

3. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S1, the segmentation is segmented according to a preset overlapping interval, the preset overlapping interval is the overlapping length of adjacent basic planning units, and the overlapping length is greater than or equal to the detection chord length of the longest reference chord used in the rail state optimization model.

4. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S2, the rail state evaluation function is one or more of internal and external geometric parameters of the track, poor deviation, and deviation change rate.

5. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S3, the sensitive wavelength is determined by the natural frequency of the vehicle body and the running speed of the train, and the typical sensitive wavelength is one or more wavelengths with the most serious unevenness among the sensitive wavelengths.

6. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S3, the inequality constraint model includes: When the adjustment object of the track is a reference track, the type of the inequality constraint model includes a height / track irregularity constraint of a typical sensitive wavelength, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, and deviation change rate; When the adjustment object of the track is a non-reference track, and the reference track state cannot be used as an adjustment reference, the type of the inequality constraint model includes the height / track irregularity constraints of typical sensitive wavelengths, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, and deviation change rate; When the adjustment object of the track is a non-reference track, and the reference track state can be used as an adjustment reference, the type of the inequality constraint model includes the height / track irregularity constraints of typical sensitive wavelengths, and one or more constraints of vertical / lateral deviation, conventional 30m chord check / 300m chord check / 10m chord verse / 20m chord verse height / track irregularity, fastener adjustment amount, deviation difference, deviation change rate, level / gauge, distortion / gauge change rate; When the adjustment object of the track is a double track, the reference track can be used as the adjustment object first according to the above method, and then the non-reference track can be used as the adjustment object to establish a constraint model; or the non-reference track can be used as the adjustment object first, and then the reference track can be used as the adjustment object to establish a constraint model; or both can be adjusted simultaneously; When adjusting simultaneously, the type of the inequality constraint model includes one or more constraints of height / track irregularity of a typical sensitive wavelength of the left track, and vertical / lateral deviation of the left track, height / track irregularity of conventional 30m chord check / 300m chord check / 10m chord serpentine / 20m chord serpentine of the left track, amount of left rail fasteners to be adjusted, poor left rail deviation, and rate of change of left rail deviation, and height / track irregularity of a typical sensitive wavelength of the right track, and vertical / lateral deviation of the right track, height / track irregularity of conventional 30m chord check / 300m chord check / 10m chord serpentine / 20m chord serpentine of the right track, amount of right rail fasteners to be adjusted, poor right rail deviation, and rate of change of right rail deviation, and one or more constraints of level / gauge, distortion / gauge change rate; When the inequality constraint model is a height / track constraint, the versine difference constraint is incorporated into the height / track constraint.

7. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S5, the evaluation value of the rail state is a function value of the mean statistics of the statistical results of the typical sensitive wavelength unevenness and the track geometry state parameters, the track geometry state parameters include one or more of deviation, height / track direction, deviation difference, deviation change rate, level / track gauge, distortion / track gauge change rate, the statistical results include one or more of the maximum value, minimum value, mean, and mean error of each parameter, and the mean statistics of the statistical results include one or more of the mean of the maximum value, the mean of the minimum value, the mean of the mean, and the mean error of each parameter.

8. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1 is characterized in that: In step S6, the calculation process of adjusting the constraint boundary value includes: if the solution of the rail to be optimized is completed in the previous i times, the correction step length of the constraint boundary value used in the i+1th solution is the constraint boundary value used in the i-th solution. times, that is, the constraint limits of various inequality constraint models used in the i+1th solution are the constraint limits used when the solution is first solved. times; and so on, determine the constraint boundary value of the inequality constraint model in the next iterative calculation.

9. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1, characterized in that: In step S8, the set of rail states is the adjusted rail shape and position represented by a state evaluation value of a combination of optimal, optimal and suboptimal, optimal and suboptimal, and suboptimal.

10. The rail high smoothness optimization method combining variable wavelength and fixed wavelength according to claim 1, characterized in that: In step S8, the smoothing information includes one or more of a deviation difference and a deviation change rate.

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