Multidisciplinary simulation detection method, device, equipment, storage medium and program product

Abstract

Embodiments of the present application provide a kind of multidisciplinary simulation detection method, device, equipment, storage medium and program product, involve simulation verification field.The method comprises: obtaining the first parameter of first submodel updated, first submodel is the any submodel in the multiple submodels that the multidisciplinary model includes;According to the first parameter updated, at least one second parameter is updated, at least one second parameter is the parameter associated with first parameter in at least one second submodel, at least one second submodel is other submodel except first submodel in multiple submodels;Determine the scene information corresponding to updated first parameter and updated at least one second parameter;According to updated first parameter, updated at least one second parameter and scene information, generate simulation detection report by multidisciplinary model.The method provided in the present application improves the efficiency of multidisciplinary simulation detection.

Description

Technical Field

[0001] This application relates to the field of simulation testing, and in particular to a multidisciplinary simulation testing method, apparatus, equipment, storage medium, and program product. Background Technology

[0002] As rail transit equipment evolves towards "high speed, lightweight, intelligent, and standardized" development, system-level architecture reconfiguration has become a core technical means to meet new operational needs. Current rail transit product development involves the collaborative design of multiple systems, including car body, bogie, traction, braking, and signaling. This is particularly true in the development of complex equipment such as high-speed trains, intelligent subways, and urban EMUs, where it is necessary to simultaneously consider multidisciplinary coupling factors such as aerodynamic performance, multibody dynamics, structural strength, and control algorithms.

[0003] In the traditional development model, design changes often lead to inconsistencies in data from multiple models, such as 3D geometric models, control algorithm models, and fluid dynamics models. Furthermore, cross-disciplinary verification requires a prototype testing cycle of up to 6 months, with a single round of rectification costing over 10 million yuan, resulting in low efficiency of simulation testing. Summary of the Invention

[0004] This application provides a multidisciplinary simulation testing method, apparatus, equipment, storage medium, and program product to solve the technical problem of low efficiency in simulation testing.

[0005] In a first aspect, this application provides a multidisciplinary simulation testing method, comprising: obtaining the first parameter after the first sub-model is updated, wherein the first sub-model is any one of the multiple sub-models included in the multidisciplinary model, and the multidisciplinary model includes at least two sub-models among the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model and multibody dynamics sub-model;

[0006] Update at least one second parameter based on the updated first parameter, wherein at least one second parameter is a parameter associated with the first parameter in at least one second sub-model, and at least one second sub-model is another sub-model other than the first sub-model among multiple sub-models;

[0007] Determine the scene information corresponding to the updated first parameter and at least one updated second parameter;

[0008] Based on the updated first parameter, at least one updated second parameter, and scene information, a simulation test report is generated using a multidisciplinary model.

[0009] In one possible implementation of the first aspect, updating at least one second parameter based on the updated first parameter includes: determining the parameter identifier and parameter semantic information of the updated first parameter; updating at least one associated second parameter based on the parameter identifier and parameter semantic information of the updated first parameter, and a preset parameter mapping rule, wherein the parameter identifier and / or parameter semantic information of the at least one second parameter is associated with the parameter identifier and / or parameter semantic information of the updated first parameter, and the parameter mapping rule is determined based on the association relationship between multiple sub-models.

[0010] In one possible implementation of the first aspect, the preset parameter mapping rules are determined based on the interface definition rules of multiple sub-models and standard specification information, including industrial data standards and railway data specifications.

[0011] In one possible implementation of the first aspect, a simulation test report is generated through a multidisciplinary model based on the updated first parameter, the second parameter, and scene information, including: determining at least two sub-models in the multidisciplinary model based on the updated first parameter, at least one updated second parameter, and scene information; and generating a simulation test report based on the at least two sub-models, the updated first parameter, at least one updated second parameter, and scene information.

[0012] In one possible implementation of the first aspect, a simulation detection report is generated based on at least two sub-models, according to the updated first parameter, second parameter, and scene information, including: scheduling at least two sub-models through a partitioned strong coupling mechanism and a distributed time advancement algorithm, and generating a simulation detection report based on the updated first parameter, at least one updated second parameter, and scene information.

[0013] In one possible implementation of the first aspect, the simulation test report also includes a comprehensive confidence level and / or a traceability matrix, wherein the comprehensive confidence level is used to indicate the confidence level of the test results in the simulation test report, the comprehensive confidence level being determined based on the calculated confidence levels of at least two sub-models, and the traceability matrix is ​​used to indicate the simulation requirements and simulation results.

[0014] In one possible implementation of the first aspect, the simulation test report includes at least one of chart information, animation information, and text information.

[0015] In one possible implementation of the first aspect, the multidisciplinary model includes a unified data interface, the standard of which is determined based on interface definition rules and standard specification information.

[0016] Secondly, this application provides a multidisciplinary simulation testing device, comprising:

[0017] The acquisition module is used to acquire the first parameter after the first sub-model is updated. The first sub-model is any one of the multiple sub-models included in the multi-disciplinary model. The multi-disciplinary model includes at least two sub-models from the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model and multibody dynamics sub-model.

[0018] The update module is used to update at least one second parameter based on the updated first parameter. The at least one second parameter is a parameter associated with the first parameter in at least one second sub-model. The at least one second sub-model is another sub-model other than the first sub-model among multiple sub-models.

[0019] The determination module is used to determine the scene information corresponding to the updated first parameter and at least one updated second parameter;

[0020] The generation module is used to generate a simulation test report based on the updated first parameter, at least one updated second parameter, and scene information through a multidisciplinary model.

[0021] Thirdly, this application provides an electronic device, including: a processor and a memory communicatively connected to the processor;

[0022] The memory stores instructions that the computer executes;

[0023] The processor executes computer-executable instructions stored in memory to implement any of the methods of the first aspect.

[0024] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the method of any one of the first aspects.

[0025] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the method of any one of the first aspects.

[0026] The multidisciplinary simulation testing method, apparatus, equipment, storage medium, and products provided in this application solve the data silo problem caused by semantic inconsistencies in heterogeneous models through the construction of multidisciplinary models; the parameter synchronization bus mechanism ensures the real-time propagation of architecture reconstruction parameters among various disciplinary models, avoiding data inconsistencies caused by manual updates; the real-time coupling algorithm supports closed-loop verification of strongly coupled scenarios such as aerodynamics-structure-control, enabling key issues to be identified in a timely manner during the conceptual design stage; and the standardized interface of the dedicated model library significantly improves verification efficiency. This not only shortens the design verification cycle, improves simulation efficiency, and reduces prototype testing costs, but also enhances the credibility and traceability of verification results through confidence-based quantitative evaluation and standard report generation. Attached Figure Description

[0027] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0028] Figure 1 A schematic diagram of a scenario for a multidisciplinary simulation detection method provided in an embodiment of this application;

[0029] Figure 2 A flowchart illustrating a multidisciplinary simulation detection method provided in this application embodiment;

[0030] Figure 3 A schematic diagram of the structure of a multidisciplinary simulation testing device provided in this application embodiment;

[0031] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0032] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0033] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0034] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of the relevant data all comply with the relevant laws, regulations, and standards of the relevant countries and regions, have taken necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation access points for users to choose to authorize or refuse.

[0035] Figure 1 This is a schematic diagram illustrating a scenario where the multidisciplinary simulation detection method of this application is applied. For example... Figure 1As shown, in a multidisciplinary simulation testing scenario, users can interact with the simulation testing equipment, which can then execute the multidisciplinary simulation testing method of this application embodiment to provide interactive services to the user. The simulation testing equipment can also interact with a server to obtain the relevant data required for executing the multidisciplinary simulation testing method of this application embodiment.

[0036] As rail transit equipment evolves towards "high speed, lightweight, intelligent, and standardized" development, system-level architecture reconfiguration has become a core technical means to meet new operational needs. Current rail transit product development involves the collaborative design of multiple systems, including car body, bogie, traction, braking, and signaling. This is particularly true in the development of complex equipment such as high-speed trains, intelligent subways, and urban EMUs, where it is necessary to simultaneously consider multidisciplinary coupling factors such as aerodynamic performance, multibody dynamics, structural strength, and control algorithms.

[0037] In the traditional development model, design changes often lead to inconsistencies in data from multiple models, such as 3D geometric models, control algorithm models, and fluid dynamics models. Furthermore, cross-disciplinary verification requires a prototype testing cycle of up to 6 months, with a single round of rectification costing over 10 million yuan. The efficiency of multi-disciplinary simulation testing is also low.

[0038] For example, the following questions may be included:

[0039] (1) The system cannot achieve unified integration of system modeling language (SysML) system model, Modelica multiphysics model, three-dimensional computer-aided design (CAD), three-dimensional computer-aided engineering (CAE) model, aerodynamic model and multibody dynamics model, resulting in semantic inconsistency between models and data silos.

[0040] (2) When the architecture reconstruction involves changes in key parameters (such as the aspect ratio of the car body head and the wheelbase of the bogie), the existing technology cannot achieve automatic synchronous updates of three-dimensional geometry, computational fluid dynamics (CFD) mesh and multibody dynamics model, resulting in data inconsistency.

[0041] (3) Existing technologies cannot achieve real-time closed-loop verification of strongly coupled scenarios such as aerodynamics-structure-control during the conceptual design stage, which leads to the inability to discover key issues in the early stages.

[0042] (4) Existing technologies lack standardized models for rail transit (such as head templates for China Railway High-speed (CRH) series trains, Chinese rail parameters, pantograph-catenary coupling models, etc.), requiring users to repeatedly develop them, which affects verification efficiency.

[0043] (5) Lack of confidence quantification assessment: Existing technologies cannot conduct unified confidence quantification assessment of multidisciplinary comprehensive verification results, and lack the ability to automatically generate compliance verification reports.

[0044] The multidisciplinary simulation testing methods, devices, equipment, storage media, and program products provided in this application are intended to solve the above-mentioned technical problems in the prior art.

[0045] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0046] Figure 2 This is a flowchart illustrating a multidisciplinary simulation detection method provided in an embodiment of this application, as shown below. Figure 2 As shown, the method includes:

[0047] S201. The simulation testing equipment obtains the first parameter after the first sub-model is updated. The first sub-model is any one of the multiple sub-models included in the multi-disciplinary model. The multi-disciplinary model includes at least two sub-models among the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model and multibody dynamics sub-model. The first parameter is any parameter in the first sub-model.

[0048] It is understandable that the multidisciplinary model in the simulation and testing equipment can include multiple sub-models, and each sub-model can correspond to a different discipline. For example, it can include sub-models of disciplines such as mechanical sub-model, electrical sub-model, control sub-model, aerodynamics sub-model, and multibody dynamics sub-model. In addition, it can also include sub-models of other disciplines or other types, which are not limited here.

[0049] For example, the multidisciplinary model can be a collection of models generated by different modeling tools from different disciplines, such as a collection of models generated by combining system modeling models (SysML), multiphysics modeling models (Modelica), three-dimensional geometric modeling models (CAD), fluid dynamics modeling models (CFD), and multibody dynamics modeling models (multibody dynamics tools).

[0050] For example, a multidisciplinary model may include the following sub-model engines:

[0051] 1) SysML Interface Engine: Built-in MagicDraw / Rhapsody API, automatically parses block definition diagram (BDD) / internal block diagram (IBD) / parameter diagram, and extracts system parameters and interfaces;

[0052] 2) Modelica compilation engine: Calls OpenModelica Compiler to generate executable C code, supporting multiphysics (mechanical, electrical, thermal, fluid) integration;

[0053] 3) Lightweight 3D Engine: Converts the original CATIA / NX model to the GLTF 2.0 lightweight format, preserving geometry, materials, and motion pair coordinate systems;

[0054] 4) CFD adaptation engine: Automatically reads STAR-CCM+ / Fluent mesh and exposes boundary condition ports via CoSim API;

[0055] 5) Multibody dynamics adaptation engine: Provides real-time interfaces for Simpack, UM, and Adams, and supports automatic mapping of wheel-rail contact tables and flexible body modal information.

[0056] In addition, more sub-model engines can be included, but this is not limited here.

[0057] S202. The simulation testing equipment updates at least one second parameter according to the updated first parameter. The at least one second parameter is a parameter associated with the first parameter in at least one second sub-model. The at least one second sub-model is another sub-model other than the first sub-model among multiple sub-models.

[0058] After obtaining the updated first parameter, the simulation testing equipment can update at least one related second parameter in other sub-models. Understandably, to avoid distortion of simulation results due to outdated related parameters in other sub-models, the simulation testing equipment needs to update the related parameters (at least one second parameter) in other sub-models.

[0059] S203. The simulation testing equipment determines the scene information corresponding to the updated first parameter and at least one updated second parameter.

[0060] Understandably, before conducting a simulation, the simulation testing equipment needs to determine the scenario to be simulated.

[0061] For example, it can include templates for various typical rail transit scenarios such as "high-speed passing on open track", "passing through tunnel", "crosswind 25 m / s", and "low wheel-rail adhesion".

[0062] S204. The simulation testing equipment generates a simulation testing report based on the updated first parameter, the updated second parameter, and the scene information through a multidisciplinary model.

[0063] The simulation testing equipment can generate a simulation testing report that integrates multidisciplinary results based on the updated first parameter, the updated second parameter, and scene information through a multidisciplinary model.

[0064] For example, after adjusting the head-body slenderness ratio from 0.225 to 0.245 in the SysML front-end, the simulation and testing equipment can automatically drive the CATIA 3D model, CFD mesh, and multibody mass distribution updates, and select the "350 km / h oncoming traffic meeting on a clear line" scenario to run a 120-second multidisciplinary coupled simulation. The simulation results show that the lateral forces at the front and rear of the new vehicle are reduced by 8.7%, the peak value of the oncoming traffic pressure wave is reduced by 6.2%, the vertical vibration acceleration of the vehicle body is reduced by 11.4%, and the overall confidence level CIglobal=96.5%. A verification report conforming to EN 14067-4 is automatically generated.

[0065] It is understood that, in this embodiment of the application, the problem of data silos caused by the heterogeneity of multidisciplinary models is solved by constructing a unified data model and defining parameter relationships. Specifically, models that originally used different modeling tools and data formats (such as SysML system models, Modelica multiphysics models, CAD 3D models, CFD fluid models, and multibody dynamics models) are converted into a unified data structure, ensuring semantic consistency and interface compatibility between models. For example, in the scenario of rail transit architecture reconstruction, when design changes involve adjusting the slenderness ratio of the car body, the semantic mapping rules in the unified data model can automatically drive the synchronous update of the 3D geometric model, CFD mesh model, and multibody dynamics parameters. This eliminates the error risks caused by traditional manual conversion and provides a reliable data foundation for subsequent multidisciplinary real-time coupled simulation, improving the verification efficiency and result credibility of complex system architecture reconstruction.

[0066] In some embodiments, the simulation detection device updates at least one second parameter based on the updated first parameter, which may specifically include:

[0067] The simulation testing equipment determines the parameter identifier and parameter semantic information of the updated first parameter; then, based on the parameter identifier and parameter semantic information of the updated first parameter, and a preset parameter mapping rule, it updates at least one associated second parameter, wherein the parameter identifier and / or parameter semantic information of the at least one second parameter is associated with the parameter identifier and / or parameter semantic information of the updated first parameter, and the parameter mapping rule is determined based on the relationship between multiple sub-models.

[0068] It is understandable that the preset parameter mapping rules include a first parameter and at least one second parameter associated with the first parameter. The association between the first parameter and at least one second parameter can be between parameter identifiers or between semantic information of the parameters. For example, if they have the same semantics (such as both indicating wheelbase), the simulation detection device can update the at least one second parameter.

[0069] For example, the simulation testing equipment may include a parameter synchronization bus that employs a "semantic ID + version hash" mechanism to ensure a one-to-one mapping between SysML parameters and Modelica, CFD, and multi-body models. For instance, semantic IDs can identify the relationships between cross-disciplinary parameters in a unified data model, and version hashes can be generated to detect changes in cross-disciplinary parameters. For example, when a hash collision is detected in the first parameter of a SysML sub-model, an automatic update workflow is triggered to update the corresponding parameters of other sub-models.

[0070] Among them, the semantic ID is a unique identifier that identifies the semantic relationship between cross-disciplinary parameters, and is used to ensure the synchronous update of multi-disciplinary models after parameter changes. For example, the unified identifier for the body slenderness ratio is SID_001 in SysML, Modelica, and STEP.

[0071] Version hashes are hash values ​​generated based on parameter values ​​and are used to detect parameter changes and trigger synchronous updates. For example, after the body aspect ratio is adjusted from 0.225 to 0.245, a new hash value "HASH_001_v2" is generated.

[0072] For example, semantic identifiers can be used to locate parameter change events (such as adjustments to the body's slenderness ratio), and version hash verification mechanisms can be used to detect these changes. When an inconsistency in hash values ​​is detected, a model update process is triggered, automatically driving the synchronous update of the 3D geometric model, CFD mesh model, and multibody dynamics model. For instance, when the SysML front-end modifies the bogie wheelbase parameter, semantic identifiers automatically associate the 3D CATIA model with the multibody dynamics mass distribution parameters, and version hash verification ensures that the change event is accurately identified. Ultimately, the model update process achieves consistent adjustments across disciplines.

[0073] In this possible implementation, automatic propagation of parameter changes in multidisciplinary models is achieved through semantic identifiers and version hash verification mechanisms. This mechanism ensures the precise correlation of parameter change events through semantic identifiers and avoids data inconsistencies caused by manual updates through version hash verification. For example, in the scenario of subway bogie wheelbase adjustment, when the wheelbase parameter changes, the 3D geometric model, CFD mesh domain, and multibody dynamics mass distribution parameters can be automatically and synchronously updated based on semantic identifiers and version hash verification, thereby significantly reducing data latency and consistency risks in design iterations.

[0074] In some embodiments, the preset parameter mapping rules are determined based on the interface definition rules of multiple sub-models and standard specification information, including industrial data standards and railway data specifications.

[0075] Among them, parameter mapping rules are the transformation logic that defines the semantic associations between data from heterogeneous models (different sub-models), used to eliminate semantic inconsistencies between cross-disciplinary models. Examples include the association rules between SysML parameters and STEP geometric parameters, or the mapping relationship between Modelica components and CFD meshing parameters.

[0076] For example, in the step of establishing a unified data model, the simulation testing equipment defines an extended interface by combining industrial data standards (such as ISO 10303-242) with railway domain data specifications (such as RailML 3.3). This extended interface constructs the semantic mapping logic of the interdisciplinary model through parameter mapping rules (such as the association between SysML parameters and Modelica component attributes) and interface definition rules (such as the boundary condition interface between CFD models and multibody dynamics models). Subsequently, a semantic framework is generated based on the extended interface. This framework clarifies the relationships between multidisciplinary models (such as parameter dependencies and data exchange interfaces) through structured definitions. The entire process, through standardized interface design, provides a basic framework for the unified data transformation of multidisciplinary models.

[0077] This possible implementation addresses the issue of inconsistent semantic mapping rules across multidisciplinary models by defining parameter mapping rules through interface rules for multiple sub-models and standard specification information. The structured design of parameter mapping rules and interface definition rules ensures semantic consistency between different disciplinary models (such as SysML system models, Modelica multiphysics models, and CFD fluid models), thereby achieving precise cross-disciplinary model association during the construction of a unified data model. For example, in the scenario of rail transit architecture reconstruction, this extended interface can automatically identify the mapping relationship between the vehicle body slenderness ratio parameter and the 3D geometric model and the CFD mesh model, providing a reliable data foundation for subsequent parameter synchronization and real-time coupled simulation.

[0078] In some embodiments, the simulation testing device generates a simulation testing report based on the updated first parameter, the updated second parameter, and scene information using a multidisciplinary model, which may specifically include:

[0079] The simulation testing equipment determines at least two sub-models in the multidisciplinary model based on the updated first parameter, the updated second parameter, and the scene information.

[0080] The simulation testing equipment is based on at least two sub-models and generates a simulation testing report based on the updated first parameter, the updated second parameter, and scene information.

[0081] It is understandable that when the simulation testing equipment performs simulation calculations, it may not require the participation of all sub-modules. Therefore, it is possible to identify at least two sub-models among multiple sub-models that match the updated first parameter, the updated second parameter, and the scene information for simulation calculations.

[0082] In some embodiments, the simulation testing device generates a simulation testing report based on at least two sub-models, according to the updated first parameter, second parameter, and scene information, which may specifically include:

[0083] The simulation testing equipment schedules at least two sub-models through a partitioned strong coupling mechanism and a distributed time-progression algorithm, and generates a simulation testing report based on the updated first parameter, the updated second parameter, and scene information.

[0084] Among them, the partitioned strong coupling mechanism refers to an algorithm that divides a multidisciplinary model into subdomains such as fluid, structure, and control, and achieves cross-domain data interaction through physical rules. For example, it can include displacement-force exchange rules between the fluid domain and the structure domain, and acceleration-control feedback rules between the structure domain and the control domain.

[0085] The displacement-force exchange rule is the displacement-force interaction rule between the fluid domain (CFD) and the structural domain (Modelica finite element model) at the fluid-solid interface.

[0086] Acceleration-control feedback rules refer to the interaction rules by which the acceleration signal from the structural domain is fed back to the control domain (Modelica control model).

[0087] The simulation testing equipment can achieve time advancement using a distributed time advancement algorithm with an adaptive step size, satisfying both the Courant-Friedrichs-Lewy (CFL) condition and the stability constraint of the multibody solver.

[0088] Understandably, in the construction of multidisciplinary models, a partitioned, strongly coupled algorithm is configured to support real-time interaction among multiple disciplines. This algorithm establishes cross-domain data links through displacement-force exchange rules (such as fluid-structure interaction between the fluid domain and the structural domain) and acceleration-control feedback rules (such as the structural domain acceleration signal driving the control domain algorithm). For example, in the scenario of high-speed trains passing each other, the aerodynamic loads of the CFD fluid domain are fed back to the finite element model of the structural domain through displacement-force exchange rules, and the acceleration signal of the structural domain is then transmitted to the active suspension algorithm of the control domain through acceleration-control feedback rules, forming a closed-loop verification link.

[0089] In this possible implementation, a partitioned strong coupling algorithm enables real-time closed-loop verification of aerodynamic-structural-control strongly coupled scenarios. This algorithm defines cross-domain data interaction logic through physical rules, overcoming the limitation of traditional discipline-based offline simulations in capturing dynamic coupling problems. For example, in the scenario of rail transit architecture reconfiguration, this algorithm can identify aerodynamic-structural-control interaction effects in real time during the conceptual design phase, thereby significantly improving the reliability of the verification results.

[0090] In some embodiments, the simulation test report also includes a comprehensive confidence level and / or a traceability matrix. The comprehensive confidence level is used to indicate the confidence level of the test results in the simulation test report. The comprehensive confidence level is determined based on the calculated confidence levels of at least two sub-models. The traceability matrix is ​​used to indicate the simulation requirements and simulation results.

[0091] For example, after generating multi-dimensional confidence assessment indicators, the simulation testing equipment automatically generates a verification report that conforms to industry standards based on these indicators. This report links simulation results with design requirements through a requirement traceability matrix and uses confidence levels to illustrate the credibility of the verification results. For instance, in a subway bogie wheelbase adjustment scenario, the simulation testing equipment automatically generates a report including derailment coefficient, wear index, and CIglobal confidence indicators, meeting the requirements of standards such as EN 15227.

[0092] The traceability matrix refers to the traceability relationship table between the verification results and the design requirements.

[0093] Overall confidence level refers to the credibility analysis of verification results generated based on multi-dimensional confidence evaluation indicators.

[0094] This possible implementation solves the problems of low efficiency and difficulty in quantifying the reliability of traditional manual report compilation by automatically generating verification reports that conform to industry standards. The report provides traceable and verifiable technical evidence for design reviews through a requirements traceability matrix and confidence level statements, significantly enhancing the engineering guidance value of the verification results.

[0095] In some embodiments, the simulation test report includes at least one of chart information, animation information, and text information.

[0096] It is understood that the simulation results in the embodiments of this application can be presented in various forms such as charts, animations, and text. For example, a line graph can be used to show the change curve of the derailment coefficient with time. Other methods are also possible, and no specific limitations are made here.

[0097] In some embodiments, the multidisciplinary model includes a unified data interface, the standard of which is determined based on interface definition rules and standard specification information.

[0098] For example, the simulation testing equipment is based on ISO 10303-242 and RailML3.3 extensions to establish a unified data model for rail transit (RT-UDM) and realize the same source management of SysML blocks, Modelica components, STEP 3D, CGNS meshes, and multibody dynamics input files.

[0099] In some embodiments, during the step of acquiring a multidisciplinary heterogeneous model, the simulation testing equipment can preferentially call parametric templates (such as CRH series head shape templates) from a dedicated rail transit model library, and read user-defined 3D geometric models, CFD mesh models, and multibody dynamics input files. For example, in a high-speed train head shape reconstruction scenario, the user can directly call parametric templates to adjust the slenderness ratio without repeatedly developing an aerodynamic model. That is, the sub-models in the multidisciplinary model can be called professional model templates, such as the following templates from the dedicated rail transit model library:

[0100] 1) Aerodynamics: CRH2 / CRH3 / CR400 series head shape parametric templates, supporting three levels of parameter drive: aspect ratio, nose wing angle, and airflow channel depth;

[0101] 2) Multibody dynamics: Includes officially recognized parameters such as Chinese 60 kg / m rails, LM wear-resistant treads, and TBU tapered roller bearings;

[0102] 3) Pantograph-catenary coupling: The pantograph aerodynamic lifting force model based on EN 50367 can be directly linked with CFD boundary conditions;

[0103] Tunnel-Train-Aerodynamics: A hybrid method of one-dimensional feature lines and three-dimensional local meshes is adopted, and the tunnel wave dynamics are used as the CFD inlet boundary, which reduces the calculation time by 50%.

[0104] In this possible implementation, the parametric templates in a dedicated rail transit model library significantly shorten the model development cycle. These templates enhance the model's engineering applicability by incorporating official parameters (such as Chinese rail parameters and LM wear-prone treads), while parametric design lowers the verification threshold. For example, in rail transit architecture reconstruction scenarios, users can quickly generate CFD mesh models that conform to industry standards, thereby improving verification efficiency.

[0105] This application embodiment realizes full-process digital verification of rail transit equipment system architecture reconstruction. By constructing a multi-disciplinary model, the data silo problem caused by semantic inconsistencies in heterogeneous models is solved; the parameter synchronization bus mechanism ensures the real-time propagation of architecture reconstruction parameters among various disciplinary models, avoiding data inconsistencies caused by manual updates; the real-time coupling algorithm supports closed-loop verification of strongly coupled scenarios such as aerodynamics-structure-control, enabling key issues to be identified in a timely manner during the conceptual design phase; and the standardized interface of the dedicated model library significantly improves verification efficiency. This not only shortens the design verification cycle and reduces prototype testing costs, but also enhances the credibility and traceability of verification results through confidence-based quantitative evaluation and standard report generation, providing reliable technical support for the innovative research and development of rail transit equipment.

[0106] Figure 3 This is a schematic diagram of the structure of a multidisciplinary simulation testing device provided in an embodiment of this application, as shown below. Figure 3 As shown, the multidisciplinary simulation testing device 300 provided in this embodiment includes, in some embodiments:

[0107] The acquisition module 301 is used to acquire the first parameter after the first sub-model is updated. The first sub-model is any one of the multiple sub-models included in the multi-disciplinary model. The multi-disciplinary model includes at least two sub-models from the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model and multibody dynamics sub-model.

[0108] The update module 302 is used to update at least one second parameter according to the updated first parameter, wherein the at least one second parameter is a parameter associated with the first parameter in at least one second sub-model, and the at least one second sub-model is another sub-model other than the first sub-model among multiple sub-models;

[0109] The determination module 303 is used to determine the scene information corresponding to the updated first parameter and at least one updated second parameter;

[0110] The generation module 304 is used to generate a simulation test report based on the updated first parameter, at least one updated second parameter, and scene information through a multidisciplinary model.

[0111] In one possible implementation, the update module 302 is specifically used to: determine the parameter identifier and parameter semantic information of the updated first parameter; update at least one associated second parameter according to the parameter identifier and parameter semantic information of the updated first parameter and a preset parameter mapping rule, wherein the parameter identifier and / or parameter semantic information of the at least one second parameter is associated with the parameter identifier and / or parameter semantic information of the updated first parameter, and the parameter mapping rule is determined according to the association relationship between multiple sub-models.

[0112] In one possible implementation, the preset parameter mapping rules are determined based on the interface definition rules of multiple sub-models and standard specification information, including industrial data standards and railway data specifications.

[0113] In one possible implementation, the generation module 304 is specifically used to: determine at least two sub-models in the multidisciplinary model based on the updated first parameter, at least one updated second parameter, and scene information; and generate a simulation test report based on the at least two sub-models, the updated first parameter, at least one updated second parameter, and scene information.

[0114] In one possible implementation, the generation module 304 is specifically used to: schedule at least two sub-models through a partitioned strong coupling mechanism and a distributed time advancement algorithm, and generate a simulation detection report based on the updated first parameter, at least one updated second parameter, and scene information.

[0115] In one possible implementation, the simulation test report also includes a comprehensive confidence level and / or a traceability matrix. The comprehensive confidence level is used to indicate the confidence level of the test results in the simulation test report. The comprehensive confidence level is determined based on the calculated confidence levels of at least two sub-models. The traceability matrix is ​​used to indicate the simulation requirements and simulation results.

[0116] In one possible implementation, the simulation test report includes at least one of chart information, animation information, and text information.

[0117] In one possible implementation, the multidisciplinary model includes a unified data interface, the standard of which is determined based on interface definition rules and standard specification information.

[0118] The multidisciplinary simulation detection device provided in this embodiment can execute the methods implemented in the above method embodiments. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0119] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4As shown, the electronic device 400 may include a memory 401 and a processor 402. Optionally, the electronic device may also include a transceiver 403, wherein the memory 401 and the processor 402 communicate with each other; for example, the memory 401, the processor 402 and the transceiver 403 may communicate via a communication bus 404, the memory 401 is used to store a computer program, and the processor 402 executes the computer program to implement the method of the above embodiments.

[0120] Optionally, the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps in the method embodiments disclosed in this application can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0121] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the methods in any of the above method embodiments.

[0122] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the methods in any of the above method embodiments.

[0123] All or part of the steps in the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a readable memory. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned memory (storage medium) includes: read-only memory (ROM), RAM, flash memory, hard disk, solid-state drive, magnetic tape, floppy disk, optical disk, and any combination thereof.

[0124] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should 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 processing unit 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 processing unit of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] 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.

[0126] 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.

[0127] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

[0128] In this application, the term "comprising" and its variations can refer to non-limiting inclusion; the term "or" and its variations can refer to "and / or". The terms "first", "second", etc., in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0129] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0130] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0131] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0132] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0133] When integrated units / modules are implemented in hardware, the hardware can be digital circuits, analog circuits, etc. The physical implementation of the hardware structure includes, but is not limited to, transistors, memristors, etc. Unless otherwise specified, the processor can be any suitable hardware processor, such as a CPU, GPU, FPGA, DSP, and ASIC, etc. Unless otherwise specified, the storage unit can be any suitable magnetic or magneto-optical storage medium, such as Resistive Random Access Memory (RRAM), Dynamic Random Access Memory (DRAM), Static Random Access Memory (SRAM), Enhanced Dynamic Random Access Memory (EDRAM), High-Bandwidth Memory (HBM), Hybrid Memory Cube (HMC), etc.

[0134] If the integrated unit / module is implemented as a software program module and sold or used as an independent product, it can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as a USB flash drive, read-only memory (ROM), random access memory (RAM), portable hard drive, magnetic disk, or optical disk.

[0135] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0136] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0137] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A multidisciplinary simulation detection method, characterized in that, The method includes: Obtain the first parameter after the first sub-model is updated. The first sub-model is any one of the multiple sub-models included in the multi-disciplinary model. The multi-disciplinary model includes at least two sub-models from the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model, and multibody dynamics sub-model. At least one second parameter is updated based on the updated first parameter, wherein the at least one second parameter is a parameter associated with the first parameter in at least one second sub-model, and the at least one second sub-model is another sub-model other than the first sub-model among the plurality of sub-models; Determine the scene information corresponding to the updated first parameter and the updated at least one second parameter; A simulation detection report is generated using a multidisciplinary model based on the updated first parameter, the updated at least one second parameter, and the scene information.

2. The method according to claim 1, characterized in that, Updating at least one second parameter based on the updated first parameter includes: Determine the parameter identifier and parameter semantic information of the updated first parameter; Based on the parameter identifier and parameter semantic information of the updated first parameter, and at least one second parameter associated with the update and a preset parameter mapping rule, the parameter identifier and / or parameter semantic information of the at least one second parameter are associated with the parameter identifier and / or parameter semantic information of the updated first parameter, and the parameter mapping rule is determined based on the association relationship between the multiple sub-models.

3. The method according to claim 1, characterized in that, The preset parameter mapping rules are determined based on the interface definition rules of the multiple sub-models and standard specification information, which includes industrial data standards and railway data specifications.

4. The method according to claim 1, characterized in that, The step of generating a simulation detection report using a multidisciplinary model based on the updated first parameter, the second parameter, and the scene information includes: Based on the updated first parameter, the updated at least one second parameter, and the scene information, at least two sub-models in the multidisciplinary model are determined; Based on the at least two sub-models, a simulation detection report is generated according to the updated first parameter, the updated at least one second parameter, and the scene information.

5. The method according to claim 4, characterized in that, The step of generating a simulation detection report based on the at least two sub-models, according to the updated first parameter, the second parameter, and the scene information, includes: The at least two sub-models are scheduled using a partitioned strong coupling mechanism and a distributed time-advancement algorithm, and a simulation detection report is generated based on the updated first parameter, the updated at least one second parameter, and the scene information.

6. The method according to claim 4, characterized in that, The simulation test report also includes a comprehensive confidence level and / or a traceability matrix. The comprehensive confidence level is used to indicate the confidence level of the test results in the simulation test report. The comprehensive confidence level is determined based on the calculated confidence levels of the at least two sub-models. The traceability matrix is ​​used to indicate the simulation requirements and simulation results.

7. The method according to claim 1, characterized in that, The simulation test report includes at least one of the following: chart information, animation information, and text information.

8. The method according to claim 1, characterized in that, The multidisciplinary model includes a unified data interface, the standard of which is determined based on interface definition rules and standard specification information.

9. A multidisciplinary simulation testing device, characterized in that, The multidisciplinary simulation testing device includes: The acquisition module is used to acquire the first parameter after the first sub-model is updated. The first sub-model is any one of the multiple sub-models included in the multi-disciplinary model. The multi-disciplinary model includes at least two sub-models from the mechanical sub-model, electrical sub-model, control sub-model, aerodynamic sub-model and multibody dynamics sub-model. An update module is configured to update at least one second parameter based on the updated first parameter, wherein the at least one second parameter is a parameter associated with the first parameter in at least one second sub-model, and the at least one second sub-model is another sub-model other than the first sub-model among the plurality of sub-models; The determining module is used to determine the scene information corresponding to the updated first parameter and the updated at least one second parameter; The generation module is used to generate a simulation detection report based on the updated first parameter, the updated at least one second parameter, and the scene information through a multidisciplinary model.

10. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the method as described in claims 1 to 8.

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