Rail vehicle simulation data processing method, device, equipment and medium

Abstract

The invention relates to the technical field of data processing, in particular to a rail vehicle simulation data processing method and device, equipment and a medium. The method comprises the following steps: acquiring a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scene; and importing the rail vehicle simulation model into a preset simulation environment for simulation operation to obtain simulation operation data. According to the invention, the operation process of the target vehicle in the preset rail operation scene can be simulated, the simulation operation data can be determined quickly, accurately, reliably and efficiently, the demand of field operation testing is reduced, and time and cost are saved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method, device, equipment and medium for processing rail vehicle simulation data. Background Art

[0002] Trams are gradually becoming an important means of transportation in urban rail transit. At present, multiple field operation tests are often required in the design, testing and operation of trams. For example, when one or more design or test parameters are changed in the same operation scenario, a field operation test may need to be conducted again to verify its operation safety and reduce operation risks. This is not only time-consuming, but also has high operating costs. Summary of the invention

[0003] The present invention aims at the technical problems in the prior art such as long time consumption and high operation cost of tram operation test, and provides a tram vehicle simulation data processing method, device, equipment and medium.

[0004] The present invention provides a rail vehicle simulation data processing method, comprising:

[0005] Acquire a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario;

[0006] The rail vehicle simulation model is imported into a preset simulation environment for simulation operation to obtain simulation operation data.

[0007] The present invention also provides a rail vehicle simulation data processing device, comprising:

[0008] An acquisition module is used to acquire a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario;

[0009] The simulation operation module is used to import the rail vehicle simulation model into a preset simulation environment for simulation operation to obtain simulation operation data.

[0010] The present invention also provides a computer device, comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, wherein the processor implements the above-mentioned rail vehicle simulation data processing method when executing the computer-readable instructions.

[0011] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the above-mentioned rail vehicle simulation data processing method is implemented.

[0012] In the rail vehicle simulation data processing method, device, equipment and medium provided by the present invention, a rail vehicle simulation model is first constructed based on the target vehicle and the preset rail operation scenario, and then the rail vehicle simulation model is imported into a preset simulation environment to simulate the target vehicle running in the preset rail operation scenario. In this way, the operation process of the target vehicle in the preset rail operation scenario can be simulated and displayed, and then the simulation operation data corresponding to the target vehicle when running in the preset rail operation scenario can be obtained.

[0013] The rail vehicle simulation data processing method of the present invention can display the operation process of the target vehicle in the preset rail operation scenario through precise simulation, and then quickly, accurately, reliably and efficiently determine the simulation operation data. In this way, the operation characteristics and performance optimization of the target vehicle in the preset rail operation scenario can be analyzed according to the above-mentioned simulation operation data, and then the reliability and design parameters of the target vehicle and the track in the preset rail operation scenario can be verified or optimized, thereby improving the safety, economy and environmental adaptability of the operation process. In this way, the present invention greatly reduces the demand for field operation testing through simulation, thereby saving time and cost, and also provides a reliable basis for the design, testing and optimization of the rail transit system, and ultimately promotes the safety, reliability and design efficiency of the rail transit system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 is a flow chart of a method for processing rail vehicle simulation data in one embodiment of the present invention;

[0016] Figure 2 is a flow chart of step S10 of a method for processing rail vehicle simulation data in one embodiment of the present invention;

[0017] Figure 3 It is a principle block diagram of a rail vehicle simulation data processing device in one embodiment of the present invention;

[0018] Figure 4 is a schematic diagram of a computer device in one embodiment of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0020] The present invention provides a method for processing rail vehicle simulation data. In one embodiment, Figure 1 As shown, the rail vehicle simulation data processing method includes the following steps S10-S20:

[0021] S10, obtaining a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario; wherein, the preset rail operation scenario must have a target track for the target vehicle to operate, and the preset rail operation scenario also includes the physical environment around the target track, etc. Understandably, the preset rail operation scenario may refer to a historical scenario in which the target vehicle has operated, or may refer to a test scenario in which the target vehicle has not operated, but the specific operation process of the preset rail operation scenario is to be tested using the rail vehicle simulation data processing method of the present invention. The target vehicle may include one or more rail vehicles, and the target vehicle may be a tram, a train, or other rail vehicle that must rely on the target track to operate.

[0022] In one embodiment, if Figure 2 As shown, the step S10, i.e., obtaining the rail vehicle simulation model, includes the following steps S101-S103:

[0023] S101, constructing a track scene model according to the preset track operation scene; understandably, since the preset track operation scene includes a target track for the target vehicle to run and a physical environment around the target track, etc., the track scene model constructed according to the preset track operation scene also includes a corresponding track model and environment model.

[0024] In one embodiment, the step S101, i.e., constructing a track scene model according to the preset track running scene, includes:

[0025] Obtain the environmental data and track line data of the preset rail operation scene; wherein the environmental data refers to the environmental data corresponding to the physical environment around the target track, such as trees, obstacles, etc. next to the target track. And the track line data refers to the line planning data corresponding to the target track in the above environmental data. In one aspect of this embodiment, if the preset rail operation scene refers to a historical scene in which the target vehicle has run before, at this time, the environmental data and track line data collected by the sensor module of the target vehicle during the operation of the preset rail operation scene of the target vehicle can be obtained. In another aspect of this embodiment, if the preset rail operation scene refers to a test scene in which the target vehicle has not run, the test scene will be pre-set and the corresponding environmental data and track line data will be stored in the storage area. Therefore, when you want to use the rail vehicle simulation data processing method of the present invention to test the specific operation process of the target vehicle in the preset rail operation scene, you only need to directly retrieve the environmental data and track line data of the preset rail operation scene from the storage area.

[0026] A track scene model corresponding to the preset track operation scene is constructed according to the environmental data and the track line data. That is, after determining the above environmental data and the track line data, an environmental model can be constructed according to the environmental data, and a track model can be constructed according to the track line data in the environmental model, thereby obtaining a track scene model after the environmental model and the track model are integrated.

[0027] Furthermore, the reverse construction of a track scene model corresponding to the preset track operation scene according to the environmental data and the track line data includes:

[0028] The environmental data and the track line data are formatted to obtain converted operation data, and a track scene model corresponding to the preset track operation scene is constructed according to a marching cube algorithm and the converted operation data.

[0029] In one embodiment, when the preset rail operation scene is a historical scene, the sensor module includes sensor devices such as a laser radar and a camera installed on the target vehicle, and the environmental data and the track line data are point cloud data collected by the sensor module (laser radar and camera, etc.). At this time, in order to facilitate the smooth progress of the subsequent moving cube algorithm, it is necessary to first convert the format of the above point cloud data to convert it into the converted operation data in the PLY format, and then build the track scene model according to the converted operation data in the PLY format. It is understandable that when the preset rail operation scene is a test scene, the environmental data and the track line data can also be directly set to a format suitable for the moving cube algorithm, such as directly setting the environmental data and the track line data in the PLY format. At this time, the track scene model corresponding to the preset rail operation scene can be directly constructed according to the moving cube algorithm and the environmental data and the track line data without format conversion.

[0030] It can be understood that the construction of the track scene model corresponding to the preset track operation scene according to the moving cube algorithm specifically includes: first, dividing the space into a large number of hexahedral grids, then preprocessing the environmental data in PLY format and the track line data and reading them into a specific array, and then finding the hexahedral grid through which the isosurface passes, and at the same time obtaining all the information of the hexahedral grid through which the isosurface passes, including the values ​​of the vertices of the hexahedral grid, coordinate positions, etc. Afterwards, the values ​​of the vertices of the current hexahedral mesh are compared with the preset isosurface values, and then a state table of the hexahedral mesh that passes through the isosurface is generated, and then the state table is used as an index to find the edge of the hexahedral mesh that intersects the isosurface, and the linear interpolation method is used to calculate the position coordinates of each intersection point between the isosurface and the hexahedral mesh; then, the central difference method is used to find the normal vector of the vertex of the hexahedral mesh, and then the linear interpolation method is used to obtain the normal vector of each vertex of the triangular face (the triangular face refers to the triangular face formed by the connection between the intersection points of the hexahedral mesh mentioned above), and then the direction of each triangular face is determined according to the coordinates of the vertices of each triangular face, so as to further connect each triangular face to obtain a track scene model corresponding to the preset track operation scene. That is, when the preset track operation scene is a historical scene, the physical scene reverse reconstruction of the historical scene can be achieved through the above-mentioned moving cube algorithm. It is understandable that after the above-mentioned preset rail operation scenario is constructed, the geometric shape, curvature, slope and other design parameters of the target track have been determined in the preset rail operation scenario.

[0031] S102, establish a vehicle model corresponding to the target vehicle, and configure the parameters of the vehicle model; that is, in this step, the target vehicle can be first modeled by a preset modeling software to obtain a vehicle model; then, the vehicle model is configured with parameters so that the vehicle model has motion characteristics that can run on the target track. It can be understood that the above-mentioned vehicle model includes a car body (when the target vehicle is a tram, the vehicle model can include a head car body, a tail car body, etc.), a bogie, a driving wheel, a guide wheel, in-car equipment, a vehicle multi-unit gangway connector, etc.

[0032] In one embodiment, the step S102, i.e., establishing a vehicle model corresponding to the target vehicle and configuring parameters of the vehicle model, includes: determining the three-dimensional model corresponding to the target vehicle constructed by the preset modeling software as the vehicle model, and configuring the model parameters of the vehicle model based on the unified robot description format. Understandably, the preset modeling software can be a mechanical structure modeling tool, such as three-dimensional modeling software such as SolidWorks or Blender, and the three-dimensional model corresponding to the target vehicle is created according to a 1:1 ratio through the above-mentioned preset modeling software. The three-dimensional model is the vehicle model. Afterwards, the model parameters of the above-mentioned vehicle model are configured through the unified robot description format (URDF, Unified Robot Description Format), and then the vehicle model is converted into a power model that can be simulated to finally obtain simulation operation data.

[0033] Among them, the unified robot description format is the robot model description format in the robot operating system. The robot operating system also provides a C++ parser for files in the unified robot description format, which can parse the robot models described in the XML format in the files in the unified robot description format. In this embodiment, the above-mentioned vehicle model can be regarded as a robot model. Therefore, after configuring the model parameters of the vehicle model based on the unified robot description format, the robot operating system can parse the above-mentioned vehicle model and control its operation through control instructions.

[0034] In one embodiment, the model parameters include: attribute parameters, sensor equipment parameters, brake control parameters and traction control parameters of the target vehicle. Specifically, in this embodiment, the model parameters configured for the vehicle model may include attribute parameters of the target vehicle, i.e., inherent attribute parameters of the vehicle such as vehicle weight and wheel radius; the model parameters may also include traction control parameters associated with the traction system of the target vehicle; the model parameters may also include brake control parameters associated with the brake system of the target vehicle; the model parameters may also include sensor equipment parameters corresponding to the sensor modules installed on the target vehicle, specifically including sensor equipment parameters corresponding to sensor equipment such as integrated navigation, laser radar, millimeter wave radar, and camera. It can be understood that the target vehicle may include one or more rail vehicles, therefore, the above model parameters may be grouped into different parameter groups, each parameter group corresponding to a type of rail vehicle, so that when the types of different target vehicles are the same, the corresponding parameter group may be directly determined according to the vehicle type, and then the parameter group may be directly configured to the target vehicle, so that the configuration management efficiency may be accelerated.

[0035] In a further embodiment, the configuration process of the above model parameters specifically includes:

[0036] Rigid body tag configuration: The rigid body tag is used to describe the appearance and physical properties of a rigid body of the vehicle model, including the configuration of size, color, shape, inertia matrix, collision parameters, etc.

[0037] Joint label configuration: Joint labels are used to describe the kinematic and dynamic properties of each joint of the vehicle model, including the configuration of the position and speed limits of joint movement. The above joints include the following six categories: first rotation joints that can rotate infinitely around a single axis, second rotation joints with rotation angle limits, sliding joints that move along a certain axis and have position limits, planar joints that allow movement or rotation in the orthogonal direction of the plane, floating joints that allow translation and rotation, and special fixed joints that do not allow movement. The main function of the above joints is to connect the two specified rigid bodies in the wheel model.

[0038] Machine tag configuration: The machine tag is the top-level tag of the complete vehicle model. Both the rigid body tag and the joint tag must be included in the machine tag. The name corresponding to the vehicle model can be set in the machine tag.

[0039] Simulation tag configuration: The simulation tag is used to describe the parameters required for the robot model to perform simulation in the preset simulation software, including the material properties of each rigid body of the vehicle model, the simulation plug-ins that need to be installed, etc.; specifically including: chassis, lead vehicle, tail vehicle, through-aisle rotation along the Z-axis, through-aisle rotation along the Z-axis in the direction of the lead vehicle, through-aisle rotation along the X-axis in the direction of the lead vehicle, through-aisle rotation along the Y-axis in the direction of the tail vehicle, through-aisle rotation along the Z-axis in the direction of the tail vehicle, through-aisle rotation along the X-axis in the direction of the tail vehicle, through-aisle rotation along the Y-axis in the direction of the tail vehicle, right driving wheel of the lead vehicle, left driving wheel of the lead vehicle, right guide wheel, left guide wheel, right driving wheel of the tail vehicle, left driving wheel of the tail vehicle, right guide wheel, left guide wheel, etc., and the corresponding three-dimensional models and the corresponding unified robot description format parameter configurations, as well as the three-dimensional model of the sensor module and the corresponding unified robot description format parameter configurations, etc.

[0040] Specifically, the sensing modules include: laser radar, millimeter-wave radar, camera, integrated navigation, etc.; among them, the unified robot description format parameters corresponding to the laser radar include number of lines, field of view angle, ranging accuracy, angular resolution, frequency, etc.; the unified robot description format parameters corresponding to the millimeter-wave radar include resolution, detection distance, frequency, etc.; the unified robot description format parameters corresponding to the camera include focal length, field of view angle, resolution; integrated navigation modeling, etc.; the unified robot description format parameters corresponding to the integrated navigation include positioning accuracy, frequency, etc.

[0041] At the same time, the configuration of the above-mentioned simulation tag can also include the configuration of the vehicle controller and its parameters for controlling the target vehicle in the robot operating system. The above-mentioned vehicle controller can be configured as a PID (Proportional Integral Derivative) controller or an MPC (Model Predictive Control) controller, etc., which is not limited here. In the configuration process of the simulation parameters of the above-mentioned embodiment, the hardware abstraction and driver interface can be provided, so that the process of integrating the real sensor module and vehicle controller into the simulation environment is simple.

[0042] S103, generating the rail vehicle simulation model according to the track scene model and the vehicle model after parameter configuration is completed. That is, in this embodiment, the track scene model and the vehicle model after parameter configuration are completed can be directly recorded as the rail vehicle simulation model, or the rail vehicle simulation model can be generated after necessary processing of the track scene model and / or the vehicle model (such as the same format of the two models or the positioning alignment between the two models, etc.), which is not limited here.

[0043] S20, import the rail vehicle simulation model into a preset simulation environment for simulation operation, and obtain simulation operation data. In one embodiment, the preset simulation environment is a Gazebo simulation environment, but the preset simulation environment can also be other simulation environments except the Gazebo simulation environment, as long as the vehicle model can be controlled by the robot operating system (ROS, Robot Operating System) in the preset simulation environment to simulate the preset rail operation scene in the track scene model, and obtain simulation operation data. In this embodiment, the rail vehicle simulation model actually imported into the preset simulation environment includes the track model and the environment model in the track scene model, and also includes the vehicle model and the sensor module installed in the vehicle model. In this embodiment, the communication between the vehicle model and the track model can be realized by the robot operating system through rostopic (wherein rostopic refers to the topic information of the robot operating system) in the Gazebo simulation environment, and the wheel speed of the vehicle model is controlled by the level, so that the vehicle model simulates the preset rail operation scene on the track model in the track scene model to obtain simulation operation data.

[0044] In one embodiment, after step S20, that is, after obtaining the simulation operation data, the method includes: optimizing the operation performance parameters of the target vehicle according to the simulation operation data. That is, in this embodiment, after determining the simulation operation data, the simulation operation data can be used to analyze whether the operation performance parameters of the target vehicle and the target track (such as the operation characteristics and performance of the target vehicle and the target track, the performance and reliability of the sensor module in the target vehicle, etc.) are reasonable, and then when they are unreasonable, they can be aligned and adjusted to achieve parameter optimization, thereby improving the safety, economy and environmental adaptability of the operation of the target vehicle, thereby greatly reducing the demand for field operation tests, thereby saving time and cost.

[0045] In the above embodiment of the present invention, firstly, based on the target vehicle and the preset rail operation scene of the target vehicle during historical operation, a rail vehicle simulation model is constructed, and then the rail vehicle simulation model is imported into a preset simulation environment to simulate the preset rail operation scene, so that the historical operation process of the target vehicle in the preset rail operation scene can be reproduced, and then the simulation operation data corresponding to the target vehicle when it runs in the preset rail operation scene can be obtained. The rail vehicle simulation data processing method of the present invention can display the operation process of the target vehicle in the preset rail operation scene through precise simulation, and then quickly, accurately, reliably and efficiently determine the simulation operation data, so that the operation characteristics and performance optimization of the target vehicle in the preset rail operation scene can be analyzed according to the above simulation operation data, and then the reliability and design parameters of the target vehicle and the track in the preset rail operation scene can be verified or optimized, so as to improve the safety, economy and environmental adaptability of the operation process, so that the present invention greatly reduces the demand for field operation testing through simulation, thereby saving time and cost, and also provides a reliable basis for the design, testing and optimization of the rail transit system, and finally promotes the safety, reliability and design efficiency of the rail transit system. The present invention can be widely used in the design, operation and maintenance of vehicles and tracks, and has broad application prospects and market value.

[0046] In one embodiment, if Figure 3 As shown, the step S20, that is, the simulation operation of the rail vehicle simulation model into the preset simulation environment, and the simulation operation data is obtained, including: the rail vehicle simulation model is imported into the preset simulation environment, and the vehicle model is controlled by the robot operating system to simulate the preset rail operation scene in the track scene model to obtain the simulation operation data. In this embodiment, the robot operating system and the Gazebo simulation software are used to simulate the rail vehicle simulation model, because the robot operating system can manage the operation and control of the vehicle model corresponding to the target vehicle. The preset simulation environment (such as the Gazebo simulation environment) can be used to simulate the behavior of the target vehicle at run time and provide a visualization effect. Therefore, the present embodiment constructs a rail vehicle simulation model according to the target vehicle and the preset rail operation scene, and simulates the rail vehicle simulation model in the preset simulation environment, and then communicates and controls through the robot operating system to realize the simulation of the target vehicle running on the target track of the preset rail operation scene, so that the control algorithm and safety system of the target vehicle can be verified under various conditions by realistic physical simulation, so as to verify the rationality of the design scheme corresponding to the target vehicle running on the target track, and improve the accuracy and reliability of the design scheme.

[0047] In one embodiment, the using of the robot operating system to control the vehicle model to simulate the preset track running scenario in the track scenario model to obtain simulation running data includes:

[0048] Setting scene data corresponding to the track scene model, and updating the track scene model to a simulation scene according to the scene data; wherein the scene data includes ambient lighting, air pressure, wind speed, local longitude and latitude coordinate information, etc. corresponding to the environmental model in the track scene model, and updating the environmental model of the track scene model to a simulation scene according to the scene data, that is, configuring the environmental model in the track scene model according to the scene data, and then obtaining a simulation model.

[0049] The operating attributes corresponding to the vehicle model are set, and the vehicle model is updated to a simulated vehicle according to the operating attributes; that is, in this embodiment, the operating attributes include some operating-related data of the vehicle model, such as the static friction between the wheels of the target vehicle and the target track.

[0050] The simulated vehicle is controlled by the robot operating system to simulate the preset rail operation scene in the simulation scene to obtain simulation operation data. In this embodiment, the launch node can be used to start the rail vehicle simulation model into the Gazebo simulation environment, and then the above-mentioned scene data is set for the track scene model to obtain the simulation model, and the above-mentioned operation attributes are configured for the above-mentioned wheel model to obtain the simulated vehicle; after that, the robot operating system can be used for communication and control, and the wheel speed of the target vehicle can be controlled by the level, so as to control the simulated vehicle to simulate the preset rail operation scene in the simulation scene, and finally the simulation operation data is obtained by the sensor module configured in the vehicle model (ie, the simulated vehicle), and the simulation operation data may include the motion state information (such as speed, acceleration, position, etc.) and environmental information (such as real-time coordinate information) of the simulated vehicle in the simulation scene. The above-mentioned simulation operation data can be used for trajectory planning, navigation, positioning, etc. of the target vehicle.

[0051] In one embodiment, the scene data includes local longitude and latitude coordinate information corresponding to the preset rail operation scene; that is, when the scene data includes local longitude and latitude coordinate information, in the above embodiment, when the track scene model is updated to a simulation scene according to the scene data, the simulation scene will be aligned with the longitude and latitude coordinates of the target track in the preset rail operation scene based on the local longitude and latitude coordinates.

[0052] The simulation operation data includes the real-time coordinate information of the simulated vehicle, and the real-time coordinate information refers to the two-dimensional coordinate information determined according to the local longitude and latitude coordinate information and the spatial coordinates of the simulated vehicle in the preset simulation environment. Specifically, the spatial coordinates of the simulated vehicle in the preset simulation environment are the longitude and latitude coordinates in the WGS84 ellipsoid coordinate system, and the above-mentioned spatial coordinates can first be projected to the Gaussian plane rectangular coordinate system through the Gaussian 3-degree band, and then the points corresponding to the spatial coordinates in the Gaussian plane rectangular coordinate system can be converted to the local plane coordinate system corresponding to the local longitude and latitude coordinate information through translation and rotation through the preset four parameters to obtain the two-dimensional coordinate information. Among them, the above-mentioned four preset parameters include: X-coordinate translation, Y-coordinate translation, rotation angle of plane coordinate axis, and scale factor; among them, X-coordinate translation refers to the X-coordinate difference between the coordinate origins of the Gaussian plane rectangular coordinate system and the local plane coordinate system; Y-coordinate translation refers to the Y-coordinate difference between the coordinate origins of the Gaussian plane rectangular coordinate system and the local plane coordinate system; and the rotation angle of the plane coordinate axis is a preset angle, and rotating one of the Gaussian plane rectangular coordinate system and the local plane coordinate system by the preset angle can make the X-axis and Y-axis of the two coordinate systems coincide with each other; the scale factor refers to the ratio of the lengths of the same straight line in the Gaussian plane rectangular coordinate system and the local plane coordinate system. The scale ratio conversion of the coordinate position can be achieved through the above-mentioned scale factor, and the K value is usually approximately equal to 1.

[0053] In one embodiment, the target vehicle includes at least one rail vehicle; the vehicle model includes sub-models corresponding to each rail vehicle; the target vehicle may be a tram, a train or other rail vehicle that must rely on the target track to run.

[0054] Furthermore, the use of the robot operating system to control the vehicle model to simulate the preset track running scenario in the track scenario model to obtain simulation running data includes:

[0055] The operation scheduling information corresponding to all the rail vehicles is obtained, and the operation time and operation parameters corresponding to each rail vehicle are determined according to the operation scheduling information; that is, no matter how many rail vehicles need to run on the target track in the preset rail operation scenario, the operation scheduling information corresponding to the preset rail operation scenario can be obtained first, and then the operation time and operation parameters corresponding to each rail vehicle are determined in the operation scheduling information, and each operation parameter will correspond to a different operation time.

[0056] The robot operating system controls each of the sub-models to simulate the preset rail operation scenario in the track scene model according to the operation time and operation parameters corresponding to each of the rail vehicles, and obtains simulation operation data. For each rail vehicle, the operation parameters and operation time corresponding to the rail vehicle can be determined. Therefore, the robot operating system can control the operation of the rail vehicle according to the operation parameters and operation time corresponding to each rail vehicle; in this way, for multiple rail vehicles, the robot operating system can use the above-mentioned operation scheduling information to simultaneously control all rail vehicles to operate in the preset rail operation scenario without mutual interference. Therefore, the simulation process of the present invention can truly restore the actual operation process of one or more rail vehicles in the preset rail operation scenario, thereby improving the simulation accuracy.

[0057] It is understandable that, in this embodiment, before controlling each of the sub-models to simulate the preset rail operation scene in the track scene model, the track scene model may be updated to a simulation scene according to the scene data, and all the sub-models may be updated to simulation vehicles according to the operation attributes. Afterwards, the robot operating system controls each of the simulation models to simulate the preset rail operation scene in the simulation model according to the operation time and operation parameters corresponding to each of the rail vehicles. For details, please refer to the above embodiments, which will not be repeated here.

[0058] Further, the robot operating system controls each of the sub-models to simulate the preset rail operation scene in the track scene model according to the operation time and operation parameters corresponding to each of the rail vehicles to obtain simulation operation data, including:

[0059] Generate a control instruction according to the operating parameters corresponding to the rail vehicle, and let the robot operating system send the control instruction to the sub-model at the operating time associated with the operating parameters, so that the sub-model runs in the track scene model according to the control instruction, and records the simulation operation data of the sub-model. That is, for each rail vehicle, all the operation arrays corresponding to the rail vehicle can be determined, and each operation array contains a set of operating parameters and the operating time corresponding to the set of operating parameters, that is, during the operating time, the sub-model corresponding to the rail vehicle needs to run in the track scene model with the set of operating parameters corresponding to it. Therefore, the robot operating system can generate a control instruction according to each set of operating parameters, and then send the control instruction to the vehicle controller of the sub-model at the operating time corresponding to the set of operating parameters, so that the vehicle controller controls the operation of the sub-model according to the control instruction.

[0060] In one embodiment, after the step S20, that is, after the simulation operation data is obtained, it includes: when the preset rail operation scenario is determined to be an emergency scenario according to the simulation operation data, an emergency response strategy is determined according to the simulation operation data, and the emergency response strategy is associated with the preset rail operation scenario and stored. That is, in this embodiment, if the preset rail operation scenario is some emergency scenarios that require emergency treatment, such as a scenario where the target vehicle fails, or a scenario where the target vehicle collides with an obstacle, etc. It can be understood that when the target vehicle runs on the target track corresponding to the preset rail operation scenario, it does not yet have an emergency response strategy corresponding to the emergency scenario, and the vehicle may be in danger. In this embodiment, the emergency response strategy corresponding to the emergency scenario (that is, the preset rail operation scenario) can be obtained directly through the simulation operation data determined by the rail vehicle simulation data processing method of the present invention, and the emergency response strategy is associated with the relevant information of the preset rail operation scenario, etc. and stored, and then, when the target vehicle encounters the preset rail operation scenario again, the emergency response strategy can be directly executed. The above process of determining the corresponding emergency strategy can quickly display the fault scenario through simulation and repair the fault in a targeted manner or avoid collision with obstacles, etc., providing a good testing environment without the need for field operation testing, avoiding safety risks of the target vehicle in the field operation test, and reducing testing costs and time.

[0061] Furthermore, after determining the emergency response strategy according to the simulation operation data and associating and storing the emergency response strategy with the preset rail operation scenario, it also includes: during the operation of the target vehicle, if it is detected that the current operation scenario of the target vehicle matches the preset rail operation scenario, the target vehicle is ordered to execute the emergency response strategy. In this embodiment, after the emergency response strategy corresponding to the preset rail operation scenario is determined and stored by the rail vehicle simulation data processing method, during the operation of the target vehicle, if the current operation scenario matches the preset rail operation scenario (the environmental data and track line data corresponding to the preset rail operation scenario are similar or consistent), the emergency response strategy corresponding to the preset rail operation scenario can be directly executed, and then the emergency scenario can be processed in a targeted manner, fault repair can be achieved, or collision with obstacles can be avoided, etc., to ensure vehicle safety.

[0062] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and does not constitute any limitation on the implementation process of the embodiment of the present invention.

[0063] In one embodiment, if Figure 3As shown, a rail vehicle simulation data processing device is provided, and the rail vehicle simulation data processing device corresponds to the rail vehicle simulation data processing method in the above embodiment. The rail vehicle simulation data processing device includes:

[0064] An acquisition module 100 is used to acquire a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario;

[0065] The simulation operation module 200 is used to import the rail vehicle simulation model into a preset simulation environment for simulation operation to obtain simulation operation data.

[0066] In the device of the above embodiment of the present invention, firstly, based on the target vehicle and the preset rail operation scene of the target vehicle during historical operation, a rail vehicle simulation model is constructed, and then the rail vehicle simulation model is imported into the preset simulation environment to simulate the preset rail operation scene, so that the historical operation process of the target vehicle in the preset rail operation scene can be reproduced, and then the simulation operation data corresponding to the target vehicle when it runs in the preset rail operation scene can be obtained. The rail vehicle simulation data processing device of the present invention can display the operation process of the target vehicle in the preset rail operation scene through precise simulation, and then quickly, accurately, reliably and efficiently determine the simulation operation data, so that according to the above simulation operation data, the operation characteristics and performance optimization of the target vehicle in the preset rail operation scene can be analyzed, and then the reliability and design parameters of the target vehicle and the track in the preset rail operation scene can be verified or optimized, so as to improve the safety, economy and environmental adaptability of the operation process, so that the present invention greatly reduces the demand for field operation testing through simulation, thereby saving time and cost, and also provides a reliable basis for the design, testing and optimization of the rail transit system, and finally promotes the safety, reliability and design efficiency of the rail transit system. The present invention can be widely applied to the design, operation and maintenance of vehicles and tracks, and has broad application prospects and market value.

[0067] The specific definition of the rail vehicle simulation data processing device can be found in the definition of the rail vehicle simulation data processing method above, which will not be repeated here. Each module in the above rail vehicle simulation data processing device can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0068] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory, a network interface and a database connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a readable storage medium and an internal memory. The readable storage medium stores an operating system, computer-readable instructions and a database. The internal memory provides an environment for the operation of the operating system and computer-readable instructions in the readable storage medium. When the computer-readable instructions are executed by the processor, a method for processing rail vehicle simulation data is implemented. The readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.

[0069] In one embodiment, a computer device is provided, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor. When the processor executes the computer-readable instructions, the steps of the above-mentioned rail vehicle simulation data processing method are implemented.

[0070] In one embodiment, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the steps of the above-mentioned rail vehicle simulation data processing method are implemented.

[0071] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through computer-readable instructions, and the computer-readable instructions can be stored in a readable storage medium, and the readable storage medium includes a non-volatile readable storage medium and a volatile readable storage medium. When the computer-readable instructions are executed, they may include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0072] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units or modules is used as an example. In actual applications, the above-mentioned functions can be assigned to different functional units or modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0073] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for processing rail vehicle simulation data, characterized in that: include: Acquire a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario; The rail vehicle simulation model is imported into a preset simulation environment for simulation operation to obtain simulation operation data.

2. The rail vehicle simulation data processing method according to claim 1, characterized in that: The step of obtaining a rail vehicle simulation model comprises: Constructing a track scene model according to the preset track operation scene; Establishing a vehicle model corresponding to the target vehicle, and configuring parameters of the vehicle model; The rail vehicle simulation model is generated according to the track scene model and the vehicle model after parameter configuration is completed.

3. The rail vehicle simulation data processing method according to claim 2, characterized in that: The step of constructing a track scene model according to the preset track operation scene includes: Acquire environmental data and track line data of the preset rail operation scenario; A track scene model corresponding to the preset track operation scene is constructed according to the environmental data and the track line data.

4. The rail vehicle simulation data processing method according to claim 3, characterized in that: The step of constructing a track scene model corresponding to the preset track operation scene according to the environmental data and the track line data includes: The environmental data and the track line data are formatted to obtain converted operation data, and a track scene model corresponding to the preset track operation scene is constructed according to a marching cube algorithm and the converted operation data.

5. The rail vehicle simulation data processing method according to claim 2, characterized in that: The step of establishing a vehicle model corresponding to the target vehicle and configuring parameters of the vehicle model includes: The three-dimensional model corresponding to the target vehicle constructed by the preset modeling software is determined as the vehicle model, and the model parameters of the vehicle model are configured based on the unified robot description format.

6. The rail vehicle simulation data processing method according to claim 5, characterized in that: The model parameters include: The target vehicle's attribute parameters, sensor equipment parameters, brake control parameters, and traction control parameters.

7. The rail vehicle simulation data processing method according to claim 2, characterized in that: The step of importing the rail vehicle simulation model into a preset simulation environment for simulation operation to obtain simulation operation data includes: The rail vehicle simulation model is imported into a preset simulation environment, and a robot operating system is used to control the vehicle model to simulate the preset rail operation scene in the track scene model to obtain simulation operation data.

8. The rail vehicle simulation data processing method according to claim 7, characterized in that: The using of the robot operating system to control the vehicle model to simulate the preset track running scenario in the track scenario model to obtain simulation running data includes: Setting scene data corresponding to the track scene model, and updating the track scene model to a simulation scene according to the scene data; Setting operation attributes corresponding to the vehicle model, and updating the vehicle model to a simulated vehicle according to the operation attributes; The simulation vehicle is controlled by a robot operating system to run in the simulation scene in a manner similar to the preset track running scene, thereby obtaining simulation running data.

9. The rail vehicle simulation data processing method according to claim 8, characterized in that: The scene data includes local longitude and latitude coordinate information corresponding to the preset rail operation scene; the simulation operation data includes real-time coordinate information of the simulated vehicle, and the real-time coordinate information refers to two-dimensional coordinate information determined based on the local longitude and latitude coordinate information and the spatial coordinates of the simulated vehicle in the preset simulation environment.

10. The rail vehicle simulation data processing method according to claim 7, characterized in that: The target vehicle includes at least one rail vehicle; the vehicle model includes sub-models corresponding to each rail vehicle; The using of the robot operating system to control the vehicle model to simulate the preset track running scenario in the track scenario model to obtain simulation running data includes: Acquire operation scheduling information corresponding to all the rail vehicles, and determine the operation time and operation parameters corresponding to each of the rail vehicles according to the operation scheduling information; The robot operating system controls each of the sub-models to simulate the preset rail operation scene in the track scene model according to the operation time and operation parameters corresponding to each of the rail vehicles, so as to obtain simulation operation data.

11. The rail vehicle simulation data processing method according to claim 10, characterized in that: The robot operating system controls each of the sub-models to simulate the preset rail operation scene in the track scene model according to the operation time and operation parameters corresponding to each of the rail vehicles to obtain simulation operation data, including: Generate a control instruction according to the operating parameters corresponding to the rail vehicle, and enable the robot operating system to send the control instruction to the sub-model at a running time associated with the operating parameters, so that the sub-model runs in the track scene model according to the control instruction, and record the simulation running data of the sub-model.

12. The rail vehicle simulation data processing method according to claim 1, characterized in that: After obtaining the simulation operation data, the method includes: The operating performance parameters of the target vehicle are optimized according to the simulation operating data.

13. The rail vehicle simulation data processing method according to claim 1, characterized in that: After obtaining the simulation operation data, the method includes: When it is determined that the preset rail operation scenario is an emergency scenario, an emergency response strategy is determined according to the simulation operation data, and the emergency response strategy is associated with the preset rail operation scenario and stored.

14. The rail vehicle simulation data processing method according to claim 13, characterized in that: After determining the emergency response strategy according to the simulation operation data and associating and storing the emergency response strategy with the preset rail operation scenario, the method further includes: During the operation of the target vehicle, if it is detected that the current operation scenario of the target vehicle matches the preset track operation scenario, the target vehicle is ordered to execute the emergency response strategy.

15. The rail vehicle simulation data processing method according to claim 1, characterized in that: The preset simulation environment is the Gazebo simulation environment.

16. A rail vehicle simulation data processing device, characterized in that: include: An acquisition module is used to acquire a rail vehicle simulation model; the rail vehicle simulation model is constructed based on a target vehicle and a preset rail operation scenario; The simulation operation module is used to import the rail vehicle simulation model into a preset simulation environment for simulation operation to obtain simulation operation data.

17. A computer device comprising a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, characterized in that: When the processor executes the computer-readable instructions, the rail vehicle simulation data processing method according to any one of claims 1 to 15 is implemented.

18. A computer-readable storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by a processor, the rail vehicle simulation data processing method according to any one of claims 1 to 15 is implemented.

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