Catenary model optimization system and method based on digital twinning and dynamic simulation

Abstract

This application relates to the field of railway catenary engineering design and operation and maintenance technology, and in particular to a catenary model optimization system and method based on digital twins and dynamic simulation. The system includes acquiring geometric parameters, physical attribute parameters, and operating environment parameters; generating verified structured data through a rule parsing engine; simulating the pantograph-catenary contact relationship using a dynamic simulation module and generating simulation results; and finally generating optimization suggestions to update the model based on design specifications. This application integrates multi-source data and dynamic simulation technology, solving the problems of data fragmentation and rule lag in traditional solutions, improving the physical coupling accuracy and intelligence level of the catenary model, achieving full lifecycle management, and reducing the subjectivity and inefficiency of manual verification.

Description

Technical Field

[0001] This invention relates to the field of railway catenary engineering design and operation and maintenance technology, and more specifically, to a catenary model optimization system and method based on digital twins and dynamic simulation. Background Technology

[0002] With the rapid development of high-speed railways and urban rail transit, the overhead contact system, as a core component of the electrified railway power supply system, directly affects the safety and stability of train operation due to its design accuracy and operation and maintenance quality. The overhead contact system consists of various components such as conductors, supports, positioners, and suspension devices, exhibiting a complex structure and dense spatial relationships, and must maintain stable current collection performance under high-speed operating conditions. Traditional two-dimensional design and manual review methods are no longer sufficient to meet the high standards of spatial accuracy, design efficiency, and collaborative management required by modern railway engineering. Therefore, the railway construction field is gradually introducing three-dimensional design technology based on Building Information Modeling (BIM) to achieve model-based, digital, and visualized full lifecycle management.

[0003] However, existing BIM design and review of overhead contact lines still face numerous technical bottlenecks. On the one hand, overhead contact line engineering is highly specialized, involving multiple industry standards such as pantograph-catenary relationship, conductor tension, support span, and clearance control. Traditional manual verification methods are inefficient, subjective, and prone to omissions and misjudgments. On the other hand, existing BIM review platforms are mostly general-purpose building software, lacking dedicated rule bases and spatial constraint logic for overhead contact line engineering, making it difficult to achieve automated, batch, and accurate verification. Simultaneously, the lack of a unified coordinate system and data interface between models of different disciplines (such as lines, bridges, and tunnels) leads to low efficiency in cross-disciplinary collision detection and information exchange. Furthermore, existing technologies still have shortcomings in terms of intelligence and data fusion. Current review methods mostly remain at the static geometric level, unable to dynamically simulate the contact relationship under pantograph operating conditions, and also difficult to achieve closed-loop management by combining data from the construction and operation and maintenance phases. Problems such as lagging rule base updates, untraceable review results, and poor cross-project adaptability make it difficult for existing BIM model review to support the high-precision, high-reliability, and full-cycle intelligent management requirements of railway overhead contact line engineering. Summary of the Invention

[0004] This invention provides a contact network model optimization system and method based on digital twins and dynamic simulation, which solves the problems of low automation in contact network BIM model review, poor rule consistency, insufficient professional collaboration, and data fragmentation in the prior art.

[0005] This invention proposes a method for optimizing overhead contact line models based on digital twins and dynamic simulation, comprising: Obtain the geometric parameters, physical property parameters, and operating environment parameters from the overhead contact system model; The geometric parameters and physical property parameters are input into the rule parsing engine, which performs logical verification on the geometric parameters and physical property parameters to generate verified structured data. Based on the verified structured data and operating environment parameters, the pantograph-catenary contact relationship under the pantograph operating state is simulated to generate dynamic simulation results; The dynamic simulation results are compared with the preset design specifications to generate optimization suggestions and update the catenary model.

[0006] Furthermore, geometric parameters include conductor height deviation, support span, and component spatial position; physical property parameters include material properties, surface roughness coefficient, and thermal radiation parameters; and operating environment parameters include train speed, ambient temperature, and wind speed.

[0007] Furthermore, the geometric parameters and physical property parameters are input into the rule parsing engine. The rule parsing engine performs logical validation on the geometric parameters and physical property parameters, generating validated structured data, including: When the conductor height deviation exceeds the preset height threshold or the support span exceeds the preset span range, the rule parsing engine is used to analyze the correlation between geometric parameters and physical property parameters in the historical verification data of the contact network model. The rule parsing engine is a multi-dimensional rule matching model based on decision trees. The multi-dimensional rule matching model includes a conditional branching layer and a weight allocation layer, which are used to extract the global constraint features and local conflict features of geometric parameters and physical attribute parameters in historical verification data, respectively. Based on global constraint characteristics and local conflict characteristics, optimize the dynamic weights corresponding to each parameter in the joint verification model of preset geometric parameters and physical property parameters; After the dynamic weight optimization is completed, the optimized joint verification model is used to generate verified structured data.

[0008] Furthermore, when optimizing the dynamic weights corresponding to each parameter in the pre-defined geometric and physical property parameter joint verification model based on global constraint characteristics and local conflict characteristics, the following steps are taken: Based on the frequency characteristics of the conductor height deviation exceeding the preset height threshold in the global constraint features, the dynamic weight corresponding to the conductor height deviation is adjusted according to the preset linear increment rule; Based on the cumulative characteristics of the support span exceeding the preset span range in the global constraint features, the dynamic weight corresponding to the support span is calculated through a piecewise function; Based on the coupling characteristics between geometric parameters and physical property parameters in local conflict features, the dynamic weights corresponding to material properties are calculated.

[0009] Furthermore, based on the verified structured data and operating environment parameters, when simulating the pantograph-catenary contact relationship under pantograph operating conditions and generating dynamic simulation results, the following are included: Based on the conductor height deviation and support span in the verified structured data, the pantograph-catenary contact force distribution under different train operating speeds is simulated, and contact force distribution curves are generated. Based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, the thermal balance state of the pantograph-catenary contact area is determined, and a thermal balance distribution map is generated. The contact force distribution curve is superimposed with the heat balance distribution diagram to generate dynamic simulation results.

[0010] Furthermore, based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, the thermal equilibrium state of the pantograph-catenary contact area is calculated using a thermodynamic simulation unit. When generating the thermal equilibrium distribution map, the following is included: Based on thermal radiation parameters, Fourier's law of heat conduction is used to determine the heat conduction power in the pantograph-catenary contact area. The natural convection heat transfer coefficient is determined based on wind speed and ambient temperature, and the convective heat transfer power is determined based on the natural convection heat transfer coefficient. Based on the heat conduction power and convective heat transfer power, a heat balance analysis is performed on the pantograph-catenary contact area to generate a heat balance distribution map.

[0011] Furthermore, when comparing the dynamic simulation results with the preset design specifications to generate optimization suggestions and update the catenary model, this includes: Based on the contact force distribution curve in the dynamic simulation results, combined with the preset contact force safety threshold, abnormal contact force areas are identified. Based on the heat balance distribution map in the dynamic simulation results, combined with the preset heat balance safety threshold, abnormal heat balance areas are identified. Mapping areas of abnormal contact force and areas of abnormal thermal balance to the catenary model to generate optimization suggestions; Adjust the geometric and physical property parameters in the overhead contact line model according to the optimization suggestions to complete the model update.

[0012] Furthermore, when mapping contact force anomaly regions and thermal balance anomaly regions to the catenary model and generating optimization suggestions, the following are included: Obtain the contact force distribution curve and heat balance distribution map from the dynamic simulation results. Based on the spatial coordinate information of the simulation results, establish the spatial correspondence between the simulation coordinate system and the catenary model coordinate system, and generate a spatial index mapping table. Based on the spatial index mapping table, the coordinate points of the abnormal contact force area and the abnormal thermal balance area are back-mapped to the catenary model point by point to determine the model components and their spatial positions corresponding to the abnormal areas. Call the model database to obtain the geometric and physical property parameters of the abnormal components and generate an abnormal component attribute dataset; Based on the abnormal component attribute dataset, the influence of abnormal contact force and thermal balance on the model performance index is calculated, and the parameter optimization direction matrix is ​​generated. Input the parameter optimization direction matrix into the parameter optimization rule base to generate a structured optimization suggestion data package for each abnormal component.

[0013] Furthermore, based on the optimization suggestions, the geometric and physical property parameters in the overhead contact line model are adjusted. The model update process includes: When the deviation between the optimized catenary model and the actual operating data exceeds the preset deviation threshold, the deviation optimization gradient is determined based on the genetic algorithm. Iterative optimization is performed on the weight allocation layer of the joint verification model of geometric parameters and physical property parameters in the rule parsing engine based on the bias optimization gradient to obtain the joint verification model with adjusted parameters.

[0014] Compared with existing technologies, the advantages of this invention are as follows: By acquiring geometric parameters, physical property parameters, and operating environment parameters from the overhead contact line model, a multi-dimensional data-driven model description system is established, realizing a full-element digital expression from static geometric structure to dynamic operating environment. This process enables the model to not only reflect the structural relationships in the design stage but also to realistically characterize the stress, temperature, and environmental response under train operating conditions, providing an accurate data foundation for subsequent simulation analysis. Secondly, by using a rule parsing engine to logically verify the geometric and physical property parameters, a multi-dimensional rule model oriented towards the engineering characteristics of the overhead contact line is constructed. This engine can automatically identify the coupling relationship between conductor height deviation, support span, and material properties, dynamically judge and correct abnormal parameters, significantly improving the consistency and accuracy of model data and reducing the risk of subjective errors and omissions caused by manual review. Thirdly, based on the verified structured data and operating environment parameters, the pantograph-catenary contact relationship under pantograph operating conditions is accurately simulated through a dynamic simulation module, generating dynamic simulation results including contact force distribution and thermal equilibrium state. This simulation process can intuitively reflect the operational stability of the pantograph under different speed, temperature, and wind speed conditions, providing a visual and quantitative evaluation basis for catenary design and compensating for the inability of traditional static models to reflect dynamic interactive performance. Finally, by comparing the dynamic simulation results with preset design specifications, optimization suggestions are generated and the catenary model is automatically updated, realizing a closed-loop improvement mechanism from model verification, simulation validation to parameter optimization. This mechanism can dynamically adjust geometric and physical property parameters based on simulation feedback, ensuring that the catenary model continuously approaches the optimal design state, thereby improving overall design accuracy and operational reliability.

[0015] On the other hand, this application also provides a catenary model optimization system based on digital twin and dynamic simulation, including: The acquisition module is used to acquire geometric parameters, physical property parameters, and operating environment parameters from the overhead contact line model. The verification module is used to input geometric parameters and physical property parameters into the rule parsing engine, and use the rule parsing engine to perform logical verification on the geometric parameters and physical property parameters to generate verified structured data. The simulation module is used to simulate the pantograph-catenary contact relationship under the pantograph's operating state based on the verified structured data and operating environment parameters, and generate dynamic simulation results. The optimization module is used to compare the dynamic simulation results with the preset design specifications, generate optimization suggestions, and update the catenary model.

[0016] It is understood that the contact network model optimization system and method based on digital twin and dynamic simulation in the above embodiments of this application have the same beneficial effects, and will not be described again. Attached Figure Description

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart illustrating a catenary model optimization method based on digital twin and dynamic simulation provided in an embodiment of the present invention; Figure 2 A flowchart illustrating a catenary model optimization method based on digital twin and dynamic simulation provided in an embodiment of the present invention; Figure 3 This is a functional block diagram of a catenary model optimization system based on digital twin and dynamic simulation, provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] like Figures 1-2 As shown in some embodiments of this application, this embodiment provides a method for optimizing a catenary model based on digital twins and dynamic simulation, including: Step S100: Obtain the geometric parameters, physical property parameters, and operating environment parameters of the overhead contact line model.

[0020] Specifically, geometric parameters include conductor height deviation, support span, and component spatial location; physical property parameters include material properties, surface roughness coefficient, and thermal radiation parameters; and operating environment parameters include train speed, ambient temperature, and wind speed.

[0021] Specifically, the overhead contact line model is a virtual simulation model that digitally represents the structure and operating characteristics of the overhead contact line in a railway power supply system. It is used to accurately describe the geometric relationship, mechanical properties, and environmental response between the pantograph and the contact wire. This model typically consists of geometric parameters, physical property parameters, and operating environment parameters, reflecting the impact of multiple factors such as conductor height, support layout, material properties, thermal effects, and wind speed on the performance of the pantograph-catenary system. By establishing the overhead contact line model, key indicators such as current collection quality, contact force fluctuations, and thermal equilibrium state can be dynamically simulated and evaluated in a virtual environment, providing reliable theoretical basis and decision support for the design optimization, operation and maintenance, and fault prediction of the overhead contact line.

[0022] Understandably, by acquiring the geometric parameters, physical property parameters, and operating environment parameters of the overhead contact system model, a multi-dimensional physical mapping model was established. This model not only describes the spatial geometry of the overhead contact system but also comprehensively considers its material properties and the influence of the external environment, providing a complete input foundation for subsequent mechanical and thermal analysis. Secondly, geometric parameters such as conductor height deviation, support span, and component spatial position directly determine the spatial layout and force path of the overhead contact system, reflecting the influence of structural morphology on current collection performance. By quantifying geometric deviations, structural hazards that may lead to uneven contact or pantograph-catenary disconnection can be identified during the modeling stage. Thirdly, physical property parameters (including material properties, surface roughness coefficient, and thermal radiation parameters) reflect the physical behavior characteristics of the contact interface between the conductor and the pantograph. The thermal conductivity and elastic modulus of the material affect the heat diffusion and deformation response at the contact point, while surface roughness and thermal radiation parameters affect the frictional heating and energy loss process, thus determining the temperature rise distribution law of the contact area. Finally, operating environment parameters (such as train speed, ambient temperature, and wind speed) reflect the coupling effect of the system's external dynamic conditions on thermodynamic balance and current collection stability. Operating speed determines the power of frictional flow, while ambient temperature and wind speed affect heat dissipation capacity. By inputting these parameters along with the aforementioned geometric and physical characteristics into the model, dynamic simulation and anomaly identification of contact force and thermal equilibrium can be achieved, providing data support for subsequent mapping of abnormal regions and generation of optimization suggestions.

[0023] Step S200: Input the geometric parameters and physical property parameters into the rule parsing engine, and use the rule parsing engine to perform logical verification on the geometric parameters and physical property parameters to generate verified structured data.

[0024] Specifically, when geometric and physical property parameters are input into a rule parsing engine, and the engine performs logical verification on them to generate verified structured data, the following steps are taken: when the conductor height deviation exceeds a preset height threshold or the support span exceeds a preset span range, the rule parsing engine analyzes the correlation between geometric and physical property parameters in the historical verification data of the overhead contact system model. The rule parsing engine is a multi-dimensional rule matching model based on a decision tree. This model includes a conditional branching layer and a weight allocation layer, used to extract global constraint features and local conflict features of geometric and physical property parameters from the historical verification data. Based on these global constraint features and local conflict features, the dynamic weights corresponding to each parameter in the preset joint verification model of geometric and physical properties are optimized. After the dynamic weight optimization is completed, the optimized joint verification model is used to generate verified structured data.

[0025] Specifically, when optimizing the dynamic weights of each parameter in the pre-defined joint verification model of geometric and physical property parameters based on global constraint features and local conflict features, the optimization includes: adjusting the dynamic weights corresponding to the conductor height deviation according to a pre-defined linear increment rule based on the frequency characteristics of conductor height deviation exceeding a pre-defined height threshold in the global constraint features; calculating the dynamic weights corresponding to the support span through a piecewise function based on the cumulative characteristics of support span exceeding a pre-defined span range in the global constraint features; and calculating the dynamic weights corresponding to material properties based on the coupling characteristics between geometric and physical property parameters in the local conflict features.

[0026] Specifically, the rule parsing engine is a core computational module used to logically judge and dynamically adjust the geometric and physical property parameters of the overhead contact line model. Through its built-in rule base and optimization algorithms, it parses and matches the input parameter optimization direction matrix, and calls corresponding optimization rules or computational models based on different anomaly types and component characteristics to generate parameter adjustment schemes that conform to design specifications and operational constraints. In other words, the rule parsing engine is equivalent to the "decision center" of model optimization, capable of transforming the optimization directions obtained from simulation analysis into executable parameter update instructions. This achieves full automation from data identification and rule matching to parameter correction, making the model update process interpretable, controllable, and intelligently adaptable.

[0027] Specifically, the multidimensional rule matching model is an intelligent judgment model used to comprehensively analyze multi-source feature data of complex systems. Its core lies in constructing a rule set encompassing multi-dimensional features such as geometry, physics, environment, and operational status. It then achieves high-dimensional correlation reasoning and pattern recognition through feature vector mapping and matching algorithms. During runtime, the model dynamically invokes rule templates of different dimensions based on the input structured data, comprehensively evaluating the coupling relationships and weighted influences between various features, thereby achieving accurate judgment of the system state and adaptive generation of optimization strategies. The application of the multidimensional rule matching model can effectively improve the accuracy of anomaly identification and the intelligence of model optimization, enabling dynamic control and continuous optimization of complex engineering systems.

[0028] It is understandable that the geometric parameters and physical property parameters of the catenary model are logically verified by a rule parsing engine. The core principle lies in using a knowledge-driven rule matching mechanism to achieve data consistency and model credibility verification. The geometric parameters and physical property parameters have significant correlations and constraint relationships in the catenary. For example, the wire height and the strut span jointly affect the geometric stability of the current collection structure, and the material properties determine its response characteristics during the force and heat balance processes. By inputting these two types of parameters into the rule parsing engine, joint verification between the structural characteristics and physical characteristics can be achieved, avoiding model distortion or simulation errors caused by parameter conflicts. Secondly, the rule parsing engine is constructed based on a multi-dimensional rule matching model of a decision tree. This model extracts global constraint features through the "conditional branch layer" to reflect the overall dependence relationship and variation law between different parameters; at the same time, local conflict features are extracted through the "weight assignment layer" to identify the non-linear coupling and mutation relationships between local abnormal parameters. Through this hierarchical feature extraction mechanism, the deep associations between geometric parameters and physical properties can be captured at the logical level, achieving multi-dimensional and dynamic rule parsing. Thirdly, during the dynamic weight optimization process, the parameter weights in the joint verification model are adaptively adjusted according to the global constraint features and local conflict features. Specifically, when the deviation of the wire height exceeds the preset threshold, its weight is increased using a linear increasing rule based on its historical occurrence frequency, thus highlighting the sensitivity to geometric deviations; when the strut span deviates from the preset range, the weight is adjusted using a piecewise function according to its cumulative overrun characteristics to more accurately reflect the impact of the structural span on the overall stability; at the same time, for the coupling relationship between geometric and physical properties (such as the synergistic effect of height deviation and material elastic modulus), the dynamic weight of the material property is calculated through local conflict features to achieve linkage optimization between parameters. Finally, through the optimization of the above dynamic weights, an adaptive geometric-physical parameter joint verification model can be constructed, which automatically adjusts the parameter sensitivity and verification threshold under different working conditions and historical data characteristics, and finally generates the verified structured data. This structured data not only ensures the logical consistency and physical rationality of the input model parameters, but also provides a highly credible data basis for subsequent abnormal mapping of contact force and heat balance.

[0029] It can be seen that by inputting geometric parameters and physical property parameters into the rule parsing engine for logical verification, multi-dimensional parameter consistency verification of the catenary model is achieved. Traditional verification methods usually detect single parameters or fixed logical relationships, easily ignoring the coupling effect between geometric structure and physical characteristics, leading to missed anomalies or misjudgments. This method, however, achieves joint verification of geometric and physical dimensions through multi-dimensional rule parsing, enabling the detection of implicit conflicts across parameter domains at an early stage, significantly improving the accuracy and completeness of the model input data. Secondly, by utilizing a decision tree-based multi-dimensional rule matching model, global constraint features and local conflict features in historical data can be extracted at the logical level, thereby achieving a structured understanding of the dependencies between parameters. This mechanism not only identifies explicit errors (such as parameters exceeding thresholds) but also detects implicit constraint conflicts (such as deviation coupling between different parameters), fundamentally improving the intelligence and generalization ability of the verification. Thirdly, by introducing a dynamic weight optimization mechanism, the importance weights of each parameter in the joint verification model can be adaptively adjusted according to the distribution of historical features and operational patterns. Taking conductor height deviation and support span as examples, when a certain type of deviation occurs frequently or has a large cumulative deviation in historical data, the corresponding weight will be automatically increased, thereby enhancing the influence of this parameter on the final verification result. This dynamic adjustment strategy effectively avoids the distortion problem caused by fixed weights in traditional methods, enabling the model to have continuous learning and self-optimization capabilities. Finally, through the above-mentioned multi-layer logical verification and dynamic weight optimization, this invention can output highly consistent and reliable structured data, providing a solid data foundation for subsequent simulation modeling and optimization analysis. Compared with manual or static rule verification methods, this technology significantly improves accuracy, robustness, and adaptability, significantly reducing modeling errors and improving the reliability and maintenance efficiency of the catenary model throughout its entire life cycle.

[0030] Step S300: Based on the verified structured data and operating environment parameters, simulate the pantograph-catenary contact relationship under the pantograph operating state and generate dynamic simulation results.

[0031] Specifically, based on the verified structured data and operating environment parameters, the simulation of the pantograph-catenary contact relationship under pantograph operating conditions and the generation of dynamic simulation results include: simulating the pantograph-catenary contact force distribution at different train operating speeds based on the conductor height deviation and support span in the verified structured data, and generating contact force distribution curves; determining the thermal equilibrium state of the pantograph-catenary contact area based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, and generating a thermal equilibrium distribution map; and superimposing the contact force distribution curves and the thermal equilibrium distribution map to generate dynamic simulation results.

[0032] Specifically, based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, the thermal equilibrium state of the pantograph-catenary contact area is calculated using a thermodynamic simulation unit to generate a thermal equilibrium distribution map. This includes: determining the thermal conduction power of the pantograph-catenary contact area based on the thermal radiation parameters and using Fourier's law of heat conduction; determining the natural convection heat transfer coefficient based on wind speed and ambient temperature, and determining the convective heat transfer power based on the natural convection heat transfer coefficient; and performing thermal equilibrium analysis on the pantograph-catenary contact area based on the thermal conduction power and the convective heat transfer power to generate a thermal equilibrium distribution map.

[0033] Understandably, the core principle of dynamically simulating the interaction between the pantograph and the overhead contact system based on verified structured data and operating environment parameters lies in utilizing a multi-physics coupled modeling method to comprehensively incorporate geometric, material, and environmental factors into the simulation system, thereby achieving a high-precision reproduction of the dynamic evolution of the pantograph-catenary contact state. By introducing structured data verified by a rule-based parsing engine, the logical consistency and physical validity of the input parameters are ensured, enabling the simulation model to accurately reflect the force and thermal behavior characteristics during actual operation based on reliable data. Secondly, a contact force distribution model and a thermal balance analysis model were established during the dynamic simulation process. The contact force distribution model, based on geometric constraints such as conductor height deviation and support span, combined with the dynamic response characteristics of the pantograph at different train speeds, simulates the variation law of the contact force between the pantograph and the overhead contact system. This process is essentially based on the principles of mechanical dynamics, solving the contact force distribution curve of the pantograph and the overhead contact system through time-varying constraints and equations of motion, thereby revealing the stability and uniformity of the pantograph-catenary contact under different speeds and geometric deviations. Furthermore, in the thermal balance analysis, a thermodynamic simulation unit for the pantograph-catenary contact area was constructed based on material properties and thermal radiation parameters, combined with external ambient temperature and wind speed. Specifically, the thermal conductivity of the pantograph-catenary conductive components was calculated using Fourier's law of heat conduction to reflect the energy transfer behavior within the material. Simultaneously, the natural convection heat transfer coefficient was determined based on ambient wind speed and temperature, and the convection heat transfer power was further calculated to characterize the heat exchange capacity of the pantograph-catenary surface to the air. Through a comprehensive balance analysis of the power from both thermal conduction and convection heat transfer, the steady-state temperature distribution of the pantograph-catenary contact area can be accurately obtained, thus generating a thermal balance distribution map. Finally, by overlaying the contact force distribution curve with the thermal balance distribution map, a synergistic mapping of the mechanical and thermal fields was achieved, yielding dynamic simulation results that comprehensively reflect the pantograph's operating state. These results not only reveal the coupling characteristics of force and heat dissipation in the pantograph-catenary under different operating conditions but also provide a quantitative basis for subsequent abnormal area identification and structural optimization. In other words, this technology achieves full-link physical mechanism modeling from structural parameters to operational performance through joint simulation of force-thermal multiphysics fields, providing scientific decision support for the optimization of catenary models.

[0034] It can be seen that by combining the verified structured data with the operating environment parameters, the pantograph-catenary contact process under pantograph operating conditions can be reproduced with high precision in a digital twin environment. Traditional simulations are usually based only on idealized static models or simplified parameters, which cannot accurately reflect the influence of external factors such as train speed changes, wind disturbances, and temperature fluctuations in actual operation. However, this method, by introducing operating environment parameters (such as train speed, temperature, and wind speed), realizes the simulation calculation coupled with dynamic environmental conditions and physical characteristics, significantly improving the realism and dynamic response consistency of the pantograph-catenary system simulation. Secondly, in terms of pantograph-catenary contact force analysis, this technology establishes a contact force distribution model under multi-speed conditions based on geometric parameters such as conductor height deviation and support span, which can quantify the variation law of pantograph-conductor contact force at different operating speeds. By generating contact force distribution curves, not only can local abnormal stress areas be identified, but data support can also be provided for subsequent structural optimization and fatigue life assessment, thereby realizing the transformation from qualitative judgment to quantitative mechanical analysis. Thirdly, this invention introduces a thermodynamic simulation unit to calculate the thermal balance distribution of the pantograph-catenary contact area based on material properties, thermal radiation parameters, ambient temperature, and wind speed. This section utilizes a combination of Fourier's law of heat conduction and a natural convection heat transfer model to accurately simulate the temperature rise and heat dissipation processes at the contact points during long-term operation. This overcomes the limitations of traditional analyses that neglect thermal effects or rely on empirical coefficients for estimation, achieving coupled simulation of contact mechanics and thermal effects. It can effectively predict risks such as conductor relaxation, bow head wear, or material degradation caused by high temperatures. Finally, by overlaying the contact force distribution curve with the heat balance distribution diagram, a dynamic simulation result with multi-dimensional information fusion characteristics is generated, enabling a comprehensive evaluation from a single physics field to a multi-physics field. This result not only enhances the completeness and decision support value of the simulation but also allows subsequent model optimization to simultaneously consider mechanical performance and thermal stability, achieving an optimal balance between structural strength and durability.

[0035] Step S400: Compare the dynamic simulation results with the preset design specifications, generate optimization suggestions, and update the catenary model.

[0036] Specifically, when comparing the dynamic simulation results with the preset design specifications to generate optimization suggestions and update the catenary model, the process includes: identifying abnormal contact force areas based on the contact force distribution curve in the dynamic simulation results and a preset contact force safety threshold; identifying abnormal heat balance areas based on the heat balance distribution map in the dynamic simulation results and a preset heat balance safety threshold; mapping the abnormal contact force areas and abnormal heat balance areas to the catenary model to generate optimization suggestions; and adjusting the geometric and physical property parameters in the catenary model according to the optimization suggestions to complete the model update.

[0037] Specifically, when mapping contact force anomaly regions and thermal balance anomaly regions to the catenary model to generate optimization suggestions, the process includes: obtaining the contact force distribution curve and thermal balance distribution map from the dynamic simulation results; establishing a spatial correspondence between the simulation coordinate system and the catenary model coordinate system based on the spatial coordinate information of the simulation results, and generating a spatial index mapping table; based on the spatial index mapping table, back-mapping the coordinate points of the contact force anomaly regions and thermal balance anomaly regions to the catenary model point by point to determine the model components corresponding to the anomaly regions and their spatial locations; calling the model database to obtain the geometric parameters and physical property parameters of the anomaly components, and generating an anomaly component attribute dataset; based on the anomaly component attribute dataset, calculating the influence of contact force and thermal balance anomalies on model performance indicators, and generating a parameter optimization direction matrix; and inputting the parameter optimization direction matrix into the parameter optimization rule base to generate a structured optimization suggestion data package for each anomaly component.

[0038] Specifically, when updating the model, the geometric and physical property parameters in the overhead contact line model are adjusted according to the optimization suggestions. This includes: when the deviation between the optimized overhead contact line model and the actual operating data is greater than a preset deviation threshold, the deviation optimization gradient is determined based on a genetic algorithm; and the weight allocation layer of the joint verification model of geometric and physical property parameters in the rule parsing engine is iteratively optimized based on the deviation optimization gradient to obtain the joint verification model after parameter adjustment.

[0039] Understandably, the performance of the overhead contact system model is quantitatively evaluated through a comparative analysis mechanism between dynamic simulation results and design specifications. Specifically, the contact force distribution curve and thermal balance distribution map are extracted from the dynamic simulation results and compared with the corresponding safety thresholds to identify areas of abnormal stress and thermal imbalance. The core principle of this process is to transform multiphysics simulation data (force field and thermal field) into constraint judgment problems that conform to design standards, constructing a closed-loop verification path from "simulation response" to "engineering specifications," so that model verification no longer relies on empirical judgment but on quantitative results based on physical consistency. Secondly, this technology achieves a one-to-one correspondence between simulation data and actual model components at the spatial level through a mapping mechanism between the simulation coordinate system and the model coordinate system. Based on the spatial coordinate information of the simulation results, a spatial index mapping table is established to accurately map the abnormal areas identified in the simulation back to the specific components in the overhead contact system model. This spatial inverse mapping principle relies on a unified coordinate transformation matrix and index matching algorithm, enabling virtual anomalies in the simulation domain to be accurately located within the actual model structure. This supports subsequent parameter correlation analysis and optimization operations, ensuring high-fidelity mapping between the virtual and real digital twin data. Third, this invention achieves data-driven model performance impact analysis and optimization direction identification through the generation mechanism of anomaly component attribute datasets and parameter optimization direction matrices. Specifically, the principle involves extracting the geometric and physical properties of anomaly components to form a multi-dimensional feature set describing stress, temperature rise, and structural response. Then, based on performance index impact calculations, the sensitivity weights of each parameter change to performance are obtained, constructing a parameter optimization direction matrix. This matrix is ​​essentially a multi-dimensional gradient vector field, used to characterize the optimal adjustment path for each anomaly component in the performance improvement direction. Subsequently, this matrix is ​​input into a parameter optimization rule base, and structured optimization suggestions are generated through rule matching and empirical weight correction, realizing the knowledge-based and automated optimization of the model. Finally, in the model update stage, an adaptive iterative optimization mechanism based on a genetic algorithm is introduced. When the optimized model output deviates from the actual running data, the deviation is treated as a fitness function. Through selection, crossover, and mutation operations using a genetic algorithm, the deviation optimization gradient is iteratively sought, thereby dynamically adjusting the weight allocation layer of the joint verification model in the rule parsing engine. This mechanism, based on the principle of evolutionary computation, enables the parameter adjustment process to have global search capabilities and self-learning characteristics. It can gradually approach the optimal model structure under complex constraints, forming a self-circulating optimization chain of "simulation-verification-optimization-re-simulation".

[0040] As can be seen, by comparing the dynamic simulation results with the preset design specifications, the system can automatically identify abnormal areas based on the contact force distribution curve and the heat balance distribution diagram, realizing the quantitative location of potential instability points and unevenly heated areas in the pantograph-catenary system. This method avoids the subjectivity of traditional manual experience judgment, improves the accuracy and real-time performance of anomaly identification, and provides a precise data foundation for model optimization. Secondly, the technical solution establishes a spatial index mapping table between the simulation coordinate system and the model coordinate system, realizing a one-to-one spatial correspondence between the simulation results and the catenary structure model, thereby accurately mapping the identified abnormal areas to specific components of the model. This enables the system to generate differentiated optimization suggestions for specific components, thereby supporting quantitative adjustments based on the parameter optimization direction matrix, significantly improving the targeting and engineering feasibility of model optimization. Finally, the introduction of a genetic algorithm and a weighted iteration mechanism of the joint verification model in the model update stage enables the system to adaptively adjust geometric and physical parameters according to the deviation of actual operating data, constructing a self-evolving model with closed-loop learning capabilities. This process not only ensures the long-term stability and reliability of the optimization results, but also enables the catenary model to be continuously optimized under different operating environments and conditions, thereby achieving efficient integration of catenary system design, simulation and verification.

[0041] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0042] During the design phase of railway overhead contact line engineering, engineers need to accurately verify the contact line model to ensure it meets design specifications. First, an acquisition module collects geometric parameters, physical property parameters, and operating environment parameters from the contact line model. For example, in a high-speed railway project, the acquisition module uses 3D scanning equipment to extract geometric parameters such as conductor height deviation, support span, and component spatial position, and obtains physical property parameters such as material properties, surface roughness coefficient, and thermal radiation parameters through laboratory testing. Simultaneously, a sensor network collects operating environment parameters in real time, such as train speed, ambient temperature, and wind speed. This data is then structured, stored, and transmitted to the verification module.

[0043] In the verification module, the rule parsing engine performs logical verification of geometric and physical property parameters based on a multi-dimensional rule matching model using decision trees. For example, when a height deviation of a conductor segment exceeds a preset threshold, the rule parsing engine calls global constraint features and local conflict features from historical verification data for correlation analysis. Global constraint features describe the overall relationship between geometric and physical property parameters, while local conflict features capture anomalies under specific parameter combinations. Based on this, the rule parsing engine optimizes the dynamic weights in the joint verification model. For example, the dynamic weight of conductor height deviation is adjusted according to its frequency of exceeding the threshold using a linearly increasing rule to enhance the cumulative effect of high-frequency anomalies; the dynamic weight of support span is calculated using a piecewise function to accurately quantify the nonlinear characteristics of the span criticality. After weight optimization, the rule parsing engine generates verified structured data and transmits it to the simulation module.

[0044] In the simulation module, the kinematic simulation unit and the thermodynamic simulation unit simulate the pantograph-catenary contact force distribution and thermal equilibrium state under pantograph operation, respectively. For example, the kinematic simulation unit, based on the conductor height deviation and support span in the verified structured data, combined with the train speed, simulates the pantograph-catenary contact force distribution at different operating speeds, generating a contact force distribution curve. The thermodynamic simulation unit, based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, calculates the thermal equilibrium state of the pantograph-catenary contact area, generating a thermal equilibrium distribution map. Specifically, the thermodynamic simulation unit first calculates the heat conduction power in the pantograph-catenary contact area using Fourier's law of heat conduction, then calculates the natural convection heat transfer coefficient based on wind speed and ambient temperature, and further calculates the convective heat transfer power. Finally, it generates a thermal equilibrium distribution map through a comprehensive analysis of the heat conduction power and the convective heat transfer power. The simulation module overlays the contact force distribution curve and the thermal equilibrium distribution map to generate dynamic simulation results, which are then transferred to the optimization module.

[0045] In the optimization module, dynamic simulation results are compared with preset design specifications to generate optimization suggestions and update the overhead contact line model. For example, when the contact force in a certain area exceeds the safety threshold, the optimization module will suggest adjusting the conductor height deviation or support span in that area; when the thermal equilibrium state in a certain area exceeds the safety threshold, the optimization module will suggest adjusting the material properties or thermal radiation parameters in that area. The optimization module adjusts the geometric and physical property parameters in the overhead contact line model according to the optimization suggestions, completing the model update.

[0046] After the model update is complete, the optimization module also performs closed-loop correction on the optimized catenary model. For example, when the deviation between the optimized catenary model and the actual operating data exceeds a preset deviation threshold, the optimization module calculates the deviation optimization gradient using a genetic algorithm and performs iterative optimization calculations on the weight allocation layer of the joint verification model of geometric and physical property parameters in the rule parsing engine based on the deviation optimization gradient, to obtain the joint verification model with adjusted parameters. This process ensures that the rule parsing engine can continuously optimize as the actual operating data changes, thereby improving the accuracy and reliability of the catenary model.

[0047] Throughout the implementation process, the acquisition module, verification module, simulation module, and optimization module are seamlessly connected via data interfaces. The acquisition module transmits the collected geometric parameters, physical property parameters, and operating environment parameters to the verification module in the form of structured data; the verification module generates verified structured data, which is then transmitted to the simulation module via the data interface; the dynamic simulation results generated by the simulation module are transmitted to the optimization module via the data interface; and the optimization suggestions and updated catenary model generated by the optimization module are fed back to the acquisition module, forming a closed-loop management system. This modular design not only improves the system's scalability and maintainability but also ensures efficient collaboration between the modules.

[0048] As can be seen from the above steps, this invention integrates multi-source data and dynamic simulation technology. While ensuring the accuracy of the overhead contact line model, it captures multi-dimensional information such as geometric parameters, physical property parameters, and operating environment parameters in real time, solving the problems of data fragmentation and rule lag in traditional solutions. Simultaneously, this invention utilizes historical verification data to train a rule parsing engine, replacing static rules with a dynamic weighted model jointly verified by geometric and physical property parameters. It also introduces thermodynamic simulation factors to correct the thermal equilibrium distribution in real time, eliminating prediction biases in static empirical formulas under complex operating environments. Based on the dynamically verified structured data, real-time operating environment parameters, and dynamic simulation results, this invention optimizes the calculation logic of contact force distribution and thermal equilibrium distribution, establishes a real-time interaction mechanism between geometric parameters, physical property parameters, and operating environment parameters, and improves the physical coupling accuracy of the overhead contact line model. Finally, this invention achieves full lifecycle management of the overhead contact line model by comparing dynamic simulation results with design specifications and using a genetic algorithm to correct deviations through actual operating data, reducing the subjectivity and inefficiency of traditional manual verification methods.

[0049] The above scenarios are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0050] In the above embodiments, by acquiring the geometric parameters, physical property parameters, and operating environment parameters of the overhead contact system model, a multi-dimensional data-driven model description system was established, realizing the full-element digital expression from static geometric structure to dynamic operating environment. This process enables the model to not only reflect the structural relationships in the design stage but also to realistically characterize the stress, temperature, and environmental response under train operating conditions, providing an accurate data foundation for subsequent simulation analysis. Secondly, by using a rule parsing engine to logically verify the geometric and physical property parameters, a multi-dimensional rule model oriented towards the engineering characteristics of the overhead contact system was constructed. This engine can automatically identify the coupling relationship between conductor height deviation, support span, and material properties, dynamically judge and correct abnormal parameters, significantly improving the consistency and accuracy of model data and reducing the risk of subjective errors and omissions caused by manual review. Thirdly, based on the verified structured data and operating environment parameters, the pantograph-catenary contact relationship under the pantograph operating state is accurately simulated through a dynamic simulation module, generating dynamic simulation results including contact force distribution and thermal equilibrium state. This simulation process can intuitively reflect the operational stability of the pantograph under different speed, temperature, and wind speed conditions, providing a visual and quantitative evaluation basis for catenary design and compensating for the inability of traditional static models to reflect dynamic interactive performance. Finally, by comparing the dynamic simulation results with preset design specifications, optimization suggestions are generated and the catenary model is automatically updated, realizing a closed-loop improvement mechanism from model verification, simulation validation to parameter optimization. This mechanism can dynamically adjust geometric and physical property parameters based on simulation feedback, ensuring that the catenary model continuously approaches the optimal design state, thereby improving overall design accuracy and operational reliability.

[0051] In another preferred embodiment based on the above embodiments, such as Figure 3 As shown, this embodiment provides a catenary model optimization system based on digital twin and dynamic simulation, including: an acquisition module, a verification module, a simulation module and an optimization module.

[0052] Specifically, the acquisition module is used to acquire the geometric parameters, physical property parameters, and operating environment parameters in the catenary model; the verification module is used to input the geometric parameters and physical property parameters into the rule parsing engine, and use the rule parsing engine to perform logical verification on the geometric parameters and physical property parameters to generate verified structured data; the simulation module is used to simulate the pantograph-catenary contact relationship under the pantograph operating state based on the verified structured data and operating environment parameters, and generate dynamic simulation results; the optimization module is used to compare the dynamic simulation results with the preset design specifications, generate optimization suggestions, and update the catenary model.

[0053] It is understood that the contact network model optimization system and method based on digital twin and dynamic simulation in the above embodiments of this application have the same beneficial effects, and will not be described again.

[0054] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program goods. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program goods 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.

[0055] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. 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... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0056] 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.

[0057] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment 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.

[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A method for optimizing overhead contact line models based on digital twins and dynamic simulation, characterized in that, include: Obtain the geometric parameters, physical property parameters, and operating environment parameters from the overhead contact system model; The geometric parameters and physical property parameters are input into the rule parsing engine, which performs logical verification on the geometric parameters and physical property parameters to generate verified structured data. Based on the verified structured data and operating environment parameters, the pantograph-catenary contact relationship under the pantograph operating state is simulated to generate dynamic simulation results; The dynamic simulation results are compared with the preset design specifications to generate optimization suggestions and update the catenary model.

2. The method for optimizing a catenary model based on digital twins and dynamic simulation as described in claim 1, characterized in that, Geometric parameters include conductor height deviation, support span, and component spatial location; physical property parameters include material properties, surface roughness coefficient, and thermal radiation parameters; operating environment parameters include train speed, ambient temperature, and wind speed.

3. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 2, characterized in that, The geometric and physical property parameters are input into the rule parsing engine. The rule parsing engine performs logical validation on the geometric and physical property parameters, generating validated structured data, including: When the conductor height deviation exceeds the preset height threshold or the support span exceeds the preset span range, the rule parsing engine is used to analyze the correlation between geometric parameters and physical property parameters in the historical verification data of the contact network model. The rule parsing engine is a multi-dimensional rule matching model based on decision trees. The multi-dimensional rule matching model includes a conditional branching layer and a weight allocation layer, which are used to extract the global constraint features and local conflict features of geometric parameters and physical attribute parameters in historical verification data, respectively. Based on global constraint characteristics and local conflict characteristics, optimize the dynamic weights corresponding to each parameter in the joint verification model of preset geometric parameters and physical property parameters; After the dynamic weight optimization is completed, the optimized joint verification model is used to generate verified structured data.

4. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 3, characterized in that, When optimizing the dynamic weights corresponding to each parameter in the pre-defined joint verification model of geometric and physical property parameters based on global constraint characteristics and local conflict characteristics, the following steps are taken: Based on the frequency characteristics of the conductor height deviation exceeding the preset height threshold in the global constraint features, the dynamic weight corresponding to the conductor height deviation is adjusted according to the preset linear increment rule; Based on the cumulative characteristics of the support span exceeding the preset span range in the global constraint features, the dynamic weight corresponding to the support span is calculated through a piecewise function; Based on the coupling characteristics between geometric parameters and physical property parameters in local conflict features, the dynamic weights corresponding to material properties are calculated.

5. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 1, characterized in that, Based on the verified structured data and operating environment parameters, the pantograph-catenary contact relationship under pantograph operating conditions is simulated, and the dynamic simulation results are generated, including: Based on the conductor height deviation and support span in the verified structured data, the pantograph-catenary contact force distribution under different train operating speeds is simulated, and contact force distribution curves are generated. Based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, the thermal balance state of the pantograph-catenary contact area is determined, and a thermal balance distribution map is generated. The contact force distribution curve is superimposed with the heat balance distribution diagram to generate dynamic simulation results.

6. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 5, characterized in that, Based on the material properties and thermal radiation parameters in the verified structured data, combined with ambient temperature and wind speed, the thermal equilibrium state of the pantograph-catenary contact area is calculated using a thermodynamic simulation unit. When generating the thermal equilibrium distribution map, the following is included: Based on thermal radiation parameters, Fourier's law of heat conduction is used to determine the heat conduction power in the pantograph-catenary contact area. The natural convection heat transfer coefficient is determined based on wind speed and ambient temperature, and the convective heat transfer power is determined based on the natural convection heat transfer coefficient. Based on the heat conduction power and convective heat transfer power, a heat balance analysis is performed on the pantograph-catenary contact area to generate a heat balance distribution map.

7. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 1, characterized in that, When comparing dynamic simulation results with preset design specifications to generate optimization suggestions and update the catenary model, the following steps are included: Based on the contact force distribution curve in the dynamic simulation results, combined with the preset contact force safety threshold, abnormal contact force areas are identified. Based on the heat balance distribution map in the dynamic simulation results, combined with the preset heat balance safety threshold, abnormal heat balance areas are identified. Mapping areas of abnormal contact force and areas of abnormal thermal balance to the catenary model to generate optimization suggestions; Adjust the geometric and physical property parameters in the overhead contact line model according to the optimization suggestions to complete the model update.

8. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 7, characterized in that, When mapping contact force anomaly regions and thermal balance anomaly regions to the catenary model and generating optimization suggestions, the following are included: Obtain the contact force distribution curve and heat balance distribution map from the dynamic simulation results. Based on the spatial coordinate information of the simulation results, establish the spatial correspondence between the simulation coordinate system and the catenary model coordinate system, and generate a spatial index mapping table. Based on the spatial index mapping table, the coordinate points of the abnormal contact force area and the abnormal thermal balance area are back-mapped to the catenary model point by point to determine the model components and their spatial positions corresponding to the abnormal areas. Call the model database to obtain the geometric and physical property parameters of the abnormal components and generate an abnormal component attribute dataset; Based on the abnormal component attribute dataset, the influence of abnormal contact force and thermal balance on the model performance index is calculated, and the parameter optimization direction matrix is ​​generated. Input the parameter optimization direction matrix into the parameter optimization rule base to generate a structured optimization suggestion data package for each abnormal component.

9. The catenary model optimization method based on digital twin and dynamic simulation as described in claim 7, characterized in that, Adjusting the geometric and physical property parameters in the overhead contact line model based on optimization suggestions, and completing the model update includes: When the deviation between the optimized catenary model and the actual operating data exceeds the preset deviation threshold, the deviation optimization gradient is determined based on the genetic algorithm. Iterative optimization is performed on the weight allocation layer of the joint verification model of geometric parameters and physical property parameters in the rule parsing engine based on the bias optimization gradient to obtain the joint verification model with adjusted parameters.

10. A catenary model optimization system based on digital twin and dynamic simulation, applicable to the catenary model optimization method based on digital twin and dynamic simulation as described in any one of claims 1-9, characterized in that, include: The acquisition module is used to acquire geometric parameters, physical property parameters, and operating environment parameters from the overhead contact line model. The verification module is used to input geometric parameters and physical property parameters into the rule parsing engine, and use the rule parsing engine to perform logical verification on the geometric parameters and physical property parameters to generate verified structured data. The simulation module is used to simulate the pantograph-catenary contact relationship under the pantograph's operating state based on the verified structured data and operating environment parameters, and generate dynamic simulation results. The optimization module is used to compare the dynamic simulation results with the preset design specifications, generate optimization suggestions, and update the catenary model.

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