Heavy haul railway group simulation system, method, equipment and medium

Abstract

The invention relates to the technical field of rail transit, in particular to a heavy haul railway group simulation system, method and device and a medium. The method comprises the following steps: deploying a plurality of sensors on site to collect train, track or signal data in real time, and cleaning and fusing the collected data to obtain preprocessed data; based on the preprocessed data, constructing digital twin models of the heavy haul railway system by adopting a three-dimensional modeling technology, and establishing an association relationship and an interaction mechanism between the digital twin models; performing real-time calculation and simulation on the digital twin model through numerical calculation and dynamic simulation; displaying the digital twin model by adopting an interactive interface of a virtual reality (VR), augmented reality (AR) technology or a naked eye 3D technology, and performing interactive operation with the digital twin model through the interactive interface; and comparing and analyzing a simulation result with actual operation data, verifying the accuracy and reliability of the digital twinborn model by calculating errors, and adjusting and optimizing the model according to a verification result.

Description

Technical Field

[0001] The present disclosure relates to the technical field of rail transit, and particularly to a heavy-haul railway group simulation system, method, device, and medium. Background Art

[0002] In recent years, the development of heavy-haul railway transportation has been rapid. As an innovative means to improve train operation efficiency, train group technology has been applied in multiple lines and various scenarios. However, group technology involves numerous devices and requires multi-disciplinary collaboration and multi-worker cooperation, which undoubtedly increases the difficulty of learning and experimentation. At the same time, the industry's requirements for its operation safety and efficiency are also increasing day by day. In this context, accurate and reliable simulation technology is of great significance for the planning, design, and operation management of heavy-haul railways.

[0003] Traditional heavy-haul railway simulation technology is mainly based on mathematical models and empirical formulas. For example, some models describe the train operation state through simple mechanical equations, ignoring the complex interactions between trains and many uncertain factors in the actual operation environment. When simulating the operation of heavy-haul railway groups, these models can often only provide relatively rough results and cannot accurately reflect the true operation conditions of trains under different loads, speeds, and line conditions. There are also some computer simulation-based methods. Although they can consider more factors to a certain extent, most of them simulate parts such as trains, tracks, or signal systems in isolation and lack a comprehensive and coordinated simulation of the entire heavy-haul railway system. Moreover, these methods are difficult to update and feedback changes in actual operation in real time and cannot adjust the simulation results in a timely manner to adapt to the actual situation. In addition, existing simulation display means are mostly static charts or simple animated videos, lacking intuitiveness and interactivity, which is not conducive to operation management personnel to deeply understand the operation state of heavy-haul railway groups and make accurate decisions.

[0004] In summary, the existing simulation technologies generally have the following disadvantages: Limited parameter simulation ability: Most traditional simulation technologies rely on mathematical models and empirical formulas. During the simulation process, it is difficult for them to carry out multi-parameter simulation work and usually can only receive or output a single parameter. Just like a machine that can only handle single tasks, it cannot take into account the interactive effects of multiple factors at the same time, greatly reducing the comprehensiveness and accuracy of the simulation results.

[0005] Insufficient complex system simulation: The heavy-haul railway group technology constructs a complex system with multi-device and multi-disciplinary collaborative cooperation. Traditional simulation technologies cannot achieve full-factor simulation and effective cooperation between multi-systems and only stay at the single-system training level.

[0006] The form of simulation display is single: The display form of traditional simulation systems is relatively simple and primitive, mainly presented in traditional ways such as lists and curves. It lacks vividness and immersion, and cannot create a lifelike simulation effect. Summary of the Invention

[0007] In view of the above problems, the present disclosure provides a heavy-haul railway group simulation system, method, device, and medium.

[0008] In a first aspect, a heavy-haul railway group simulation system includes: A data acquisition and processing module, a digital twin model construction module, a simulation calculation module, a real-time interaction and display module, and a model verification and update module; The data acquisition and processing module is used to collect train, track, or signal data in real time by deploying a variety of sensors on-site, and clean and fuse the collected data to obtain preprocessed data; The digital twin model construction module, based on the preprocessed data, uses three-dimensional modeling technology to construct a digital twin model of the heavy-haul railway system, and establishes the association relationship and interaction mechanism between the digital twin models; The simulation calculation module is used to perform real-time calculation and simulation on the digital twin model through numerical calculation and dynamic simulation to obtain simulation results; The real-time interaction and display module is used to display the digital twin model using an interaction interface of virtual reality (VR), augmented reality (AR) technology, or naked-eye 3D technology, and perform interactive operations with the digital twin model through the interaction interface; The model verification and update module is used to compare and analyze the simulation results with the actual operation data, verify the accuracy and reliability of the digital twin model by calculating the error, and adjust and optimize the model according to the verification results.

[0009] Further, the sensors include: a speed sensor, an acceleration sensor, a load sensor, a track stress sensor, a bridge vibration sensor, and a signal status sensor.

[0010] Further, the collection of train, track, or signal data in the data acquisition and processing module includes: collecting train operation parameters, track status information, bridge structure health data, and signal system status data; Among them, the train operation parameters include: train position, speed, acceleration, and load parameters; the track status information includes: gauge change and track deformation information; the bridge structure health data includes: vibration frequency and stress-strain data; the signal system status data includes: signal lamp color and signal instruction data.

[0011] Further, the cleaning and fusion of the collected data in the data acquisition and processing module includes: Perform data cleaning, data fusion, and data normalization on the collected data; Among them, data cleaning includes removing noise, outliers, and duplicate data; data fusion includes aligning the data from different sensors according to timestamps or other associated fields to form a unified dataset; data normalization processing includes converting the data from different sensors to the same dimension and adopting a unified numerical range.

[0012] Furthermore, the digital twin model includes a train model, a track model, a bridge model, and a signal system model; Among them, the train model is used to simulate the appearance, structure, load distribution, and dynamic characteristics of the train; the track model is used to describe the geometric shape, material characteristics, and laying parameters of the track; the bridge model is used to simulate the structural form, mechanical properties, and vibration response of the bridge; the signal system model is used to simulate the working principle, signal transmission mechanism, and signal control logic of the signal equipment.

[0013] Furthermore, the association relationships and interaction mechanisms between the digital twin models include: The mechanical interaction between the train operation in the train model and the track in the track model; the interaction between the train operation in the train model and the signal in the signal system model; the mechanical interaction between the train and the bridge in the bridge model when the train passes through the bridge; the interaction between the track and the bridge in the vibration response in the track model and the bridge model.

[0014] Furthermore, the simulation calculation module is specifically used for: According to the input initial conditions and preprocessed data, through numerical simulation calculations of the train running position, speed, and acceleration, simulate the running process of the heavy-haul railway group trains, and calculate the interaction between trains, the coupling effect between trains and infrastructure, and the control effect of the signal system on train operation; among them, the initial conditions include the initial position, speed, and load of the train; Consider various operating conditions and dynamically adjust and optimize the simulation results; among them, the operating conditions include different weather conditions, line gradients, curve radii, and train formation methods.

[0015] In the second aspect, a heavy-haul railway group simulation method includes: Deploy various sensors on-site to collect train, track, or signal data in real time, and clean and fuse the collected data to obtain preprocessed data; Based on the preprocessed data, use three-dimensional modeling technology to construct a digital twin model of the heavy-haul railway system, and establish the association relationships and interaction mechanisms between the digital twin models; Based on the digital twin model, through numerical calculation and dynamic simulation, real-time calculation and simulation are carried out to obtain the simulation results; Based on the digital twin model, it is presented through an interactive interface using virtual reality (VR), augmented reality (AR) technology or naked-eye 3D technology, and interactive operations are performed with the digital twin model through the interactive interface; Based on the digital twin model, the simulation results are compared and analyzed with the actual operation data, and the accuracy and reliability of the digital twin model are verified by calculating the error. According to the verification results, the model is adjusted and optimized.

[0016] Further, the sensors include: speed sensors, acceleration sensors, load sensors, track stress sensors, bridge vibration sensors, and signal status sensors.

[0017] Further, collecting train, track or signal data includes: collecting train operation parameters, track status information, bridge structural health data, and signal system status data; Among them, the train operation parameters include: train position, speed, acceleration, and load parameters; the track status information includes: gauge change and track deformation information; the bridge structural health data includes: vibration frequency and stress-strain data; the signal system status data includes: signal light color and signal instruction data.

[0018] Further, cleaning and fusing the collected data includes: Performing data cleaning, data fusion, and data normalization processing on the collected data; Among them, data cleaning includes: removing noise, outliers, and duplicate data; data fusion includes: aligning the data of different sensors according to timestamps or other associated fields to form a unified data set; data normalization processing includes: converting the data of different sensors to the same dimension and adopting a unified numerical range.

[0019] Further, the digital twin model includes: train model, track model, bridge model, and signal system model; Among them, the train model is used to simulate the appearance, structure, load distribution, and dynamic characteristics of the train; the track model is used to describe the geometric shape, material characteristics, and laying parameters of the track; the bridge model is used to simulate the structural form, mechanical properties, and vibration response of the bridge; the signal system model is used to simulate the working principle, signal transmission mechanism, and signal control logic of the signal equipment.

[0020] Further, the association relationships and interaction mechanisms between the digital twin models include: The mechanical interaction between the train operation in the train model and the track in the track model; the interaction between the train operation in the train model and the signals in the signal system model; the mechanical interaction between the train passing through the bridge in the train model and the bridge in the bridge model; the interaction between the track in the track model and the bridge in the bridge model in terms of vibration response.

[0021] Furthermore, through numerical calculation and dynamic simulation, real-time calculation and simulation are carried out on the digital twin model, including: According to the input initial conditions and preprocessed data, through numerical simulation calculation of the train running position, speed and acceleration, simulate the running process of heavy-haul railway group trains, and calculate the interaction between trains, the coupling effect between trains and infrastructure, and the control effect of the signal system on train operation; among them, the initial conditions include: the initial position, speed and load of the train. Considering various operating conditions, dynamically adjust and optimize the simulation results; among them, the operating conditions include: different weather conditions, line gradients, curve radii, and train formation methods.

[0022] In a third aspect, an electronic device includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory stores a computer program; The processor is configured to implement the above-mentioned heavy-haul railway group simulation method when executing the computer program stored on the memory.

[0023] In a fourth aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned heavy-haul railway group simulation method is implemented.

[0024] The present disclosure has at least the following beneficial effects: The accuracy and consistency of the present disclosure are higher. Compared with traditional technologies based on mathematical models and empirical formulas, only relatively simple and rough models can be constructed, ignoring many complex factors and making it difficult to accurately reflect the real train operation situation. However, the model carefully constructed by the present disclosure using digital twin technology precisely corresponds to the indoor and outdoor environment of heavy-haul railways in a 1:1 ratio, just like a "clone" of the real world, ensuring a high degree of consistency in the spatial structure and physical characteristics between the virtual world and the real scenario, and providing a more reliable basis for subsequent simulation.

[0025] The present disclosure is more comprehensive and collaborative. Compared with traditional computer simulation-based methods that mostly simulate parts such as trains, tracks, or signal systems in isolation and lack comprehensive and collaborative simulation, the digital twin model of the present disclosure can accurately simulate various dynamic behaviors and interaction relationships of heavy-haul railway group trains, and can also comprehensively and real-time reflect the coupling effects between infrastructure and train operations, and conduct all-round and collaborative simulation of the entire heavy-haul railway system.

[0026] The present disclosure takes into account more factors. Compared with traditional technologies that ignore the complex interactions between trains and the uncertainties in the actual operating environment during simulation and can only provide rough results, the present disclosure can not only simulate the normal states of trains such as starting, accelerating, decelerating, and stopping, but also simulate fault situations, and can consider special working conditions such as large load, long formation, and complex terrain, making the simulation more in line with the actual situation.

[0027] The present disclosure has better real-time performance and flexibility. Compared with traditional methods that are difficult to update and feedback actual operating changes in real time and cannot adjust the simulation results in time, the present disclosure can flexibly set various train arrival and departure operation plans, weather, fault information, etc. artificially. The virtual trains can run orderly in the digital twin simulation environment strictly according to the pre-designed plans, and can be flexibly adjusted according to actual needs and present the simulation situation in real time.

[0028] The present disclosure has more intuitive display and stronger interactivity. Compared with traditional simulation display means that are mostly static charts or simple animated videos and lack intuitiveness and interactivity, the present disclosure can develop an interactive interface based on technologies such as virtual reality (VR) or augmented reality (AR) with highly realistic simulation, allowing trainees to experience complex scenarios immersive in the virtual environment, enabling operation and management personnel to understand the operating state more deeply, and helping to make accurate decisions.

[0029] The present disclosure has more significant application value. Compared with the limited value of traditional technologies in actual applications, the present disclosure can not only be used for training to improve trainees' ability to handle actual situations, but also accumulate rich data through a large number of simulation tests, providing a solid basis for subsequent technical improvement, operation optimization, and safety assessment, and promoting the continuous development and progress of the heavy-haul railway field.

[0030] Other features and advantages of the present disclosure will be described in the following specification, and some of them will become obvious from the specification or be understood by implementing the present disclosure. The objectives and other advantages of the present disclosure can be achieved and obtained through the structures pointed out in the specification and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0032] Figure 1 Schematic diagram of the simulation system structure for the embodiments of the present disclosure; Figure 2 Schematic diagram of the structural principle of the simulation system for the embodiments of the present disclosure; Figure 3 Schematic diagram of the electronic device structure for the embodiments of the present disclosure. Specific implementation manners

[0033] To make the objectives, technical solutions, and advantages of the embodiments of the present disclosure clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present disclosure with reference to the accompanying drawings in the embodiments of the present disclosure. Obviously, the described embodiments are some, but not all, of the embodiments of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present disclosure.

[0034] The present disclosure is based on digital twin simulation technology, which deeply integrates the cutting-edge concept of digital twin into the innovative results of train group operation simulation. Digital twin realizes the precise mapping and dynamic monitoring of the physical entity state by constructing a virtual model highly consistent with the physical entity, collecting the data of the physical entity in real time through devices such as sensors, and feeding it back into the virtual model. In the field of train groups, this technology aims to create a virtual simulation space corresponding 1:1 to the actual train group operation environment, and comprehensively and realistically simulate the operation state, interaction relationship, and various complex scenarios of the train group.

[0035] As Figure 1 shown, a heavy-haul railway group simulation system includes: A data acquisition and processing module, a digital twin model construction module, a simulation calculation module, a real-time interaction and display module, and a model verification and update module; The data acquisition and processing module is used to collect train, track, or signal data in real time by deploying various sensors on site, and clean and fuse the collected data to obtain preprocessed data; The digital twin model construction module constructs a digital twin model of the heavy-haul railway system based on the preprocessed data by using three-dimensional modeling technology, and establishes the association relationship and interaction mechanism between the digital twin models; The simulation calculation module is used to perform real-time calculation and simulation on the digital twin model through numerical calculation and dynamic simulation to obtain simulation results; The real-time interaction and display module is used to present the digital twin model through an interaction interface adopting virtual reality (VR), augmented reality (AR) technology or naked-eye 3D technology, and perform interaction operations with the digital twin model through the interaction interface; The model verification and update module is used to compare and analyze the simulation results with the actual operation data, verify the accuracy and reliability of the digital twin model by calculating the error, and adjust and optimize the model according to the verification results.

[0036] Specifically, the implementation is introduced as follows: As Figure 2 shown schematically, the data acquisition and processing module is used to deploy a variety of sensors, collect various types of data in real time and perform data preprocessing; after data preprocessing, it is transmitted to the digital twin model construction module; By deploying a variety of sensors in the on-site computer room or through local twin simulation, including but not limited to speed, acceleration, load, track stress, bridge vibration sensors, and signal status sensors, etc. Real-time collection of train operation parameters (such as position, speed, acceleration, load, etc.), track status information (such as gauge change, track deformation, etc.), bridge structure health data (such as vibration frequency, stress and strain, etc.), and signal system status data (such as signal light color, signal instruction, etc.).

[0037] Perform preprocessing on the collected massive multi-source heterogeneous data, including data cleaning (removing noise, outliers and duplicate data), data fusion (integrating data from different sensors to form a unified data set), and data normalization processing for subsequent analysis and modeling.

[0038] Shown schematically, the digital twin model construction module is used to build a model based on the preprocessed data, build various types of models and establish model association and interaction mechanisms; Based on the preprocessed data, use 3D modeling technology to build a digital twin model of the heavy-haul railway system, including a train model (accurately simulating the appearance, structure, load distribution and dynamic characteristics of the train), a track model (detailed description of the track geometry, material properties, laying parameters, etc.), a bridge model (accurately reflecting the structural form, mechanical properties and vibration response of the bridge), a signal system model (simulating the working principle of signal equipment, signal transmission mechanism and signal control logic), etc.

[0039] By establishing the correlation relationships and interaction mechanisms among various models, a comprehensive and collaborative modeling of the heavy-haul railway system is achieved. For example, the operation of a train exerts forces on the track, and the deformation of the track in turn affects the operation state of the train. This interaction relationship is accurately reflected in the digital twin model.

[0040] Schematically, a simulation calculation module is used for numerical calculation and dynamic simulation to simulate the train operation process, and to adjust the simulation results considering multiple working conditions; record the simulated train operation process to obtain simulation results, which are used for comparing the simulation with actual data in the model verification and update module, and at the same time are used for presenting the operation state information in the real-time interaction and display module. Using numerical calculation methods and dynamic simulation algorithms, real-time calculation and simulation of the digital twin model are carried out. According to the input initial conditions (such as the initial position, speed, load, etc. of the train) and the real-time collected data, simulate the operation process of the heavy-haul railway group trains, calculate the interaction between trains, the coupling effect between trains and the infrastructure, and the control effect of the signal system on the train operation, etc.

[0041] Considering various actual operation conditions, such as different weather conditions (rain, snow, fog, etc.), line gradients, curve radii, and train formation modes, etc., dynamically adjust and optimize the simulation results to improve the accuracy and reliability of the simulation.

[0042] Schematically, a real-time interaction and display module is used for developing VR / AR interaction interfaces, presenting operation state information, and user interaction operations and result observation; adjust the numerical calculation and dynamic simulation of the simulation calculation module according to user interaction operations. Develop an interaction interface based on virtual reality VR, augmented reality AR technology, and naked-eye 3D technology, and present the digital twin model to users in an intuitive and three-dimensional way. Operation and management personnel can wear VR / AR devices to view the operation state of the heavy-haul railway group in real time, including the position, speed, operation trajectory of the train, and the state information of the infrastructure, etc.

[0043] Realize the interaction operations between users and the digital twin model, such as changing the operation parameters of the train, adjusting the settings of the signal system, simulating fault scenarios, etc., and observe the changes in the simulation results in real time. Through this interactive method, it is convenient for operation and management personnel to carry out decision-making analysis and scheme optimization.

[0044] Schematically, a model verification and update module is used for comparing the simulation with actual data, verifying the model accuracy, and updating the model parameters and structure; after updating the model parameters and structure, reconstruct the model based on the preprocessed data again. Compare the simulation results with the actual operation data, and verify the accuracy and reliability of the digital twin model by calculating error metrics (such as position error, speed error, stress error, etc.). According to the verification results, adjust and optimize the model, such as correcting model parameters, improving model structure, etc.

[0045] As the heavy-haul railway system operates and develops, update the digital twin model in real time, incorporating new equipment parameters, operation data, and environmental change information to ensure that the model can always accurately reflect the state of the actual system.

[0046] This disclosure uses digital twin technology to carefully construct a digital twin model that precisely corresponds to the indoor and outdoor environment of the heavy-haul railway at a 1:1 ratio. The construction of this model is like building a virtual "clone" of the real heavy-haul railway, ensuring a high degree of consistency in spatial structure and physical characteristics between the virtual world and the real scenario. To endow the virtual model with real operating characteristics, simulate the operation data of the heavy-haul group trains and accurately assign them to the digital twin model. Through this operation, the virtual trains have the same key characteristics as the real trains, such as driving speed, operating status, etc., including the start, acceleration, deceleration, stop, and faults of the trains. Specifically, it includes accurately simulating various dynamic behaviors and interaction relationships of the heavy-haul group trains during actual operation; comprehensively and real-time reflecting the coupling effects between infrastructure such as tracks, bridges, and signals and train operation in the heavy-haul railway system; special working conditions (such as large load, long formation, complex terrain, etc.). On this basis, various train receiving and dispatching operation plans, weather, and fault information can be flexibly set by humans. In the digital twin simulation environment, these virtual trains operate in an orderly manner according to the preset plans. With the help of this highly realistic simulation, systematic and comprehensive training work can be carried out. Trainees can experience various complex train operation scenarios immersive in the virtual environment, improving their ability to handle actual situations. At the same time, through a large number of simulation tests, rich test data can be accumulated. These data will provide a solid basis for subsequent technical improvement, operation optimization, and safety assessment, promoting the continuous development and progress of the heavy-haul railway field.

[0047] A method for simulating a heavy-haul railway group, comprising: Deploy a variety of sensors on-site to collect train, track, or signal data in real time, and clean and fuse the collected data to obtain preprocessed data; Based on the preprocessed data, use three-dimensional modeling technology to construct a digital twin model of the heavy-haul railway system, and establish the association relationship and interaction mechanism between each digital twin model; Based on the digital twin model, through numerical calculation and dynamic simulation, perform real-time calculation and simulation to obtain simulation results; Based on the digital twin model, it is presented through an interactive interface using virtual reality (VR), augmented reality (AR) technology or naked-eye 3D technology, and interactive operations are performed with the digital twin model through the interactive interface. Based on the digital twin model, the simulation results are compared and analyzed with the actual operation data, and the accuracy and reliability of the digital twin model are verified by calculating the error. According to the verification results, the model is adjusted and optimized.

[0048] The present disclosure introduces digital twin technology to achieve an accurate mapping of the heavy-haul railway system from physical entities to virtual models, comprehensively and real-time simulating the operating states of heavy-haul railway groups and the interaction relationships among various elements.

[0049] The present disclosure comprehensively applies various technical means such as multi-source data acquisition, three-dimensional modeling, numerical calculation, VR / AR, etc., to construct a complete heavy-haul railway group simulation system integrating data acquisition, model construction, simulation calculation, real-time interaction, and model verification and update.

[0050] The present disclosure emphasizes real-time and interactivity. By real-time collecting data and dynamically updating the model, and providing an intuitive VR / AR interactive interface, users can timely understand and intervene in the operating states of heavy-haul railway groups, providing strong support for operation management.

[0051] The heavy-haul railway group simulation method based on digital twin technology in the present disclosure includes a complete process of data acquisition and processing, digital twin model construction, simulation calculation, real-time interaction and display, and model verification and update.

[0052] The heavy-haul railway group simulation system based on digital twin in the present disclosure includes a data acquisition and processing module, a digital twin model construction module, a simulation calculation module, a real-time interaction and display module, and a model verification and update module.

[0053] The specific technologies and methods for digital twin modeling of various elements (trains, tracks, bridges, signal systems, etc.) of the heavy-haul railway system in the method and system of the present disclosure, as well as the correlation relationships and interaction mechanisms among various models.

[0054] The methods and systems for realizing real-time interaction and intuitive display using VR / AR technology in the present disclosure, as well as the mechanism for verifying and updating the digital twin model based on actual operation data.

[0055] As Figure 3 shown, the present disclosure provides an electronic device, including a processor 301, a communication interface 302, a memory 303, and a communication bus 304. Among them, the processor 301, the communication interface 302, and the memory 303 complete mutual communication through the communication bus 304; The memory 303 stores a computer program. The processor 301 is configured to implement the above-mentioned method when executing the computer program stored in the memory 303.

[0056] The present disclosure provides a computer-readable storage medium storing a computer program, and the computer program, when executed by a processor, implements the above-mentioned method.

[0057] The computer-readable storage medium may be included in the device / apparatus described in the above embodiments; or may exist alone without being assembled into the device / apparatus. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to the embodiments of the present disclosure is implemented.

[0058] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, for example, including but not limited to: portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, device, or apparatus.

[0059] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A heavy-haul railway group simulation system, characterized in that: include: Data acquisition and processing module, digital twin model construction module, simulation calculation module, real-time interaction and display module, and model verification and update module; The data acquisition and processing module is used to collect train, track or signal data in real time by deploying multiple sensors on site, and clean and fuse the collected data to obtain pre-processed data; The digital twin model construction module uses 3D modeling technology to build a digital twin model of the heavy-haul railway system based on preprocessed data, and establishes the association relationship and interaction mechanism between digital twin models; The simulation calculation module is used to perform real-time calculation and simulation of the digital twin model through numerical calculation and dynamic simulation to obtain simulation results; Real-time interaction and display module, which is used to display the digital twin model through the interactive interface of virtual reality VR, augmented reality AR technology or naked-eye 3D technology, and to interact with the digital twin model through the interactive interface; The model verification and update module is used to compare and analyze the simulation results with the actual operation data, verify the accuracy and reliability of the digital twin model by calculating the error, and adjust and optimize the model based on the verification results.

2. A heavy-haul railway group simulation system according to claim 1, characterized in that: Sensors include: speed sensors, acceleration sensors, load sensors, track stress sensors, bridge vibration sensors and signal status sensors.

3. A heavy-haul railway group simulation system according to claim 1, characterized in that: The data acquisition and processing module collects train, track or signal data, including: collecting train operation parameters, track status information, bridge structure health data and signal system status data; Among them, train operation parameters include: train position, speed, acceleration and load parameters; track status information includes: track gauge change and track deformation information; bridge structure health data includes: vibration frequency and stress strain data; signal system status data includes: signal light color and signal instruction data.

4. A heavy-haul railway group simulation system according to claim 1, characterized in that: The data collection and processing module cleans and integrates the collected data, including: Perform data cleaning, data fusion and data normalization on the collected data; Among them, data cleaning includes: removing noise, outliers and duplicate data; data fusion includes: aligning data from different sensors according to timestamps or other related fields to form a unified data set; data normalization processing includes: converting data from different sensors to the same dimension and using a unified numerical range.

5. A heavy-haul railway group simulation system according to claim 1, characterized in that: Digital twin models, including train models, track models, bridge models, and signal system models; Among them, the train model is used to simulate the appearance, structure, load distribution and dynamic characteristics of the train; the track model is used to describe the geometry, material properties and laying parameters of the track; the bridge model is used to simulate the structural form, mechanical properties and vibration response of the bridge; the signal system model is used to simulate the working principle of the signal equipment, the signal transmission mechanism and the signal control logic.

6. A heavy-haul railway group simulation system according to claim 5, characterized in that: The relationship and interaction mechanism between digital twin models include: The mechanical interaction between the train operation in the train model and the track in the track model; the mutual interaction between the train operation in the train model and the signal in the signal system model; the mechanical interaction between the train in the train model and the bridge in the bridge model when the train passes through the bridge; the interaction between the track in the track model and the bridge in the bridge model in vibration response.

7. A heavy-haul railway group simulation system according to claim 1, characterized in that: The simulation calculation module is specifically used for: According to the input initial conditions and preprocessed data, the operation process of heavy-load railway group trains is simulated through numerical simulation calculation of train operation position, speed and acceleration, and the interaction between trains, the coupling effect between trains and infrastructure, and the control effect of signal system on train operation are calculated; among which, the initial conditions include: the initial position, speed and load of the train; The simulation results are dynamically adjusted and optimized taking into account a variety of operating conditions, including different weather conditions, line slopes, curve radii, and train formations.

8. A heavy-haul railway group simulation method, characterized in that: include: By deploying a variety of sensors on site to collect train, track or signal data in real time, and cleaning and fusing the collected data, pre-processed data is obtained; Based on the preprocessed data, a digital twin model of the heavy-haul railway system is constructed using 3D modeling technology, and the association relationship and interaction mechanism between the digital twin models are established; Based on the digital twin model, real-time calculation and simulation are performed through numerical calculation and dynamic simulation to obtain simulation results; Based on the digital twin model, the interactive interface of virtual reality VR, augmented reality AR technology or naked-eye 3D technology is used for display, and interactive operations are performed with the digital twin model through the interactive interface; Based on the digital twin model, the simulation results are compared and analyzed with the actual operation data. The accuracy and reliability of the digital twin model are verified by calculating the error. According to the verification results, the model is adjusted and optimized.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; a memory storing a computer program; The processor is used to implement the heavy-load railway group simulation method described in claim 8 when executing the computer program stored in the memory.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the heavy-load railway group simulation method described in claim 8 is implemented.

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