Astronaut working load relieving implementation method based on virtual reality technology in microgravity environment

Abstract

The invention provides an astronaut working load relieving implementation method based on a virtual reality technology in a microgravity environment, and belongs to the field of microgravity environment psychological recovery. The problems that an existing working load relieving system is difficult to provide higher immersion and interactivity for astronauts in a microgravity environment, and is poor in individuation and intervention effect are solved. According to the method, physiological state data and psychological state data of astronauts are collected, task execution data are integrated, workloads are quantified, workload types are identified after comprehensive evaluation, preliminary matching is carried out according to defined matching conditions, a virtual scene is selected through machine learning and multi-dimensional data analysis, and a virtual scene is obtained. And performing continuous fine adjustment in the loaded and presented optimized virtual scene, and recording the process. According to the method, an effective pressure relieving means can be flexibly and accurately provided according to the real-time state of the astronaut and the requirements of the working environment, and the cognitive reconstruction and psychological recovery process is enhanced through the immersion of the virtual reality technology.

Description

Technical Field

[0001] The present invention relates to the technical field of psychological recovery in a microgravity environment, and in particular to a method for alleviating astronaut workload based on virtual reality technology in a microgravity environment. Background Art

[0002] Workload relief refers to reducing or optimizing the workload, complexity, or psychological and physical stress of individuals at work through various methods and means, thereby mitigating negative impacts on physical and mental health and improving work efficiency and psychological stability. Emotional support and psychological counseling are provided to employees or teams to help them cope with anxiety and stress at work. In the astronaut environment, a virtual reality workload relief system is an effective means of reducing stress, helping astronauts adjust their mental state and alleviate psychological burdens.

[0003] With the development of technology and the updating of health awareness, today's health has expanded from traditional physical health to comprehensive physical and mental health. The health protection for astronauts has also gradually evolved from a single focus on physical health to comprehensive psychological support. During long-term in-orbit service, astronauts face sensory deprivation and negative emotional feedback brought about by long-term isolation and confinement. These psychological problems astronauts face are one of the key factors leading to space mission risks.

[0004] The application of traditional on-site workload relief technology is relatively dependent on fixed physical infrastructure. Virtual reality workload relief system refers to content presented by virtual reality technology as the main medium. Compared with the psychological recovery facilities of the real physical environment, workload relief based on virtual reality system has the following three significant advantages: first, the virtual reality system can compensate for the psychological recovery benefits of the natural environment to a certain extent; second, the workload relief system based on virtual reality technology has the characteristics of personal customization, and can establish an independent database for individuals to a certain extent, so as to maximize the workload relief benefits for individuals; third, the virtual reality system has a certain degree of interactivity, and individuals go from one-way passive perception to two-way information exchange. This interactive behavior process can also help astronauts improve their psychological controllability of the environment, thereby establishing a new cognitive system. In a microgravity environment, compared with the on-site virtual reality workload relief system, it still faces the following problems:

[0005] 1) Under the constraints of the special environment, traditional workload relief measures are difficult to provide effective intervention for astronauts, resulting in the inability to effectively achieve the effect of workload relief. It is difficult for astronauts to adjust their negative thinking patterns, that is, it is difficult for astronauts to relieve the anxiety, depression and mental fatigue caused by high pressure and special working environment.

[0006] 2) Due to the particularity of astronauts' work, existing workload relief methods are difficult to provide highly personalized services, especially in data processing systems and analysis algorithms. Without the cooperation of professional psychological practitioners, it is difficult for the system to analyze and match the cognitive reconstruction virtual environment that best suits their current psychological state.

[0007] 3) Traditional workload relief methods (such as face-to-face psychological counseling or paper-and-pencil exercises) do not have significant advantages in terms of adjustment efficiency and accuracy. Astronauts cannot wait for regular counseling appointments, and their immediate needs cannot be met. In other words, targeted workload relief cannot be provided, resulting in generalized and less effective interventions. Summary of the Invention

[0008] The technical problems to be solved by the present invention are:

[0009] In order to solve the problems that the existing workload mitigation system is difficult to provide astronauts with higher immersion and interactivity in a microgravity environment, has poor personalization and poor intervention effect.

[0010] The present invention is to solve the above technical problems using the following technical solutions:

[0011] The present invention provides a method for alleviating astronaut workload in a microgravity environment based on virtual reality technology, comprising the following steps:

[0012] S100, Workload Assessment, is used to identify and assess the astronauts' current workload and quantify their work stress and fatigue levels, including: real-time collection of astronauts' physiological and psychological status data; analysis of astronauts' tasks and quantification of workload;

[0013] S200, database call and matching: Based on the results of step S100, the intelligent matching engine recommends the most suitable virtual scene, including: identifying the astronaut's workload type and defining matching rules; matching with the system's built-in virtual environment database; selecting scenes that match the astronaut's current emotional and physiological state through machine learning algorithms and multi-dimensional data analysis, and generating a recommendation list based on the matching accuracy;

[0014] S300 , realistic presentation of the load relief scenario, converting the analysis and matching results of steps S100 and S200 into an actual virtual experience scene for loading and presentation.

[0015] Furthermore, in step S100, it specifically includes:

[0016] S110. Use smart wearable devices to collect real-time physiological status data of astronauts. Utilize interactive interfaces placed on information terminals or tablet devices to have astronauts regularly complete standardized psychological questionnaires to assess individual stress levels, fatigue levels, and emotional states.

[0017] S120, Task Analysis and Workload Quantification: Integrate astronauts' task execution data to calculate the task workload index and concentration level; By calling the standardized psychological questionnaires and interactive test results previously stored in the database, understand the astronauts' preferences for different digital natural scenes, providing a reference for subsequent scene matching;

[0018] S130, performing a comprehensive assessment of the astronaut's emotional and physiological conditions according to steps S110 and S120, i.e., utilizing a machine learning algorithm to integrate physiological state data, psychological state data, quantified mission load, and environmental factor data, calculate a comprehensive workload index, and quantify the astronaut's overall workload level;

[0019] The task load index was calculated by assessing workload based on five 7-point scales, with increments of high, medium, and low estimates at each point resulting in 21 levels on the scale;

[0020] The task performance load index is calculated as follows:

[0021]

[0022] Among them, ω i is the weight, r i It is a 21-level rating;

[0023] By taking the weighted sum of the six dimensions, the total task load index is obtained;

[0024] Physiological state indicators are used to calculate workload using the heart rate variability method:

[0025]

[0026] By continuously monitoring and using predictive models to predict the changing trends of workload and psychological state, early warning of overload conditions and timely recommendations for load relief can be provided.

[0027] Furthermore, in step S200, it specifically includes:

[0028] S210, based on the evaluation result of step S100, identifying the astronaut's workload type and defining matching rules to convert the workload into a form that can be processed by fuzzy logic;

[0029] S220, matching with the system's built-in virtual environment database;

[0030] S230. Using a machine learning algorithm, select scenes that match the astronauts' current emotional and physiological states, and generate a recommendation list based on the matching accuracy.

[0031] Furthermore, in step S210, it specifically includes:

[0032] Based on the value range of WLI, the workload state is divided into several fuzzy sets, and a membership function is defined for each fuzzy set;

[0033] Build a fuzzy rule base and formulate if-then matching rules.

[0034] IF Workload = Low THEN Load Relief Requirement = Small

[0035] IF Workload = Medium THEN Load Relief Requirement = Medium

[0036] IF Workload = High THEN Load Relief Requirement = High

[0037] According to the activated rules, a fuzzy output value is obtained using the fuzzy inference engine, and then defuzzification is performed. When the actual workload is calculated, the system converts the WLI into the membership of each fuzzy set through the defined membership function, thus entering the fuzzy rule matching process.

[0038] Furthermore, in step S220, it specifically includes:

[0039] S221. Parameterize the virtual natural scene, including the sensory stimulation level, rhythm dynamic change speed, and interactivity;

[0040] The sensory stimulation level includes the richness of visual elements, sound atmosphere, and tactile feedback intensity;

[0041] The rhythm and dynamic changes include the speed of scene changes;

[0042] The interaction complexity includes the degree to which virtual elements in the scene can be interacted with;

[0043] S222, define matching logic, including,

[0044] When astronauts exhibit high workload, cognitively lightweight scenarios are selected;

[0045] When astronauts are in a state of emotional tension and high physiological stress, they choose scenes with natural scenery, soft music and low interaction;

[0046] When mission data indicates astronauts face distraction issues, choose scenarios that aid focus;

[0047] S223, fuzzy matching and preliminary screening, using the characteristics of fuzzy logic, set corresponding membership functions for scene parameters, convert the scene description into a fuzzy set, and compare the astronaut's state with the fuzzy characteristics of the scene to obtain a matching score;

[0048] S224, dynamic tuning and mechanism feedback: After the preliminary matching in step S223 is completed, dynamic tuning is performed according to the actual situation.

[0049] Furthermore, in step S230, it specifically includes:

[0050] S231. Using an indexed retrieval mechanism, locate scene resources that meet the feature combination;

[0051] S232: Convert the demand characteristics obtained in step S220 into search conditions, and set weights for different conditions;

[0052] S233. Based on the comprehensive scoring and sorting algorithm, a list of candidate scenarios is finally output and arranged according to the matching degree and priority. During the actual execution process, the system updates or fine-tunes the recommendation list according to the performance of the mission execution platform, real-time environmental parameters and the current feedback of the astronauts.

[0053] Furthermore, in step S300, it specifically includes:

[0054] S310, loading and implementing virtual scenes, combining multi-channel sensory stimulation with virtual reality experience, and displaying virtual scenes using high-resolution virtual reality headsets;

[0055] S320: Based on the loaded and preliminarily optimized virtual scenes, provide astronauts with a multi-channel, multi-sensory comprehensive experience;

[0056] S330. Incorporate the experience process into a closed-loop feedback mechanism to continuously collect and analyze the physiological and psychological state data of astronauts in virtual scene experiences, timely evaluate the intervention effect, and provide a reference basis for scene recommendations and strategy adjustments.

[0057] Furthermore, in step S310, it specifically includes:

[0058] S311, based on the scene recommendation list formed in step S200, quickly retrieve and call all resources required for the target scene from the virtual scene database, including high-precision 3D models, texture maps, lighting parameters, terrain data-related audio and video files, and interactive scripts;

[0059] S312, graphics rendering and visual performance optimization, with the support of high-performance image processing unit, real-time rendering of scenes;

[0060] S313, dynamic adjustment, in the early stage of scene presentation, reduce visual stimulation through gradual brightness increase and soft transition of picture cutting.

[0061] Furthermore, in step S320, it specifically includes:

[0062] S321. Synchronize hardware and software, including time synchronization and spatial mapping. During time synchronization, use timing control to synchronize visual, auditory, and tactile changes within milliseconds. During spatial mapping, calibrate sound sources and tactile feedback points to achieve spatial consistency.

[0063] S323. Feedback and shaping of multi-sensory stimulation. Based on the presented visual scene, soft natural sounds, low-intensity instrumental music, or soothing music are selected according to the scene type and astronaut needs. Atmos or binaural recording technology is used to give the sound a sense of directionality and layering in the virtual space.

[0064] S324. Stabilize and improve the sensory fusion experience. While multi-channel stimulation brings a huge amount of information, fine-tune the sensory stimulation to avoid causing additional load.

[0065] Furthermore, in step S330, it specifically includes:

[0066] S331, real-time monitoring of multi-dimensional physiological and psychological state data and dynamic adjustment of scenario strategies. During the astronauts' experience of the virtual scene, the system continuously analyzes the above data to determine the intervention effect of the current stimulation method on the astronauts and make real-time fine-tuning of sensory elements and interaction methods;

[0067] S332. Quantitative evaluation of the effectiveness of scenario interventions. To objectively measure the contribution of scenarios to astronauts' psychological recovery, the system extracts features from the aforementioned multidimensional data and conducts quantitative analysis. Through time window averaging, trend analysis, and threshold judgment, it determines whether the intervention has achieved its intended goals, or identifies areas and potential for improvement.

[0068] S333, data archiving, long-term feedback loop and visual presentation. After the experience is over, the data, fine-tuning records and effect evaluation results of this intervention process will be archived and compared with historical records and other astronaut data to provide resources for long-term improvement and personalized strategy optimization.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] The present invention provides a method for relieving astronaut workload in a microgravity environment based on virtual reality technology. By adopting an evaluation-matching-presentation method, it can flexibly and accurately provide effective stress relief measures based on the astronaut's immediate state and the needs of the work environment, and enhance cognitive reconstruction and psychological recovery processes through the immersive feeling of virtual reality technology:

[0071] 1. Applying virtual reality technology to alleviate workload in microgravity environments

[0072] Based on the deep integration of the space environment and virtual reality (VR) technology, the traditional psychological adjustment program is extended to microgravity conditions, enabling astronauts to obtain appropriate psychological support and stress relief in the cramped and extreme cabin environment, breaking the limitations of space and environment, and providing reliable, flexible and readily available technical means for the maintenance of the mental health of on-orbit personnel.

[0073] 2. Personalized solutions based on real-time assessment

[0074] Through wearable sensors and multimodal data analysis, the physiological and psychological states of astronauts are continuously and dynamically assessed in real time. Based on this, fuzzy logic and intelligent matching algorithms are used to generate highly personalized load mitigation strategies and virtual scenarios for different individuals, making the intervention plans more in line with the actual needs and preferences of astronauts.

[0075] 3. Multi-sensory immersive experience integrating digital and natural scenes

[0076] Integrating concepts including digital nature into virtual reality creates highly realistic natural environments, such as forests, oceans, mountains, etc.; these scenes are not only more realistic visually, but also combine multi-sensory stimulation such as hearing (natural sounds) and touch (tactile feedback) to enhance the sense of immersion; in the special scenario of space where there is a lack of natural environment, providing astronauts with the opportunity to come into contact with nature helps to relieve psychological stress, improve emotional state, and enhance mental health.

[0077] 4. Interactive system with real-time feedback and dynamic adjustment

[0078] By continuously monitoring the astronauts' physiological and psychological feedback data (such as heart rate, respiratory rate, EEG characteristics, emotional reports and interactive behaviors), highly dynamic adjustments can be achieved during the multi-sensory immersive experience. When the astronauts' response to the current scene or sensory stimulation is not as expected, the scene parameters and presentation forms are optimized in a short period of time: based on immediate feedback, the scene's visual elements (brightness, color combination), sound intensity (volume, frequency), and tactile and somatosensory feedback (vibration frequency, pressure intensity) are adjusted to ensure that the stimulation intensity is optimally matched to the astronauts' state. If it is found that a single sensory stimulation has reached saturation or caused discomfort, The system can reduce the intensity of sensory stimulation while maintaining the overall sense of immersion; the interactive mode is dynamically switched: if the interactive elements are too complex or not enough to attract attention, the system can increase or decrease the interactive objects, adjust the frequency and difficulty of the interaction, to ensure that the astronauts are neither overly nervous nor bored during the experience; through this flexible and maneuverable adaptation and fine-tuning, the system builds a virtual intervention system that is truly "sensitive to the needs of astronauts"; whether it is short-term emotional fluctuations or long-term workload changes, they can get efficient and timely responses in this process, providing an efficient interactive experience for astronauts to maintain their mental health in harsh environments.

[0079] 5. Comprehensively integrated full-process closed-loop system

[0080] This invention establishes a complete closed-loop technology system, from physiological and psychological data collection and real-time workload assessment to virtual scene matching, multi-sensory experience presentation, and feedback and iterative strategy optimization. This process not only enables real-time, data-driven decision-making but also continuously accumulates and analyzes historical data to provide long-term support for future solution improvements and personalized intelligent upgrades. This comprehensive, continuously optimized closed-loop system provides the system with high adaptability and stability over long periods of time and in specialized environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 The present invention is a flowchart of a method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to an embodiment of the present invention. DETAILED DESCRIPTION

[0082] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0083] Specific implementation plan 1: Combined Figure 1 As shown, the present invention provides a method for alleviating astronaut workload based on virtual reality technology in a microgravity environment, comprising the following steps:

[0084] S100, Workload Assessment, is used to identify and assess the astronauts' current workload and quantify work stress and fatigue levels, including:

[0085] S110. Using the physiological detection system to collect the astronauts' physiological status data in real time, and using the psychological detection system to collect psychological status data;

[0086] The system uses smart wearable devices to monitor the astronauts' physiological and psychological states in real time. In terms of physiological data, the combination of traditional indicators (such as heart rate, respiratory rate, blood oxygen saturation, skin galvanic response, and body temperature) and the introduction of electrocardiogram data enables the physiological detection system to gain an in-depth understanding of the astronauts' cognitive load, attention level, and emotional state. In terms of psychological data collection, astronauts can regularly fill out standardized psychological questionnaires through an interactive interface set up on an information terminal or tablet device to assess the individual's stress level, fatigue level, and emotional state. The content of the questionnaire is based on recognized psychological assessment scales, including but not limited to the Perceived Stress Scale (PSS), the Fatigue Assessment Scale (FAS), the NASA Task Load Index (NASA-TLX), and the Affective State Scale (POMS, PANAS). The interactive interface supports touch and voice input, and can also be connected to a variety of data input devices to achieve human-computer interaction.

[0087] The system further integrates facial expression recognition and speech emotion analysis modules. The facial recognition module collects user facial images through an integrated camera and uses a visual Transformer model to perform feature analysis on key points (68 facial action units) extracted from the image to automatically determine the astronaut's emotions (such as joy, anxiety, and sadness). The speech recognition module collects user voice through a high-sensitivity microphone and combines acoustic feature extraction algorithms (including but not limited to Mel-frequency cepstral coefficients MFCC and fundamental frequency pitch) and sequence modeling networks (such as RNN or Transformer) to identify their voice emotional state. The data from the above two modules can be integrated and analyzed in a multimodal manner through an emotion fusion algorithm to achieve real-time dynamic monitoring of the astronaut's psychological state and early warning prompts of potential emotional fluctuations. The technical foundation refers to existing open source tools and algorithm models, such as OpenFace, FER+, EmoNet, and ResNet.

[0088] S120, Mission Analysis and Workload Quantification, integrates astronauts' mission execution data to calculate the mission workload index;

[0089] Automatically record the astronauts' mission parameters through a cloud storage device and, combined with the electrocardiogram data obtained in step S110, analyze the astronauts' cognitive load and concentration level while performing the mission. The mission parameters include mission type, duration, complexity, and frequency, which are used to quantify their mission load. Simultaneously, monitor environmental factors within the cabin, such as noise, temperature, lighting, vibration, and radiation conditions, to assess their impact on the astronauts' workload and psychological state. By calling standardized psychological questionnaires and interactive test results previously stored in a database, understand the astronauts' preferences for different digital natural scenes (e.g., forests, oceans, and mountains), providing a reference for subsequent scene matching.

[0090] S130, performing a comprehensive assessment of the astronaut's emotional and physiological conditions according to step S110 and step S120;

[0091] Using machine learning algorithms, physiological state data, psychological state data, quantified mission load and environmental factor data are integrated to calculate a comprehensive workload index to quantify the overall workload level of astronauts;

[0092] The task load index method assesses workload based on five 7-point scales, with high, medium, and low estimate increments at each point resulting in 21 levels on the scale;

[0093] The workload WLI (Workload Index) formula is calculated using the comprehensive index method:

[0094] WLI = α·physiological state data + y·task performance

[0095] Among them, α and y are weight coefficients;

[0096] The task performance load index of the present invention is calculated as follows:

[0097]

[0098] Among them, ω i is the weight, r i It is a 21-level score, and the total workload index is obtained by weighted summation of these six dimensions;

[0099] Physiological status indicators are used to calculate workload using the heart rate variation method, as follows:

[0100]

[0101] Taking into account the individual differences of astronauts (such as physiological baselines and psychological characteristics), personalized workload assessments are carried out to ensure the accuracy of the assessment results; the system continuously monitors and uses prediction models to predict the changing trends of workload and psychological state, warns of possible overload situations in advance, and recommends timely load relief; the system integrates a prediction model module, trains the collected data based on a machine learning algorithm (support vector regression SVR), and constructs an individual psychological state and workload prediction model; the prediction model is used to dynamically predict the changing trends of astronauts' mission load and the risk of psychological fluctuations in the future, and can be linked to corresponding intervention mechanisms to achieve early warning prompts and auxiliary decision-making. Based on the prediction model, early warnings can be predicted and timely load relief can be carried out;

[0102] S200, database call and matching, based on the evaluation results of step S100, recommend the most suitable virtual scene through the intelligent matching engine, including:

[0103] S210, identifying the astronaut's workload type and defining a matching rule based on the evaluation result of step S100;

[0104] Convert the workload into a form that can be processed by fuzzy logic. The specific steps are as follows:

[0105] Based on the value range of WLI, the workload state is divided into several fuzzy sets ("Low", "Medium"

[0106] "Medium", "High", or more refined levels such as "very low" and "high" as needed), workloads in the range [0,0.3] correspond to "low" workloads, [0.3,0.7] correspond to "medium" workloads, and [0.7,1] correspond to "high" workloads; a membership function is defined for each fuzzy set, and these membership functions can map workloads to membership values of a finite set;

[0107] Build a fuzzy rule base and formulate "if-then" matching rules.

[0108] IF Workload = Low THEN Load Relief Requirement = Small

[0109] IF Workload = Medium THEN Load Relief Requirement = Medium

[0110] IF Workload = High THEN Load Relief Requirement = High

[0111] When the actual WLI is calculated, the system converts the WLI into the membership of each fuzzy set through the defined membership function, thus entering the fuzzy rule matching process; according to the activated rules, the fuzzy inference engine (Mamdani or Sugeno reasoning method) is used to obtain a fuzzy output value, which is then defuzzified. When the actual workload is calculated, the system converts the WI into the membership of each fuzzy set through the defined membership function, thus entering the fuzzy rule matching process. The results of fuzzy inference are defuzzified to obtain specific decision values, such as outputting a relatively quantitative "load relief demand degree" index, which provides a clear reference for subsequent scenario matching and database calls;

[0112] S220, the process of matching with the system's built-in virtual environment database includes:

[0113] S221. Parameterize the virtual natural scene, including the sensory stimulation level, rhythm dynamic change speed, and interactivity;

[0114] Sensory stimulation level: richness of visual elements, sound atmosphere (natural sound effects, music, white noise), and intensity of tactile feedback;

[0115] Rhythm and dynamics: the speed of scene change (slow weather changes, slow landscape changes, versus rapidly changing moving objects or dynamic environments);

[0116] Interaction complexity: The degree to which virtual elements in a scene can be interacted with, such as simplified viewing, light interaction (simple gestures or gaze interactions), and deeper interaction (simple gamified experiential therapy or meditation space intervention);

[0117] These parameters are usually stored in the database in the form of metadata and can be defined as quantitative indicators as needed;

[0118] S222, define matching logic, including,

[0119] When astronauts exhibit "high workload," the system tends to choose "cognitively lightweight" scenarios, such as simple nature viewing or meditation spaces, to reduce additional information input;

[0120] If astronauts are in a state of "emotional tension" and "high physiological stress", it is appropriate to choose scenes with natural scenery, soft music and low interaction to reduce the overall stimulation intensity and promote physical and mental relaxation;

[0121] When mission data indicates that astronauts are facing attention problems, scenes that help focus can be selected, such as spaces with light interactions but simple environmental elements, to help astronauts restore their concentration;

[0122] S223. Fuzzy matching and preliminary screening. Utilizing the characteristics of fuzzy logic, we set corresponding membership functions for scenario parameters and convert scenario descriptions into fuzzy sets (the "quietness" and "visual richness" of the environment can be divided into three fuzzy levels: low, medium, and high). We then compare the astronaut's state (e.g., "high workload" corresponds to "need for strong relaxation") with the fuzzy characteristics of the scenario to obtain a matching score. This score can be calculated using simple rules or by using a fuzzy inference engine to combine multiple indicators to determine the "fit" between the scenario and the current payload state. Through this process, we obtain a preliminary set of candidate scenarios that are theoretically most likely to meet the astronaut's current psychological and physiological needs.

[0123] S224, dynamic tuning and mechanism feedback, including

[0124] After the preliminary matching in step S223 is completed, the system can dynamically adjust according to the actual situation. If there are few relaxation scenarios in the database that match high cognitive load, the system can relax the matching criteria and provide astronauts with relatively suboptimal scenarios that still have a certain relief effect. In subsequent use, the system can record the scenario selection results and astronaut feedback (physiological data drop, emotional stability improvement), and continuously revise and refine the mapping rules and weight distribution to make the matching results increasingly meet the actual needs of astronauts.

[0125] S230. The system automatically selects scenes that match the astronaut's current emotional and physiological state through machine learning algorithms and multi-dimensional data analysis, and generates a recommendation list based on the matching accuracy.

[0126] S231. Scene metadata indexing: In the early stages of preparation, each virtual scene is described using standardized metadata. This metadata includes scene type (e.g., natural, artistic, social), sensory stimulation characteristics (complexity of visual, auditory, and tactile elements), interaction mode (passive viewing, light interaction, deep interaction), rhythm and dynamic changes, and psychological goals suitable for intervention (relaxation, cognitive reconstruction, emotional balance, etc.). At this point, the system uses an indexed retrieval mechanism to quickly identify scene resources that meet specific feature combinations.

[0127] S232. Based on the results of the first two steps, the system has obtained a set of "demand characteristics," including fuzzy classifications of workload (e.g., moderate workload, emotional tension), desired scene characteristics (mild sensory stimulation, low interactivity), and preferences for natural elements or cognitive restructuring guidance. The system converts these requirements into search conditions and assigns weights to different conditions. If preliminary matching confirms that "relieving emotional tension" is the current primary goal, the corresponding scene tag (e.g., natural relaxation, meditation space) will receive a higher weight during search and ranking. If cognitive restructuring is also a secondary requirement, the weight of the relevant scene tag will be slightly lower, but it will still have a positive impact on ranking.

[0128] S233. Generation and dynamic fine-tuning of a candidate list of virtual scenarios. Based on a comprehensive scoring and ranking algorithm, the system ultimately outputs a list of candidate scenarios, ranked by matching degree and priority. This list contains several suitable scenarios for subsequent call-up and presentation. During actual execution, the system can quickly update or fine-tune the recommended list based on the performance of the mission execution platform, real-time environmental parameters (such as changes in cabin lighting), and the astronaut's current micro-feedback (such as fine-tuning of real-time physiological data). For example, if an astronaut's heart rate drops slightly while waiting for presentation, the system can appropriately select a more interactive scenario to achieve a better psychological adjustment effect.

[0129] S300, realistic presentation of the load relief scenario. After converting the analysis and matching results of steps S100 and S200 into an actual virtual experience scenario, the loading and presentation strategies are used to ensure that the astronauts can obtain appropriate visual experience and psychological relief foundation the moment they come into contact with the scenario, including:

[0130] S310, loading and implementation of virtual scenes, combining multi-channel sensory stimulation with virtual reality experience, the system uses high-resolution virtual reality head-mounted devices to display virtual scenes; calls scene resources that are highly consistent with the astronauts' current status from the virtual scene database; through high-performance image processing units, VR head-mounted displays and related hardware equipment, the selected scenes are loaded quickly and stably to ensure visual realism and scene smoothness; during the loading process, the system will adjust the basic parameters of the scene (such as light brightness, color matching, and visual hierarchy) according to the real-time physiological and psychological conditions of the astronauts to ensure that a suitable experience environment is created for the astronauts at the initial stage of scene presentation, including:

[0131] S311: The virtual environment resource library is called for workload data input. Based on the scene recommendation list formed in step S200, the system quickly retrieves and calls all the resources required for the target scene from the virtual scene database. These resources include high-precision 3D models, texture maps, lighting parameters, terrain data, and related audio and video files and interactive scripts.

[0132] S312: Graphics rendering and visual performance optimization. With the support of a high-performance graphics processing unit (GPU), the system renders scenes in real time, which involves optimizing polygon meshes, textures, lighting effects, and special effect particles (such as fog and light penetration). Performance balance: Utilizing Level of Detail (LOD) technology, model accuracy and rendering overhead are properly controlled while ensuring scene realism, avoiding screen freezes and delays caused by hardware resource limitations in microgravity environments. Anti-shake and anti-distortion processing: Targeting the display characteristics of VR displays, distortion correction algorithms and stabilization processing are used to ensure that the scene remains clear and smooth even with rapid head rotation or line of sight movement.

[0133] At the same time, during the loading process, the system will make appropriate fine-tuning of visual parameters according to the astronauts' physiological and psychological data; Light brightness: If the astronauts are monitored for visual fatigue or physiological indicators indicate sensitivity to strong light, the overall brightness of the scene will be appropriately reduced or switched to soft warm light; Color matching: According to the astronauts' current emotional state, softer, lower-saturation color combinations are selected to reduce the intensity of visual stimulation, thereby helping to reduce the psychological burden; Visual hierarchy and composition: The proportion of depth of field, distant view and near view elements is appropriately adjusted to make the scene appear open and stretched when it is first presented, creating a sense of spatial hierarchy, thereby giving astronauts psychological breathing space;

[0134] S313. Dynamic adjustment and matching. In the initial stage of scene presentation, the system can reduce sudden visual stimulation through gradual brightness increase and soft transition of image cut-in (such as fade-in effect). Through the above refined loading and presentation process, the system not only achieves fast and stable virtual scene initialization, but also lays a good foundation for the subsequent multi-sensory immersive experience, ensuring to the greatest extent possible that astronauts can achieve comfort, relaxation, and physical and mental adjustment from the visual point of view.

[0135] S320, based on the loaded and preliminarily optimized virtual scene, the system provides astronauts with a multi-channel, multi-sensory comprehensive experience; this link aims to create a highly immersive, enjoyable and psychologically restorative virtual environment for astronauts through multi-dimensional stimulation of vision, hearing, touch and even smell, thereby maximizing the effectiveness of scene intervention, specifically including:

[0136] S321. In the initial stimulation phase, the system coordinates and integrates multi-channel sensory stimulation to ensure effective synergy between the various sensory stimuli. This synchronization strategy encompasses two dimensions: temporal synchronization and spatial mapping. Temporal synchronization utilizes precise timing control to synchronize visual, auditory, and tactile changes within milliseconds. For example, when a visual scene transition occurs, it is accompanied by the gradual entry or exit of a corresponding sound, as well as appropriate tactile feedback. Spatial mapping ensures that sensory stimulation aligns with the spatial location of objects and events in the scene. The system calibrates sound sources and tactile feedback points within the virtual scene's 3D coordinate system to achieve spatial consistency. This ensures that when astronauts turn their heads or make gestures, they still experience the spatial correspondence of sensory information.

[0137] S323. Feedback and shaping of multi-sensory stimulation. In the previous step S322, the main focus was on visual stimulation. However, the application of multi-sensory stimulation is the advantage and innovation of the immersive payload relief system. Therefore, based on the presented visual scene, auditory elements provide key support for emotional adjustment and attention guidance. Ambient sound effects and atmospheric music: Based on the scene type and astronauts' needs, soft natural sounds (light breeze, flowing water, birdsong), low-intensity instrumental performances, or soothing music are selected to help reduce tension. Spatial audio or binaural recording technology is used to give sound directionality and layering in the virtual space, enhancing immersion and realism.

[0138] S324. To stabilize and enhance the sensory fusion experience, while multi-channel stimulation delivers a vast amount of information, the system also needs to fine-tune sensory stimulation to avoid excessive load and maintain the astronaut within the ideal relaxation zone. Over time, if physiological indicators and mental state continue to improve, the intensity of stimulation can be gradually reduced, allowing the astronaut to naturally transition back to a more neutral state, facilitating feedback and summary for the next step.

[0139] S330, the system incorporates the previous immersive experience process into a closed-loop feedback mechanism. The goal of this link is to continuously collect and analyze the physiological and psychological state data of astronauts in the virtual scene experience, timely evaluate the intervention effect and provide a reference basis for future scene recommendations and strategy adjustments, including:

[0140] S331. Real-time monitoring of multi-dimensional physiological and psychological state data and dynamic adjustment of scenario strategies. While astronauts are experiencing virtual scenarios, the system synchronously collects their multi-dimensional physiological and psychological state data, including heart rate, respiration, electroencephalogram (EEG), and galvanic skin response, through smart wearable devices equipped with physiological measurement sensors. Based on continuous analysis of this data, the system can quickly determine the intervention effect of the current stimulation method on the astronauts and, if necessary, make instant fine-tuning of sensory elements and interaction methods. For example, if the astronaut's stress index is observed to be high, the system can lower the volume, soften the lighting, or simplify the interaction logic to seek a more ideal psychological adjustment balance.

[0141] S332. Quantitative evaluation of the effectiveness of scenario interventions. To objectively measure the contribution of scenarios to astronauts' psychological recovery, the system extracts features and conducts quantitative analysis on the aforementioned multidimensional data, constructing measurable indicators such as "relaxation index" and "attention concentration." Through time window averaging, trend analysis, and threshold judgment, the system can determine whether the intervention has achieved its intended goals or identify areas for improvement. This quantitative evaluation helps the system quickly identify problematic areas and provides a data basis for subsequent strategy adjustments.

[0142] S333. Data archiving, long-term feedback loop and visual presentation: After the overall experience is completed, the system will archive the data, fine-tuning records and effect evaluation results of the intervention process, compare them with historical records and other astronaut data, and provide resources for long-term improvement and personalized strategy optimization; based on accumulated experience and machine learning algorithms, the system continuously updates model parameters and scenario matching logic to improve the accuracy and adaptability of future interventions; at the same time, key indicator trends, scenario performance and intervention results are presented to relevant experts and decision makers in the form of visual charts and reports, so that they can clearly understand the changes in astronaut status and the effectiveness of system intervention, and provide intuitive reference for further management and support measures.

[0143] experiment

[0144] The scenario is set as follows: After completing a long-term mission, an astronaut's heart rate is high (normal is 90, and currently remains at 126 after resting), and his workload (assessed by NASA-TLX) is high. He needs to quickly adjust his psychology and relieve his workload.

[0145] Input conditions:

[0146] Physiological state data: The normal heart rate is 90bpm, and it remains at 126bpm after resting, indicating a high level of physiological activation and potential stress.

[0147] Workload measurement: The NASA-TLX (Task Load Index) was used to quantitatively evaluate the tasks performed. The results showed that the workload was high and the cognitive and emotional stress were significantly high.

[0148] Long working hours: Astronauts have been working for several hours continuously and are showing signs of physical and mental fatigue.

[0149] Step 1: Data collection and physiological testing

[0150] The system uses wearable sensors (such as wristband heart rate sensors, galvanic skin response sensors, and lightweight EEG devices that measure brain waves) to obtain real-time physiological data from the astronauts. Among them, the heart rate was 126 bpm (significantly higher than the normal value of 90 bpm), the skin conductivity may be high, and the breathing rate was accelerated.

[0151] Example action: The system detects that the astronaut's heart rate is continuously high, records this status, and associates it with the current time in the cabin and the mission phase to provide a data basis for subsequent analysis.

[0152] Step 2: Task Analysis and Workload Quantification

[0153] The system collects task execution records (including task duration, task type, complexity, error rate, and required attention level) and calculates the task workload index using a pre-defined algorithm. The NASA-TLX questionnaire (or similar assessment) is completed by astronauts during a short rest break. Results indicate that this task scores high on dimensions such as mental demand, time pressure, and effort.

[0154] Example action: The system inputs the mission data just completed by the astronaut into the calculation model to obtain a higher mission payload score (for example, a high-load range with a NASA-TLX comprehensive score ≥ 70 points).

[0155] Step 3: Comprehensive Assessment of Physical and Emotional Conditions

[0156] The system integrates elevated heart rate, accelerated breathing rate, increased galvanic skin response, and the astronauts' self-reported fatigue and anxiety after the mission. Using emotion recognition algorithms and psychological stress analysis models, it assesses the astronauts' current condition. The result, "high physiological activation + emotional tension," suggests the astronauts are under high stress and require timely intervention.

[0157] Example action: The system determines that the astronaut is in a high-load and high-stress state, and recommends that the subsequent scene be a natural and quiet virtual environment that is relaxing and has low cognitive load.

[0158] Step 4: Workload type identification and matching rule definition

[0159] Based on the evaluation results, the system categorizes the astronauts' current working state as "high physiological stress + high cognitive load." In the matching rules, this type of situation corresponds to virtual scenarios that focus on reducing psychological stress, sensory overload, and information input. For example, the rules specify:

[0160] If physiological stress is high, choose a natural, low-stimulation, low-interaction scenario.

[0161] If the cognitive load is high, reduce complex interactive instructions and choose a simple, passive appreciation environment.

[0162] Step 5: Virtual environment database matching

[0163] The system searches its internal database for virtual scenes tagged with "natural relaxation," "stress relief," and "low cognitive stimulation." The database includes a variety of natural environments, including "forest breeze," "lakeside sunset," and "warmly lit meadow."

[0164] Example action: The system retrieves and filters out several matching scene candidates, such as the "tranquil forest" scene and the "quiet lakeshore" scene.

[0165] Step 6: Generate a recommendation list using machine learning and multidimensional data analysis

[0166] The system automatically selects the most suitable scenario using a multi-dimensional analysis model (combining physiological indicators, historical feedback data, and the astronaut's previous preferences for virtual scenes). For example, based on previous data, if the astronaut's response to the "surround forest" scenario is better when the heart rate is high, the "tranquil forest" scenario will be prioritized as a candidate.

[0167] Example action: The ML algorithm generates a list of recommendations, with the "Tranquil Forest" scene having the highest match (matching score 0.85), followed by the "Quiet Lakeshore" (0.75).

[0168] Step 7: Loading and implementing virtual scenes

[0169] The system retrieved the "Tranquil Forest" scene resources from storage, including a high-quality 3D forest model, soft lighting, and crisp bird song sound effects, and rendered them on the VR display device using the GPU. During the loading process, the system slightly lowered the scene brightness and reduced the complexity of the visual information to reduce the intensity of sensory stimulation due to the astronauts' still-high heart rates.

[0170] Sample action: A forest scene with soft lighting appears in the VR display, with slightly lower color saturation and uniform lighting, reducing the stimulation caused by the contrast between light and dark.

[0171] Step 8: Multi-channel, multi-sensory integrated experience

[0172] After the scene is presented, the system adds appropriate sounds: the rustling of leaves in the breeze, the gentle sound of flowing water in the distance, and uses mild tactile feedback devices (such as a slight vibration of the handle to simulate the feeling of a breeze on the back of the hand). If conditions permit, a trace of natural fragrance (such as the light scent of grass and trees) can be released. This full range of sensory stimulation creates a comfortable and relaxing immersive experience. Example action: Soft forest sounds are transmitted through the headphones, and the slight vibration of the handle simulates the feeling of wind. The astronaut looks around in VR and feels the openness and tranquility.

[0173] Step 9: Continuous Monitoring and Feedback Loop

[0174] During the astronaut's experience, the system continuously records heart rate, respiration, skin conductance changes, head posture, and eye movements. After three minutes of the scenario, if the heart rate gradually decreases from 126 bpm to 110 bpm, the system will determine that the intervention is initially effective. If it remains high, the system may slightly reduce the volume or further dim the lights. After the experience, the system archives the data and evaluates the effectiveness of the scenario.

[0175] Example action: After the experience, the system finds that the astronaut's heart rate has dropped significantly and the psychological workload indicator has improved. It records this feedback and prioritizes similar scenarios the next time the workload is high.

[0176] During this implementation process, the system efficiently matched the astronauts' actual physiological and psychological states with specific virtual scenarios through a nine-step process, performing real-time fine-tuning and continuous evaluation during the experience. The ultimate result is that in microgravity and high-stress environments, astronauts can achieve significant emotional relief and stress relief through the immersive virtual reality scenes, laying a more stable psychological and physiological foundation for their subsequent mission execution.

[0177] Although the present invention is disclosed as above, the scope of protection disclosed by the present invention is not limited thereto. Those skilled in the art of the present invention may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for alleviating astronaut workload in a microgravity environment based on virtual reality technology, characterized in that: The following steps are involved: S100, Workload Assessment, is used to identify and assess the astronauts' current workload and quantify their work stress and fatigue levels, including: real-time collection of astronauts' physiological and psychological status data; analysis of astronauts' tasks and quantification of workload; S200, database call and matching: Based on the results of step S100, the intelligent matching engine recommends the most suitable virtual scene, including: identifying the astronaut's workload type and defining matching rules; matching with the system's built-in virtual environment database; selecting scenes that match the astronaut's current emotional and physiological state through machine learning algorithms and multi-dimensional data analysis, and generating a recommendation list based on the matching accuracy; S300 , realistic presentation of the load relief scenario, converting the analysis and matching results of steps S100 and S200 into an actual virtual experience scene for loading and presentation.

2. The method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to claim 1, characterized in that: In step S100, specifically including: S110. Use smart wearable devices to collect real-time physiological status data of astronauts. Utilize interactive interfaces placed on information terminals or tablet devices to have astronauts regularly complete standardized psychological questionnaires to assess individual stress levels, fatigue levels, and emotional states. S120, Mission Analysis and Workload Quantification, integrates astronauts' mission execution data and calculates the mission workload index and concentration level; By calling the standardized psychological questionnaires and interactive test results previously stored in the database, we can understand the astronauts' preferences for different digital natural scenes and provide a reference for subsequent scene matching; S130, performing a comprehensive assessment of the astronaut's emotional and physiological conditions according to steps S110 and S120, i.e., utilizing a machine learning algorithm to integrate physiological state data, psychological state data, quantified mission load, and environmental factor data, calculate a comprehensive workload index, and quantify the astronaut's overall workload level; The task load index was calculated by assessing workload based on five 7-point scales, with increments of high, medium, and low estimates at each point resulting in 21 levels on the scale; The task performance load index is calculated as follows: Among them, ω i is the weight, r i It is a 21-level rating; By taking the weighted sum of the six dimensions, the total task load index is obtained; Physiological state indicators are used to calculate workload using the heart rate variability method: By continuously monitoring and using predictive models to predict the changing trends of workload and psychological state, early warning of overload conditions and timely recommendations for load relief can be provided.

3. The method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to claim 1, characterized in that: In step S200, specifically including: S210, based on the evaluation result of step S100, identifying the astronaut's workload type and defining matching rules to convert the workload into a form that can be processed by fuzzy logic; S220, matching with the system's built-in virtual environment database; S230. Using a machine learning algorithm, select scenes that match the astronauts' current emotional and physiological states, and generate a recommendation list based on the matching accuracy.

4. The method for alleviating astronaut workload in a microgravity environment based on virtual reality technology according to claim 3, characterized in that: In step S210, specifically including: Based on the value range of WLI, the workload state is divided into several fuzzy sets, and a membership function is defined for each fuzzy set; Build a fuzzy rule base and formulate if-then matching rules. IF Workload = Low THEN Load Relief Requirement = Small IF Workload = Medium THEN Load Relief Requirement = Medium IF Workload = High THEN Load Relief Requirement = High According to the activated rules, a fuzzy output value is obtained using the fuzzy inference engine, and then defuzzification is performed. When the actual workload is calculated, the system converts the WLI into the membership of each fuzzy set through the defined membership function, thus entering the fuzzy rule matching process.

5. The method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to claim 4, characterized in that: In step S220, specifically including: S221. Parameterize the virtual natural scene, including the sensory stimulation level, rhythm dynamic change speed, and interactivity; The sensory stimulation level includes the richness of visual elements, sound atmosphere, and tactile feedback intensity; The rhythm and dynamic changes include the speed of scene changes; The interaction complexity includes the degree to which virtual elements in the scene can be interacted with; S222. Define matching logic, including: When astronauts exhibit high workload, cognitively lightweight scenarios are selected; When astronauts are in a state of emotional tension and high physiological stress, they choose scenes with natural scenery, soft music and low interaction; When mission data indicates astronauts face distraction issues, choose scenarios that aid focus; S223, fuzzy matching and preliminary screening, using the characteristics of fuzzy logic, set corresponding membership functions for scene parameters, convert the scene description into a fuzzy set, and compare the astronaut's state with the fuzzy characteristics of the scene to obtain a matching score; S224, dynamic tuning and mechanism feedback: After the preliminary matching in step S223 is completed, dynamic tuning is performed according to the actual situation.

6. The method for alleviating astronaut workload in a microgravity environment based on virtual reality technology according to claim 5, characterized in that: In step S230, specifically including: S231. Using an indexed retrieval mechanism, locate scene resources that meet the feature combination; S232: Convert the demand characteristics obtained in step S220 into search conditions, and set weights for different conditions; S233. Based on the comprehensive scoring and sorting algorithm, a list of candidate scenarios is finally output and arranged according to the matching degree and priority. During the actual execution process, the system updates or fine-tunes the recommendation list according to the performance of the mission execution platform, real-time environmental parameters and the current feedback of the astronauts.

7. The method for alleviating astronaut workload in a microgravity environment based on virtual reality technology according to claim 6, characterized in that: In step S300, specifically including: S310, loading and implementing virtual scenes, combining multi-channel sensory stimulation with virtual reality experience, and displaying virtual scenes using high-resolution virtual reality headsets; S320: Based on the loaded and preliminarily optimized virtual scenes, provide astronauts with a multi-channel, multi-sensory comprehensive experience; S330. Incorporate the experience process into a closed-loop feedback mechanism to continuously collect and analyze the physiological and psychological state data of astronauts in virtual scene experiences, timely evaluate the intervention effect, and provide a reference basis for scene recommendations and strategy adjustments.

8. The method for alleviating astronaut workload in a microgravity environment based on virtual reality technology according to claim 7, characterized in that: In step S310, specifically including: S311, based on the scene recommendation list formed in step S200, quickly retrieve and call all resources required for the target scene from the virtual scene database, including high-precision 3D models, texture maps, lighting parameters, terrain data-related audio and video files, and interactive scripts; S312, graphics rendering and visual performance optimization, with the support of high-performance image processing unit, real-time rendering of scenes; S313, dynamic adjustment, in the early stage of scene presentation, reduce visual stimulation through gradual brightness increase and soft transition of picture cutting.

9. The method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to claim 8, characterized in that: In step S320, specifically including: S321. Synchronize hardware and software, including time synchronization and spatial mapping. During time synchronization, use timing control to synchronize visual, auditory, and tactile changes within milliseconds. During spatial mapping, calibrate sound sources and tactile feedback points to achieve spatial consistency. S323. Feedback and shaping of multi-sensory stimulation. Based on the presented visual scene, soft natural sounds, low-intensity instrumental music, or soothing music are selected according to the scene type and astronaut needs. Atmos or binaural recording technology is used to give the sound a sense of directionality and layering in the virtual space. S324. Stabilize and improve the sensory fusion experience. While multi-channel stimulation brings a huge amount of information, fine-tune the sensory stimulation to avoid causing additional load.

10. The method for alleviating astronaut workload based on virtual reality technology in a microgravity environment according to claim 9, characterized in that: In step S330, specifically including: S331, real-time monitoring of multi-dimensional physiological and psychological state data and dynamic adjustment of scenario strategies. During the astronauts' experience of the virtual scene, the system continuously analyzes the above data to determine the intervention effect of the current stimulation method on the astronauts and make real-time fine-tuning of sensory elements and interaction methods; S332. Quantitative evaluation of the effectiveness of scenario interventions. To objectively measure the contribution of scenarios to astronauts' psychological recovery, the system extracts features from the aforementioned multidimensional data and conducts quantitative analysis. Through time window averaging, trend analysis, and threshold judgment, it determines whether the intervention has achieved its intended goals, or identifies areas and potential for improvement. S333, data archiving, long-term feedback loop and visual presentation. After the experience is over, the data, fine-tuning records and effect evaluation results of this intervention process will be archived and compared with historical records and other astronaut data to provide resources for long-term improvement and personalized strategy optimization.

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