Intelligent operation and maintenance digital twin modeling method and system for urban rail vehicle pantograph device

Abstract

The invention belongs to the technical field of urban rail vehicle operation and maintenance intelligentization, and discloses an intelligent operation and maintenance digital twin modeling method and system for an urban rail vehicle pantograph device. According to the method, a geometric physical model of the pantograph device is established based on physical characteristics and actual working conditions of the urban rail pantograph device; constructing a pantograph-catenary system electromechanical coupling dynamic model of the urban rail vehicle by referring to the related parameters; constructing a pantograph-catenary system behavior model of the urban rail vehicle, and associating multi-model data; according to a specific real-time operation and maintenance scene of a pantograph-contact network system of the urban rail vehicle, a twin database is built, data visualization processing is carried out, and an operation and maintenance result is displayed. According to the invention, the real-time interactivity in the operation and maintenance operation of the pantograph-contact network system is enhanced, and workers are helped to carry out real-time monitoring, intelligent diagnosis and predictive maintenance on the device more timely and effectively, so that the operation reliability of the pantograph and contact network equipment is obviously enhanced.

Description

Technical Field

[0001] This invention belongs to the field of intelligent operation and maintenance technology for urban rail vehicles, and particularly relates to a digital twin modeling method and system for intelligent operation and maintenance of pantograph devices for urban rail vehicles. Background Technology

[0002] The construction of urban rail transit systems is the best option for effectively alleviating traffic congestion in large and medium-sized cities and solving commuting problems between urban and suburban areas. The pantograph of urban rail vehicles is a key device used by trains to obtain the electrical energy required for operation by contacting the overhead power line. Therefore, effective modeling and analysis of the pantograph is a necessary measure for maintaining urban rail vehicle systems and ensuring their smooth operation.

[0003] Digital twin technology, which has emerged in recent years, combines a physical entity with its digital virtual model to achieve a high degree of simulation and real-time monitoring of the physical entity. Driven by digital twin technology, urban rail transit systems can collect, transmit, and analyze large amounts of data in real time, compare and optimize the actual operation with the virtual model, thereby effectively improving system operating efficiency and reducing its maintenance costs.

[0004] The current operation and maintenance system for pantograph devices on urban rail vehicles largely adopts traditional operating methods. This involves analyzing and maintaining the equipment in a relatively static environment based on historical operating data from a database during periods of downtime for maintenance. Consequently, this method lacks real-time interactivity and flexibility in feedback, making it difficult to adjust in a timely manner according to changes in operating status. Furthermore, due to hardware limitations, the stored historical data may be subject to some loss and variation, significantly weakening its timeliness and data reliability, thus making it difficult to achieve a satisfactory maintenance effect. Summary of the Invention

[0005] To overcome the problems existing in the operation and maintenance of pantograph devices in urban rail transit systems, this invention provides an intelligent operation and maintenance digital twin modeling method and system for pantograph devices in urban rail transit vehicles based on digital twin technology. The purpose of this invention is to solve the problem of unsatisfactory maintenance results due to the lack of real-time data interaction during the operation of pantograph devices in urban rail transit vehicles.

[0006] The technical solution is as follows: A digital twin modeling method for intelligent operation and maintenance of pantograph devices on urban rail vehicles, comprising the following steps:

[0007] S1. Based on the physical characteristics and actual working conditions of the pantograph device in urban rail transit, a geometric-physical model of the pantograph device is established.

[0008] S2. Based on the established geometric and physical model of the pantograph device and referring to relevant parameters, construct the electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle.

[0009] S3, based on the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles, constructs a behavioral model of the pantograph-contact network system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance;

[0010] S4. For specific real-time operation and maintenance scenarios of pantograph-overhead contact system of urban rail vehicles, build a twin database and perform data visualization processing to display operation and maintenance results; based on cloud-fog-edge collaborative architecture, support the storage and processing of multi-source heterogeneous data; data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

[0011] In step S1, based on the physical characteristics and actual working conditions of the pantograph device for urban rail transit, a geometrical physical model of the pantograph device is established, including: S11, simplifying the structure of the pantograph assembly into a base, lower arm, tie rod, balance bar, upper frame, and pantograph head; S12, in the 3D modeling software Solidworks, defining the shape, size, structure, and relative constraint relationships of each component, modeling and assembling each component to obtain a multi-rigid-body physical model of the pantograph device for urban rail transit vehicles; wherein, the multi-rigid-body physical model of the pantograph device... The theoretical model includes: the bottom end of the lower arm is hinged and fixed to the base; the lower part of the lower arm is connected to the front end of the compression cylinder rod through a rotatable structure, and the rear end of the compression cylinder rod is fixed to the base; the upper part of the lower arm is hinged to the middle of the lower crossbar of the upper frame; the bottom of the pull rod is hinged and fixed to the base, the upper part of the pull rod is hinged to the rear end of the balance bar, and the bottom end of the balance bar is hinged to the lower crossbar of the upper frame; the bow head fixing rods are respectively hinged and fixed to both ends of the upper crossbar of the upper frame; the upper end of the balance bar is fixed to the middle of the bow head fixing rod through a rotatable component.

[0012] In step S2, referring to relevant parameters, an electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle is constructed, including: S21, establishing a dynamic model of the pantograph device in the multibody dynamics analysis software platform Simpack; S22, establishing a contact network model in the Ansys platform and importing it into Simpack to generate the contact network elastomer; S23, referring to relevant input and output parameters, performing dynamic simulation analysis on the electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle; wherein, the relevant parameters are contact network structural parameters, pantograph operating parameters, and train operation parameters.

[0013] In step S21, the dynamic model of the pantograph device is established. The geometric-physical model of the pantograph device is the basis of the rigid-flexible coupling model. The geometric-physical model of the pantograph device defines the geometric shape and assembly relationship of the components. The rigid-flexible coupling model regards the easily deformable upper frame and the bow head as flexible bodies, thus establishing a more accurate dynamic model.

[0014] The upper frame and bow head, which experience relatively large deformations, are classified as flexible bodies, while the remaining parts with smaller deformations are considered rigid bodies; the dynamic equilibrium equations for the upper frame of the rigid-flexible coupled pantograph are as follows:

[0015]

[0016] In the formula, The mass matrix of the upper frame. Here is the damping matrix of the upper frame. Here is the stiffness matrix of the upper frame. These represent the displacement, velocity, and acceleration of the node, respectively. For the load of the upper frame;

[0017] In step S22, a catenary model is established in the Ansys platform and imported into Simpack to generate the catenary elastic body, including: establishing the catenary model using the finite element method, and performing vibration analysis and substructure analysis to obtain the mass matrix and stiffness matrix of the catenary.

[0018] The dynamic differential equation of the catenary model established using the finite element method is:

[0019]

[0020] In the formula, The quality matrix of the overhead contact line. Here is the damping matrix of the overhead contact line. Here is the stiffness matrix of the overhead contact line. These represent the vertical displacement, velocity, and acceleration of the overhead contact line, respectively. for The contact pressure between the pantograph and the catenary at any given time; after generating the SID file from the catenary model file generated in the Ansys platform using the FEMBS module, it is imported into Simpack to generate the catenary elastomer; for the contact pressure between the pantograph and the catenary, a penalty function is used to couple the pantograph device and the catenary to obtain an effective representation of the pantograph-catenary moving contact pressure:

[0021]

[0022] In the formula, For the dynamic contact pressure of the pantograph and catenary, This refers to the displacement of the contact point on the contact line. This represents the displacement of the bow head contact point. For the stiffness of the overhead contact line;

[0023] It is implemented in the form of contact units; when there is no penetration between the contact area of ​​the bow head and the contact line, the contact pressure is 0; if penetration occurs, the contact pressure is obtained based on the displacement of the bow-catenary contact point and the calculated stiffness.

[0024] In step S3, a behavioral model of the pantograph-catenment system of the urban rail vehicle is constructed, and multi-model data is associated, including:

[0025] S31. Construct a behavioral model of the pantograph-contact network system in a three-dimensional simulation and virtual debugging platform;

[0026] S32. Establish a communication connection mechanism between the Simpack client and the virtual platform client to realize data communication between the two ports, and perform collaborative simulation and runtime testing;

[0027] S33. Conduct multi-model data management and correlation analysis to achieve effective digital twin applications.

[0028] In step S31, a behavioral model of the pantograph-contact network system is constructed in the 3D simulation and virtual debugging platform, including:

[0029] Based on the established multi-rigid-body physical model of the pantograph device of urban rail vehicles and the constructed electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles, a behavioral model of the pantograph-contact network system is built in a virtual platform, including elements such as pantograph lifting control logic, real-time evaluation model of pantograph-contact network contact quality, and abnormal working condition response strategy, so as to realize the digital mapping of the system operation behavior.

[0030] The pantograph raising and lowering control logic adopts a finite state machine model for pantograph raising and lowering control, and the system state set is defined as follows:

[0031]

[0032] In the formula, This is a set of states for the pantograph system, including pantograph lowering, pantograph raising, working state, and emergency pantograph lowering.

[0033] State transitions are triggered by a set of input events, which is represented as:

[0034]

[0035] Define the state transition function to define the dynamic behavior of the system:

[0036] Then we have:

[0037]

[0038] The real-time pantograph-catenary contact quality evaluation model includes: real-time pantograph-catenary contact force based on the electromechanical coupling dynamics model of the pantograph-catenary system. The behavior model of the pantograph-contact network system is used to evaluate the current collection quality online; offline event detection: when Duration Exceeding the threshold When an offline event occurs, it is determined that an offline event has occurred; let's assume... Given the total duration, the total offline rate is... The calculation is as follows: ; Calculation of excellent contact force rate: Define the contact force as being within the ideal range The probability within Evaluation indicators: In the formula, The evaluation time window visually reflects the stability of the current receiving quality; Time is the integral variable in the integral formula of the real-time evaluation model for the contact quality of the bow and netting. This is an indicator function.

[0039] In step S32, a communication connection mechanism is established between the Simpack end and the virtual platform end to realize data communication between the two ports and to conduct collaborative simulation and operation testing, including: establishing a real-time data channel between the Simpack and the virtual platform through the TCP / IP protocol to realize bidirectional interaction between dynamic simulation data and behavioral models; and using the collaborative simulation platform to conduct joint simulation tests on the behavior of the pantograph under different operating speeds, heights, and load conditions.

[0040] Establishing a real-time data channel between Simpack and the virtual platform includes:

[0041] (1) Communication protocol and data packet design: TCP / IP protocol is adopted, and a Socket connection is established between Simpack and the virtual platform; in each simulation step... At the end, the dynamic data packet is encapsulated and sent by Simpack. : In the formula, The dynamic simulation data package encapsulated and sent by Simpack includes information such as simulation time, pantograph-catenary contact force, displacement, velocity, acceleration, and status flags; For simulation time, For the contact force between the bow and the fire net, These represent the pantograph head displacement, velocity, and acceleration, respectively.

[0042] (2) Behavioral feedback and control: when the virtual debugging platform receives... Subsequently, the behavior model of the pantograph-contact network system is calculated according to preset rules; the preset rules include: when continuous detection... If the duration exceeds the threshold, the behavior model of the pantograph-contact network system generates a contact force deficiency event, denoted as... The event is then reported to Simpack; based on this event, the Simpack platform adjusts its internal damping parameters. or stiffness parameters This enables closed-loop simulation of behavior and dynamics.

[0043] In step S33, multi-model data management and correlation analysis are performed, and effective digital twin applications are realized, including:

[0044] (1) Unify spatiotemporal indexing and data fusion to establish timestamps for all data originating from different platforms. and device space coordinates A unified index is used as the core; for sensor data and simulation data with different sampling rates, data alignment is performed using methods based on linear interpolation or spline interpolation, ensuring they are on the same time reference. The following association analysis is performed; the expression is:

[0045]

[0046] In the formula, For timestamp reference The following multi-model data vectors, Geometric, dynamic, and behavioral models, respectively. Attribute vectors, Transpose of a vector;

[0047] (2) Feature mining based on association rules: Using algorithms such as Apriori, strong association rules between dynamic parameters and behavioral indicators under different working conditions are mined. The strong association rules include the following discovery rules:

[0048]

[0049] These rules are used to predict system risks under specific operating conditions;

[0050] (3) Twin data service: By encapsulating it into a RESTful API, the processed multi-model fusion data, health assessment results and association rules are provided to the upper-level operation and maintenance applications to realize the utilization of digital twin data.

[0051] In step S4, for the specific real-time operation and maintenance scenario of the pantograph-overhead contact system of urban rail vehicles, a twin database is built and data visualization processing is performed, including:

[0052] S41, Construct a twin database for the pantograph-contact network system, integrating historical databases, operation and maintenance knowledge bases, model libraries, and real-time twin databases;

[0053] Based on the cloud-fog-edge collaborative architecture, a twin database supporting multi-source heterogeneous data storage is constructed. The historical database stores the pantograph's historical operating data, maintenance records, and fault cases. The operation and maintenance knowledge base integrates expert rules, fault trees, maintenance strategies, and other knowledge. The model library stores various simulation models and their parameter versions. The real-time twin database receives and updates real-time data from sensors and simulation platforms.

[0054] S42 utilizes Python and visualization libraries to develop visualizations of twin data; based on the twin database of the pantograph-contact network system, it obtains a visual human-machine interface for real-time display of pantograph displacement, pantograph-contact network contact force, and key parameter curves of vibration spectrum; it dynamically displays the relative position and contact status of the pantograph and the contact network in three dimensions; it provides fault warning prompts and maintenance suggestions; and it supports historical data playback and multi-condition comparative analysis.

[0055] S43 achieves efficient data processing and visualization rendering through a cloud-fog-edge collaborative architecture. The cloud is used for big data analysis and model training, the edge is responsible for real-time data collection and lightweight inference, and the fog edge undertakes relay and collaboration tasks.

[0056] Another objective of this invention is to provide an intelligent operation and maintenance digital twin modeling system for pantograph devices on urban rail vehicles. This system implements the aforementioned intelligent operation and maintenance digital twin modeling method for pantograph devices on urban rail vehicles. The system includes:

[0057] The geometric physical model building module establishes a geometric physical model of the pantograph device based on its physical characteristics and actual working conditions.

[0058] The module for constructing the electromechanical coupling dynamic model of the pantograph-contact network system constructs the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles based on the established geometric and physical model of the pantograph device and with reference to relevant parameters.

[0059] The pantograph-overhead contact system behavior model construction module, based on the constructed electromechanical coupling dynamic model of the pantograph-overhead contact system of urban rail vehicles, constructs a behavior model of the pantograph-overhead contact system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance;

[0060] The twin database construction and display module is designed for the specific real-time operation and maintenance scenarios of the pantograph-overhead contact system of urban rail vehicles. It builds a twin database, performs data visualization processing, and displays the operation and maintenance results. Based on the cloud-fog-edge collaborative architecture, it supports the storage and processing of multi-source heterogeneous data. The data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

[0061] Combining all the above technical solutions, the beneficial effects of this invention are as follows:

[0062] First, existing technologies are typically limited to: using single software for offline simulation analysis (e.g., using only Simpack for dynamic analysis); or having only a 3D visualization model, lacking mechanistic models and data-driven approaches; or having a database, but limited to historical data storage, unable to link with the simulation model in real time. This invention systematically integrates the capabilities of SolidWorks, Simpack, Ansys, and Python software to construct a digital twin closed-loop system for real-time operation and maintenance. This invention achieves a closed loop from data perception to intelligent decision-making through the fusion of multiple models (geometry, dynamics, and behavior) and the linkage of four databases (history, model, knowledge, and real-time), which is a relatively cutting-edge exploration in existing literature and patents.

[0063] This invention realizes the concept and implementation of "behavioral models" (such as the finite state machine for pantograph lifting control and the real-time evaluation model for pantograph-catenary contact quality described in the text), which is an advanced modeling approach in the field of pantograph operation and maintenance. The real-time TCP / IP communication and closed-loop simulation mechanism between Simpack and the virtual platform are the key to realizing the "virtual-real interaction" of digital twins. This method is groundbreaking in the combination of high-fidelity dynamic simulation and real-time operation and maintenance scenarios. The cloud-fog-edge collaborative twin database architecture for digital twins is a dedicated data base designed for the characteristics of rail transit operation and maintenance scenarios.

[0064] Secondly, this invention establishes a geometric-physical model of the pantograph device based on its physical characteristics and actual operating conditions. Referring to relevant parameters, it constructs an electromechanical coupling dynamic model of the pantograph-catenfold system of urban rail vehicles. It also constructs a behavioral model of the pantograph-catenfold system and associates it with data from multiple models. For specific real-time operation and maintenance scenarios of the pantograph-catenfold system, it builds a twin database and performs data visualization processing to concretely display the operation and maintenance results. This invention enhances the real-time interactivity of the pantograph-catenfold system operation and maintenance, helping staff to more timely and effectively monitor, intelligently diagnose, and predictively maintain the device, thereby significantly improving the reliability of the pantograph and catenfold equipment.

[0065] Third, the technical solution of this invention can effectively advance digital twin technology from the proof-of-concept level to the engineering and intelligent operation and maintenance applications of key equipment in urban rail vehicles. The expected benefits and commercial value of this technical solution after transformation are mainly reflected in the following aspects: Significantly reduced operation and maintenance costs: Through predictive maintenance, potential faults can be accurately located, avoiding over- or under-maintenance, which can greatly reduce operational interruption losses caused by unplanned downtime maintenance processes and emergency support. At the same time, it can moderately shift the operation and maintenance strategy from "periodic maintenance" to "condition-based maintenance," optimizing the allocation of human resources and spare parts, which can significantly reduce operation and maintenance costs. Comprehensive improvement of safety and reliability: Real-time monitoring and intelligent diagnostic functions can immediately detect potential safety hazards such as abnormal pantograph-catenary contact force and offline arcing, and provide early warnings through a visualization system, providing a valuable time window for handling faults; thereby significantly improving the reliability and safety of urban rail vehicle operation and reducing the risk of safety accidents. Significantly improved decision-making efficiency and digitalization level: This solution integrates scattered model data, sensor data, and operation and maintenance knowledge into a unified decision dashboard, changing the traditional model that relies on the personal experience of skilled technicians, making operation and maintenance decisions more data-driven, more intuitive, and more efficient. This solution can help urban rail transit operators accumulate important digital assets, improve their overall digital management level, and lay a solid foundation for future upgrades to autonomous driving and intelligent operation and maintenance mechanisms. It will also drive the upgrading of related industry chains: the system described in this solution, with its model building, data fusion, and visualization technologies, can be combined to form a standardized solution. This standardized solution is applicable not only to pantographs but can also be extended to other key components such as doors and bogies, thereby creating a new high-end technology service market and driving the technological upgrading and value enhancement of the entire rail transit operation and maintenance industry chain.

[0066] Fourth, this invention, through research on existing technologies, reveals a widespread technological gap in the field of pantograph operation and maintenance for urban rail vehicles, both domestically and internationally, characterized by "disconnection between models and data" and "separation of simulation and operation and maintenance." Existing technologies lack an integrated digital twin architecture that supports real-time interaction. They largely remain at the level of offline simulation analysis using single models (such as geometric or dynamic models), failing to effectively integrate geometric models, dynamic models, behavioral logic, and real-time data. This invention, however, establishes a complete, real-time interactive digital twin by constructing multiple models of "geometry, dynamics, and behavior" and achieving data association between them, filling the gap in the process from static models to dynamic twins. Existing technologies lack a closed-loop data flow and application ecosystem for intelligent operation and maintenance. Traditional operation and maintenance databases have relatively limited functionality, making it difficult to support predictive decision-making. This invention, by building a "four-database linkage" architecture integrating a historical database, an operation and maintenance knowledge base, a model library, and a real-time twin database, and based on this, provides data applications for real-time monitoring, intelligent diagnosis, and predictive maintenance, forming a closed loop from data perception to operation and maintenance decision-making, filling the gap from data recording to intelligent decision-making.

[0067] Fifth, this invention successfully solves a long-standing technical problem in the industry: how to achieve real-time health status perception, accurate fault early warning, and proactive maintenance decision-making for the pantograph-catenfold system of urban rail vehicles—a high-speed dynamic coupling system. The main challenges and corresponding solutions are as follows: Addressing the difficulty of "complex system dynamic behavior, making it difficult to accurately describe with a single model," this invention does not attempt to establish a universal model. Instead, it employs a strategy of multi-model collaboration and data association, allowing the geometric model, high-fidelity dynamic model, and intelligent behavior model to each perform their respective functions, jointly shaping the complete picture of the system. Addressing the difficulty of "strong transient nature of fault symptoms, while offline analysis based on historical data has a lag," this invention establishes a real-time pantograph-catenfold contact quality evaluation model and a real-time data channel between Simpack and the virtual platform, achieving millisecond-level online monitoring and evaluation of key indicators such as insufficient contact force and offline events, transforming the fault identification process from "post-event tracing" to "in-event capture." Addressing the challenge of "operation and maintenance decisions relying on experience and lacking quantitative and visualized scientific basis," this paper utilizes a twin database and data visualization to transform complex system states and simulation results into intuitive health indicators, 3D animated scenes, and early warning information. This enables operation and maintenance personnel to intuitively understand, clearly judge, and quickly make decisions, ultimately achieving a crucial shift in the operation and maintenance model from passive response to proactive prediction.

[0068] Sixth, addressing the traditional view that "high-fidelity dynamics simulation platforms are not suitable for real-time operation and maintenance," this invention clarifies that high-precision dynamics simulation platforms such as Simpack and Ansys are computationally time-consuming and only suitable for early-stage design and offline analysis, unable to be integrated into real-time operation and maintenance processes. This invention simplifies the rigid-flexible coupling model, analyzes substructures, and constructs a "behavior-dynamics" closed-loop simulation mechanism. Instead of performing full-model calculations at every step, it achieves effective application of the dynamics simulation mechanism in near-real-time scenarios while ensuring sufficient accuracy through real-time data interaction and key parameter adjustments. Regarding the industry's tendency to equate "digital twins with 3D visualization models," this invention breaks through this cognitive limitation, clearly stating that a truly industrial-service-oriented digital twin system must include a mechanistic model, real-time data, behavioral logic, and decision support. To this end, this invention constructs a complete system including a behavioral model and an operation and maintenance knowledge base, enabling the digital twin to possess intelligent utilities for simulation, analysis, observation, and decision support, reflecting a deeper understanding of the connotation of digital twins. Attached Figure Description

[0069] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure;

[0070] Figure 1 This is a flowchart of the intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles provided by the present invention;

[0071] Figure 2 This is a schematic diagram of the multi-rigid-body physical model of the pantograph device of the urban rail vehicle in this invention.

[0072] Figure 3 This is a schematic diagram of the dynamic model of the pantograph-contact network system of the urban rail vehicle described in this invention, and the curve of output contact pressure versus pantograph displacement.

[0073] Figure 4 This is a schematic diagram of the behavioral model construction and collaborative simulation system architecture of the urban rail vehicle pantograph-contact network system described in this invention;

[0074] Figure 5 This is a schematic diagram of the pantograph-overhead contact twin behavior model of urban rail vehicles described in this invention;

[0075] Figure 6 This is a schematic diagram of the twin database architecture and cloud-fog-edge collaborative system of the pantograph-contact network system for urban rail vehicles described in this invention;

[0076] Figure 7 This is a flowchart illustrating the visualization and data processing of the intelligent operation and maintenance system for pantograph-overhead contact network of urban rail vehicles described in this invention.

[0077] In the diagram: 1. Base; 2. Lower arm; 3. Tie rod; 4. Balance bar; 5. Upper frame; 6. Bow head; 7. Rotatable structure; 8. Compression cylinder rod; 9. Bow head fixing rod; 10. Lower crossbar; 11. Upper crossbar; 12. Rotatable component. Detailed Implementation

[0078] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0079] The innovation of this invention lies in the following: By constructing a multi-model fusion mechanism of "geometry-dynamics-behavior" and real-time data association technology, this invention realizes the leap from static simulation to dynamic twinning of the pantograph-contact network system; and based on the "four-database linkage" twin database and visualization system oriented towards operation and maintenance needs, it forms a closed loop from data perception to intelligent decision-making, solving the technical problems of real-time monitoring, accurate diagnosis and predictive maintenance, and fully changing the passive operation and maintenance mode that relies on experience in the traditional case.

[0080] This invention represents a leap from "static simulation" to "dynamic twin." This structure ensures the integrity of the technical solution and highlights the overall synergy of the solution through logical connections between steps. This invention constructs a pantograph-contact network system behavior model, realizing a digital mapping of system operation behavior; by associating multi-model data, it achieves deep integration of the mechanistic model and real-time data, supporting intelligent diagnosis and predictive maintenance functions. A four-database linked twin database is built, based on a cloud-fog-edge collaborative architecture, supporting the storage and processing of multi-source heterogeneous data; data visualization processing covers parameter curve display, 3D dynamic display, fault early warning push, and other functions, forming a closed loop from data perception to decision support, realizing a transformation from passive maintenance to proactive prediction.

[0081] Example 1, such as Figure 1 As shown in the figure, the intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles provided in this embodiment of the invention includes the following steps:

[0082] S1. Based on the physical characteristics and actual working conditions of the pantograph device in urban rail transit, a geometric-physical model of the pantograph device is established.

[0083] S2. Based on the established geometric and physical model of the pantograph device and referring to relevant parameters, construct the electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle.

[0084] S3, based on the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles, constructs a behavioral model of the pantograph-contact network system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance;

[0085] S4. For specific real-time operation and maintenance scenarios of pantograph-overhead contact system of urban rail vehicles, build a twin database and perform data visualization processing to display operation and maintenance results; based on cloud-fog-edge collaborative architecture, support the storage and processing of multi-source heterogeneous data; data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

[0086] In step S1, based on the physical characteristics and actual operating conditions of the urban rail pantograph, a geometric-physical model of the pantograph is established, including:

[0087] S11. The structure of the pantograph assembly is simplified to base 1, lower arm 2, tie rod 3, balance bar 4, upper frame 5, and pantograph head 6. The geometric accuracy of the pantograph components refers to the degree of consistency between the contour shape, key feature dimensions (such as aperture, rod length, and installation distance), and geometric tolerances (such as parallelism and coaxiality) of each component (e.g., base, lower arm, and pantograph head) and their allowable tolerances as specified in the design drawings or national standards, relative to their physical entities. This accuracy is ensured through model-driven constraints and verification with measured data, and is the foundation for ensuring the accuracy and reliability of subsequent dynamic simulation results.

[0088] S12. In the 3D modeling software Solidworks, the shape, size, structure, and relative constraints of each component are defined. Modeling and assembling of each component yields a multi-rigid-body physical model of the pantograph device for urban rail vehicles. In Solidworks, the shapes of each pantograph component are generated through feature modeling (extrude, revolve, sweep, loft); dimensions are driven by parametric sketches and strictly follow the design input; structure refers to the functional topology of the components, such as the mounting flange of the base and the carbon sliding plate clamping mechanism of the pantograph head. Relative constraints are clearly defined in the assembly through mechanical fits ("coinciding" surface constraints, "concentric" axis constraints, "distance" control, "angle" limitations, etc.). For example, the bottom end of the lower boom is hinged to the base, the upper frame is rotated to the upper part of the lower boom, and the two ends of the balance bar form "spherical hinge" or "rotational" constraints with the upper frame and the pantograph head fixing rod, respectively, thus ensuring the complete transmission of the kinematic chain.

[0089] The multi-rigid-body physical model of the pantograph device includes: the bottom end of the lower arm 2 is hinged and fixed to the base 1; the lower part of the lower arm 2 is connected to the front end of the compression cylinder rod 8 through a rotatable structure 7, and the rear end of the compression cylinder rod 8 is fixed to the base 1; the upper part of the lower arm 2 is hinged to the middle of the lower crossbar 10 of the upper frame 5; the bottom of the pull rod 3 is hinged and fixed to the base 1, the upper part of the pull rod 3 is hinged to the rear end of the balance bar 4, and the bottom end of the balance bar 4 is hinged to the lower crossbar 10 of the upper frame 5; the bow head fixing rod 9 of the bow head 6 is hinged and fixed to both ends of the upper crossbar 11 of the upper frame 5; the upper end of the balance bar 4 is fixed to the middle of the bow head fixing rod 9 through a rotatable component 12.

[0090] Modeling and assembling of each component yields a multi-rigid-body physical model of the pantograph device for urban rail vehicles; such as... Figure 2As shown. The modeling process for each component was completed using existing technologies: in the Solidworks environment, 3D solid models of components such as the base, lower arm, tie rod, stabilizer bar, upper frame, and bow head were generated through parametric sketch-driven features (such as extrusion and rotation). Subsequently, in the assembly module, standard mechanical fit constraints such as coincidence, concentricity, and distance were defined to complete the overall assembly. The innovation of this step lies in using the multi-rigid-body physical model constructed according to this conventional process as the geometric and topological foundation for the subsequent realization of digital twins.

[0091] The multi-rigid-body physical model of the pantograph device includes: a base 1, a lower arm 2, a tie rod 3, a balance bar 4, an upper frame 5, and a pantograph head 6; the bottom end of the lower arm 2 is hinged and fixed to the base 1; the lower part of the lower arm 2 is connected to the front end of the compression cylinder rod 8 through a rotatable structure 7, and the rear end of the compression cylinder rod 8 is fixed to the base 1; the upper part of the lower arm 2 is hinged to the middle of the lower crossbar 10 of the upper frame 5; the bottom of the tie rod 3 is hinged and fixed to the base 1, the upper part of the tie rod 3 is hinged to the rear end of the balance bar 4, and the bottom end of the balance bar 4 is hinged to the lower crossbar 10 of the upper frame 5; the pantograph head fixing rod 9 of the pantograph head 6 is hinged and fixed to both ends of the upper crossbar 11 of the upper frame 5; the upper end of the balance bar 4 is fixed to the middle of the pantograph head fixing rod 9 through a rotatable component 12.

[0092] The upper frame 5 is connected to the lower arm 2 and the tie rod 3, so that the upper and lower parts of the device are spliced ​​into a whole; the bow head 6 is hinged to the upper frame 5, and the balance bar 4 connects the upper frame 5 and the bow head 6 to ensure that the bow head 6 is as horizontal as possible.

[0093] For example, in step S2, referring to relevant parameters, an electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle is constructed, including:

[0094] S21. Establish a dynamic model of the pantograph device in the Simpack multibody dynamics analysis software platform;

[0095] S22. Create a catenary model in the Ansys platform and import it into Simpack to generate the catenary elastomer;

[0096] S23. Referring to the relevant input and output parameters, perform dynamic simulation analysis on the electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle. The relevant parameters mainly include contact network structural parameters (such as dropper spacing, catenary tension, and contact wire tension), pantograph operating parameters (such as pantograph lifting height and lifting torque), and train operating parameters (such as speed). These parameters are derived from the actual line data and component attributes of a certain city's rail transit line 1. The connection between steps S1 and S2 is as follows: the multi-rigid-body physical model established in S1 provides the geometric basis and assembly constraints for the dynamic modeling in S2. S2 further defines mechanical properties (such as mass and stiffness) and introduces flexible bodies and the contact network on this physical model, thereby constructing the electromechanical coupling dynamic model.

[0097] In step S21, the dynamic model of the pantograph device is established. The geometric-physical model of the pantograph device is the basis of the rigid-flexible coupling model. The geometric-physical model of the pantograph device defines the geometric shape and assembly relationship of the components. The rigid-flexible coupling model regards the easily deformable upper frame and the bow head as flexible bodies, thus establishing a more accurate dynamic model.

[0098] The upper frame (5) and bow head (6) that undergo relatively large deformation are considered flexible bodies, while the remaining parts with small deformation are considered rigid bodies; the dynamic equilibrium equation of the upper frame of the rigid-flexible coupled pantograph is:

[0099]

[0100] In the formula, The mass matrix of the upper frame. Here is the damping matrix of the upper frame. Here is the stiffness matrix of the upper frame. These represent the displacement, velocity, and acceleration of the node, respectively. For the load of the upper frame;

[0101] S21. In the multibody dynamics analysis software platform Simpack, a dynamic model of the pantograph device is established. Based on the relevant parameters of the pantograph device of a certain city's rail transit line 1, the upper frame 5, the pantograph head 6, and the lower frame of the pantograph are regarded as concentrated masses, and connected by parallel springs and dampers. That is, the pantograph is equivalent to a three-mass block model, and its kinematic equations are:

[0102]

[0103] In the formula, the subscript These are the pantograph head 6, upper frame 5, and lower frame, respectively. The mass of the pantograph head. The acceleration of the pantograph head. For the pantograph head damping, The velocity of the pantograph tip. For the speed of the upper frame, The stiffness of the pantograph head. This refers to the displacement of the pantograph head. This is the displacement of the upper frame. For the quality of the upper frame, To accelerate the upper frame, For upper frame damping, For the speed of the lower frame, The stiffness of the pantograph head. For the stiffness of the upper frame, This represents the displacement of the lower frame. For the quality of the lower frame, Acceleration for the lower frame For the lower frame damping, For the stiffness of the lower frame;

[0104] In establishing the dynamic model of the pantograph, the geometric-physical model of the pantograph is the basis of the rigid-flexible coupling model. The geometric-physical model of the pantograph defines the geometric shape and assembly relationship of the components, while the rigid-flexible coupling model treats the easily deformable upper frame and the pantograph head as flexible bodies, thus establishing a more accurate dynamic model.

[0105] The upper frame 5 and the bow head 6, which undergo relatively large deformation, are considered flexible bodies, while the remaining parts with small deformation are considered rigid bodies; the dynamic equilibrium equation of the upper frame of the rigid-flexible coupled pantograph is:

[0106]

[0107] In the formula, The mass matrix of the upper frame. Here is the damping matrix of the upper frame. Here is the stiffness matrix of the upper frame. These represent the displacement, velocity, and acceleration of the node, respectively. The load is for the upper frame; for the rest of the device other than the upper frame 5, the general form of the multi-rigid-body mechanical equilibrium equations can be used to express it.

[0108] In step S22, a catenary model is established in the Ansys platform and imported into Simpack to generate the catenary elastic body, including: establishing the catenary model using the finite element method, and performing vibration analysis and substructure analysis to obtain the mass matrix and stiffness matrix of the catenary.

[0109] The dynamic differential equation of the catenary model established using the finite element method is:

[0110]

[0111] In the formula, The quality matrix of the overhead contact line. Here is the damping matrix of the overhead contact line. Here is the stiffness matrix of the overhead contact line. These represent the vertical displacement, velocity, and acceleration of the overhead contact line, respectively. for The contact pressure between the pantograph and the catenary at any given time; after generating the SID file from the catenary model file generated in the Ansys platform using the FEMBS module, it is imported into Simpack to generate the catenary elastomer. For the contact pressure between the pantograph and the catenary, a penalty function is used to couple the pantograph device and the catenary to obtain an effective representation of the pantograph-catenary moving contact pressure, i.e.:

[0112]

[0113] In the formula, For the dynamic contact pressure of the pantograph and catenary, This refers to the displacement of the contact point on the contact line. This represents the displacement of the bow head contact point. For the stiffness of the overhead contact line;

[0114] The pantograph-catenary contact model is implemented using contact elements. When there is no penetration between the pantograph head and the contact line, the contact pressure is 0. If penetration occurs, the contact pressure can be obtained based on the pantograph-catenary contact point displacement and calculated stiffness.

[0115] S23. Referring to the relevant input and output parameters, a dynamic simulation analysis of the electromechanical coupling dynamic model of the pantograph-catenfold system of urban rail vehicles is performed. Different operating heights and speeds are set to adjust the relevant parameters of the electromechanical coupling dynamic model of the pantograph-catenfold system of urban rail vehicles, and simulation calculations are performed to obtain effective output parameters, which are then exported. The pantograph displacement and pantograph-catenfold dynamic contact pressure curves under different operating conditions are plotted in the Matlab interface; this intuitively reflects the influence of specific related factors on the current collection quality of the pantograph-catenfold system, such as... Figure 3 .

[0116] The electromechanical coupling dynamic model of the pantograph-catenhead system of urban rail vehicles is a co-simulation model implemented in the Simpack environment. The coupling relationship of this model has been clearly expressed through the subsystem dynamic equations (the pantograph upper frame equilibrium equation and the catenary differential equation), and the penalty function calculation model for the pantograph-catenhead contact pressure in S22. The coupled solution of this co-model is essentially an implicit numerical integration process based on the contact algorithm, which does not require, and typically does not use, an explicit "overall formula" for description; the current descriptions of the subsystem equations and contact conditions have fully defined the model coupling relationship.

[0117] Setting different working heights and speeds, and adjusting relevant model parameters, refers to directly modifying specific input parameters listed in the established electromechanical coupling dynamics model. For example, setting the pantograph height hc to different working conditions such as 1.6m and 1.8m; setting the train speed v to different levels such as 72km / h and 108km / h; and adjusting the simulation step size accordingly. With simulation duration To ensure computational stability and valid results, the model's output parameters mainly include: pantograph-catenary dynamic contact force F, and average contact force. Maximum contact force Minimum contact force Standard deviation of contact force Offline rate Positioning point lifting amount and the dynamic stress of the contact wire These output parameters are used together to comprehensively and quantitatively evaluate the current collection quality of the pantograph-catenary system and the dynamic performance of the system. Among them, the core parameters in the input stage include: catenary tension. Contact line tension Bow height Pantograph lifting torque Train speed Lift spring stiffness Lifting bow damping wait.

[0118] For example, in step S3, constructing a pantograph-catenhead system behavior model for urban rail vehicles and associating it with multi-model data includes:

[0119] S31. Construct a behavioral model of the pantograph-contact network system in a 3D simulation and virtual debugging platform (such as Visual-Components);

[0120] Based on the multi-rigid-body physical model of the pantograph device of the urban rail vehicle established in step S1 and the electromechanical coupling dynamic model of the pantograph-catenfold system of the urban rail vehicle constructed in step S2, a behavioral model of the pantograph-catenfold system is built in a virtual platform. This model includes elements such as pantograph lifting control logic, real-time pantograph-catenfold contact quality evaluation model, and abnormal operating condition response strategies, achieving a digital mapping of the system's operational behavior. The behavioral model construction and collaborative simulation system architecture of the pantograph-catenfold system are as follows: Figure 4 As shown.

[0121] For example, as a link between physical entities and operational decisions, a behavioral model containing the following key core logic is constructed in a 3D simulation and virtual debugging platform:

[0122] (1) Finite state machine model for pantograph lifting control: This model can accurately simulate the state transition process of pantograph in response to train control commands.

[0123] Define the system state set as follows:

[0124]

[0125] In the formula, This is a set of states for the pantograph system, including pantograph lowering, pantograph raising, working state, and emergency pantograph lowering.

[0126] State transitions are triggered by a set of input events, which is represented as:

[0127]

[0128] Define the state transition function to define the dynamic behavior of the system:

[0129] Then we have:

[0130]

[0131] The finite state machine model for pantograph lifting control ensures that the control logic of the virtual pantograph is completely consistent with that of the physical entity.

[0132] (2) Real-time evaluation model of pantograph-catenary contact quality; based on the real-time pantograph-catenary contact force input from the electromechanical coupling dynamics model of the pantograph-catenary system. The behavior model of its pantograph-contact network system is used to evaluate the current collection quality online.

[0133] Offline event detection: When Duration Exceeding the threshold When an offline event occurs, it is determined that an offline event has occurred; let's assume... Given the total duration, the total offline rate is... The calculation is as follows:

[0134]

[0135] Calculation of excellent contact force rate: The ideal contact force range is defined as... The probability within Evaluation indicators:

[0136]

[0137] In the formula, The evaluation time window visually reflects the stability of the current receiving quality; Time is the integral variable in the integral formula of the real-time evaluation model for the contact quality of the bow and netting. This is an indicator function.

[0138] S32 establishes a communication connection mechanism between the Simpack client and the virtual platform client, realizes data communication between the two ports, and performs collaborative simulation and runtime testing;

[0139] A real-time data channel is established between Simpack and the virtual platform via TCP / IP protocol to achieve bidirectional interaction between dynamic simulation data and behavioral models. Using this collaborative simulation platform, the behavior of the pantograph under different operating conditions such as speed, height, and load is jointly simulated and tested to verify the consistency and reliability of the model.

[0140] To achieve closed-loop interaction between dynamics and behavioral models, a cross-platform real-time data channel is constructed, including the following elements:

[0141] (1) Communication Protocol and Data Packet Design: The TCP / IP protocol is adopted, and a Socket connection is established between Simpack and the virtual platform. At each simulation step... At the end, the dynamic data packet is encapsulated and sent by Simpack. :

[0142]

[0143] In the formula, The dynamic simulation data package encapsulated and sent by Simpack includes information such as simulation time, pantograph-catenary contact force, displacement, velocity, acceleration, and status flags; For simulation time, For the contact force between the bow and the fire net, These represent the pantograph head displacement, velocity, and acceleration, respectively.

[0144] (2) Behavioral feedback and control: When the virtual debugging platform receives... Then, the behavior model will perform calculations according to preset rules. For example, when continuous detection... If the duration exceeds the threshold, the behavioral model will generate an "insufficient contact force event," denoted as... The event is then fed back to Simpack. Based on this event, the Simpack platform can adjust its internal damping parameters. or stiffness parameters This enables closed-loop simulation of "behavior-dynamics," greatly enhancing the model's adaptability and realism.

[0145] S33 enables the management and correlation analysis of multi-model data, and facilitates effective digital twin applications.

[0146] Establish a unified data interface specification to integrate and manage multi-source model data from Solidworks, Simpack, Ansys, and virtual platforms. Through data correlation analysis, extract key characteristic parameters (such as pantograph-catenary contact force, displacement response, vibration frequency, etc.) and construct a health status assessment index system for the pantograph system to support real-time diagnosis and predictive maintenance of the digital twin system.

[0147] To achieve unified management and value extraction of all-element data, a multi-model data association framework was constructed:

[0148] (1) Unified spatiotemporal indexing and data fusion: Establish a time stamp t and device space coordinates for all data from different platforms (Solidworks, Simpack, Ansys, virtual test platform). A unified index is used as the core; for sensor data and simulation data from different sampling rates, data alignment is performed using methods based on linear interpolation or spline interpolation to ensure that they are on the same time reference. The following association analysis is performed. The expression is as follows:

[0149]

[0150] In the formula, For timestamp reference The following multi-model data vectors, Geometric, dynamic, and behavioral models, respectively. Attribute vectors, Transpose of a vector;

[0151] (2) Feature mining based on association rules: Using algorithms such as Apriori, strong association rules between dynamic parameters and behavioral indicators under different working conditions are mined. The strong association rules include the following discovery rules:

[0152]

[0153] These rules are used to predict system risks under specific operating conditions;

[0154] (3) Twin data service: By encapsulating it into a RESTful API, the processed multi-model fusion data, health assessment results and association rules are provided to the upper-level operation and maintenance applications to realize the utilization of digital twin data.

[0155] like Figure 5 The pantograph-overhead contact network twin behavior model shown in the diagram forms a technical chain that runs through "data-algorithm-mechanism-drive" by integrating the model and data interface, thereby effectively enabling the twin device to autonomously perceive environmental changes.

[0156] For example, in step S4, the process of building a twin database and performing data visualization processing for the specific real-time operation and maintenance scenario of the pantograph-contact network system of urban rail vehicles, thereby visually displaying the operation and maintenance results, includes:

[0157] S41, Construct a twin database for the pantograph-contact network system, integrating its historical database, operation and maintenance knowledge base, model library, and real-time twin database;

[0158] Based on a cloud-fog-edge collaborative architecture, a twin database supporting multi-source heterogeneous data storage is constructed. This includes: a historical database storing historical pantograph operating data, maintenance records, and fault cases; an operation and maintenance knowledge base integrating expert rules, fault trees, maintenance strategies, and other knowledge; a model library storing various simulation models and their parameter versions; and a real-time twin database receiving and updating real-time data from sensors and the simulation platform. For example, the twin database architecture of the pantograph-contact network system and the structure of the cloud-fog-edge collaborative system are as follows: Figure 6 As shown.

[0159] A four-database linkage architecture of "history-model-knowledge-real-time" is adopted to build a twin database that supports cloud-fog-edge collaboration.

[0160] (1) Historical Database: The time-series database InfluxDB is used, and its data model is highly optimized for time-series data. A typical data point structure is as follows:

[0161] text

[0162] Measurement: Pantograph_Data

[0163] Tags:line=Line1,train=T001,component= pantograph_head

[0164] Fields:contact_force=125.6,displacement=1.75,acceleration=12.3

[0165] Timestamp:2024-10-26T10:30:00Z

[0166] This structure can greatly improve the efficiency of time-range queries and aggregate analysis.

[0167] (2) Model library: Based on the version control system Git, the library manages 3D models, dynamic model parameter files (.mod), finite element mesh files (.cdb), and behavioral model scripts. Each model modification corresponds to a unique version number (e.g., v2.1.3) to ensure the traceability of the model evolution process.

[0168] (3) Operation and Maintenance Knowledge Base: An operation and maintenance knowledge graph is constructed based on the Neo4 graph database, the core of which is a triple in the following form:

[0169] "(Entity)-[Relationship]-(Entity)". For example:

[0170] (:Fault{name:'Abnormal wear on carbon skateboard'})-[:CAUSED_BY]->(:Condition{name:'Excessive contact force'})

[0171] (:Condition{name:'Excessive Contact Force'})-[:MITIGATED_BY]->(:Action{name:'Adjust Bow Lifting Pressure',tool:'Torque Wrench'})

[0172] The Cypher language can be used to efficiently trace the root cause of faults and reason about repair paths.

[0173] (4) Real-time twin database: Redis is used as a data buffer and subscription / publishing center. This database receives real-time sensor data and high-frequency simulation results from the edge gateway and exists in the form of key-value pairs or message queues, providing millisecond-level data response for visualization dashboards.

[0174] S42 utilizes Python and related visualization libraries (Matplotlib / Dash) to develop visualizations for twin data;

[0175] Based on the twin database of the pantograph-contact network system, a visual human-machine interface was developed to achieve the following functions: real-time display of key parameter curves such as pantograph displacement, pantograph-contact network contact force, and vibration spectrum; three-dimensional dynamic display of the relative position and contact status of the pantograph and the contact network; fault warning prompts and maintenance suggestions; and support for historical data playback and multi-condition comparative analysis.

[0176] For example, the process of building and processing data for an intelligent operation and maintenance visualization system is as follows: Figure 7 As shown.

[0177] Based on the Dash framework, we developed an interactive, multi-view intelligent operation and maintenance visualization panel.

[0178] (1) Real-time running status panel: Using Matlotlib to draw the bow-catenary contact force and pantograph displacement The real-time curve is obtained; and the raw data is smoothed using Kalman filtering to eliminate sensor noise.

[0179] (2) System health status comprehensive assessment panel: Based on multi-feature fusion, the real-time health status of the pantograph system is calculated. The calculation formula is as follows:

[0180]

[0181] In the formula, This refers to the specific time point corresponding to the real-time assessment of the system's health status. For the excellent contact force rate at this time, The rate of excellent contact force for The maximum value, For the standard deviation of contact force, The total offline rate, Let be the weight of each indicator, and , A constant to prevent division by zero errors; This represents the standard deviation of the contact force between the bow and the firebox.

[0182] (3) Three-dimensional virtual scene synchronization panel: Render the three-dimensional scene in the browser using WebGL technology (There.js), receive real-time pose data from the virtual debugging platform through WebSocket, and drive the three-dimensional model to perform synchronous movement, so as to realize immersive and visual monitoring of the interaction process of the pantograph-contact network system.

[0183] S43 achieves efficient data processing and visualization rendering through a cloud-fog-edge collaborative architecture, improving the real-time performance and accuracy of operational decisions. Specifically, the cloud handles big data analysis and model training, the edge is responsible for real-time data collection and lightweight inference, while the fog edge undertakes relay and collaboration tasks.

[0184] For example, to achieve load balancing and low-latency response in data processing, a three-tiered cloud-fog-edge collaborative architecture was constructed:

[0185] (1) Edge end: Deploy edge computing nodes on the on-board equipment of urban rail trains, responsible for: real-time acquisition of sensor data; running a lightweight Kalman filter algorithm and performing data preprocessing; and executing real-time alarms based on simple threshold rules.

[0186] (2) Fog End: Deploy fog computing servers in the regional control center, responsible for: aggregating data from multiple edge nodes; running behavior models and lightweight Simpack simulations to perform preliminary aggregation and cross-vehicle comparative analysis of multi-vehicle data; and undertaking the rendering and push tasks of 3D visualization scenes.

[0187] (3) Cloud: Deployed in the central cloud computing center, responsible for: Deep mining of big data: using distributed computing frameworks such as Spark to mine the data of the entire network in order to train a more accurate prediction model; High-fidelity model re-simulation: when the edge or fog end reports a complex fault, start a high-precision full system model in the cloud to reproduce the fault process and conduct in-depth root cause analysis; Global optimization and decision-making: based on the health status of the pantograph devices of the entire network, optimize the maintenance plan and resource allocation strategy.

[0188] Through the close collaboration of the above three-tier architecture, comprehensive coverage from real-time perception and edge intelligence to cloud intelligence can be achieved. Ultimately, the complete closed loop of intelligent operation and maintenance can be visualized on the visualization platform, thereby significantly improving the real-time performance, accuracy, and economy of the operation and maintenance of the pantograph system of urban rail vehicles.

[0189] As can be seen from the above embodiments, the present invention enhances the real-time interactivity in the operation and maintenance of the pantograph-contact network system by constructing a full-link digital twin system from physical model, dynamic simulation model, behavioral logic model to data visualization. This helps staff to monitor the device in real time, perform intelligent diagnosis and predictive maintenance more timely and effectively, thereby significantly enhancing the reliability of the pantograph and contact network equipment operation.

[0190] Example 2, the intelligent operation and maintenance digital twin modeling system for pantograph devices of urban rail vehicles provided in this embodiment of the invention includes the following steps:

[0191] The geometric physical model building module establishes a geometric physical model of the pantograph device based on its physical characteristics and actual working conditions.

[0192] The module for constructing the electromechanical coupling dynamic model of the pantograph-contact network system constructs the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles based on the established geometric and physical model of the pantograph device and with reference to relevant parameters.

[0193] The pantograph-overhead contact system behavior model construction module, based on the constructed electromechanical coupling dynamic model of the pantograph-overhead contact system of urban rail vehicles, constructs a behavior model of the pantograph-overhead contact system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance;

[0194] The twin database construction and display module is designed for the specific real-time operation and maintenance scenarios of the pantograph-overhead contact system of urban rail vehicles. It builds a twin database, performs data visualization processing, and displays the operation and maintenance results. Based on the cloud-fog-edge collaborative architecture, it supports the storage and processing of multi-source heterogeneous data. The data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

[0195] To further illustrate the effects of the embodiments of the present invention, the following experiment was conducted: This embodiment is based on the actual parameters of a certain city's rail transit line 1 and adopts the technical solution of the present invention. First, steps S1 and S2 were completed to construct the geometric and physical model of the pantograph device. Figure 2 ) and electromechanical coupling dynamics model of pantograph-contact network system ( Figure 3 Subsequently, in the Simpack and Matlab co-simulation environment, the pantograph-catenary dynamic interaction process of the train at its maximum working height and at three different speeds (36km / h, 72km / h, and 108km / h) was simulated, and the pantograph-catenary dynamic contact force curves were obtained (see...). Figure 3 , Figure 5 (Lower section) and key statistical indicators (as shown in Table 1).

[0196] Table 1. Statistical values ​​of pantograph-catenary contact pressure simulation results at maximum working height and different speeds.

[0197] Simulation results show that the average value of the pantograph-catenary contact force (F) at different operating speeds is... m All currents were within the normal current-receiving range (e.g., 100N-150N), and the offline rate was 0. This result preliminarily proves that the rigid-flexible coupling dynamic model established in this invention can effectively and stably simulate the dynamic behavior of the pantograph-catenary system, laying an accurate and solid model foundation for the subsequent construction of a highly reliable digital twin.

[0198] Verification of the real-time evaluation function of the pantograph-overhead contact system behavior model: Figure 3 , Figure 5 The dynamic contact force curve shown is input as a real-time data stream into the "real-time evaluation model of pantograph-catenary contact quality" constructed in step S3 of this invention; taking the working condition of 72km / h as an example (see... Figure 3 (See upper right and Table 1) The contact force values ​​fluctuated significantly (standard deviation). =18.502N), and produces an extremely low valley (F). min=25.813N); In this embodiment, the contact force threshold for offline events is set to 30N. Although this valley value indicates that the system has experienced a momentary contact force lower than the preset threshold, it is not included in the offline rate because the duration is short and the offline event criterion is not met.

[0199] In the real-time operation and maintenance digital twin system built by this invention, the behavior model of the pantograph-contact network system will be captured in real time. , for Real-time contact force at all times The minimum threshold is set to 30N. If such events occur consecutively within a short time window, the system will immediately trigger an "insufficient contact" event warning and report the event to the operations and maintenance personnel. This achieves "real-time capture" of potential offline risks, avoiding the risk from being discovered only during post-event analysis reports in the traditional operations and maintenance model.

[0200] Demonstration of Twin Data Association Analysis and Predictive Maintenance: The twin database built by this invention associates and stores simulation results at different operating speeds (as shown in all data in Table 1) with operating conditions (speed, altitude). Example of Association Rule Mining: Applying the association rule mining method described in S33 of this invention, massive historical simulation data and real-time operating data can be mined. For example, a strong association rule may be mined: {Operating speed ≈ 70-75 km / h} → {Increased standard deviation of contact force, high risk of minimum contact force} (confidence level: 85%).

[0201] In the real-time operation and maintenance digital twin system built by this invention, when the system detects that the train speed has entered the 70-75 km / h range through edge sensors, the operation and maintenance visualization interface will automatically push a prompt message: "Pay attention to monitoring the stability of the pantograph-catenary contact force." In this way, operation and maintenance personnel can pay attention to this risky condition in advance, thereby achieving predictive maintenance for specific problems.

[0202] Traditional approach: Relies on periodic inspections and reactive repairs. The risk of instantaneous low contact force at 72 km / h is likely to be overlooked in this approach, only being addressed after it develops into a more serious offline arcing or abnormal wear of the carbon slide plate, leading to significantly increased maintenance costs and safety risks. Invention approach: A complete digital twin closed loop—"precise simulation of dynamic models - real-time evaluation of behavioral models - correlation analysis of twin databases - early warning push notifications via visual interfaces"—achieves a significant shift from "passive response" to "proactive early warning and predictive maintenance." Furthermore, in this invention's system, the simulation data in Table 1 is no longer static, isolated report values, but rather a dynamic, continuous data stream driving intelligent operation and maintenance decisions.

[0203] The intelligent operation and maintenance digital twin modeling method and system for pantograph devices of urban rail vehicles provided by this invention can not only simulate the dynamic behavior of the system with high fidelity, but more importantly, through its innovative behavioral model, real-time data association and visualization architecture, it can transform simulation data and models into real-time and actionable decision support for operation and maintenance. This invention significantly improves the timeliness, effectiveness and foresight of pantograph-catenary system operation and maintenance, and can effectively solve the technical problems of insufficient real-time interactivity and delayed operation and maintenance decision-making in traditional operation and maintenance.

[0204] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by those skilled in the art within the scope of the technology disclosed in the present invention and within the spirit and principles of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A digital twin modeling method for intelligent operation and maintenance of pantograph devices on urban rail vehicles, characterized in that, The method includes the following steps: S1. Based on the physical characteristics and actual working conditions of the pantograph device in urban rail transit, a geometric-physical model of the pantograph device is established. S2. Based on the established geometric and physical model of the pantograph device and referring to relevant parameters, construct the electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle. S3, based on the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles, constructs a behavioral model of the pantograph-contact network system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance; S4. For specific real-time operation and maintenance scenarios of pantograph-overhead contact system of urban rail vehicles, build a twin database and perform data visualization processing to display operation and maintenance results; based on cloud-fog-edge collaborative architecture, support the storage and processing of multi-source heterogeneous data; data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

2. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 1, characterized in that, In step S1, based on the physical characteristics and actual operating conditions of the urban rail pantograph, a geometric-physical model of the pantograph is established, including: S11. The structure of the pantograph assembly is simplified to a base (1), a lower arm (2), a tie rod (3), a balance bar (4), an upper frame (5), and a pantograph head (6). S12. In the 3D modeling software Solidworks, define the shape, size, structure and relative constraint relationship of each component, model and assemble each component to obtain the multi-rigid-body physical model of the pantograph device of the urban rail vehicle. The multi-rigid-body physical model of the pantograph device includes: the bottom end of the lower arm (2) is hinged and fixed to the base (1); the lower part of the lower arm (2) is connected to the front end of the compression cylinder rod (8) through a rotatable structure (7), and the rear end of the compression cylinder rod (8) is fixed to the base (1); the upper part of the lower arm (2) is hinged to the middle of the lower crossbar (10) of the upper frame (5); the bottom of the pull rod (3) is hinged and fixed to the base (1), the upper part of the pull rod (3) is hinged to the rear end of the balance rod (4), and the bottom end of the balance rod (4) is hinged to the lower crossbar (10) of the upper frame (5); the bow head fixing rod (9) of the bow head (6) is hinged and fixed to both ends of the upper crossbar (11) of the upper frame (5); the upper end of the balance rod (4) is fixed to the middle of the bow head fixing rod (9) through a rotatable component (12).

3. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 1, characterized in that, In step S2, referring to relevant parameters, an electromechanical coupling dynamic model of the pantograph-contact network system of the urban rail vehicle is constructed, including: S21. Establish a dynamic model of the pantograph device in the Simpack multibody dynamics analysis software platform; S22. Create a catenary model in the Ansys platform and import it into Simpack to generate the catenary elastomer; S23. Referring to the relevant input and output parameters, perform dynamic simulation analysis on the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles; Among them, the relevant parameters are the overhead contact system structure parameters, pantograph operation parameters, and train operation parameters.

4. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 3, characterized in that, In step S21, the dynamic model of the pantograph device is established. The geometric-physical model of the pantograph device is the basis of the rigid-flexible coupling model. The geometric-physical model of the pantograph device defines the geometric shape and assembly relationship of the components. The rigid-flexible coupling model regards the easily deformable upper frame and the bow head as flexible bodies, thus establishing a more accurate dynamic model. The upper frame (5) and bow head (6) that undergo relatively large deformation are considered flexible bodies, while the remaining parts with small deformation are considered rigid bodies; the dynamic equilibrium equation of the upper frame of the rigid-flexible coupled pantograph is: ； In the formula, The mass matrix of the upper frame. Here is the damping matrix of the upper frame. Here is the stiffness matrix of the upper frame. These represent the displacement, velocity, and acceleration of the node, respectively. For the load of the upper frame; In step S22, a catenary model is established in the Ansys platform and imported into Simpack to generate the catenary elastic body, including: establishing the catenary model using the finite element method, and performing vibration analysis and substructure analysis to obtain the mass matrix and stiffness matrix of the catenary. The dynamic differential equation of the catenary model established using the finite element method is: ； In the formula, The quality matrix of the overhead contact line. Here is the damping matrix of the overhead contact line. Here is the stiffness matrix of the overhead contact line. These represent the vertical displacement, velocity, and acceleration of the overhead contact line, respectively. for Constant pressure of the bow and catenary contact; After generating a SID file from the catenary model file in the Ansys platform using the FEMBS module, import it into Simpack to generate the catenary elastomer. For the contact pressure between the pantograph and the catenary, a penalty function is used to couple the pantograph and the catenary to obtain an effective representation of the pantograph-catenary moving contact pressure. ； In the formula, For the dynamic contact pressure of the pantograph and catenary, This refers to the displacement of the contact point on the contact line. This represents the displacement of the bow head contact point. For the stiffness of the overhead contact line; It is implemented in the form of contact units; when there is no penetration between the contact area of ​​the bow head and the contact line, the contact pressure is 0; if penetration occurs, the contact pressure is obtained based on the displacement of the bow-catenary contact point and the calculated stiffness.

5. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 1, characterized in that, In step S3, a behavioral model of the pantograph-catenment system of the urban rail vehicle is constructed, and multi-model data is associated, including: S31. Construct a behavioral model of the pantograph-contact network system in a three-dimensional simulation and virtual debugging platform; S32. Establish a communication connection mechanism between the Simpack client and the virtual platform client to realize data communication between the two ports, and perform collaborative simulation and runtime testing; S33. Conduct multi-model data management and correlation analysis to achieve effective digital twin applications.

6. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 5, characterized in that, In step S31, a behavioral model of the pantograph-contact network system is constructed in the 3D simulation and virtual debugging platform, including: Based on the established multi-rigid-body physical model of the pantograph device of urban rail vehicles and the constructed electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles, a behavioral model of the pantograph-contact network system is built in a virtual platform, including elements such as pantograph lifting control logic, real-time evaluation model of pantograph-contact network contact quality, and abnormal working condition response strategy, so as to realize the digital mapping of the system operation behavior. The pantograph raising and lowering control logic adopts a finite state machine model for pantograph raising and lowering control, and the system state set is defined as follows: ； In the formula, This is a set of states for the pantograph system, including pantograph lowering, pantograph raising, working state, and emergency pantograph lowering. State transitions are triggered by a set of input events, which is represented as: ； Define the state transition function to define the dynamic behavior of the system: ； Then we have: ； ； ； The real-time assessment model for pantograph-catenary contact quality includes: Real-time pantograph-catenary contact force based on input from the electromechanical coupling dynamics model of the pantograph-catenary system The behavior model of the pantograph-contact network system is used to evaluate the current collection quality online. Offline event detection: When Duration Exceeding the threshold When an offline event occurs, it is determined that an offline event has occurred; let's assume... Given the total duration, the total offline rate is... The calculation is as follows: ； Calculation of excellent contact force rate: The ideal contact force range is defined as... The probability within Evaluation indicators: ； In the formula, The evaluation time window visually reflects the stability of the current receiving quality; Time is the integral variable in the integral formula of the real-time evaluation model for the contact quality of the bow and netting. This is an indicator function.

7. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 5, characterized in that, In step S32, a communication connection mechanism is established between the Simpack end and the virtual platform end to realize data communication between the two ports and to conduct collaborative simulation and operation testing, including: establishing a real-time data channel between the Simpack and the virtual platform through the TCP / IP protocol to realize bidirectional interaction between dynamic simulation data and behavioral models; and using the collaborative simulation platform to conduct joint simulation tests on the behavior of the pantograph under different operating speeds, heights, and load conditions. Establishing a real-time data channel between Simpack and the virtual platform includes: (1) Communication protocol and data packet design: TCP / IP protocol is adopted, and a Socket connection is established between Simpack and the virtual platform; in each simulation step... At the end, the dynamic data packet is encapsulated and sent by Simpack. : ； In the formula, The dynamic simulation data package encapsulated and sent by Simpack includes information such as simulation time, pantograph-catenary contact force, displacement, velocity, acceleration, and status flags; For simulation time, For the contact force between the bow and the fire net, These represent the pantograph head displacement, velocity, and acceleration, respectively. (2) Behavioral feedback and control: when the virtual debugging platform receives... Subsequently, the behavior model of the pantograph-contact network system is calculated according to preset rules; the preset rules include: when continuous detection... If the duration exceeds the threshold, the behavior model of the pantograph-contact network system generates a contact force deficiency event, denoted as... The event is then reported to Simpack; based on this event, the Simpack platform adjusts its internal damping parameters. or stiffness parameters This enables closed-loop simulation of behavior and dynamics.

8. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 5, characterized in that, In step S33, multi-model data management and correlation analysis are performed, and effective digital twin applications are realized, including: (1) Unify spatiotemporal indexing and data fusion to establish timestamps for all data originating from different platforms. and device space coordinates A unified index is used as the core; for sensor data and simulation data with different sampling rates, data alignment is performed using methods based on linear interpolation or spline interpolation, ensuring they are on the same time reference. The following association analysis is performed; the expression is: ； In the formula, For timestamp reference The following multi-model data vectors, Geometric, dynamic, and behavioral models, respectively. Attribute vectors, Transpose of a vector; (2) Feature mining based on association rules: Using algorithms such as Apriori, strong association rules between dynamic parameters and behavioral indicators under different working conditions are mined. The strong association rules include the following discovery rules: ； These rules are used to predict system risks under specific operating conditions; (3) Twin data service: By encapsulating it into a RESTful API, the processed multi-model fusion data, health assessment results and association rules are provided to the upper-level operation and maintenance applications to realize the utilization of digital twin data.

9. The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles according to claim 1, characterized in that, In step S4, for the specific real-time operation and maintenance scenario of the pantograph-overhead contact system of urban rail vehicles, a twin database is built and data visualization processing is performed, including: S41, Construct a twin database for the pantograph-contact network system, integrating historical databases, operation and maintenance knowledge bases, model libraries, and real-time twin databases; Based on the cloud-fog-edge collaborative architecture, a twin database supporting multi-source heterogeneous data storage is constructed. The historical database stores the pantograph's historical operating data, maintenance records, and fault cases. The operation and maintenance knowledge base integrates expert rules, fault trees, maintenance strategies, and other knowledge. The model library stores various simulation models and their parameter versions. The real-time twin database receives and updates real-time data from sensors and simulation platforms. S42 utilizes Python and visualization libraries to develop visualizations of twin data; based on the twin database of the pantograph-contact network system, it obtains a visual human-machine interface for real-time display of pantograph displacement, pantograph-contact network contact force, and key parameter curves of vibration spectrum; it dynamically displays the relative position and contact status of the pantograph and the contact network in three dimensions; it provides fault warning prompts and maintenance suggestions; and it supports historical data playback and multi-condition comparative analysis. S43 achieves efficient data processing and visualization rendering through a cloud-fog-edge collaborative architecture. The cloud is used for big data analysis and model training, the edge is responsible for real-time data collection and lightweight inference, and the fog edge undertakes relay and collaboration tasks.

10. A digital twin modeling system for intelligent operation and maintenance of pantograph devices on urban rail vehicles, characterized in that, The intelligent operation and maintenance digital twin modeling method for pantograph devices of urban rail vehicles as described in any one of claims 1, the system comprising: The geometric physical model building module establishes a geometric physical model of the pantograph device based on its physical characteristics and actual working conditions. The module for constructing the electromechanical coupling dynamic model of the pantograph-contact network system constructs the electromechanical coupling dynamic model of the pantograph-contact network system of urban rail vehicles based on the established geometric and physical model of the pantograph device and with reference to relevant parameters. The pantograph-overhead contact system behavior model construction module, based on the constructed electromechanical coupling dynamic model of the pantograph-overhead contact system of urban rail vehicles, constructs a behavior model of the pantograph-overhead contact system of urban rail vehicles and associates data from multiple models; realizes the digital mapping of system operation behavior, and achieves deep integration of mechanism model and real-time data by associating data from multiple models, supporting intelligent diagnosis and predictive maintenance; The twin database construction and display module is designed for the specific real-time operation and maintenance scenarios of the pantograph-overhead contact system of urban rail vehicles. It builds a twin database, performs data visualization processing, and displays the operation and maintenance results. Based on the cloud-fog-edge collaborative architecture, it supports the storage and processing of multi-source heterogeneous data. The data visualization processing forms a closed loop from data perception to decision support through parameter curve display, 3D dynamic display, and fault early warning push, realizing the transformation from passive maintenance to proactive prediction.

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