Electric locomotive three-dimensional digital-analog simulation teaching system constructed based on unreal engine technology

Abstract

The invention discloses an electric locomotive three-dimensional digital-analog simulation teaching system constructed based on an unreal engine technology, and relates to the technical field of electric locomotive teaching. Comprising a three-dimensional model construction module which is used for generating a hierarchical three-dimensional model of electric locomotive equipment through combination of polygon modeling and parametric modeling; and the physical engine interaction module is integrated with an unreal engine physical system and is configured to process the real-time solution of the locomotive kinetic parameter equation set. According to the method, a highly accurate three-dimensional model is created by combining polygon modeling and parametric modeling on the complex equipment of the electric locomotive, the model can support detailed level-of-detail rendering so as to adapt to different observation distances, and in the physical engine interaction module, the model can be applied to the physical engine interaction module. Through the train longitudinal dynamics calculation unit, the traction transmission simulation unit and the braking system modeling unit, the system can simulate non-linear changes in the wheel-rail contact force, traction motor torque transmission and tread braking process in real time, so that various dynamic effects can be visually displayed in the teaching process.

Description

Technical Field

[0001] The present invention relates to the technical field of electric locomotive teaching, and specifically to a three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology. Background Technique

[0002] Today, as an important part of modern rail transit, electric locomotives have complex power systems, electrical systems, and mechanical systems, which pose challenges to teaching and training. Traditional teaching methods rely on the operation practice of actual equipment, but this method has problems such as high cost, many safety hazards, and inconvenient operation. Considering these factors, by introducing virtual simulation technology and taking advantage of its ability to create realistic scenarios, repeated operations, and scenario simulations, a new teaching platform is provided for the operation and troubleshooting of electric locomotives.

[0003] In existing electric locomotive simulation systems, most rely on the construction of complex software and hardware, and usually it is difficult to process complex dynamic and electrical phenomena in real time. The patent described in this article aims to overcome these existing problems, especially focusing on using Unreal Engine technology to build a highly realistic and interactive three-dimensional digital simulation teaching system for electric locomotives through its powerful graphics processing capabilities and rich physical simulation functions. Through this system, users can not only intuitively learn various operations of electric locomotives, but also achieve the goal of deeply understanding the causes of faults and troubleshooting strategies through its backtracking fault injection and operation evaluation functions.

[0004] In addition, the system also has powerful data communication and visualization capabilities, supports docking with real physical control systems, and uses advanced time series analysis algorithms and technologies such as dynamic weather and catenary effect simulation to make the virtual simulation environment closer to the real operation environment. The integration of these technologies greatly improves the practicality and educational value of the system, providing a new solution for the teaching of electric locomotives and related majors. Summary of the Invention

[0005] The purpose of the present invention is to provide a three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology to solve the problems raised in the above background technique.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology, including:

[0007] A three-dimensional model construction module configured to generate a hierarchical three-dimensional model of electric locomotive equipment by combining polygon modeling and parametric modeling;

[0008] A physical engine interaction module integrating the Unreal Engine physical system and configured to process the real-time solution of the locomotive dynamics parameter equations;

[0009] The core teaching logic module includes a fault injection unit and an operation evaluation unit, configured to implement dynamic logic switching of teaching scenarios through a finite state machine;

[0010] The data communication interface module supports bidirectional data channels for the OPC UA protocol and the MODBUS / TCP protocol, and is configured to connect the virtual simulation environment and the physical PLC control system;

[0011] The visualization rendering module adopts the virtual micro-polygon geometry framework of the Unreal Engine and is configured to implement LOD dynamic level-of-detail rendering.

[0012] As a specific solution of the technical solution of this application, the physical engine interaction module includes:

[0013] The train longitudinal dynamics calculation unit processes the wheel-rail contact mechanical relationship based on the improved Hunt-Crossley contact model;

[0014] The traction drive simulation unit uses a two-mass block model to simulate the torque transmission characteristics of the main circuit, traction motor, and gearbox;

[0015] The braking system modeling unit integrates the Kelvin-Voigt viscoelastic model to achieve non-linear simulation of the tread braking process.

[0016] As a specific solution of the technical solution of this application, the core teaching logic module includes:

[0017] The dynamic fault tree generation algorithm constructs a visual topology of the fault propagation path based on the Bayesian network;

[0018] The operation sequence verification engine uses the Petri net model for formal verification of the operation process;

[0019] The real-time scoring matrix establishes a multi-dimensional evaluation system including timing compliance, operation accuracy, and troubleshooting efficiency.

[0020] As a specific solution of the technical solution of this application, the system further includes:

[0021] The spatio-temporal synchronization controller coordinates the simulation step sizes of multiple physical fields using the IEEE 1588 precise clock protocol;

[0022] The hardware-in-the-loop interface is configured to achieve hard real-time data interaction between the FPGA and the Unreal Engine through the ADM6656 chipset;

[0023] The distributed rendering cluster realizes frame synchronization output of multi-channel visual scenes based on the nDisplay technology.

[0024] As a specific embodiment of the technical solution of the present application, the visualization rendering module includes:

[0025] A dynamic weather simulation unit, which uses a virtual engine volume cloud system and a fluid dynamics particle system;

[0026] An overhead contact line arc simulation unit, which generates a dynamic discharge effect based on the FBM fractal algorithm;

[0027] A material response system, which realizes the variable reflection characteristics of the device surface state through Substance parameterized materials.

[0028] As a specific embodiment of the technical solution of the present application, the simulation teaching method includes:

[0029] Construct a parametric model library of locomotive equipment based on BRep-NURBS hybrid modeling;

[0030] Design a dynamic simulation scenario configuration file including traction / braking characteristic curves and overhead contact line parameters;

[0031] Generate a teaching observation path for the virtual camera through the Jerk-limited trajectory planning algorithm;

[0032] A dynamic generation strategy for fault cases optimized based on the genetic algorithm in real time;

[0033] Adopt an improved DTW algorithm to perform timing matching analysis between the operation process and the standard operation.

[0034] Compared with the prior art, the beneficial effects of the present invention are:

[0035] Construct a three-dimensional digital simulation teaching system for electric locomotives based on Unreal Engine technology. By combining polygon modeling and parametric modeling of complex electric locomotive equipment, a highly accurate three-dimensional model is created. This model can support detailed level-of-detail rendering to adapt to different viewing distances. In the physical engine interaction module, through the train longitudinal dynamics calculation unit, traction drive simulation unit, and braking system modeling unit, the system can real-time simulate the non-linear changes in wheel-rail contact force, traction motor torque transmission, and tread braking process, enabling various dynamic effects to be intuitively displayed during the teaching process.

[0036] In the teaching logic core module, the dynamic fault tree generation algorithm visualizes the fault propagation path based on the Bayesian network. The operation sequence verification engine verifies the operation process through the Petri network model. The real-time scoring aggregation regular evaluates the operation ability of the college from multiple dimensions such as timing compliance, operation accuracy, and troubleshooting efficiency, constructing a highly interactive and challenging teaching environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic flowchart of the 3D digital simulation teaching system of the present invention;

[0038] Figure 2 Schematic flowchart of the physical engine interaction module of the present invention;

[0039] Figure 3 Schematic flowchart of the core teaching logic module of the present invention. Specific implementation manners

[0040] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention.

[0042] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of the present invention, "a plurality" means at least two, such as two, three, etc., unless otherwise specifically defined.

[0043] In the present invention, unless otherwise clearly defined and limited, the terms "installed", "connected", "connected", "fixed", etc. should be understood in a broad sense. For example, it may be a fixed connection, a detachable connection, or integrated; it may be a mechanical connection or an electrical connection; it may be directly connected or indirectly connected through an intermediate medium, and it may be the internal communication of two elements or the interaction relationship between two elements, unless otherwise clearly limited. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0044] In the present invention, unless otherwise clearly specified and defined, the first feature being "on" or "under" the second feature may mean that the first and second features are in direct contact, or the first and second features are in indirect contact through an intermediate medium. Moreover, the first feature being "above", "over" and "on top of" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the horizontal height of the first feature is higher than that of the second feature. The first feature being "under", "beneath" and "underneath" the second feature may mean that the first feature is directly above or obliquely above the second feature, or simply means that the horizontal height of the first feature is less than that of the second feature.

[0045] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0046] As Figures 1 - 3 shown, the present invention provides a technical solution: a three-dimensional digital simulation teaching system for electric locomotives based on Unreal Engine technology, including:

[0047] A three-dimensional model construction module, configured to generate a hierarchical three-dimensional model of electric locomotive equipment by combining polygon modeling and parametric modeling. It should be clear that this module generates a hierarchical three-dimensional model of electric locomotive equipment by combining polygon modeling and parametric modeling. Polygon modeling can finely adjust the shape and details of the model, while parametric modeling can quickly generate model instances with different parameters. For example, use modeling software such as 3dsMAX and Maya to create models of each component of the electric locomotive, and then import these models into the Unreal Engine through the Datasmith plug-in of the Unreal Engine. It should also be clear that in this application, during the modeling process, parameters such as the scale, size, and material of the model need to be considered to ensure the accuracy and authenticity of the model.

[0048] The physical engine interaction module integrates the Unreal Engine physical system and is configured to handle the real-time solution of the locomotive dynamics parameter equations. It should be noted that in the embodiments of this application, this module integrates the Unreal Engine physical system to handle the real-time solution of the locomotive dynamics parameter equations. The physical system of the Unreal Engine is based on NVIDIA PhysX technology and can simulate physical effects such as object collisions, friction, and gravity. In the simulation of electric locomotives, it is necessary to establish a locomotive dynamics model, including parameters such as mass inertia, traction force, and braking force. By setting physical materials and collision bodies, the real movement of the locomotive in the virtual environment can be achieved, which is represented by the locomotive dynamics equation: , where m is the locomotive mass, v is the speed, and F 牵引 is the traction force, and F 阻力 is the sum of air resistance and track resistance, and F 制动力 is the braking force.

[0049] The teaching logic core module includes a fault injection unit and an operation evaluation unit and is configured to achieve dynamic logic switching of teaching scenarios through a finite state machine. It should be noted that in the embodiments of this application, this module includes a fault injection unit and an operation evaluation unit and achieves dynamic logic switching of teaching scenarios through a finite state machine. A finite state machine is a teaching model used to model a finite number of states and the transitions and actions between states. In the Unreal Engine, a blueprint visual scripting system can be used to implement a finite state machine. For example, different teaching scenario states can be defined, such as normal operation state, fault injection state, operation evaluation state, etc., and the transition conditions and trigger actions between states can be set. The fault injection unit can simulate faults of electric locomotives, such as motor faults and braking system faults, in specific states, while the operation evaluation unit can record the operations of trainees and score them according to preset evaluation criteria.

[0050] The data communication interface module supports two-way data channels for the OPCUA protocol and the MODBUS / TCP protocol and is configured to connect the virtual simulation environment and the physical PLC control system. It should be noted that in this application, this module supports two-way data channels for the OPUA protocol and the MODBUS / TCP protocol to connect the virtual simulation environment and the physical PLC control system. Through these protocols, the virtual simulation system can perform real-time data interaction with the PLC control system to achieve remote monitoring and operation of the electric locomotive. For example, an OPUA client can be used to connect to the PLC server in the Unreal Engine to read and write register data in the PLC, thereby achieving control and monitoring of the electric locomotive.

[0051] The visual rendering module adopts the virtualized micro-polygon geometry framework of the Unreal Engine and is configured to implement LOD dynamic level-of-detail rendering. It should be clear that in the embodiments of this application, this module adopts the virtualized micro-polygon geometry framework of the Unreal Engine to achieve LOD dynamic level-of-detail rendering. The Unreal Engine has powerful rendering capabilities and can create highly realistic virtual scenes. The virtualized micro-polygon geometry construction can handle complex geometric models and improve rendering efficiency. The LOD technology can automatically adjust the level of detail of the model according to the distance between the model and the observer to optimize performance and maintain visual quality. For example, when the observer is far away from the electric locomotive, the system can reduce the polygon count and texture resolution of the model, while increasing the level of detail at close range to ensure smooth and realistic rendering.

[0052] The teaching logic core module includes:

[0053] The dynamic fault tree generation algorithm constructs a visual topology of the fault propagation path based on the Bayesian network;

[0054] The operation sequence verification engine uses the Petri net model for formal verification of the operation process;

[0055] A real-time scoring matrix is established to construct a multi-dimensional evaluation system including timing compliance, operation precision, and troubleshooting efficiency. It should be clear that in this application, a fault tree is an analysis tool for system reliability, which can decompose the faults of a system into combinations of multiple basic events. By converting the fault tree into a Bayesian network, the probability inference ability of the Bayesian network can be utilized to dynamically analyze the occurrence probability and propagation path of faults. In the Unreal Engine, the construction and inference of the Bayesian network can be achieved using blueprints or plugins. For example, different fault events can be defined as nodes of the Bayesian network, and the conditional probabilities between the nodes can be determined through expert evaluation or historical data, and then the probability of fault occurrence can be calculated using the inference algorithm of the Bayesian network. As can be seen from the foregoing, this engine uses a Petri net model for formal verification of the operation process. A Petri net is a mathematical model used to describe and analyze the behavior of concurrent systems, which can be used to model and verify the operation process of an electric locomotive. For example, different operation steps can be defined as transitions of the Petri net, and the operation conditions and resources can be used as places, and the execution of the operation process can be simulated through the flow of tokens. By analyzing the properties of the Petri net such as reachability, boundedness, and liveness, the correctness and feasibility of the operation process can be verified. It should also be clear that the real-time scoring matrix is a multi-dimensional evaluation system including timing compliance, operation precision, and troubleshooting efficiency. The timing compliance can be calculated by comparing the similarity between the time series of the trainee's operation and the standard operation time series. The operation precision can be evaluated by measuring the deviation between the trainee's operation and the standard operation in terms of space and parameters. The troubleshooting efficiency can be measured by recording the time and steps required for the trainee to troubleshoot the fault.

[0056] The system further includes:

[0057] A spatio-temporal synchronization controller that coordinates the simulation step sizes of multiple physical fields using the IEEE 1588 Precision Time Protocol; it should be clear that in the embodiments of this application, this controller uses the IEEE 1588 Precision Time Protocol to coordinate the simulation step sizes of multiple physical fields. The IEEE 1588 protocol is a protocol used to synchronize the clocks of devices in a computer network, which can achieve sub-microsecond-level clock synchronization accuracy within a local area network. In the simulation of multiple physical fields, by using the IEEE 1588 protocol, it can be ensured that the time step sizes between various simulation modules are consistent, thereby achieving spatio-temporal synchronization. For example, in the simulation of an electric locomotive, switches and devices that support the IEEE 1588 protocol can be used, and time synchronization can be performed through the master-slave clock model. The master clock serves as the authoritative source of time, and the slave clock synchronizes with the master clock, thus ensuring the clock consistency of the entire simulation system. Then, the time deviation is calculated, and the time deviation calculation formula is as follows: , where t1, t2, t3, and t4 are respectively the transmission time, arrival time, transmission time of the delay request message, and arrival time of the synchronization message.

[0058] A hardware-in-the-loop interface configured to achieve hard real-time data interaction between the FPGA and the Unreal Engine through the ADM6656 chipset; it should be clear that in this application, this interface is configured to achieve hard real-time data interaction between the FPGA and the Unreal Engine through the ADM6656 chipset. ADM6656 is a high-speed data conversion chip that can achieve the conversion between analog signals and digital signals, and supports high precision and high sampling rate. In the electric locomotive simulation system, the FPGA can be used to process the sensor data and control signals of the locomotive in real time, and transmit these data to the Unreal Engine through the ADM6656 chip to achieve hardware-in-the-loop simulation. For example, the FPGA can collect the speed sensor signal and current sensor signal of the locomotive, and after processing, send the data to the Unreal Engine through the ADM6656 chipset. The Unreal Engine updates the virtual model state of the locomotive according to these data and feeds back the control instructions to the FPGA, so as to achieve closed-loop real-time data interaction.

[0059] A distributed rendering cluster that realizes frame synchronization output of multi-channel visual scenes based on the nDisplay technology; it should be clear that this cluster realizes frame synchronization output of multi-channel visual scenes based on the nDisplay technology. nDisplay is a distributed rendering technology of the Unreal Engine, which can distribute rendering tasks to multiple networked computers to achieve efficient rendering and display of large-scale scenes. In the electric locomotive simulation teaching system, the nDisplay technology can be used to divide the virtual scene into multiple views, render them on different computers respectively, and then synchronize the frame data of these views through the network to ensure frame synchronization output of multi-channel visual scenes. For example, in a large electric locomotive training center, multiple projectors or monitors can be used to form a surround display system, and the scene rendered by the Unreal Engine can be synchronously displayed on these devices through the nDisplay technology, providing an immersive training experience for trainees. The nDisplay technology can automatically process the splicing and fusion between views to ensure seamless display of the entire scene.

[0060] The visualization rendering module includes:

[0061] A dynamic weather simulation unit that adopts the virtual engine volume cloud system and the fluid dynamics particle system;

[0062] An overhead line arc simulation unit that generates a dynamic discharge effect based on the FBM fractal algorithm;

[0063] The material response system realizes the variable reflection characteristics of the device surface state through Substance parametric materials. It should be clear that in this application, the dynamic weather simulation unit uses the Unreal Engine volume system and the fluid dynamics particle system. The volume cloud system can simulate the shape, density, and illumination effect of clouds, and the fluid dynamics particle system can simulate the movement and interaction of particles such as rain and snow. In the Unreal Engine, the parameters of these particle systems, such as the size, speed, and life cycle of particles, can be controlled through blueprints or material expressions. For example, the falling speed of raindrops can be set to 10 meters per second, and the falling speed of snow particles can be set to 2 meters per second, and these parameters can be dynamically adjusted according to weather conditions. In this application, the catenary arc simulation unit generates a dynamic discharge effect based on the FBM fractal algorithm. The FBM algorithm is an algorithm for generating natural noise textures, which can simulate irregular patterns with fractal characteristics. In the Unreal Engine, the generation of FBM noise can be achieved through material expressions or blueprints and applied to the simulation of arcs. For example, FBM noise can be used to control the changes in the brightness, color, and shape of the arc, making the arc look more realistic and dynamic. The generation formula of FBM noise is as follows: , where x is the input coordinate, noise is the basic noise function, and n is the number of fractal layers. It should also be clear that this system realizes the variable reflection characteristics of the device surface state through Substance parametric materials. Substance is a powerful material creation tool that can create materials with high detail and realism. In the Unreal Engine, Substance materials can be imported and their parameters can be dynamically adjusted through blueprints or material expressions. For example, parameters such as the roughness, metallicity, and reflection intensity of the device surface can be set, and the appearance of the device can be changed in real time according to different environmental conditions and operating states. For example, when the device surface is covered with oil, the surface roughness can be increased and the reflection intensity can be reduced to make the device look more realistic.

[0064] The simulation teaching method includes:

[0065] Construct a parametric model library of locomotive equipment based on BRep-NURBS hybrid modeling;

[0066] Design a dynamic simulation scenario configuration file including traction / braking characteristic curves and catenary parameters;

[0067] Generate a teaching observation path for the virtual camera through the Jerk-limited trajectory planning algorithm;

[0068] A dynamic generation strategy for fault cases optimized based on the genetic algorithm in real time;

[0069] The improved DTW algorithm is used to perform the timing matching analysis between the operation process and the standard operation. It should be clear that in the embodiments of this application, BRep (Boundary Representation) and NURBS (Non-Uniform Rational B-Spline) are two common 3D modeling techniques. BRep is used to represent the boundary information of an object, while NURBS is used to generate smooth curves and surfaces. In the modeling of electric locomotive equipment, these two methods can be combined. BRep is used to define the boundary and topological structure of the equipment, and NURBS is used to generate the details and surfaces of the equipment. Through the parametric model library, equipment models with different parameters can be quickly generated, such as locomotive components with different sizes, shapes, and materials. For example, parameters such as the length, width, and height of the locomotive body can be defined, and different models of locomotives can be generated by adjusting these parameters. In the Unreal Engine, these parametric models can be imported using plugins such as Datasmith, and the parameters of the models can be dynamically adjusted through blueprints or material expressions. The traction and braking characteristic curves are important performance parameters of electric locomotives, reflecting the traction force and braking force of the locomotive at different speeds. The catenary parameters include information such as the voltage, current, and resistance of the catenary. In the Dingtai simulation scenario configuration file, these parameters can be defined to accurately simulate the operating state of the locomotive in the simulation. For example, the traction characteristic curve can be expressed as a relationship curve between the traction force and the speed, and the braking characteristic curve can be expressed as a relationship curve between the braking force and the speed. The catenary parameters can be simulated through a circuit model, including voltage drop, current distribution, etc. In the Unreal Engine, these configuration files can be read through blueprints or plugins, and the parameters can be passed to the physics engine and the control system to achieve precise control of the locomotive behavior. The Jerk-limited trajectory planning algorithm is an algorithm used to generate smooth motion trajectories, taking into account the limitation of jerk (jerk). In the path planning of virtual cameras, using this algorithm can generate natural and comfortable viewing paths. The basic idea of the algorithm is to limit the change in acceleration within a certain range to avoid visual discomfort caused by sudden acceleration changes. For example, parameters such as the starting position, target position, speed, and acceleration of the virtual camera can be defined, and then the motion trajectory of the camera can be calculated through the Jerk-limited algorithm. In the Unreal Engine, this algorithm can be implemented using blueprints or plugins, and the generated trajectory can be applied to the control of the virtual camera. For example, the formulas for the acceleration and speed changing with time are as follows:

[0070] , where A is the maximum acceleration, t0 is the time for acceleration to rise and fall, t1 is the time for acceleration to be maintained, and V is the target speed. It should also be clear that the DTW (Dynamic Time Warping) algorithm is an algorithm for measuring the similarity of two time series sequences and is applicable to the time series matching analysis of the operation process and the standard operation. The improved DTW algorithm can, on the basis of the traditional DTW, improve the efficiency and accuracy of matching by introducing constraint conditions or optimizing the search strategy. In the Unreal Engine, the DTW algorithm can be implemented through blueprints or plugins, and the operation process of the trainee can be compared with the standard operation to generate matching results and evaluation metrics. For example, the basic formula of the DTW algorithm is as follows: , where D(i,j) represents the minimum distance metric value between sequences Xi and Yj, and d(i,j) represents the distance metric value between sequences Xi and Yj. By recursively calculating each element in matrix D and traversing along the optimal path from (1,1) to (N,M), the minimum distance metric value can be obtained, thus realizing the time series matching analysis.

[0071] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended embodiments and their equivalents.

Claims

1. A three-dimensional digital simulation teaching system for electric locomotives is constructed based on Unreal Engine technology, characterized in that Including: A 3D model construction module, configured to generate a hierarchical 3D model of electric locomotive equipment through a combination of polygon modeling and parametric modeling; A physical engine interaction module, integrating the Unreal Engine physical system, configured to process the real-time solution of the locomotive dynamics parameter equations; A teaching logic core module, including a fault injection unit and an operation evaluation unit, configured to realize the dynamic logic switching of teaching scenarios through a finite state machine; A data communication interface module, supporting a two-way data channel for the OPCUA protocol and the MODBUS / TCP protocol, configured to connect the virtual simulation environment and the physical PLC control system; A visualization rendering module, adopting the virtualized micro-polygon geometry framework of the Unreal Engine, configured to implement LOD dynamic detail level rendering.

2. The three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology according to claim 1, characterized in that: The physical engine interaction module includes: A train longitudinal dynamics calculation member, based on the improved Hunt-Crossley contact model to process the wheel-rail contact mechanical relationship; A traction drive simulation unit, using a two-mass block model to simulate the torque transmission characteristics of the main circuit, traction motor, and gearbox; A braking system modeling unit, integrating the Kelvin-Voigt viscoelastic model to realize the non-linear simulation of the tread braking process.

3. The three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology according to claim 1, wherein: The teaching logic core module includes: A dynamic fault tree generation algorithm, constructing a visual topology of the fault propagation path based on a Bayesian network; An operation sequence verification engine, using a Petri net model for formal verification of the operation process; A real-time scoring matrix, establishing a multi-dimensional evaluation system including timing compliance, operation accuracy, and troubleshooting efficiency.

4. The three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology according to claim 1, characterized in that: The system further includes: A spatio-temporal synchronization controller, using the IEEE 1588 precise clock protocol to coordinate the simulation step sizes of multiple physical fields; A hardware-in-the-loop interface, configured to achieve hard real-time data interaction between the FPGA and the Unreal Engine through the ADM6656 chipset; A distributed rendering cluster, based on the nDisplay technology to achieve frame synchronization output of multi-channel visual scenes.

5. The three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology according to claim 1, wherein: The visualization rendering module includes: A dynamic weather simulation unit, using the virtual engine volume cloud system and the fluid mechanics particle system; An overhead contact line arc simulation unit, generating a dynamic discharge effect based on the FBM fractal algorithm; A material response system, realizing the variable reflection characteristics of the equipment surface state through Substance parametric materials.

6. The three-dimensional digital simulation teaching system for electric locomotives constructed based on Unreal Engine technology according to claim 1, characterized in that: The simulation teaching method includes: Constructing a parametric model library of locomotive equipment based on BRep-NURBS hybrid modeling; Designing a dynamic simulation scenario configuration file including traction / braking characteristic curves and overhead contact line parameters; Generating a teaching observation path for the virtual camera through the Jerk-limited trajectory planning algorithm; A real-time dynamic generation strategy for fault cases optimized based on the genetic algorithm; Using an improved DTW algorithm for timing matching analysis of the operation process and the standard operation.

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