A full-field stress reconstruction method and system for key parts of rail vehicles

Abstract

The present invention provides a method and system for full-field stress reconstruction of key rail vehicle parts. The method comprises: determining the placement of sensors at key rail vehicle parts; gradually selecting sensors using a sequence method until the number of sensors reaches a preset value; deploying fiber Bragg grating (FBG) sensors at selected locations in the region of interest; and inverting the stress field distribution in the measured region using a stress-strain reconstruction method based on modal superposition and regularization, based on multi-point strain measurement data provided by the FBG sensors. This invention achieves full-field stress reconstruction of rail vehicles using the modal superposition method.

Description

Technical Field

[0001] The present invention belongs to the field of monitoring technology, and in particular relates to a full-field stress reconstruction method and system for key parts of a rail vehicle. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Rail transit operations are becoming faster and more intensive. Vehicles in service must not only withstand fatigue and impact loads, but also harsh environments such as temperature, humidity, and salt spray. This places higher demands on the safety and reliability of key load-bearing parts of trains.

[0004] At present, rail vehicle operation and maintenance have generally adopted a safety assurance system of "operational maintenance + advanced maintenance" and online auxiliary monitoring, but still face various problems such as the need for manual intervention, low efficiency, large demand for parts replacement, and high cost, and the conditions for implementing "condition-based maintenance" are not yet in place.

[0005] To accurately monitor the damage state of critical high-speed train structures in real time and rapidly assess its impact on structural reliability, some rail transit practitioners have proposed drawing on structural health management concepts from the aerospace industry. Building on existing monitoring technologies, they aim to develop state assessment, fault prediction, and decision-making support technologies applicable to critical rail vehicle structures. "Digital twins" are a crucial foundation for structural health monitoring technology. Digital twins leverage data from physical models, sensor updates, and operational history to integrate multidisciplinary, multi-physics, multi-scale, and multi-probabilistic simulation processes, mapping them in virtual space to reflect the entire lifecycle of the corresponding physical equipment. However, systematic research on the theoretical and technical aspects of digital twins in China began relatively late, and research on their application in the rail transit industry is fragmented, lacking a comprehensive theoretical and technical framework. Consequently, full-field stress reconstruction of critical rail vehicle components, and consequently, the internal stress state of the structure, remains inaccessible. Summary of the Invention

[0006] To address the above-mentioned issues, the present invention proposes a full-field stress reconstruction method for key parts of rail vehicles. The present invention uses the structural surface stress (strain) data collected by sensors to obtain full-field strain information of the three-dimensional normal strain and three-dimensional shear strain of the complete structure through a reconstruction method.

[0007] According to some embodiments, the present invention adopts the following technical solutions:

[0008] In the first aspect, a method for full-field stress reconstruction of key parts of a rail vehicle is disclosed, comprising:

[0009] Determine the placement of sensors at key locations on rail vehicles;

[0010] The sequential method is used to select sensors step by step until the number of sensors reaches the preset value;

[0011] deploying fiber Bragg grating sensors at selected locations in the region of interest;

[0012] Based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, the stress field distribution of the measured area is inverted by using a stress-strain reconstruction method based on the modal superposition method and the regularization method.

[0013] As a further technical solution, the following are specifically considered when determining the placement of sensors at key locations on rail vehicles:

[0014] Perform finite element meshing on the three-dimensional model of the rail vehicle structure and determine the external constraints based on the structure's fixed form in the vehicle;

[0015] Calculate the finite element strain mode. The number of modes should include the overall vibration mode and local vibration mode of the structure. Finally, the strain mode mode matrix and modal strain energy are obtained.

[0016] Based on the Bayesian system identification theory, with the goal of minimizing the information entropy of the modal coordinates, the step-by-step accumulation method is used to obtain the optimal measurement point position. The number of measurement points should not be less than the number of modes.

[0017] As a further technical solution, after the optimal measuring point position is determined, it also includes: based on the fact that the sensor can only measure unidirectional strain, combined with the three-dimensional model of the structure, deleting the measuring points in the optimal measuring point set that are not convenient for actual arrangement, or fine-tuning their positions for later arrangement.

[0018] As a further technical solution, a sequence method is used to select sensors step by step. The specific steps are as follows:

[0019] Step (1) Calculate the strain modes and modal strain energy of the frame, and select the large strain energy degree of freedom contained in all modes as the candidate set;

[0020] Step (2) selects one degree of freedom from the candidate set in turn, adds it to the sensor set, calculates the information entropy of the modal coordinates corresponding to the sensor set, retains the degree of freedom with the smallest information entropy, and deletes it from the candidate set;

[0021] Step (3) repeats step (2) until the number of sensors reaches a preset value.

[0022] As a further technical solution, fiber Bragg grating sensors are arranged at selected locations in the area of interest:

[0023] Before manufacturing the fiber Bragg grating sensor, the three-dimensional coordinates of the measuring points on the structure are measured to determine the length of the fiber optic cable;

[0024] Fiber Bragg grating temperature sensors that are not affected by strain are deployed separately for temperature compensation.

[0025] As a further technical solution, the multi-point strain measurement data provided by the fiber Bragg grating sensor is processed to eliminate abnormal points and reduce noise. Then, a stress-strain reconstruction method based on modal superposition and regularization method is used to invert the stress field distribution of the measured area.

[0026] As a further technical solution, the stress field distribution of the measured area is inverted. The specific process is as follows:

[0027] The measured strain data d in a certain direction is obtained through the fiber Bragg grating sensor. These measurement points correspond to the strain mode vibration matrix;

[0028] According to d and the variable mode vibration matrix corresponding to the strain sensor, the modal coordinate vector q is obtained by using the regularization method;

[0029] Based on the modal coordinate vector and the modal superposition method, the full-field stress expression of the structure is obtained: ε=ψ all q, where ψ all are the strain mode shapes of all nodes in the finite element model.

[0030] In the second aspect, a full-field stress reconstruction system for key parts of a rail vehicle is disclosed, comprising:

[0031] The position determination module is configured to: determine the placement position of the sensor at a key position of the rail vehicle;

[0032] The sensor selection module is configured to: select sensors step by step using a sequence method until the number of sensors reaches a preset value;

[0033] deploying fiber Bragg grating sensors at selected locations in the region of interest;

[0034] The full-field stress reconstruction module is configured to: based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, adopt a stress-strain reconstruction method based on the modal superposition method and the regularization method to invert the stress field distribution of the measured area.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] This paper proposes a modal superposition method for full-field stress reconstruction of rail vehicles. Specifically, through rational design and layout, unidirectional fiber Bragg grating (FBG) sensors are deployed at key locations on the rail vehicle. Data processing is used to obtain the measured stresses at these monitoring points. Finite element modal calculations are then performed on the structure. Based on the strain modal shapes and measured stresses, the full-field stress of the structure is reconstructed. Because all degrees of freedom in the structure share the same modal coordinate q, the modal shapes derived from the finite element calculations for all degrees of freedom, including the normal stresses in three directions and the shear stresses in three directions, can be combined to reconstruct the full-field stress of the structure. This method offers the advantage of being able to determine the internal stress state of the structure simply by placing unidirectional sensors on the surface.

[0037] The proposed modal identification method based on optimized sensor configuration, based on Bayesian system identification theory, uses the reciprocal of the sum of normalized modal strain energies as the main diagonal element of the prediction error covariance matrix; the off-diagonal elements are represented by exponential correlation equations that combine the distance between measurement points and the response level. With the goal of minimizing information entropy, a stepwise accumulation method is used to determine the optimal measurement point locations. This method has the advantage of being able to quickly identify optimal sensor locations even when the number of structural degrees of freedom is large, improving computational efficiency.

[0038] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention.

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0041] Figure 1 This is a schematic diagram of the monitoring position of the rail vehicle body bolster structure;

[0042] Figure 2 This is a schematic diagram of the fiber Bragg grating sensor layout;

[0043] Figure 3 Optimize the configuration flow chart for the sensor. DETAILED DESCRIPTION

[0044] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0045] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0046] It should be noted that the terms used herein are intended only to describe specific embodiments and are not intended to limit the exemplary embodiments of the present invention. As used herein, unless the context clearly indicates otherwise, the singular is intended to include the plural. Furthermore, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they specify the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0047] Example 1:

[0048] This embodiment discloses a method for reconstructing full-field stresses in critical rail vehicle parts. Fiber Bragg grating sensors are deployed at key locations on the rail vehicle, with sensor positions determined through mathematical derivation. Based on the measured sensor data and in conjunction with the finite element method, the stress field distribution of the entire structure is reconstructed. Because the structural stress field includes stress information at all external and internal locations, it achieves the goal of assessing the integrity of critical rail vehicle parts. Furthermore, the reconstructed full-field stresses are updated as the measured data obtained by the sensors is updated, achieving real-time monitoring.

[0049] The technical solution of the present invention can combine the data provided by structural health monitoring technology, map it to a virtual model of the entire complete structure and display it in real time, thereby achieving real-time diagnosis of the structural status of key parts of in-service rail vehicles and realizing real-time monitoring and evaluation of vehicle structural integrity.

[0050] Full-field stress reconstruction: This involves reconstructing the surface stress (strain) data collected by sensors to obtain full-field strain information for the complete structure, including three-dimensional normal strain and three-dimensional shear strain. This method maps the measured data onto the simulation model, bridging the gap between the real and the twin, enabling structural integrity monitoring.

[0051] In the technical solution disclosed in the present disclosure, a method for full-field stress reconstruction of key parts of a rail vehicle is disclosed, which mainly includes the following steps:

[0052] Step 1: Determine the sensor location:

[0053] (1) Use 3D modeling software to build a 3D model of the vehicle body underframe bolster structure, perform finite element meshing on the 3D model, and determine external constraints, such as the fixing form and fixing position, based on the installation form of the bolster under the vehicle;

[0054] (2) According to the dynamics theory, the modal analysis module of the finite element commercial software is used to calculate the strain mode. The modal number should include the overall vibration mode and local vibration mode of the structure. Finally, the strain modal vibration mode matrix and modal strain energy are obtained. The mathematical derivation is as follows:

[0055] The structural dynamic response of a complex linear system with N degrees of freedom can be expressed by the following second-order differential equation:

[0056]

[0057] Where: u(t)∈N d is the node displacement; M, C, K are the structural mass, damping, and stiffness matrices; Nd is the total degree of freedom of the node; F is the external excitation (load or acceleration) vector;

[0058] Expand equation (1) in the modal space and let:

[0059]

[0060] Where: is the displacement modal matrix of all nodes of the structure, q(t) is the modal coordinate, which indicates the contribution of each mode of the structure to the deformation and is independent of the coordinate.

[0061] According to the principles of elastic mechanics, the relationship between the strain tensor and spatial displacement of a structure can be expressed as

[0062]

[0063] The spatial shear strain can be expressed as:

[0064]

[0065] According to the expression of displacement in each direction in formula (3), combining formulas (3)-(4), we can obtain:

[0066]

[0067]

[0068] Where: ψ=[ψ xx ψ yy ψ zz ψ xy ψ xz ψ yz ] T This is the strain modal matrix of the structure.

[0069] It can be seen from Equations (5) and (6) that the strain components in all directions of the structure have the same modal coordinates. Since the strain mode shapes can be calculated by the finite element method, after the modal coordinates are identified through the measured data, all the strain components of the structure can be inverted.

[0070] The modal strain energy of the nth degree of freedom of the structure can be expressed as:

[0071]

[0072] Where: MSEn is the modal strain energy, kn is the stiffness matrix of the nth degree of freedom.

[0073] (3) The optimal selection of sensor positions can be attributed to the problem of identifying modal coordinates (system parameters) through measured data (system output response). Based on Bayesian system identification theory, the process of parameter identification is also the process of minimizing parameter uncertainty. When the uncertainty of the parameters reaches the minimum value, it can be considered that the optimal parameters are obtained. According to information theory, the degree of uncertainty can be characterized by the information entropy of the parameters. The expression of the information entropy of the modal coordinates (parameters to be identified) is:

[0074]

[0075] h(q,L,C)=[LΨ] T [LCL T ] -1 [LΨ] (9)

[0076] Where H(L, C, q) is the information entropy, h(q, L, C) is the information matrix, C is the covariance matrix of the error in the modal parameter identification process, L is the position observation matrix corresponding to the selected degree of freedom, and det(·) represents the determinant of the matrix.

[0077] From formula (8), we can see that in order to maximize the amount of test information and reduce the uncertainty of parameter identification results, the information entropy H(L, C, q) should be minimized as much as possible, that is, the information matrix The determinant of is maximized and is determined only by the strain modal matrix Ψ, the observation matrix L and the covariance matrix C of the error.

[0078] The inverse of the sum of the normalized modal strain energies is used as the main diagonal element of the prediction error covariance matrix; the off-diagonal elements are represented by exponential correlation equations that combine the distance between the measurement points and the response level, specifically:

[0079]

[0080]

[0081] Where: δ ij represents the distance between degrees of freedom; b represents the correlation index between degrees of freedom; ∑ s are the off-diagonal elements of the error covariance matrix; Σ t is the main diagonal element of the error covariance matrix.

[0082] With the goal of minimizing information entropy, the sequence method is used to obtain the optimal measurement point location. According to the principle of modal objectivity, the number of measurement points should not be less than the number of modes.

[0083] (4) Since the sensor can only measure unidirectional strain, combined with the three-dimensional model of the structure, the measuring points in the optimal measuring point set that are not convenient for actual arrangement are deleted, or their positions are fine-tuned to facilitate later arrangement.

[0084] Step 2: Use the sequence method to select sensors step by step. The steps are as follows:

[0085] (1) Calculate the structural strain mode shape and modal strain energy through the modal analysis module in the finite element software, and select the large strain energy degree of freedom contained in all modes as the candidate set;

[0086] (2) Select one degree of freedom from the candidate set in turn and add it to the observation matrix L. Calculate the information matrix according to formula (9): Determinant, retain the L corresponding to the maximum value of the determinant, and delete the degree of freedom of this measure from the alternative set;

[0087] (3) Repeat step (2) until the number of sensors reaches the preset value. Figure 3 shown.

[0088] Step 3: Arrange fiber Bragg grating sensors at selected locations in the area of interest, such as Figure 1 and Figure 2 Before manufacturing the fiber Bragg grating sensor, the three-dimensional coordinates of the measuring points on the structure are measured to determine the length of the fiber optic cable.

[0089] The sensor layout should comply with the following principles:

[0090] (1) Having the ability to monitor areas of concern;

[0091] (2) Make full use of the monitoring characteristics and monitoring range of fiber Bragg grating monitoring technology;

[0092] (3) The sensor and its circuit layout do not occupy the space of the original vehicle structure;

[0093] (4) Adapt to the complex service environment, vibration, shock and strong electromagnetic interference of rail vehicles.

[0094] In addition, in order to eliminate the influence of ambient temperature on the strain monitoring results of fiber Bragg grating sensors and ultrasonic guided waves of piezoelectric smart layer, fiber Bragg grating temperature sensors and piezoelectric smart layer temperature sensors that are not affected by strain and ultrasonic guided waves are deployed respectively for temperature compensation.

[0095] Step 4: Eliminate outliers and reduce noise in the fiber Bragg grating sensor data. Based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, a stress-strain reconstruction method based on modal superposition and regularization is used to invert the stress field distribution in the measured area. This includes the following steps:

[0096] (1) The measured strain data d in a certain direction are obtained by the fiber Bragg grating sensor. The strain mode matrix corresponding to these measuring points is Ψ (m×n) ,m>n, where m represents the number of measurement points, n represents the number of modes, and Ψ represents the strain mode shape;

[0097] (2) According to d and Ψ (m×n) , m>n, the modal coordinate vector q can be obtained. This process is a typical inversion problem, which is characterized by the fact that the slight noise in the data will cause the results to diverge seriously and lose practical significance. In order to alleviate the degree of divergence of the results, the Tikhonov regularization method is used to solve it.

[0098] The Tikhonov regularization method reduces ill-posedness by defining the optimization problem. The optimization function can be expressed as:

[0099]

[0100] Where: represents the square of the matrix norm; λ is the regularization parameter, which balances the relative size between the norm of the solution and the norm of the residual.

[0101] The damped least squares solution of formula (12) is:

[0102] q=(ψ T ψ+λ 2 I) -1 ψ T d (13)

[0103] right Perform singular value decomposition:

[0104]

[0105] Where: r It is a diagonal matrix whose elements are all non-zero singular values κ arranged from large to small; U and V are square matrices of order m and n respectively.

[0106] Formula (13) can be rewritten as:

[0107]

[0108] (3) According to the modal superposition method, the full-field strain expression of the structure is obtained: ε = ψ all q, where is the modal vibration shape of all nodes of the finite element model, which is calculated by the modal analysis module of the finite element commercial software, and ε is the full-field strain.

[0109] In the implementation examples of the present disclosure, a full-field stress reconstruction method for key parts of rail vehicles is proposed based on sensor optimization theory and a regularized solution method for ill-posed problems. Fiber Bragg grating monitoring technology is used to obtain the time history of stress components in key parts. Combined with the finite element modal calculation results, a regularized method is used to reconstruct the global stress field distribution of the entire structure, thereby performing accurate and real-time structural integrity assessment of key parts of rail vehicles.

[0110] This invention overcomes the drawback of the common practice in the rail vehicle field of placing sensors in limited locations based on experience, which prevents real-time acquisition of the global stress field of the complete structure. It enables the comprehensive application of structural health monitoring technology in the rail vehicle field. This invention facilitates the development of real-time visualization systems for monitoring data evaluation, as well as the association of monitoring data with virtual sensor channels in simulation models.

[0111] Example 2:

[0112] Based on the method of embodiment 1, a full-field stress reconstruction system for key parts of a rail vehicle is disclosed, comprising:

[0113] The position determination module is configured to: determine the placement position of the sensor at a key position of the rail vehicle;

[0114] The sensor selection module is configured to: select sensors step by step using a sequence method until the number of sensors reaches a preset value;

[0115] deploying fiber Bragg grating sensors at selected locations in the region of interest;

[0116] The full-field stress reconstruction module is configured to: based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, adopt a stress-strain reconstruction method based on the modal superposition method and the regularization method to invert the stress field distribution of the measured area.

[0117] Example 3:

[0118] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method of the first embodiment described above when executing the program.

[0119] Example 4

[0120] The purpose of this embodiment is to provide a computer-readable storage medium.

[0121] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the method in the first embodiment.

[0122] The steps involved in the apparatuses of Examples 2, 3, and 4 above correspond to those of Method Example 1. For detailed implementations, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media containing one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any method of the present invention.

[0123] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0124] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0125] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0127] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

[0128] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A full-field stress reconstruction method for key parts of a rail vehicle, characterized by: include: Determine the placement of sensors at key locations on rail vehicles; specifically: Perform finite element meshing on the three-dimensional model of the rail vehicle structure and determine external constraints; Calculate the finite element strain mode. The number of modes should include the overall vibration mode and local vibration mode of the structure. Finally, the strain mode mode matrix and modal strain energy are obtained. Based on Bayesian system identification theory, the inverse of the sum of normalized modal strain energies is used as the main diagonal element of the prediction error covariance matrix; the off-diagonal elements are represented by exponential correlation equations that combine the distance between the measurement points and the response level. With the goal of minimizing information entropy, the optimal measurement point position is obtained using the step-by-step accumulation method. The number of measurement points should not be less than the number of modes. The sequential method is used to select sensors step by step until the number of sensors reaches the preset value; deploying fiber Bragg grating sensors at selected locations in the region of interest; Based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, the stress field distribution of the measured area is inverted by using a stress-strain reconstruction method based on the modal superposition method and the regularization method.

2. A method for full-field stress reconstruction of key parts of a rail vehicle as claimed in claim 1, characterized in that: After the optimal measuring point location, it also includes: based on the fact that the sensor can only measure unidirectional strain, combined with the three-dimensional model of the structure, deleting the measuring points in the optimal measuring point set that are not convenient for actual arrangement, or fine-tuning their positions for later arrangement.

3. A method for full-field stress reconstruction of key parts of a rail vehicle according to claim 1, characterized in that: The sequential method is used to select sensors step by step. The specific steps are as follows: Step (1) Calculate the strain modes and modal strain energy of the frame, and select the large strain energy degree of freedom contained in all modes as the candidate set; Step (2) selects one degree of freedom from the candidate set in turn, adds it to the sensor set, calculates the information entropy of the modal coordinates corresponding to the sensor set, retains the degree of freedom with the smallest information entropy, and deletes it from the candidate set; Step (3) repeats step (2) until the number of sensors reaches a preset value.

4. A method for full-field stress reconstruction of key parts of a rail vehicle as claimed in claim 1, characterized in that: Arrange fiber Bragg grating sensors at selected locations in the area of interest: Before manufacturing the fiber Bragg grating sensor, the three-dimensional coordinates of the measuring points on the structure are measured to determine the length of the fiber optic cable; Fiber Bragg grating temperature sensors that are not affected by strain are deployed for temperature compensation.

5. A method for full-field stress reconstruction of key parts of a rail vehicle as claimed in claim 1, characterized in that: The multi-point strain measurement data provided by the fiber Bragg grating sensor are processed by eliminating abnormal points and reducing noise. Then, a stress-strain reconstruction method based on modal superposition and regularization method is used to invert the stress field distribution of the measured area.

6. A method for full-field stress reconstruction of key parts of a railway vehicle as claimed in claim 1, characterized in that: Invert the stress field distribution of the measured area. The specific process is as follows: The measured strain data d in a certain direction is obtained through the fiber Bragg grating sensor. These measurement points correspond to the strain mode vibration matrix; According to d and the strain mode shape matrix, the modal coordinate vector q can be obtained by regularization method; Based on the modal coordinate vector and the modal superposition method, the full-field stress expression of the structure is obtained: ε=ψ all q, where ψ all are the mode shapes of all nodes of the finite element model.

7. A rail vehicle key part full-field stress reconstruction system, using a rail vehicle key part full-field stress reconstruction method according to claim 1, characterized in that: include: The position determination module is configured to: determine the placement of sensors at key locations of the rail vehicle; The sensor selection module is configured to: select sensors step by step using a sequence method until the number of sensors reaches a preset value; deploying fiber Bragg grating sensors at selected locations in the region of interest; The full-field stress reconstruction module is configured to: based on the multi-point strain measurement data provided by the fiber Bragg grating sensor, adopt a stress-strain reconstruction method based on the modal superposition method and the regularization method to invert the stress field distribution of the measured area.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are performed.

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