Vehicle bridge dynamic coupling optimization simulation method based on dynamics and finite elements

Abstract

The invention discloses a dynamic coupling optimization simulation method for a vehicle bridge based on dynamics and finite elements, and belongs to the field of urban rail transit, and the method comprises the steps: obtaining vehicle operation parameters and track beam parameters of a straddle type monorail train, collecting a track beam surface image, obtaining track beam surface wear data, and constructing a track beam wear evaluation model; the track beam surface wear data is imported into a track beam wear evaluation model to evaluate the track beam surface wear condition, a wheel load acting force evaluation model is constructed, track beam parameters are imported into the wheel load acting force evaluation model to evaluate the distribution condition of the wheel load acting force on the beam, and a tire stress evaluation model is constructed; the wear degree of the track beam, the wheel load acting force and the vehicle operation parameters are imported into the tire stress evaluation model to evaluate the tire stress condition, the train operation stability evaluation model is constructed, the train operation stability condition is evaluated through the tire stress condition, and the operation safety and the riding comfort are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of urban rail transit, and in particular to a vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite elements. BACKGROUND

[0002] Straddle-type monorail trains are widely used due to their small footprint and compact structure. However, when they run on bridges, curves, and uneven tracks, problems such as large vehicle vibration, stress concentration on the track beam, and excessive wheel-rail force may occur. The smoothness of train operation directly affects the ride comfort and the service life of the vehicle and the track. With long-term operation of the vehicle and the track, track wear and tire wear gradually accumulate, changing the wheel-rail contact state and affecting the vehicle vibration transmission, wheel-rail force distribution, and bridge stress state. The existing technology relies mainly on periodic manual detection or experience-based maintenance strategies for train smoothness evaluation, and does not fully utilize tire wear and track wear data for future smoothness prediction. There is a lack of real-time and quantitative prediction methods, making it difficult to accurately evaluate the long-term running smoothness of the train.

[0003] The application comprehensively considers vehicle operating parameters, track beam structural characteristics, bridge surface damage data, and tire material properties, establishes a dynamic interaction model between the vehicle and the bridge, uses beam vibration modal and modal shape information, combines material elastic modulus, cross-sectional moment of inertia, and other parameters to evaluate wheel load force and calculate stress uniformity, thereby reflecting the mutual influence of vehicle load and bridge vibration, evaluating the running smoothness of the vehicle, and improving the running safety and ride comfort. SUMMARY

[0004] In view of the deficiencies of the prior art, the application provides a vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite elements To achieve the above-mentioned purpose, the application provides the following technical solutions: The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite elements comprises the following specific steps: Obtain the vehicle operating parameters and track beam parameters of the straddle-type monorail train, and simultaneously collect track beam surface images to obtain track beam surface wear data; Construct a track beam wear evaluation model, and import the track beam surface wear data into the track beam wear evaluation model to evaluate the track beam surface wear; Construct a wheel load force evaluation model, and import the track beam parameters into the wheel load force evaluation model to evaluate the distribution of the wheel load force on the beam; Construct a tire stress evaluation model, and import the track beam wear degree, wheel load force, and vehicle operating parameters into the tire stress evaluation model to evaluate the tire stress; A train operation stability assessment model is constructed, and the stress conditions of the tires are imported into the train operation stability assessment model to evaluate the stability of train operation.

[0005] Preferably, the steps of obtaining the vehicle operating parameters and track beam parameters of the straddle-type monorail train, and simultaneously acquiring track beam surface images and obtaining track beam surface wear data, include the following specific steps: S11. Obtain the vehicle operating parameters of the straddle-type monorail, including train speed, tire standard material modulus, tire standard radius, and vehicle deflection angle. S12. Obtain the parameters of the track beam, including the number of beam vibration modes, mode shapes, standard material modulus of the track, beam material data, and beam cross-sectional area; S13. Acquire images of the track beam surface using a 3D laser scanner and a high-definition camera, and obtain crack data and steel structure corrosion data on the track beam surface through image processing.

[0006] Preferably, the construction of the track beam wear assessment model and the import of track beam surface wear data into the track beam wear assessment model to assess the track beam surface wear includes the following specific steps: S21. Substitute the crack data on the track beam surface into the crack damage index calculation formula to assess the crack damage. The crack damage index calculation formula is as follows: ,in, This is a crack density correction factor, determined based on the number of cracks per unit area. Let x be the number of cracks at position x. This represents the crack weighting coefficient at location x. Let q be the length of the q-th crack. This represents the limit value for crack length. Let q be the width of the q-th crack. denoted as the limit value of the crack width, and x as the spatial position along the longitudinal direction of the beam; S22. Substitute the steel structure corrosion data into the steel structure corrosion damage index calculation formula to assess the steel structure corrosion damage. The steel structure corrosion damage index calculation formula is as follows: ,in, This is the corrosion weighting coefficient. Let x be the area of ​​corrosion at location x. d is the area of ​​the steel structure, and d is the thickness of the steel structure. The depth of corrosion at location x; S23. Substitute the crack damage index and the steel structure corrosion damage index into the track beam wear degree calculation formula to assess the track beam wear condition. The track beam wear degree calculation formula is as follows: ,in, The weight of crack impact, The weight is affected by corrosion.

[0007] Preferably, the construction of the wheel load force assessment model, and the importation of the track beam parameters into the wheel load force assessment model to assess the distribution of wheel load forces on the beam, includes the following specific steps: S31. Substitute the number of vibration modes and mode shapes of the beam into the vibration response formula of the beam to evaluate the vibration of the track beam. The vibration response formula for the beam at position x at time t is: Where n is the number of beam vibration modes, For Liang's first Each mode shape has a functional form determined by the boundary conditions. For Liang in the Modal amplitude under different modes; S32. Substitute the beam's vibration response, beam material data, and beam cross-sectional area into the wheel load force calculation formula based on the Euler-Bernoulli beam equation to evaluate the distribution of wheel load forces on the beam. The wheel load force calculation formula is as follows: Where E is the elastic modulus of the beam material, reflecting the rigidity of the beam. The moment of inertia of the beam section determines the beam's bending capacity. Let be the vibration response of the beam at position x and time t. Let A be the density of the beam material and A be the mass term in the beam cross-sectional area equation. This represents the inertial effect of the beam.

[0008] Preferably, the construction of the tire stress assessment model, which imports the track beam wear degree, wheel load force, and vehicle operating parameters into the tire stress assessment model to assess the tire stress condition, includes the following specific steps: S41. The impact of track beam wear on train tires is evaluated in the tire stress calculation formula by substituting the track beam wear degree, wheel load force, and vehicle operating parameters. The formula for calculating the stress of the i-th tire at position x at time t is: ,in, The stress on the tires is caused by the wear of the track beam. The degree of wear on the track beam, This refers to the standard material modulus of tires. Here, r is the standard material modulus of the track, and r is the standard radius of the tire. Let velocity be the effect function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let x be the average speed of the vehicle at position x. The vehicle's base speed. As a speed-sensitive factor, the stress on all tires at the same moment is calculated. The tire-to-rail stiffness ratio is used to measure the tire's ability to buffer the rail force, affecting wheel-rail force, vibration, and wear. When the stiffness ratio is large, the beam vibration frequency is close to the modal frequency, which can easily cause resonance. When the stiffness ratio is small, the tire absorbs most of the vibration, reducing the resonance response amplitude. A high stiffness ratio accelerates tire wear and intensifies rail wear. A low stiffness ratio slows down tire wear, but the wheel-rail force is relatively stable.

[0009] Preferably, the construction of the train running stability assessment model, which involves importing the tire stress conditions into the model to assess the train's running stability, includes the following specific steps: S51. Substitute the stress of all tires at the same moment into the tire stress uniformity calculation formula to evaluate the stability of tire stress. The tire stress uniformity calculation formula is as follows: Where m is the total number of tires. The average stress on the tire at time t; S52. Compare the tire force uniformity calculation results with the train running stability threshold. If it is within the threshold range, the train is running safely and smoothly. If it exceeds the threshold range, a danger warning is issued immediately.

[0010] The vehicle-bridge dynamic coupling optimization simulation system based on dynamics and finite element method is implemented based on the aforementioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method, and specifically includes: The data acquisition module is used to acquire vehicle operating parameters and track beam parameters of straddle-type monorail trains, and at the same time collect track beam surface images and acquire track beam surface wear data. The track beam wear assessment module is used to assess the wear condition of the track surface through track beam surface wear data. The wheel load force assessment module is used to assess the distribution of wheel load forces on the beam based on the track beam parameters. The tire stress assessment module is used to assess the tire stress based on track beam wear, wheel load, and vehicle operating parameters. The train running stability assessment module is used to assess the smoothness of train operation by analyzing the stress on the tires.

[0011] An electronic device includes: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the above-mentioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method by calling the computer program stored in the memory.

[0012] A computer-readable storage medium is characterized by storing instructions that, when executed on a computer, cause the computer to perform the aforementioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method.

[0013] Compared with the prior art, the beneficial effects of this application are: This application obtains vehicle operating parameters and track beam parameters of straddle-type monorail trains, simultaneously acquires track beam surface images, obtains track beam surface wear data, constructs a track beam wear assessment model, imports the track beam surface wear data into the track beam wear assessment model to evaluate the track beam surface wear condition, constructs a wheel load force assessment model, imports the track beam parameters into the wheel load force assessment model to evaluate the distribution of wheel load force on the beam, constructs a tire stress assessment model, imports the track beam wear degree, wheel load force, and vehicle operating parameters into the tire stress assessment model to evaluate the tire stress condition, and constructs a train running stability assessment model, importing the tire stress condition into the train running stability assessment model to evaluate the train running stability condition, thereby improving operational safety and passenger comfort. Attached Figure Description

[0014] Figure 1 This is a schematic diagram of the overall process of the vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method in this application; Figure 2 This is a flowchart of the tire stress uniformity calculation for this application; Figure 3 This is a schematic diagram of the overall framework of the vehicle-bridge dynamic coupling optimization simulation system based on dynamics and finite element method in this application. Detailed Implementation

[0015] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0016] Example 1 Please see Figure 1 One embodiment provided in this application is as follows: Figure 1 As shown, the vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method includes the following specific steps: The vehicle operation parameters and track beam parameters of the straddle-type monorail train are obtained, and images of the track beam surface are collected to obtain track beam surface wear data. A track beam wear assessment model was constructed, and track beam surface wear data was imported into the track beam wear assessment model to evaluate the track beam surface wear condition; A wheel load force assessment model is constructed, and the track beam parameters are imported into the wheel load force assessment model to evaluate the distribution of wheel load forces on the beam; A tire stress assessment model was constructed, and the wear degree of the track beam, wheel load force and vehicle operating parameters were imported into the tire stress assessment model to evaluate the tire stress situation; A train operation stability assessment model is constructed, and the stress conditions of the tires are imported into the train operation stability assessment model to evaluate the stability of train operation.

[0017] In this embodiment, it should be specifically explained that obtaining the vehicle operating parameters and track beam parameters of the straddle-type monorail train, and simultaneously acquiring track beam surface images and obtaining track beam surface wear data, includes the following specific steps: S11. Obtain the vehicle operating parameters of the straddle-type monorail, including train speed, tire standard material modulus, tire standard radius, and vehicle deflection angle. S12. Obtain the parameters of the track beam, including the number of beam vibration modes, mode shapes, standard material modulus of the track, beam material data, and beam cross-sectional area; S13. Acquire images of the track beam surface using a 3D laser scanner and a high-definition camera, and obtain crack data and steel structure corrosion data on the track beam surface through image processing.

[0018] In this embodiment, it is necessary to specifically explain that constructing a track beam wear assessment model and importing track beam surface wear data into the track beam wear assessment model to assess the track beam surface wear includes the following specific steps: S21. Substitute the crack data on the track beam surface into the crack damage index calculation formula to assess the crack damage. The crack damage index calculation formula is as follows: ,in, This is a crack density correction factor, determined based on the number of cracks per unit area. Let x be the number of cracks at position x. Here is the crack weighting coefficient at location x. This location weight reflects the greater hazard caused by cracks in critical stress areas. Let q be the length of the q-th crack. This represents the limit value for crack length. Let q be the width of the q-th crack. The crack width limit is given by x, which represents the spatial position along the longitudinal direction of the beam. The severity of the cracks is comprehensively assessed by considering the quantity, length, width, and position weights, reflecting the potential threat of the cracks to the bridge structure performance. S22. Substitute the steel structure corrosion data into the steel structure corrosion damage index calculation formula to assess the steel structure corrosion damage. The steel structure corrosion damage index calculation formula is as follows: ,in, This is the corrosion weighting coefficient. Let x be the area of ​​corrosion at location x. d is the area of ​​the steel structure, and d is the thickness of the steel structure. The depth of corrosion at location x; S23. Substitute the crack damage index and the steel structure corrosion damage index into the track beam wear degree calculation formula to assess the track beam wear condition. The track beam wear degree calculation formula is as follows: ,in, The weight of crack impact, The weight is affected by corrosion.

[0019] In this embodiment, it is necessary to specifically explain that constructing a wheel load force assessment model and importing the track beam parameters into the wheel load force assessment model to evaluate the distribution of wheel load forces on the beam includes the following specific steps: S31. Substitute the number of vibration modes and mode shapes of the beam into the vibration response formula of the beam to evaluate the vibration of the track beam. The vibration response formula for the beam at position x at time t is: Where n is the number of beam vibration modes, For Liang's first The mode shape describes the beam in the _th ... The shape and functional form of each vibration mode are determined by the boundary conditions. For Liang in the The modal amplitude under the modal conditions reflects the beam's modal amplitude in the first modal. The magnitude of vibration over time under each vibration mode, and the multi-mode representation of the complexity of beam vibration; S32. Substitute the beam's vibration response, beam material data, and beam cross-sectional area into the wheel load force calculation formula based on the Euler-Bernoulli beam equation to evaluate the distribution of wheel load forces on the beam. The wheel load force calculation formula is as follows: Where E is the elastic modulus of the beam material, reflecting the rigidity of the beam. The moment of inertia of the beam section determines the beam's bending capacity. Let be the vibration response of the beam at position x and time t. Let A be the density of the beam material and A be the cross-sectional area of ​​the beam. The bending response of the beam is described using Euler-Bernoulli beam theory. The mass term in the equation... This represents the inertial effect of the beam, and the wheel load force is calculated through the bending and vibration of the beam.

[0020] In this embodiment, it is necessary to specifically explain that constructing a tire stress assessment model and importing track beam wear, wheel load, and vehicle operating parameters into the tire stress assessment model to evaluate the tire stress includes the following specific steps: S41. The impact of track beam wear on train tires is evaluated in the tire stress calculation formula by substituting the track beam wear degree, wheel load force, and vehicle operating parameters. The formula for calculating the stress of the i-th tire at position x at time t is: ,in, The stress on the tires is caused by the wear of the track beam. The degree of wear on the track beam, This refers to the standard material modulus of tires. Here, r is the standard material modulus of the track, and r is the standard radius of the tire. Let velocity be the effect function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let x be the average speed of the vehicle at position x. The vehicle's base speed. As a speed-sensitive factor, the stress on all tires at the same moment is calculated. Cracks, corrosion, and track wear on the bridge are transmitted to the tires through the wheel load. The tire-to-track stiffness ratio is used to measure the tire's ability to buffer track forces, affecting wheel-rail forces, vibration, and wear. When the stiffness ratio is large, the beam vibration frequency is close to the modal frequency, which can easily cause resonance. When the stiffness ratio is small, the tire absorbs most of the vibration, reducing the resonance response amplitude. A high stiffness ratio accelerates tire wear and exacerbates track wear. A low stiffness ratio slows down tire wear, but the wheel-rail forces remain relatively stable.

[0021] In this embodiment, it should be specifically explained that constructing a train running stability assessment model and importing the tire stress conditions into the model to assess the train's running stability includes the following specific steps: S51. Substitute the stress of all tires at the same moment into the tire stress uniformity calculation formula to evaluate the stability of tire stress. The tire stress uniformity calculation formula is as follows: Where m is the total number of tires. The average stress on the tires at time t is the uniformity of the stress on the train tires. Large differences in stress can lead to increased vibration, train shaking, and decreased comfort. S52. Compare the calculated tire force uniformity with the train running stability threshold. If it is within the threshold range, the train is running safely and smoothly. If it exceeds the threshold range, an immediate danger warning is issued. The stability threshold is determined based on vehicle dynamics characteristics, ride comfort standards, and safety limits for wheel-rail interaction. It reflects the acceptable range of tire force differences during train operation. When the tire force uniformity index is within the threshold range, it indicates that the force distribution of each tire is relatively uniform, the force state of the vehicle on the track is balanced, the running vibration is small, and the train is in a safe and stable running state. If the tire force uniformity index exceeds the threshold range, it indicates that the force difference of each tire is large, and there may be abnormalities such as a tire bearing excessive impact or severe local wear of the track, which leads to a decrease in train running stability and increased vehicle vibration. An immediate danger warning signal is triggered, prompting the operator to take corresponding measures.

[0022] It should be noted that the values ​​of various setting parameters in this embodiment are obtained as follows: acquire representative historical vehicle data and track data during the operation of straddle-type monorail trains, acquire historical train operation stability data, hire experts to manually judge whether the train operation stability meets the requirements, and substitute the acquired historical data into the calculation results and judgment results of each step in this embodiment, and then substitute them into the fitting software to output the values ​​of various setting parameters that meet the highest judgment accuracy. The advantages of this embodiment compared to the prior art are: This application obtains vehicle operating parameters and track beam parameters of straddle-type monorail trains, simultaneously acquires track beam surface images, obtains track beam surface wear data, constructs a track beam wear assessment model, imports the track beam surface wear data into the track beam wear assessment model to evaluate the track beam surface wear condition, constructs a wheel load force assessment model, imports the track beam parameters into the wheel load force assessment model to evaluate the distribution of wheel load force on the beam, constructs a tire stress assessment model, imports the track beam wear degree, wheel load force, and vehicle operating parameters into the tire stress assessment model to evaluate the tire stress condition, and constructs a train running stability assessment model, importing the tire stress condition into the train running stability assessment model to evaluate the train running stability condition, thereby improving operational safety and passenger comfort.

[0023] Example 2 like Figure 2As shown, the vehicle-bridge dynamic coupling optimization simulation system based on dynamics and finite element method is implemented based on the aforementioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method. Specifically, it includes a data acquisition module, a track beam wear assessment module, a wheel load force assessment module, a tire stress assessment module, and a train running stability assessment module. The data acquisition module is used to acquire vehicle operating parameters and track beam parameters of the straddle-type monorail train, and simultaneously collect track beam surface images to obtain track beam surface wear data. The track beam wear assessment module is used to assess the track surface wear condition using the track beam surface wear data. The wheel load force assessment module is used to assess the distribution of wheel load forces on the beam using the track beam parameters. The tire stress assessment module is used to assess the tire stress condition using the track beam wear degree, wheel load force, and vehicle operating parameters. The train running stability assessment module is used to assess the train running stability using the tire stress condition.

[0024] Example 3 This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the aforementioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method by calling the computer program stored in memory.

[0025] This electronic device can vary considerably depending on its configuration or performance. It may include one or more Central Processing Units (CPUs) and one or more memories, wherein the memory stores at least one computer program. This computer program is loaded and executed by the processor to implement the dynamic coupling optimization simulation method for vehicle-bridge based on dynamics and finite element methods provided in the above-described embodiment. The electronic device may also include other components for implementing its functions. For example, it may have wired or wireless network interfaces and input / output interfaces for data input and output. Details will not be elaborated upon in this embodiment.

[0026] Example 4 This embodiment proposes a computer-readable storage medium on which an erasable and rewritable computer program is stored. When the computer program runs on the computer device, it causes the computer device to perform the above-mentioned vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method.

[0027] For example, computer-readable storage media can be read-only memory (ROM), random access memory (RAM), compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage devices.

[0028] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired network and / or wireless network. A computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

Claims

1. A vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method, characterized in that, It includes the following specific steps: The vehicle operation parameters and track beam parameters of the straddle-type monorail train are obtained, and images of the track beam surface are collected to obtain track beam surface wear data. A track beam wear assessment model was constructed, and track beam surface wear data was imported into the track beam wear assessment model to evaluate the track beam surface wear condition; A wheel load force assessment model is constructed, and the track beam parameters are imported into the wheel load force assessment model to evaluate the distribution of wheel load forces on the beam; A tire stress assessment model was constructed, and the wear degree of the track beam, wheel load force and vehicle operating parameters were imported into the tire stress assessment model to evaluate the tire stress situation; A train operation stability assessment model is constructed, and the stress conditions of the tires are imported into the train operation stability assessment model to evaluate the stability of train operation.

2. The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in claim 1, characterized in that, The process of obtaining vehicle operating parameters and track beam parameters for straddle-type monorail trains, and simultaneously acquiring track beam surface images and track beam surface wear data, includes the following specific steps: S11. Obtain the vehicle operating parameters of the straddle-type monorail, including train speed, tire standard material modulus, tire standard radius, and vehicle deflection angle. S12. Obtain the parameters of the track beam, including the number of beam vibration modes, mode shapes, standard material modulus of the track, beam material data, and beam cross-sectional area; S13. Acquire images of the track beam surface using a 3D laser scanner and a high-definition camera, and obtain crack data and steel structure corrosion data on the track beam surface through image processing.

3. The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in claim 2, characterized in that, The construction of the track beam wear assessment model, and the importation of track beam surface wear data into the track beam wear assessment model to assess the track beam surface wear, includes the following specific steps: S21. Substitute the crack data on the track beam surface into the crack damage index calculation formula to assess the crack damage. The crack damage index calculation formula is as follows: ,in, This is the crack density correction factor. Let x be the number of cracks at position x. This represents the crack weighting coefficient at location x. Let q be the length of the q-th crack. This represents the maximum crack length. Let q be the width of the q-th crack. denoted as the limit value of the crack width, and x as the spatial position along the longitudinal direction of the beam; S22. Substitute the steel structure corrosion data into the steel structure corrosion damage index calculation formula to assess the steel structure corrosion damage. The steel structure corrosion damage index calculation formula is as follows: ,in, This is the corrosion weighting coefficient. Let x be the area of ​​corrosion at location x. d represents the area of ​​the steel structure, and d represents the thickness of the steel structure. The depth of corrosion at location x; S23. Substitute the crack damage index and the steel structure corrosion damage index into the track beam wear degree calculation formula to assess the track beam wear condition. The track beam wear degree calculation formula is as follows: ,in, The weight of crack impact, The weight is affected by corrosion.

4. The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in claim 3, characterized in that, The construction of the wheel load force assessment model, and the importation of the track beam parameters into the wheel load force assessment model to evaluate the distribution of wheel load forces on the beam, includes the following specific steps: S31. Substitute the number of vibration modes and mode shapes of the beam into the vibration response formula of the beam to evaluate the vibration of the track beam. The vibration response formula for the beam at position x at time t is: Where n is the number of beam vibration modes, For Liang's first Each mode shape has a functional form determined by the boundary conditions. For Liang in the Modal amplitude under different modes; S32. Substitute the beam's vibration response, beam material data, and beam cross-sectional area into the wheel load force calculation formula based on the Euler-Bernoulli beam equation to evaluate the distribution of wheel load forces on the beam. The wheel load force calculation formula is as follows: Where E is the elastic modulus of the beam material. Let the moment of inertia of the beam section be . Let be the vibration response of the beam at position x and time t. Let A be the density of the beam material and A be the cross-sectional area of ​​the beam.

5. The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in claim 4, characterized in that, The construction of the tire stress assessment model, which incorporates track beam wear, wheel load, and vehicle operating parameters to evaluate tire stress, includes the following specific steps: S41. The impact of track beam wear on train tires is evaluated in the tire stress calculation formula by substituting the track beam wear degree, wheel load force, and vehicle operating parameters. The formula for calculating the stress of the i-th tire at position x at time t is: ,in, The stress on the tires is caused by the wear of the track beam. The degree of wear on the track beam, This refers to the standard material modulus of tires. Here, r is the standard material modulus of the track, and r is the standard radius of the tire. Let velocity be the influence function. Let be the vehicle deflection angle, where the formula for calculating the speed influence function is: ,in, Let x be the average speed of the vehicle at position x. The vehicle's base speed. As a speed-sensitive factor, calculate the stress on all tires at the same moment.

6. The vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in claim 5, characterized in that, The construction of the train operation stability assessment model, which incorporates the tire stress conditions into the model to evaluate the train's operational stability, includes the following specific steps: S51. Substitute the stress of all tires at the same moment into the tire stress uniformity calculation formula to evaluate the stability of tire stress. The tire stress uniformity calculation formula is as follows: Where m is the total number of tires. The average stress on the tire at time t; S52. Compare the tire force uniformity calculation results with the train running stability threshold. If it is within the threshold range, the train is running safely and smoothly. If it exceeds the threshold range, a danger warning is issued immediately.

7. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor is characterized in that it executes the vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in any one of claims 1-6 by calling the computer program stored in the memory.

8. A computer-readable storage medium, characterized in that, The system stores instructions that, when executed on a computer, cause the computer to perform the vehicle-bridge dynamic coupling optimization simulation method based on dynamics and finite element method as described in any one of claims 1-6.

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