Rigidity parameter collaborative matching and dynamic iteration updating method and system for passenger and freight collinear ballastless track

Abstract

The invention provides a stiffness parameter collaborative matching and dynamic iteration updating method and system for a passenger and freight collinear ballastless track, and belongs to the technical field of structural design and optimization in the field of railway track engineering. Vehicle-track structure dynamic response characteristics of a track system under different rigidity combinations are collected, wherein the vehicle response characteristics comprise vehicle body vertical acceleration, wheel rail vertical force and the like, and the track system response characteristics comprise steel rail vertical displacement, fastener vertical force and the like. A sensitive response feature evaluation matrix is constructed, and objective weights of all features are calculated by using an information entropy theory, so that a dynamic response feature weighted standardized evaluation matrix is established. And determining the relative fitting degree of multi-stiffness combinations of different track systems, realizing collaborative matching of multi-stiffness parameters of the track systems, and identifying a high-matching-degree stable region and a parameter combination with strong robustness.

Description

Technical Field

[0001] This invention relates to the field of structural design and optimization technology in railway track engineering, specifically to a method and system for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks used in both passenger and freight lines. This method addresses the problems of strong subjectivity, poor adaptability, and lack of dynamic adjustment mechanisms in existing track stiffness design, and provides a systematic, operable, and sustainable optimization technical solution for ballastless track structures in complex operating environments. Background Technology

[0002] The mixed passenger and freight operation mode is becoming increasingly common in intercity railways, suburban railways, and some trunk railways. These lines must simultaneously handle the transport tasks of high-speed passenger trains and heavy-haul freight trains, with significant differences in vehicle axle load, operating speed, and load frequency, placing higher demands on the dynamic performance of the track structure. Ballastless track, due to its advantages of high smoothness, low maintenance, and long service life, has become the primary choice for high-speed and heavy-haul railways. However, under mixed passenger and freight conditions, traditional ballastless track stiffness design methods have revealed many problems, making it difficult to effectively accommodate the operational needs of different types of trains.

[0003] Currently, the design of track system stiffness parameters (such as fastener stiffness, inter-layer connection stiffness, and foundation stiffness) largely relies on empirical values ​​or static optimization methods based on a single vehicle model. These methods typically assume that the track structure is in an ideal homogeneous state, neglecting the complexity of the vehicle-track coupled dynamic response and failing to fully consider the impact of different stiffness combinations on the overall service performance of the system. For example, while excessively high fastener stiffness is beneficial for controlling rail deformation, it can exacerbate wheel-rail impact forces, leading to accelerated fatigue damage to track components; conversely, excessively low stiffness may cause excessive vehicle vibration, affecting ride stability and passenger comfort. Especially under mixed passenger and freight transport conditions, lightweight high-speed passenger cars prefer softer supports to reduce vibration transmission, while heavy-duty freight cars require higher stiffness to suppress track settlement and structural displacement, resulting in a significant conflict in their stiffness requirements.

[0004] Existing research attempts to find compromise solutions for stiffness configuration through multi-objective optimization methods, but most remain at the offline analysis stage, lacking real-time feedback mechanisms and failing to adapt to performance degradation caused by material aging, foundation settlement, and environmental changes during long-term service of track structures. Furthermore, most models rely on subjective weighting to determine the importance of each evaluation index, making them susceptible to human interference and resulting in a lack of objectivity and repeatability in the optimization results. Although some scholars have introduced entropy weighting methods for weight allocation, in practical engineering applications, they are rarely combined with complete collaborative matching processes and visualization methods, making it difficult to provide intuitive support for on-site decision-making.

[0005] More importantly, current stiffness design systems generally lack the ability to dynamically update from a life-cycle perspective. Once the initial construction is completed, the track stiffness parameters are fixed, and even if subsequent monitoring detects performance degradation or changes in operating conditions, it is difficult to adjust them in a timely manner. This "one-time design, lifelong use" model can no longer meet the comprehensive requirements of modern intelligent rail transit for safety, economy, and sustainability.

[0006] Therefore, there is an urgent need for a track stiffness parameter collaborative matching method that can integrate multi-source data, conduct scientific quantitative evaluation, meet both passenger and freight requirements, and possess closed-loop feedback and dynamic optimization capabilities. This method should be able to establish an objective stiffness evaluation system based on the actual dynamic response characteristics of the vehicle-track system, identify robust high-matching parameter combinations, and drive iterative model updates through real-time monitoring data, thereby achieving a shift from "static design" to "dynamic evolution," and ultimately comprehensively improving the service performance and intelligent operation and maintenance level of ballastless tracks for both passenger and freight lines. Summary of the Invention

[0007] The purpose of this invention is to provide a method and system for coordinated matching and dynamic iterative updating of stiffness parameters for ballastless tracks with both passenger and freight traffic. By combining vehicle-track coupled dynamic simulation data under different foundation conditions, it enables objective evaluation and visual optimization of different fastener stiffness and buffer layer stiffness combination schemes under different subfoundation conditions. This provides quantitative decision support for track structure design under conditions of parallel high-speed passenger and heavy-haul freight transport, thereby solving at least one of the technical problems existing in the background art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution:

[0009] In a first aspect, the present invention provides a method for coordinated matching and dynamic iterative updating of stiffness parameters for ballastless tracks with both passenger and freight traffic, comprising:

[0010] The dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system are collected based on monitoring equipment or virtual simulation models.

[0011] Based on dynamic response characteristics, the correlation between different stiffness combinations and structural response characteristics is clarified, and a sensitive response characteristic evaluation matrix for track system stiffness is established.

[0012] Based on information entropy, the objective weights of dynamic response sensitive features are calculated, and a weighted standardized evaluation matrix of dynamic response features is established.

[0013] Based on the weighted standardized evaluation matrix of response characteristics, the relative fit of different track systems with multiple stiffness combinations is clarified;

[0014] Based on the fit of multiple stiffness combinations, the collaborative matching and effect visualization of multiple stiffness parameters of the track system are realized;

[0015] Based on real-time feedback data from the track system, dynamic iterative updates of multiple stiffness optimal parameters of the track system are achieved throughout its entire life cycle.

[0016] As a further limitation of the first aspect of the present invention, the dynamic response characteristics of the vehicle-track structure are collected based on the monitoring device; or, the dynamic response characteristics of the vehicle-track structure are collected based on virtual body modeling and simulation.

[0017] Different stiffness combinations of the track system include different fastener system stiffness, different inter-story stiffness of the track structure, and different substructure stiffness;

[0018] The selected vehicle response characteristics include at least: car body vertical acceleration response, wheel-rail vertical force response, and derailment coefficient response; the selected track system response characteristics include at least: rail vertical displacement response, fastener vertical force response, and vibration damping pad vertical force response.

[0019] As a further limitation of the first aspect of the present invention, calculating the objective weights of the dynamic response characteristics includes:

[0020] The dynamic response characteristics are standardized by range, where positive indices are:

[0021] ;

[0022] As for contrarian indicators:

[0023] ;

[0024] Calculate the first The first characteristic indicator The proportion of each stiffness combination :

[0025] ;

[0026] Calculate the first The degree of dispersion of each characteristic indicator:

[0027] ;

[0028] In the formula, , such that 0≤ ≤1;

[0029] Calculate the first Weights of each feature indicator :

[0030] ;

[0031] In the formula, .

[0032] As a further limitation of the first aspect of the present invention, the calculation of the objective weights of the dynamic response characteristics requires the introduction of respective correction coefficients for different track structures and substructure types:

[0033]

[0034] in, Based on weights, Correction factors for different track structures and different substructures.

[0035] As a further definition of the first aspect of the present invention, the relative fit of different track systems with multiple stiffness combinations is specified. This includes: the existence of positive and negative ideal solutions for different orbital systems under multiple stiffness combinations, where the distance between different stiffness combinations and the positive ideal solution is... :

[0036] ;

[0037] Distance between different stiffness combinations and negative ideal solutions for:

[0038] ;

[0039] Relative fit of different stiffness combinations :

[0040] .

[0041] As a further limitation of the first aspect of the present invention, the coordinated matching of multiple stiffness parameters of the track system includes:

[0042] Constructing the cooperative matching degree relationship of multiple stiffness parameters of the track system:

[0043] ;

[0044] In the formula, Set different dynamic proportions for different lower bases. ∈[0,1]; α is the matching degree coordination factor, α∈[0,1];

[0045] The visualization of the effect includes:

[0046] ;

[0047] ;

[0048] In the formula, and For multiple stiffness parameters of the track system;

[0049] Dynamic iterative updates, including:

[0050] ;

[0051] In the formula, For real-time data feedback; These are the model's predicted values; Let be the historical standard deviation of the k-th indicator.

[0052] Secondly, this invention provides a stiffness parameter collaborative matching and dynamic iterative update system for ballastless tracks used for both passenger and freight traffic, comprising a feature acquisition module, a feature analysis module, a feature weighting module, and a stiffness parameter matching and iterative update module; wherein:

[0053] The feature acquisition module collects dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system based on monitoring equipment or virtual simulation models.

[0054] The feature analysis module, based on dynamic response characteristics, clarifies the correlation between different stiffness combinations and structural response characteristics, and establishes a sensitive response characteristic evaluation matrix for the stiffness of the track system.

[0055] The feature weighting module calculates the objective weights of dynamic response sensitive features based on information entropy and establishes a weighted standardized evaluation matrix for dynamic response features.

[0056] The stiffness parameter matching and iterative update module: based on the response feature weighted standardized evaluation matrix, it clarifies the relative fit of multiple stiffness combinations of different track systems; based on the fit of multiple stiffness combinations, it realizes the collaborative matching and effect visualization of multiple stiffness parameters of the track system; based on the real-time feedback data of the track system, it realizes the dynamic iterative update of the optimal multiple stiffness parameters of the track system throughout its entire life cycle.

[0057] Thirdly, the present invention provides a non-transitory computer-readable storage medium for storing computer instructions, which, when executed by a processor, implement the stiffness parameter collaborative matching and dynamic iterative update method for ballastless track with passenger and freight co-tracking as described in the first aspect.

[0058] Fourthly, the present invention provides a computer device including a memory and a processor, wherein the processor and the memory communicate with each other, the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the stiffness parameter collaborative matching and dynamic iterative update method for ballastless track with passenger and freight co-track as described in the first aspect.

[0059] Fifthly, the present invention provides an electronic device, comprising: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the stiffness parameter collaborative matching and dynamic iterative update method for passenger and freight co-track ballastless track as described in the first aspect.

[0060] The beneficial effects of this invention are as follows: By constructing a multi-scenario coupled dynamic simulation data system, comprehensive coverage of stiffness combinations under different foundation conditions is achieved; human bias is avoided and the importance of key indicators is highlighted; multi-objective fusion optimization is achieved by combining passenger and freight coordination matching functions; high-matching stability zones are located through three-dimensional surface visualization and sensitivity analysis, improving the robustness of the scheme; and finally, a closed-loop mechanism of "collection-analysis-application-feedback" is formed, enabling track stiffness design to shift from experience-driven to data-driven, solving the problems of poor stiffness matching adaptability, subjective evaluation, and lack of dynamic optimization capabilities under complex operating environments, and significantly improving the safety, durability, and engineering decision-making efficiency of track structures.

[0061] The advantages of additional aspects of the invention will be set forth more clearly in the following description or will be learned by practice of the invention. Attached Figure Description

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0063] Figure 1 This is a flowchart of the stiffness parameter collaborative matching and dynamic iterative update method for ballastless track with passenger and freight lines as described in an embodiment of the present invention.

[0064] Figure 2 This is a schematic diagram of the finite element model of the ballastless track according to an embodiment of the present invention.

[0065] Figure 3 This is a schematic diagram illustrating the variation of the maximum vertical displacement of the rail obtained according to an embodiment of the present invention.

[0066] Figure 4 This is a schematic diagram illustrating the variation of the maximum vertical acceleration of the rail obtained according to an embodiment of the present invention.

[0067] Figure 5 This is a schematic diagram illustrating the change in the maximum lateral acceleration of the vehicle body obtained according to an embodiment of the present invention.

[0068] Figure 6 This is a schematic diagram illustrating the variation of the maximum vertical acceleration of the vehicle body obtained according to an embodiment of the present invention.

[0069] Figure 7 This is a schematic diagram illustrating the variation of the maximum value of the wheel-rail lateral force obtained according to an embodiment of the present invention.

[0070] Figure 8 This is a schematic diagram illustrating the variation of the maximum vertical force between the wheel and rail obtained according to an embodiment of the present invention.

[0071] Figure 9 This is a schematic diagram illustrating the change in the maximum value of the derailment coefficient obtained according to an embodiment of the present invention.

[0072] Figure 10 This is a schematic diagram illustrating the change in the maximum wheel load reduction rate obtained according to an embodiment of the present invention.

[0073] Figure 11 This is a visualization of the three-dimensional surface result of the collaborative matching described in an embodiment of the present invention. Detailed Implementation

[0074] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0075] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0076] It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with their meanings in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as here.

[0077] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, and / or groups thereof.

[0078] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0079] To facilitate understanding of the present invention, the present invention will be further explained and described below with reference to the accompanying drawings and specific embodiments. However, the specific embodiments do not constitute a limitation on the embodiments of the present invention.

[0080] Those skilled in the art should understand that the accompanying drawings are merely schematic diagrams of embodiments, and the components in the drawings are not necessarily essential for implementing the present invention.

[0081] This invention discloses a method, storage medium, and system for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks used in both passenger and freight lines. The method first collects the dynamic response characteristics of the vehicle-track structure under different stiffness combinations (covering fastener system stiffness, inter-layer stiffness of the track structure, and substructure stiffness) based on monitoring equipment or a virtual simulation model. This includes vehicle response characteristics such as car body vertical acceleration and wheel-rail vertical force, and track system response characteristics such as rail vertical displacement and fastener vertical force. Subsequently, a sensitive response characteristic evaluation matrix is ​​constructed, and the objective weights of each characteristic are calculated using information entropy theory, thereby establishing a weighted standardized evaluation matrix for dynamic response characteristics. Based on this, the relative fit of different track system stiffness combinations is clarified, achieving collaborative matching of multiple stiffness parameters of the track system, and providing effect visualization functions to identify high-matching stability zones and robust parameter combinations. Finally, based on real-time feedback data from the track system, the optimal parameters of multiple stiffness parameters of the track system are dynamically iteratively updated throughout its entire lifecycle, ensuring that the track system continuously maintains the optimal stiffness configuration. Furthermore, this invention provides a computer-readable storage medium and a corresponding system implementation to support the execution of the above-described methods. This invention effectively improves the stability and safety of ballastless tracks for both passenger and freight transport, resulting in significant economic and social benefits.

[0082] Example 1

[0083] This embodiment provides a stiffness parameter collaborative matching and dynamic iterative update system for ballastless tracks with both passenger and freight trains. It aims to solve the problems in the prior art, such as unreasonable track structure stiffness configuration, rapid service performance degradation, and high maintenance costs caused by large differences in the operating speed and axle load of passenger and freight trains.

[0084] The system includes a feature acquisition module, a feature analysis module, a feature weighting module, and a stiffness parameter matching and iterative update module. The feature acquisition module collects dynamic response characteristics of the vehicle-track structure under different stiffness combinations based on monitoring equipment or a virtual simulation model. The feature analysis module, based on the dynamic response characteristics, clarifies the correlation between different stiffness combinations and structural response characteristics, and establishes a sensitive response characteristic evaluation matrix for the track system stiffness. The feature weighting module calculates the objective weights of the dynamic response sensitive characteristics based on information entropy, and establishes a weighted standardized evaluation matrix for the dynamic response characteristics. The stiffness parameter matching and iterative update module: based on the weighted standardized evaluation matrix of response characteristics, clarifies the relative fit of multiple stiffness combinations in different track systems. Based on the fit of multiple stiffness combinations, the collaborative matching and effect visualization of multiple stiffness parameters of the track system are realized; based on the real-time feedback data of the track system, the dynamic iterative update of the optimal parameters of multiple stiffness of the track system throughout its entire life cycle is realized.

[0085] In this embodiment, based on the above system, a stiffness parameter collaborative matching method integrating multi-source data acquisition, information entropy weight analysis, multi-attribute decision evaluation, and visualization is implemented. A real-time feedback-driven dynamic iterative update mechanism is introduced to achieve scientific configuration and continuous optimization of track system stiffness parameters. The stiffness parameter collaborative matching and dynamic iterative update method for ballastless tracks used for both passenger and freight transport includes the following steps:

[0086] Dynamic response data of vehicle-track systems under different stiffness combinations can be obtained based on monitoring equipment or virtual simulation models.

[0087] The stiffness combination encompasses key parameters such as the stiffness of the fastener system, the inter-layer stiffness of the track structure, and the stiffness of the substructure.

[0088] The collected dynamic response characteristics should be operable, independent, scientific, simple, and complete, specifically including: vehicle response indicators such as vehicle body vertical acceleration, wheel-rail vertical force, and derailment coefficient; and track structure response indicators such as rail vertical displacement, fastener vertical force, and vibration damping pad vertical force.

[0089] Based on dynamic response characteristics, the correlation between different stiffness combinations and structural response characteristics is clarified, and a sensitive response characteristic evaluation matrix for track system stiffness is established.

[0090] The objective weights of the dynamic response features are used to standardize the original index data according to its range, with the positive indexes transformed using the following formula:

[0091] The contrarian indicator is converted using the following formula:

[0092] Calculate the first The first indicator The proportion of each option :

[0093] Calculate the degree of dispersion of each feature using information entropy theory:

[0094] In the formula , such that 0≤ ≤1; Calculate the first Weight of each indicator : In the formula ;

[0095] Based on information entropy, objective weights of dynamic response sensitive features are calculated, and a weighted standardized evaluation matrix of dynamic response features is established. ,in ;

[0096] To calculate the objective weights of dynamic response characteristics, different correction coefficients need to be introduced for different track structures and subbase types. ;in, The basic weights of the algorithm are... Correction factors for different track structures and different substructures;

[0097] Based on the response characteristic weighted standardized evaluation matrix, positive and negative ideal solutions are defined for different stiffness combinations to clarify the relative fit of different track systems with multiple stiffness combinations. ,in:

[0098] Distance between each solution and the ideal solution for:

[0099] Distance between each solution and the negative ideal solution for:

[0100] Relative fit of each scheme :

[0101] The larger this value, the closer the stiffness combination is to the optimal state.

[0102] Based on the multi-stiffness combination fit, the cooperative matching relationship of multiple stiffness parameters of the track system is constructed:

[0103] ;

[0104] In the formula, γ is the weighting factor, γ∈[0,1]; α is the matching degree coordination factor, α∈[0,1]; Set the dynamic proportions for different lower bases.

[0105] Based on the matching relationship of multiple stiffness parameters of the track system, the collaborative matching and effect visualization of multiple stiffness parameters of the track system are realized, including:

[0106]

[0107]

[0108] In the formula, and For multiple stiffness parameters of the track system;

[0109] Specifically, local maxima regions are identified in the matching degree distribution map and defined as "high matching degree stable regions". If the matching degree fluctuation caused by changes in stiffness parameters within this region is less than 5%, the combination is considered to have good robustness and is suitable as a recommended design scheme.

[0110] Based on real-time feedback data from the orbital system, dynamic iterative updates of multiple stiffness-optimal parameters of the orbital system are achieved throughout its entire lifecycle. Combining real-time monitoring data during the orbital system's service life, error assessment is performed on the model's prediction results, and the root mean square error normalization index is calculated: during iteration... When, calculate:

[0111]

[0112] in, For the first The actual measured value of the indicator These are the model's predicted values. This represents the historical standard deviation of the indicator. If... When the model retraining and parameter reoptimization process is triggered, the coordination factor is adjusted based on actual operational data. This enables dynamic iterative updates of the optimal stiffness parameters of the orbital system, ensuring stable performance during long-term service.

[0113] In this embodiment, a typical ballastless track line for both passenger and freight transport with a design speed of 200 km / h under subgrade conditions is selected as the research object. A parameter combination system with different fastener stiffness and buffer pad stiffness is constructed as an example. A combination parameter set with different fastener stiffness (40-80 kN / mm) and buffer pad stiffness (200-1000 MPa / m) is established. The parameter combination set is constructed using a full combination method, forming a total of 25 stiffness matching schemes (A1 to A25), as shown in Table 1.

[0114] Table 1 Stiffness Combination Types

[0115]

[0116] For each set of parameters, the passing conditions of passenger cars and freight cars were simulated to obtain vehicle-track coupled dynamic response data. Seven key performance indicators were extracted, including: lateral acceleration of the car body, wheel-rail vertical force, derailment coefficient, vertical displacement of the rail, vertical acceleration of the rail, vertical force of the subbase, and vertical force of the fastener. These indicators formed a complete initial evaluation index matrix, providing basic data support for subsequent multi-index fusion analysis.

[0117] In this embodiment, a finite element modeling scheme for ballastless track vehicle-track coupling is further provided.

[0118] A coupled vehicle-track model for a passenger and freight mixed-track railway was established, including rails, track slabs, self-compacting concrete, buffer pads, and base plates. Figure 2 This is the finite element model of the ballastless track provided in this example, where 1 is the rail, 2 is the track slab, 3 is the self-compacting concrete, 4 is the buffer layer, and 5 is the base plate.

[0119] Preferably, in this embodiment, the rail and the track slab are connected by fastener units, and the stiffness of the fasteners and the stiffness of the buffer pad can be parameterized according to different combinations of values ​​in Embodiment 1.

[0120] The key parameters of each structure are shown in Table 2:

[0121] Table 2 Parameters of the Finite Element Model for Ballastless Track on Passenger and Freight Co-operation Line

[0122]

[0123] In this example, the vehicle model adopts a multi-rigid-body dynamics model, including key components such as the car body, bogies, and wheelsets. Based on multi-body dynamics theory, passenger train and freight train models were established respectively. The passenger train model contains 7 rigid bodies, including the car body, two bogies, and four wheel sets. Due to differences in structural form and vibration characteristics, the freight train model contains 11 rigid bodies, including the car body, side frames, sleeper beams, and wheel sets. The dynamic equations of the vehicle subsystem are as follows:

[0124]

[0125] in, , and These are the mass matrix, damping matrix, and stiffness matrix of the vehicle subsystem, respectively. This is the displacement vector of the vehicle subsystem; This is the vehicle's self-weight load vector. For wheel-rail force.

[0126] The main parameters of the vehicles are listed in Table 3. The CRH2 type EMU was selected as the passenger train, and the typical C80 type train was selected as the freight train.

[0127] Table 3 Vehicle Parameter Values

[0128]

[0129] The wheel-rail contact relationship is determined using Hertzian nonlinear contact theory. Wheel-rail interaction forms the coupling interface between the vehicle and the ballastless track. It mainly involves determining the wheel-rail spatial contact geometry and calculating the wheel-rail contact forces. The displacement of the wheel-rail spatial contact point is obtained using a tracking method, where the contact position is determined by finding the minimum distance between the wheel profile track and the rail top profile. The normal wheel-rail force is calculated using a non-Hertzian contact algorithm based on virtual penetration, while the tangential creeping force is determined using Kalker's Fastsim algorithm. Its basic expression is:

[0130]

[0131]

[0132] in It is a virtual penetration coefficient; and These are the elastic modulus and Poisson's ratio, respectively. It is a correction factor; It is the rolling radius of the wheel.

[0133] The wheel-rail force is fed back to the track structure finite element model in real time through the wheel-rail relationship, forming a complete vehicle-track bidirectional coupling system.

[0134] Track irregularities constitute the primary excitation source of vehicle-track coupled vibration. To obtain a dynamic response that better reflects actual operating conditions, the track irregularities used in this example consist of measured long-wavelength irregularities recorded by the track inspection vehicle and the Sato roughness spectrum.

[0135] During the simulation, dynamic time-history analysis was performed on 25 stiffness combinations under both passenger car and freight car operating conditions to obtain extreme response data for each evaluation index under stable operating conditions, such as... Figures 3-11 As shown.

[0136] Based on the simulation data obtained above, this embodiment performs multi-index weight calculations for passenger car and freight car operating conditions. Using the original data matrix composed of seven dynamic indices, the objective weights of dynamic response sensitive features are calculated based on information entropy. The specific method is as follows:

[0137] Based on the above method of range standardization of the original index data, range standardization is performed on the dynamic response characteristics, including positive indicators, negative indicators, and calculation of the first... The first characteristic indicator The proportion of each stiffness combination Calculate the first The degree of dispersion of the first characteristic index, and the calculation of the first characteristic index. Weights of each feature indicator Different correction coefficients are introduced for different track structures and substructure types. ;in, As the basic weights of the algorithm, Correction factors for different track structures and different substructures.

[0138] In this example, using the subgrade conditions, the corrected coefficients were calculated, and the objective weights of each index under the subgrade conditions for passenger cars and freight cars are shown in Tables 4 and 5:

[0139] Table 4 Weighting coefficients for passenger vehicle stiffness matching index parameters

[0140]

[0141] Table 5 Weighting coefficients of truck stiffness matching index parameters

[0142]

[0143] Based on the aforementioned weighting coefficients, the matching degree rankings of the two types of trains are obtained through calculation.

[0144] Constructing the cooperative matching degree relationship of multiple stiffness parameters of the track system:

[0145]

[0146] In the formula, Set different dynamic proportions for different lower bases. ∈[0,1]; α is the matching degree coordination factor, α∈[0,1].

[0147] This example uses a large number of freight trucks as an example to set up... The overall matching degree was calculated, and the results showed that the A2 scheme (fastener stiffness 40kN / mm, buffer pad stiffness 400 MPa / m) had the highest overall score.

[0148] Further drawing of three-dimensional surface plots, such as Figure 11 As shown, it can be seen in kN / mm There is a clear "peak plateau" in the MPa / m range, which is defined as a high-matching stability region. It is recommended to select a design scheme within this range to improve robustness.

[0149] To verify its effectiveness in full lifecycle management, a dynamic iterative update mechanism is further introduced.

[0150] After the track is put into operation, a distributed health monitoring system will be deployed along the line to continuously collect dynamic response data of the vehicle-track system. The data collection cycle is once a month, and each time no less than 50 passenger trains and 80 freight trains will be collected as valid passage records. The focus will be on extracting the time series extreme values ​​of the aforementioned 7 evaluation indicators, and then performing noise reduction and normalization processing.

[0151] Using the prediction model trained on the previously established virtual simulation database, theoretical response values ​​under the corresponding working conditions are generated. and compared with the average measured value during the same period. A comparison was made. The historical standard deviation of each indicator was derived from the statistical data of the previous six months and used as a normalization benchmark.

[0152] Calculate the current time Normalized error index:

[0153]

[0154] The preset determination rule in this embodiment is:

[0155] If the error index is less than or equal to the first threshold, the system is considered to be in normal condition, and the current model is maintained.

[0156] If the first threshold < error index ≤ second threshold, then model calibration needs to be initiated to fine-tune the correlation coefficient.

[0157] If the error index is greater than the second threshold, then the process needs to be completely re-optimized.

[0158] In this embodiment, the first threshold = 1.2; the second threshold = 1.5;

[0159] In a more preferred embodiment, the highest recommendation level, second-highest recommendation level, general recommendation level, and no-recommendation level are respectively rated as "Excellent," "Good," "Average," and "No-Recommendation."

[0160] Recorded monitoring data showed that the average vertical displacement of the rails increased compared to the initial value, and the vertical force fluctuation of the fasteners intensified, indicating a significant shift in structural performance, which may be due to aging of the buffer pad material or partial detachment of the base plate.

[0161] Using existing data, recalculate the stiffness combinations for all stiffness combinations. and It was found that the original optimal solution A2 had a significantly reduced fit, while A7 ( =50, =400) and A8 ( =60, =600) enters the new "high matching stability zone".

[0162] The system output recommends prioritizing the replacement of some low-stiffness fasteners during the next maintenance window, and implementing grouting reinforcement or partial replacement of the buffer pad layer in weak sections to bring the actual stiffness closer to the recommended combination. Simultaneously, the 3D visualization surface map will be updated to dynamically display the migration trajectory of the optimal parameter area for the maintenance department's reference.

[0163] All update processes automatically generate logs, which are stored in the track lifecycle management system, forming a closed-loop mechanism of "design-verification-feedback-optimization".

[0164] Example 2

[0165] This embodiment 2 provides a non-transitory computer-readable storage medium for storing computer instructions. When these instructions are executed by a processor, they implement the method described above for the coordinated matching and dynamic iterative update of stiffness parameters for ballastless tracks used for both passenger and freight lines. This method includes: collecting dynamic response characteristics of the vehicle-track structure under different stiffness combinations based on monitoring equipment or a virtual simulation model; clarifying the correlation between different stiffness combinations and structural response characteristics based on the dynamic response characteristics, and establishing a sensitive response characteristic evaluation matrix for track system stiffness; calculating the objective weights of the sensitive dynamic response characteristics based on information entropy, and establishing a weighted standardized evaluation matrix for dynamic response characteristics; clarifying the relative fit of different track system multi-stiffness combinations based on the weighted standardized evaluation matrix; realizing coordinated matching and effect visualization of multiple stiffness parameters of the track system based on the fit of multiple stiffness combinations; and realizing dynamic iterative update of the optimal multi-stiffness parameters of the track system throughout its entire lifecycle based on real-time feedback data from the track system.

[0166] Example 3

[0167] This embodiment 3 provides a computer device, including a memory and a processor. The processor and the memory communicate with each other. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the above-described method for coordinated matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks. The method includes: collecting dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system based on monitoring equipment or virtual simulation models; clarifying the correlation between different stiffness combinations and structural response characteristics based on the dynamic response characteristics, and establishing a sensitive response characteristic evaluation matrix for track system stiffness; calculating the objective weights of the sensitive dynamic response characteristics based on information entropy, and establishing a weighted standardized evaluation matrix for dynamic response characteristics; clarifying the relative fit of different multi-stiffness combinations of the track system based on the weighted standardized evaluation matrix for response characteristics; realizing coordinated matching and effect visualization of multi-stiffness parameters of the track system based on the fit of multi-stiffness combinations; and realizing dynamic iterative updating of the optimal multi-stiffness parameters of the track system throughout its entire life cycle based on real-time feedback data from the track system.

[0168] Example 4

[0169] This embodiment 4 provides an electronic device, including: a processor, a memory, and a computer program; wherein, the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes instructions to implement the above-described method for coordinated matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks. The method includes: collecting dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system based on monitoring equipment or virtual simulation models; clarifying the correlation between different stiffness combinations and structural response characteristics based on the dynamic response characteristics, and establishing a sensitive response characteristic evaluation matrix for track system stiffness; calculating the objective weights of the sensitive dynamic response characteristics based on information entropy, and establishing a weighted standardized evaluation matrix for dynamic response characteristics; clarifying the relative fit of different multi-stiffness combinations of the track system based on the weighted standardized evaluation matrix for response characteristics; realizing coordinated matching and effect visualization of multi-stiffness parameters of the track system based on the fit of multi-stiffness combinations; and realizing dynamic iterative updating of the optimal multi-stiffness parameters of the track system throughout its entire life cycle based on real-time feedback data of the track system.

[0170] In summary, the method and system for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks used in both passenger and freight lines, as described in this invention, have practical application value in the following aspects: Design decision support: By using matching degree ranking and three-dimensional visualization, the scientific nature and efficiency of stiffness parameter configuration in track structure design are significantly improved; Avoidance of engineering mismatch risks: Clearly defined parameter-sensitive areas are provided, effectively avoiding engineering problems such as slab cracking and excessive vibration caused by improper stiffness selection; Strong scalability: This method can be flexibly integrated with different load levels, track structures, and index systems, possessing good scalability and engineering adaptability. This invention provides a collaborative matching and dynamic iterative updating method for the design of ballastless tracks used in both passenger and freight lines, which can significantly improve the overall performance of the track system in terms of comfort, safety, and durability.

[0171] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0172] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations 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, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0173] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0174] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment, whereby a series of operational steps are performed to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that, based on the technical solutions disclosed in the present invention, various modifications or variations that can be made by those skilled in the art without creative effort should be included within the scope of protection of the present invention.

Claims

1. A method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with both passenger and freight traffic, characterized in that, include: The dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system are collected based on monitoring equipment or virtual simulation models. Based on dynamic response characteristics, the correlation between different stiffness combinations and structural response characteristics is clarified, and a sensitive response characteristic evaluation matrix for track system stiffness is established. Based on information entropy, the objective weights of dynamic response sensitive features are calculated, and a weighted standardized evaluation matrix of dynamic response features is established. Based on the weighted standardized evaluation matrix of response characteristics, the relative fit of different track systems with multiple stiffness combinations is clarified; Based on the fit of multiple stiffness combinations, the collaborative matching and effect visualization of multiple stiffness parameters of the track system are realized; Based on real-time feedback data from the track system, dynamic iterative updates of multiple stiffness optimal parameters of the track system are achieved throughout its entire life cycle.

2. The method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks according to claim 1, characterized in that, The dynamic response characteristics of the vehicle-track structure are collected based on the monitoring device; or, the dynamic response characteristics of the vehicle-track structure are collected based on virtual body modeling and simulation. Different stiffness combinations of the track system include different fastener system stiffness, different inter-story stiffness of the track structure, and different substructure stiffness; The selected vehicle response characteristics include at least: car body vertical acceleration response, wheel-rail vertical force response, and derailment coefficient response; the selected track system response characteristics include at least: rail vertical displacement response, fastener vertical force response, and vibration damping pad vertical force response.

3. The method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks according to claim 1, characterized in that, Calculating the objective weights of dynamic response features includes: The dynamic response characteristics are standardized by range, where positive indices are: ； As for contrarian indicators: ； Calculate the first The first characteristic indicator The proportion of each stiffness combination : ； Calculate the first The degree of dispersion of each characteristic indicator: ； In the formula, , such that 0≤ ≤1; Calculate the first Weights of each feature indicator : ； In the formula, .

4. The method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks according to claim 1, characterized in that, To calculate the objective weights of dynamic response characteristics, different correction coefficients need to be introduced for different track structures and substructure types. ； in, Based on weights, Correction factors for different track structures and different substructures.

5. The method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks according to claim 1, characterized in that, Determine the relative fit of different track systems with multiple stiffness combinations This includes: the existence of positive and negative ideal solutions for different orbital systems under multiple stiffness combinations, where the distance between different stiffness combinations and the positive ideal solution is... : ； Distance between different stiffness combinations and negative ideal solutions for: ； Relative fit of different stiffness combinations : 。 6. The method for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless tracks with passenger and freight co-tracks according to claim 1, characterized in that, Cooperative matching of multiple stiffness parameters of the track system, including: Constructing the cooperative matching degree relationship of multiple stiffness parameters of the track system: ； In the formula, Set different dynamic proportions for different lower bases. ∈[0,1]; α is the matching degree coordination factor, α∈[0,1]; The visualization of the effect includes: ； ； In the formula, and For multiple stiffness parameters of the track system; Dynamic iterative updates, including: ； In the formula, For real-time data feedback; These are the model's predicted values; Let be the historical standard deviation of the k-th indicator.

7. A system for collaborative matching and dynamic iterative updating of stiffness parameters for ballastless track with both passenger and freight lines, characterized in that, The system includes a feature acquisition module, a feature analysis module, a feature weighting module, and a stiffness parameter matching and iterative update module; wherein: The feature acquisition module collects dynamic response characteristics of the vehicle-track structure under different stiffness combinations of the track system based on monitoring equipment or virtual simulation models. The feature analysis module, based on dynamic response characteristics, clarifies the correlation between different stiffness combinations and structural response characteristics, and establishes a sensitive response characteristic evaluation matrix for the stiffness of the track system. The feature weighting module calculates the objective weights of dynamic response sensitive features based on information entropy and establishes a weighted standardized evaluation matrix for dynamic response features. The stiffness parameter matching and iterative update module: based on the response feature weighted standardized evaluation matrix, it clarifies the relative fit of multiple stiffness combinations of different track systems; based on the fit of multiple stiffness combinations, it realizes the collaborative matching and effect visualization of multiple stiffness parameters of the track system; based on the real-time feedback data of the track system, it realizes the dynamic iterative update of the optimal multiple stiffness parameters of the track system throughout its entire life cycle.

8. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium is used to store computer instructions, which, when executed by a processor, implement the stiffness parameter collaborative matching and dynamic iterative update method for ballastless track with passenger and freight co-tracking as described in any one of claims 1-6.

9. A computer device, characterized in that, The system includes a memory and a processor, which communicate with each other. The memory stores program instructions that can be executed by the processor. The processor calls the program instructions to execute the stiffness parameter collaborative matching and dynamic iterative update method for ballastless track with passenger and freight co-track as described in any one of claims 1-6.

10. An electronic device, characterized in that, include: The device includes a processor, a memory, and a computer program; wherein the processor is connected to the memory, and the computer program is stored in the memory. When the electronic device is running, the processor executes the computer program stored in the memory to cause the electronic device to execute instructions for implementing the stiffness parameter collaborative matching and dynamic iterative update method for passenger and freight co-track ballastless track as described in any one of claims 1-6.

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