Railway locomotive operation dynamic simulation method and system

Abstract

The invention discloses a railway locomotive operation dynamic simulation method and system. The railway locomotive operation dynamic simulation method comprises the following steps: acquiring locomotive model data, track data and railway environment data; simulating the running state of the locomotive based on the locomotive model data, the track data and the railway environment data; wherein the operation state simulation of the locomotive is carried out according to the simulation step length within the set total simulation time; the operation states of the locomotive are different in the time corresponding to the at least two simulation step lengths; and acquiring operation data of the locomotive in the simulation process, and determining a simulation result according to the operation data. According to the railway locomotive operation dynamic simulation method, locomotive model data, track data and railway environment data are obtained, the operation states of different simulation step lengths are dynamically simulated based on multiple simulation step lengths, and the simulation result is determined. According to the dynamic simulation method for the operation of the railway locomotive, the simulation authenticity is improved through step-by-step simulation and dynamic parameter adjustment.

Description

Technical Field

[0001] The present invention relates to the field of railway locomotive testing, and in particular to a railway locomotive running dynamic simulation method and system. Background Art

[0002] Railways are a vital component of modern transportation, boasting advantages such as large carrying capacity, low energy consumption, and minimal capital expenditure. They are widely used in both passenger and freight transport. During locomotive operation, the operating status of a locomotive is affected by numerous factors, including the powertrain, braking system, trackbed condition, and environmental conditions. Existing locomotive testing relies on on-site inspections, which are costly and time-consuming, making comprehensive evaluations in diverse environments difficult. Summary of the Invention

[0003] The present invention provides a method and system for dynamic simulation of railway locomotive operation, which can comprehensively evaluate the locomotive under different climatic conditions and track conditions through simulation, reduce costs and improve efficiency.

[0004] According to one aspect of the present invention, a method for dynamic simulation of railway locomotive operation is provided, the method comprising:

[0005] Obtain locomotive model data, track data and railway environment data;

[0006] simulating the running state of the locomotive based on the locomotive model data, the track data, and the railway environment data; wherein the running state of the locomotive is simulated within a set total simulation time and according to a simulation step size; and the running state of the locomotive is different within at least two times corresponding to the simulation step sizes;

[0007] The operation data of the locomotive during the simulation process is collected, and the simulation result is determined based on the operation data.

[0008] Optionally, the performing the operation state simulation of the locomotive based on the locomotive model data, the track data, and the railway environment data includes:

[0009] Different operating conditions of the locomotive are simulated based on the track data, the locomotive model data and the railway environment data to obtain the operating status of the locomotive under different operating conditions.

[0010] Optionally, the simulating different operating conditions of the locomotive based on the track data, the locomotive model data and the railway environment data includes:

[0011] Based on the simulation step length and the set total simulation time, stepwise calculating the operating condition of the locomotive at each simulation step length according to the track data, the locomotive model data and the railway environment data;

[0012] The operating conditions include: at least one of a normal operating condition, an acceleration or deceleration operating condition, an emergency braking operating condition, and a fault operating condition;

[0013] The normal operating condition includes at least one of: the locomotive is running on a smooth track, the locomotive is running on an uneven track, and the locomotive is running at a constant speed on a curved track;

[0014] The acceleration or deceleration operating condition includes at least one of a locomotive startup process operating state, a locomotive acceleration process operating state, and a locomotive braking process operating state;

[0015] The emergency braking operating conditions include: the operating state of the locomotive when emergency braking is performed in an emergency situation;

[0016] The fault operating conditions include: the operating status of the locomotive under different fault conditions.

[0017] Optionally, the railway environment data is different in at least two different simulation steps;

[0018] The simulating different operating conditions of the locomotive based on the track data, the locomotive model data and the railway environment data includes:

[0019] Based on the track data and the locomotive model data, the running state of the locomotive is adjusted according to different railway environment data in different simulation steps.

[0020] Optionally, before performing the locomotive running state simulation based on the locomotive model data, the track data, and the railway environment data and / or during performing the locomotive running state simulation based on the locomotive model data, the track data, and the railway environment data, the process further includes:

[0021] Acquiring line state data; wherein, in at least two different simulation steps, the line state data is different;

[0022] The process of simulating the running state of the locomotive based on the locomotive model data, the track data and the railway environment data includes:

[0023] In different simulation steps, the locomotive's operating status is adjusted according to different line status data based on locomotive model data, track data, and railway environment data.

[0024] Optionally, before performing the simulation of the locomotive's operating status based on the locomotive model data, the track data, and the railway environment data, the method further includes: selecting the simulation step size according to simulation accuracy.

[0025] Optionally, the locomotive model data includes at least one of: geometric dimensions, mass, power system parameters, historical operating data, and brake system parameters of the locomotive;

[0026] The track data includes at least one of: track geometry, track type, and switch location;

[0027] The geometric shape includes at least one of a curve radius and a slope; the track type includes at least one of a normal track and a high-speed track;

[0028] The railway environment data includes at least one of topographic data, weather data and environmental data;

[0029] The landform data includes at least one of mountain landform data, river landform data, city landform data, station landform data, and bridge and tunnel landform data;

[0030] The weather data includes at least one of rainy day data, foggy day data and snowy day data;

[0031] The environmental data includes at least one of wind speed data, air temperature data and humidity data.

[0032] Optionally, the operating data includes locomotive operating performance parameters; and collecting the locomotive operating data during the simulation process and determining the simulation results based on the operating data includes:

[0033] Acquiring the locomotive operating performance parameters according to the locomotive operating data during the simulation process;

[0034] Evaluating safety indicators, comfort indicators, and energy consumption indicators based on the locomotive operating performance parameters; and / or predicting locomotive performance and failures based on the locomotive operating performance parameters;

[0035] The locomotive operating performance parameters include:

[0036] At least one of the position of the locomotive relative to the track, the speed, the acceleration, the locomotive tractive force, the locomotive braking force, the resistance between the locomotive and the track, and the air resistance of the locomotive.

[0037] Optionally, the locomotive model data includes: historical operation data;

[0038] The predicting of locomotive performance and faults based on the locomotive operating performance parameters includes:

[0039] Use historical operating data to establish training sets and validation sets to train machine learning models;

[0040] Inputting the locomotive operating performance parameters into a preset machine learning model, the output of the preset machine learning model being locomotive performance and fault prediction data;

[0041] The locomotive performance and fault prediction data includes: at least one of performance prediction data and fault prediction data;

[0042] The performance prediction data includes at least one of acceleration prediction data, friction prediction data, track adhesion prediction data, future braking distance change prediction data, and passenger comfort change prediction data.

[0043] The fault prediction data includes at least one of signal failure rate prediction data and brake failure rate prediction data.

[0044] Optionally, a data acquisition module is used to acquire locomotive model data, track data and railway environment data;

[0045] a simulation module configured to simulate the locomotive's operating state based on the locomotive model data, the track data, and the railway environment data; wherein the locomotive's operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive's operating state is different within at least two simulation step sizes;

[0046] The data analysis module is used to collect the operation data of the locomotive during the simulation process and determine the simulation results based on the operation data.

[0047] An embodiment of the present invention provides a method and system for dynamic simulation of railway locomotive operation, wherein the method includes: obtaining track data and railway environment data, dynamically simulating the operating status of different simulation steps in multiple simulation steps, collecting the operating data of the locomotive during the simulation process, and determining the simulation results. Dynamic simulation is performed by multi-dimensional data to achieve a comprehensive performance evaluation of the train under different environments and working conditions, making the simulation process closer to reality and improving the accuracy of the simulation. The dynamic simulation method for railway locomotive operation is simulated by dividing the steps, so that dynamic parameter adjustment can be performed within different simulation steps to improve the simulation authenticity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0049] Figure 1 A flow chart of a method for dynamic simulation of railway locomotive operation is provided for an embodiment of the present invention;

[0050] Figure 2 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0051] Figure 3 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0052] Figure 4 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0053] Figure 5 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0054] Figure 6 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0055] Figure 7 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0056] Figure 8 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0057] Figure 9 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention;

[0058] Figure 10 A schematic structural diagram of a railway locomotive operation dynamic simulation system provided by an embodiment of the present invention;

[0059] Figure 11 A schematic structural diagram of a simulation module provided by an embodiment of the present invention;

[0060] Figure 12 A structural diagram of a data analysis module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0062] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0063] Figure 1 The present invention provides a flowchart of a method for dynamic simulation of railway locomotive operation. This embodiment is applicable to railway locomotive testing. The method can be executed by a dynamic simulation device for railway locomotive operation. The dynamic simulation device for railway locomotive operation can be implemented in the form of hardware and / or software. The dynamic simulation device for railway locomotive operation can be configured in a storage medium. Figure 1 As shown, the method includes:

[0064] S101. Obtain locomotive model data, track data, and railway environment data.

[0065] Specifically, the locomotive model data, track data and railway environmental data are obtained, where the locomotive model data includes: at least one of the locomotive's geometric dimensions, mass, power system parameters, historical operation data and braking system parameters; the track data includes: at least one of the track's geometric shape, track type and switch position; the geometric shape includes at least one of the curve radius and the slope; the track type includes at least one of an ordinary track and a high-speed track; the railway environmental data includes: at least one of landform data, weather data and environmental data; the landform data includes at least one of mountain landform data, river landform data, urban landform data, station landform data, bridge and tunnel landform data; the weather data includes at least one of rainy day data, foggy day data and snowy day data; the environmental data includes at least one of wind speed data, temperature data and humidity data.

[0066] Locomotive model data includes the train's geometry, mass, powertrain parameters, brake system parameters, and historical operating data. Powertrain parameters refer to the locomotive's dynamic parameters, including traction or power. Braking system parameters include the locomotive's braking force and response time. Historical operating data refers to the locomotive's historical operating parameters and can also include speed curves or energy consumption records, though this is not a specific requirement. Locomotive model data is specific to the locomotive being simulated to ensure that the simulation results match the dynamic characteristics of the actual locomotive.

[0067] Track data includes track geometry, track type, and turnout locations. The geometry includes curve radius and slope. The track type specifies whether the track being simulated is conventional or high-speed. Track data defines the line conditions under which trains operate, affecting dynamic behaviors such as locomotive acceleration and braking distance.

[0068] Railway environmental data includes topography, weather, and environmental parameters. Topography types include mountains, bridges, or tunnels. Weather types include rain, snow, or fog. Environmental parameters include wind speed, temperature, and humidity. Railway environmental data simulates the impact of the external environment on train operation. For example, rainy days can increase locomotive resistance, while topography can affect locomotive stability.

[0069] Furthermore, locomotive model data, track data and railway environment data are defined according to typical simulation scenarios.

[0070] Typical simulation scenarios are pre-designed combinations of locomotive model data, track data, and railway environmental data that represent typical conditions encountered in actual locomotive operation. These are used to systematically test locomotive performance and reliability. For example, typical simulation scenarios might include a "rainy day scenario" to simulate the effects of rainfall on track friction and air resistance, or a "snowy day scenario" to test braking system response and track adhesion changes at low temperatures. Track data can also include curved track scenarios to test cornering capability and wheel-rail contact force.

[0071] A typical simulation scenario can be a simulation scenario composed of locomotive model data, track data, and railway environment data. For example, a typical simulation scenario may include "rapid locomotive emergency braking + curved track + snowy weather" to verify comprehensive performance under complex conditions. "Rapid locomotive emergency braking" is the definition of locomotive model data in a typical simulation scenario, "curved track" is the definition of track data in a typical simulation scenario, and "snowy weather" is the definition of railway environment data in a typical simulation scenario.

[0072] Typical simulation scenarios combine different locomotive model data, track data, and railway environment data to ensure that the simulation covers all types of situations in actual operation and avoid test blind spots.

[0073] S102: Execute a locomotive operating state simulation based on the locomotive model data, track data, and railway environment data. The locomotive operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive operating state is different within at least two simulation step sizes.

[0074] Specifically, the simulation step size is set to gradually calculate the train's operating status. The locomotive's operating status includes operating conditions under different operating conditions. For example, under normal operating conditions, the train's operating conditions include smooth track operation, uneven track operation, and curved track operation. Uneven track operation includes vertical unevenness, lateral unevenness, and lateral horizontal curvature, while curved track operation includes transition curves and circular curves.

[0075] By dynamically adjusting the operating status of different simulation steps, the real-time changes of the train under actual complex conditions are simulated.

[0076] For example, the simulation engine is driven by input data, the Runge-Kutta method is used for dynamic calculations, and the PID algorithm is used to control the state, gradually simulating the dynamic behavior of the train at different time points to ensure that the simulation process is close to reality.

[0077] S103: Collecting the operation data of the locomotive during the simulation process, and determining the simulation results based on the operation data.

[0078] Specifically, operational data includes real-time parameters such as position, speed, acceleration, traction, braking force, and resistance. This operational data is collected to determine simulation results, including safety, comfort, energy consumption, and fault prediction. Simulation data is collected and analyzed in real time to predict performance trends and fault risks.

[0079] An embodiment of the present invention provides a method for dynamic simulation of railway locomotive operation. The method obtains track data and railway environment data, dynamically simulates the operating status of different simulation steps in multiple simulation steps, collects the operating data of the locomotive during the simulation process, and determines the simulation results. Dynamic simulation is performed by multi-dimensional data to achieve comprehensive performance evaluation of the train under different environments and working conditions, making the simulation process closer to reality and improving the accuracy of the simulation. The method for dynamic simulation of railway locomotive operation is simulated by dividing the steps, so that dynamic parameter adjustment can be performed within different simulation steps to improve the simulation authenticity.

[0080] Based on the above embodiments, Figure 2 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 2 As shown, the method includes:

[0081] S201. Obtain locomotive model data, track data, and railway environment data.

[0082] S202. Simulate different operating conditions of the locomotive based on the track data, the locomotive model data, and the railway environment data to obtain the operating status of the locomotive under different operating conditions; wherein the simulation of the locomotive operating status is performed within a set total simulation time and according to a simulation step size; and the locomotive operating status is different within a time corresponding to at least two simulation steps.

[0083] The operating conditions include normal operating conditions, acceleration / deceleration, emergency braking or fault conditions.

[0084] The operating condition is an operating scenario obtained by simulation based on track data, locomotive model data and railway environment data, and the operating state is the specific dynamic performance of the locomotive in this scenario. Exemplarily, the operating condition includes: at least one of normal operating condition, acceleration or deceleration condition, emergency braking condition and fault condition. Then the motion state includes: at least one of the locomotive running on a smooth track under normal operating conditions, the locomotive running on an uneven track and the locomotive running at a constant speed on a curved track; at least one of the locomotive's starting process operating state, the locomotive's acceleration process operating state and the locomotive's braking process operating state under acceleration or deceleration conditions; the locomotive's operating state when performing emergency braking in an emergency under emergency braking conditions; and the locomotive's operating state under different fault conditions under fault conditions.

[0085] S203: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0086] Based on the above embodiments, Figure 3 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 3 As shown, the method includes:

[0087] S301. Obtain locomotive model data, track data, and railway environment data.

[0088] S302: Based on the simulation step size and the set total simulation time, the locomotive's operating condition at each simulation step is calculated stepwise according to the track data, locomotive model data, and railway environment data. The locomotive's operating state is simulated within the set total simulation time and according to the simulation step size; the locomotive's operating state differs within at least two simulation steps.

[0089] The operating conditions include: at least one of normal operating conditions, acceleration or deceleration operating conditions, emergency braking conditions and fault conditions;

[0090] Normal operating conditions include: at least one of the locomotive running on a smooth track, the locomotive running on an uneven track, and the locomotive running at a constant speed on a curved track; acceleration or deceleration operating conditions include: at least one of the locomotive's starting process operating state, the locomotive's acceleration process operating state, and the locomotive's braking process operating state; emergency braking operating conditions include: the operating state of the locomotive when emergency braking is performed in an emergency situation; fault operating conditions include: the operating state of the locomotive under different fault conditions.

[0091] Specifically, the simulation step size determines the temporal resolution of the simulation, while the total simulation time defines the total duration of the simulation. Through step-by-step dynamic calculations, combined with real-time updated track data, locomotive models, and environmental data, the real-time state of the locomotive under different operating conditions is simulated.

[0092] For example, the simulation process is divided into multiple time segments based on the total simulation time and step size. At the beginning of each step, updated railway environment data is loaded. Based on the track data, locomotive model data, and environmental data, the locomotive state at that step is calculated using methods such as the Runge-Kutta method. For example, during rainy days, the friction coefficient decreases, and the operating mode is dynamically switched based on environmental changes, such as switching from normal operation to acceleration or deceleration, to ensure smooth state transitions.

[0093] In the railway locomotive operation dynamic simulation method of the embodiment of the present invention, the simulation accuracy and authenticity are improved and the field test cost is reduced by dynamically adjusting the operating conditions in steps.

[0094] S303: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0095] Based on the above embodiments, Figure 4 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 4 As shown, the method includes:

[0096] S401. Obtain locomotive model data, track data, and railway environment data.

[0097] S402: Based on the track data and locomotive model data, adjust the locomotive operating state according to different railway environment data at different simulation steps. The locomotive operating state simulation is performed within a set total simulation time and according to the simulation step size; the locomotive operating state is different within at least two simulation steps.

[0098] The railway environment data are different in at least two different simulation steps;

[0099] Specifically, by dynamically adjusting railway environmental data and combining it with track and locomotive model data, the locomotive's operating status is updated in real time, ensuring that the simulation process can reflect the impact of dynamic environmental changes on locomotive behavior.

[0100] For example, during the simulation, the weather changes from sunny to rainy. The track and locomotive model data are combined to update the locomotive's operating status in real time, such as slowing down to cope with slippery tracks.

[0101] According to the set simulation time and simulation accuracy, the simulation process is divided into multiple time segments to determine the simulation step length. At the beginning of each simulation step length, new railway environment data is loaded.

[0102] Based on the updated environmental data, combined with the locomotive model data and track data, the running status of the locomotive is recalculated by using the Runge-Kutta method to calculate the dynamic equations through the simulation engine.

[0103] PID control algorithm can also be used to dynamically adjust locomotive control instructions according to environmental changes to perform control operations such as traction output or braking response, thereby achieving control of the train's operating status.

[0104] For example, a sunny day is simulated in step t1, while a rainy day is simulated in step t2. When the rainy day begins at step t2, the simulation engine triggers the locomotive's braking system to increase braking force and adjust the operating speed based on track data showing a reduced friction coefficient. In the event of a sudden strong wind, the PID algorithm adjusts traction in real time to offset wind resistance and maintain the set speed.

[0105] S403: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0106] In the railway locomotive operation dynamic simulation method of the embodiment of the present invention, by dynamically adjusting the railway environmental data, the simulation can adapt to complex and changeable real-world scenarios, improve the test coverage and result credibility, and provide more accurate data support for locomotive performance optimization and fault prediction.

[0107] Based on the above embodiments, Figure 5 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the method includes:

[0108] S501. Obtain locomotive model data, track data, and railway environment data.

[0109] S502: Acquire line state data, wherein the line state data is different in at least two different simulation steps.

[0110] Specifically, the line status data includes: track smooth state, track uneven state and curved track state; wherein, the track uneven state includes: at least one of vertical uneven state, lateral uneven state and lateral horizontal bending state; the curved track state includes: at least one of gentle curve state and circular arc curve state.

[0111] In line status data, track smoothness refers to the absence of abnormal undulations or deformations, such as in straight track segments. Track irregularity refers to vertical irregularities, such as unevenness, lateral irregularities, such as left-right deviation, and lateral curvature, such as lateral curvature. Curved track status refers to gentle curves, such as transition curves, and circular curves, such as fixed-radius curves.

[0112] S503. Adjust the locomotive's operating state based on the locomotive model data, track data, and railway environment data, and in accordance with different line status data, at different simulation steps. The locomotive's operating state simulation is performed within a set total simulation time and according to the simulation step size; and the locomotive's operating state differs within at least two simulation steps.

[0113] Specifically, the system dynamically adjusts locomotive operating data based on track status data to respond to track changes in real time. For example, if the track status data at step length t1 indicates vertical irregularity, the braking system is triggered to increase braking force and reduce speed to minimize track impact. If the track status data at step length t2 indicates a circular curve, steering control parameters are adjusted to prevent derailment risk.

[0114] S504: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0115] The dynamic simulation method for railway locomotive operation, presented in this embodiment of the present invention, dynamically acquires and responds to track status data, enabling refined simulation of complex track conditions and accurate evaluation of locomotive performance under various track scenarios. This reduces the high costs and risks of relying on field testing and provides data support for locomotive design optimization and operation and maintenance strategies.

[0116] Based on the above embodiments, Figure 6 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 6 As shown, the method includes:

[0117] S601. Obtain locomotive model data, track data, and railway environment data.

[0118] S602: Acquire line status data. At different simulation steps, the locomotive's operating state is adjusted based on the locomotive model data, track data, and railway environment data according to the different line status data. The locomotive's operating state simulation is performed within a set total simulation time and at each simulation step; the locomotive's operating state differs within at least two simulation steps.

[0119] Acquiring the line state data may be performed in the step of performing a running state simulation of the locomotive based on the locomotive model data, the track data, and the railway environment data.

[0120] S603: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0121] Based on the above embodiments, Figure 7 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 7 As shown, the method includes:

[0122] S701. Obtain locomotive model data, track data, and railway environment data.

[0123] S702: Select a simulation step size according to simulation accuracy.

[0124] Simulation accuracy can include multiple precision options, each corresponding to a preset simulation step range. For example, high precision has a step size of Δt = t1 seconds; medium precision has a step size of Δt = t2 seconds; and low precision has a step size of Δt = t3 seconds. The relationship between t1, t2, and t3 is: t1 < t2 < t3.

[0125] Step sizes of different precisions correspond to different simulation requirements. When emergency braking simulation of a locomotive is required, a high-precision step size is selected to accurately capture transient behavior. When long-term energy consumption simulation of the locomotive is required, a low-precision step size is selected to significantly reduce computing resource consumption.

[0126] S703: Execute a locomotive operating state simulation based on the locomotive model data, track data, and railway environment data. The locomotive operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive operating state is different within at least two simulation step sizes.

[0127] S704: Collect the operation data of the locomotive during the simulation process, and determine the simulation results based on the operation data.

[0128] An embodiment of the present invention provides a method for dynamic simulation of railway locomotive operation. By dynamically selecting the simulation step size based on the required simulation accuracy, this method achieves an optimal balance between computational efficiency and result accuracy. This design not only enhances simulation test flexibility but also reduces hardware costs, providing efficient and reliable technical support for multi-scenario, long-term locomotive performance evaluation.

[0129] Based on the above embodiments, Figure 8 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 8 As shown, the method includes:

[0130] S801. Obtain locomotive model data, track data, and railway environment data.

[0131] S802: Execute a locomotive operating state simulation based on the locomotive model data, track data, and railway environment data. The locomotive operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive operating state is different within at least two simulation step sizes.

[0132] S803. Obtain locomotive operating performance parameters according to the locomotive operating data during the simulation process.

[0133] The operating data includes locomotive operating performance parameters, which include at least one of the locomotive's position relative to the track, speed, acceleration, locomotive traction, locomotive braking force, resistance between the locomotive and the track, and air resistance during locomotive travel.

[0134] Specifically, among the operational performance parameters, position, speed, and acceleration reflect the locomotive's fundamental motion state. Traction and braking force parameters reflect the real-time output capability of the locomotive's power system. The locomotive-track resistance and air resistance parameters reflect the locomotive's energy consumption and environmental impact during operation.

[0135] S804. Evaluate safety indicators, comfort indicators, and energy consumption indicators based on locomotive operating performance parameters; and / or predict locomotive performance and faults based on locomotive operating performance parameters.

[0136] Specifically, safety indicators may include acceleration extremes and braking distances. Comfort indicators may include acceleration rate of change and vibration frequency, reflecting the passenger experience. Energy consumption indicators may include energy consumption per unit distance and total energy consumption, used to optimize energy efficiency. Fault prediction predicts potential faults based on historical data and real-time parameters.

[0137] Based on the above embodiments, Figure 9 A flowchart of another method for dynamic simulation of railway locomotive operation provided by an embodiment of the present invention is shown in FIG. Figure 9As shown, the method includes:

[0138] S901. Obtain locomotive model data, track data, and railway environment data.

[0139] S902: Execute a locomotive operating state simulation based on the locomotive model data, track data, and railway environment data. The locomotive operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive operating state is different within at least two simulation step sizes.

[0140] S903. Obtain locomotive operating performance parameters according to the locomotive operating data during the simulation process.

[0141] S904. Evaluate safety indicators, comfort indicators, and energy consumption indicators based on locomotive operating performance parameters; and / or, use historical operating data to establish a training set and a validation set to train a machine learning model;

[0142] The locomotive model data includes: historical operation data; the locomotive performance and fault prediction data includes: at least one of performance prediction data and fault prediction data; wherein the performance prediction data includes: at least one of acceleration prediction data, friction prediction data, track adhesion prediction data, future braking distance change prediction data, and passenger comfort change prediction data.

[0143] Specifically, the historical operating data of the locomotive model includes fault records and performance parameters accumulated from past simulations or actual operations. This historical operating data provides training samples for the machine learning model, enabling the model to achieve predictive capabilities.

[0144] When training a machine learning model, historical data is divided into a training set and a validation set to optimize model parameters. For example, the model is trained using historical brake failure data, with inputs such as traction fluctuations and brake force attenuation, and outputs the probability of failure.

[0145] S905. Input the locomotive operating performance parameters into a preset machine learning model. The output of the preset machine learning model is locomotive performance and fault prediction data.

[0146] Fault prediction includes: at least one of signal failure rate prediction data and brake failure rate prediction data.

[0147] The performance parameters acquired in real time during the simulation are fed into the trained model. Performance and fault prediction are performed. For example, in performance prediction, the braking distance trend over the first time period is predicted based on the current acceleration and track adhesion. In fault prediction, the braking force parameters and historical failure patterns are combined to calculate the probability of brake system failure over the second time period.

[0148] Optionally, the prediction results are compared with thresholds to generate maintenance recommendations.

[0149] In the railway locomotive operation dynamic simulation method provided by the embodiment of the present invention, accurate evaluation and prediction of locomotive performance is achieved by combining real-time performance parameters with a machine learning model driven by historical data.

[0150] Based on the above embodiments, Figure 10 A schematic diagram of a railway locomotive operation dynamic simulation system provided by an embodiment of the present invention is shown in FIG. Figure 10 As shown, including:

[0151] Data acquisition module 1001, used to acquire locomotive model data, track data and railway environment data;

[0152] The simulation module 1002 is used to perform a simulation of the locomotive's operating status based on the locomotive model data, the track data, and the railway environment data. The simulation of the locomotive's operating status is performed within a set total simulation time and according to a simulation step size. The locomotive's operating status is different within at least two simulation step sizes.

[0153] The data analysis module 1003 is used to collect the operation data of the locomotive during the simulation process and determine the simulation results based on the operation data.

[0154] Specifically, the data acquisition module 1001 , the simulation module 1002 and the data analysis module 1003 are program modules, which are stored in a memory and can be executed by a processor.

[0155] A railway locomotive operation dynamic simulation system provided by an embodiment of the present invention is used to execute the railway locomotive operation dynamic simulation method of any of the above embodiments of the present invention, and has the beneficial effects of the railway locomotive operation dynamic simulation method of any of the above embodiments of the present invention.

[0156] Based on the above embodiments, data acquisition module 1001 includes a database unit that stores and provides locomotive model data, track data, and railway environment data. The database unit stores various necessary data, either pre-entered or updated in real time. When needed during the simulation, the database unit provides this data to other modules.

[0157] Based on the above embodiments, Figure 11 A schematic diagram of the structure of a simulation module provided by an embodiment of the present invention is shown in FIG. Figure 11 As shown, the simulation module 1002 includes: an environment simulation unit 10021, a working condition simulation unit 10022 and a simulation adjustment unit 10223

[0158] The environmental simulation unit 10021 simulates the actual railway operating environment based on railway environmental data. The railway environmental data includes at least one of topographic data, weather data, and environmental data. The topographic data includes at least one of mountain topographic data, river topographic data, urban topographic data, station topographic data, and bridge and tunnel topographic data. The weather data includes at least one of rainy day data, foggy day data, and snowy day data. The environmental data includes at least one of wind speed data, temperature data, and humidity data.

[0159] The operating condition simulation unit 10022 simulates different operating conditions of the locomotive based on track data, locomotive model data, and railway environment data to obtain the operating status of the locomotive under different operating conditions. Operating conditions include at least one of normal operating conditions, acceleration or deceleration conditions, emergency braking conditions, and fault conditions. Normal operating conditions include at least one of the following: the locomotive operating on smooth track, the locomotive operating on uneven track, and the locomotive operating on a curved track at a constant speed. Acceleration or deceleration conditions include at least one of the locomotive operating during the starting process, the locomotive operating during acceleration, and the locomotive operating during braking. Emergency braking conditions include the locomotive operating during emergency braking in an emergency situation. Fault conditions include the locomotive operating under different fault conditions.

[0160] Furthermore, the operating condition simulation unit 10022 gradually calculates the operating condition of the locomotive at each simulation step based on the simulation step and the set total simulation time according to the track data, locomotive model data and railway environment data.

[0161] The simulation adjustment unit 10223 is used to obtain line state data and, at different simulation steps, adjust the locomotive's operating state based on the locomotive model data, track data, and railway environment data. The locomotive's operating state is also adjusted based on the track data, locomotive model data, and railway environment data at different simulation steps.

[0162] Among them, the line status data includes: track smooth state, track uneven state and curved track state; among them, the track uneven state includes: at least one of vertical uneven state, lateral uneven state and lateral horizontal bending state; the curved track state includes: at least one of gentle curve state and circular arc curve state.

[0163] Based on the above embodiments, Figure 12 A structural diagram of a data analysis module provided by an embodiment of the present invention is shown in FIG. Figure 12 As shown,

[0164] Furthermore, the data analysis module 1003 includes a data analysis unit 10031 and a model acquisition unit 10032 .

[0165] The data analysis unit 10031 obtains locomotive operating performance parameters based on the locomotive operating data during the simulation process; evaluates safety indicators, comfort indicators and energy consumption indicators based on the locomotive operating performance parameters; and / or predicts locomotive performance and faults based on the locomotive operating performance parameters.

[0166] The model acquisition unit 10032 is used to use historical operating data to establish a training set and a validation set to train the machine learning model. The data analysis unit 10031 is also used to input locomotive operating performance parameters into the preset machine learning model, and the output of the preset machine learning model is the locomotive performance and fault prediction data.

[0167] Among them, the performance prediction data includes: acceleration prediction data, friction prediction data, track adhesion prediction data, future braking distance change prediction data, and passenger comfort change prediction data; the fault prediction includes: signal failure rate prediction data and braking failure rate prediction data.

[0168] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0169] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for dynamic simulation of railway locomotive operation, characterized in that: include: Obtain locomotive model data, track data and railway environment data; simulating the running state of the locomotive based on the locomotive model data, the track data, and the railway environment data; wherein the running state of the locomotive is simulated within a set total simulation time and according to a simulation step size; and the running state of the locomotive is different within at least two times corresponding to the simulation step sizes; The operation data of the locomotive during the simulation process is collected, and the simulation result is determined based on the operation data.

2. The method for dynamic simulation of railway locomotive operation according to claim 1, characterized in that: The performing of the locomotive operation state simulation based on the locomotive model data, the track data and the railway environment data comprises: Different operating conditions of the locomotive are simulated based on the track data, the locomotive model data and the railway environment data to obtain the operating status of the locomotive under different operating conditions.

3. The method for dynamic simulation of railway locomotive operation according to claim 2, characterized in that: The simulating different operating conditions of the locomotive based on the track data, the locomotive model data and the railway environment data includes: Based on the simulation step length and the set total simulation time, stepwise calculating the operating condition of the locomotive at each simulation step length according to the track data, the locomotive model data and the railway environment data; The operating conditions include: at least one of a normal operating condition, an acceleration or deceleration operating condition, an emergency braking operating condition, and a fault operating condition; The normal operating condition includes at least one of: the locomotive is running on a smooth track, the locomotive is running on an uneven track, and the locomotive is running at a constant speed on a curved track; The acceleration or deceleration operating condition includes at least one of a locomotive startup process operating state, a locomotive acceleration process operating state, and a locomotive braking process operating state; The emergency braking operating conditions include: the operating state of the locomotive when emergency braking is performed in an emergency situation; The fault operating conditions include: the operating status of the locomotive under different fault conditions.

4. The method for dynamic simulation of railway locomotive operation according to claim 2, characterized in that: In at least two different simulation steps, the railway environment data is different; The simulating different operating conditions of the locomotive based on the track data, the locomotive model data and the railway environment data includes: Based on the track data and the locomotive model data, the running state of the locomotive is adjusted according to different railway environment data in different simulation steps.

5. The method for dynamic simulation of railway locomotive operation according to claim 1, characterized in that: Before performing the simulation of the running state of the locomotive based on the locomotive model data, the track data and the railway environment data and / or during the simulation of the running state of the locomotive based on the locomotive model data, the track data and the railway environment data, the further step includes: Acquiring line state data; wherein, in at least two different simulation steps, the line state data is different; The process of simulating the running state of the locomotive based on the locomotive model data, the track data and the railway environment data includes: In different simulation steps, the locomotive's operating status is adjusted according to different line status data based on locomotive model data, track data, and railway environment data.

6. The method for dynamic simulation of railway locomotive operation according to claim 1, characterized in that: Before simulating the running state of the locomotive based on the locomotive model data, the track data and the railway environment data, the method further includes: selecting the simulation step size according to simulation accuracy.

7. The method for dynamic simulation of railway locomotive operation according to claim 1, characterized in that: in, The locomotive model data includes: at least one of the locomotive's geometric dimensions, mass, power system parameters, historical operating data, and brake system parameters; The track data includes at least one of: track geometry, track type, and switch location; The geometric shape includes at least one of a curve radius and a slope; the track type includes at least one of a normal track and a high-speed track; The railway environment data includes at least one of topographic data, weather data and environmental data; The landform data includes at least one of mountain landform data, river landform data, city landform data, station landform data, and bridge and tunnel landform data; The weather data includes at least one of rainy day data, foggy day data and snowy day data; The environmental data includes at least one of wind speed data, air temperature data and humidity data.

8. The method for dynamic simulation of railway locomotive operation according to claim 1, characterized in that: The operating data includes locomotive operating performance parameters; The collecting of the locomotive operation data during the simulation process and determining the simulation result according to the operation data includes: Acquiring the locomotive operating performance parameters according to the locomotive operating data during the simulation process; Evaluating safety indicators, comfort indicators, and energy consumption indicators based on the locomotive operating performance parameters; and / or predicting locomotive performance and failures based on the locomotive operating performance parameters; The locomotive operating performance parameters include: At least one of the position of the locomotive relative to the track, the speed, the acceleration, the locomotive tractive force, the locomotive braking force, the resistance between the locomotive and the track, and the air resistance of the locomotive.

9. The method for dynamic simulation of railway locomotive operation according to claim 8, characterized in that: The locomotive model data includes: historical operation data; The predicting of locomotive performance and faults based on the locomotive operating performance parameters includes: Use historical operating data to establish training sets and validation sets to train machine learning models; Inputting the locomotive operating performance parameters into a preset machine learning model, the output of the preset machine learning model being locomotive performance and fault prediction data; The locomotive performance and fault prediction data includes: at least one of performance prediction data and fault prediction data; The performance prediction data includes at least one of acceleration prediction data, friction prediction data, track adhesion prediction data, future braking distance change prediction data, and passenger comfort change prediction data. The fault prediction data includes at least one of signal failure rate prediction data and brake failure rate prediction data.

10. A railway locomotive operation dynamic simulation system, characterized in that: include: Data acquisition module, used to obtain locomotive model data, track data and railway environment data; a simulation module configured to simulate the locomotive's operating state based on the locomotive model data, the track data, and the railway environment data; wherein the locomotive's operating state simulation is performed within a set total simulation time and according to a simulation step size; and the locomotive's operating state is different within at least two simulation step sizes; The data analysis module is used to collect the operation data of the locomotive during the simulation process and determine the simulation results based on the operation data.

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