Analysis Method and System for Integrating the Dynamics and Structural Reliability of Rail Vehicles

Abstract

The present application relates to the field of track technology, and provides an analysis method and system for integrating the dynamics and structural reliability of rail vehicles, including Step 1: obtaining the track operation topology data of the target vehicle, and identifying typical operation scenarios based on the line engineering characteristics; Step 2: constructing the track dynamics equations for each typical operation scenario; Step 3: constructing the rigid-flexible dynamics equations of the car body; Step 4: coupling the rigid-flexible dynamics equations of the car body and the track dynamics equations to generate a coupling equation; Step 5: obtaining the load curve of the target vehicle when passing through a typical track scenario, and extracting vibration characteristics from the load curve, where the vibration characteristics are equivalent load spectra; Step 6: inputting the equivalent load spectra into a pre-established structural reliability integration analysis model for the spatio-temporal evolution of dynamic performance to generate the reliability probability of the vehicle during long-term operation on this line.

Description

Technical Field

[0001] The present application relates to the field of rail technology, and in particular to an analysis method and system for integrating rail vehicle dynamics and structural reliability. Background Art

[0002] The contents of this section merely provide background information related to the present application and may not constitute prior art.

[0003] Rail transit systems are the core components of modern integrated transportation systems, and the service safety of their key equipment directly affects the safe operation of public transportation. In the technological evolution of contemporary rail trains that continuously improve their operating speed and load capacity, core load-bearing components such as car bodies, bogies, wheelsets and couplers continue to bear high-frequency cyclic loads and complex excitations, resulting in progressive degradation of structural dynamic performance. It is worth noting that there is a significant coupling correlation between the cumulative damage effect of components and the instability of dynamic behavior, which may cause major traffic safety accidents with cascading characteristics.

[0004] In current engineering practice, structural reliability assessment and dynamic performance verification of rail vehicles are usually implemented independently as two types of key performance assessment systems: the former verifies the design margin based on static strength criteria, and the latter confirms operational stability through dynamic response analysis. This disciplinary separation leads to dual limitations in traditional reliability analysis: first, static parameters of the initial healthy state are used to characterize the full life cycle performance, and the constitutive relationship between the time-varying load spectrum and material performance degradation is not established; second, the spatial distribution characteristics of dynamic excitation parameters and the cross-scale evolution law of structural fatigue damage are ignored, resulting in the reliability model being established on the basis of quasi-static assumptions. This decoupled analysis method makes it difficult to accurately characterize the nonlinear damage accumulation process caused by the dynamic-static coupling effect during service, resulting in significant theoretical deviations in the safety assessment results. Summary of the invention

[0005] In view of this, the purpose of the present application is to provide an analysis method and system for the fusion of rail vehicle dynamics and structural reliability. The analysis method and system for the fusion of rail vehicle dynamics and structural reliability disclosed in the present application can be realized.

[0006] The purpose of this application is achieved through the following technical solutions:

[0007] An analysis method for integrating rail vehicle dynamics and structural reliability includes the following steps:

[0008] Step 1: Obtain the track operation topology data of the target vehicle and identify typical operation scenarios based on line engineering characteristics;

[0009] Step 2: Construct the orbital dynamics equations for each typical operation scenario;

[0010] ;

[0011] M r represents the track mass matrix, C r represents the track damping matrix, K r represents the track stiffness matrix, x r (t) represents the track displacement vector, F r (t) represents the track external force vector;

[0012] Step 3: Construct the rigid-flexible dynamics equation of the car body;

[0013] M ta q + C ta q + K ta q = F ta q;

[0014] where, M ta represents the vehicle mass matrix, C ta represents the vehicle damping matrix, K ta represents the vehicle stiffness matrix, F ta q represents the vehicle external force vector, q represents the vehicle rigid body degree of freedom vector;

[0015] Step 4: Couple the rigid-flexible dynamics equation of the car body and the track dynamics equation to generate a coupled equation;

[0016] The coupled equation is:

[0017] ;

[0018] M ta q + C ta q + K ta q = F ta q;

[0019] Step 5: Obtain the load curve of the target vehicle when passing through a typical track scenario, and extract the vibration characteristics from the load curve, and the vibration characteristics are the equivalent load spectrum;

[0020] Step 6: Input the equivalent load spectrum into the pre-established structural reliability fusion analysis model of the spatio-temporal evolution of dynamic performance to generate the reliability probability of the vehicle running for a long time on this line.

[0021] In some possible embodiments, Step 2 includes the following steps:

[0022] Step 21: Divide the track into multiple finite elements, define two nodes at both ends of each finite element, and generate a node sequence N1, N2,... N n ; Record the geometric coordinates of each node, and n represents the total number of nodes;

[0023] Step 22: Collect the track density and cross-sectional area of the track to construct the track mass matrix M r ;

[0024] Step 23: Collect the elastic modulus E, Poisson's ratio v, and vertical support stiffness matrix K of the track bed, and construct the track stiffness matrix Kr; b , and construct the track stiffness matrix Kr;

[0025] Step 24: Collect the damping coefficient and modal damping ratio of the track, and calculate the track damping matrix C r ;

[0026] Step 25: According to the track mass matrix M r , the track stiffness matrix K r , and the track damping matrix C r Construct the track dynamic equation.

[0027] In some possible embodiments, Step 3 includes the following steps:

[0028] Step 31: Divide the vehicle into a number of finite elements, and use the head and tail ends of the finite elements as mass points to generate a sequence of mass points P1, P2,... P PG ; PG represents the total number of mass points;

[0029] Step 32: Collect the rigid body dynamics parameters, flexible car body parameters, modal reduction parameters, coupling term calculation parameters, and external excitation parameters of the vehicle;

[0030] Step 33: Construct the vehicle mass matrix M ta ;

[0031] Step 34: Construct the vehicle stiffness matrix K ta ;

[0032] Step 35: Construct the vehicle damping matrix C ta ;

[0033] Step 36: According to the vehicle mass matrix M ta , the vehicle stiffness matrix K ta , and the vehicle damping matrix C ta Construct the rigid-flexible dynamics equation of the car body.

[0034] In some possible embodiments, Step 21 includes the following steps:

[0035] Step 211: Collect the track surface information and determine the minimum node density ρ under typical operating scenarios q ;

[0036] Step 212: Divide the track into N E finite elements, and the length of the finite element isl， Define the two ends of each finite element as nodes;

[0037] Step 213: Obtain the minimum vibration wavelength λ of the track, and set the finite element length based on the minimum vibration wavelength λ l , where l＜ 10 / λ;

[0038] Step 214: Continuously adjust the finite element length until the node density ρ0 of the track is equal to the minimum node density ρ q ;

[0039] Step 215: Traverse the entire track, add new nodes to the worn part and joint part of the track, and obtain the node sequence N1, N2,... N n , where n represents the total number of nodes;

[0040] Step 216: Add translational degrees of freedom and rotational degrees of freedom in the x, y, and z directions to each node, and record the geometric coordinates of each node.

[0041] In some possible embodiments, the track mass matrix M r is:

[0042] ;

[0043] where, I x1 represents the equivalent moment of inertia of the first node about the x-axis, I y1 represents the equivalent moment of inertia of the first node about the y-axis, I z1 represents the equivalent moment of inertia of the first node about the z-axis, I xn represents the equivalent moment of inertia of the nth node about the x-axis, I yn represents the equivalent moment of inertia of the nth node about the y-axis, I zn represents the equivalent moment of inertia of the nth node about the z-axis, m x1 represents the translational mass of the first node in the x-axis, m y1 represents the translational mass of the first node in the y-axis, m z1 represents the translational mass of the first node in the z-axis, m xn represents the translational mass of the nth node in the x-axis, m yn represents the translational mass of the nth node in the y-axis, m zn represents the translational mass of the nth node in the z-axis; diag represents a diagonal matrix;

[0044] ; ;

[0045] where, i represents the index of the node, A represents the cross-sectional area of the track, ρ represents the density of the track, le represents the finite element length, e represents the index of the finite element, J represents the torsional constant of the track section, I y and I z respectively represent the inertia matrices of the track cross-section about the y-axis and z-axis, m i represents the equivalent translational mass of the i-th node, I xi represents the equivalent moment of inertia of the i-th node about the x-axis, I yi represents the equivalent moment of inertia of the i-th node about the y-axis, I zi represents the equivalent moment of inertia of the i-th node about the z-axis;

[0046] The track stiffness matrix K r is:

[0047] ;

[0048] ;

[0049] ;

[0050] where, l e represents the finite element length, e represents the index of the finite element, J represents the torsional constant of the track interface, G represents an intermediate variable, ⊕ represents assembling the element matrix into the global matrix according to the degree-of-freedom numbering, K b represents the vertical support stiffness matrix of the ballast bed, E represents the elastic modulus of the track, v represents the Poisson's ratio, I y and I z respectively represent the inertia matrices of the track cross-section about the y-axis and z-axis, N E represents the number of finite elements;

[0051] The track damping matrix is C r :

[0052] ; ; ;

[0053] where, M r is the track mass matrix, K r is the track stiffness matrix, α1 and β1 are the first track damping coefficient and the second track damping coefficient respectively, are the first damping factor and the second damping factor respectively, is the modal damping ratio.

[0054] In some possible embodiments, the rigid body dynamics parameters include the total mass of the car body, the material density of the car body, the moment of inertia, and the coordinates of the suspension points;

[0055] The flexible car body parameters include the density of the car body material, elastic modulus, shear modulus, cross-sectional set parameters, and mass point division information;

[0056] The modal reduction parameters include the number of key degrees of freedom, the number of retained modal orders, suspension stiffness, and damping;

[0057] The coupling term calculation parameters include the rigid body Jacobian matrix and modal shape functions. The coupling term calculation parameters select the key degrees of freedom and the number of retained modal orders according to the simulation objective, and are obtained by combining the suspension system parameters.

[0058] The external excitation parameters include wheel-rail forces and track irregularity excitations.

[0059] In some possible embodiments, the vehicle mass matrix M ta is:

[0060] ;

[0061] where M rr represents the rigid body mass matrix, M rm represents the inertia coupling matrix, M mm represents the modal matrix; M mr represents the transpose matrix of the inertia coupling matrix;

[0062] ; T represents the transpose symbol;

[0063] ;

[0064] ; ;

[0065] ;

[0066] where cI x1 represents the equivalent moment of inertia of the first mass point about the x-axis, cI y1 represents the equivalent moment of inertia of the first mass point about the y-axis, cI z1 represents the equivalent moment of inertia of the first mass point about the z-axis, cI xn represents the equivalent moment of inertia of the nth mass point about the x-axis, cI yn represents the equivalent moment of inertia of the nth mass point about the y-axis, cI zn represents the equivalent moment of inertia of the nth mass point about the z-axis, cm x1 represents the translational mass of the first mass point in the x-axis, cm y1 represents the translational mass of the first mass point in the y-axis, cm z1 represents the translational mass of the first mass point in the z-axis, cm xn represents the translational mass of the nth mass point in the x-axis, cm ynRepresents the translational mass of the nth particle on the y-axis, cm zn Represents the translational mass of the nth particle on the z-axis; diag represents a diagonal matrix;

[0067] Represents the transformation matrix that maps physical coordinates to modal coordinates, M represents the un-reduced overall mass matrix of the flexible car body, Represents the volume integral of the entire car body structure, R is the free quantity matrix, 0 is the zero matrix of PB×L, L is the number of retained elastic modal orders, Is the boundary-induced displacement transfer matrix, PB is the number of key degrees of freedom;

[0068] ; K II Represents the internal degree of freedom stiffness matrix, K IB Represents the internal-boundary degree of freedom coupling stiffness matrix, Represents the fixed interface modal matrix; ; M II Represents the internal degree of freedom mass matrix, Represents the diagonal matrix of the first K eigenvalues; ρ represents the density of the car body material, Represents the modal shape function matrix, JD represents the Jacobian matrix of rigid body motion;

[0069] The vehicle stiffness matrix is K ta :

[0070] ;

[0071] K rr Represents the vehicle rigid body stiffness matrix, K mm Represents the modal stiffness matrix, K rm Represents the stiffness coupling matrix, K mr Is K rm The transpose matrix of;

[0072] ; ; ;

[0073] T r Represents the transformation matrix from rigid body displacement to the suspension point, K s Represents the local stiffness matrix of the suspension system, Represents the transformation matrix that maps physical coordinates to modal coordinates, K represents the un-reduced vehicle overall stiffness matrix, T p Represents the transformation matrix from modal coordinates to the suspension point;

[0074] The vehicle damping matrix C ta Is:

[0075] ;

[0076] Among them, C rr represents the damping matrix, C rm represents the damping coupling matrix, C mm represents the modal damping matrix, C mr represents the transposed matrix of the damping coupling matrix;

[0077] Among them, ; ; ;

[0078] T r represents the transformation matrix from the rigid body displacement to the suspension point, C s represents the suspension damping, α2 and β2 are the first vehicle damping coefficient and the second vehicle damping coefficient respectively, T p represents the transformation matrix from the modal coordinates to the suspension point, and T represents the transposed matrix.

[0079] In some possible embodiments, the typical operating scenarios include the operating route with the maximum turning radius, the uphill route with the maximum gradient, the downhill route with the maximum gradient, and the operating route at the highest speed.

[0080] In some possible embodiments, step 5 includes the following steps:

[0081] Step 51: Input the coupling equation into the DAP-Rail software;

[0082] Step 52: Input the operating scenario parameters, and the operating scenario parameters include line parameters, vehicle parameters, and load conditions;

[0083] Step 53: The DAP-Rail software outputs a load curve corresponding to time, and extracts an equivalent load spectrum from the load curve.

[0084] In some possible embodiments, step 53 includes the following steps:

[0085] Step 531: Filter the load curve with a high-pass filter to obtain the time series X(t);

[0086] Step 532: Identify the maximum and minimum values of the time series X(t), and extract the local maximum values and local minimum values ; represents the s-th local maximum value, represents the s-th local minimum value, and generates an extreme value sequence ST;

[0087] ST = ;

[0088] Step 533: Starting from the first value in the extreme value sequence ST, gradually traverse the entire extreme value sequence ST. When the preset closing condition is met, extract a cycle SY, and record the amplitude SY1 and mean value SY2 of this cycle;

[0089] ; ;

[0090] Extract all cycles SY to generate a cycle list;

[0091] Step 534: Define the amplitude binning interval, and assign the amplitude of each cycle SY to the corresponding amplitude binning interval; output the binning count table;

[0092] Step 535: Obtain the amplitude SY1, number of cycles Q, and material S-N curve parameters of each cycle, generate an equivalent constant amplitude load amplitude, and convert the equivalent constant amplitude load amplitude into an equivalent load spectrum;

[0093] ;

[0094] Among them, BY represents the number of amplitude binning intervals, b represents the index of the amplitude binning interval, B b represents the number of cycles in the b-th amplitude binning interval, SY1 b represents the maximum amplitude in the b-th amplitude binning interval, mba represents the slope of the material S-N curve; N total represents the sum of the number of cycles.

[0095] In some possible embodiments,

[0096] Step 6 includes the following steps:

[0097] Step 61: Construct an LSTM model to extract temporal features from the equivalent load spectrum;

[0098] Step 62: Construct two sub-network groups with the same structure and weight sharing;

[0099] Step 63: Pre-construct a database related to temporal features and reliability probability;

[0100] Step 64: Input the temporal features to be predicted into one sub-network group, and gradually input the temporal features in the database into the other sub-network group, calculate the similarity between the temporal features and the temporal features in the database, screen out the sample with the highest similarity from the database, and obtain the reliability probability of this temporal feature.

[0101] An analysis system for the integration of railway vehicle dynamics and structural reliability uses the aforementioned analysis method for the integration of railway vehicle dynamics and structural reliability to generate the reliability probability of the vehicle running on this line for a long time.

[0102] The technical solution of the embodiment of the present application has at least the following advantages and beneficial effects:

[0103] (1) In the technical solution proposed in the present application, aiming at the problem of reliability probability assessment of the railway vehicle system, a multi-physics field coupling dynamics model of wheel-rail contact is established, and the dynamic load history of the vehicle under all working conditions is obtained through numerical solution. Based on the non-linear random vibration theory, a multi-axial equivalent fatigue load spectrum is derived, and combined with the material S-N curve and the Miner linear cumulative damage criterion, a time-varying reliability assessment function is constructed. This method breaks through the limitations of traditional static reliability analysis. By analyzing the frequency-domain energy distribution characteristics of the load spectrum, it effectively identifies the cross-domain correlation of stress amplitude - cycle number - phase difference in the structural dynamic response, and realizes the accurate prediction of the initiation and propagation rate of micro-cracks during the whole life cycle.

[0104] (2) When constructing the wheel-rail coupling dynamics model in this solution, a rigid-flexible hybrid multi-body system modeling method is adopted, and the two-way coupling of rigid body motion and elastic deformation is realized by introducing the flexible body modal neutral file. Compared with the traditional pure rigid body model or the decoupled rigid-flexible separation model, this method completely retains the geometric non-linear terms and inertial coupling terms in the dynamic equation, and can accurately characterize the modulation effect of the structural dynamic flexibility on the wheel-rail contact force under high-frequency excitation, significantly improving the time-frequency domain prediction accuracy of the wheel-rail impact load.

[0105] (3) In the rigid-flexible coupling modeling of the car body, the degrees of freedom are compressed. By retaining the rigid body degrees of freedom of the interface main nodes and the low-order elastic modes, the original distributed parameter model with 6N + 6 degrees of freedom is reduced to a condensed model with K + 6 degrees of freedom. While ensuring the accuracy of the first six-order rigid body motion, this reduction algorithm effectively suppresses the truncation error of the high-order modes, and maintains a kinetic energy conservation rate of more than 99.5% while reducing the calculation scale by two orders of magnitude.

[0106] (4) In terms of dynamic excitation modeling, a modal space projection algorithm for distributed loads is proposed. By constructing a load influence coefficient matrix, complex excitations such as wheel-rail contact forces and aerodynamic loads are accurately mapped to the generalized modal coordinates. This algorithm breaks through the limitations of traditional concentrated force loading and can accurately characterize the wave propagation effect and strain gradient distribution characteristics on the surface of the elastic body under high-frequency vibration.

[0107] (5) During the finite element mesh discretization process, a mesh convergence criterion based on wave theory is established, and the critical mesh size is determined according to the longitudinal wave / transverse wave propagation speed of the material and the highest frequency component of the excitation. By constructing a reference division scale, while suppressing the numerical dispersion effect, the mesh density is reduced by 40% - 60% compared with the traditional empirical method, and the accurate analysis of the micro-meter level strain gradient is realized.

[0108] (6) In this solution, when generating the equivalent load spectrum, the time history of the random load is converted into an equivalent constant amplitude or multi-level load block to ensure the same fatigue damage as the original load, avoiding the defect of ignoring the non-linear damage contribution of high-amplitude loads in traditional methods. According to the S-N curve parameters, the damage weights of different load amplitudes are quantified, which can well integrate the damage characteristics of the material and dynamically display the load distribution and development of the material under long-term operation, thus accurately generating the equivalent load diagram.

[0109] (7) This application uses a high-pass filter to filter out low-frequency and invalid information in the load curve, reducing the impact of error information and noise information on the equivalent load diagram.

[0110] (8) This application uses a twin network model for similarity matching. Compared with the scheme using cosine similarity matching, in this application, the similar analysis situation can be generated from the perspective of the relationship between features. Compared with the pure numerical comparison, the similarity in this scheme can better consider the influence between features and the evolution direction of features, so that higher recognition accuracy can be achieved with a smaller data sample.

[0111] (9) In the technical solution provided by this application, the track constructs the track dynamics equation, and the vehicle constructs the rigid-flexible dynamics equation. The reason is that when the vehicle is running, there will be a resonance phenomenon between the flexible part of the vehicle and the flexible part of the track. Introducing flexible information into both will increase the computational complexity of the model and the simulation difficulty, but reduce the accuracy. In this application, only the flexible characteristics of the vehicle are considered. The reason is that the vehicle integrates a suspension system and has more connection points, so the flexible influence of the vehicle is greater. By establishing the rigid-flexible dynamics equation of the vehicle and then coupling the rigid-flexible dynamics equation of the vehicle with the track dynamics equation, the flexible part in the vehicle-track system can be well described, increasing the simulation ability of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 It is a schematic structural diagram of an analysis method for integrating track vehicle dynamics and structural reliability provided by some embodiments of this application.

[0113] Figure 2 It is a rendering of the track in the DAP-Rail software.

[0114] Figure 3 It is a rendering of the vehicle in the DAP-Rail software.

[0115] Figure 4 It is a rendering of the track and the vehicle in DAP-Rail.

[0116] Figure 5It is a load curve graph, with the abscissa being time in seconds and the ordinate being the load ratio.

[0117] Figure 6 It is an equivalent load spectrum. Specific implementation manners

[0118] To make the objectives, technical solutions and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with specific implementation manners. The same reference numerals in the drawings represent the same components. It should be noted that the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the described embodiments of the present application without creative efforts fall within the scope of protection of the present application.

[0119] Compared with the embodiments shown in the drawings, the feasible implementation solutions within the scope of protection of the present application may have fewer components, have other components not shown in the drawings, different components, differently arranged components or differently connected components, etc. In addition, two or more components in the drawings may be implemented in a single component, or a single component shown in the drawings may be implemented as multiple separate components.

[0120] Unless otherwise defined, the technical terms or scientific terms used herein shall have the ordinary meanings understood by those of ordinary skill in the art to which the present application pertains. The "first", "second" and similar terms used in the specification and claims of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. Similarly, words such as "a" or "one" do not necessarily indicate a quantity limitation. "Upper", "lower", etc. are only used to indicate relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly. Embodiment 1:

[0121] Refer to Figure 1 The first embodiment of the present application discloses an analysis method for integrating the dynamics and structural reliability of rail vehicles, including the following steps:

[0122] Step 1: Obtain the track operation topology data of the target vehicle, and identify typical operation scenarios based on line engineering characteristics. The typical operation scenarios include the operation route with the maximum turning radius, the uphill route with the maximum gradient, the downhill route with the maximum gradient, and the operation route at the highest speed. The line engineering characteristics are the track turning radius, the track speed limit, and the track gradient.

[0123] In the field of track engineering, the full-line simulation of locomotives and rolling stock faces the problem of the computational complexity of multi-physical field coupling: the dynamic structural stability analysis of the complete line requires the construction of a global time-varying load model, resulting in exponential consumption of computing resources and the risk of iterative error propagation. The present invention proposes a dimensionality reduction analysis method based on the characteristics of line engineering, and constructs a typical operation scenario by extracting the key sections with the most structural dynamics bearing characteristics in the line. It is demonstrated from the perspective of engineering mechanics that when the vehicle meets the structural reliability threshold under boundary conditions such as maximum curvature, maximum slope direction, and limit speed, its dynamic response envelope must cover the threshold of the conventional working condition domain, thereby establishing a mathematical completeness proof of the structural integrity of the full line. This screening mechanism based on characteristic working conditions not only maintains the engineering accuracy of the dynamic simulation but also realizes a step-by-step improvement in computing efficiency.

[0124] Step 2: Construct the track dynamics equation under each typical operation scenario;

[0125] ;

[0126] M r represents the track mass matrix, C r represents the track damping matrix, K r represents the track stiffness matrix, x r (t) represents the track displacement vector, F r (t) represents the track external force vector.

[0127] For each typical operation scenario, the structure of its track is different, and there will be certain differences when constructing the track dynamics equation. Therefore, the track dynamics equations under each typical operation scenario need to be established independently.

[0128] Further, Step 2 includes the following steps:

[0129] Step 21: Divide the track into multiple finite elements, define two nodes at the head and tail of each finite element, and generate a node sequence N1, N2,...; record the geometric coordinates of each node.

[0130] Specifically, Step 21 includes the following steps:

[0131] Step 211: Collect the track surface information and determine the minimum node density ρ q .

[0132] When constructing the track dynamics equation, the discretization node scale is positively correlated with the model solution accuracy, but it will lead to a significant increase in computational complexity. In view of this, this solution innovatively introduces an adaptive node density configuration strategy, establishes a gradient grid division criterion for different characteristic working conditions, and realizes the dynamic balance between computational accuracy and efficiency.

[0133] At the engineering implementation level, a node density regulation function is established based on the health status of the track surface structure: taking the historical maintenance frequency of the track unit as the key input parameter, there is a monotonically increasing relationship between the maintenance frequency threshold and the node distribution density. Engineering practice shows that the track subgrade will exhibit differential wear distribution characteristics under the action of the wheel-rail contact stress field. The high-frequency maintenance section often corresponds to the area of accumulated plastic deformation of the material or the area where contact fatigue cracks initiate. In numerical simulation, it is necessary to improve the discretization accuracy to accurately characterize its mechanical response characteristics. By establishing a quantitative mapping model among maintenance records - structural degradation sensitivity - grid resolution, ensure that the simulation accuracy of key weak areas meets the requirements of micron-level deformation resolution.

[0134] Step 212: Divide the track into N E finite elements, and the length of the finite element is l， Define the head and tail ends of each finite element as nodes;

[0135] Step 213: Obtain the minimum vibration wavelength λ of the track, and set the finite element length based on the minimum vibration wavelength λ l where l＜ 10 / λ;

[0136] Step 214: Continuously adjust the finite element length until the node density ρ0 of the track is equal to the minimum node density ρ q .

[0137] In the technical solution proposed in this application, during the grid discretization process, the finite element cell division scale is determined based on the minimum characteristic wavelength of the excitation wave spectrum, and it is ensured that the grid density meets the critical convergence threshold. This optimization method strictly follows the convergence criterion of wave theory, and on the premise of ensuring the modal truncation error, realizes the optimal compression of the calculation scale. This dual constraint mechanism not only effectively suppresses the Gibbs phenomenon of high-frequency vibration signals, but also enables the numerical model to accurately capture the micron-level strain gradient distribution characteristics after reducing the order dimension by adaptively adjusting the unit topology structure, achieving the coordinated improvement of calculation resource optimization and dynamic fidelity.

[0138] Step 215: Traverse the entire track, add new nodes in the worn part and joint part of the track, and obtain the node sequence N1, N2,... N n , where n represents the total number of nodes.

[0139] For relatively special positions, more nodes need to be added to supplement the details of the model. For the identification of the worn area of the track, an edge inspection algorithm can be used to automatically add new nodes.

[0140] Step 216: Add translational degrees of freedom and rotational degrees of freedom in the x, y, and z directions to each node, and record the geometric coordinates of each node.

[0141] Thus, each node has six degrees of freedom.

[0142] Step 22: Collect the orbital density and orbital cross-sectional area of the orbit to construct the orbital mass matrix M r ;

[0143] The essence of the mass matrix is to evenly distribute the mass in the orbit to each node. Specifically, the orbital mass matrix M r is:

[0144] ;

[0145] where, I x1 represents the equivalent moment of inertia of the first node about the x-axis, I y1 represents the equivalent moment of inertia of the first node about the y-axis, I z1 represents the equivalent moment of inertia of the first node about the z-axis, I xn represents the equivalent moment of inertia of the nth node about the x-axis, I yn represents the equivalent moment of inertia of the nth node about the y-axis, I zn represents the equivalent moment of inertia of the nth node about the z-axis, m x1 represents the translational mass of the first node on the x-axis, m y1 represents the translational mass of the first node on the y-axis, m z1 represents the translational mass of the first node on the z-axis, m xn represents the translational mass of the nth node on the x-axis, m yn represents the translational mass of the nth node on the y-axis, m zn represents the translational mass of the nth node on the z-axis; diag represents a diagonal matrix;

[0146] ; ;

[0147] where, i represents the index of the node, A represents the orbital cross-sectional area, ρ represents the orbital density, l e represents the finite element length, e represents the index of the finite element, J represents the torsional constant of the orbital cross-section, I y and I z respectively represent the inertia matrices of the orbital cross-section about the y-axis and z-axis, m i represents the equivalent translational mass of the ith node, I xi represents the equivalent moment of inertia of the ith node about the x-axis, I yi represents the equivalent moment of inertia of the ith node about the y-axis, I zi represents the equivalent moment of inertia of the ith node about the z-axis, where, m i =m xi =myi =m zi ;

[0148] Among them, the orbital interface torsion constant, and the moments of inertia of the orbital cross-section about the y-axis and z-axis are pre-calculated from the surface data, which are data that need to be collected in advance. After collecting the above information, the mass and inertia characteristics of the orbit can be incorporated into the corresponding nodes.

[0149] Step 23: Collect the elastic modulus E, Poisson's ratio v, and the vertical support stiffness matrix K of the track bed, and construct the track stiffness matrix K b , r ;

[0150] ;

[0151] ;

[0152] ;

[0153] Among them, l e represents the finite element length, e represents the index of the finite element, J represents the orbital interface torsion constant, G represents an intermediate variable, ⊕ represents assembling the element matrix into the global matrix according to the degree-of-freedom numbering, I y and I z respectively represent the inertia matrices of the orbital cross-section about the y-axis and z-axis, N E represents the number of finite elements.

[0154] Step 24: Collect the damping coefficient and modal damping ratio of the track, and calculate the track damping matrix C r ;

[0155] ; ; ;

[0156] Among them, M r is the track mass matrix, K r is the track stiffness matrix, α1 and β1 are the first and second track damping coefficients respectively, are the first and second damping factors respectively; is the modal damping ratio.

[0157] Step 25: Construct the track dynamic equation according to the track mass matrix M r , the track stiffness matrix K r , and the track damping matrix C r .

[0158] In this way, in Step 2, the above steps can be used to construct a corresponding track dynamic equation for each typical operating scenario.

[0159] Step 3: Construct the rigid-flexible dynamics equation of the car body;

[0160] M ta q + C ta q + K ta q = F ta q;

[0161] Among them, M ta represents the vehicle mass matrix, C ta represents the vehicle damping matrix, K ta represents the vehicle stiffness matrix, F ta q represents the vehicle external force vector, and q represents the vehicle rigid body degree-of-freedom vector.

[0162] Furthermore, Step 3 includes the following steps:

[0163] Step 31: Divide the vehicle into several finite elements, take the head and tail ends of the finite elements as mass points, and generate a sequence of mass points P1, P2,... P PG ; PG represents the total number of mass points;

[0164] In Step 31, it is mainly necessary to divide the vehicle into several finite elements, then return the mass within the finite elements to the corresponding mass points, and simplify the vehicle into a model composed of mass points. The method of dividing the vehicle into mass points is the same as the method of dividing the track into nodes, which will not be elaborated here.

[0165] Step 32: Collect the rigid body dynamics parameters, flexible car body parameters, modal reduction parameters, coupling term calculation parameters, and external excitation parameters of the vehicle;

[0166] Among them, the rigid body dynamics parameters include the total mass of the car body, the material density of the car body, the moment of inertia, and the suspension point coordinates; the rigid body dynamics parameters are extracted from the CAD model or test report.

[0167] The flexible car body parameters include the material density of the car body, elastic modulus, shear modulus, cross-sectional set parameters, and mass point division information; the flexible car body parameters are obtained through finite element preprocessing software.

[0168] The modal reduction parameters include the number of key degrees of freedom, the number of retained modal orders, suspension stiffness, and damping.

[0169] The coupling term calculation parameters include the rigid body Jacobian matrix and modal shape functions. The coupling term calculation parameters select the key degrees of freedom and the number of retained modal orders according to the simulation target, and are obtained by combining the suspension system parameters.

[0170] The external excitation parameters include wheel-rail forces and track irregularity excitations. The external excitation parameters are loaded and obtained from the multi-body dynamics model or measured data.

[0171] The elastic mode order, the internal-boundary degree-of-freedom coupling stiffness matrix, the fixed interface mode matrix, the internal degree-of-freedom mass matrix, the eigenvalue diagonal matrix, the modal shape function matrix, the rigid body displacement to suspension point conversion matrix, the local stiffness matrix of the suspension system, the transformation matrix from physical coordinates to modal coordinates, the overall vehicle stiffness matrix, and the modal coordinate to suspension point conversion matrix can be generated using the rigid body dynamics parameters, flexible vehicle body parameters, modal reduction parameters, coupling term calculation parameters, and external excitation parameters.

[0172] Step 33: Construct the vehicle mass matrix M ta ;

[0173] ;

[0174] where M rr represents the rigid body mass matrix, M rm represents the inertia coupling matrix, M mm represents the mode matrix; M mr represents the transpose matrix of the inertia coupling matrix;

[0175] ; T represents the transpose symbol;

[0176] ;

[0177] ; ;

[0178] ;

[0179] where cI x1 represents the equivalent moment of inertia of the first particle about the x-axis, cI y1 represents the equivalent moment of inertia of the first particle about the y-axis, cI z1 represents the equivalent moment of inertia of the first particle about the z-axis, cI xn represents the equivalent moment of inertia of the nth particle about the x-axis, cI yn represents the equivalent moment of inertia of the nth particle about the y-axis, cI zn represents the equivalent moment of inertia of the nth particle about the z-axis, cm x1 represents the translational mass of the first particle in the x-axis, cm y1 represents the translational mass of the first particle in the y-axis, cm z1 represents the translational mass of the first particle in the z-axis, cm xn represents the translational mass of the nth particle in the x-axis, cm yn represents the translational mass of the nth particle in the y-axis, cm znrepresents the translational mass of the nth particle along the z-axis; diag represents a diagonal matrix;

[0180] represents the transformation matrix that maps physical coordinates to modal coordinates, M represents the overall mass matrix of the flexible car body without reduction, represents the volume integral of the entire car body structure, dV represents the integral symbol, R is the free quantity matrix, 0 is the zero matrix of PB×L, and L is the number of retained elastic modal orders, is the boundary-induced displacement transfer matrix, and PB is the number of key degrees of freedom;

[0181] ; K II represents the internal degree-of-freedom stiffness matrix, K IB represents the internal-boundary degree-of-freedom coupling stiffness matrix, represents the fixed interface modal matrix; ; M II represents the internal degree-of-freedom mass matrix, represents the diagonal matrix of the first K eigenvalues; ρ represents the density of the car body material, represents the modal shape function matrix, and JD represents the Jacobian matrix of rigid body motion.

[0182] Step 34: Construct the vehicle stiffness matrix K ta ;

[0183] ;

[0184] K rr represents the vehicle rigid body stiffness matrix, K mm represents the modal stiffness matrix, K rm represents the stiffness coupling matrix, K mr is the transpose matrix of K rm ;

[0185] ; ; ;

[0186] T r represents the transformation matrix from rigid body displacement to the suspension point, K s represents the local stiffness matrix of the suspension system, represents the transformation matrix that maps physical coordinates to modal coordinates, K represents the overall stiffness matrix of the vehicle without reduction, T p represents the transformation matrix from modal coordinates to the suspension point.

[0187] Assume that the car body has 10 particles, the total number of degrees of freedom is 66, the number of key degrees of freedom is 8, and 10 orders of modes are retained:

[0188] , describe the relationship between the displacements of 8 suspension points and 6 rigid body degrees of freedom.

[0189] , the reduced modal stiffness matrix.

[0190] , the stiffness coupling connecting the rigid body motion and the 10th order elastic mode.

[0191] Through the above parameter definitions, the rigid-flexible coupling dynamic equation can be systematically constructed to realize the co-simulation of vehicle vibration and rigid body motion.

[0192] Step 35: Construct the vehicle damping matrix C ta ;

[0193] ;

[0194] where, C rr represents the damping matrix, C rm represents the damping coupling matrix, C mm represents the modal damping matrix, C mr represents the transpose matrix of the damping coupling matrix;

[0195] where, ; ; ;

[0196] T r represents the transformation matrix from rigid body displacement to suspension point, C s represents the suspension damping, α2 and β2 are the first vehicle damping coefficient and the second vehicle damping coefficient respectively, T p represents the transformation matrix from modal coordinate to suspension point, T represents the transpose matrix.

[0197] Step 36: Construct the rigid-flexible dynamics equation of the vehicle body according to the vehicle mass matrix M ta , the vehicle stiffness matrix K ta , and the vehicle damping matrix C ta ;

[0198] ;

[0199] where, q represents the vector of the rigid body degrees of freedom of the vehicle, including translational and rotational components; F r represents the external force vector of the rigid body degrees of freedom, F m represents the external force vector of the modal coordinates;

[0200] ; represents the modal shape function matrix, f ext represents the external distributed force density, denotes the volume integral of the entire vehicle body structure, and dV denotes the integral symbol.

[0201] Step 4: Couple the vehicle rigid-flexible dynamics equation and the track dynamics equation to generate a coupled equation;

[0202] The coupled equation is:

[0203] ;

[0204] M ta q + C ta q + K ta q = F ta q;

[0205] where M r denotes the track mass matrix, C r denotes the track damping matrix, K r denotes the track stiffness matrix, x r (t) denotes the track displacement vector, and F r (t) denotes the track external force vector;

[0206] M ta denotes the vehicle mass matrix, C ta denotes the vehicle damping matrix, K ta denotes the vehicle stiffness matrix, q denotes the vehicle rigid body degree-of-freedom vector, and F ta q denotes the vehicle external force vector.

[0207] Reference Figures 2 to 4 , Step 5: Obtain the load curve of the target vehicle when passing through a typical track scenario, and extract the vibration characteristics from the load curve, where the vibration characteristics are the equivalent load spectrum.

[0208] The equivalent load spectrum needs to be obtained through the coupled equation, and the specific method is as follows:

[0209] Step 51: Input the coupled equation into the DAP-Rail software.

[0210] Step 52: Input the operating scenario parameters, where the operating scenario parameters include line parameters, vehicle parameters, and load conditions.

[0211] The line parameters include gradient, curve radius, and track spectrum; the vehicle parameters include speed and axle load; the load conditions include traction force and braking force.

[0212] Reference Figure 5 , Step 53: The DAP-Rail software outputs the load curve corresponding to time, and extracts the equivalent load spectrum from the load curve. Figure 5The abscissa represents time in seconds, and the ordinate represents the load ratio KU, where KU = (Nk - N0)Nk / N0. N0 represents the standard load, N0 = 180 kN, and Nk represents the load at the current time point. When KU is greater than zero, it indicates that the current load is compressive, and when KU is less than zero, it indicates that the current load is tensile.

[0213] Reference Figure 6 , further, step 53 includes the following steps:

[0214] Step 531: Filter the load curve with a high-pass filter to obtain the time series X(t);

[0215] There is a lot of noise information in the load curve, so it needs to be filtered with a high-pass filter. The high-pass filter will filter out the lower-frequency information. In this way, the higher-frequency information can be retained. In practice, the probability of these higher-frequency information belonging to the correct information is higher, and it is more valuable to retain.

[0216] Step 532: Identify the maximum and minimum values of the time series X(t), and extract the local maximum and local minimum ; represents the s-th local maximum, represents the s-th local minimum, and generate the extreme value sequence ST;

[0217] ST = ;

[0218] Step 533: Starting from the first value in the extreme value sequence ST, gradually traverse the entire extreme value sequence ST. When the preset closing condition is met, extract a cycle SY, and record the amplitude SY1 and mean SY2 of this cycle;

[0219] ; ;

[0220] Extract all the cycles SY to generate a cycle list;

[0221] Step 534: Define the amplitude binning interval, and assign the amplitude of each cycle SY to the corresponding amplitude binning interval; output the binning count table;

[0222] Step 535: Obtain the amplitude SY1, cycle number Q of each cycle, and the material S-N curve parameters, generate the equivalent constant amplitude load amplitude, and convert the equivalent constant amplitude load amplitude into an equivalent load spectrum;

[0223] ;

[0224] where BY represents the number of amplitude binning intervals, b represents the index of the amplitude binning interval, Bb represents the number of cycles in the b-th amplitude bin interval, SY1 b represents the maximum amplitude of the b-th amplitude bin interval, mba represents the slope of the material S-N curve; N total represents the sum of the number of cycles.

[0225] The equivalent load spectrum is to convert the complex random load time history into a simplified and standardized load sequence through the fatigue damage equivalence principle. In the case of knowing the equivalent constant amplitude load amplitude, converting the equivalent load spectrum is the prior art and will not be elaborated here.

[0226] Step 6: Input the equivalent load spectrum into the pre-established structural reliability fusion analysis model for the spatio-temporal evolution of dynamic performance to generate the reliability probability of the vehicle running on this line for a long time.

[0227] Step 6 includes the following steps:

[0228] Step 61: Construct an LSTM model to extract temporal features from the equivalent load spectrum.

[0229] The LSTM model includes an input layer, an LSTM layer, and an output layer; the input layer is used to input the equivalent load spectrum, the LSTM layer is used to extract the temporal characteristics in the equivalent load spectrum, and the output layer is used to output the temporal features corresponding to the temporal characteristics.

[0230] The LSTM model in this solution essentially converts the information of an equivalent load spectrum into the temporal features of a temporal sequence. Compared with the spectrogram, the information density of the temporal features is higher.

[0231] Step 62: Construct two sub-network groups with the same structure and weight sharing;

[0232] The sub-network group is a CNN convolutional network model, which has the same structure, input, and output. Using two sub-network groups with the same structure can better extract the hidden information in the temporal features, identify the actual differences between the temporal features, and then accurately predict the reliability probability.

[0233] Step 63: Pre-construct a database related to the temporal features and reliability probability.

[0234] A large number of samples are stored in the database, and each sample includes a set of temporal features and reliability probability. The samples are obtained through actual experiments.

[0235] Step 64: Input the temporal features to be predicted into one sub-network group, and gradually input the temporal features in the database into the other sub-network group, calculate the similarity between the temporal features and the temporal features in the database, screen out the sample with the highest similarity from the database, and use the reliability probability of this temporal feature.

[0236] The essential reason for using two sub-network groups with the same structure and shared weights to calculate the similarity between samples in this application is that it is difficult to obtain samples, there are significant differences between samples, and it is impossible to obtain a sufficient number of samples in practice. Therefore, it is necessary to accurately analyze the internal differences between temporal features. For this reason, in this solution, using two sub-network groups with the same structure and shared weights to calculate similarity can better capture the differences between features, and then, with a smaller number of samples, the input temporal features can be regressed to the corresponding reliability probabilities.

[0237] The structural reliability fusion analysis model for the spatio-temporal evolution of dynamic performance in this application consists of two sub-network groups with the same structure and shared weights, as well as a database related to the temporal features and reliability probabilities.

[0238] Furthermore, the sub-network groups need to be trained before use. When training the sub-network groups, the loss function is:

[0239] ;

[0240] where, represents the loss function, NU represents the number of training samples, il represents the index of the training sample, y il represents the il th il represents the il th true label of the training sample, P

[0241] Embodiment 2: An analysis system for the fusion of the dynamics and structural reliability of a rail vehicle uses the aforementioned analysis method for the fusion of the dynamics and structural reliability of a rail vehicle to generate the reliability probability of the vehicle running on this line for a long time.

[0242] The above are only the preferred embodiments of this application and are not used to limit this application. For those skilled in the art, this application can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of this application shall be included in the protection scope of this application.

Claims

1. An analysis method for the integration of the dynamics and structural reliability of rail vehicles, characterized in that, The method includes the following steps: Step 1: Obtain the track operation topology data of the target vehicle, and identify typical operation scenarios based on line engineering characteristics; Step 2: Construct the track dynamics equations for each typical operation scenario; ； In the formula, M r represents the track mass matrix, C r represents the track damping matrix, K r represents the track stiffness matrix, x r (t) represents the track displacement vector, F r (t) represents the track external force vector; Step 3: Construct the rigid-flexible dynamics equations of the carbody; M ta q + C ta q + K ta q = F ta q; Among them, M ta represents the vehicle mass matrix, C ta represents the vehicle damping matrix, K ta represents the vehicle stiffness matrix, F ta q represents the vehicle external force vector, and q represents the vehicle rigid body degree of freedom vector; Step 4: Couple the rigid-flexible dynamics equations of the carbody and the track dynamics equations to generate coupled equations; The coupled equations are as follows: ； M ta q + C ta q + K ta q = F ta q; Step 5: Obtain the load curve of the target vehicle when passing through the typical track scenario, and extract the vibration characteristics from the load curve, where the vibration characteristics are the equivalent load spectrum; Step 6: Input the equivalent load spectrum into the pre-established structural reliability fusion analysis model for the spatio-temporal evolution of dynamic performance to generate the reliability probability of the vehicle during long-term operation on this line.

2. The analysis method for integrating the dynamics and structural reliability of rail vehicles according to claim 1, characterized in that Step 2 includes the following steps: Step 21: Divide the track into multiple finite elements, define two nodes at the head and tail ends of each finite element, and generate a node sequence N1, N2, … N n ; Record the geometric coordinates of each node, where n represents the total number of nodes; Step 22: Collect the orbital density and orbital cross-sectional area of the orbit to construct the orbital mass matrix M r ; Step 23: Collect the elastic modulus E, Poisson's ratio v, and vertical support stiffness matrix K of the track b , and construct the track stiffness matrix Kr; Step 24: Collect the damping coefficient and modal damping ratio of the track, and calculate the track damping matrix C r ; Step 25: Construct the track dynamic equation according to the track mass matrix M r , the track stiffness matrix K r , and the track damping matrix C r .

3. The analysis method for integrating the dynamics and structural reliability of a rail vehicle according to claim 1, wherein Step 3 includes the following steps: Step 31: Divide the vehicle into a number of finite elements, and take the head and tail ends of the finite elements as mass points to generate a sequence of mass points P1, P2, … P PG ; PG represents the total number of mass points; Step 32: Collect the rigid body dynamics parameters, flexible carbody parameters, modal reduction parameters, coupling term calculation parameters, and external excitation parameters of the vehicle; Step 33: Construct the vehicle mass matrix M ta ; Step 34: Construct the vehicle stiffness matrix K ta ; Step 35: Construct the vehicle damping matrix C ta ; Step 36: Construct the rigid-flexible dynamics equation of the car body according to the vehicle mass matrix M ta , the vehicle stiffness matrix K ta , the vehicle damping matrix C ta .

4. The analysis method for the integration of the dynamics and structural reliability of rail vehicles according to claim 2, characterized in that, Step 21 includes the following steps: Step 211: Collect the information of the track surface and determine the minimum node density ρ under typical operating scenarios q ; Step 212: Divide the track into N E finite elements, where the length of each finite element is l， Define the head and tail ends of each finite element as nodes; Step 213: Obtain the minimum vibration wavelength λ of the track, and set the finite element length based on the minimum vibration wavelength λ l , where l＜ 10 / λ; Step 214: Continuously adjust the finite element length until the node density ρ0 of the track is equal to the minimum node density ρ q ; Step 215: Traverse the entire track, add new nodes to the worn part and joint part of the track, and obtain the node sequence N1, N2, … N of the track n , where n represents the total number of nodes; Step 216: Add translational degrees of freedom and rotational degrees of freedom in the x, y, and z directions to each node, and record the geometric coordinates of each node.

5. The analysis method for integrating the dynamics and structural reliability of a rail vehicle according to claim 2, wherein Orbital mass matrix M r is as follows: ； Among them, I x1 represents the equivalent moment of inertia of the first node about the x-axis, I y1 represents the equivalent moment of inertia of the first node about the y-axis, I z1 represents the equivalent moment of inertia of the first node about the z-axis, I xn represents the equivalent moment of inertia of the nth node about the x-axis, I yn represents the equivalent moment of inertia of the nth node about the y-axis, I zn represents the equivalent moment of inertia of the nth node about the z-axis, m x1 represents the translational mass of the first node on the x-axis, m y1 represents the translational mass of the first node on the y-axis, m z1 represents the translational mass of the first node on the z-axis, m xn represents the translational mass of the nth node on the x-axis, m yn represents the translational mass of the nth node on the y-axis, m zn represents the translational mass of the nth node on the z-axis; diag represents a diagonal matrix; ； ； where, i represents the index of the node, A represents the cross-sectional area of the track, ρ represents the track density, l e represents the finite element length, e represents the index of the finite element, J represents the torsional constant of the track section, I y and I z respectively represent the inertia matrices of the track cross-section about the y-axis and the z-axis, m i represents the equivalent translational mass of the i-th node, I xi represents the equivalent moment of inertia of the i-th node about the x-axis, I yi represents the equivalent moment of inertia of the i-th node about the y-axis, I zi represents the equivalent moment of inertia of the i-th node about the z-axis; Track stiffness matrix K r is as follows: ； ； ； Among them, l e represents the finite element length, e represents the index of the finite element, J represents the track interface torsional constant, G represents the intermediate variable, ⊕ represents assembling the element matrix into the global matrix according to the degree of freedom numbering, K b represents the vertical support stiffness matrix of the ballast bed, E represents the elastic modulus of the track, v represents the Poisson's ratio, I y and I z respectively represent the inertia matrices of the track cross-section about the y-axis and z-axis, N E represents the number of finite elements; The orbital damping matrix is C r : ； ； ； Among them, M r is the track mass matrix, K r is the track stiffness matrix, α1 and β1 are the first track damping coefficient and the second track damping coefficient respectively, are the first damping factor and the second damping factor respectively, is the modal damping ratio.

6. The analysis method for the integration of the dynamics and structural reliability of rail vehicles according to claim 3, characterized in that, The rigid body dynamics parameters include the total mass of the carbody, the material density of the carbody, the moment of inertia, and the suspension point coordinates; The flexible carbody parameters include the material density of the carbody, the elastic modulus, the shear modulus, the cross-section set parameters, and the mass point division information; The modal reduction parameters include the number of key degrees of freedom, the retained modal order, the suspension stiffness, and the damping; The coupling term calculation parameters include the rigid body Jacobian matrix and the modal shape function, and the coupling term calculation parameters select the key degrees of freedom and the retained modal order according to the simulation target; The external excitation parameters include wheel-rail force and track irregularity excitation.

7. The analysis method for the integration of the dynamics and structural reliability of a rail vehicle according to claim 6, characterized in that Vehicle mass matrix M ta is as follows: ； Among them, M rr represents the rigid body mass matrix, M rm represents the inertia coupling matrix, M mm represents the modal matrix; M mr represents the transposed matrix of the inertia coupling matrix; ; T represents the transpose symbol; ； ； ； ; Among them, cI x1 represents the equivalent moment of inertia of the first particle about the x-axis, cI y1 represents the equivalent moment of inertia of the first particle about the y-axis, cI z1 represents the equivalent moment of inertia of the first particle about the z-axis, cI xn represents the equivalent moment of inertia of the nth particle about the x-axis, cI yn represents the equivalent moment of inertia of the nth particle about the y-axis, cI zn represents the equivalent moment of inertia of the nth particle about the z-axis, cm x1 represents the translational mass of the first particle on the x-axis, cm y1 represents the translational mass of the first particle on the y-axis, cm z1 represents the translational mass of the first particle on the z-axis, cm xn represents the translational mass of the nth particle on the x-axis, cm yn represents the translational mass of the nth particle on the y-axis, cm zn represents the translational mass of the nth particle on the z-axis; diag represents a diagonal matrix; represents the transformation matrix that maps physical coordinates to modal coordinates, M represents the overall mass matrix of the flexible car body without reduction, represents the volume integral of the entire car body structure, dV represents the integral symbol, R is the free quantity matrix, 0 is the zero matrix of PB×L, and L is the number of retained elastic modal orders, is the boundary-induced displacement transfer matrix, and PB is the number of key degrees of freedom; ; K II represents the internal degree-of-freedom stiffness matrix, K IB represents the internal-boundary degree-of-freedom coupling stiffness matrix, represents the fixed interface modal matrix; ; M II represents the internal degree-of-freedom mass matrix, represents the diagonal matrix of the first K eigenvalues; ρ represents the density of the car body material, represents the modal shape function matrix, JD represents the Jacobian matrix of rigid body motion; The vehicle stiffness matrix is K ta : ； K rr represents the vehicle rigid body stiffness matrix, K mm represents the modal stiffness matrix, K rm represents the stiffness coupling matrix, K mr is rm the transpose matrix of K; ； ； ; T r represents the transformation matrix for the rigid body displacement to the suspension point, K s represents the local stiffness matrix of the suspension system, represents the transformation matrix that maps physical coordinates to modal coordinates, K represents the unreduced overall stiffness matrix of the vehicle, T p represents the transformation matrix from modal coordinates to the suspension point; Vehicle damping matrix C ta is as follows: ； Among them, C rr represents the damping matrix, C rm represents the damping coupling matrix, C mm represents the modal damping matrix, C mr represents the transposed matrix of the damping coupling matrix; Among them, ; ; ; T r represents the transformation matrix from the rigid body displacement to the suspension point, C s represents the suspension damping, α2 and β2 are the first vehicle damping coefficient and the second vehicle damping coefficient respectively, T p represents the transformation matrix from the modal coordinates to the suspension point, and T represents the transpose matrix.

8. The analysis method for integrating the dynamics and structural reliability of a rail vehicle according to claim 1, characterized in that The typical operation scenarios include the operation route with the maximum turning radius, the uphill route with the maximum gradient, the downhill route with the maximum gradient, and the operation route at the highest speed.

9. The analysis method for the integration of the dynamics and structural reliability of a rail vehicle according to claim 1, characterized in that Step 5 includes the following steps: Step 51: Input the coupled equations into the DAP-Rail software; Step 52: Input the operation scenario parameters, where the operation scenario parameters include line parameters, vehicle parameters, and load conditions; Step 53: The DAP-Rail software outputs the load curve corresponding to time, and extracts the equivalent load spectrum from the load curve.

10. An analysis system integrating the dynamics and structural reliability of rail vehicles, characterized in that, Use the analysis method for the integration of track vehicle dynamics and structural reliability described in any one of claims 1 to 9 to generate the reliability probability of the vehicle during long-term operation on this line.

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