A VR-based hydrogen fuel cell vehicle fire rescue disposal training and drilling system

Abstract

This invention relates to the field of fire rescue training technology for hydrogen fuel cell vehicles, and discloses a VR-based training and drill system for fire rescue and disposal of hydrogen fuel cell vehicles. The system includes a storage module for storing training data, constructing a knowledge graph based on the training data, and a construction module for building a basic vehicle model using the knowledge graph, and generating adversarial examples based on the training data. This invention integrates advanced technologies such as knowledge graphs, adversarial example generation, physical simulation, natural language interaction, and intelligent behavior analysis to construct a highly realistic, dynamically evolving, multi-user collaborative, and adaptive immersive training environment. Operators can repeatedly practice standardized handling procedures for various hydrogen fuel cell vehicle accidents under zero-risk conditions, and receive real-time explanations of principles and operational corrections, significantly improving emergency response capabilities and safety awareness.

Description

Technical Field

[0001] This invention relates to the field of fire rescue training technology for hydrogen fuel cell vehicles, specifically a VR-based training and drill system for fire rescue and disposal of hydrogen fuel cell vehicles. Background Technology

[0002] Hydrogen fuel cell vehicles are cars that use hydrogen as their primary energy source for propulsion. While conventional internal combustion engines typically use diesel or gasoline, hydrogen cars use gaseous hydrogen. Fuel cells and electric motors replace conventional engines. The principle of a hydrogen fuel cell is that hydrogen is fed into the fuel cell, where electrons from the hydrogen atoms are blocked by a proton exchange membrane and conducted from the negative electrode to the positive electrode through an external circuit, generating electricity to drive the electric motor. Protons, however, can pass through the proton exchange membrane and combine with oxygen to form pure water mist, which is then discharged.

[0003] Existing VR-based emergency rescue training systems for hydrogen fuel cell vehicles can only conduct simulation training based on pre-set scenarios. However, the actual construction of hydrogen fuel cell vehicles varies from manufacturer to manufacturer, making it impossible to promptly adapt the rescue knowledge learned in the simulation during actual rescue operations. This results in the system being qualified in simulation training but too rigid in actual rescue, failing to quickly formulate a rescue plan and leading to slow rescue efficiency. Summary of the Invention

[0004] This invention provides a VR-based training and drill system for fire rescue and disposal of hydrogen fuel cell vehicles. It has the beneficial effect of good simulation training effect and solves the problem mentioned in the background technology that the rescue knowledge learned by simulation cannot be updated in a timely manner, resulting in qualified simulation training, but too rigid in actual rescue, unable to form a rescue quickly, and thus slow rescue efficiency.

[0005] This invention provides the following technical solution: a VR-based training and drill system for fire rescue and response of hydrogen fuel cell vehicles, comprising:

[0006] A storage module is used to store training data and construct a knowledge graph based on the training data;

[0007] The construction module constructs a basic vehicle model using a knowledge graph and generates adversarial examples based on the training data.

[0008] The construction module further utilizes the adversarial examples to perturb and train the basic vehicle model, generating a random vehicle model with a combination of random fault types, damage locations, and system states.

[0009] The VR module, based on the training data and random vehicle models, builds a simulated emergency rescue training scenario.

[0010] The language module integrates a natural language processing model to receive and understand the voice or text commands input by the operator in the simulated emergency rescue training scenario in real time, and generate corresponding virtual rescue actions based on the semantic parsing results.

[0011] The analysis module is used to record operation logs, environmental status, and interactive behavior data in the simulated rescue emergency training scenario;

[0012] The analysis module reverse-engineers the random vehicle model based on the knowledge graph, extracts several basic principles related to the current scenario, and combines these basic principles to make real-time judgments on the safety, compliance, and effectiveness of the rescue actions, and provides targeted learning guidance and corrective feedback to the operator.

[0013] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and drill system of the present invention, the storage module is also used to record and store all rescue actions performed by the operator in the simulated emergency rescue training scenario and their corresponding timestamps, operation paths and decision-making basis, for subsequent review and analysis and personalized training optimization.

[0014] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and exercise system of the present invention, the analysis module is further used to collect and analyze the force data generated when the rescue action is applied to the random vehicle model;

[0015] The force data includes the magnitude, direction, point of application, and duration of the force.

[0016] The analysis module dynamically adjusts the structural state or fault performance of the random vehicle model based on the stress data and preset mechanical response rules, so as to reflect the changes in the deformation, displacement, component detachment or hydrogen system leakage risk of the vehicle after being subjected to force in the real physical world.

[0017] As an optional solution of the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and exercise system described in this invention, the analysis module is further used to dynamically deduce and generate at least one rescue accident situation after updating the random vehicle model according to the force data and combining the association information on hydrogen fuel cell system structure, material properties, safety threshold and accident evolution path in the knowledge graph.

[0018] The unexpected situations encountered during the rescue included aggravated hydrogen leakage, displacement of high-pressure hydrogen storage cylinders, short circuit and fire of fuel cell stack, and secondary structural collapse.

[0019] The unexpected rescue situation is fed back to the VR module in real time, so as to present corresponding visual, auditory and interactive warnings in the simulated rescue emergency training scenario.

[0020] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and response training and drill system of the present invention, it further includes:

[0021] The acquisition module is used to collect several sound signals and several image data in a simulated emergency rescue training scenario in real time;

[0022] A sensing module is used to detect the operator's emotional fluctuation state based on the sound signal and image data, the emotional fluctuation state including tension, confusion, anxiety or distraction;

[0023] The output module is used to select audio prompts or visual guidance content that matches the emotional fluctuation state from a preset teaching resource library, and dynamically generate and output personalized teaching guidance videos in combination with the corresponding basic principles in the knowledge graph, so as to assist the operator in adjusting the operation strategy and strengthening the learning of key safety knowledge.

[0024] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and response training and drill system of the present invention, it further includes at least one sensor, which is used to collect the physiological and / or behavioral status data of the operator in real time.

[0025] The analysis module is also used to assess the operator's current focus, stability and operational proficiency based on the status data, and dynamically match the preset force threshold and operational precision range required for the current rescue action based on the assessment results.

[0026] The force threshold and the range of operational precision are associated with the safety specification nodes of the corresponding rescue steps in the knowledge graph, and are used to determine whether the operator's actions comply with the emergency response standards for hydrogen fuel cell vehicles.

[0027] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and drill system of the present invention, the storage module is also used to record the emotional fluctuation points marked by the operator during the training process, and each emotional fluctuation point corresponds to a training content.

[0028] The analysis module is used to acquire current social news hotspot data and, in combination with the semantic content associated with the emotional fluctuation points, identify the situational themes that the operator may pay attention to or easily resonate with.

[0029] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and drill system of the present invention, wherein: based on the distribution pattern of historical emotional fluctuation points, the operator's preference range for character types and voice styles in the teaching content is predicted;

[0030] Based on the aforementioned preference range and the monitoring results of the real-time emotion fluctuation sensor, the virtual character image, gender, age, occupational characteristics, and the tone, speed, and emotional color of the voice-over in the teaching guidance screen of the VR module are dynamically adjusted to enhance the operator's emotional engagement and learning acceptance.

[0031] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and response training and drill system of the present invention, there are several operators;

[0032] Several operators simultaneously access the same simulated emergency rescue training scenario through their respective VR terminals;

[0033] The analysis module is also used to monitor the task collaboration status between operators in real time, evaluate the temporal logic, division of responsibilities and operational consistency of team collaboration based on the emergency response process specifications in the knowledge graph, and generate a collaborative training feedback report.

[0034] As an optional solution to the VR-based hydrogen fuel cell vehicle fire rescue and disposal training and drill system of the present invention, the status data includes heart rate, hand tremor amplitude, operation speed, limb posture and eye movement trajectory.

[0035] The present invention has the following beneficial effects:

[0036] 1. This VR-based training and drill system for fire rescue and emergency response in hydrogen fuel cell vehicles integrates advanced technologies such as knowledge graphs, adversarial example generation, physical simulation, natural language interaction, and intelligent behavior analysis to construct a highly realistic, dynamically evolving, multi-user collaborative, and adaptive immersive training environment. Operators can repeatedly practice standardized procedures for handling various hydrogen fuel cell vehicle accidents under zero-risk conditions, and receive real-time explanations of principles and operational corrections, significantly improving their emergency response capabilities and safety awareness.

[0037] 2. This VR-based training and drill system for fire rescue and emergency response of hydrogen fuel cell vehicles adheres to the technical standards for emergency response of hydrogen fuel cell vehicles while providing flexible error tolerance based on the operator's real-time status. This avoids ineffective failures due to short-term tension or insufficient skills, thereby improving the completion rate of training and learning confidence. It also ensures the internalization and implementation of safety regulations, making emergency training truly intelligent, personalized, and practical.

[0038] 3. This VR-based training and drill system for fire rescue and disposal of hydrogen fuel cell vehicles achieves individualized instruction through its output module, and moves further towards situational instruction. It deeply integrates the operator's physiological state, psychological preferences, social cognitive background, and knowledge graph to build an immersive learning partner with emotional intelligence. This significantly enhances the operator's emotional engagement, trust, and knowledge acceptance, thereby achieving the dual goals of skills improvement and psychological resilience building in the high-pressure, high-risk emergency training of hydrogen fuel cell vehicles. Attached Figure Description

[0039] Figure 1 This is a schematic diagram of the overall system architecture of the present invention.

[0040] Figure 2 This is a schematic diagram of the random vehicle model generation and perturbation training process of the present invention.

[0041] Figure 3 This is a schematic diagram of the closed-loop process of emotion perception and personalized teaching guidance in this invention. Detailed Implementation

[0042] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0043] Example 1

[0044] Please see Figures 1-3 A VR-based training and drill system for fire rescue and response of hydrogen fuel cell vehicles, comprising:

[0045] The storage module is used to store training data and construct a knowledge graph based on the training data.

[0046] The construction module constructs a basic vehicle model using a knowledge graph and generates adversarial examples based on training data.

[0047] The construction module further utilizes adversarial examples to perturb and train the basic vehicle model, generating a stochastic vehicle model with a combination of random fault types, damage locations, and system states.

[0048] The VR module is used to build simulated emergency rescue training scenarios based on training data and random vehicle models.

[0049] The language module integrates a natural language processing model to receive and understand the voice or text commands input by the operator in the simulated emergency rescue training scenario in real time, and generate corresponding virtual rescue actions based on the semantic parsing results.

[0050] The analysis module is used to record operation logs, environmental status, and interactive behavior data in simulated emergency rescue training scenarios.

[0051] The analysis module uses a knowledge graph to reverse-engineer a random vehicle model, extracting several fundamental principles relevant to the current scenario. Combining these principles, the analysis module makes real-time judgments on the safety, compliance, and effectiveness of rescue actions, and provides operators with targeted learning guidance and corrective feedback.

[0052] On the hardware side, the system is deployed on a high-performance GPU server (such as a workstation equipped with an NVIDIA RTX A6000 graphics card) to run the physics engine, AI model and knowledge graph services; the operation end uses a VR all-in-one machine that supports 6DoF (six degrees of freedom) tracking (such as MetaQuest Pro or HTC ViveFocus3), equipped with a controller, microphone and optional physiological sensors (such as heart rate belt, eye tracker).

[0053] The network environment uses gigabit LAN or Wi-Fi 6 to ensure low-latency synchronization for multiple users.

[0054] On the software side, the system develops VR scenes based on the Unity 2022.3LTS engine and integrates the NVIDIA PhysX 5.1 physics engine to realize vehicle deformation and component interaction; AI inference is trained using the PyTorch framework and exported as an ONNX format model, which is accelerated by ONNXRuntime; the knowledge graph is stored and queried using the Neo4j graph database; inter-module communication adopts the gRPC protocol, and real-time data exchange between the VR terminal and the server is achieved through WebSocket.

[0055] The storage module is responsible for collecting, organizing, and managing all structured and unstructured data related to training. Its core data includes:

[0056] The historical accident case database contains real accident reports of hydrogen fuel cell vehicles from home and abroad, including accident types (frontal collision, rollover, rear-end collision, etc.), damaged parts, leakage concentration, handling measures and consequences.

[0057] The standards and specifications library integrates national standards (such as GB / T37124 "Safety Requirements for Hydrogen Fuel Cell Electric Vehicles"), industry guidelines (such as NFPA2 "Hydrogen Technology Specification"), and enterprise operating procedures.

[0058] User interaction logs record each operator's operation sequence, voice commands, number of errors, completion time, and self-evaluation emotion markers during training;

[0059] The teaching resource library includes supplementary teaching materials such as 3D animations, audio explanations, and picture cards.

[0060] Based on the above data, the storage module automatically constructs a domain knowledge graph using natural language processing technology. The graph is organized in the form of entities, relationships, and attributes.

[0061] The entities include vehicle components (such as 70MPa hydrogen storage cylinders, fuel cell stacks, and MSD maintenance switches), risk types (hydrogen explosion, high-voltage electric shock), and response actions (closing the main valve, disconnecting the MSD), etc.

[0062] Relationships include semantic links such as containment, triggering, prohibition, and execution prior to. For example, the graph contains a path where a hydrogen storage tank leaks hydrogen if impacted, and explodes if exposed to a spark; therefore, the hydrogen concentration must be detected first. This graph is stored in the Neo4j database, supporting efficient semantic queries and providing knowledge support for subsequent model building and behavior evaluation.

[0063] The core task of the construction module is to generate highly uncertain and realistic random vehicle fault models, avoiding the solidification of training scenarios. First, based on the vehicle topology in the knowledge graph, the construction module builds a parameterized basic vehicle digital twin model in Unity. This model includes not only external geometry but also embedded physical properties (e.g., hydrogen storage tank mass 85kg, stiffness coefficient), electrical properties (high-voltage circuit voltage ≥650V), and hydrogen energy properties (pipeline sealing rating IP67). Then, the construction module trains a Wasserstein generative adversarial network (WGAN-GP) using historical accident data to generate adversarial examples, simulating rare but dangerous complex fault modes in reality. For example, the network can output a set of perturbation vectors: a 30% reduction in the strength of the hydrogen storage tank support, micro-cracks in the high-voltage wiring harness insulation, and coolant pump failure. These perturbations are injected into the basic model, and the PhysX engine modifies the physical parameters of the corresponding components (e.g., lowering the collision threshold, increasing thermal conductivity), thereby generating a random vehicle model. Before each training session, the system generates a new random model, ensuring that the operator cannot rely on memory to complete the task.

[0064] The VR module transforms random vehicle models into interactive, immersive training scenarios. Based on the accident context, such as a rear-end collision on a highway at night, it dynamically configures ambient lighting, weather, background sound effects, and smoke effects. Hydrogen leaks are visualized through a semi-transparent white particle system; the higher the concentration, the greater the particle density, accompanied by a pungent odor warning (simulated through VR headset vibration or voice). Operators can pick up virtual tools (hydraulic spreaders, hydrogen detectors, fire blankets, etc.) using VR controllers to perform precise operations. For example, when rotating a valve, torque and angle are calculated in real time; excessive force or incorrect direction will trigger component damage. All interactions are physically simulated using PhysX to ensure realistic mechanical feedback. Furthermore, the VR module supports multi-user collaborative training. Multiple operators can take on roles such as commander, inspector, and demolition expert, coordinating actions through voice communication. The system uses a Photon Fusion network plugin for status synchronization, ensuring that all terminals see consistent vehicle status and operation results, with synchronization latency controlled within 50 milliseconds.

[0065] The language interaction module enables operators to interact with the system using natural language. When an operator says "close the main hydrogen valve" or "is there an open flame?", the language interaction module converts the speech into text using the Whisper speech recognition model. Subsequently, a fine-tuned domain-specific BERT model performs intent classification and slot filling on the text, identifying the action type (close / detect / call) and the target object (main valve / fuel stack / support). The parsed results are mapped to predefined action commands, driving the VR character to perform the corresponding operation. For example, closing the main hydrogen valve will trigger the RotateValve("main_h2_valve", -90°) function, causing the virtual valve to rotate to the closed position. If the statement is ambiguous, such as "turn off the blue switch," the system will combine the visible components in the current field of view with the knowledge graph to intelligently infer the most likely target (e.g., the blue handle corresponds to the MSD switch).

[0066] The analysis module monitors the entire training process and provides feedback. It records operation logs in real time, including timestamps, tool usage, action trajectories, and environmental parameters (hydrogen concentration, temperature). Whenever the operator performs an action, the module immediately queries the knowledge graph to extract fundamental principles related to the current vehicle state. For example, if the operator attempts to strike the hydrogen storage tank area with a metal tool, the system will recognize that impacts to the hydrogen storage tank area are prohibited and could potentially cause hydrogen leakage. Based on this principle, the module performs a three-dimensional evaluation of the action.

[0067] Safety, and whether it will cause secondary accidents (such as exacerbating the leakage);

[0068] Compliance, whether it conforms to standard procedures (such as whether the power is cut off before operation);

[0069] Effectiveness, whether the disposal objective has been achieved (e.g., successful valve closure);

[0070] If a violation is detected, the system intervenes immediately: freezing the operation in VR, playing a warning voice (such as a warning that a high-pressure hydrogen pipe exists in the area), and highlighting the danger zone in the field of view. Simultaneously, the system retrieves a matching 3D animation or knowledge card from the teaching resource library to explain why it is not permissible to dismantle the device in that location, achieving a closed-loop learning process of making a mistake, understanding, and correcting it.

[0071] In summary, taking the example of a hydrogen fuel cell vehicle experiencing a slow hydrogen leak due to a rear-end collision on an urban road:

[0072] Scenario initialization: The system generates a random vehicle with a detached rear bumper and a hydrogen concentration of 2.1%, and places it at an intersection;

[0073] Risk assessment phase: The operator uses voice commands to detect the hydrogen concentration; the system invokes a virtual detector, which displays the concentration has risen to 3.9%.

[0074] Incorrect attempt: The operator walks towards the trunk to retrieve tools, and Leap Motion detects the hand approaching the DC / DC converter area;

[0075] Intelligent intervention: The analysis module determines that the MSD is not disconnected, posing a risk of electric shock. It immediately freezes the operation and provides a voice prompt: Please unplug the maintenance switch first.

[0076] Correct procedure: The operator locates and removes the MSD; the system confirms the high-voltage circuit is disconnected and the alarm is deactivated.

[0077] Completed handling: The operator closed the main hydrogen valve, the hydrogen concentration decreased, the system generated a scoring report, the operation was compliant, and the time taken was 2 minutes and 18 seconds. It is recommended to strengthen the awareness of early warning of leaks.

[0078] In summary, this example integrates advanced technologies such as knowledge graphs, adversarial example generation, physical simulation, natural language interaction, and intelligent behavior analysis to construct a highly realistic, dynamically evolving, multi-user collaborative, and adaptive immersive training environment. Operators can repeatedly practice standardized handling procedures for various hydrogen fuel cell vehicle accidents (such as collision leaks, fuel cell stack fires, and high-voltage short circuits) under zero-risk conditions, and receive real-time explanations of principles and operational corrections, significantly improving emergency response capabilities and safety awareness.

[0079] It should be noted that the objective function formula for stochastic vehicle model generation and mechanical response in WGAN-GP is as follows:

[0080]

[0081] The goal is to generate realistic adversarial samples, namely, composite failure modes that are rare but dangerous in reality (such as "weakened hydrogen storage tank support + high-voltage line insulation failure").

[0082] Where G is the generator, which takes random noise z as input and outputs a fault disturbance vector;

[0083] D is the discriminator, which determines whether the input is real accident data or generated data;

[0084] This represents the distribution of real historical accident data;

[0085] The noise distribution is either standard normal or uniform.

[0086] The sample is obtained by random interpolation between the real sample x and the generated sample G(z);

[0087] For the discriminator to input The gradient;

[0088] This is the gradient penalty coefficient (usually taken as 10).

[0089] Example 2

[0090] This embodiment is an improvement upon embodiment 1. For details, please refer to [link / reference]. Figures 1-3 The storage module is also used to record and store all rescue actions performed by the operator in the simulated emergency rescue training scenario, as well as their corresponding timestamps, operation paths and decision-making basis, for subsequent review and analysis and personalized training optimization.

[0091] The storage module is also configured to record and structure all rescue actions performed by the operator and related contextual information in real time during simulated emergency rescue training. For each operation interaction, the storage module not only saves the action type (such as "close the main hydrogen valve", "use hydraulic shears to break the B-pillar", "start the gas detector"), but also simultaneously records the following data:

[0092] The timestamp, accurate to the millisecond, is used to reconstruct the operation sequence logic and determine whether the order of steps conforms to the emergency procedure specifications (e.g., whether the high-voltage component operation was performed before the power outage).

[0093] Operation path, including the operator's movement trajectory in virtual space, spatial coordinate sequence of handles or tools, and changes in gaze focus (if equipped with eye tracking), is used to analyze operation efficiency, spatial judgment ability, and attention distribution.

[0094] The decision-making basis includes the content of the voice commands triggered before the operation, system prompts, and environmental status snapshots (such as the hydrogen concentration at that time, the vehicle damage status, and the teammates' positions), which are used to trace the operator's judgment logic and situational awareness level.

[0095] The data is stored in the database as structured logs, with each record associated with a unique training session ID, operator ID, and scenario configuration parameters. The system supports replaying the entire rescue process along a timeline and automatically annotates key nodes (such as "compliant operation points," "high-risk misoperation points," and "collaboration waiting delays") using a knowledge graph.

[0096] Based on this, the analysis module can construct an operator's emergency response capability profile, identifying their strengths and weaknesses in areas such as risk identification, process adherence, tool usage, and team collaboration, and generate personalized reinforcement training plans accordingly. For example, for users who frequently make mistakes in high-voltage areas, a special training package on electrical safety isolation can be automatically pushed; for users who hesitate too long in making decisions, pressure adaptability training for sudden leakage scenarios can be added.

[0097] Example 3

[0098] This embodiment is an improvement upon embodiment 2. For details, please refer to [link / reference]. Figures 1-3 The analysis module is also used to collect and analyze the force data generated when rescue actions are applied to a random vehicle model;

[0099] Force data includes the magnitude, direction, point of application, and duration of the force;

[0100] The analysis module dynamically adjusts the structural state or fault behavior of the random vehicle model based on the force data and the preset mechanical response rules, so as to reflect the changes in the deformation, displacement, component detachment or hydrogen system leakage risk of the vehicle after being subjected to force in the real physical world.

[0101] The analysis module is further configured to collect and analyze in real time the force data exerted on a random vehicle model by the rescue actions performed by the operator during the simulated rescue. This force data is generated collaboratively by the VR interaction system and the physics engine, and specifically includes four dimensions:

[0102] The magnitude of the force, expressed in Newtons (N), represents the instantaneous or continuous load applied by the operator using virtual tools such as hydraulic spreaders, window breakers, and traction ropes.

[0103] The direction of force application is represented by a three-dimensional spatial vector (x, y, z).

[0104] The point of application is identified by the three-dimensional position (Px, Py, Pz) in the world coordinate system, indicating the specific part of the vehicle model where the force is applied (such as the upper edge of the A-pillar, the root of the hydrogen storage tank bracket, and the high-voltage connector housing).

[0105] Duration records the start and end times of the force application and the total duration, used to distinguish between impact loads and steady-state loads;

[0106] The analysis module inputs the aforementioned stress data into a pre-defined mechanical response rule base. This rule base is built upon structural strength test data of real hydrogen fuel cell vehicles, material mechanical parameters (such as yield strength of aluminum alloys and fracture toughness of composite materials), and industry safety standards (such as ISO 15869 impact resistance requirements for hydrogen storage containers). It employs simplified finite element models or rigid body dynamics rules to achieve rapid reasoning. For example:

[0107] If an impulse exceeding 500 N·s is applied to the hydrogen storage tank installation area, perpendicular to the tank axis, plastic deformation of the support structure will be triggered. If a lateral force greater than 200 N is continuously applied to the high-voltage electrical connector for more than 3 seconds, it will be considered a connector detachment, activating the risk of high-voltage leakage. If a concentrated load is applied to the edge of the fuel cell stack casing, causing local stress to exceed the threshold, it will lead to seal failure and activate the hydrogen micro-leakage model.

[0108] Based on the above rules, the analysis module dynamically updates the state of the random vehicle model, including:

[0109] Geometric deformation is achieved by modifying the vertex offset of SkinnedMeshRenderer in Unity to create local depressions or bends.

[0110] Component displacement is used to adjust the position and rotation of rigid body components, simulating component loosening or detachment;

[0111] Fault evolution activates new fault nodes (such as "hydrogen pipe joint leakage" or "DC / DC short circuit") and updates environmental parameters (hydrogen concentration rise rate, temperature anomaly).

[0112] When a risk escalates, the new risk is marked in the knowledge graph, and the VR module is notified to render the corresponding warning (such as flashing red or alarm sound effects).

[0113] In summary, this embodiment, through a physical and logical closed loop of operation, force, structural response, fault evolution, and risk feedback, enables operators to deeply understand the core safety concept that improper force application may trigger secondary disasters. For example, when a trainee uses an expander to forcibly pry open the hydrogen system hatch, the system not only displays door deformation but also triggers hydrogen leakage due to support breakage, thus requiring an immediate switch to gas control procedures. This dynamic chain reaction cannot be achieved through a preset script but relies on the force-driven model evolution mechanism of this invention.

[0114] It should be noted that the formula for determining the consistency of force direction is:

[0115]

[0116] The purpose is to determine whether the force applied by the operator is acting on critical components from a "dangerous direction" (such as knocking on the hydrogen storage cylinder laterally).

[0117] Symbol explanation:

[0118] The actual applied force vector (unit: N);

[0119] The permissible safe force direction for this component (e.g., hydrogen storage tanks are only allowed to be subjected to axial pressure).

[0120] The angle between the two vectors;

[0121] This is the maximum permissible deviation angle (e.g., 30°).

[0122] Engineering significance: Even if the force is within the limit, leakage may still occur if the direction is wrong (such as lateral impact). This formula is used for directional constraint judgment in the rule base.

[0123] Example 4

[0124] This embodiment is an improvement upon embodiment 6. For details, please refer to [link / reference]. Figures 1-3 The analysis module is also used to dynamically deduce and generate at least one rescue accident scenario after updating the random vehicle model based on the stress data and combining the association information in the knowledge graph about the structure, material properties, safety threshold and accident evolution path of the hydrogen fuel cell system.

[0125] Unexpected situations during the rescue included escalation of hydrogen leakage, displacement of high-pressure hydrogen storage cylinders, short circuit and fire of fuel cell stack, and secondary structural collapse;

[0126] The system provides real-time feedback on unexpected rescue situations to the VR module, enabling the presentation of corresponding visual, auditory, and interactive alerts in simulated emergency rescue training scenarios.

[0127] After dynamically updating the structural state or fault behavior of the random vehicle model based on the stress data, the analysis module further combines pre-built domain knowledge in the knowledge graph to perform multi-step causal inference to generate rescue contingency scenarios that conform to engineering logic and safety specifications. The knowledge graph has structured storage of key attributes and association rules for hydrogen fuel cell vehicles, including:

[0128] System structure information (such as the spatial layout of hydrogen storage tanks and fuel cell stacks, and the routing of high-pressure pipelines);

[0129] Material property parameters (such as the impact resistance threshold of carbon fiber hydrogen storage tanks and the thermal stability of battery bipolar plates).

[0130] Safety threshold standards (such as the lower explosive limit of hydrogen 4%, and the power outage delay time of high-pressure systems ≤500ms).

[0131] Accident evolution path (e.g., support breakage leads to bottle displacement, bottle displacement leads to pipeline strain, pipeline strain leads to joint leakage, joint leakage leads to concentration accumulation, concentration accumulation leads to static electricity ignition).

[0132] When a random vehicle model experiences a change in state due to stress (e.g., plastic deformation of a hydrogen storage tank support), the analysis module immediately queries the knowledge graph to identify potential downstream risk chains triggered by this change. Based on this, the system dynamically simulates and generates at least one emergency rescue scenario, typical of which include:

[0133] The hydrogen leak worsened, with the leakage rate increasing from 0.5 g / s to 3.0 g / s due to loose pipe joints;

[0134] The high-pressure hydrogen storage cylinder shifted, with the cylinder body deflecting by more than 15 centimeters, which may have caused it to collide with adjacent high-pressure components.

[0135] The fuel cell stack short-circuited and caught fire. The deformation of the vehicle body squeezed the fuel cell stack, causing an internal short circuit and producing an open flame.

[0136] The secondary collapse of the structure caused the A-pillar to become unstable due to the initial demolition, which in turn led to the partial collapse of the roof, endangering the trapped people.

[0137] Once the aforementioned unexpected situation is deduced, the analysis module immediately encodes it into a multimodal warning command and pushes it to the VR module in real time via a standard interface. Based on this, the VR module simultaneously presents corresponding visual, auditory, and interactive feedback in the simulated rescue emergency training scenario, for example:

[0138] Visually, high-density white smoke particles are generated at the leak point, orange-red dynamic flame effects are rendered in the flame area, and a semi-transparent red warning halo is superimposed on the danger zone.

[0139] On the auditory level, the system plays harsh noises such as gas hissing, electric arc cracking, or structural metal twisting, and uses spatial audio technology to locate the direction of the sound source.

[0140] At the interaction level, operators are restricted from performing high-risk actions (such as automatically locking demolition tools), or a virtual HUD prompt is forcibly displayed. For example, a warning may be issued: "Displacement of hydrogen storage cylinder detected, posing an explosion risk. Please evacuate immediately to at least 10 meters upwind."

[0141] In summary, this application transforms the original static or single-step response virtual training scenario into an intelligent emergency sandbox with dynamic risk evolution capabilities. Operators not only face the initial accident but also need to deal with the chain reaction caused by their own improper operation or environmental disturbances, thereby truly experiencing the uncertainty in rescue and deeply understanding the core safety concept that standardized operation is the key to breaking the accident chain.

[0142] Among them, impulse calculation (used for impact loads) assesses the destructive potential of an instantaneous impact (such as hammering or collision) on a structure, and the formula is:

[0143]

[0144] Symbol explanation:

[0145] Impulse (unit: N·s);

[0146] The average force;

[0147] This refers to the duration of action (usually very short, such as 0.1 seconds).

[0148] Example 5

[0149] This embodiment is an improvement upon embodiment 4. For details, please refer to [link / reference]. Figures 1-3 The acquisition module is used to collect several sound signals and several image data in the simulated rescue emergency training scenario in real time.

[0150] The sensing module is used to detect the operator's emotional fluctuations based on sound signals and image data. These emotional fluctuations include tension, confusion, anxiety, or distraction.

[0151] The output module is used to select audio prompts or visual guidance content that matches the emotional fluctuation state from the preset teaching resource library, and dynamically generate and output personalized teaching guidance videos in combination with the corresponding basic principles in the knowledge graph, so as to assist the operator in adjusting the operation strategy and strengthening the learning of key safety knowledge.

[0152] It also includes at least one sensor for real-time acquisition of the operator’s physiological and / or behavioral status data;

[0153] The analysis module is also used to assess the operator's current focus, stability and operational proficiency based on status data, and dynamically match the preset force threshold and operational precision range required for the current rescue action based on the assessment results.

[0154] Among them, the force threshold and the range of operational precision are associated with the safety specification nodes of the corresponding rescue steps in the knowledge graph, and are used to determine whether the operator's actions comply with the emergency response standards for hydrogen fuel cell vehicles.

[0155] Several sensors can be integrated into VR headsets, controllers, wearable devices, or external tracking systems, and the specific data collected includes heart rate, hand tremor amplitude, operation speed, limb posture, and eye movement trajectory.

[0156] Heart rate can be obtained through photoplethysmography (PPG) sensors or chest-cuff ECG devices.

[0157] The amplitude of hand tremors and the speed of operation are calculated by the accelerometer and gyroscope built into the VR controller;

[0158] Limb posture can be captured by external optical cameras or inertial motion capture devices to capture changes in the angle of upper limb joints;

[0159] The eye movement trajectory uses the built-in infrared eye tracking system of the VR headset to record parameters such as gaze point, saccade path and pupil diameter.

[0160] The analysis module receives the aforementioned multi-source state data and, based on a preset evaluation model, quantifies and judges the operator's current state, mainly in three aspects: first, focus, calculated comprehensively based on eye movement stability, response delay to system prompts, and head shaking frequency; second, stability, assessed based on hand tremor amplitude, fluctuation in operation speed, and limb posture deviation; and third, operational proficiency, judged by comparing the current operational behavior with the behavior patterns of historical high-performing users, determining the smoothness of the movements and the rationality of the steps executed.

[0161] Based on the above evaluation results, the analysis module will dynamically match the preset force threshold and operational accuracy range required for the current rescue action. For example, when the operator performs the critical action of closing the main hydrogen valve, if the system determines that the operator's stability is low (e.g., hand tremors exceed 2 cm / s), it will automatically relax the operational accuracy tolerance (e.g., allow rotation angle error ±15°) to reduce the risk of operational failure due to tension. Conversely, if the operator is in a highly focused and skilled state, it will activate a stricter safety threshold (e.g., the rotation angle must be controlled within ±5°, and the applied force must not exceed 80 Newtons) to closely match the technical requirements of real high-pressure operations.

[0162] It is important to note that the force threshold and operational precision range are not fixed, but are directly related to the safety specification nodes of the corresponding rescue steps in the knowledge graph. For example, in the knowledge graph, the action node of closing the main valve of the 70MPa hydrogen storage cylinder is associated with structured attributes such as national standards (e.g., GB / T 37124-2018, Clause 7.2), maximum permissible force (100 Newtons), angle tolerance (5 degrees), recommended tool type (dedicated explosion-proof wrench), and consequences of violations (e.g., valve seal failure leading to hydrogen leakage).

[0163] During training, the analysis module queries the corresponding nodes in the knowledge graph in real time for the specified parameters and dynamically adjusts the compliance judgment boundaries based on the operator's current physiological and behavioral state. If the force or precision of the operator's movements exceeds the safe range, the system not only records the violation but also triggers a subsequent risk simulation mechanism (such as simulating an escalation of hydrogen leakage or component damage) and feeds the results back to the VR module, presenting corresponding visual, auditory, and interactive warnings in the virtual scene.

[0164] In summary, this embodiment achieves human-factor-adaptive safety and compliance assessment, adhering to the technical standards for emergency response in hydrogen fuel cell vehicles while providing flexibility and tolerance based on the operator's real-time condition, avoiding ineffective failures due to brief periods of tension or insufficient skills. This not only improves training completion rates and learning confidence but also ensures the internalization and implementation of safety regulations, truly unifying intelligent, personalized, and practical emergency training.

[0165] Example 6

[0166] This embodiment is an improvement upon embodiment 5. For details, please refer to [link / reference]. Figures 1-3 The storage module is also used to record the emotional fluctuation points that the operator marks during the training process, with each emotional fluctuation point corresponding to a segment of training content;

[0167] The analysis module is used to acquire current social news hotspots data and, combined with the semantic content associated with emotional fluctuation points, identify the situational themes that the operator may pay attention to or easily resonate with.

[0168] Based on the distribution pattern of historical emotional fluctuation points, predict the range of operators' preferences for character types and voice styles in teaching content.

[0169] Based on the range of preferences and the monitoring results of real-time emotion fluctuation sensors, the virtual character image, gender, age, occupation characteristics, and the tone, speed, and emotional color of the voice-over in the teaching guidance screen of the VR module are dynamically adjusted to enhance the operator's emotional engagement and learning acceptance.

[0170] The acquisition module is used to collect multimodal perception data in the scene in real time during simulated rescue emergency training, including several sound signals (such as the operator's voice commands, breathing sounds, and non-verbal sounds) and several image data (such as the operator's facial expressions, head posture, and upper body movements captured by the VR headset's built-in camera or an external RGB / infrared camera).

[0171] Based on the aforementioned sound and image data, the sensing module runs a multimodal emotion recognition model to detect the operator's emotional fluctuations in real time. After specialized training for emergency rescue scenarios, it can accurately identify four key states: tension (manifested as high-frequency speech enhancement, dilated pupils, and slight hand tremors), confusion (manifested as prolonged staring at a component without operation, and repeating ineffective commands), anxiety (manifested as rapid speech, rapid breathing, and disordered operation rhythm), and distraction (manifested as frequent deviation of gaze from the task area and lack of response to system prompts).

[0172] The output module intelligently selects appropriate guidance content from a pre-set teaching resource library based on the identified emotional fluctuations. This resource library includes various styles of audio prompts (such as calm explanations, urgent reminders, and encouraging guidance), visual guidance elements (such as AR annotations, 3D animation clips, and safety warning icons), and structured knowledge units. The output module further integrates corresponding basic principle nodes from the knowledge graph (such as hydrogen being lighter than air and rising after a leak, and the need to disconnect power before operating a high-pressure system) to dynamically generate a personalized instructional video. This video is not simply played; instead, it integrates into the current VR scene as a virtual narrator, assisting the operator in adjusting their operational strategies in real time and reinforcing their understanding and memory of key safety knowledge.

[0173] In addition, the storage module allows operators to mark emotional fluctuations during training. For example, when an operator feels particularly nervous or confused, they can mark these points on the timeline using voice commands (such as "I was panicking just now") or by using a shortcut key on the controller. Each mark is associated with a specific training segment (such as the hydrogen tank valve closing stage), forming a behavioral log with subjective emotional annotations.

[0174] The analytics module further utilizes this historical data to perform two advanced cognitive modeling tasks:

[0175] Acquire current social news hotspots (such as accessing mainstream media or emergency management department announcements via API to obtain recent hydrogen energy accidents, fire drill reports, etc.), and combine them with the semantic content associated with emotional fluctuations (such as leak handling, fuel cell fire) to identify situational themes that operators may be concerned about or easily evoke emotional resonance (such as hydrogen leak on a bus, rescue in a tunnel).

[0176] It should be noted that when the analysis module acquires data on current social news hotspots, it not only accesses authoritative information released by mainstream media, government emergency management departments, or industry regulatory agencies, but also simultaneously collects content of public concern from social media platforms, especially including internet celebrity events, controversial online topics, famous short video scenes, or typical user-generated content (UGC) with high dissemination and emotional appeal.

[0177] For example: a recent viral video on a short video platform showing a hydrogen fuel cell vehicle leaking on the street, with passersby filming it and attracting a crowd; or a firefighter demonstrating how to properly handle a new energy vehicle fire in a live stream, garnering millions of likes; or a dramatic scene in a movie or TV show where the improper dismantling of a hydrogen fuel cell vehicle leads to an explosion, which is widely discussed.

[0178] By using natural language processing and multimodal content understanding technologies (such as CLIP-based image and text matching and hot topic clustering), the core semantics related to hydrogen safety, emergency response, and public misunderstanding are automatically identified, and their emotional tendencies (such as panic, curiosity, and skepticism) and cognitive bias types (such as using water to extinguish hydrogen fire and pulling out high-voltage lines by hand) are marked.

[0179] Subsequently, the analysis module cross-references these trending topics with the training semantics associated with the emotional fluctuations marked by the operators themselves. For example, if an operator repeatedly marks confusion during a hydrogen leak evacuation, and there is currently widespread public discussion about an online celebrity filming a leak video without evacuating, the system infers that the operator may be influenced by such online content and has a cognitive conflict regarding when to evacuate and whether it is permissible to film.

[0180] Based on this insight, the output module can dynamically generate more targeted instructional guidance content, such as:

[0181] Note that someone is filming the leaking vehicle in the online video, which is extremely dangerous. According to GB / T 37124, when the hydrogen concentration is unknown, you must immediately evacuate to at least 10 meters upwind. Your safety is always more important than a video.

[0182] Meanwhile, the virtual guide's image and language can be adjusted accordingly. If a trending event is triggered by a young internet celebrity, a virtual character with a peer-like style (such as a 25-year-old female safety science popularization blogger) can be used, along with expressions that are closer to the online context (such as "Don't follow those death challenges, proper operation is really cool"), thereby enhancing emotional resonance and behavioral guidance.

[0183] By introducing internet celebrity events and famous online scenes as a social cognitive context, this system expands emergency training from closed technical drills to an open learning process that interacts with the real public opinion environment. This helps operators not only master standard procedures, but also identify and resist wrong examples and risky temptations from social media, so as to truly know not only what to do, but also why to do it, and more importantly, why not to do it.

[0184] Based on the distribution patterns of historical emotional fluctuations (e.g., a user consistently marks anxiety during "multi-person collaboration" segments), the system analyzes the user's preference range for character types and voice styles in the teaching content. For example, if a user's anxiety markings significantly decrease under the guidance of a female instructor, the system infers that the user prefers gentle, clear, and low-to-medium speaking female professional roles.

[0185] Ultimately, based on the aforementioned preference range and the monitoring results from real-time emotion fluctuation sensors, the system dynamically adjusts the virtual character presented in the instructional guidance screen of the VR module. This includes the character's gender, age, occupational characteristics (such as fire engineer, senior instructor, safety supervisor), facial expressions, and the tone, speed, and emotional tone of the voice-over (such as calm, concerned, and firm). For example, if the system detects that the operator is in a highly tense state and has a historical preference for a 40-year-old male technical expert with a calm tone, it will immediately summon the virtual instructor of that character, who will slowly and firmly remind the user to keep breathing, follow the steps, and that they have mastered the correct procedure.

[0186] Through the above mechanisms, this system not only achieves individualized instruction but also moves towards emotion-based instruction. It deeply integrates the operator's physiological state, psychological preferences, social cognitive background, and knowledge graph to build an immersive learning partner with emotional intelligence. This significantly enhances the operator's emotional engagement, trust, and knowledge acceptance, thereby achieving the dual goals of skills enhancement and psychological resilience building in the high-pressure, high-risk emergency training of hydrogen fuel cell vehicles.

[0187] Example 7

[0188] This embodiment is an improvement upon embodiment 6. For details, please refer to [link / reference]. Figures 1-3 There are several operators;

[0189] Several operators simultaneously access the same simulated emergency rescue training scenario through their respective VR terminals;

[0190] The analysis module is also used to monitor the task collaboration status between operators in real time. Based on the emergency response process specifications in the knowledge graph, it evaluates the temporal logic of team collaboration, the division of responsibilities and the consistency of operations, and generates a collaborative training feedback report.

[0191] It supports multiple operators participating simultaneously in the same simulated emergency rescue training scenario. Several operators access the same virtual environment through their respective VR terminals (such as Meta Quest Pro, HTC Vive Focus 3, etc.) to jointly execute emergency response tasks for hydrogen fuel cell vehicle accidents. Each terminal synchronizes with the central server through a low-latency network (such as gigabit LAN or 5G private network) to ensure that the scene status, vehicle model, environmental parameters, and role behavior remain consistent in the field of vision of all participants.

[0192] In this multi-user collaborative mode, the analysis module not only monitors the behavior of individual operators but also monitors the task collaboration status between operators in real time. Specifically, the system tracks the following collaboration dimensions based on timestamp-aligned operation logs:

[0193] Timing logic, whether each key step is executed in the standard procedure order (e.g., whether the commander issues the power-off command before the demolition worker touches the vehicle body).

[0194] The division of responsibilities, whether each role (such as commander, inspector, demolition worker, medical worker) performs the corresponding tasks within its authorized scope, and whether there is any overlap or omission of responsibilities;

[0195] Operational consistency: whether there are conflicts (such as two people trying to close different valves at the same time, resulting in contradictory system states) or complementarities (such as one person stabilizing the vehicle body while another person performs demolition).

[0196] The above assessment was conducted strictly based on the pre-built emergency response process specifications nodes in the knowledge graph. For example, the knowledge graph defines the coordination rules for a hydrogen fuel cell bus rollover and leakage scenario:

[0197] The inspector must complete the hydrogen concentration test before the demolition begins. If the concentration is >2%, the commander should immediately stop all metal tool operations. Medical personnel may only approach the injured person after the vehicle is powered off and there is no risk of leakage.

[0198] The analysis module dynamically matches the real-time operation flow with this rule chain to identify coordination deviations. For example, if a demolition operator starts hydraulic shears before receiving the inspection results, it is determined as an unauthorized operation; if two operators pull on the same high-voltage harness at the same time, it is marked as an operational conflict and may trigger damage to virtual components.

[0199] Based on the above analysis, the system automatically generates a collaborative training feedback report, which includes:

[0200] Overall team compliance score (e.g., process compliance rate of 82%)

[0201] Replay of key coordination failure times (e.g., 14:23, demolition occurred before gas detection).

[0202] Role performance heatmap (showing the task coverage and response latency of each member);

[0203] Improvement suggestions (such as clearly defining and standardizing command commands to avoid ambiguity).

[0204] The report can be pushed to each operator's terminal after training and can also be used by instructors for debriefing and teaching. In addition, during training, the team can receive real-time collaborative correction information (such as pausing demolition if the inspection is not completed) through a virtual command panel or voice prompts within VR, achieving a dual closed loop of process intervention and result evaluation. Through this mechanism, individual skills training is upgraded to a high-fidelity team emergency drill platform, effectively improving the coordination efficiency, communication clarity, and overall safety of professional teams such as fire fighting, traffic, and hydrogen energy operation and maintenance in real accidents.

[0205] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0206] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A VR-based hydrogen fuel cell vehicle fire rescue handling training exercise system, characterized in that, The application relates to a simulation rescue emergency training system, which comprises the following modules: a storage module for storing training data and constructing a knowledge graph based on the training data; a construction module for constructing a basic vehicle model through the knowledge graph and generating an adversarial sample based on the training data; the construction module further uses the adversarial sample to perform perturbation training on the basic vehicle model, thereby generating a random vehicle model with a random combination of fault types, damage positions and system states; a VR module for building a simulation rescue emergency training scene based on the training data and the random vehicle model; a language module integrated with a natural language processing model, which is used for receiving and understanding voice or text instructions input by an operator in the simulation rescue emergency training scene in real time, and generating corresponding virtual rescue actions based on semantic analysis results; an analysis module for recording operation logs, environment states and interactive behavior data in the simulation rescue emergency training scene; the analysis module reversely disassembles the random vehicle model based on the knowledge graph, extracts a plurality of basic principles related to the current scene, and combines the basic principles to judge the safety, compliance and effectiveness of the rescue actions in real time, and provides targeted learning guidance and correction feedback to the operator; the analysis module is also used for collecting and analyzing force data generated when the rescue actions act on the random vehicle model; the force data includes force size, direction, action point and duration; the analysis module dynamically adjusts the structure state or fault performance of the random vehicle model based on the force data and a preset mechanical response rule, so as to reflect the deformation, displacement, component shedding or hydrogen system leakage risk change of the vehicle in the real physical world after being subjected to force.

2. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 1, wherein: The storage module is also used for recording and storing all rescue actions performed by the operator in the simulation rescue emergency training scene, and corresponding time stamps, operation paths and decision basis, for subsequent review analysis and personalized training optimization.

3. The VR-based hydrogen fuel cell vehicle fire rescue handling training exercise system of any one of claims 1-2, wherein: After updating the random vehicle model according to the force data, the analysis module dynamically deduces and generates at least one rescue accident situation in combination with the associated information about the hydrogen fuel cell system structure, material properties, safety threshold and accident evolution path in the knowledge graph; the rescue accident situation includes hydrogen leakage aggravation, high-pressure hydrogen storage bottle displacement, electric pile short-circuit fire and structure secondary collapse; the rescue accident situation is fed back to the VR module in real time, so as to present corresponding visual, auditory and interactive warnings in the simulation rescue emergency training scene.

4. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 3, wherein, The application further comprises: an acquisition module for collecting a plurality of sound signals and a plurality of image data in the simulation rescue emergency training scene in real time; a sensing module for detecting the emotional fluctuation state of the operator based on the sound signals and image data, wherein the emotional fluctuation state includes nervousness, confusion, anxiety or distraction; An output module is configured to select a sound prompt or visual guide content matching the emotional fluctuation from a preset teaching resource library according to the emotional fluctuation state, and dynamically generate and output a personalized teaching guide video in combination with a corresponding basic principle in the knowledge graph, so as to assist the operator in adjusting the operation strategy and strengthening the learning of key safety knowledge.

5. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 4, wherein, Further comprising at least one sensor configured to collect physiological and / or behavioral state data of the operator in real time; The analysis module is further configured to evaluate the current concentration, stability and operation proficiency of the operator according to the state data, and dynamically match a preset force threshold and operation precision range required for the current rescue action based on the evaluation result; The force threshold and operation precision range are associated with a safety specification node of a corresponding rescue step in the knowledge graph, and are used to determine whether the operation of the operator conforms to the emergency disposal standard of the hydrogen fuel cell vehicle.

6. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 4, wherein: The storage module is further configured to record emotional fluctuation points marked by the operator during the training process, each of the emotional fluctuation points corresponding to a piece of training content; The analysis module is configured to obtain current social news hotspot data, and identify a situation theme that the operator is likely to pay attention to or resonate with in combination with content semantics associated with the emotional fluctuation points.

7. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 4, wherein: Based on the distribution pattern of historical emotional fluctuation points, a preference range of the operator for character types and voice styles in the teaching content is predicted; According to the preference range and the monitoring result of the real-time emotional fluctuation sensor, the virtual character image, gender, age, professional characteristics, and the intonation, speed and emotional color of the dubbing voice presented by the teaching guide picture in the VR module are dynamically adjusted to enhance the emotional involvement and learning acceptance of the operator.

8. The VR-based hydrogen fuel cell vehicle fire rescue incident training exercise system of claim 1, wherein: There are several operators; The several operators synchronously access the same simulated rescue emergency training scene through respective VR terminals; The analysis module is further configured to monitor the task coordination state between the operators in real time, evaluate the timing logic, responsibility division and operation consistency of team cooperation based on the emergency disposal process specification in the knowledge graph, and generate a cooperative training feedback report.

9. The VR-based hydrogen fuel cell vehicle fire rescue handling training drill system of claim 5, wherein: The state data includes heart rate, hand tremor amplitude, operation speed, body posture and eye movement trajectory.

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