Track system matching parameter determination method, device and equipment and readable storage medium

Abstract

The invention provides a method, a device and equipment for determining matching parameters of a vehicle rail system and a readable storage medium, and relates to the technical field of vehicle rail coupling dynamics, a plurality of system parameters are utilized to carry out orthogonal experiment design, and a plurality of groups of experiment schemes containing different system parameters are obtained; sequentially endowing the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamic model to generate a plurality of experimental models; generating a plurality of track irregularity samples based on a random harmonic function method; respectively simulating each experimental model by using the plurality of samples to obtain a plurality of groups of dynamic response and acceleration data of each experimental model so as to calculate a safety index and a comfort index of each experimental model; the optimal experiment model is determined based on the safety index and the comfort index, and system parameters of the optimal experiment model are obtained.The method is used for determining the optimal train rail system key parameter matching scheme, and the train running safety and the riding comfort of passengers are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle-rail coupling dynamics, and in particular to a method, device, equipment and readable storage medium for determining matching parameters of a vehicle-rail system. Background Art

[0002] The selection of various parameters in the train track system will directly affect the safety of train operation and the comfort of passengers. Reasonable parameter values ​​can extend the service life of the track system, reduce maintenance, and reduce operating costs. It can also reduce vibration and noise during train operation and improve passenger comfort. With economic development and technological progress in the railway industry, the safety and comfort of train operation have received more and more attention. With the development of the rail transit equipment manufacturing industry, the track system parameters available for design and selection are more extensive, and the types of track components are more diverse. How to select the track system parameter values ​​and component types so that they can make the train the safest and most comfortable is worthy of more in-depth research. Summary of the invention

[0003] The purpose of the present invention is to provide a method, device, equipment and readable storage medium for determining vehicle-rail system matching parameters to improve the above-mentioned problems. In order to achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0004] In a first aspect, the present application provides a method for determining vehicle-track system matching parameters, comprising:

[0005] Obtain the vehicle-track system parameters that need to be optimized, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters;

[0006] Construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models;

[0007] Determine the irregularity parameters required for the vehicle-track system, and generate several track irregularity samples based on the random harmonic function method; use several samples to simulate each experimental model respectively, and obtain several groups of dynamic response and acceleration data of each experimental model;

[0008] The safety index of each experimental model is calculated based on several groups of dynamic responses; the comfort index of each experimental model is calculated based on several groups of acceleration data;

[0009] An optimal experimental model is determined based on the safety index and the comfort index, and system parameters of the optimal experimental model are obtained.

[0010] In a second aspect, the present application further provides a device for determining vehicle-track system matching parameters, comprising:

[0011] Experimental design module: obtain the vehicle-track system parameters that need to be optimized, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters;

[0012] Model building module: construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models;

[0013] Simulation module: determine the irregularity parameters required for the vehicle-track system, generate several track irregularity samples based on the random harmonic function method; use several samples to simulate each experimental model respectively, and obtain several groups of dynamic response and acceleration data of each experimental model;

[0014] Calculation module: the safety index of each experimental model is calculated based on several groups of dynamic responses; the comfort index of each experimental model is calculated based on several groups of acceleration data;

[0015] Selection module: Determine the optimal experimental model based on the safety index and the comfort index, and obtain the system parameters of the optimal experimental model.

[0016] In a third aspect, the present application further provides a device for determining vehicle-track system matching parameters, including:

[0017] Memory for storing computer programs;

[0018] A processor is used to implement the steps of the method for determining the vehicle-rail system matching parameters when executing the computer program.

[0019] In a fourth aspect, the present application further provides a readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for determining vehicle-track system matching parameters are implemented.

[0020] The beneficial effects of the present invention are:

[0021] 1. The present invention uses orthogonal experimental theory to conduct orthogonal experimental design on vehicle-track system parameters and component types, and finds the optimal vehicle-track system parameter matching solution, in order to improve driving safety and passenger comfort.

[0022] 2. The present invention selects the random parameter sample point set required for constructing track irregularity through number theory methods. Compared with the sample point set selected by the Monte Carlo method, the sample point set is more representative, and the track irregularity data simulated based on the sample point set is more real and effective.

[0023] 3. The present invention establishes a rigid-flexible coupling model of train tracks based on multi-body dynamics theory, and uses the probability density evolution method to calculate the dynamic reliability of the train-track system. Compared with the traditional method, the probability density evolution method requires fewer samples. Under the same sample size, its calculation accuracy is higher than that of the traditional method, which can greatly improve the calculation efficiency and save manpower and material costs.

[0024] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0026] Figure 1 It is a flow chart of a method for determining vehicle-rail system matching parameters according to an embodiment of the present invention;

[0027] Figure 2 This is a schematic diagram of track height unevenness data in an embodiment of the present invention;

[0028] Figure 3 This is a schematic diagram of track irregularity data in an embodiment of the present invention;

[0029] Figure 4 It is a schematic diagram of track horizontal unevenness data in an embodiment of the present invention;

[0030] Figure 5 This is a schematic diagram of track gauge irregularity data in an embodiment of the present invention;

[0031] Figure 6 Schematic diagram of a simulation model in an embodiment of the present invention;

[0032] Figure 7 A probability density distribution diagram obtained by performing a probability density evolution analysis on a group of vehicle body vertical accelerations according to an embodiment of the present invention;

[0033] Figure 8 It is a schematic diagram of the structure of a device for determining vehicle-rail system matching parameters according to an embodiment of the present invention;

[0034] Fig. 9It is a schematic diagram of the structure of a device for determining vehicle-rail system matching parameters described in an embodiment of the present invention.

[0035] Markings in the figure:

[0036] 800, a device for determining matching parameters of a vehicle-track system; 801, a processor; 802, a memory; 803, a multimedia component; 804, an I / O interface; 805, a communication component. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0038] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0039] Embodiment 1:

[0040] This embodiment provides a method for determining vehicle-track system matching parameters.

[0041] See also Figure 1 , the figure shows that the method includes:

[0042] S1. Obtain the vehicle-track system parameters that need to be optimized, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters;

[0043] In this embodiment, the vehicle-track system parameters used for orthogonal experimental design are:

[0044] (A) primary suspension vertical stiffness;

[0045] (B) primary suspension vertical damping;

[0046] (C) Secondary suspension vertical stiffness;

[0047] (D) Secondary suspension vertical damping;

[0048] (E) Fastener type;

[0049] (F) Type of sleeper;

[0050] The above six track parameters all include two values, so a six-factor two-level orthogonal experimental design is carried out for the above six track parameters, as shown in Table 1:

[0051] Table 1

[0052]

[0053] For the above six factors and two levels of experimental design, select L 20 (2 19 ) Orthogonal table arranges 20 orthogonal experiments, replacing 2 6 = 64 full experiments. The design of the experiment is shown in Table 2 (take the first 6 columns of the complete table):

[0054] Table 2

[0055]

[0056]

[0057] Table 2 shows 20 sets of experimental schemes with different system parameters.

[0058] Based on the above embodiments, the method further includes:

[0059] S2. Construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models;

[0060] Specifically, step S2 includes:

[0061] S21. The train body, bogie and wheelset are regarded as rigid bodies;

[0062] S22. The primary suspension between the wheelset and the bogie is simulated by springs and dampers, and the secondary suspension between the bogie and the car body is simulated by shock absorbers and spring bars. The car body and the bogie consider five degrees of freedom, namely, lateral movement, nodding, shaking, floating and rolling. The wheelset considers four degrees of freedom, namely, lateral movement, shaking, floating and rolling, for a total of 31 degrees of freedom.

[0063] S23. The rails and sleepers of the track system are regarded as flexible bodies, and the rails are simulated by Euler beams;

[0064] S24. The rails and sleepers are connected by linear springs and dampers, and the sleepers and ballast are connected by linear springs and dampers. The vehicle and the track are coupled through the wheel-rail contact relationship. The FASTSIM model is used to simulate the wheel-rail contact relationship to construct a multi-body rigid-flexible coupling dynamic model, such as Figure 6 As shown;

[0065] Based on the above embodiments, the method further includes:

[0066] S3. Determine the irregularity parameters required for the vehicle-track system, and generate a number of track irregularity samples based on the random harmonic function method; use the several samples to simulate each experimental model respectively to obtain several groups of dynamic response and acceleration data of each experimental model. In this embodiment, the dynamic response can be a dynamic parameter such as acceleration, velocity or displacement.

[0067] In this embodiment, the vehicle-track system considers four types of track irregularities, namely, track height irregularity z(t), track azimuth irregularity y(t), track horizontal irregularity θ(t) and track gauge irregularity v(t).

[0068] Specifically, step S3 includes:

[0069] S31. Select several discrete representative point sets using the square root sequence method in number theory;

[0070] Specifically, step S31 includes:

[0071] S311. Select s different prime numbers in ascending order and generate a prime number sequence {p1, p2, ..., p s}, that is, p1=2, p2=3... and so on, the number of prime numbers selected s=2(N-1)*4, where N represents the number of harmonic components in the random harmonic function;

[0072] S312. Generate n s-dimensional hypercube point sets based on the prime number sequence;

[0073] Among them, the kth s-dimensional hypercube point set X k It can be expressed as:

[0074]

[0075] In the formula, {} means to get the decimal value in the brackets.

[0076] S313. Using the hypersphere screening method to screen the n s-dimensional hypercube point sets, obtain m sample point sets;

[0077] Specifically, the hypersphere screening method is:

[0078]

[0079] In the formula, x k,j Represents the s-dimensional hypercube point set X k The jth element in , r0 represents the screening radius, and the value range of the screening radius r0 is when , the above formula loses its screening property.

[0080] The qth sample point set X after screening q It can be expressed as:

[0081] X q =(x q,1 ,x q,2 ,…,x q,s ),q=1,2,…,m; (3)

[0082] S314. Perform affine transformation on each sample point set in turn to generate a discrete representative point set with a smaller F-deviation, and obtain m discrete representative point sets;

[0083] Specifically, the method of affine transformation is:

[0084]

[0085] In the formula, x q,j Represents the qth sample point set X q The jth element in ||X q || represents the Euclidean norm (i.e. the magnitude of the vector), Represents x q,j The affine transformation result of , g represents the function;

[0086] g(r)=αr τ +b(τ≥0,τ∈Z);

[0087] In the formula, r represents ||X q || calculation result, β represents the scaling ratio near the origin, β = 0.6, Z represents a real number set, α represents a scaling factor, τ represents a power, and ρ represents a calculation parameter;

[0088] The qth discrete representative point set obtained after affine transformation for:

[0089]

[0090] S32. Generate a set of random wavelengths and random phases using each discrete representative point set to obtain several sets of random wavelengths and random phases:

[0091] Specifically, since the wavelength of track irregularity is usually in the range of 1m to 120m, let the lower cutoff wavelength λ0 = 1m and the upper cutoff wavelength λ M =120m, divide the wavelength between the upper and lower cutoff wavelengths into N equal parts, each wavelength corresponds to a phase, preferably, let N = 150;

[0092] λ0=λ1<λ2<λ3<…<λ N-1 <λ N =λ M ; (6)

[0093] Therefore, it is necessary to use m discrete representative point sets to randomly assign values ​​to each wavelength and phase. In this embodiment, the discrete representative point sets are used. Compute a random set of random wavelengths and random phases:

[0094]

[0095] In the formula, λ i represents the i-th random wavelength, Represents the qth discrete representative point set The i-1th element in θ i represents the i-th random phase;

[0096] S33. Based on the random harmonic function, a calculation model of track irregularity parameters is constructed, namely, the calculation models of track height irregularity z(t), track direction irregularity y(t), track horizontal irregularity θ(t) and track gauge irregularity v(t):

[0097]

[0098] In the formula, S v ,S a ,S c ,S g are the power spectral density functions of track height irregularity, direction irregularity, track horizontal irregularity and track gauge irregularity, They represent the random phase, spatial frequency and the difference between adjacent spatial frequencies of the track height irregularity. and They represent the random phase, spatial frequency and the difference between adjacent spatial frequencies with uneven directions. and They represent the random phase, spatial frequency and the difference between adjacent spatial frequencies of track horizontal irregularity. and They represent the random phase, spatial frequency and the difference of adjacent spatial frequencies of track gauge irregularity respectively;

[0099] In this embodiment, the four track irregularity parameter samples calculated are as follows: Figure 2-Figure 5 shown.

[0100] Specifically, the calculation method of the spatial frequency of the track height unevenness is:

[0101]

[0102] In the formula, λ i z The wavelength represents the unevenness of the track, v represents the speed, and the value is 120km / h;

[0103] Similarly, the calculation method of spatial frequency of directional unevenness, track horizontal unevenness and track gauge unevenness is the same as the above formula.

[0104] S34. Input each set of random wavelength and random phase into the calculation model of track irregularity parameters in turn to generate several samples of irregularity parameters;

[0105] In this embodiment, a set of track irregularity data can be calculated based on a set of discrete representative point sets, and therefore, m samples θ of track irregularity data can be obtained based on m sets of discrete representative point sets. The 20 sets of experimental models are simulated using the m samples to obtain 20 sets of dynamic response and acceleration data corresponding to each set of experimental models.

[0106] Based on the above embodiments, the method further includes:

[0107] S4. Calculate the safety index of each experimental model based on m groups of dynamic responses, and calculate the comfort index of each experimental model based on several groups of acceleration data;

[0108] Specifically, step S4 includes:

[0109] S41. Construct a generalized probability density evolution model, and substitute each group of dynamic responses and their first-order derivatives into the generalized probability density evolution model in turn;

[0110] Specifically, the generalized probability density evolution model is:

[0111]

[0112] Where Z(θ,t) represents the dynamic response time history function, is its first-order derivative, Θ represents the set of irregularity parameters; P ZΘ (u,θ,t) represents the joint probability density when the dynamic response Z takes the value u and Θ takes the value θ at time t.

[0113] S42. Use the TVD bilateral difference format to numerically solve the generalized probability density evolution model and generate m probability density distribution values:

[0114]

[0115] Where u0 is the initial value of u, δ(·) is the step function, and t0 represents the initial time.

[0116] S43. Sum the probability density distribution values ​​of m to obtain the numerical solution P of the probability density distribution of the experimental model Z (u,t):

[0117]

[0118] Where P Z (u, t) represents the probability that the dynamic response Z takes the value u at time t, such as Figure 7 As shown, the figure shows the probability density distribution diagram obtained by the numerical solution of the calculated probability density distribution when the dynamic response is the vertical acceleration of the vehicle body.

[0119] S44. Calculating the safety index of the experimental model based on the numerical solution of the probability density distribution;

[0120] Specifically, determine the safety region Ω of the dynamic response s , according to the security domain Ω s Calculate the dynamic reliability of the vehicle-track system:

[0121]

[0122] Where Pr represents the probability function, Z(t) represents the dynamic response of the vehicle-track system at time t, and Z(t)∈Ω s Indicates that the dynamic response Z(t) is in the safe region Ω s ;

[0123] In this embodiment, the power reliability is used to judge the safety index of the experimental model, and the higher the power reliability is, the stronger the safety is.

[0124] Specifically, the step S4 further includes:

[0125] S45. Perform frequency weighting on the acceleration data in each direction to obtain a corresponding acceleration signal;

[0126] Specifically, the directions of the acceleration data include horizontal X, longitudinal Y and vertical Z;

[0127] Using the lateral acceleration weighting function W x Perform frequency weighting on the lateral acceleration to obtain the lateral acceleration signal

[0128] Using the longitudinal acceleration weighting function W y Perform frequency weighting on the longitudinal acceleration to obtain the longitudinal acceleration signal

[0129] Using the vertical acceleration weighting function W z Perform frequency weighting on the vertical acceleration to obtain the vertical acceleration signal

[0130] S46. Calculate the weighted root mean square value of the acceleration signal in each direction within a preset time period;

[0131] In this embodiment, the preset time length is 5s, and the weighted root mean square value of the acceleration signal in each direction within every 5s is calculated. Preferably, the time span is 5min (i.e., 300 5-second time windows), and the average of the weighted root mean square value of the acceleration signal in each direction within 5min is calculated, which are respectively

[0132] S47. Calculate the comfort index of the experimental model using the weighted root mean square value of each direction;

[0133] Specifically, take The 95% confidence point is

[0134]

[0135] Calculate the comfort index N MV :

[0136]

[0137] The comfort level is assessed according to the calculated comfort value. The comfort registration table is shown in Table 3:

[0138] Table 3

[0139]

[0140] Based on the above embodiments, the method further includes:

[0141] S5. Determine the optimal experimental model based on the safety index and the comfort index, and obtain the system parameters of the optimal experimental model;

[0142] In this embodiment, in view of the huge economic losses and fatal threats to people's lives caused by safety problems in the vehicle-track system, its safety factor should be considered as the main factor, and the comfort factor should be considered as a secondary factor. When comparing the schemes, the comfort level of the vehicle-track system is not considered first, and the scheme with higher power reliability is selected as the better scheme. When the power reliability is similar, the secondary factor is considered, and the scheme with a higher comfort level is selected as the better scheme.

[0143] Embodiment 2:

[0144] like Figure 8 As shown, this embodiment provides a device for determining vehicle-track system matching parameters, the device comprising:

[0145] Experimental design module: obtain multiple system parameters contained in the vehicle-track system, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters;

[0146] Model building module: construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models;

[0147] Simulation module: determine the irregularity parameters required for the vehicle-track system, generate several track irregularity samples based on the random harmonic function method; use several samples to simulate each experimental model respectively, and obtain several groups of dynamic response and acceleration data of each experimental model;

[0148] Calculation module: the safety index of each experimental model is calculated based on several groups of dynamic responses; the comfort index of each experimental model is calculated based on several groups of acceleration data;

[0149] Selection module: Determine the optimal experimental model based on the safety index and the comfort index, and obtain the system parameters of the optimal experimental model.

[0150] Based on the above embodiment, the model building module includes:

[0151] First simulation unit: the train body, bogie and wheelset are treated as rigid bodies;

[0152] The second simulation unit: simulates the primary suspension between the wheelset and the bogie through springs and dampers, and simulates the secondary suspension between the bogie and the vehicle body through shock absorbers and spring bars;

[0153] The third simulation unit: the rails and sleepers of the track system are taken as flexible bodies, and the Euler beam is used to simulate the rails;

[0154] Model building unit: connect the rails and sleepers through linear springs and dampers, connect the sleepers and ballast through linear springs and dampers to build a multi-body rigid-flexible coupling dynamic model.

[0155] Based on the above embodiment, the simulation module includes:

[0156] The first selection unit: select a number of discrete representative point sets using number theory methods;

[0157] Wavelength generation unit: using each discrete representative point set to generate a set of random wavelengths and random phases, to obtain several sets of random wavelengths and random phases;

[0158] Model construction unit: constructs a calculation model of track irregularity parameters based on random harmonic functions;

[0159] Input unit: input each group of random wavelength and random phase into the calculation model of the roughness parameter in turn to generate several samples of the roughness parameter.

[0160] Based on the above embodiment, the first selection unit includes:

[0161] The second selection unit: selects a plurality of mutually different prime numbers;

[0162] Set generation unit: generates several hypercube point sets based on multiple different prime numbers;

[0163] Screening unit: use the hypersphere screening method to screen several hypercube point sets to obtain several sample point sets;

[0164] Affine transformation unit: Perform affine transformation on each sample point set in turn to generate a discrete representative point set with a small F-deviation, and obtain several discrete representative point sets.

[0165] Based on the above embodiment, the calculation module includes:

[0166] Substitution unit: construct a generalized probability density evolution model, and substitute each group of dynamic responses and their first-order derivatives into the generalized probability density evolution model in turn;

[0167] Solving unit: numerically solving the generalized probability density evolution model using the total variation non-increasing format to generate several probability density distribution values;

[0168] Fitting unit: sum up several probability density distribution values ​​to obtain the numerical solution of the probability density distribution of the experimental model;

[0169] The first calculation unit is used to calculate the safety index of the experimental model based on the numerical solution of the probability density distribution.

[0170] Based on the above embodiment, the calculation module further includes:

[0171] A frequency weighting unit: performing frequency weighting on the acceleration data in each direction to obtain a corresponding acceleration signal, wherein the direction of the acceleration data includes horizontal, longitudinal and vertical directions;

[0172] The second calculation unit is used to calculate the weighted root mean square value of the acceleration signal in each direction within a preset time period;

[0173] The third calculation unit: the comfort index of the experimental model is calculated using the weighted root mean square value of each direction.

[0174] It should be noted that, regarding the device in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment of the method, and will not be elaborated here.

[0175] Embodiment 3:

[0176] Corresponding to the above method embodiment, this embodiment also provides a device for determining vehicle-track system matching parameters. The device for determining vehicle-track system matching parameters described below and the method for determining vehicle-track system matching parameters described above can refer to each other.

[0177] Fig. 9 8 is a block diagram of a vehicle-track system matching parameter determination device 800 according to an exemplary embodiment. Fig. 9 As shown, the vehicle-track system matching parameter determination device 800 may include: a processor 801 and a memory 802. The vehicle-track system matching parameter determination device 800 may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0178] The processor 801 is used to control the overall operation of the vehicle-track system matching parameter determination device 800 to complete all or part of the steps in the above-mentioned vehicle-track system matching parameter determination method. The memory 802 is used to store various types of data to support the operation of the vehicle-track system matching parameter determination device 800, and these data may include, for example, instructions for any application or method operating on the vehicle-track system matching parameter determination device 800, and application-related data, such as contact data, sent and received messages, pictures, audio, video, etc. The memory 802 can be implemented by 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, disk or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input an audio signal. For example, the audio component may include a microphone, which is used to receive an external audio signal. The received audio signal may be further stored in the memory 802 or sent via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 804 provides an interface between the processor 801 and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component 805 is used for wired or wireless communication between the vehicle-track system matching parameter determination device 800 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component 805 can include: Wi-Fi module, Bluetooth module, NFC module.

[0179] In an exemplary embodiment, the vehicle-rail system matching parameter determination device 800 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned vehicle-rail system matching parameter determination method.

[0180] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned method for determining the vehicle-track system matching parameters are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 802 including program instructions, and the above-mentioned program instructions may be executed by the processor 801 of the device 800 for determining the vehicle-track system matching parameters to complete the above-mentioned method for determining the vehicle-track system matching parameters.

[0181] Embodiment 4:

[0182] Corresponding to the above method embodiment, a readable storage medium is also provided in this embodiment. The readable storage medium described below and the method for determining the vehicle-rail system matching parameters described above can refer to each other.

[0183] A readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for determining the vehicle-rail system matching parameters of the above method embodiment.

[0184] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0185] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0186] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for determining vehicle-track system matching parameters, characterized in that: include: Obtain the vehicle-track system parameters that need to be optimized, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters; Construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models; Determine the track irregularity parameters required for the vehicle-track system, and generate several track irregularity samples based on the random harmonic function method; use several samples to simulate each experimental model respectively, and obtain several groups of dynamic response and acceleration data of each experimental model; The safety index of each experimental model is calculated based on several groups of dynamic responses; the comfort index of each experimental model is calculated based on several groups of acceleration data; An optimal experimental model is determined based on the safety index and the comfort index, and system parameters of the optimal experimental model are obtained.

2. The method for determining the vehicle-rail system matching parameters according to claim 1, characterized in that ,Based on the random harmonic function method, several track irregularity samples are generated, including: A number of discrete representative point sets are selected using number theory methods; Using each discrete representative point set to generate a set of random wavelengths and random phases, several sets of random wavelengths and random phases are obtained; Construct a calculation model of track irregularity parameters based on random harmonic functions; Each set of random wavelength and random phase is input into the calculation model of the irregularity parameter in turn to generate a number of track irregularity samples.

3. The method for determining the matching parameters of the vehicle-rail system according to claim 2, characterized in that ,Using number theory methods to select several discrete representative point sets, including: Select multiple different prime numbers; Generate several hypercube point sets based on multiple different prime numbers; Use the hypersphere screening method to screen each hypercube point set and remove the points that fall in the corners and edges of the hypercube; Perform affine transformation on each filtered point set in turn to obtain the corresponding discrete representative point set.

4. The method for determining the vehicle-rail system matching parameters according to claim 1, characterized in that ,According to several groups of dynamic responses, the safety indicators of each experimental model are calculated, including: A generalized probability density evolution model is constructed, and each group of dynamic responses and their first-order derivatives are substituted into the generalized probability density evolution model in turn; The generalized probability density evolution model is numerically solved using the total variation non-increasing format to generate several probability density distribution values; The numerical solution of the probability density distribution of the experimental model is obtained by summing up several probability density distribution values; The safety index of the experimental model is calculated based on the numerical solution of the probability density distribution.

5. A device for determining vehicle-track system matching parameters, characterized in that: include: Experimental design module: obtain the vehicle-track system parameters that need to be optimized, use multiple system parameters to perform orthogonal experimental design, and obtain several groups of experimental schemes containing different system parameters; Model building module: construct a multi-body rigid-flexible coupling dynamics model of the vehicle-track system, and assign the system parameters of each group of experimental schemes to the multi-body rigid-flexible coupling dynamics model to generate multiple experimental models; Simulation module: determine the track irregularity parameters required by the vehicle-track system, generate several track irregularity samples based on the random harmonic function method; use several samples to simulate each experimental model respectively, and obtain several groups of dynamic response and acceleration data of each experimental model; Calculation module: the safety index of each experimental model is calculated based on several groups of dynamic responses; the comfort index of each experimental model is calculated based on several groups of acceleration data; Selection module: Determine the optimal experimental model based on the safety index and the comfort index, and obtain the system parameters of the optimal experimental model.

6. The device for determining vehicle-rail system matching parameters according to claim 5, characterized in that: The simulation module comprises: The first selection unit: select a number of discrete representative point sets using number theory methods; Wavelength generation unit: using each discrete representative point set to generate a set of random wavelengths and random phases, to obtain several sets of random wavelengths and random phases; Model construction unit: constructs a calculation model of track irregularity parameters based on random harmonic functions; Input unit: input each group of random wavelength and random phase into the calculation model of the irregularity parameter in turn to generate a number of track irregularity samples.

7. The device for determining vehicle-rail system matching parameters according to claim 6, characterized in that: The first selection unit includes: The second selection unit: selects a plurality of mutually different prime numbers; Set generation unit: generates several hypercube point sets based on multiple different prime numbers; Screening unit: use the hypersphere screening method to screen several hypercube point sets to obtain several sample point sets; Affine transformation unit: Perform affine transformation on each sample point set in turn to generate a corresponding discrete representative point set, and obtain several discrete representative point sets.

8. The device for determining vehicle-rail system matching parameters according to claim 5, characterized in that: The calculation module comprises: Substitution unit: construct a generalized probability density evolution model, and substitute each group of dynamic responses and their first-order derivatives into the generalized probability density evolution model in turn; Solving unit: numerically solving the generalized probability density evolution model using the total variation non-increasing format to generate several probability density distribution values; Fitting unit: sum up several probability density distribution values ​​to obtain the numerical solution of the probability density distribution of the experimental model; The first calculation unit is used to calculate the safety index of the experimental model based on the numerical solution of the probability density distribution.

9. A device for determining matching parameters of a vehicle-track system, characterized in that: include: Memory for storing computer programs; A processor, configured to implement the steps of the method for determining vehicle-rail system matching parameters as claimed in any one of claims 1 to 4 when executing the computer program.

10. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method for determining the vehicle-rail system matching parameters as claimed in any one of claims 1 to 4 are implemented.

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