A virtual simulation teaching system and method for college students

Abstract

The application provides a virtual simulation teaching system and method for college students, which comprises the following steps: collecting real-time position data of key moving parts when a student operates an entity device; driving a virtual model consistent with the geometric topology of the entity device in a virtual simulation environment according to the real-time position data, and then generating synchronous driving state data; receiving a command sequence input by the student beyond the safe operation range of the entity in the state of the virtual model represented by the synchronous driving state data; performing finite element mechanics analysis on the virtual model by combining the command sequence with a physical simulation engine, obtaining the simulated stress and strain distribution of key parts in the virtual model, comparing the simulated stress and strain distribution with pre-stored material mechanics performance parameters, and generating a safety warning report of the student operating the entity device according to the comparison result. The scheme of the application can quantize abstract mechanics concepts into specific risk warning teaching feedback loops.

Description

Technical Field

[0001] This application relates to the field of virtual simulation teaching technology, and more specifically, to a virtual simulation teaching system and method for college students. Background Technology

[0002] Virtual simulation teaching refers to the integration of technologies such as computer graphics, physics engines, human-computer interaction, and system simulation to construct a highly immersive and interactive digital virtual environment and experimental scenarios. This simulates teaching elements and process rules under real or theoretical conditions, enabling learners to construct knowledge in an embodied and exploratory manner.

[0003] In traditional university engineering experiment teaching, to ensure the safety of expensive equipment and students, teaching procedures must strictly prohibit any dangerous or extreme operations that exceed safety thresholds. This prevents students from developing a deep understanding of risks and engineering intuition through intuitively experiencing the "engineering consequences of incorrect operations," creating a fundamental contradiction between safety constraints and cognitive needs. For example, existing alternative methods, such as verbal warnings or pre-set animation demonstrations, lack dynamic correlation with real operating conditions, are not interactive, and the consequence simulations are not scientifically calculated (such as finite element analysis). Their teaching effectiveness is superficial, making it difficult for students to accept, and they cannot accurately map virtual consequences back to the specific risks of real equipment. Therefore, how to quantify abstract mechanical concepts into a closed-loop teaching feedback mechanism for specific risk warnings has become a challenge for the industry. Summary of the Invention

[0004] This application provides a virtual simulation teaching system and method for college students, which can quantify abstract mechanical concepts into a teaching feedback loop with specific risk warnings.

[0005] Firstly, this application provides a virtual simulation teaching method for college students, comprising the following steps: Collect real-time pose data of key moving parts when students operate the physical device; Based on the real-time pose data, a virtual model with the same geometric topology as the physical device is driven in a virtual simulation environment, thereby generating synchronous drive state data; In the virtual model state represented by the synchronous drive state data, a sequence of instructions that exceeds the safe operation range of the entity is received from the student. The virtual model is subjected to finite element mechanical analysis by combining the physical simulation engine with the instruction sequence to obtain the simulated stress and strain distribution of key parts in the virtual model. The simulated stress-strain distribution is compared with the pre-stored material mechanical property parameters, and a safety warning report is generated based on the comparison results when students operate the physical device.

[0006] In some embodiments, driving a virtual model with the same geometric topology as the physical device in a virtual simulation environment based on the real-time pose data, and then generating synchronous drive state data specifically includes: The real-time pose data is mapped into virtual driving commands in a virtual environment according to a preset coordinate transformation relationship. The virtual drive instructions are subjected to inverse kinematics solution to obtain the motion parameters of each key moving component in the virtual model that is consistent with the geometric topology of the physical device. At each simulation frame refresh, motion parameters of all joint components in the virtual model are collected, thereby generating synchronous drive state data.

[0007] In some embodiments, in the virtual model state represented by the synchronous drive state data, receiving a sequence of instructions from the student that exceeds the scope of physical safety operations specifically includes: The real-time running status of the corresponding virtual model is analyzed based on the synchronous drive state data; Based on the real-time operating status, a human-computer interaction input interface is displayed in the virtual simulation environment, allowing input of parameters that exceed the safe operating range of the physical entity. The system receives parameters for violations set by students through the human-computer interaction input interface, and then obtains a sequence of instructions input by students that exceed the safe operating range of the physical entity.

[0008] In some embodiments, performing finite element mechanical analysis on the virtual model using a physical simulation engine in conjunction with the instruction sequence to obtain the simulated stress-strain distribution of key parts in the virtual model specifically includes: Based on the instruction sequence and the synchronous drive state data, the simulation load and boundary conditions applied to the virtual model are determined; The finite element solver of the physical simulation engine is invoked to apply the simulated loads and boundary conditions to the virtual model and perform mechanical calculations to solve the problem. Based on the solution results of the mechanical calculations, the simulated stress and strain distribution of key parts in the virtual model is extracted.

[0009] In some embodiments, comparing the simulated stress-strain distribution with pre-stored material mechanical property parameters specifically includes: Extract the strength thresholds corresponding to the materials of each key part of the virtual model from the pre-stored material mechanical property parameters; By comparing the simulated stress-strain distribution with all strength thresholds, the stress state of each key component during student operation can be obtained.

[0010] In some embodiments, generating a safety warning report based on the comparison results when students operate physical devices specifically includes: Determine the safety risk level of each critical component based on all stress states; A safety warning report is generated when students operate the physical device by combining all safety risk levels with the instruction sequence.

[0011] In some embodiments, the physical device is a physics teaching instrument with movable parts used in engineering experimental teaching in universities.

[0012] Secondly, this application provides a virtual simulation teaching system for college students, comprising: The data acquisition module is used to collect real-time pose data of key moving parts when students operate the physical device; The processing module is used to drive a virtual model with the same geometric topology as the physical device in a virtual simulation environment based on the real-time pose data, thereby generating synchronous drive state data. The processing module is also used to receive a sequence of instructions input by the student that exceeds the scope of physical safe operation in the virtual model state represented by the synchronous drive state data. The processing module is also used to perform finite element mechanical analysis on the virtual model by combining the instruction sequence with the physical simulation engine, so as to obtain the simulated stress and strain distribution of key parts in the virtual model. The execution module is used to compare the simulated stress-strain distribution with the pre-stored material mechanical property parameters, and generate a safety warning report for students operating the physical device based on the comparison results.

[0013] Thirdly, this application provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the virtual simulation teaching method for college students described above.

[0014] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the virtual simulation teaching method for college students described above.

[0015] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: The virtual simulation teaching system and method for university students provided in this application firstly collects the pose data of key moving parts of a physical device in real time and drives its high-fidelity dynamic geometric model in a virtual environment, thereby generating synchronous driving state data. This mirrors the student's intentions and processes from the physical world to the digital world without lag or loss, transforming abstract operational behaviors into a specific spatiotemporal state sequence that can be continuously tracked, analyzed, and intervened by the computer. This process establishes a millisecond-level synchronous dynamic mapping relationship between the physical and virtual worlds, laying the foundation for data synchronization in associating abstract mechanical concepts with specific operational risks. Subsequently, after receiving the student's input instruction sequence, a physical simulation engine combined with finite element analysis calculates the simulated stress and strain distribution, transforming abstract and dangerous operations that exceed the physical safety threshold and cannot be realistically executed into specific mechanical state fields of key parts within the virtual model. This process solves the solid mechanics governing equations. This approach deconstructs vague, qualitative, empirical judgments that may lead to damage into a series of physical quantities with clear engineering significance, such as peak stress, strain gradient, and plastic zone range. This elevates risk perception from a perceptual warning level to a rational, quantitative level based on materials mechanics and computational science. Furthermore, it compares the calculated simulated stress-strain distribution with pre-stored material mechanical property parameters (such as yield strength and fatigue limit) to generate a structured safety warning report. This process dynamically transforms abstract mechanical analysis results into concrete, tiered risk warnings through direct comparison with material failure thresholds, potentially linking them to specific failure modes and descriptions of equipment damage consequences. Through pre-defined engineering criteria, this process compresses and refines complex continuous mechanical field data into targeted feedback information oriented towards teaching objectives, completing the final transformation from scientific calculation to teaching decision-making. In summary, this scheme quantifies abstract mechanical concepts into a closed-loop teaching feedback system for specific risk warnings. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating a virtual simulation teaching method for college students, according to some embodiments of this application. Figure 2 This is a schematic diagram of the process for generating synchronous drive state data according to some embodiments of this application; Figure 3 This is a schematic flowchart illustrating the determination of stress state according to some embodiments of this application; Figure 4 This is a schematic diagram of the structure of a virtual simulation teaching system for college students, as shown in some embodiments of this application; Figure 5 This is an internal structural diagram of a computer device for implementing a virtual simulation teaching method for college students, according to some embodiments of this application. Detailed Implementation

[0017] To better understand the technical solutions in this embodiment, the technical solutions in this embodiment will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0018] refer to Figure 1 The figure is a flowchart illustrating a virtual simulation teaching method for university students according to some embodiments of this application. This virtual simulation teaching method for university students mainly includes the following steps: In step 101, real-time pose data of key moving parts are collected when students operate the physical device.

[0019] In practical implementation, raw motion signals can be acquired by installing a multi-source sensor combination on the key moving parts of the physical device (such as the rotating shaft of a rotary joint, the slider of a movable slide, and the end effector of a robotic arm). The multi-source sensor combination is configured according to the type of motion; for example, a photoelectric encoder is used to measure angular displacement for rotational motion, a laser rangefinder or a draw-wire encoder is used to measure linear displacement for linear motion, and an inertial measurement unit is added to measure angular velocity and acceleration for complex spatial motion. All sensors communicate with a host computer via a data acquisition card or embedded microcontroller, converting analog voltage or digital pulse signals into processable digital sequences. The data processing module in the host computer first processes the received multi-channel raw digital sequences... After timestamp synchronization and outlier removal, Kalman filtering or complementary filtering algorithms are used to fuse and denoise multi-source signals (especially data from the inertial measurement unit) to obtain smooth and accurate displacement, angle, or acceleration estimates. Finally, based on the kinematic model (such as the DH parameter model or geometric constraint equations) pre-established by the physical device, the processed sensor measurements are used as input, and the six-degree-of-freedom pose data of the key moving parts in the physical world coordinate system is output in real time through forward kinematics calculation, i.e., real-time pose data. The real-time pose data includes three-dimensional position coordinates (X, Y, Z) and three-dimensional attitude angles (such as rotation angles Rx, Ry, Rz about the X, Y, and Z axes, or represented by quaternions).

[0020] It should be noted that the real-time pose data mentioned in this application refers to a set of values ​​that change continuously over time and can uniquely determine the position and orientation of a specified moving part in the physical device in three-dimensional space. Its real-time requirement is that the total latency of data processing and transmission is lower than the frame rate refresh threshold of the virtual simulation system (usually within 100 milliseconds) to ensure the synchronization of the virtual drive. The physical device is a physical teaching instrument with movable parts in engineering experimental teaching in universities, such as a materials mechanics testing machine, a rotor dynamics experimental platform, an industrial robot teaching unit, etc. The key moving parts refer to physical parts in the physical device whose motion state is directly related to the core experimental principle, operational safety, or the target to be simulated and analyzed. The multi-source sensors refer to a combination of measuring devices based on different physical principles used to obtain complete motion information. Their selection and installation must meet the requirements of measurement accuracy, response speed, and mechanical integration with the physical device.

[0021] In step 102, a virtual model with the same geometric topology as the physical device is driven in a virtual simulation environment based on the real-time pose data, thereby generating synchronous drive state data.

[0022] In some embodiments, reference Figure 2 As shown in the figure, this is a schematic diagram of the process for generating synchronous drive state data according to some embodiments of this application. The generation of synchronous drive state data by driving a virtual model with the same geometric topology as the physical device in a virtual simulation environment based on the real-time pose data can be achieved through the following steps: First, in step 1021, the real-time pose data is mapped to virtual driving commands in a virtual environment according to a preset coordinate transformation relationship; Then, in step 1022, the virtual drive command is subjected to inverse kinematics solution to obtain the motion parameters of each key moving component in the virtual model that is consistent with the geometric topology of the physical device. Finally, in step 1023, when each simulation frame is refreshed, the motion parameters of all joint components in the virtual model are collected, thereby generating synchronous drive state data.

[0023] In specific implementation, mapping the real-time pose data into virtual driving commands in the virtual environment according to a preset coordinate transformation relationship can be achieved in the following ways: for example, the spatial mapping relationship between the physical world coordinate system and the virtual simulation environment coordinate system is determined through a calibration process, and the spatial mapping relationship is specifically defined by a homogeneous coordinate transformation matrix; when the real-time pose data is input, matrix multiplication is performed on the three-dimensional position coordinates contained in the real-time pose data through the transformation matrix, and corresponding rotation transformation calculations are performed on the three-dimensional attitude angles contained therein; finally, the obtained three-dimensional position and three-dimensional attitude data corresponding to the virtual coordinate system are used as virtual driving commands in the virtual environment.

[0024] It should be noted that the virtual driving command described in this application refers to the control command used to drive the corresponding moving parts in the virtual model to reach a specific target pose after mapping the real-time pose data in the physical world coordinate system to the virtual environment.

[0025] In specific implementation, the inverse kinematics solution of the virtual drive command is used to obtain the motion parameters of each key moving component in the virtual model that is consistent with the geometric topology of the physical device. This can be achieved in the following ways: First, based on the kinematic chain structure of the physical device reproduced by the virtual model, a corresponding inverse kinematics solver is configured for it; for common series kinematic chains composed of rotary joints and links, the inverse solver is solved using analytical geometry or numerical iteration methods; when using analytical geometry, the inverse solver directly calculates the parameters that make the end of the model reach the virtual drive command based on the geometric parameters of the model, such as the link length and joint offset, through trigonometric functions and geometric relationships. When a pose is required, the required rotation angles of each joint are specified. In a preferred embodiment, when using a numerical iteration method, the inverse kinematics (IK) starts from the current joint state of the model, calculates the deviation between the current pose of the model's end effector and the target pose, and uses the Jacobian matrix to establish the differential relationship between joint motion and end effector pose change. It continuously adjusts the angles of each joint through an iterative algorithm (such as gradient descent) until the end effector pose deviation converges to within a preset threshold. The series of joint rotation angle values ​​output by the IK output, which are required for the virtual model's end effector to accurately match the target pose, serve as the motion parameters of each key moving component in the virtual model that is consistent with the geometric topology of the physical device.

[0026] It should be noted that the motion parameters mentioned in this application refer to the quantitative values ​​obtained by inverse kinematics calculation, which are used to directly control the joints or key moving parts in the virtual model to achieve specific motion (such as rotation or translation).

[0027] In specific implementation, the motion parameters of all joint components in the virtual model are collected during each simulation frame refresh, and then the synchronous drive state data is generated. This can be achieved in the following way: In each frame update cycle of the virtual simulation engine, the current kinematic state of all driven key moving parts in the virtual model is read in real time through the state query interface provided by the engine. The state includes at least its position and rotation angle. Subsequently, the motion parameters of all key moving parts collected are organized and encapsulated according to a predefined structured data format. This encapsulated data packet containing the instantaneous complete kinematic state of the virtual model is used as the synchronous drive state data.

[0028] It should be noted that the synchronous drive state data described in this application is a data set used to characterize the instantaneous kinematic state of the virtual model, and is the data basis for maintaining real-time synchronous mirroring between the virtual model and the physical device.

[0029] In step 103, under the virtual model state represented by the synchronous drive state data, a sequence of instructions that exceeds the safe operating range of the entity is received from the student.

[0030] In some embodiments, receiving a sequence of instructions input by a student that exceeds the safe operating range of the entity, under the virtual model state represented by the synchronous drive state data, can be achieved through the following steps: The real-time running status of the corresponding virtual model is analyzed based on the synchronous drive state data; Based on the real-time operating status, a human-computer interaction input interface is displayed in the virtual simulation environment, allowing input of parameters that exceed the safe operating range of the physical entity. The system receives parameters for violations set by students through the human-computer interaction input interface, and then obtains a sequence of instructions input by students that exceed the safe operating range of the physical entity.

[0031] In specific implementation, the real-time running state of the corresponding virtual model can be parsed based on the synchronous drive state data in the following ways: For example, the synchronous drive state data is parsed according to its predefined encapsulation format to extract the identifiers of each key moving component in the virtual model, as well as their corresponding position coordinates and rotation angles; then, according to the preset component hierarchy and kinematic constraints in the virtual model, the extracted position and rotation data of each component in the local coordinate system are transformed step by step to the virtual world coordinate system to obtain the global pose of each component in the virtual space; at the same time, according to the global pose and the preset model state determination rules, the specific working stage of the virtual model is determined, and the working stage includes at least one of loading, rotation, and movement; finally, the structured data object containing the global pose and the working stage information is used as the real-time running state of the parsed virtual model; in other embodiments, the end pose can also be directly calculated by forward kinematics, or the state continuity can be improved by interpolation smoothing, which is not limited in this application.

[0032] It should be noted that the real-time operating status mentioned in this application refers to the analysis results used to comprehensively describe the spatial pose, motion attributes and working stage of the virtual model at a specific moment, providing context for receiving and understanding subsequent virtual violation operations.

[0033] In specific implementation, the human-computer interaction input interface that allows input of parameters exceeding the physical safety operation range in the virtual simulation environment based on the real-time operating status can be implemented in the following way: First, obtain the work stage information contained in the real-time operating status, and obtain the list of adjustable physical parameters and their physical safety operation range thresholds for the corresponding stage from the preset configuration file based on the work stage information; then, dynamically generate a graphical control panel in the virtual simulation rendering screen, and create a corresponding input control for each parameter in the parameter list, setting the upper and / or lower limits of the settable value range of each input control to exceed the physical safety operation range threshold; finally, use the generated graphical control panel with parameters exceeding the limit and bound to the current work stage as the displayed human-computer interaction input interface; in other embodiments, the layout, style, and prompt information of the interface elements can be customized according to teaching needs, and this application does not limit this.

[0034] In specific implementation, receiving the violation operation parameters set by the student through the human-computer interaction input interface, and then obtaining the sequence of instructions input by the student that exceeds the physical safety operation range, can be achieved in the following way: continuously monitoring the value change events and confirmation submission events of each input control on the human-computer interaction input interface; when a confirmation submission operation is captured, recording the control identifier involved in this operation, the set parameter value, the target virtual component identifier, and the current simulation timestamp, and associating the real-time running state corresponding to this moment as a baseline state snapshot to form a single-step violation operation instruction; in a single continuous virtual trial and error process, temporarily storing all generated single-step violation operation instructions in chronological order; when the trial and error process is indicated to end, sorting all the temporarily stored single-step instructions according to their timestamps and encapsulating them into an ordered data list; finally, using the ordered data list as the sequence of instructions input by the student that exceeds the physical safety operation range; in other embodiments, the encapsulation format of the instructions can be JSON, XML, or a custom binary format, which is not limited in this application.

[0035] It should be noted that the instruction sequence described in this application refers to a data sequence organized in chronological order, used to record a series of operation commands and their parameters that exceed the physical security range input by students in the virtual trial-and-error process.

[0036] In step 104, the virtual model is subjected to finite element mechanical analysis by a physical simulation engine in conjunction with the instruction sequence to obtain the simulated stress and strain distribution of key parts in the virtual model.

[0037] In some embodiments, the simulated stress-strain distribution of key parts in the virtual model can be obtained by performing finite element mechanical analysis on the virtual model using a physical simulation engine in conjunction with the instruction sequence, through the following steps: Based on the instruction sequence and the synchronous drive state data, the simulation load and boundary conditions applied to the virtual model are determined; The finite element solver of the physical simulation engine is invoked to apply the simulated loads and boundary conditions to the virtual model and perform mechanical calculations to solve the problem. Based on the solution results of the mechanical calculations, the simulated stress and strain distribution of key parts in the virtual model is extracted.

[0038] In specific implementation, the simulation loads and boundary conditions applied to the virtual model based on the instruction sequence and the synchronous drive state data can be determined in the following ways: First, each instruction in the instruction sequence is read sequentially. According to the instruction type (such as "overspeed rotation" or "overload loading") and its parameter values, a preset load mapping rule is queried to convert the violation parameters into corresponding physical loads. For example, for an overspeed command, it is calculated as a centrifugal force load acting on the center of mass of the rotating component based on the principle of rotational dynamics. At the same time, the synchronous drive state data is parsed to extract the accurate pose of the virtual model before the start of the violation operation. The part of the model corresponding to the installation foundation or fixed support of the physical device is set as a fixed constraint boundary condition, and the current initial velocity or displacement of the model is set as the initial condition for transient analysis. Finally, all the converted loads, extracted constraints, and initial conditions are combined to determine the complete set for finite element analysis. The simulation load and boundary condition set; in other embodiments, the instruction sequence can also be subjected to instructional semantic parsing to identify the illegal operation intentions contained therein (e.g., "significantly increase static load", "rapidly accelerate rotation" or "rapid reciprocating impact"); then, a preset instruction-engineering parameter conversion rule library is called to map the illegal operation intentions and their parameter values ​​to engineering load types and parameters that conform to the physical simulation engine definition; for example, for the instruction "increase the rotational speed to X revolutions per minute", the conversion rule will automatically calculate the corresponding centrifugal force load and gyroscopic effect torque according to the rigid body rotation dynamics formula, and determine their application position and direction on the rotating component; at the same time, combined with the initial pose of the model extracted from the synchronous drive state data, the initial coordinate system and boundary support conditions for load application are determined; finally, the engineering load definition and boundary conditions obtained through instructional semantic parsing and rule conversion are used as the simulation loads and boundary conditions applied to the virtual model.

[0039] It should be noted that the simulation loads and boundary conditions mentioned in this application refer to the set of parameters used in finite element analysis to simulate the physical effects (such as force and acceleration) exerted by students exceeding the safe operation of the physical entity, as well as to define the model fixing and constraint methods.

[0040] In practice, the finite element solver of the physical simulation engine is invoked to apply the simulated loads and boundary conditions to the virtual model, and mechanical calculations are performed. This can be achieved in the following way: First, preprocessing for finite element analysis is performed: corresponding material property parameters are assigned to each component of the virtual model from a pre-set material library, and the model is discretized into a mesh, dividing it into a large number of tiny elements with specific shapes (such as tetrahedrons and hexahedrons); then, each item in the set of simulated loads and boundary conditions is applied to the corresponding nodes, element surfaces, or element volumes of the mesh model according to its location and type; finally, the physical simulation is configured. The analysis type and parameters of the engine (such as calling the solver module of ANSYS Mechanical or the Abaqus / Standard solver) are specified, and a complete solution task including the model, properties, mesh, loads, and boundary conditions is submitted. The solver numerically solves linear or nonlinear equations based on the finite element method to calculate the mechanical response of the model under load. Finally, the mechanical calculation solution results containing detailed data of all nodal displacements, element stresses, and element strains of the model are obtained. In other embodiments, the mesh generation can adopt adaptive mesh refinement technology, and the solver can also adopt an explicit dynamic solver to simulate transient processes such as impacts. This application does not limit this.

[0041] It should be noted that the mechanical calculation and solution described in this application refers to the process by which the physical simulation engine performs numerical calculations on the virtual model based on the simulated load and boundary conditions in order to solve for its internal mechanical responses such as stress, strain, and displacement.

[0042] In specific implementation, the simulated stress-strain distribution of key parts in the virtual model can be extracted based on the solution results of the mechanical calculations in the following ways: For example, according to a predefined list of parts of interest (the parts in the list correspond to known stress concentration areas or key weak components of the physical device in the virtual model), all finite element elements and their nodes belonging to the key parts are located and screened; then, for each screened element, all stress tensor components and strain tensor components of each integration point or node are read, and its equivalent stress (such as Mises stress) and equivalent strain are calculated according to a pre-selected strength theory (such as the fourth strength theory); finally, by associating the identification information of each element with its calculated equivalent stress and equivalent strain values, a mapping relationship is generated with key part elements as the index and stress-strain data as the content, which serves as the simulated stress-strain distribution of key parts in the virtual model; in other embodiments, the distribution data can be further processed into cloud map data for visualization, or statistically generated summary information such as the maximum value and average value of each key part, which is not limited in this application.

[0043] It should be noted that the simulated stress-strain distribution mentioned in this application refers to the detailed data distribution used to quantify and visualize the internal stress and deformation of key parts of the virtual model under conditions exceeding the safe operation of the physical entity.

[0044] In step 105, the simulated stress-strain distribution is compared with the pre-stored material mechanical property parameters, and a safety warning report is generated based on the comparison results when students operate the physical device.

[0045] In some embodiments, comparing the simulated stress-strain distribution with pre-stored material mechanical property parameters can be achieved using the following steps: Extract the strength thresholds corresponding to the materials of each key part of the virtual model from the pre-stored material mechanical property parameters; By comparing the simulated stress-strain distribution with all strength thresholds, the stress state of each key component during student operation can be obtained.

[0046] In practice, extracting the strength thresholds corresponding to the materials of each key part of the virtual model from the pre-stored material mechanical property parameters can be achieved in the following way: First, read the identification information of each key part recorded in the simulated stress-strain distribution, and determine the specific material type used for each key part according to the predefined component-material mapping relationship in the virtual model; then, access the pre-stored material mechanical property parameter database, which stores various strength parameters such as yield strength, tensile strength, and fatigue limit, indexed by material type; based on the determined material type, query and extract the corresponding key strength thresholds from the database (usually, yield strength is used as the main comparison threshold under static load, or fatigue limit is used as the comparison threshold under cyclic load); finally, organize the strength values ​​extracted for each key part and used to determine failure into a mapping set with part identifier as key and strength threshold as value, as the strength thresholds corresponding to the materials of each key part of the virtual model.

[0047] It should be noted that the strength threshold mentioned in this application refers to the mechanical property benchmark value used to determine whether the material of the key parts of the virtual model has yielded or failed.

[0048] For specific implementation, refer to Figure 3As shown in the figure, this is a flowchart illustrating the process of determining stress state in some embodiments of this application. The stress state of each key component during student operation is obtained by comparing the simulated stress-strain distribution with all strength thresholds. This can be achieved in the following way: For example, iterate through each key component included in the simulated stress-strain distribution. For each key component, read the calculated maximum equivalent stress value from its distribution data, and simultaneously search for the corresponding strength threshold from the set of strength thresholds. Then, calculate the ratio of the maximum equivalent stress value to the strength threshold to obtain the stress ratio of that component. Finally, according to a preset judgment rule, the stress state of each key component is determined. The stress ratio is used to determine whether the critical part is in a safe elastic state. If the stress ratio is less than 1, the corresponding critical part is determined to be in a safe elastic state. If the stress ratio is greater than or equal to 1, the corresponding critical part is determined to have yielded. Finally, the identifier of each critical part, its maximum equivalent stress value, the corresponding strength threshold, the calculated stress ratio, and the safety state determination result are combined into a complete record. All such records of critical parts constitute the stress state of each critical part during student operation. In other embodiments, the determination rule can be further subdivided, for example, according to the range of stress ratio, different levels such as low risk and high risk are divided. This application does not limit this.

[0049] It should be noted that the stress state described in this application refers to the judgment result obtained by comparing simulated stress with material strength threshold, which is used to quantitatively evaluate the safety margin or degree of danger of each key part under the current operation.

[0050] In some embodiments, generating a safety warning report for students operating physical devices based on the comparison results can be achieved through the following steps: Determine the safety risk level of each critical component based on all stress states; A safety warning report is generated when students operate the physical device by combining all safety risk levels with the instruction sequence.

[0051] In specific implementation, determining the safety risk level of each key component based on all stress states can be achieved in the following way: for example, traversing each record in the stress state data set of each key component during student operation and reading the stress ratio contained therein; then, mapping the stress ratio to the corresponding safety risk level according to a preset risk level classification rule; the classification rule defines at least two risk level intervals, for example, when the stress ratio is less than a preset first threshold, it is mapped to a low risk level, indicating that the material is within the safe elastic range; when the stress ratio is greater than or equal to the first threshold and less than a preset second threshold, it is mapped to a medium risk level, indicating that the material is close to or has reached yield but has not yet failed immediately; when the stress ratio is greater than or equal to the second threshold, it is mapped to a high risk level, indicating that the material has yielded or is at risk of fracture; finally, a record containing its component identifier, stress ratio, and determined safety risk level is generated for each key component, and all records together constitute the safety risk level of each key component; in other embodiments, the risk level classification rule can be adjusted according to teaching objectives and material characteristics, for example, by adding a critical safety level, which is not limited in this application.

[0052] It should be noted that the safety risk level mentioned in this application refers to the severity level of the actual engineering risks (such as failure or damage) that may be caused by students' violations of regulations in various key parts.

[0053] In specific implementation, the safety warning report for students operating the physical device can be generated by combining all safety risk levels with the instruction sequence in the following manner: First, a pre-set report template is loaded, which defines the structure of the report, including sections such as report overview, virtual operation reproduction, risk analysis, and improvement suggestions, as well as their corresponding data placeholders; then, data is filled in: key operating parameters (such as excessive load values ​​and rotational speed) and operating context from the instruction sequence are filled into the virtual operation reproduction section; the risk level, excessive value, and location information of each key part in the safety risk level set are filled into the risk analysis section, and the corresponding stress cloud map data in the simulated stress-strain distribution is associated to generate a visual illustration; based on the risk level and location information, targeted improvement suggestions are matched and generated from a pre-set failure mode and consequence analysis knowledge base; finally, a document generation engine is called to serialize the template filled with all data and graphic content into a standard format document as a safety warning report for students operating the physical device; in other embodiments, natural language generation technology can also be introduced into the report generation process to automatically summarize the analysis results, which is not limited in this application.

[0054] It should be noted that the structured safety warning report mentioned in this application refers to a comprehensive teaching document used to systematically provide students with feedback on their operational errors, reveal potential engineering consequences, relate them to mechanical principles, and provide suggestions for improvement.

[0055] Furthermore, in another aspect of this application, in some embodiments, this application provides a virtual simulation teaching system for university students, with reference to... Figure 4 The figure is a schematic diagram of the structure of a virtual simulation teaching system for college students according to some embodiments of this application. The virtual simulation teaching system 200 for college students includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to acquire real-time pose data of key moving parts when students operate the physical device. Processing module 202, in this application, is mainly used to drive a virtual model with the same geometric topology as the physical device in a virtual simulation environment based on the real-time pose data, thereby generating synchronous drive state data; In addition, the processing module 202 in this application is also used to receive a sequence of instructions input by the student that exceeds the scope of physical safe operation in the virtual model state represented by the synchronous drive state data; In addition, the processing module 202 in this application is also used to perform finite element mechanical analysis on the virtual model by combining the instruction sequence with the physical simulation engine, so as to obtain the simulated stress and strain distribution of key parts in the virtual model; The execution module 203 in this application is mainly used to compare the simulated stress and strain distribution with the pre-stored material mechanical property parameters, and generate a safety warning report when students operate the physical device based on the comparison results.

[0056] In addition, this application also provides a computer device, which includes a memory and a processor. The memory stores code, and the processor is configured to acquire the code and execute the virtual simulation teaching method for college students described above.

[0057] In some embodiments, reference Figure 5 This figure is an internal structural diagram of a computer device implementing a virtual simulation teaching method for university students, according to some embodiments of this application. The virtual simulation teaching method for university students in the above embodiments can be implemented through... Figure 5 The computer device shown is used to implement this, and the computer device 300 includes at least one processor 301, a communication bus 302, a memory 303, and at least one communication interface 304.

[0058] The processor 301 may be a general-purpose central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more devices used to control the execution of the virtual simulation teaching method for college students in this application.

[0059] The communication bus 302 is used to transmit information between the aforementioned components.

[0060] Memory 303 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CDROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disks or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory 303 may exist independently and be connected to processor 301 via communication bus 302. Memory 303 may also be integrated with processor 301.

[0061] The memory 303 stores program code for executing the scheme of this application, and its execution is controlled by the processor 301. The processor 301 executes the program code stored in the memory 303. The program code may include one or more software modules. The virtual simulation teaching method for college students in the above embodiments can be implemented by the processor 301 and one or more software modules in the program code in the memory 303.

[0062] Communication interface 304 uses any transceiver-like device to communicate with other devices or communication networks, such as Ethernet, radio access network (RAN), wireless local area networks (WLAN), etc.

[0063] In a specific implementation, as one example, a computer device may include multiple processors, each of which may be a single-core processor or a multi-core processor. Here, a processor may refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0064] The aforementioned computer device can be a general-purpose computer device or a special-purpose computer device. In specific implementations, the computer device may be a desktop computer, a portable computer, a network server, a handheld digital assistant (PDA), a mobile phone, a tablet computer, a wireless terminal device, a communication device, or an embedded device. This application does not limit the type of computer device.

[0065] In addition, this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned virtual simulation teaching method for college students.

[0066] In summary, the virtual simulation teaching system and method for university students disclosed in this application collects real-time pose data of key moving parts when students operate the physical device; drives a virtual model with the same geometric topology as the physical device in the virtual simulation environment based on the real-time pose data, thereby generating synchronous drive state data; receives a sequence of instructions from students that exceed the safe operating range of the physical device under the virtual model state represented by the synchronous drive state data; performs finite element mechanical analysis on the virtual model using a physical simulation engine in conjunction with the instruction sequence to obtain the simulated stress and strain distribution of key parts in the virtual model; compares the simulated stress and strain distribution with pre-stored material mechanical property parameters, and generates a safety warning report for students operating the physical device based on the comparison results; and creates a teaching feedback loop that quantifies abstract mechanical concepts into specific risk warnings.

[0067] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0068] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A virtual simulation teaching method for college students, characterized in that, Includes the following steps: Collect real-time pose data of key moving parts when students operate the physical device; Based on the real-time pose data, a virtual model with the same geometric topology as the physical device is driven in a virtual simulation environment, thereby generating synchronous drive state data; In the virtual model state represented by the synchronous drive state data, a sequence of instructions that exceeds the safe operation range of the entity is received from the student. The virtual model is subjected to finite element mechanical analysis by combining the physical simulation engine with the instruction sequence to obtain the simulated stress and strain distribution of key parts in the virtual model. The simulated stress-strain distribution is compared with the pre-stored material mechanical property parameters, and a safety warning report is generated based on the comparison results when students operate the physical device.

2. The method as described in claim 1, characterized in that, Based on the real-time pose data, a virtual model with the same geometric topology as the physical device is driven in a virtual simulation environment, thereby generating synchronous drive state data, specifically including: The real-time pose data is mapped into virtual driving commands in a virtual environment according to a preset coordinate transformation relationship. The virtual drive instructions are subjected to inverse kinematics solution to obtain the motion parameters of each key moving component in the virtual model that is consistent with the geometric topology of the physical device. At each simulation frame refresh, motion parameters of all joint components in the virtual model are collected, thereby generating synchronous drive state data.

3. The method as described in claim 1, characterized in that, In the virtual model state represented by the synchronous drive state data, the sequence of instructions received from the student that exceeds the scope of physical safety operation specifically includes: The real-time running status of the corresponding virtual model is analyzed based on the synchronous drive state data; Based on the real-time operating status, a human-computer interaction input interface is displayed in the virtual simulation environment, allowing input of parameters that exceed the safe operating range of the physical entity. The system receives parameters for violations set by students through the human-computer interaction input interface, and then obtains a sequence of instructions input by students that exceed the safe operating range of the physical entity.

4. The method as described in claim 1, characterized in that, By using a physical simulation engine in conjunction with the instruction sequence to perform finite element mechanical analysis on the virtual model, the simulated stress and strain distribution of key parts in the virtual model is obtained, specifically including: Based on the instruction sequence and the synchronous drive state data, the simulation load and boundary conditions applied to the virtual model are determined; The finite element solver of the physical simulation engine is invoked to apply the simulated loads and boundary conditions to the virtual model and perform mechanical calculations to solve the problem. Based on the solution results of the mechanical calculations, the simulated stress and strain distribution of key parts in the virtual model is extracted.

5. The method as described in claim 1, characterized in that, The comparison of the simulated stress-strain distribution with the pre-stored material mechanical property parameters specifically includes: Extract the strength thresholds corresponding to the materials of each key part of the virtual model from the pre-stored material mechanical property parameters; By comparing the simulated stress-strain distribution with all strength thresholds, the stress state of each key component during student operation can be obtained.

6. The method as described in claim 1, characterized in that, The safety warning report generated based on the comparison results when students operate the physical device specifically includes: Determine the safety risk level of each critical component based on all stress states; A safety warning report is generated when students operate the physical device by combining all safety risk levels with the instruction sequence.

7. The method as described in claim 1, characterized in that, The physical device is a physics teaching instrument with movable parts used in engineering experimental teaching in universities.

8. A virtual simulation teaching system for university students, characterized in that, include: The data acquisition module is used to collect real-time pose data of key moving parts when students operate the physical device; The processing module is used to drive a virtual model with the same geometric topology as the physical device in a virtual simulation environment based on the real-time pose data, thereby generating synchronous drive state data. The processing module is also used to receive a sequence of instructions input by the student that exceeds the scope of physical safe operation in the virtual model state represented by the synchronous drive state data. The processing module is also used to perform finite element mechanical analysis on the virtual model by combining the instruction sequence with the physical simulation engine, so as to obtain the simulated stress and strain distribution of key parts in the virtual model. The execution module is used to compare the simulated stress-strain distribution with the pre-stored material mechanical property parameters, and generate a safety warning report for students operating the physical device based on the comparison results.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the virtual simulation teaching method for college students as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the virtual simulation teaching method for college students as described in any one of claims 1 to 7.

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