VR-based high-altitude simulation system and use method thereof

Abstract

The invention relates to the technical field of VR high-altitude simulation, and discloses a VR-based high-altitude simulation system, which comprises floating plate type bridge crossing equipment, VR helmet and tracking equipment, a VR handle and tracker and a physiological monitor, the high-altitude simulation module comprises a hardware integrated control module which integrates floating plate type bridge crossing equipment, a VR helmet and tracking equipment, a VR handle and tracker and physiological monitor hardware equipment, and carries out data interaction and sharing; the virtual reality environment construction module is used for designing and developing multiple high-altitude virtual scenes through 3D modeling and texture rendering; the data acquisition module is used for acquiring test data of a user through hardware integration and in a virtual reality environment; and the data analysis module is used for constructing subjective scale scores, physiological index changes and performance task completion condition data based on actually acquired data.

Description

Technical Field

[0001] The present invention relates to the field of VR high-altitude simulation technology, and in particular to a VR-based high-altitude simulation system and a use method thereof. Background Art

[0002] With the enhancement of computer graphics processing capabilities, the improvement of head-mounted display (HMD) resolution and refresh rate, and the precision of tracking technology, VR technology provides users with an increasingly immersive three-dimensional virtual world experience. This technological advancement makes it possible to simulate high-altitude environments, making users feel as if they are in a real high-altitude situation;

[0003] At the same time, with the integration of physiological monitoring technology and the development of wearable devices and physiological sensor technology, users' heart rate, blood pressure, skin conductance and other physiological indicators can be monitored in real time and non-invasively, providing objective data support for the evaluation of emotional responses.

[0004] After searching, the invention patent with Chinese patent number CN110910698A discloses a parachuting simulation system, control method and storage medium. Compared with the prior art, the invention patent with Chinese patent number CN110910698A adopts a cylinder drive mode, and drives the spatial movement of the parachuting simulation cabin through a cylinder drive device and a pulley transmission device, giving the parachuting trainee a real weightlessness experience, and can simulate the free fall process, parachute opening process, gliding process and landing process during the parachuting descent process. Combined with the VR virtual unit, it provides users with virtual visual effects during the parachuting process, giving users a double weightlessness stimulation in terms of physiology and psychology, and improving the realism of the parachuting simulation training;

[0005] However, although VR can provide consistent visual and auditory stimulation, the user's subjective feelings, such as fear, tension, excitement, etc., are affected by multiple factors such as personal experience and psychological state, and are difficult to be accurately quantified and compared like physiological indicators, which will affect the consistency and comparability of the evaluation. Therefore, a VR-based high-altitude simulation system and its use method are proposed. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art that subjective experience cannot be accurately quantified and only relies on auditory and visual simulation evaluation, and to propose a VR-based high-altitude simulation system and its use method.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A high-altitude simulation system based on VR, including a floating plate bridge device, a VR helmet and tracking device, a VR handle and tracker, and a physiological monitor. The high-altitude simulation module includes:

[0009] Hardware integrated control module: Integrates floating bridge equipment, VR helmet and tracking equipment, VR handle and tracker, and physiological monitor hardware equipment for data interaction and sharing;

[0010] Virtual reality environment construction module: design and develop multi-altitude virtual scenes through 3D modeling and texture rendering;

[0011] Data acquisition module: collects user test data through hardware integration and virtual reality environment;

[0012] Data analysis module: Based on the actual acquired data, construct subjective scale scores, physiological index changes, and performance task completion data;

[0013] Evaluation model construction module: Integrate subjective scale scores, changes in physiological indicators, and performance task completion, and use statistical and machine learning algorithms to build an emotion control ability evaluation model.

[0014] The data acquisition module includes a physiological data acquisition subsystem, the data analysis module includes a performance task design unit, and the physiological monitor activates the physiological data acquisition subsystem to transmit data to the data acquisition module.

[0015] The physiological data acquisition subsystem is based on the physiological monitor to obtain multiple physiological data in the virtual environment. During the development of the high-altitude simulation system based on VR virtual reality, the quality of integration efficiency is improved through:

[0016] Integration_Quality=g(R,I,C)

[0017] R represents the code reuse rate, I represents the degree of module interface standardization, and C represents the unit test coverage. The goal of function g is to maximize the positive effects of these three key factors and measure the data integration quality based on the formula;

[0018] The performance task design unit is designed to simulate fear situations in real life, including walking at heights, jumping, or passing through unstable surface environments, to stimulate the subjects' fear response and provide a real source of emotional stimulation for the study.

[0019] A method based on a VR high-altitude simulation system, the high-altitude simulation method steps are:

[0020] S1: Design and develop a variety of high-altitude stimulating scenes, and integrate a variety of hardware devices to build a virtual reality and interactive system;

[0021] S2: Collect user interaction data and physiological data based on the server host, client host, and physiological monitor;

[0022] S3: Develop specific task scenarios and complete specific tasks in a virtual high-altitude environment to evaluate their cognitive functions and behavioral performance in fear situations;

[0023] S4: Construct an emotion control ability assessment model by combining subjective scales, physiological indicators, and performance tasks.

[0024] In S1, a floating bridge that can simulate the feeling of real walking was designed and installed to ensure stability and synchronization with the movements in the virtual environment;

[0025] The selected VR helmet, combined with the indoor space tracking system, captures every tiny movement and feeds it back to the virtual environment in real time;

[0026] Equipped with a VR controller with high-precision tracking function, users can have a natural hand interaction experience in the virtual environment;

[0027] Choose compatible physiological signal detectors, heart rate monitors, and skin conductance sensors, and transmit data to the system wirelessly to monitor and record the subject's physiological responses in real time;

[0028] Based on the game engine, combined with 3D modeling software, we designed and produced highly realistic high-altitude environment models, including bridges, cliff edges, and high-rise building rooftops, focusing on the authenticity of texture mapping and lighting effects;

[0029] Programming realizes the environmental sound effects of wind and bird calls, as well as the visual and sound effects of sudden objects falling and wind speed changes, increasing the dynamism and unpredictability of the environment;

[0030] Use the engine's built-in or third-party physics engine to design the interaction logic between the handle and the virtual object, and the vibration feedback when the handle touches the virtual railing;

[0031] Write software interfaces to enable smooth communication between hardware devices such as physiological monitors, VR helmets, and system software;

[0032] Apply modular design principles combined with microservice architecture, use service grid and container orchestration technology to dynamically manage and deploy services, and consider scalability factor S and fault tolerance F during design:

[0033] System efficiency = f(S,F)

[0034] Improve overall system efficiency by optimizing communication and load balancing strategies between services;

[0035] Carefully calibrate all hardware devices, design experimental operation interfaces, manage user accounts, launch experiments, monitor physiological data, and adjust the virtual environment.

[0036] In S2, based on S1, HDR high dynamic range lighting is used to simulate natural lighting effects, and streaming loading technology is used to dynamically load and unload scene resources according to the user's location, reducing memory usage and improving operating efficiency.

[0037] Optimize the LOD of the 3D model, adjust the model complexity according to the distance, ensure that both near and far scenes present good visual effects without affecting performance, use low-latency tracking algorithms and optimized hardware synchronization mechanisms to reduce delay points in the data processing pipeline, use intelligent algorithms to process physiological monitor data, develop algorithms to integrate physiological data with virtual behavior data for analysis, comprehensively evaluate the user's emotional state, optimize the communication protocol between hardware, and ensure that the motion sensing of the floating board bridge device is synchronized with the movement in the VR environment;

[0038] Build an efficient real-time data storage pipeline and use edge computing or cloud computing resources to instantly store and analyze physiological data and user behavior data.

[0039] In S3, quantifiable cognitive function indicators are defined, such as task completion time, decision accuracy, and the number of attention shifts, which are evaluated through interaction records and task completion in the virtual environment;

[0040] Design observation indicators, gait stability, physical feedback through the cross-bridge device, hesitation time of hand movements, operation data through the handle, and reaction speed to unexpected situations;

[0041] Conduct a small-scale pilot experiment to test hardware compatibility, task flow, and data collection integrity, adjust and optimize based on feedback, organize participants to participate, record behavioral performance and physiological reactions in the virtual environment, pre-process the collected test data, remove outliers, and combine experimental observation and data analysis to evaluate the cognitive function and behavioral performance of the participants in fear situations;

[0042] Use the data for model building.

[0043] In S4, before the model was constructed, the specific goal of assessing emotion control ability was established to identify individuals’ emotion regulation strategies, emotional response intensity, and resilience in stressful situations;

[0044] Use validated psychological scales to obtain subjects' self-reports of subjective emotion perception and regulation strategies;

[0045] Select physiological parameters that are closely related to emotional responses, such as heart rate, skin conductance response, respiratory rate, and changes in facial muscle potential, to reflect the activity of the autonomic nervous system;

[0046] Develop tasks that induce emotional responses, such as virtual reality high-altitude walking, stress interview simulation, and cognitive conflict tasks, to assess individuals’ cognitive performance and task completion in an emotionally aroused state;

[0047] According to the research design, a representative sample group was recruited and the baseline physiological data of the subjects were collected before the experiment for subsequent comparative analysis;

[0048] Ask the subjects to fill out questionnaires related to emotion regulation to collect their subjective feelings about their emotional state and regulation strategies;

[0049] Record physiological data in real time through physiological monitor;

[0050] Implement the designed performance tasks under controlled conditions, record task completion, reaction time, and error rate, and proceed to the next step of data analysis.

[0051] In S4, subjective scale scores, changes in physiological indicators, and performance task scores were integrated into a unified database;

[0052] Perform correlation analysis to measure the strength and direction of the linear relationship between two variables, based on:

[0053]

[0054] The user's physiological indicators of heart rate and skin conductance response in the VR high-altitude simulation environment were recorded as quantitative indicators of emotional response. At the same time, their behavioral performance, time to complete the task, and movement stability were recorded. The Pearson correlation coefficient between these variables was calculated based on the above formula to understand whether there was a positive correlation, negative correlation, or no correlation between emotional response and behavioral performance.

[0055] The sampling neural network constructs an emotion control ability assessment model that comprehensively considers the complex interactions among subjective feelings, physiological reactions, and task performance.

[0056] Based on the model results, analyze which subjective feelings, physiological indicators and performance can best predict emotional control ability;

[0057] Based on the model, the problem is discovered and personalized suggestions or intervention measures are proposed to improve the emotion regulation ability. The present invention has the following beneficial effects:

[0058] 1. In the present invention, a model for evaluating the emotional control ability of subjects in dealing with fear is constructed by combining subjective scales, physiological indicators and performance tasks. This multi-dimensional evaluation method can comprehensively examine the individual's response to a fear situation. It not only takes into account the subjective self-reported fear level, but also incorporates objective physiological reaction heart rate, skin conductance changes and actual behavioral performance task completion, which can accurately reflect the individual's true emotional control level in VR simulation.

[0059] 2. In the present invention, by automating the data collection and processing process, the manual burden is reduced, the experimental efficiency and the accuracy of data analysis are improved, and large-scale sample research is facilitated. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A system block diagram of a VR-based high-altitude simulation system and a method of using the same proposed by the present invention;

[0061] Figure 2 This is a method step diagram of a VR high-altitude simulation system and its use method proposed by the present invention.

[0062] In the figure: S1: Design and develop a variety of high-altitude stimulation scenes, and integrate a variety of hardware devices to build a virtual reality and interactive system;

[0063] S2: Collect user interaction data and physiological data based on the server host, client host, and physiological monitor;

[0064] S3: Develop specific task scenarios and complete specific tasks in a virtual high-altitude environment to evaluate their cognitive functions and behavioral performance in fear situations;

[0065] S4: Construct an emotion control ability assessment model by combining subjective scales, physiological indicators, and performance tasks. DETAILED DESCRIPTION

[0066] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Embodiment 1

[0068] like Figure 1-2 As shown, the present invention proposes a VR high-altitude simulation system, including a floating plate bridge device, a VR helmet and a tracking device, a VR handle and a tracker, and a physiological monitor. The high-altitude simulation module includes:

[0069] Hardware integrated control module: Integrates floating bridge equipment, VR helmet and tracking equipment, VR handle and tracker, and physiological monitor hardware equipment for data interaction and sharing;

[0070] Virtual reality environment construction module: design and develop multi-altitude virtual scenes through 3D modeling and texture rendering;

[0071] Data acquisition module: collects user test data through hardware integration and virtual reality environment;

[0072] Data analysis module: Based on the actual acquired data, construct subjective scale scores, physiological index changes, and performance task completion data;

[0073] Evaluation model construction module: Integrate subjective scale scores, changes in physiological indicators, and performance task completion, and use statistical and machine learning algorithms to build an emotion control ability evaluation model.

[0074] The data acquisition module includes a physiological data acquisition subsystem, the data analysis module includes a performance task design unit, and the physiological monitor activates the physiological data acquisition subsystem to transmit data to the data acquisition module.

[0075] The physiological data acquisition subsystem is based on the physiological monitor to obtain multiple physiological data in the virtual environment. During the development of the high-altitude simulation system based on VR virtual reality, the quality of integration efficiency is improved through:

[0076] Integration_Quality=g(R,I,C)

[0077] R represents the code reuse rate, I represents the degree of module interface standardization, and C represents the unit test coverage. The goal of function g is to maximize the positive effects of these three key factors and measure the data integration quality based on the formula;

[0078] The performance task design unit is designed to simulate fear situations in real life, including walking at heights, jumping, or passing through unstable surface environments, to stimulate the subjects' fear response and provide a real source of emotional stimulation for the study.

[0079] In this embodiment, during the development of a high-altitude simulation system based on VR virtual reality, the formula Integration_Quality = g(R, I, C) describes the system integration quality or efficiency as a composite function, which depends on three key factors: code reuse rate R, module interface standardization degree I and unit test coverage C. This function g is intended to express how these factors work together in the integration process to achieve the best overall integration effect;

[0080] R (code reuse rate): refers to the efficiency of reusing the same or similar code segments in a project. A high code reuse rate means that the development team can reduce the workload of writing new code, reduce the error rate, speed up the development process, and make the code base more concise and easy to maintain;

[0081] I (module interface standardization): emphasizes the consistency and standardization of the interaction interfaces between modules within the system. A standardized interface design can make the integration between modules smoother, reduce the adaptation work during integration, facilitate the replacement and upgrade of modules, and also help the collaboration between team members;

[0082] C (unit test coverage): measures the proportion of each module or code unit in the system that is covered by unit tests. High test coverage helps to find errors in advance, improve software quality, reduce problems during later integration and deployment, and also enhance confidence in code changes;

[0083] In summary, Integration_Quality = g(R,I,C) emphasizes that in the system integration stage, by optimizing code reuse, strengthening interface standardization and improving test coverage, the integration quality, stability, future maintainability and scalability of the entire VR high-altitude simulation system can be significantly improved.

[0084] Embodiment 2

[0085] like Figure 1-2 As shown, based on the first embodiment, a method used by a VR high-altitude simulation system, the steps of the high-altitude simulation method are:

[0086] S1: Design and develop a variety of high-altitude stimulating scenes, and integrate a variety of hardware devices to build a virtual reality and interactive system;

[0087] S2: Collect user interaction data and physiological data based on the server host, client host, and physiological monitor;

[0088] S3: Develop specific task scenarios and complete specific tasks in a virtual high-altitude environment to evaluate their cognitive functions and behavioral performance in fear situations;

[0089] S4: Construct an emotion control ability assessment model by combining subjective scales, physiological indicators, and performance tasks.

[0090] In S1, a floating bridge that can simulate the feeling of real walking was designed and installed to ensure stability and synchronization with the movements in the virtual environment;

[0091] The selected VR helmet, combined with the indoor space tracking system, captures every tiny movement and feeds it back to the virtual environment in real time;

[0092] Equipped with a VR controller with high-precision tracking function, users can have a natural hand interaction experience in the virtual environment;

[0093] Choose compatible physiological signal detectors, heart rate monitors, and skin conductance sensors, and transmit data to the system wirelessly to monitor and record the subject's physiological responses in real time;

[0094] Based on the game engine, combined with 3D modeling software, we designed and produced highly realistic high-altitude environment models, including bridges, cliff edges, and high-rise building rooftops, focusing on the authenticity of texture mapping and lighting effects;

[0095] Programming realizes the environmental sound effects of wind and bird calls, as well as the visual and sound effects of sudden objects falling and wind speed changes, increasing the dynamism and unpredictability of the environment;

[0096] Use the engine's built-in or third-party physics engine to design the interaction logic between the handle and the virtual object, and the vibration feedback when the handle touches the virtual railing;

[0097] Write software interfaces to enable smooth communication between hardware devices such as physiological monitors, VR helmets, and system software;

[0098] Apply modular design principles combined with microservice architecture, use service grid and container orchestration technology to dynamically manage and deploy services, and consider scalability factor S and fault tolerance F during design:

[0099] System efficiency = f(S,F)

[0100] Improve overall system efficiency by optimizing communication and load balancing strategies between services;

[0101] Carefully calibrate all hardware devices, design experimental operation interfaces, manage user accounts, launch experiments, monitor physiological data, and adjust the virtual environment.

[0102] In S2, based on S1, HDR high dynamic range lighting is used to simulate natural lighting effects, and streaming loading technology is used to dynamically load and unload scene resources according to the user's location, reducing memory usage and improving operating efficiency.

[0103] Optimize the LOD of the 3D model, adjust the model complexity according to the distance, ensure that both near and far scenes present good visual effects without affecting performance, use low-latency tracking algorithms and optimized hardware synchronization mechanisms to reduce delay points in the data processing pipeline, use intelligent algorithms to process physiological monitor data, develop algorithms to integrate physiological data with virtual behavior data for analysis, comprehensively evaluate the user's emotional state, optimize the communication protocol between hardware, and ensure that the motion sensing of the floating board bridge device is synchronized with the movement in the VR environment;

[0104] Build an efficient real-time data storage pipeline and use edge computing or cloud computing resources to instantly store and analyze physiological data and user behavior data.

[0105] In S3, quantifiable cognitive function indicators are defined, such as task completion time, decision accuracy, and the number of attention shifts, which are evaluated through interaction records and task completion in the virtual environment;

[0106] Design observation indicators, gait stability, physical feedback through the cross-bridge device, hesitation time of hand movements, operation data through the handle, and reaction speed to unexpected situations;

[0107] Conduct a small-scale pilot experiment to test hardware compatibility, task flow, and data collection integrity, adjust and optimize based on feedback, organize participants to participate, record behavioral performance and physiological reactions in the virtual environment, pre-process the collected test data, remove outliers, and combine experimental observation and data analysis to evaluate the cognitive function and behavioral performance of the participants in fear situations;

[0108] Use the data for model building.

[0109] In S4, before the model was constructed, the specific goal of assessing emotion control ability was established to identify individuals’ emotion regulation strategies, emotional response intensity, and resilience in stressful situations;

[0110] Use validated psychological scales to obtain subjects' self-reports of subjective emotion perception and regulation strategies;

[0111] Select physiological parameters that are closely related to emotional responses, such as heart rate, skin conductance response, respiratory rate, and changes in facial muscle potential, to reflect the activity of the autonomic nervous system;

[0112] Develop tasks that induce emotional responses, such as virtual reality high-altitude walking, stress interview simulation, and cognitive conflict tasks, to assess individuals’ cognitive performance and task completion in an emotionally aroused state;

[0113] According to the research design, a representative sample group was recruited and the baseline physiological data of the subjects were collected before the experiment for subsequent comparative analysis;

[0114] Ask the subjects to fill out questionnaires related to emotion regulation to collect their subjective feelings about their emotional state and regulation strategies;

[0115] Record physiological data in real time through physiological monitor;

[0116] Implement the designed performance tasks under controlled conditions, record task completion, reaction time, and error rate, and proceed to the next step of data analysis.

[0117] In S4, subjective scale scores, changes in physiological indicators, and performance task scores were integrated into a unified database;

[0118] Perform correlation analysis to measure the strength and direction of the linear relationship between two variables, based on:

[0119]

[0120] The user's physiological indicators of heart rate and skin conductance response in the VR high-altitude simulation environment were recorded as quantitative indicators of emotional response. At the same time, their behavioral performance, time to complete the task, and movement stability were recorded. The Pearson correlation coefficient between these variables was calculated based on the above formula to understand whether there was a positive correlation, negative correlation, or no correlation between emotional response and behavioral performance.

[0121] The sampling neural network constructs an emotion control ability assessment model that comprehensively considers the complex interactions among subjective feelings, physiological reactions, and task performance.

[0122] Based on the model results, analyze which subjective feelings, physiological indicators and performance can best predict emotional control ability;

[0123] Identify problems based on the model and provide personalized suggestions or interventions to improve emotion regulation ability.

[0124] In this embodiment, based on the Pearson correlation coefficient

[0125] By calculating the Pearson correlation coefficient between these variables, we can understand whether there is a positive, negative or no correlation between emotional response and behavioral performance. If an increase in heart rate (emotional excitement) is positively correlated with a longer time to complete the task, this indicates that poor emotional control may affect task efficiency.

[0126] If the above indicators are repeatedly measured for the same user at different training stages, the Pearson correlation coefficient can help assess whether the relationship between the user's emotional control ability and behavioral performance changes as the training progresses; if the correlation coefficient gradually decreases, it means that after training, the user can better control emotions, so that their influence on behavioral performance is weakened;

[0127] By analyzing data from different users or user groups, correlation coefficients can help identify which emotional indicators have the greatest impact on specific types of behavioral performance, thereby providing a basis for developing personalized training or intervention strategies.

[0128] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A VR-based high-altitude simulation system, including a floating bridge device, a VR helmet and tracking device, a VR handle and tracker, and a physiological monitor, characterized in that: The high altitude simulation module includes: Hardware integrated control module: Integrates floating bridge equipment, VR helmet and tracking equipment, VR handle and tracker, and physiological monitor hardware equipment for data interaction and sharing; Virtual reality environment construction module: design and develop multi-altitude virtual scenes through 3D modeling and texture rendering; Data acquisition module: collects user test data through hardware integration and virtual reality environment; Data analysis module: Based on the actual acquired data, construct subjective scale scores, physiological index changes, and performance task completion data; Evaluation model construction module: Integrate subjective scale scores, changes in physiological indicators, and performance task completion, and use statistical and machine learning algorithms to build an emotion control ability evaluation model. The data acquisition module includes a physiological data acquisition subsystem, the data analysis module includes a performance task design unit, and the physiological monitor activates the physiological data acquisition subsystem to transmit data to the data acquisition module.

2. A VR-based high-altitude simulation system according to claim 1, characterized in that: The physiological data acquisition subsystem is based on the physiological monitor to obtain multiple physiological data in the virtual environment. During the development of the high-altitude simulation system based on VR virtual reality, the quality of integration efficiency is improved. 3.Integration_Quality=g(R,I,C) R represents the code reuse rate, I represents the degree of module interface standardization, and C represents the unit test coverage. The goal of function g is to maximize the positive effects of these three key factors and measure the data integration quality based on the formula; The performance task design unit is designed to simulate fear situations in real life, including walking at heights, jumping, or passing through unstable surface environments, to stimulate the subjects' fear response and provide a real source of emotional stimulation for the study.

4. A method based on a VR high-altitude simulation system according to any one of claims 1-2, characterized in that: The steps of high altitude simulation method are as follows: S1: Design and develop a variety of high-altitude stimulating scenes, and integrate a variety of hardware devices to build a virtual reality and interactive system; S2: Collect user interaction data and physiological data based on the server host, client host, and physiological monitor; S3: Develop specific task scenarios and complete specific tasks in a virtual high-altitude environment to evaluate their cognitive functions and behavioral performance in fear situations; S4: Construct an emotion control ability assessment model by combining subjective scales, physiological indicators, and performance tasks.

5. The method for using a VR high-altitude simulation system according to claim 3, characterized in that: In S1, a floating bridge that can simulate the feeling of real walking was designed and installed to ensure stability and synchronization with the movements in the virtual environment; The selected VR helmet, combined with the indoor space tracking system, captures every tiny movement and feeds it back to the virtual environment in real time; Equipped with a VR controller with high-precision tracking function, users can have a natural hand interaction experience in the virtual environment; Choose compatible physiological signal detectors, heart rate monitors, and skin conductance sensors, and transmit data to the system wirelessly to monitor and record the subject's physiological responses in real time; Based on the game engine, combined with 3D modeling software, we designed and produced highly realistic high-altitude environment models, including bridges, cliff edges, and high-rise building rooftops, focusing on the authenticity of texture mapping and lighting effects; Programming realizes the environmental sound effects of wind and bird calls, as well as the visual and sound effects of sudden objects falling and wind speed changes, increasing the dynamism and unpredictability of the environment; Use the engine's built-in or third-party physics engine to design the interaction logic between the handle and the virtual object, and the vibration feedback when the handle touches the virtual railing; Write software interfaces to enable smooth communication between hardware devices such as physiological monitors, VR helmets, and system software; Apply modular design principles combined with microservice architecture, use service grid and container orchestration technology to dynamically manage and deploy services, and consider scalability factor S and fault tolerance F during design: System efficiency = f(S,F) Improve overall system efficiency by optimizing communication and load balancing strategies between services; Carefully calibrate all hardware devices, design experimental operation interfaces, manage user accounts, launch experiments, monitor physiological data, and adjust the virtual environment.

6. The method for using a VR high-altitude simulation system according to claim 3, characterized in that: In S2, based on S1, HDR high dynamic range lighting is used to simulate natural lighting effects, and streaming loading technology is used to dynamically load and unload scene resources according to the user's location, reducing memory usage and improving operating efficiency. Optimize the LOD of the 3D model, adjust the model complexity according to the distance, ensure that both near and far scenes present good visual effects without affecting performance, use low-latency tracking algorithms and optimized hardware synchronization mechanisms to reduce delay points in the data processing pipeline, use intelligent algorithms to process physiological monitor data, develop algorithms to integrate physiological data with virtual behavior data for analysis, comprehensively evaluate the user's emotional state, optimize the communication protocol between hardware, and ensure that the motion sensing of the floating board bridge device is synchronized with the movement in the VR environment; Build an efficient real-time data storage pipeline and use edge computing or cloud computing resources to instantly store and analyze physiological data and user behavior data.

7. The method for using a VR high-altitude simulation system according to claim 3, characterized in that: In S3, quantifiable cognitive function indicators are defined, such as task completion time, decision accuracy, and the number of attention shifts, which are evaluated through interaction records and task completion in the virtual environment; Design observation indicators, gait stability, physical feedback through the cross-bridge device, hesitation time of hand movements, operation data through the handle, and reaction speed to unexpected situations; Conduct a small-scale pilot experiment to test hardware compatibility, task flow, and data collection integrity, adjust and optimize based on feedback, organize participants to participate, record behavioral performance and physiological reactions in the virtual environment, pre-process the collected test data, remove outliers, and combine experimental observation and data analysis to evaluate the cognitive function and behavioral performance of the participants in fear situations; Use the data for model building.

8. The method for using a VR high-altitude simulation system according to claim 3, characterized in that: In S4, before the model was constructed, the specific goal of assessing emotion control ability was established to identify individuals’ emotion regulation strategies, emotional response intensity, and resilience in stressful situations; Use validated psychological scales to obtain subjects' self-reports of subjective emotion perception and regulation strategies; Select physiological parameters that are closely related to emotional responses, such as heart rate, skin conductance response, respiratory rate, and changes in facial muscle potential, to reflect the activity of the autonomic nervous system; Develop tasks that induce emotional responses, such as virtual reality high-altitude walking, stress interview simulation, and cognitive conflict tasks, to assess individuals’ cognitive performance and task completion in an emotionally aroused state; According to the research design, a representative sample group was recruited and the baseline physiological data of the subjects were collected before the experiment for subsequent comparative analysis; Ask the subjects to fill out questionnaires related to emotion regulation to collect their subjective feelings about their emotional state and regulation strategies; Record physiological data in real time through physiological monitor; Implement the designed performance tasks under controlled conditions, record task completion, reaction time, and error rate, and proceed to the next step of data analysis.

9. The method for using a VR high-altitude simulation system according to claim 3, characterized in that: In S4, subjective scale scores, changes in physiological indicators, and performance task scores were integrated into a unified database; Perform correlation analysis to measure the strength and direction of the linear relationship between two variables, based on: The user's physiological indicators of heart rate and skin conductance response in the VR high-altitude simulation environment were recorded as quantitative indicators of emotional response. At the same time, their behavioral performance, time to complete the task, and movement stability were recorded. The Pearson correlation coefficient between these variables was calculated based on the above formula to understand whether there was a positive correlation, negative correlation, or no correlation between emotional response and behavioral performance. The sampling neural network constructs an emotion control ability assessment model that comprehensively considers the complex interactions among subjective feelings, physiological reactions, and task performance. Based on the model results, analyze which subjective feelings, physiological indicators and performance can best predict emotional control ability; Identify problems based on the model and provide personalized suggestions or interventions to improve emotion regulation ability.

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