Rail laying line shape fine adjustment method and device with smoothness and design line shape cooperative control

Abstract

The application provides a track-laying line shape fine adjustment method and device for smoothness and design line shape cooperation control, relates to the technical field of bridge track-laying line shape optimization, and comprises the following steps: acquiring bridge line shape data and design line shape data; limiting the order of Fourier series according to the sensitive wavelength of vehicle dynamics, constructing the Fourier series of the bridge line shape data through the order, and obtaining a track line shape fitting model; constructing a deviation index function of the Fourier series coefficient of the track line shape fitting model, the smoothness index of a driving vehicle and the design line shape data, and obtaining a multi-objective optimization function; taking the difference between the bridge line shape data and the design line shape data as a nonlinear constraint, solving the multi-objective optimization function through a fuzzy maximum minimum algorithm, and obtaining optimal Fourier series coefficients; inputting the optimal Fourier series coefficients into the track line shape fitting model for target track-laying line shape fine adjustment, and constructing a target track-laying line shape scheme. The application solves the smoothness problem of train operation.

Description

Technical Field

[0001] This invention relates to the field of bridge track laying alignment optimization technology, and more specifically, to a method and apparatus for fine-tuning track laying alignment by coordinating smoothness and design alignment control. Background Technology

[0002] In existing bridge track alignment optimization technologies, due to factors such as construction errors, structural creep, temperature effects, and operational loads, the track alignment on the bridge often deviates from the design alignment, requiring correction through track fine-tuning. Existing methods typically determine track height adjustment based on measured bridge alignment data, using discrete point correction or low-order function fitting. However, these methods primarily focus on the geometric deviation between the alignment and the design alignment, making it difficult to simultaneously consider the overall smoothness requirements of the alignment. This can easily lead to abrupt changes in alignment in local areas, resulting in train operation stability issues.

[0003] Therefore, there is an urgent need for a method and device for fine-tuning the track laying alignment that coordinates smoothness and design alignment control, which would solve the problem of train running stability. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for fine-tuning track laying alignment through coordinated control of smoothness and design alignment, thereby improving the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows: Firstly, this application provides a method for fine-tuning track laying alignment by coordinating smoothness and design alignment control, including: Obtain the measured alignment data and design alignment data of the bridge; The order of the Fourier series is determined by the preset vehicle dynamics sensitive wavelength. The Fourier series is constructed by applying the order to the bridge alignment data to obtain the track alignment fitting model. The Fourier series coefficients of the track alignment fitting model, the ride comfort index of the vehicle, and the deviation index function of the design alignment data are used to construct a multi-objective optimization function. The difference between the bridge alignment data and the preset target track alignment elevation is used as a nonlinear constraint. The multi-objective optimization function is solved by the fuzzy minima algorithm to obtain the optimal Fourier series coefficients. The optimal Fourier series coefficients are input into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

[0005] Secondly, this application also provides a track laying alignment fine-tuning device for coordinated control of ride comfort and design alignment, including: The acquisition module is used to acquire the measured and designed alignment data of the bridge. The limiting module is used to limit the order of the Fourier series according to the preset vehicle dynamics sensitive wavelength, and to construct the Fourier series of the bridge alignment data by the order to obtain the track alignment fitting model. The construction module is used to construct the Fourier series coefficients of the track alignment fitting model, the ride comfort index of the driving vehicle, and the deviation index function of the design alignment data to obtain a multi-objective optimization function; The solution module is used to solve the multi-objective optimization function by using the difference between the bridge alignment data and the preset target track alignment elevation as a nonlinear constraint, and obtaining the optimal Fourier series coefficients. The fine-tuning module is used to input the optimal Fourier series coefficients into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

[0006] The beneficial effects of this invention are as follows: This invention limits the order of the Fourier series by the vehicle dynamics-sensitive wavelength, thereby eliminating high-frequency interference signals unrelated to vehicle dynamics. This simplifies the complexity of the alignment fitting model while maintaining fitting accuracy, thus improving the efficiency and rationality of track alignment construction. Based on this, a multi-objective optimization function is used to fuse the running vehicle and the designed alignment data, incorporating smoothness and deviation indices. This achieves a synergistic consideration of the two core objectives: track alignment conforming to design requirements and ensuring ride comfort. Simultaneously, it achieves coordinated control of geometric alignment accuracy and vehicle ride comfort. Subsequently, a fuzzy minima algorithm is used to handle nonlinear constraints and multi-objective optimization, quickly obtaining the optimal track laying alignment adapted to the actual working state of the bridge. This achieves a balanced optimization of track smoothness and design deviation, effectively avoiding the subjective defects in the target weight setting of traditional weighted methods. In summary, this invention solves the problem of train running stability.

[0007] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0008] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0009] Figure 1This is a schematic diagram of the track laying alignment fine-tuning method for coordinated control of smoothness and design alignment as described in an embodiment of the present invention; Figure 2 This is a schematic diagram of the bridge alignment data and the design alignment data described in the embodiments of the present invention; Figure 3 This is a schematic diagram of the power spectral density at driving speeds of 200km / h, 250km / h, 300km / h, and 350km / h in an embodiment of the present invention; Figure 4 This is a schematic diagram of the optimal Fourier series coefficients described in an embodiment of the present invention; Figure 5 This is a schematic diagram of the track laying alignment after fine-tuning as described in this embodiment of the invention; Figure 6 This is a schematic diagram of the track laying alignment fine-tuning equipment for coordinated control of smoothness and design alignment as described in an embodiment of the present invention.

[0010] The markings in the diagram are: 800, track laying alignment fine-tuning equipment for coordinated control of smoothness and design alignment; 801, processor; 802, memory; 803, multimedia component; 804, I / O interface; 805, communication component. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0012] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0013] In the actual engineering environment of track laying and subsequent operation of long-span high-speed railway bridges, the actual bridge alignment inevitably deviates from the initial design alignment due to the combined effects of multiple factors such as bridge construction errors, concrete structure creep and shrinkage, periodic temperature changes, and long-term effects of train operation loads. Traditional track alignment fine-tuning techniques often use discrete point-by-point correction or low-order polynomial fitting to determine the track height adjustment, focusing only on controlling the geometric deviation between the alignment and the design alignment. This can easily lead to local abrupt changes in track alignment, short-wave irregularities, and other phenomena, which in turn cause increased vertical vibration of the train body and excessive centrifugal acceleration during train operation, ultimately resulting in poor train running stability and affecting passenger comfort.

[0014] Example 1: This embodiment provides a method for fine-tuning track laying alignment by coordinating smoothness and design alignment control.

[0015] See Figure 1 The figure shows that the method includes steps S1 to S5, including: S1: Obtain the measured and designed alignment data of the bridge; like Figure 2 As shown, this step utilizes professional measuring equipment and methods such as high-precision control networks, deflectometers, and displacement sensors to accurately measure the tracks on long-span high-speed railway bridges, collecting elevation data of the actual bridge alignment and retrieving the design alignment elevation data corresponding to the bridge tracks. This embodiment specifically uses a 532m main span cable-stayed bridge for both road and rail use as an example.

[0016] S2: The order of the Fourier series is limited according to the preset vehicle dynamics sensitive wavelength. The Fourier series is constructed on the bridge alignment data by the order to obtain the track alignment fitting model. To clarify the specific method for obtaining the vehicle dynamics-sensitive wavelength, step S2 includes S20 to S23, specifically: S20: Obtain random irregularity samples of the track; In this step, the random track irregularity samples are inverted using the German low-interference spectrum to generate multiple sets of random track irregularity samples under actual track engineering conditions.

[0017] S21: A dynamic model is constructed based on the coupling relationship between high-speed vehicles and track structures; In this step, a high-speed vehicle-track coupled dynamics model is built by using a multibody dynamics simulation model (SIMPACK), which considers the parameters of the high-speed vehicle, the parameters of the track structure, and the interaction between the high-speed vehicle and the track structure. The dynamics model is used to realistically reproduce the dynamic interaction characteristics between the vehicle and the track during train operation.

[0018] S22: The random track irregularity sample is used as an excitation input into the dynamic model to calculate the vertical vibration acceleration of the vehicle body and obtain the power spectral density; like Figure 3 As shown, the random irregularity sample of the track is used as an external excitation input into the dynamic model. Simulation calculations are carried out at four typical train speeds of 200km / h, 250km / h, 300km / h and 350km / h to obtain the vertical vibration acceleration of the vehicle body at different speeds. Then, the normalized power spectral density is calculated based on the vertical vibration acceleration of the vehicle body at different speeds to obtain the final power spectral density.

[0019] S23: Based on the distribution characteristics of the power spectral density, identify the dominant wavelength range that causes the vertical acceleration response of the vehicle body, and determine the vehicle dynamics sensitive wavelength.

[0020] In this step, the peak distribution and energy concentration characteristics of the power spectral density of the vehicle body's vertical vibration acceleration are analyzed. Based on the peak distribution and energy concentration characteristics, the dominant wavelength range that triggers a significant vertical vibration response of the vehicle body is identified. The calculation results at typical speeds of 200 km / h, 250 km / h, 300 km / h, and 350 km / h are integrated. Specifically, at 200 km / h, the sensitive wavelength range is 25–120 m; at 250 km / h, it is 40–150 m; at 300 km / h, it is 60–170 m; and at 350 km / h, it is 70–200 m. Therefore, 25–200 m is determined to be the sensitive wavelength range for vehicle body vertical acceleration dynamics, which is the vehicle dynamics sensitive wavelength range.

[0021] To clarify the specific method for obtaining the trajectory alignment fitting model, step S2 includes S24 to S26, specifically: S24: The order of the Fourier series is limited according to the maximum value of the vehicle dynamics sensitive wavelength to obtain a finite-order Fourier series; In this step, based on the maximum value of the vehicle dynamics sensitive wavelength of 200m as the control threshold, the minimum wavelength after Fourier series fitting is set to be greater than the control threshold as the control principle. According to the control principle, combined with the main span length of the bridge and the line parameters, the reasonable order of the Fourier series is calculated and determined to obtain a finite-order Fourier series. The finite-order Fourier series avoids the appearance of short-wavelength components in the fitted line that would cause severe vibration of the vehicle body, thereby ensuring the smoothness of the ride.

[0022] S25: Based on the finite-order Fourier series, the elevation curve along the mileage direction is superimposed with the sine wave and cosine wave harmonic components through a preset Fourier series fitting model to obtain the track alignment fitting function. In this step, based on the finite-order Fourier series, the constant term and harmonic components of sine and cosine waves of different orders are superimposed using the formula of the Fourier series fitting model to construct a track alignment fitting function with the track mileage as the independent variable and the track elevation as the dependent variable. The track alignment fitting function is used to characterize the mathematical relationship between the track elevation curve and each harmonic component.

[0023] The expression for the Fourier series fitting model is: ; In the above formula, This is the track elevation fitting curve along the track mileage direction. For the fitting constant term, For the corresponding wavelength cosine wave amplitude, For the first The wavelength of the first harmonic. The harmonic order number ( ), For cosine trigonometric functions, The fundamental angular frequency, For orbital mileage, For the corresponding wavelength The amplitude of the sine wave, It is a sine trigonometric function. is the fitting order of the Fourier series.

[0024] Wherein, the harmonic order number , No. The wavelength of the first harmonic is .

[0025] S26: Fit the bridge alignment data based on the track alignment fitting function to construct a track alignment fitting model.

[0026] In this step, the measured bridge alignment data is fitted with a high-precision model based on the track alignment fitting function, and finally a track alignment fitting model that adapts to the bridge track characteristics and meets the requirements of vehicle dynamics ride comfort control is constructed.

[0027] S3: Construct a multi-objective optimization function by combining the Fourier series coefficients of the track alignment fitting model, the ride comfort index of the vehicle, and the deviation index function of the design alignment data; To clarify the specific method for obtaining the multi-objective optimization function, step S3 includes S31 to S35, specifically: S31: Input the Fourier series coefficients into the Fourier series fitting model to perform track alignment fitting calculation and obtain the target track alignment elevation; In this step, the Fourier series coefficients of the determined order in the track alignment fitting model are input into the Fourier series fitting model to perform track alignment fitting calculations point by point along the track mileage direction, so as to obtain the target track alignment elevation.

[0028] S32: Based on the completed bridge alignment data, construct a ride comfort index for the target track alignment elevation and the traveling vehicle to obtain a ride comfort constraint index; To clarify the specific method for obtaining the smoothness constraint index, step S32 includes S321 to S323, specifically: S321: Based on the line mileage of the completed bridge alignment data, the second derivative of the target track alignment elevation is obtained by taking the second derivative of the track alignment. In this step, the second derivative of the target track alignment elevation is calculated using the track mileage as the independent variable, yielding the second derivative of the track alignment. Under engineering conditions with a small track slope angle (typically no greater than 3°, corresponding to a slope of no more than 5.24%), the track alignment curvature... It can be approximated as the second derivative.

[0029] The expression for the second derivative of the orbital shape is: ; In the above formula, For orbital curvature, For approximate equality, It is the second derivative. The differential symbol, For the track at mileage The vertical elevation value at that location. This refers to the route mileage.

[0030] S322: The second derivative of the track curve is used as the track curvature. The centrifugal acceleration of the vehicle is calculated by combining the track curvature with the speed of the moving vehicle. In this step, based on the principles of vehicle dynamics, the second derivative of the track alignment is approximated as the track curvature. The track curvature is then correlated with the train speed to obtain the centrifugal acceleration of the vehicle at the corresponding mileage position. The magnitude of the centrifugal acceleration represents the normal inertial force of the vehicle when it is moving along a curve, directly reflecting the smoothness of the track. The smaller the acceleration, the better the track smoothness.

[0031] The expression for the centrifugal acceleration of the vehicle is: ; In the above formula, For the centrifugal acceleration of the vehicle, For vehicle operating speed, For orbital curvature.

[0032] S323: Discretize and sum the square of the vehicle's centrifugal acceleration over the entire track mileage to obtain the ride comfort constraint index.

[0033] In this step, based on the proportional relationship between the vehicle's centrifugal acceleration and the second derivative of the track alignment, the square of the vehicle's centrifugal acceleration is divided into several discrete mileage points over the entire track mileage. The square values ​​of the centrifugal acceleration at each discrete mileage point are summed to obtain a smoothness constraint index. The smoothness constraint index is used to quantify and constrain track smoothness. The smaller the smoothness constraint index, the better the overall track smoothness.

[0034] The proportional relationship between the centrifugal acceleration and the second derivative of the orbital shape is expressed as follows: ; In the above formula, For the centrifugal acceleration of the vehicle, The sign is proportional. It is the second derivative. This is a function of track elevation. This refers to the route mileage.

[0035] The expression for the smoothness constraint index is: ; In the above formula, As a smoothness constraint index, This is the definite integral operator. The total length of the track line. This refers to the starting mileage location of the rail line. The second derivative of the orbital elevation function is given by . It is a second-order differential operator. The optimized target track alignment and elevation. For orbital mileage, To optimize the variable vector, For the differential unit of orbital mileage; The discrete form of the smoothness constraint index is: ; In the above formula, As a smoothness constraint index, For the summation operator, This represents the total number of measuring points on the track. For discrete measurement points, For the first At each measuring point Discrete derivative of order 1, The optimized target track alignment and elevation. For orbital mileage, To optimize the variable vector.

[0036] Among them, the optimized target trajectory alignment elevation The target track alignment elevation.

[0037] S33: The mean square deviation is calculated based on the elevation difference between the target track alignment elevation and the design alignment data at the same mileage position to obtain the degree of alignment similarity. In this step, at the same mileage location, the difference between the target track alignment elevation and the design alignment data is compared. The difference is quantified using the mean square deviation method to obtain the degree of closeness between the optimized alignment and the design alignment. The degree of closeness is used to measure the degree of fit between the optimized alignment and the design alignment.

[0038] S34: Discretize the linear similarity over the entire track mileage to obtain the design fit index; In this step, the degree of alignment closeness is discretized and accumulated along the entire track mileage to obtain the design alignment fit index, which is used to measure the magnitude of the deviation between the optimized alignment and the design alignment.

[0039] The expression for the design fit index is: ; In the above formula, To design fit index, This is the definite integral operator. The total length of the track line. This refers to the starting mileage location of the rail line. The optimized target track alignment and elevation. For orbital mileage, To optimize the variable vector, To measure the elevation function of the completed bridge, For the differential unit of orbital mileage; The discrete form of the design fit index is: ; In the above formula, To design fit index, For the summation operator, This represents the total number of measuring points on the track. For discrete measurement points, To optimize the target trajectory shape in the first... Each measuring point The elevation value at that location, The actual measured alignment of the completed bridge is in the first... Each measuring point The elevation value at that location, For orbital mileage, To optimize the variable vector.

[0040] Among them, the measured bridge alignment elevation function is the elevation function of the measured completed bridge alignment.

[0041] S35: Based on the smoothness constraint index and the design fit index, extreme value processing is performed and a function is constructed to obtain a multi-objective optimization function.

[0042] In this step, a dual-objective extremum process is performed based on the smoothness constraint index and the design fit index. The minimization of the smoothness constraint index and the minimization of the design fit index are taken as dual optimization objectives. The smoothness constraint index and the design fit index are combined as the objective function vector to construct a dual-objective collaborative optimization function that takes into account both track ride smoothness and design alignment consistency. The dual-objective collaborative optimization function is a multi-objective optimization function.

[0043] S4: Using the difference between the bridge alignment data and the preset target track alignment elevation as a nonlinear constraint, the multi-objective optimization function is solved by the fuzzy minima algorithm to obtain the optimal Fourier series coefficients. To clarify the specific method for obtaining the optimal Fourier series coefficients, step S4 includes S41 to S43, specifically: S41: Calculate the elevation difference at the same mileage position based on the bridge alignment data and the target track alignment elevation to obtain the track height adjustment amount; In this step, based on the line mileage, the difference between the target track alignment elevation and the measured bridge alignment data at the same mileage position is calculated. The difference result is the track height adjustment amount, which is used to characterize the elevation adjustment range that can be implemented during track construction.

[0044] The expression for the track elevation adjustment amount is: ; In the above formula, For track height adjustment, The target track alignment elevation, For orbital mileage, To optimize the variable vector, This refers to the measured alignment data of the completed bridge.

[0045] S42: Using the track elevation adjustment amount as a nonlinear constraint, the multi-objective optimization function is constructed using the fuzzy minima algorithm to obtain a multi-objective optimization model; In this step, a maximum height adjustment limit is set according to the allowable adjustment range of the ballastless track structure. The absolute value of the track height adjustment not exceeding the maximum allowable height adjustment limit of the ballastless track structure is used as a nonlinear constraint condition. Under this constraint condition, the smoothness constraint index and the design fit index in the multi-objective optimization function are combined using the fuzzy minima algorithm (Fminimax) to construct a multi-objective optimization model with minimizing the smoothness constraint index and the design fit index as dual optimization objectives, and minimizing the maximum value of the objective function under the constraint condition. The multi-objective optimization model realizes the equilibrium game solution of track running smoothness and design alignment consistency, ensuring that the track alignment optimization result meets the structural feasibility requirements of construction adjustment.

[0046] The constraint that track construction adjustments must not exceed the allowable range for ballastless track structures is as follows: ; In the above formula, This is the absolute value of the track elevation adjustment. For orbital mileage, This represents the maximum allowable track height adjustment for ballastless track structures.

[0047] The maximum allowable track height adjustment of the ballastless track structure is determined based on the adjustment capability of the ballastless track structure, and is taken as 20mm in this embodiment;

[0048] The optimization objective expression of the fuzzy minima algorithm (Fminimax) is: ; In the above formula, To minimize the operator, To maximize the operator, For a multi-objective optimization function, To optimize the variable vector.

[0049] S43: The multi-objective optimization model is iteratively solved according to the numerical optimization algorithm, and the optimal Fourier series coefficients are obtained by searching within the range of the nonlinear constraints.

[0050] like Figure 4As shown, the multi-objective optimization model is iteratively calculated and converged according to the numerical optimization algorithm. Under the premise of satisfying the limit constraint of the orbital elevation adjustment, the Fourier series coefficient vector is automatically searched and updated. When the iteration converges and the objective function reaches the comprehensive optimum, the optimal Fourier series coefficient is output. The optimal Fourier series coefficient is used to determine the final target orbital shape.

[0051] S5: Input the optimal Fourier series coefficients into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

[0052] like Figure 5 As shown, the optimal Fourier series coefficients are input into the track alignment fitting model, and the target track laying alignment is refitted along the entire track mileage to generate a finely adjusted target track laying alignment. The target track laying alignment simultaneously meets the requirements of track smoothness control, the minimum design alignment deviation, and the track elevation adjustment within the allowable construction range. Combined with the elevation adjustment at each mileage position, a track laying alignment fine-tuning scheme for on-site construction is formed.

[0053] The expression for the fine-tuning of the target track laying profile is: ; In the above formula, Lay the track alignment to the target. The optimal target trajectory alignment elevation function. For orbital mileage, These are the optimal Fourier series coefficients.

[0054] Example 2: This embodiment provides a track laying alignment fine-tuning device for coordinated control of smoothness and design alignment, the device comprising: The acquisition module is used to acquire the measured and designed alignment data of the bridge. The limiting module is used to limit the order of the Fourier series according to the preset vehicle dynamics sensitive wavelength, and to construct the Fourier series of the bridge alignment data by the order to obtain the track alignment fitting model. To clearly define the specific methods for obtaining the modules, the following are included: The limiting unit is used to limit the order of the Fourier series according to the maximum value of the vehicle dynamics sensitive wavelength, so as to obtain a finite-order Fourier series. The superposition unit is used to superimpose the elevation curve along the mileage direction with the sine wave and cosine wave harmonic components according to the finite-order Fourier series and through a preset Fourier series fitting model to obtain the track alignment fitting function. The fitting unit is used to fit the bridge alignment data based on the track alignment fitting function to construct a track alignment fitting model.

[0055] The construction module is used to construct the Fourier series coefficients of the track alignment fitting model, the ride comfort index of the driving vehicle, and the deviation index function of the design alignment data to obtain a multi-objective optimization function; To clarify the specific methods for obtaining the building modules, the following are included: The calculation unit is used to input the Fourier series coefficients into the Fourier series fitting model to perform track alignment fitting calculation and obtain the target track alignment elevation. A smoothness construction unit is used to construct smoothness indicators for the target track alignment elevation and the traveling vehicle based on the bridge alignment data, thereby obtaining smoothness constraint indicators; To clarify the specific methods for obtaining smooth building blocks, the following are included: The derivative subunit is used to perform a second derivative of the target track alignment elevation based on the line mileage of the bridge alignment data to obtain the second derivative of the track alignment. The calculation subunit is used to take the second derivative of the track shape as the track curvature, and calculate the vehicle centrifugal acceleration by combining the track curvature with the speed of the moving vehicle. The summation subunit is used to discretize and sum the square of the vehicle's centrifugal acceleration over the entire track mileage to obtain the ride comfort constraint index.

[0056] The deviation calculation unit is used to perform mean square deviation measurement and calculation based on the elevation difference between the target track alignment elevation and the design alignment data at the same mileage position to obtain the degree of alignment similarity. A discrete processing unit is used to discretize the linear similarity over the entire track mileage to obtain the design fit index. The function construction unit is used to perform extremum processing and function construction based on the smoothness constraint index and the design fit index to obtain a multi-objective optimization function.

[0057] The solution module is used to solve the multi-objective optimization function by using the difference between the bridge alignment data and the preset target track alignment elevation as a nonlinear constraint, and obtaining the optimal Fourier series coefficients. To clarify the specific methods for obtaining the solution module, the following are included: The difference unit is used to calculate the elevation difference at the same mileage position based on the bridge alignment data and the target track alignment elevation to obtain the track height adjustment amount. The model building unit is used to construct the multi-objective optimization function by taking the track elevation adjustment amount as a nonlinear constraint and using the fuzzy minimax algorithm to obtain a multi-objective optimization model; The iterative solution unit is used to iteratively solve the multi-objective optimization model according to the numerical optimization algorithm, and to perform the optimal search for Fourier series coefficients within the range of the nonlinear constraints to obtain the optimal Fourier series coefficients.

[0058] The fine-tuning module is used to input the optimal Fourier series coefficients into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

[0059] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0060] Example 3: Corresponding to the above method embodiments, this embodiment also provides a track laying alignment fine-tuning device for coordinated control of ride comfort and design alignment. The track laying alignment fine-tuning device for coordinated control of ride comfort and design alignment described below and the track laying alignment fine-tuning method for coordinated control of ride comfort and design alignment described above can be referred to in correspondence with each other.

[0061] Figure 6 This is a block diagram of a track-laying alignment fine-tuning device 800 that coordinates smoothness and design alignment control according to an exemplary embodiment. Figure 6 As shown, the track laying alignment fine-tuning device 800, which coordinates smoothness and design alignment control, may include: a processor 801 and a memory 802. The track laying alignment fine-tuning device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0062] The processor 801 controls the overall operation of the track laying alignment fine-tuning device 800, which coordinates smoothness and design alignment control, to complete all or part of the steps in the aforementioned track laying alignment fine-tuning method. The memory 802 stores various types of data to support the operation of the track laying alignment fine-tuning device 800, including, for example, instructions for any application or method operating on the track laying alignment fine-tuning device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as keyboards, mice, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the track-laying alignment fine-tuning device 800, which coordinates smoothness and design alignment control, and other devices. Wireless communication methods include Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0063] In an exemplary embodiment, the track laying alignment fine-tuning device 800 for smoothness and design alignment coordinated control can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the track laying alignment fine-tuning method for smoothness and design alignment coordinated control described above.

[0064] Example 4: Corresponding to the above method embodiments, this embodiment also provides a medium. The medium described below and the track laying alignment fine-tuning method for coordinated control of smoothness and design alignment described above can be referred to in correspondence.

[0065] A medium storing a computer program, which, when executed by a processor, implements the steps of the track laying alignment fine-tuning method for coordinated control of smoothness and design alignment as described in the above method embodiments.

[0066] The medium can specifically be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0067] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0068] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for fine-tuning track laying alignment through coordinated control of smoothness and design alignment, characterized in that, include: Obtain the measured alignment data and design alignment data of the bridge; The order of the Fourier series is determined by the preset vehicle dynamics sensitive wavelength. The Fourier series is constructed by applying the order to the bridge alignment data to obtain the track alignment fitting model. The Fourier series coefficients of the track alignment fitting model, the ride comfort index of the vehicle, and the deviation index function of the design alignment data are used to construct a multi-objective optimization function. The difference between the bridge alignment data and the preset target track alignment elevation is used as a nonlinear constraint. The multi-objective optimization function is solved by the fuzzy minima algorithm to obtain the optimal Fourier series coefficients. The optimal Fourier series coefficients are input into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

2. The method for fine-tuning track laying alignment by coordinating smoothness and design alignment control according to claim 1, characterized in that, Based on a preset vehicle dynamics sensitive wavelength, the order of the Fourier series is determined. The Fourier series is then used to construct the track alignment model from the bridge alignment data, including: The order of the Fourier series is limited based on the maximum value of the vehicle dynamics sensitive wavelength, resulting in a finite-order Fourier series. Based on the finite-order Fourier series, the elevation curve along the mileage direction is superimposed with the sine and cosine harmonic components through a preset Fourier series fitting model to obtain the track alignment fitting function. The bridge alignment data are fitted using the aforementioned track alignment fitting function to construct a track alignment fitting model.

3. The method for fine-tuning track laying alignment by coordinating smoothness and design alignment control according to claim 1, characterized in that, By constructing the Fourier series coefficients of the track alignment fitting model, the ride comfort index of the vehicle, and the deviation index function of the design alignment data, a multi-objective optimization function is obtained, including: The Fourier series coefficients are input into the Fourier series fitting model to perform track alignment fitting calculations and obtain the target track alignment elevation. Based on the bridge alignment data, a ride comfort index is constructed for the target track alignment elevation and the traveling vehicle to obtain a ride comfort constraint index. The mean square deviation is calculated based on the elevation difference between the target track alignment elevation and the design alignment data at the same mileage position to obtain the degree of alignment similarity. The degree of linear similarity is discretized over the entire track mileage to obtain the design fit index; Based on the smoothness constraint index and the design fit index, extreme value processing and function construction are performed to obtain a multi-objective optimization function.

4. The method for fine-tuning track laying alignment by coordinating smoothness and design alignment control according to claim 3, characterized in that, Based on the bridge alignment data, ride comfort indices are constructed for the target track alignment elevation and the vehicles in operation, resulting in ride comfort constraint indices, including: The second derivative of the target track alignment elevation is obtained by taking the second derivative of the track alignment elevation based on the track mileage of the completed bridge alignment data. The second derivative of the track shape is used as the track curvature. The centrifugal acceleration of the vehicle is calculated by combining the track curvature with the speed of the moving vehicle. The smoothness constraint index is obtained by discretizing and summing the square of the vehicle's centrifugal acceleration over the entire track mileage.

5. The method for fine-tuning track laying alignment by coordinating smoothness and design alignment control according to claim 1, characterized in that, Using the difference between the bridge alignment data and the preset target track alignment elevation as a nonlinear constraint, the multi-objective optimization function is solved using a fuzzy minima algorithm to obtain the optimal Fourier series coefficients, including: The elevation difference at the same mileage position is calculated based on the bridge alignment data and the target track alignment elevation to obtain the track height adjustment amount. Using the track elevation adjustment amount as a nonlinear constraint, the multi-objective optimization function is constructed using the fuzzy minima algorithm to obtain a multi-objective optimization model; The multi-objective optimization model is iteratively solved using a numerical optimization algorithm. Within the range of the nonlinear constraints, the optimal Fourier series coefficients are obtained through an optimal search.

6. A track laying alignment fine-tuning device for coordinated control of smoothness and design alignment, characterized in that, include: The acquisition module is used to acquire the measured and designed alignment data of the bridge. The limiting module is used to limit the order of the Fourier series according to the preset vehicle dynamics sensitive wavelength, and to construct the Fourier series of the bridge alignment data by the order to obtain the track alignment fitting model. The construction module is used to construct the Fourier series coefficients of the track alignment fitting model, the ride comfort index of the driving vehicle, and the deviation index function of the design alignment data to obtain a multi-objective optimization function; The solution module is used to solve the multi-objective optimization function by using the difference between the bridge alignment data and the preset target track alignment elevation as a nonlinear constraint, and obtaining the optimal Fourier series coefficients. The fine-tuning module is used to input the optimal Fourier series coefficients into the track alignment fitting model to fine-tune the target track laying alignment and construct the target track laying alignment scheme.

7. The track laying alignment fine-tuning device for coordinated control of smoothness and design alignment according to claim 6, characterized in that, The limiting module includes: The limiting unit is used to limit the order of the Fourier series according to the maximum value of the vehicle dynamics sensitive wavelength, so as to obtain a finite-order Fourier series. The superposition unit is used to superimpose the elevation curve along the mileage direction with the sine wave and cosine wave harmonic components according to the finite-order Fourier series and through a preset Fourier series fitting model to obtain the track alignment fitting function. The fitting unit is used to fit the bridge alignment data based on the track alignment fitting function to construct a track alignment fitting model.

8. The track laying alignment fine-tuning device for coordinated control of smoothness and design alignment according to claim 6, characterized in that, The building module includes: The calculation unit is used to input the Fourier series coefficients into the Fourier series fitting model to perform track alignment fitting calculation and obtain the target track alignment elevation. The smoothness construction unit is used to construct smoothness indicators for the target track alignment elevation and the traveling vehicle based on the bridge alignment data, and obtain smoothness constraint indicators. The deviation calculation unit is used to perform mean square deviation measurement and calculation based on the elevation difference between the target track alignment elevation and the design alignment data at the same mileage position to obtain the degree of alignment similarity. A discrete processing unit is used to discretize the linear similarity over the entire track mileage to obtain the design fit index. The function construction unit is used to perform extremum processing and function construction based on the smoothness constraint index and the design fit index to obtain a multi-objective optimization function.

9. The track laying alignment fine-tuning device for coordinated control of smoothness and design alignment according to claim 8, characterized in that, The smooth building block includes: The derivative subunit is used to perform a second derivative of the target track alignment elevation based on the line mileage of the bridge alignment data to obtain the second derivative of the track alignment. The calculation subunit is used to take the second derivative of the track shape as the track curvature, and calculate the vehicle centrifugal acceleration by combining the track curvature with the speed of the moving vehicle. The summation subunit is used to discretize and sum the square of the vehicle's centrifugal acceleration over the entire track mileage to obtain the ride comfort constraint index.

10. The track laying alignment fine-tuning device for coordinated control of smoothness and design alignment according to claim 6, characterized in that, The solution module includes: The difference unit is used to calculate the elevation difference at the same mileage position based on the bridge alignment data and the target track alignment elevation to obtain the track height adjustment amount. The model building unit is used to construct the multi-objective optimization function by taking the track elevation adjustment amount as a nonlinear constraint and using the fuzzy minimax algorithm to obtain a multi-objective optimization model; The iterative solution unit is used to iteratively solve the multi-objective optimization model according to the numerical optimization algorithm, and to perform the optimal search for Fourier series coefficients within the range of the nonlinear constraints to obtain the optimal Fourier series coefficients.

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