Two-stage optimization method for steel rail state control

Abstract

The invention discloses a two-stage optimization method for steel rail state control, and belongs to the field of precision engineering measurement technology application in the field of rail transit. Performing first-stage segmentation on the to-be-optimized steel rail, and establishing a steel rail state optimization model containing typical sensitive wavelength irregularity; adding sleeper fastener to-be-adjusted quantity limitation and constraint boundary values, and solving the inequality constraint model section by section; iteratively correcting the inequality according to the evaluation value until iteration is terminated; obtaining an optimal real number solution based on the smooth information; second-stage segmentation is carried out, and a state optimization model containing a neighborhood point residual deviation n-order difference function and a waveform consistency function is established; and adding sleeper fastener to-be-adjusted quantity limitation and constraint boundary values, and solving the inequality constraint model section by section to obtain an optimal differential integer solution. According to the method, the problems of variable wavelength irregularity causing train excitation and material level difference adjustment in general engineering are considered, the riding comfort is improved, the TQI is reduced, the problems that a fine adjustment scheme is time-consuming and labor-consuming and uneven in effect are solved, and optimal state control is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of railway track precision engineering measurement, and in particular to a two-stage optimization method for rail state control. Background Art

[0002] Ensuring high smoothness by formulating an optimization plan for the shape and position of the rails is the key to high-speed, safe and smooth operation of trains. From the fine-tuning of the double-block ballastless track or the slab ballastless track during the construction phase to the fine-tuning of long rails and the fine-tuning of tracks during 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 the Smoothness of Rails" (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. The stepless adjustment at this stage can greatly improve the track state and improve its smoothness under the requirements of the fixed-wavelength unevenness index specified in the existing specifications, but the implementation effect will be limited by the existing construction conditions. In the maintenance and optimization of track smoothness, in order to save costs and facilitate the modularization of pad fasteners, multiple pads with a step difference of 0.5mm or 1.0mm are often used to change the shape and position of the track to achieve the purpose of improving the state. If the construction personnel rely on experience and use a simple rounding method to approximate the stepless adjustment fine-tuning scheme, it will reduce the smoothness effect of the adjusted rail state, and even lead to the dilemma of unevenness exceeding the limit. Therefore, two problems need to be further solved: ① To reduce TQI, reduce the phenomenon of train shaking and vibration, and further improve the riding comfort of passengers, it is necessary to consider the influence of variable wavelength unevenness causing train excitation and incorporate it into the optimization model for effective control; ② Taking into account the problem that the thickness of the pads of the fastener system used for rail geometry adjustment has a grade difference of 0.5mm and 1.0mm, the stepless adjustment scheme is effectively converted into a graded adjustment scheme to ensure that the track state loss before and after the conversion is as small as possible, and the track state is as smooth as possible after the graded adjustment. Summary of the invention

[0003] In view of the above problems, the present invention aims to provide a two-stage optimization method for rail state control, which ensures high smoothness of rail state through a first-stage stepless optimization adjustment; on this basis, a second-stage differential optimization adjustment is implemented, and through the n-order difference constraint of the residual deviation of the neighborhood point and the waveform consistency constraint, it is ensured that the rail state of the stepless adjustment is inherited as completely as possible as the rail state of the differential adjustment. The first stage of stepless adjustment: the objective function, the typical sensitive wavelength unevenness function and the rail state evaluation function are used as the optimization model, the overall rail to be adjusted is used as the evaluation object, the constraint boundary value is adjusted according to the overall evaluation result, and it is used as the condition for determining the termination of the iteration, and then the rail line shape obtained each time is evaluated to obtain the best rail line shape set that can significantly improve the passenger riding comfort, and finally the optimal adjustment real number solution is obtained based on the smoothness condition. The second stage of step adjustment: based on the optimal adjustment real number solution, optimization based on the integer multiple of the differential is performed, and the optimal adjustment differential integer solution is obtained based on the principle of minimizing the state loss in the two stages. The present invention effectively controls the unevenness of variable wavelength and fixed wavelength, and formulates an optimal real number adjustment amount scheme that takes into account the passenger riding comfort in the process of solving, evaluating, iteratively correcting and re-evaluating the smoothness optimization control. The optimal real number adjustment amount is converted into an optimal differential integer adjustment amount by taking into account the fastener material difference, thereby further effectively improving the unevenness of the rail state, significantly reducing the jitter and shaking phenomena, and obtaining a rail fine-tuning optimization scheme suitable for general engineering conditions.

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

[0005] A two-stage optimization method for rail state control comprises the following steps:

[0006] S1: dividing the length of the rail to be optimized into first-stage segments according to a first preset length;

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

[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, and constraint boundaries corresponding to the adjustment amount, the typical sensitive wavelength irregularity and the state evaluation are set. The constraint boundaries are the limit difference values, and an inequality constraint model for one-stage 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 first-stage rail state optimization model, calculate the real number solution of the sleeper fastener adjustment amount, and if there is no solution in the current optimization calculation, set the corresponding adjustment amount to zero;

[0010] 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;

[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 real number solution of the rail smooth state;

[0014] S9: segmenting the length of the rail to be optimized into second-stage segments according to a second preset length;

[0015] S10: Based on the optimal real number solution, a rail state optimization model within a two-stage segment length interval is established, wherein the two-stage segment length interval is a two-stage basic planning unit;

[0016] S11: Based on the n-order difference function of the residual deviation of the neighborhood points, the waveform consistency function, the limit of the amount of adjustment to be made for the sleeper fasteners, and the constraint boundary value, an inequality constraint model for the two-stage rail state optimization is established;

[0017] S12: performing integer programming to solve the objective function and inequality constraint model of the second stage, and obtaining the sleeper fastener adjustment amount of the integer multiple of the fastener material difference. If there is no solution in the optimization calculation at this time, the corresponding adjustment amount is set to zero;

[0018] S13: Process the rails in each segment length interval according to S10-S12 until the processing of the rails to be optimized is completed, and the optimal differential integer solution for rail adjustment is obtained.

[0019] Preferably, in step S1, the first preset length is greater than or equal to the longest reference chord length used in the rail state optimization model.

[0020] Preferably, in step S1, the first stage segmentation is segmentation moved according to a first preset overlapping interval.

[0021] Preferably, in step S1, the first preset overlapping interval is a first overlapping length of adjacent basic planning units.

[0022] Preferably, in step S1, the first overlapping length is greater than or equal to a sleeper interval length.

[0023] Preferably, in step S2, the rail state optimization model within the one-stage segment length interval 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.

[0024] 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.

[0025] 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.

[0026] Preferably, the adjustment object of the track is a reference rail and / or a non-reference rail.

[0027] Preferably, the inequality constraint model comprises:

[0028] 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;

[0029] 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;

[0030] 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;

[0031] 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;

[0032] 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.

[0033] Preferably, when the inequality constraint model is a height / track constraint, the positron difference constraint is incorporated into the height / track constraint.

[0034] 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.

[0035] Preferably, in step S5, each segment length interval is a segment interval moved according to a first preset overlapping interval.

[0036] 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 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.

[0037] 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:

[0038] (1)

[0039] 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.

[0040] 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.

[0041] Preferably, the track geometry parameters include one or more of deviation, height / track direction, deviation difference, deviation change rate, level / track gauge, and distortion / track gauge change rate.

[0042] Preferably, the statistical results include one or more of the maximum value, minimum value, mean value and mean error of each parameter.

[0043] 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.

[0044] Preferably, in step S6, the threshold is 0.1.

[0045] 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.

[0046] 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.

[0047] Preferably, in step S9, the second preset length is greater than or equal to the length of the nth-order difference function of the neighborhood point residual deviation and the waveform consistency function used in the rail state optimization model.

[0048] Preferably, in step S9, the second stage segmentation is segmentation moved according to a second preset overlapping interval.

[0049] Preferably, in step S9, the second preset overlapping interval is a second overlapping length of adjacent basic planning units.

[0050] Preferably, in step S9, the second overlapping length is greater than or equal to a sleeper interval length.

[0051] Preferably, in step S10, the rail state optimization model within the two-stage segmented length interval includes an objective function of the amount to be adjusted of all sleeper fasteners within the interval, an nth-order difference function of the residual deviation of the neighborhood points, and a waveform consistency function.

[0052] Preferably, in step S10, the amount to be adjusted of the rail state optimization model within the two-stage segment length interval is an integer multiple of the step difference.

[0053] Preferably, in step S10, the grade difference is one of the specifications of the rail fastener adjustment material of 0.5mm, 1.0mm, 1.5mm, and 2.0mm.

[0054] Preferably, in step S11, the n-th order difference function of the residual deviation of the neighborhood points is a recursive formula:

[0055] (2)

[0056] The recursive formula can be obtained by the following formula:

[0057] (3)

[0058] In the formula, The sleeper number of the rail to be optimized. , is a natural number, , m is the number of sleepers contained in the rail to be optimized, ,and .

[0059] Preferably, in step S11, the waveform consistency function is:

[0060] (4)

[0061] In the formula, is the difference, is the number of level differences at point i, is the optimal real number solution of the second stage to be adjusted at point i and the corresponding first stage The worse.

[0062] Compared with the prior art, the present invention has the following advantages:

[0063] The present invention aims at the problem of insufficient control state of general fixed-wavelength unevenness index in the current track precision measurement and fine-tuning related specifications, and the problem of uneven effects of differential adjustment schemes caused by the use of low-precision differential adjustment materials in general engineering, and proposes a two-stage optimization method for rail state control. The present invention can obtain a fastener material differential integer multiple adjustment scheme that ensures high smoothness of the track state through a two-stage optimization method. By considering the vibration of the running train and the comfort of passengers, the sensitive wavelength that causes the resonance of the train is controlled as a key factor, and the stability of high-speed operation of the train under complex operating conditions is more accurately guaranteed, and the optimal adjustment amount real number solution to achieve this state is obtained. Based on the adjustment line shape corresponding to the optimal adjustment amount real number solution, the n-order difference function of the residual deviation of the neighborhood point and the waveform consistency function are used to implement integer programming, and the differential integer solution is obtained when the two-stage state loss is minimized, ensuring that the track smoothness obtained in the second stage is consistent with or closest to the track smoothness obtained in the first stage. The present invention can scientifically and accurately formulate a track optimization plan that conforms to the actual fastener material differential adjustment, and obtain the optimal track height smoothness in the track state corresponding to the integral multiple adjustment amount of the fastener material differential. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] 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.

[0065] Figure 1 It is a schematic diagram of the overall solution flow of an embodiment of the present invention;

[0066] Figure 2 This is a schematic diagram of the first stage optimization method flow in an embodiment of the present invention;

[0067] Figure 3 It is a rail state optimization model within a segment length interval in a first stage in an embodiment of the present invention;

[0068] Figure 4 This is a schematic diagram of the second stage optimization method flow in an embodiment of the present invention;

[0069] Figure 5 This is a rail state optimization model within the two-stage segment length interval in an embodiment of the present invention. DETAILED DESCRIPTION

[0070] 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.

[0071] like Figure 1-5 As shown, a two-stage optimization method for rail state control, the adjustment object can be for the reference rail alone, or for the non-reference rail alone, or for the reference rail first and then for the non-reference rail, or for the non-reference rail first and then for the reference rail, or for both rails at the same time. The method comprises the following steps:

[0072] S1: The length of the rail to be optimized is segmented in the first stage according to a first preset length. The first preset length is greater than or equal to the longest reference chord length used in the rail state optimization model, and the first stage segmentation is segmentation according to a first preset overlapping interval, the first preset overlapping interval is the first overlapping length of adjacent basic planning units, and the first overlapping length is greater than or equal to the length of a sleeper interval.

[0073] S2: Establish a rail state optimization model within a one-stage segment length interval, wherein the one-stage segment length interval is a one-stage basic planning unit, and the rail state optimization model within the one-stage segment length interval 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 the minimum total amount to be adjusted of all sleeper fasteners in the segment planning unit, the minimum sum of the absolute values ​​of the amount to be adjusted, or the minimum sum of the squares of the amount 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.

[0074] 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.

[0075] In a specific embodiment, a typical sensitive wavelength determination method is as follows:

[0076] (1) Determine the sensitive wavelength by the natural frequency of the vehicle body and the train running speed:

[0077] (5)

[0078] In the formula, Sensitive wavelengths determined for different speeds, ; is the running speed of the train, ; is the natural frequency of the vehicle body.

[0079] (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.

[0080] (3) Incorporate the track orientation and height irregularity of the determined typical sensitive wavelength into the track state optimization model.

[0081] In a specific embodiment, the number of typical sensitive wavelengths may be 1, 2 or 3. The running speed of the train is determined according to the specific train running speed regulations and requirements on the railway line. The natural frequency of the car body is determined according to the car body used, and generally, the natural frequency is in the range of 0.7 to 1.4 Hz.

[0082] 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.

[0083] The height / track irregularity in the track internal and external geometric parameters refers to the track irregularity / height irregularity of a certain wavelength.

[0084] 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:

[0085] 1) Vertical (or lateral) deviation

[0086] (6)

[0087] Where: is the original deviation; The amount to be adjusted.

[0088] 2) Uneven height (or track direction)

[0089] (7)

[0090] 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; .

[0091] When using 5m / 30m chord calibration, ; When using 150m / 300m chord calibration, .

[0092] 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:

[0093] (8)

[0094] 3) Level (or track gauge)

[0095] (9)

[0096] 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 (6).

[0097] 4) Distortion (or track gauge change rate)

[0098] (10)

[0099] Where: n is the number of sleepers corresponding to the action distance. The static laying of ballastless track requires a distortion action distance of 3m and a gauge change rate action distance of 0.625m. r represents the calculation parameter of distortion or gauge change rate, r=1 (or r=625).

[0100] The evaluation function with poor deviation is as follows:

[0101] (11)

[0102] Where: .

[0103] The evaluation function of the deviation change rate is as follows:

[0104] (12)

[0105] Where: d is the mileage difference between points i and j.

[0106] S3: Based on the typical sensitive wavelength irregularity function and rail state evaluation function, additional sleeper fastener adjustment amount restrictions are added, and constraint boundaries corresponding to the adjustment amount, typical sensitive wavelength irregularity and state evaluation are set. The constraint boundary value is the limit difference value, and an inequality constraint model for one-stage rail state optimization is established. The sleeper fastener adjustment amount function is as follows:

[0107] (13)

[0108] 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:

[0109] (14)

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] S4: A mathematical programming method is used to optimize and solve the objective function and inequality constraint model of a stage. The mathematical programming method may be a simplex method, a dual simplex method, an interior point method, a genetic algorithm, or a simulated annealing algorithm.

[0115] If there is no solution in the current optimization calculation, the corresponding adjustment amount will be set to zero.

[0116] S5: Process the rails in each segment length interval according to S2-S4 until the processing of the rails to be optimized is completed. Each segment length interval is a segment interval moved according to a first preset overlapping interval.

[0117] In a specific embodiment, the first preset overlapping interval may be equal to the length of one sleeper interval, or equal to the detection chord length of the longest reference chord used in the rail condition optimization model.

[0118] 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.

[0119] 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.

[0120] 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:

[0121] (15)

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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. :

[0127] (16)

[0128] Where: 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.

[0129] In other specific embodiments, Can be:

[0130] (17)

[0131] or:

[0132] (18)

[0133] or:

[0134] (19)

[0135] Where: mean is the mean calculation function.

[0136] or:

[0137] (20)

[0138] 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.

[0139] In another specific embodiment, other thresholds may be set according to optimization requirements.

[0140] S7: Repeat S1-S6 until the iterative correction is terminated.

[0141] 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 real number solution for the rail smoothness state.

[0142] 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.

[0143] 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 real number solution is obtained.

[0144] S9: The length of the rail to be optimized is segmented in the second stage according to a second preset length. The second preset length is greater than or equal to the length in the neighborhood point residual deviation n-order difference function and the waveform consistency function used in the rail state optimization model, and the second stage segmentation is segmented according to the second preset overlapping interval, the second preset overlapping interval is the second overlapping length of adjacent basic planning units, and the second overlapping length is greater than or equal to the length of one sleeper interval.

[0145] S10: Based on the optimal real number solution, a rail state optimization model within a two-stage segment length interval is established, wherein the two-stage segment length interval is a two-stage basic planning unit, and the rail state optimization model within the two-stage segment length interval includes an objective function of the amount to be adjusted of all sleeper fasteners within the interval, an n-order difference function of the residual deviation of the neighborhood points, and a waveform consistency function.

[0146] The amount to be adjusted of the rail state optimization model within the two-stage segment length interval is an integral multiple of the step difference. The step difference is one of the specifications of the rail fastener adjustment material of 0.5mm, 1.0mm, 1.5mm, and 2.0mm.

[0147] The objective function of the rail state optimization model within the two-stage segment length interval is to minimize the cumulative sum of the differences between the two-stage adjusted quantities and the corresponding one-stage optimal real number solution, or minimize the sum of the absolute values ​​of the differences, or minimize the sum of the squares of the differences.

[0148] The n-order difference function of the residual deviation of the neighborhood point is a recursive formula:

[0149] (twenty one)

[0150] The recursive formula can be obtained by the following formula:

[0151] (twenty two)

[0152] Where: is the sleeper number of the rail to be optimized, , is a natural number, , m is the number of sleepers contained in the rail to be optimized, ,and .

[0153] In the n-th order difference function, n does not exceed the number of sleepers contained in the rail to be optimized minus 1.

[0154] In a specific embodiment, , When , the nth-order difference function of the residual deviation of the neighborhood points is simplified to the difference between the residual deviation difference between the first point and the second point and the residual deviation difference between the second point and the third point in the three adjacent intervals.

[0155] The waveform consistency function is:

[0156] (twenty three)

[0157] In the formula, is the difference, is the number of differences at point i, is the optimal real number solution of the second stage to be adjusted at point i and the corresponding first stage The worse.

[0158] S11: Based on the nth-order difference function of the residual deviation of the neighborhood points, the waveform consistency function, the limit of the adjustment amount of the sleeper fasteners and the constraint boundary, an inequality constraint model for the two-stage rail state optimization is established.

[0159] The inequality constraint of the n-order difference function of the residual deviation of the neighborhood point can be described as:

[0160] (twenty four)

[0161] In the formula, is the limit difference value of the n-order difference function of the residual deviation of the neighborhood point, that is, the constraint limit value of the inequality constraint model, .

[0162] In a specific embodiment, the limit difference is:

[0163] (25)

[0164] In the formula, is the rate of change, l is the number of sleeper intervals, ,and , where m is the number of sleepers contained in the rail to be optimized.

[0165] In a specific embodiment, when , hour, ,and , is a positive real number.

[0166] The inequality constraints of the waveform consistency function can be described as:

[0167] (26)

[0168] The limit of the amount of adjustment to be made for the sleeper fasteners is the same as that in the first stage.

[0169] S12: performing integer programming to solve the objective function and inequality constraint model of the second stage, wherein the integer programming method may adopt branch and bound method, cutting plane method, implicit enumeration method, Hungarian method, or Monte Carlo method. After solving, the adjustment amount of the sleeper fasteners of the integral multiple of the fastener material difference may be obtained.

[0170] If there is no solution in the current optimization calculation, the corresponding adjustment amount will be set to zero.

[0171] S13: Process the rails in each segment length interval according to S10-S12 until the processing of the rails to be optimized is completed, and the optimal differential integer solution for rail adjustment is obtained.

[0172] Each segment length interval is a segment interval moved according to a second preset overlapping interval.

[0173] In a specific embodiment, the second preset overlapping interval may be equal to the length of one sleeper interval, or equal to the length of the nth-order difference function of the residual deviation of the neighborhood points and the waveform consistency function used in the rail state optimization model.

[0174] 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 two-stage optimization method for rail state control, characterized in that: The following steps are involved: S1: dividing the length of the rail to be optimized into first-stage segments according to a first preset length; S2: establishing a rail state optimization model within a one-stage segment length interval, wherein the one-stage segment length interval is a one-stage basic planning unit, and the rail state optimization model within the one-stage segment length interval 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 one-stage rail state optimization is established; S4: Optimize and solve the objective function and inequality constraint model of the first stage to obtain the real number solution of the sleeper fastener adjustment amount; 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 real number solution of the rail smooth state; S9: segmenting the length of the rail to be optimized into second-stage segments according to a second preset length; S10: Based on the optimal real number solution, a rail state optimization model within a two-stage segment length interval is established, wherein the two-stage segment length interval is a two-stage basic planning unit, and the rail state optimization model within the two-stage segment length interval includes an objective function of the amount to be adjusted of all sleeper fasteners within the interval, an n-order difference function of the residual deviation of the neighborhood point, and a waveform consistency function. The amount to be adjusted of the rail state optimization model within the two-stage segment length interval is an integer multiple of the level difference, and the n-order difference function of the residual deviation of the neighborhood point is a recursive formula , the recursive formula is given by Get, where is the sleeper number of the rail to be optimized, , is a natural number, , m is the number of sleepers contained in the rail to be optimized, l is the number of sleeper intervals, ,and , the waveform consistency function is ,in, is the difference, is the number of level differences at point i, is the optimal real number solution of the second stage to be adjusted at point i and the corresponding first stage of poorer; S11: Based on the n-order difference function of the residual deviation of the neighborhood points, the waveform consistency function, the limit of the amount of adjustment to be made for the sleeper fasteners, and the constraint boundary value, an inequality constraint model for the two-stage rail state optimization is established; S12: Solve the objective function and inequality constraint model of the second stage by integer programming to obtain the sleeper fastener adjustment amount of the integer multiple of the fastener material difference; S13: Process the rails in each segment length interval according to S10-S12 until the processing of the rails to be optimized is completed, and the optimal differential integer solution for rail adjustment is obtained.

2. A two-stage optimization method for rail state control according to claim 1, characterized in that: In step S1, the first preset length is greater than or equal to the longest reference chord length used in the rail state optimization model, the first stage segmentation is to move the segmentation according to the first preset overlapping interval, the first preset overlapping interval is the first overlapping length of adjacent basic planning units, and the first overlapping length is greater than or equal to the length of one sleeper interval.

3. A two-stage optimization method for rail state control according to claim 1, 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.

4. A two-stage optimization method for rail state control according to claim 1, 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.

5. A two-stage optimization method for rail state control according to claim 1, 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.

6. A two-stage optimization method for rail state control according to claim 1, 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.

7. A two-stage optimization method for rail state control according to claim 1, 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.

8. A two-stage optimization method for rail state control according to claim 1, characterized in that: 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, and the smoothing information includes one or more of a poor deviation and a deviation change rate.

9. A two-stage optimization method for rail state control according to claim 1, characterized in that: In step S9, the second preset length is greater than or equal to the length of the nth-order difference function of the residual deviation of the neighborhood points and the waveform consistency function used in the rail state optimization model, the second stage segmentation is moved segmentation according to the second preset overlapping interval, the second preset overlapping interval is the second overlapping length of adjacent basic planning units, and the second overlapping length is greater than or equal to the length of one sleeper interval.

10. A two-stage optimization method for rail state control according to claim 1, characterized in that: In step S10, the grade difference is one of the specifications of the rail fastener adjustment material of 0.5mm, 1.0mm, 1.5mm, and 2.0mm.

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