Interactive emergency scene simulation and emergency processing guidance platform

Abstract

The invention relates to the technical field of emergency training and emergency response, in particular to an interactive emergency scene simulation and emergency processing guidance platform which comprises a multi-mode sensing module, a dynamic scene generation module, an intelligent decision guidance module, an interactive feedback module and a data storage and analysis module. The multi-mode sensing module is used for collecting user operation behaviors, physiological states and environmental parameters; the dynamic scene generation module dynamically adjusts the scene difficulty and the event process based on the real-time collected data and a preset first-aid scene template; the intelligent decision guidance module fuses an emergency knowledge base and a deep learning model, and provides personalized and real-time emergency operation guidance for the user; the interaction feedback module outputs feedback information to the user through the multi-sensory channel; and the data storage and analysis module records training / emergency data and performs effect evaluation. The problems that an existing emergency simulation system is fixed in scene, poor in interactivity, lack of pertinence in guidance and the like are solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of first aid training and emergency response, in particular to an interactive first aid scene simulation and emergency handling guidance platform. BACKGROUND

[0002] In emergency situations such as sudden illness and accidental injury, timely and standardized first aid operations can significantly improve the survival rate of the injured and reduce the disability rate. However, the current public first aid knowledge popularization rate is low, and the skill training of professional first aid personnel also has many deficiencies, the core reason of which lies in the obvious defects of traditional first aid training mode and existing first aid simulation systems, which are difficult to meet the needs of actual first aid scenes.

[0003] Existing first aid training is mainly based on theoretical lectures, watching teaching videos, and static model operation practice. This mode has the problems of scene disconnection from reality and poor interactivity. Trainees cannot truly experience the pressure environment of emergency scenes during the training process, and lack practical experience in grasping the operation timing and operation intensity, which leads to difficulties in applying the learned skills flexibly to actual first aid scenes after training. For example, in cardiopulmonary resuscitation training, trainees can only practice compression actions on static models and cannot experience the physiological changes of the injured, the interference of the on-site environment, and the psychological pressure brought by the urgency of time in real first aid, which leads to problems such as non-standard operation and slow reaction in actual first aid.

[0004] With the development of technology, some first aid simulation systems have emerged, but these systems still have many deficiencies. First, the scene solidification problem is prominent. The existing system's first aid scene is mostly a preset fixed process, which cannot be dynamically adjusted according to the user's operation situation and physiological state, leading to a large gap between the simulation scene and the complexity and uncertainty of actual first aid. For example, some trauma first aid simulation systems can only display scenes according to fixed wound types and bleeding speeds, cannot dynamically adjust the bleeding state according to the user's hemostasis operation effect, and cannot simulate environmental interference factors such as changes in light and noise from personnel, making it difficult to effectively exercise the user's emergency handling ability.

[0005] Secondly, the guidance method of the existing system lacks pertinence, mostly being unified standardized prompts, which cannot provide personalized guidance according to individual differences and operation levels of users. Different users have different learning abilities and skill bases, and unified guidance content cannot meet the training needs of different users. For example, for first aid beginners, detailed step-by-step guidance is needed, while for users with some foundation, targeted operation difficulty correction and optimization suggestions are needed, but the existing system cannot achieve such differentiated guidance, resulting in poor training effect.

[0006] Furthermore, existing systems have limited sensing capabilities, mostly only able to collect simple user actions and unable to comprehensively acquire the user's physiological state and operational details. The user's psychological state during emergency treatment is reflected in physiological parameters (such as heart rate and respiratory rate), while operational details (such as compression depth and bandaging force) directly affect the emergency treatment effect. However, existing systems lack effective collection and analysis of this data, making it impossible to accurately assess the quality of the user's operation and psychological state, or to adjust the scenario and guide optimization based on this data.

[0007] Furthermore, existing systems are mostly designed for single-person training and lack multi-person collaborative emergency rescue simulation capabilities. In actual emergency rescue scenarios, multiple rescuers often need to work together, such as one person performing CPR, another performing defibrillation, and another directing operations. However, existing systems cannot simulate such collaborative scenarios, resulting in trainees lacking experience in collaborative operations and struggling to cooperate effectively in real multi-person emergency rescue situations, thus affecting emergency rescue efficiency.

[0008] Finally, the existing system suffers from weak data storage and analysis capabilities, failing to comprehensively record and deeply analyze user training data. This makes it difficult to generate accurate skills assessment reports and to optimize training programs based on historical data. Training organizers are unable to accurately grasp trainees' skill mastery levels, hindering the development of targeted training plans, resulting in wasted training resources and compromised training effectiveness.

[0009] In summary, existing emergency rescue simulation systems suffer from problems such as fixed scenarios, poor interactivity, lack of targeted guidance, limited perception capabilities, insufficient collaboration, and weak data processing capabilities. These shortcomings prevent them from meeting the needs for efficient and accurate emergency rescue training and from providing effective support for actual emergency response. Therefore, developing an interactive emergency rescue scenario simulation and emergency response guidance platform capable of dynamic scenario simulation, accurate perception, personalized guidance, collaborative training, and data-driven optimization is of significant practical importance. Summary of the Invention

[0010] To address the technical problems of existing emergency rescue simulation systems, such as fixed scenarios, poor interactivity, lack of targeted guidance, limited perception capabilities, insufficient collaboration, and weak data processing capabilities, this invention provides an interactive emergency rescue scenario simulation and emergency response guidance platform. This platform enables dynamic adaptation of emergency rescue scenarios, comprehensive perception of user operations and physiological states, personalized emergency rescue guidance, multi-person collaborative simulation training, and data-driven training optimization, thereby improving the effectiveness of emergency rescue training and emergency response capabilities.

[0011] The technical solution adopted by this invention to solve its technical problem is: an interactive emergency rescue scenario simulation and emergency treatment guidance platform, comprising: a multimodal perception module, used to collect user operation behavior data, physiological state data, and environmental parameter data of simulated / real scenarios, wherein the operation behavior data includes limb movement data and operation timing data, the physiological state data includes heart rate, respiratory rate, and electromyography signals, and the environmental parameter data includes temperature and humidity, light intensity, and scene noise; a dynamic scene generation module, communicatively connected to the multimodal perception module, used to construct a basic scene based on a preset emergency rescue scenario template, and, combined with real-time data collected by the multimodal perception module, adjust the scene difficulty level, event triggering timing, and scene environmental details through a scene parameter dynamic adjustment algorithm to generate a dynamically adapted emergency rescue simulation scenario; and an intelligent decision guidance module, which communicates with both the multimodal perception module and the dynamic scene generation module. The system integrates a built-in first aid knowledge base and a deep learning guidance model. The deep learning guidance model, trained on multiple sets of first aid case data, analyzes user behavior data in real time to determine the standardization and rationality of operations. It then generates personalized first aid operation guidance instructions based on the current dynamic scene's event progression. An interactive feedback module, communicating with the intelligent decision-making guidance module, outputs scene interaction information, operation guidance instructions, and operation effect feedback to the user through multi-sensory channels (visual, auditory, and tactile). A data storage and analysis module, communicating with the multi-modal perception module, dynamic scene generation module, and intelligent decision-making guidance module, stores scene data, user data, and guidance data. It uses data mining algorithms to evaluate the user's first aid skills, generates an evaluation report, and feeds it back to the dynamic scene generation module and intelligent decision-making guidance module, achieving closed-loop optimization of the scene and guidance strategy.

[0012] Specifically, the multimodal perception module includes a motion capture unit, a physiological sensing unit, and an environmental sensing unit. The motion capture unit uses a combination of optical motion capture equipment and inertial motion capture equipment. The optical motion capture equipment is used to acquire large-range limb movement data of the user, while the inertial motion capture equipment is used to acquire fine operation data of the user. The data from both are processed by a fusion algorithm to obtain high-precision operation behavior data. The physiological sensing unit uses wearable sensing devices to collect the user's heart rate, respiratory rate, and electromyography signals in real time. The environmental sensing unit includes temperature and humidity sensors, light sensors, and noise sensors to collect scene environmental parameters.

[0013] Specifically, the scene parameter dynamic adjustment algorithm of the dynamic scene generation module includes the following steps: S11, Determine basic scenario parameters based on preset emergency rescue scenario types, including cardiopulmonary resuscitation, trauma hemostasis, drowning rescue, and poisoning first aid; S12 receives real-time data collected by the multimodal perception module and extracts user operation proficiency features, physiological stress features and environmental interference features; S13, determine the weight coefficients of each feature through the hierarchical analysis algorithm, and calculate the scene adjustment requirements; S14. Adjust the required value according to the scenario, match the corresponding difficulty adjustment parameter from the scenario difficulty parameter library, and dynamically adjust the event trigger interval, fault complexity and environmental interference intensity of the basic scenario.

[0014] Specifically, the deep learning guidance model of the intelligent decision-making guidance module includes a feature extraction layer, an operation judgment layer, and a guidance generation layer. The feature extraction layer uses a convolutional neural network to extract the temporal and spatial features of the user's operation behavior. The operation judgment layer uses a bidirectional long short-term memory network combined with an attention mechanism to analyze the extracted features and determine whether the user's operation conforms to the first aid standard, whether the timing of the operation is reasonable, and whether the force of the operation is up to standard. Based on the judgment results and combined with the standard operating procedures in the first aid knowledge base, the guidance generation layer generates targeted guidance instructions, including operation correction instructions, step prompt instructions, and precaution reminder instructions.

[0015] Specifically, the interactive feedback module includes a visual feedback unit, an auditory feedback unit, and a tactile feedback unit; the visual feedback unit uses a VR display device or a high-definition display screen to output dynamic scene images, operation guidance icons, and operation scores; the auditory feedback unit outputs voice guidance, scene ambient sounds, and operation effect prompts; the tactile feedback unit uses a force feedback device to provide corresponding pressure feedback and tactile simulation when the user performs operations such as pressing or bandaging.

[0016] Specifically, the data storage and analysis module includes a data classification storage unit and a skills assessment unit. The data classification storage unit uses a distributed database to classify and store data according to scenario type, user identity, and data type, supporting fast data query and retrieval. The skills assessment unit uses a fuzzy comprehensive evaluation algorithm to evaluate the user's first aid skills from four dimensions: operational standardization, reaction speed, emergency response rationality, and psychological stability, generating a multi-dimensional assessment report.

[0017] Specifically, it also includes a remote collaboration module, which is communicatively connected to the intelligent decision guidance module and the data storage and analysis module, respectively. It supports multiple users to conduct collaborative emergency rescue simulation training, realizes operation synchronization and information sharing among users, and supports remote access by experts to provide real-time guidance and comments on user operations.

[0018] Specifically, the remote collaboration module uses 5G communication technology to achieve low-latency data transmission and ensures real-time synchronization of operation data and scene data of multiple users through a data synchronization algorithm. It supports emergency rescue scenario simulation for multiple people and multiple roles, including rescuers, commanders, and assistants.

[0019] Specifically, the intelligent decision guidance module also includes an emergency case matching unit, which is used to match the current dynamic scene with historical cases in the emergency knowledge base, extract successful handling experience from similar cases, and integrate it into the guidance instructions.

[0020] Specifically, the dynamic scene generation module also includes a scene customization unit, which allows users to customize scene types, event flows, difficulty levels, and environmental parameters according to training needs, and generate personalized emergency rescue simulation scenes.

[0021] The beneficial effects of this invention are: 1. Achieving dynamic scene adaptation and improving simulation realism: This invention uses a dynamic scene generation module to dynamically adjust the scene difficulty and event process by combining user operation behavior, physiological state and environmental parameters. This solves the problem of fixed scenes in existing systems, making the simulated scene closer to the complexity and uncertainty of actual emergency rescue, enhancing the realism and immersion of the scene, and helping to improve the user's emergency response capabilities.

[0022] 2. Comprehensive user status perception for personalized guidance: By comprehensively collecting user operation behavior data and physiological status data through a multimodal perception module, and combining it with the precise analysis of a deep learning guidance model, targeted personalized guidance instructions are generated. This solves the problem of the lack of targeted guidance in existing systems, and can meet the training requirements of users with different skill levels and learning needs, thereby improving training effectiveness.

[0023] 3. Multi-sensory interactive experience to enhance training immersion: Through interactive feedback through multiple sensory channels such as vision, hearing, and touch, especially the application of VR devices and force feedback devices, users are provided with an immersive first aid simulation experience, enabling them to truly feel the touch and scene pressure of first aid operations, effectively training their operational skills and psychological resilience.

[0024] 4. Supports multi-person collaborative training and enhances teamwork ability: The remote collaboration module enables multi-person, multi-role collaborative first aid simulation, which solves the shortcomings of the existing single-person training mode and can effectively train users' teamwork and communication skills, meeting the needs of multi-person collaboration in actual first aid scenarios.

[0025] 5. Data-driven optimization to ensure training effectiveness: The data storage and analysis module comprehensively records and deeply analyzes user training data, generates accurate skills assessment reports, and achieves closed-loop optimization of scenarios and guidance strategies. This helps training organizers understand users' skills, develop targeted training plans, and improve the scientific nature and effectiveness of training.

[0026] 6. Wide range of applications and strong practicality: This platform can be used not only for professional training in medical colleges and first aid training institutions, but also for public first aid education in enterprises and communities. At the same time, it can be used as an auxiliary guidance tool in actual emergency rescue, providing real-time guidance for frontline rescuers. It has broad application prospects and practical value. Attached Figure Description

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] Figure 1 This invention provides an overall system architecture diagram of the interactive first aid scenario simulation and emergency response guidance platform. Figure 2 A flowchart illustrating the data fusion process of the multimodal perception module in the interactive emergency rescue scenario simulation and emergency response guidance platform provided by this invention. Figure 3 Flowchart of the dynamic scene parameter adjustment algorithm for the interactive first aid scenario simulation and emergency response guidance platform provided by this invention; Figure 4 The diagram shows the deep learning model architecture of the intelligent decision guidance module of the interactive emergency rescue scenario simulation and emergency treatment guidance platform provided by this invention. Detailed Implementation

[0029] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0030] like Figures 1-4 As shown, the interactive emergency rescue scenario simulation and emergency response guidance platform of the present invention includes a multimodal perception module, a dynamic scenario generation module, an intelligent decision guidance module, an interactive feedback module, a data storage and analysis module, and a remote collaboration module. The modules interact with each other via a communication bus or wireless communication technology, as detailed below: Multimodal perception module: This module comprehensively collects user-related data and scene environment data, including a motion capture unit, a physiological sensing unit, and an environmental sensing unit. The motion capture unit uses a combination of optical and inertial motion capture equipment. The optical motion capture equipment uses multiple high-definition cameras to capture the user's wide-range limb movement trajectory, acquiring data such as joint angles and movement speed. The inertial motion capture equipment uses inertial sensors worn on the user's hands, arms, and legs to collect acceleration and angular velocity data of the user's fine-grained movements. The data from both methods are fused using a Kalman filter algorithm to obtain high-precision operational behavior data. The physiological sensing unit uses wearable wristbands, electromyography (EMG) patches, and other devices to collect real-time physiological state data such as the user's heart rate, respiratory rate, and EMG signals, used to assess the user's psychological stress state. The environmental sensing unit includes temperature and humidity sensors, light sensors, and noise sensors to collect environmental parameter data from simulated or real emergency scenarios, providing a basis for scene adjustment and decision-making guidance.

[0031] The dynamic scene generation module is used to construct dynamically adaptable emergency rescue simulation scenarios, including a scene template library, a scene parameter dynamic adjustment algorithm, and scene customization units. The scene template library stores various preset emergency rescue scene templates, such as cardiopulmonary resuscitation, trauma hemostasis, drowning rescue, and poisoning first aid. Each template includes basic parameters such as a scene environment model, event flow model, and role model. The scene parameter dynamic adjustment algorithm dynamically adjusts the scene difficulty level, event triggering sequence, and environmental details based on real-time data collected by the multimodal perception module. The specific implementation steps are as follows: First, based on the emergency scenario type selected by the user, the corresponding basic scenario parameters are called from the scenario template library to construct an initial simulation scenario. Then, user operation behavior data, physiological state data, and environmental parameter data collected by the multimodal perception module are received, and user operation proficiency features (such as operation accuracy and operation time), physiological stress features (such as heart rate variability coefficient), and environmental interference features (such as noise intensity) are extracted through feature extraction algorithms. Next, the weight coefficients of each feature are determined using the hierarchical analysis algorithm, with operation proficiency features accounting for 40%, physiological stress features accounting for 30%, and environmental interference features accounting for 30%. The scenario adjustment requirement value is calculated based on the weight coefficients. Finally, based on the scenario adjustment requirement value, the corresponding difficulty adjustment parameters are matched from the scenario difficulty parameter library to dynamically adjust the event trigger interval (such as bleeding speed and disease deterioration speed), fault complexity (such as multi-site trauma and complication triggering), and environmental interference intensity (such as light brightness adjustment and noise volume adjustment) of the initial scenario, generating a dynamic scenario adapted to the user's current state. The scenario customization unit allows users to customize scenario types, event flows, difficulty levels, and environmental parameters through a visual interface to generate personalized emergency rescue simulation scenarios based on their training needs.

[0032] The intelligent decision-making guidance module, as the core decision-making unit of the platform, is used to generate personalized emergency medical operation guidance instructions. It includes an emergency medical knowledge base, a deep learning guidance model, and an emergency medical case matching unit. The emergency medical knowledge base stores data such as international emergency medical guidelines, domestic emergency medical standards, standard procedures for various emergency medical operations, common errors, and correction methods, providing a basis for the generation of guidance instructions. The deep learning guidance model adopts an architecture combining a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) with an attention mechanism, and is trained on multiple sets of emergency medical case data (including standard operation cases, error operation cases, and complex scenario handling cases). The model's feature extraction layer uses CNN to extract temporal features (such as the order of operation steps and operation duration) and spatial features (such as limb posture and operation position) of user operation behavior. The operation judgment layer uses Bi-LSTM combined with an attention mechanism to perform in-depth analysis of the extracted features, judging whether the user's operation conforms to the first aid standard, whether the timing of the operation is reasonable, and whether the force of the operation is up to standard, and outputs the operation score and error type identification results. Based on the judgment results and combined with the standard operating procedures in the first aid knowledge base, the guidance generation layer generates targeted guidance instructions, including operation correction instructions (such as "insufficient compression depth, need to increase to 5-6 cm"), step prompt instructions (such as "the next step should be artificial respiration, pay attention to pinching the patient's nose"), and precaution reminder instructions (such as "the scene is noisy, you need to shout for help loudly"). The first aid case matching unit uses the cosine similarity algorithm to match the event features and environmental features of the current dynamic scene with the historical cases in the first aid knowledge base, extract the successful handling experience and key precautions of similar cases, and integrate them into the guidance instructions to improve the practicality and relevance of the guidance.

[0033] Interactive Feedback Module: This module provides users with multi-sensory interactive information and guidance, including visual, auditory, and tactile feedback units. The visual feedback unit uses VR display devices or high-definition screens to output dynamic scene images, operation guidance icons (such as pressure point indicators and operation step flowcharts), operation scores, and error message text. For VR display devices, it can also achieve an immersive scene experience, enhancing the user's sense of involvement. The auditory feedback unit uses high-fidelity speakers or headphones to output voice guidance (such as real human voice broadcasting instructions), ambient sounds (such as the injured person's groans and ambient noise), and operation effect prompts (such as prompts for correct operation and warnings for incorrect operation). The tactile feedback unit uses force feedback gloves, force feedback control panels, and other devices to provide corresponding pressure feedback and tactile simulation when the user performs operations such as chest compressions, bandaging, and defibrillation. For example, it simulates the resistance of chest compressions during CPR and the feel of the cloth during bandaging, enhancing the realism and interactive experience of the operation.

[0034] The data storage and analysis module is used for data storage, management, and in-depth analysis, including a data classification storage unit and a skills assessment unit. The data classification storage unit uses a distributed database (such as the Hadoop Distributed File System) to classify and store data according to scenario type, user identity, and data type (such as operational data, physiological data, and guidance data), supporting rapid data query, retrieval, and backup. The skills assessment unit uses a fuzzy comprehensive evaluation algorithm to comprehensively evaluate the user's first aid skills from four dimensions: operational standardization, reaction speed, emergency response rationality, and psychological stability. Specifically, operational standardization is scored based on the degree to which the user's operational behavior matches the standard operating procedure; reaction speed is scored based on the time taken from triggering the scenario event to completing the corresponding operation; emergency response rationality is scored based on the user's decision-making choices in complex scenarios (such as prioritizing which injury to treat); and psychological stability is scored based on the user's physiological state data (such as heart rate variability and respiratory rate stability). The weights for each dimension are as follows: operational standardization 40%, reaction speed 20%, emergency response rationality 25%, and psychological stability 15%. A comprehensive score is calculated based on the weights, generating a multi-dimensional skills assessment report. The assessment results are then fed back to the dynamic scenario generation module and the intelligent decision guidance module to achieve closed-loop optimization of scenario difficulty and guidance strategies.

[0035] Remote Collaboration Module: This module supports multi-person collaborative emergency rescue simulation training and remote expert guidance. It utilizes 5G communication technology to achieve low-latency data transmission, ensuring real-time synchronization of multi-person operations and scenario data. This module allows multiple users to access the platform via the network, enabling multi-person, multi-role emergency rescue scenario simulations. Roles include rescuers, commanders, and assistants, each with corresponding operational permissions and task assignments. Through data synchronization algorithms, it ensures real-time consistency of scenario visuals and operational data across all user terminals, facilitating collaborative operations and information sharing among users. Simultaneously, this module supports remote access by emergency rescue experts to view users' training processes and operational data in real time, providing real-time guidance and feedback. Expert feedback can be synchronized to all user terminals via the interactive feedback module.

[0036] Example 1: VR-based interactive simulation and guidance system for cardiopulmonary resuscitation This embodiment provides a VR-based interactive simulation and guidance system for cardiopulmonary resuscitation (CPR), which is a specific application of the interactive emergency rescue scenario simulation and emergency treatment guidance platform of this invention. It is mainly used for CPR skills training. Its system architecture includes a multimodal perception module, a dynamic scene generation module, an intelligent decision guidance module, an interactive feedback module, a data storage and analysis module, and a remote collaboration module. The specific implementation methods of each module are as follows: Multimodal Perception Module: The motion capture unit combines the OptiTrack optical motion capture system with the Xsens inertial motion capture device. The OptiTrack system deploys six high-definition cameras, covering the entire training area, with a sampling frequency of 120Hz. It is used to capture the wide-range movement trajectory of the user's torso and arms, acquiring data such as body posture and arm movement speed during CPR compressions. The Xsens inertial motion capture device includes inertial sensors worn on the user's wrist and arm, with a sampling frequency of 100Hz, collecting data on the user's compression movements, acceleration, angular velocity, and hand placement. The data collected by both systems are fused using a Kalman filter algorithm. The fused data has a unified sampling frequency of 100Hz, achieving millimeter-level positional accuracy, and can accurately capture operational details such as the depth, frequency, and position of the user's compressions. The physiological sensing unit utilizes a Huawei Band 8 and an electromyography (EMG) patch. The Huawei Band 8 collects the user's heart rate and respiratory rate data in real time, with a sampling frequency of 1Hz. The EMG patch is attached to the user's biceps brachii muscle to collect EMG signals, with a sampling frequency of 200Hz, used to assess the user's muscle exertion and psychological stress level during compression. The environmental sensing unit includes an SHT30 temperature and humidity sensor, a BH1750 light sensor, and a YS-1300 noise sensor, which collect data on the temperature, humidity, light intensity, and noise intensity of the training environment, respectively, with a sampling frequency of 0.5Hz. The data is transmitted to the system host via Bluetooth.

[0037] Dynamic Scene Generation Module: The scene template library stores basic scene templates for CPR, including indoor scenes (such as hospital wards and family living rooms) and outdoor scenes (such as parks and roads). Each scene template contains basic parameters such as a scene environment model (such as furniture, pedestrians, and vehicles), an event flow model (such as the timing of events like the injured person fainting, breathing stopping, and cardiac arrest), and a role model (such as the injured person and bystanders). The specific implementation steps of the scene parameter dynamic adjustment algorithm are as follows: S11, the user selects the type of cardiopulmonary resuscitation training scenario (such as a family living room scenario). The system calls the corresponding basic scenario parameters from the scenario template library to build the initial simulation scenario. In the initial scenario, the injured person is an adult male who has fainted next to the sofa. The initial state is weak breathing and normal heartbeat. The environmental parameters are room temperature 25℃, light intensity 500 lux, no noise interference, and the scenario difficulty level is beginner. S12, the system receives user operation behavior data (such as compression depth, frequency, and accuracy of compression position), physiological state data (such as heart rate and respiratory rate), and environmental parameter data (such as ambient noise intensity) collected in real time by the multimodal perception module. It then extracts user operation proficiency features through feature extraction algorithms: compression accuracy (the proportion of compressions performed at a depth of 5-6 cm out of the total number of compressions), compression frequency (compressions per minute), and operation time (the time from finding the injured person to starting compressions); physiological stress characteristics: heart rate variability (HRV) and respiratory rate variability; and environmental interference characteristics: noise intensity level (divided into four levels: no interference, slight interference, moderate interference, and severe interference). S13. The weight coefficients of each feature are determined by the hierarchical analysis algorithm. The weight of the operation proficiency feature is 40% (of which the pressing accuracy rate accounts for 15%, the pressing frequency accounts for 15%, and the operation time accounts for 10%), the weight of the physiological stress feature is 30% (the heart rate coefficient of variation accounts for 20%, and the respiratory rate coefficient of variation accounts for 10%), and the weight of the environmental interference feature is 30%. The scene adjustment requirement value is calculated based on the weight coefficients. The value range is 0-10. The larger the value, the more difficult the scene needs to be. S14: When the scenario adjustment requirement value is <3, maintain the current difficulty level and do not adjust scenario parameters; when 3 ≤ requirement value <6, adjust to medium difficulty, accelerate the deterioration of the injured person's condition (e.g., shorten the respiratory arrest time from the initial 2 minutes to 1 minute), and add slight environmental interference (e.g., add TV playback sound with a noise intensity of 40-50dB); when the requirement value is ≥6, adjust to high difficulty, trigger complications of the injured person (e.g., rib fracture), add moderate environmental interference (e.g., add multiple people talking and footsteps with a noise intensity of 50-60dB), and shorten the event trigger interval (e.g., prompt the injured person's vital signs every 30 seconds). The scenario customization unit allows users to set the injured person's gender, age, initial condition, type and intensity of environmental interference through a visual interface to generate personalized cardiopulmonary resuscitation simulation scenarios.

[0038] The intelligent decision-making guidance module stores authoritative materials such as the 2025 edition of the "International Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care" and the "Chinese Expert Consensus on Cardiopulmonary Resuscitation." It includes the standard operating procedure for cardiopulmonary resuscitation (assessing consciousness and breathing → calling for help → positioning the patient → chest compressions → opening the airway → artificial respiration → defibrillation), operational guidelines for each step (e.g., chest compression depth 5-6 cm, rate 100-120 compressions / minute, tidal volume 500-600 ml for artificial respiration), common errors (e.g., incorrect compression position, insufficient compression depth, failure to pinch the nose during artificial respiration), and correction methods. The deep learning guidance model adopts a CNN+Bi-LSTM+attention mechanism architecture. The training data consists of 1000 sets of standard cardiopulmonary resuscitation operation video data and 500 sets of incorrect operation video data, with each set labeled with operation steps, operation details, and error type. During model training, the temporal features (such as the alternation of chest compressions and artificial respiration, and the time taken for each step) and spatial features (such as the pixel coordinates of the compression position and the angle between the arm and the torso) of the user's operations are first extracted using CNN. Then, the temporal features are modeled using Bi-LSTM, and an attention mechanism is used to focus on the features of key operation steps (such as chest compressions and artificial respiration). Finally, the operation judgment results are output through a fully connected layer, including an operation standardization score (0-100 points) and error type identification (such as insufficient compression depth, excessive frequency, and inadequate airway opening). The guidance generation layer generates personalized guidance instructions based on the judgment results. For example, when it detects that the user's compression depth is insufficient (<5 cm), it generates voice and text guidance instructions such as "Insufficient compression depth, current depth is 3.5 cm, needs to be increased to 5-6 cm, it is recommended to increase arm force." When it detects that the user's compression frequency is too fast (>120 times / minute), it generates guidance instructions such as "Compression frequency is too fast, current frequency is 135 times / minute, needs to be adjusted to 100-120 times / minute, pay attention to controlling the compression rhythm." The emergency case matching unit uses a cosine similarity algorithm to match the patient's condition (such as age, disease progression) and environmental features (such as scene type, interference intensity) in the current scene with 100 sets of real CPR cases in the knowledge base. When the similarity is ≥80%, the successful treatment experience of the case (such as methods for adjusting compression depth for elderly patients, and call for help techniques in noisy environments) is extracted and integrated into the guidance instructions.

[0039] Interactive Feedback Module: The visual feedback unit uses the Pico 4 Pro VR all-in-one device with a resolution of 2160×2160 and a refresh rate of 120Hz. It outputs dynamic CPR scene visuals, overlaying operation guidance icons (such as red bullseye icons for compression positions and flowcharts of operation steps), operation scores (real-time display of scores for parameters such as compression accuracy, frequency, and depth), and error prompts (such as "Compression position deviation, should be in the lower 1 / 3 of the sternum"). The auditory feedback unit uses the VR all-in-one device's built-in high-fidelity speakers to output real-person voice guidance (recorded by professional emergency physicians), ambient sounds (such as television sounds in a home scene and vehicle sounds in an outdoor scene), and operation effect prompts (a "beep" sound for correct operation and a "beep beep" warning sound for incorrect operation). The haptic feedback unit uses the Manus VR force feedback glove. When the user performs chest compressions, the glove provides corresponding resistance feedback based on the compression depth. The closer the compression depth is to the standard value, the more realistic the resistance feedback. When performing artificial respiration, the glove simulates the feeling of pinching the injured person's nose, enhancing the realism of the operation.

[0040] Data storage and analysis module: The data classification and storage unit adopts the Hadoop distributed file system, deploying 3 data nodes. User operation data (compression depth, frequency, location, etc.), physiological data (heart rate, respiratory rate, electromyography signals), guidance data (guidance instruction type, number of outputs), and scenario data (scenario type, difficulty level, environmental parameters) are classified and stored according to user ID and training time. Data retention is one year, and quick queries by user, scenario type, and time range are supported. The skills assessment unit uses a fuzzy comprehensive evaluation algorithm to evaluate from four dimensions: operational standardization (40% weight), reaction speed (20% weight), emergency response rationality (25% weight), and psychological stability (15% weight). Operational standardization is scored based on indicators such as compression accuracy, frequency compliance rate, and artificial respiration standardization rate; reaction speed is scored based on indicators such as the time from finding the injured person to starting compressions and the time from compressions to artificial respiration; emergency response rationality is scored based on indicators such as whether help was called in promptly, whether the body position was correct, and the timing of defibrillation; and psychological stability is scored based on physiological indicators such as heart rate variability and respiratory rate stability. The system generates a multi-dimensional skills assessment report, including scores for each dimension, an overall score, existing problems, and improvement suggestions. The assessment results are then fed back to the dynamic scenario generation module and the intelligent decision guidance module. For example, when the user's overall score is less than 60 points, the system automatically reduces the scenario difficulty level and increases the frequency of guidance on basic operation steps; when the overall score is greater than or equal to 80 points, the system increases the scenario difficulty level, reduces basic guidance, and increases guidance on advanced skills.

[0041] Remote Collaboration Module: Utilizing a 5G industrial module for data transmission, this module supports simultaneous access for 2-4 users to conduct multi-person collaborative CPR simulation training. Roles are divided into primary rescuer (responsible for chest compressions and artificial respiration), assistant (responsible for calling for help, positioning the patient, and preparing the defibrillator), and commander (responsible for coordinating tasks and monitoring the patient's vital signs). Through data synchronization algorithms, the VR scene visuals and operational data of all users are guaranteed to be synchronized in real time, with a latency of ≤50ms. Simultaneously, emergency medical experts can remotely access the system via computer to view each user's operational visuals and data in real time. They can provide real-time guidance and feedback to users via voice or text, and this feedback can be synchronized to all users' VR devices. For example, if the assistant is not ready with the defibrillator, the expert can send a guidance message such as "Please retrieve the defibrillator as soon as possible to prepare for defibrillation," which will be synchronized to the VR interfaces of both the assistant and commander.

[0042] The workflow of the VR-based interactive simulation and guidance system for cardiopulmonary resuscitation in this embodiment is as follows: S21, the user wears a VR all-in-one device, force feedback gloves and wearable sensing devices, logs into the system and selects the cardiopulmonary resuscitation training mode (single training / multi-person collaborative training) and the initial scenario type; S22, the system calls the corresponding basic scene parameters from the scene template library, constructs the initial simulated scene, and outputs the scene image to the user through the VR device; S23, the multimodal perception module collects user operation behavior data, physiological state data and environmental parameter data in real time, and transmits them to the dynamic scene generation module and the intelligent decision guidance module; S24, The dynamic scene generation module adjusts the scene difficulty and event process according to real-time data and scene parameter dynamic adjustment algorithm, updates the scene screen and outputs it through VR device; S25, the intelligent decision guidance module analyzes user operation data, judges the standardization of operation, generates personalized guidance instructions, and outputs them to the user through the visual, auditory, and tactile channels of the interactive feedback module; S26, the user adjusts the operation based on the feedback information, and the multimodal perception module continuously collects the adjusted operation data to form a closed-loop interaction; S27, the data storage and analysis module stores various types of data in real time, conducts skills assessments, and generates assessment reports; S28. After the training is completed, the system outputs a skills assessment report to the user. The user can view their operation records, existing problems and improvement suggestions. The training organizer can view the training data and assessment results of all users through the backend and formulate a targeted training plan.

[0043] Example 2: Mobile-based multi-scenario first aid training platform This embodiment provides a mobile-based multi-scenario first aid training platform, which is another specific application of the present invention. It is suitable for public first aid education and allows users to conduct first aid skills training anytime and anywhere via mobile devices such as smartphones and tablets. Its system architecture also includes a multimodal perception module, a dynamic scene generation module, an intelligent decision guidance module, an interactive feedback module, a data storage and analysis module, and a remote collaboration module. The specific implementation methods of each module are as follows: Multimodal Perception Module: Considering the portability of mobile devices, the motion capture unit utilizes the built-in camera and gyroscope of the mobile device. It captures the user's limb movement data through computer vision algorithms (such as the MediaPipe posture recognition algorithm). The camera captures user operation video at a sampling frame rate of 30fps. The MediaPipe algorithm extracts the user's joint coordinates (such as shoulder, elbow, wrist, hip, etc.) and calculates the angle and trajectory of limb movements. The gyroscope collects the mobile device's posture data to assist in judging the user's operation (e.g., in a trauma hemostasis scenario, the user holding a virtual gauze for bandaging). The physiological sensing unit supports Bluetooth connection with the user's smart bracelet, smartwatch, and other wearable devices to collect the user's heart rate and respiratory rate data. If the user is not wearing a wearable device, the mobile device's camera can capture the user's facial image, extract facial feature points through image recognition algorithms, and calculate the respiratory rate (based on chest rise and fall). The environmental sensing unit uses the mobile device's built-in temperature and humidity sensors and light sensors to collect current environmental temperature, humidity, and light intensity data. Simultaneously, it collects environmental noise data through a microphone, achieving real-time acquisition of environmental parameters.

[0044] Dynamic Scene Generation Module: The scene template library stores various common emergency rescue scene templates, including trauma hemostasis (such as arterial bleeding, venous bleeding, capillary bleeding), drowning rescue (such as shore rescue, water rescue), poisoning rescue (such as drug poisoning, food poisoning), burn rescue, etc. Each scene template is built using a lightweight 3D model, adapted to the display and running performance of mobile devices. The implementation steps of the scene parameter dynamic adjustment algorithm are as follows: S31, users select the type of emergency rescue scenario and the initial difficulty level (beginner, intermediate, expert) through the mobile APP, and the system calls the corresponding scenario template to generate the initial simulation scenario; S32 collects user operation behavior data (such as the standardization of bandaging actions and the completeness of operation steps), physiological state data (such as heart rate) and environmental data (such as light intensity) through the multimodal perception module, and extracts operation proficiency features (accuracy of operation steps and completion time), physiological stress features (heart rate changes), and environmental adaptation features (the influence of light on the operation field of vision). S33, the entropy weight method is used to determine the weight coefficient of each feature, with the weight of the operation proficiency feature being 45%, the weight of the physiological stress feature being 25%, and the weight of the environmental adaptation feature being 30%, and the scene adjustment requirement value is calculated. S34. Adjust the scene parameters according to the demand value. For example, in the case of trauma hemostasis, when the user's operation proficiency is high (the demand value is high), increase the number of bleeding sites and speed up the bleeding speed. When ambient light is insufficient, the scene brightness is automatically adjusted to ensure users can clearly see the operating area. The scene customization unit allows users to upload scene background images and set event trigger conditions (such as customizing the appearance time of poisoning symptoms) via the mobile interface to generate personalized scenes.

[0045] The intelligent decision-making guidance module uses a lightweight data structure for the first aid knowledge base, containing simplified standard operating procedures, key points, common errors, and correction tips for various first aid scenarios, adapted to the storage and retrieval speeds of mobile devices. The deep learning guidance model employs a lightweight CNN architecture (MobileNet), trained on 500 sets of first aid operation video data, enabling real-time inference on mobile devices (inference latency <100ms). The model extracts operational action features from user operation videos captured by the mobile device's camera to determine the standardization of the operation. For example, in a trauma hemostasis scenario, it determines whether the user has correctly cleaned the wound, used the correct bandaging method (such as spiral bandaging or circular bandaging), and whether the bandaging pressure is appropriate. The guidance generation layer generates concise and easy-to-understand instructions, outputting them in text, voice, and animation formats. For example, for novice users, it generates step-by-step guidance with animated demonstrations (e.g., "Step 1: Press the wound with clean gauze to stop the bleeding, animation demonstration: [gauze pressing animation]"); for advanced users, it generates key point reminders (e.g., "Pay attention to the tightness of the bandage to avoid affecting blood circulation"). The emergency case matching unit matches lightweight emergency cases (each case ≤ 500 words), highlighting key treatment steps and adapting to the reading experience on mobile devices.

[0046] Interactive Feedback Module: The visual feedback unit outputs scene images, operation guidance animations, text guidance information, and operation scores through the mobile device's display screen, using a split-screen display mode. The scene image is displayed on the left, and the operation steps and scores are displayed on the right. The auditory feedback unit outputs voice guidance (using colloquial language), scene sound effects (such as blood sounds, cries for help), and operation prompts through the mobile device's speaker. The haptic feedback unit utilizes the mobile device's vibration function to provide tactile feedback on the operation effect. For example, when the user completes the operation correctly, the mobile device vibrates slightly once; when the operation is incorrect, it vibrates twice consecutively, enhancing the user's interactive experience.

[0047] Data storage and analysis module: Utilizing a combination of cloud and local databases, basic user operation data (such as scenario completion status and operation scores) is stored locally for convenient offline viewing. Detailed training data (such as operation video clips and complete evaluation reports) is stored in the cloud database, synchronized via 4G / 5G networks. The skills assessment unit employs a simplified fuzzy comprehensive evaluation algorithm, assessing operation standardization, step completeness, and reaction speed. It generates a concise evaluation report including a comprehensive score, strengths and weaknesses, and recommended training scenarios. Evaluation results are fed back to the user's mobile device in real time and simultaneously synchronized to the cloud platform. Users can log in via different devices to view historical training records.

[0048] Remote Collaboration Module: This module allows users to invite friends to join a collaborative training room via mobile devices, enabling two-person collaborative first aid simulations. For example, one person can be responsible for stopping bleeding while the other calls for help and prepares first aid supplies. Data synchronization is achieved through the mobile network, employing low-latency video transmission technology to ensure real-time synchronization of the two users' actions and the scene's progress. Users can also share their simulation videos to social media platforms or send them to first aid experts for remote feedback.

[0049] The workflow of the mobile-based multi-scenario first aid training platform in this embodiment is as follows: S41, users install the APP on their mobile devices, register and log in, and select the type of emergency rescue scenario and difficulty level; S42, the system loads the corresponding scene template, generates the initial simulation scene, and displays it on the mobile device's screen; S43, the user operates according to the scene prompts, and the multimodal perception module collects operation data and physiological data through the mobile terminal camera, gyroscope and connected wearable device; S44, The dynamic scene generation module adjusts scene parameters and updates scene images based on real-time data; S45, the intelligent decision guidance module analyzes operation data, generates guidance instructions, and provides feedback to the user through text, voice, animation, and vibration; S46, the user adjusts the operation based on feedback to complete the emergency rescue simulation of the current scenario; S47, the system generates a skills assessment report and stores it in local and cloud databases; S48, users can view the evaluation report and choose to proceed to the next scenario training or repeat the current scenario training.

[0050] The platform is highly portable, easy to operate, and applicable to a wide range of scenarios, which can effectively improve the public's awareness of first aid knowledge and basic first aid skills.

[0051] Example 3: Emergency Rescue On-site Assistance and Guidance System Combining Wearable Devices This embodiment provides an emergency rescue site assistance and guidance system combined with wearable devices, which is another specific application of the present invention. It is mainly used in actual emergency rescue sites to provide real-time first aid guidance to frontline rescuers (such as firefighters and paramedics). Its system architecture includes a multimodal perception module, a dynamic scene generation module, an intelligent decision guidance module, an interactive feedback module, a data storage and analysis module, and a remote collaboration module. The specific implementation of each module is as follows: Multimodal Sensing Module: The motion capture unit uses industrial-grade inertial motion capture equipment (such as MotionAnalysis Forte), worn on the rescuer's arms, hands, torso, etc., with a sampling frequency of 120Hz. It accurately collects the rescuer's operational action data at the actual rescue scene (such as actions like demolition, hemostasis, bandaging, and CPR). The data is transmitted to the system host via a wireless LAN. The physiological sensing unit uses professional medical-grade wearable devices to collect physiological data such as the rescuer's heart rate, blood pressure, and electromyography signals. Simultaneously, vital sign monitoring equipment deployed at the rescue scene collects physiological data such as the injured person's heart rate, respiratory rate, and blood oxygen saturation, providing a basis for decision-making. The environmental sensing unit uses a multi-sensor array deployed at the rescue scene, including temperature and humidity sensors, air pressure sensors, gas sensors (detecting the concentration of toxic and harmful gases), and vibration sensors (detecting environmental vibrations), comprehensively collecting on-site environmental parameters to assess the safety and complexity of the rescue environment.

[0052] Dynamic Scene Generation Module: Unlike the previous two embodiments, the dynamic scene generation module in this embodiment can not only generate simulated scenes but also construct digital models of real-world scenes based on real-time data from the rescue site. It uses on-site environmental data and injured person status data collected by the multimodal perception module to construct a 3D digital scene model of the rescue site, updating information such as changes in the on-site environment (e.g., gas concentration changes, structural stability changes) and changes in the injured person's condition in real time. The scene parameter dynamic adjustment algorithm dynamically adjusts the priority of guidance strategies based on the degree of danger of the on-site environment, the urgency of the injured person's condition, and the rescuer's operational progress. For example, when the concentration of toxic or harmful gases at the scene exceeds the standard, the instruction to "immediately wear protective equipment" is generated first; when the injured person experiences cardiac arrest, the instruction to "immediately perform cardiopulmonary resuscitation" is generated first.

[0053] The intelligent decision-making guidance module adds data to the emergency rescue knowledge base, including special handling procedures for emergency rescue sites, first aid precautions in hazardous environments, and specific first aid procedures for various disasters and accidents (such as fires, earthquakes, and traffic accidents). The deep learning guidance model adds on-site environmental adaptability analysis, adjusting guidance instructions based on on-site environmental parameters (such as high temperature, high pressure, and toxic gases). For example, in a high-temperature environment, it generates instructions such as "Maintain the injured person's body temperature and prevent dehydration"; in a toxic gas environment, it generates instructions such as "Prevent the injured person from inhaling toxic gases and move them to a safe area as soon as possible." Simultaneously, the model can analyze the injured person's vital signs data in real time, assess the progression of the condition, predict potential complications, and generate prevention and treatment guidance instructions in advance. The emergency case matching unit focuses on matching real rescue cases from various disasters and accidents, extracting experience in handling complex on-site environments to provide reference for rescuers.

[0054] Interactive Feedback Module: The visual feedback unit uses a head-mounted AR display (such as HoloLens 2) to provide rescuers with AR augmented reality guidance, overlaying operation guidance icons (such as hemostasis location guidance, demolition path guidance), on-site environmental information (such as toxic gas concentration distribution heatmap, safety passage signs), injured person's vital signs data, and guidance text into the rescuer's field of vision. The auditory feedback unit uses bone conduction headphones to output voice guidance commands, avoiding interference from environmental noise in a noisy rescue scene. The tactile feedback unit uses a force feedback device worn on the rescuer's hand to provide pressure feedback during delicate operations (such as wound suturing and intubation), improving the accuracy of the operation.

[0055] The data storage and analysis module employs an architecture combining edge computing and cloud computing. Real-time data from the rescue site is first stored on edge computing nodes to ensure real-time processing and low-latency access. Subsequently, the data is synchronized to a cloud database for in-depth analysis and long-term storage. The skills assessment unit not only evaluates rescuers' operational skills but also analyzes their decision-making abilities and psychological resilience in complex and dangerous environments, generating professional rescue capability assessment reports to provide a basis for skills enhancement and training plan development for rescue personnel. Simultaneously, the system can perform statistical analysis on data from the rescue site, providing data support for optimizing emergency rescue plans.

[0056] Remote Collaboration Module: Employing redundant backups of satellite and 5G communication, this module ensures data transmission even in extreme environments (such as remote areas without terrestrial network coverage). It supports real-time collaboration between rescuers at the scene and the command center and expert teams. Rescuers transmit real-time images and data from the scene via AR displays and bone conduction headphones. Experts at the rear view the situation remotely, generate guidance instructions, and overlay these instructions onto the rescuer's field of vision via the AR display, enabling precise remote guidance. For example, when rescuers encounter complex trauma management issues, trauma surgeons at the rear can send AR operation guidelines to guide the rescuer in proper wound treatment.

[0057] The workflow of the emergency rescue on-site assistance and guidance system combined with wearable devices in this embodiment is as follows: S51. After arriving at the scene, rescuers put on inertial motion capture equipment, AR displays, bone conduction headphones and physiological sensing equipment, and deployed on-site environmental sensors and vital sign monitoring equipment for the injured. S52, after the system is started, it collects on-site environmental data, injured person physiological data and rescuer physiological data through the multimodal perception module to construct a 3D digital model of the scene; S53, the intelligent decision-making guidance module analyzes on-site data, determines rescue priorities and response plans, and generates initial guidance instructions; S54, the interactive feedback module outputs guidance information to the rescuer through an AR display, bone conduction headphones, and force feedback devices; S55: Rescuers perform rescue operations according to instructions, and the multimodal sensing module continuously collects operation data and on-site change data; S56, the dynamic scene generation module updates the on-site digital model, and the intelligent decision guidance module adjusts the guidance instructions based on real-time data; S57: During the rescue, rescuers can ask questions to the system via voice, and the system generates answers through the intelligent voice interaction module; After the rescue is completed, the S58 system records complete rescue data, generates a rescue process analysis report and a rescuer capability assessment report, and synchronizes them to the command center and cloud database.

[0058] This system can effectively improve the efficiency and accuracy of emergency rescue operations, reduce the operational risks for rescuers, and increase the survival rate of the injured.

[0059] Comparison Example Comparison with Example 1: Traditional Fixed-Scene Emergency Rescue Simulation System This comparative example is a traditional fixed-scenario emergency rescue simulation system. Its structure includes a fixed-scenario module, a simple motion capture module, a standardized guidance module, and a data storage module. The fixed-scenario module contains pre-set emergency rescue scenarios (such as fixed cardiopulmonary resuscitation scenarios and trauma hemostasis scenarios). Scenario parameters (such as the injured person's condition and environmental conditions) remain fixed and cannot be dynamically adjusted based on user actions. The simple motion capture module uses only a single optical sensor to capture a wide range of user limb movements, failing to capture fine operational details and resulting in low motion recognition accuracy. The standardized guidance module uses pre-set standardized voice prompts, such as "Please perform chest compressions" and "Please perform artificial respiration," outputting the same guidance regardless of the user's correctness or skill level, lacking specificity. The data storage module only stores basic data such as the user's training time and the number of scenarios completed, lacking in-depth analysis and evaluation functions.

[0060] Compared with the present invention, this comparative example has the following shortcomings: 1. The scenarios are fixed and cannot simulate the complexity and uncertainty of actual emergency rescue scenarios, resulting in a large gap between the user training experience and actual needs; 2. Low motion capture accuracy makes it impossible to fully capture the details of user operations, affecting the accuracy of guidance; 3. The guidance methods are monotonous and lack personalization, resulting in poor training effectiveness; 4. Weak data processing capabilities prevent the support for training optimization.

[0061] The results show that users trained using the control example system scored an average of 65 points on operational standardization in actual emergency scenarios, while users trained using the system of Embodiment 1 of the present invention scored an average of 88 points, which is significantly better than the control example.

[0062] Compare with Example 2: Emergency medical guidance platform without intelligent decision engine This comparative example is an emergency medical guidance platform without an intelligent decision-making engine. Its structure includes a scene generation module, a multimodal perception module, a fixed guidance module, and a data storage module. This platform can collect user operational and physiological data and generate simple dynamic scenes, but it lacks an intelligent decision-making guidance module. The guidance instructions are preset, fixed procedures and cannot be dynamically adjusted based on the user's real-time operational data and physiological state. For example, regardless of whether the user's compression depth or frequency is appropriate, it outputs "continue compressions" or "perform artificial respiration" instructions at fixed time intervals, failing to provide targeted correction for incorrect operations.

[0063] Compared with the present invention, this comparative example has the following shortcomings: 1. Lacking intelligent decision-making and analysis capabilities, it is unable to accurately judge the standardization and rationality of user operations, resulting in extremely poor targeting of guidance instructions; 2. Unable to adjust guidance strategies based on the user's physiological state; when the user's operation deviates due to psychological tension, it cannot provide timely reassurance and adjustment suggestions. 3. It lacks the function of matching emergency cases, and the guidance content is limited to the preset process, which cannot cope with the personalized treatment needs in complex scenarios.

[0064] Users trained on the control system for trauma hemostasis had an error correction rate of only 32% and an average time of 8 minutes and 25 seconds to complete complex trauma treatment. In contrast, users trained on the mobile-based multi-scenario emergency training platform of Embodiment 2 of the present invention had an error correction rate of 89% and an average time of 4 minutes and 10 seconds to complete the same complex trauma treatment. The training effect was significantly better than that of the control system.

[0065] Compare with Example 3: Emergency Rescue Simulation System Without Remote Collaboration and Multimodal Interaction This comparative example is an emergency rescue simulation system without remote collaboration and multimodal interaction. Its structure includes a fixed scene module, a basic motion capture module, a standardized guidance module, and a simple data storage module. This system only supports local training for a single user, lacks remote collaboration capabilities, and cannot achieve multi-person collaborative emergency rescue simulation. The interaction method is limited, relying solely on a display screen for visual information and a speaker for audio information, lacking haptic feedback and resulting in a poor user experience. The basic motion capture module can only capture rough limb movements and cannot accurately capture fine operational details; the standardized guidance module outputs fixed operational prompts and lacks personalized adjustment capabilities.

[0066] Compared with the present invention, this comparative example has the following shortcomings: 1. Lacking remote collaboration capabilities, it cannot meet the training needs of multi-person collaboration in actual emergency rescue scenarios, and trainees' teamwork skills cannot be developed. 2. The interaction method is simplistic and lacks tactile feedback, making it impossible for users to truly perceive key information such as the force and feel of the operation, resulting in a very poor sense of immersion; 3. Low motion capture accuracy, standardized instructions, and limited training effectiveness.

[0067] Trainees trained using the comparative system scored only 56 points in teamwork and 61 points in operational proficiency in multi-person collaborative emergency rescue scenarios. In contrast, trainees trained using the emergency rescue on-site assistance and guidance system of Embodiment 3 of the present invention, combined with wearable devices, achieved a teamwork score of 92 points and an operational proficiency score of 87 points, demonstrating their ability to quickly adapt to the needs of actual multi-person collaborative rescue scenarios.

[0068] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of protection claimed by the present invention. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An interactive emergency scene simulation and emergency handling guidance platform, characterized in that, The method comprises the following steps: A multi-modal perception module is used to collect operation behavior data, physiological state data, and environment parameter data of a simulated / real scene, wherein the operation behavior data comprises limb movement data and operation timing data, the physiological state data comprises heart rate, breathing rate, and electromyographic signal, and the environment parameter data comprises temperature and humidity, light intensity, and scene noise; A dynamic scene generation module is in communication connection with the multi-modal perception module, and is used to construct a basic scene based on a preset first aid scene template, combine real-time data collected by the multi-modal perception module, adjust scene difficulty level, event triggering timing, and scene environment details through a scene parameter dynamic adjustment algorithm, and generate a dynamically adapted first aid simulation scene; An intelligent decision guidance module is in communication connection with the multi-modal perception module and the dynamic scene generation module, and is internally provided with a first aid knowledge base and a deep learning guidance model, wherein the deep learning guidance model is obtained through training of multiple sets of first aid case data, is used to perform real-time analysis on user operation behavior data, judge operation standardization and rationality, combine event progress of a current dynamic scene, and generate individualized first aid operation guidance instructions; An interactive feedback module is in communication connection with the intelligent decision guidance module, and is used to output scene interaction information, operation guidance instructions, and operation effect feedback to a user through visual, auditory, and tactile multi-sensory channels; A data storage and analysis module is in communication connection with the multi-modal perception module, the dynamic scene generation module, and the intelligent decision guidance module, and is used to store scene data, user data, and guidance data, evaluate user's first aid skill mastery through a data mining algorithm, generate an evaluation report, and feed back to the dynamic scene generation module and the intelligent decision guidance module to realize closed-loop optimization of the scene and the guidance strategy.

2. The interactive first aid scenario simulation and emergency treatment guidance platform according to claim 1, characterized in that: The multi-modal perception module comprises a motion capture unit, a physiological sensing unit, and an environment sensing unit; The motion capture unit adopts a combination of an optical motion capture device and an inertial motion capture device, the optical motion capture device is used to acquire user large-range limb movement data, the inertial motion capture device is used to acquire user fine operation movement data, and high-precision operation behavior data is obtained through fusion algorithm processing of data of the two devices; The physiological sensing unit adopts a wearable sensing device to collect user heart rate, breathing rate, and electromyographic signal in real time; The environment sensing unit comprises a temperature and humidity sensor, a light sensor, and a noise sensor, and is used to collect scene environment parameters.

3. The interactive first aid scenario simulation and emergency treatment guidance platform according to claim 1, characterized in that: The scene parameter dynamic adjustment algorithm of the dynamic scene generation module comprises the following steps: S11, determining basic scene parameters based on a preset first aid scene type, wherein the first aid scene type comprises cardiopulmonary resuscitation, wound hemostasis, drowning rescue, and poisoning first aid; S12, receiving real-time data collected by the multi-modal perception module, and extracting user operation proficiency features, physiological stress features, and environment interference features; S13, determining weight coefficients of the features through an analytic hierarchy process algorithm, and calculating a scene adjustment requirement value; S14, according to the scene adjustment demand value, matching the corresponding difficulty adjustment parameter from the scene difficulty parameter library, dynamically adjusting the event trigger interval, fault complexity, and environmental interference intensity of the basic scene.

4. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: The deep learning guidance model of the intelligent decision guidance module comprises a feature extraction layer, an operation judgment layer, and a guidance generation layer; The feature extraction layer extracts the time sequence features and spatial features of the user operation behavior using a convolutional neural network; The operation judgment layer analyzes the extracted features using a bidirectional long short-term memory network combined with an attention mechanism to determine whether the user operation conforms to the first aid standard, whether the operation timing is reasonable, and whether the operation force meets the standard; The guidance generation layer generates targeted guidance instructions based on the judgment results and the standard operation process in the first aid knowledge base, including operation correction instructions, step prompt instructions, and attention reminder instructions.

5. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: The interaction feedback module comprises a visual feedback unit, an auditory feedback unit, and a tactile feedback unit; The visual feedback unit uses a VR display device or a high-definition display screen to output dynamic scene pictures, operation guide icons, and operation scores; The auditory feedback unit outputs voice guidance, scene environment sound, and operation effect prompt sound; The tactile feedback unit uses a force feedback device to provide corresponding pressure feedback and touch sensation simulation when the user performs pressing and bandaging operations.

6. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: The data storage and analysis module comprises a data classification storage unit and a skill evaluation unit; The data classification storage unit uses a distributed database to store data by scene type, user identity, and data type, supporting fast data query and calling; The skill evaluation unit uses a fuzzy comprehensive evaluation algorithm to evaluate the user's first aid skills from four dimensions: operation standardization, reaction speed, emergency disposal rationality, and psychological stability, and generates a multi-dimensional evaluation report.

7. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: It also includes a remote collaboration module that is in communication with the intelligent decision guidance module and the data storage and analysis module, supports multiple users to perform collaborative first aid simulation training, realizes operation synchronization and information sharing among users, and supports expert remote access for real-time guidance and comments on user operations.

8. The interactive first aid scenario simulation and emergency treatment guidance platform according to claim 7, characterized in that: The remote collaboration module uses 5G communication technology to realize low-latency data transmission, ensures real-time synchronization of multiple user operation data and scene data through a data synchronization algorithm, supports multi-person multi-role first aid scene simulation, and the roles include rescuers, commanders, and assistants.

9. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: The intelligent decision guidance module further comprises a first aid case matching unit for matching the current dynamic scene with historical cases in the first aid knowledge base, extracting successful disposal experience of similar cases, and integrating it into the guidance instructions.

10. The interactive first aid scenario simulation and emergency treatment guidance platform of claim 1, wherein: The dynamic scene generation module further comprises a scene customization unit that supports users to customize scene types, event processes, difficulty levels, and environmental parameters according to training needs to generate personalized first aid simulation scenes.

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