Tactile interaction intelligent management system for virtual social scene
By constructing a tactile interaction intelligent management system, the problems of singular tactile simulation and inaccurate emotional transmission in virtual social interaction have been solved. It has achieved personalized and secure multimodal tactile feedback, and enhanced users' emotional immersion and social experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-28
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies lack multimodal haptic support in virtual social scenarios, cannot reproduce complex contact actions, lack dynamic adjustment mechanisms for physiological signal feedback, and have not established a quantitative mapping model between haptic parameters and emotional dimensions, resulting in low accuracy of emotional transmission. Furthermore, they lack differentiated designs for special groups, and are insufficient in terms of safety and comfort.
Construct a tactile interaction intelligent management system, including a data perception layer, an emotional intelligence analysis layer, a knowledge base layer, a tactile interaction layer, an execution feedback layer, a user context adaptation layer, a social scene integration layer, and an intelligent operation and maintenance management layer, to achieve multimodal data collection, emotional need recognition, personalized tactile parameter generation, security feedback, and system optimization.
It achieves high-fidelity, personalized, safe and reliable tactile emotional transmission, alleviating emotional alienation and loneliness in virtual social interactions, and enhancing users' social initiative and satisfaction.
Smart Images
Figure CN121785458A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of smart wearables and emotional computing technology, specifically to a tactile interactive smart management system for virtual social scenarios. Background Technology
[0002] With the popularization of digital communication technology, virtual social interaction has become one of the main ways of interpersonal communication in modern society. However, long-term reliance on online interaction has led to a lack of physical contact, causing psychological problems such as emotional alienation, increased loneliness, and intergenerational communication barriers, which are particularly prominent among special groups such as left-behind children, empty-nest elderly, and long-distance couples. Existing technologies mainly rely on audiovisual media such as video and voice to convey emotions, lacking support from the tactile dimension. Although some studies have proposed tactile feedback devices based on smart clothing, they generally have the following defects: (1) Single-modal tactile simulation (only pressure or temperature) cannot reproduce the complex tactile sensation of comforting actions such as hugging and patting; (2) Lack of a dynamic adjustment mechanism based on physiological signal feedback, which cannot personalize the tactile parameters according to the user's real-time emotional state; (3) No quantitative mapping model between tactile parameters and emotional dimensions has been established, resulting in low accuracy of tactile semantic transmission; (4) Lack of differentiated design for special groups, resulting in insufficient safety and comfort; (5) Fragmented system architecture, with no closed-loop optimization capability between functional modules. Therefore, there is an urgent need for a systematic solution that deeply integrates touch comfort theory, affective computing, and flexible execution technology to achieve high-fidelity, personalized, safe, and reliable tactile emotion transmission in virtual social scenarios. Summary of the Invention
[0003] (a) Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a tactile interactive intelligent management system for virtual social scenarios. It has the advantages of high realism of multimodal tactile collaboration, accurate identification of emotional needs, strong adaptability to different groups, and excellent closed-loop self-evolution capability. It solves the problems of emotional alienation, increased loneliness, and intergenerational communication barriers caused by the lack of physical contact in virtual social interactions.
[0004] (II) Technical Solution To achieve the above objectives, the present invention provides the following technical solution: a tactile interaction intelligent management system for virtual social scenarios, comprising a data perception layer, an emotional intelligence analysis layer, a knowledge base layer, a tactile interaction layer, an execution feedback layer, a user context adaptation layer, a social scenario fusion layer, and an intelligent operation and maintenance management layer; The data perception layer collects physiological states, tactile features, and contextual data in virtual social scenarios in real time, providing a multimodal data source for emotion computing; The emotional intelligence analysis layer identifies users' emotional states and demographic types based on multimodal data sources, and generates a personalized emotional need index for each user. ; The knowledge base layer is responsible for storing tactile semantics, user profiles, and scenario strategies, providing a basis for decision-making in multimodal tactile coding; The tactile interaction layer will index the user's personalized emotional needs. It is converted into tactile commands, and simultaneously adopts a multimodal tactile intensity synthesis formula to dynamically generate personalized tactile parameters, realizing temperature-pressure-vibration triaxial coordination and remote real-time control; The execution feedback layer executes tactile commands and provides real-time feedback on hardware execution status, changes in user physiological response, wearing comfort, and safety risk warning information. This information is then uploaded to the intelligent operation and maintenance management layer and the user scenario adaptation layer via a real-time data stream of 80-100ms. The user context adaptation layer enables intelligent matching and dynamic optimization of tactile patterns to meet the differentiated needs of different groups and emotions. The social scene fusion layer deeply integrates tactile feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression; The intelligent operation and maintenance management layer calculates the satisfaction level of the comfort effect based on feedback information. The system is monitored, evaluated, and optimized throughout the entire process through a multi-dimensional closed-loop feedback mechanism.
[0005] Preferably, the data perception layer includes a physiological signal acquisition sublayer, a tactile parameter acquisition sublayer, and a social context perception sublayer.
[0006] Preferably, the physiological signal acquisition sublayer acquires the user's physiological signals in real time through wearable sensors.
[0007] Preferably, the tactile parameter acquisition sublayer acquires the mechanical parameters of the user's skin contact through pressure sensors and motion sensors.
[0008] Preferably, the social context perception sublayer identifies the strength of emotional association and the quality of interaction in virtual social interactions through multi-source data fusion and analysis technology.
[0009] Preferably, the emotional intelligence analysis layer incorporates the Russell emotional loop model, which maps physiological signals to a two-dimensional coordinate system of pleasure and arousal, identifies typical negative emotions in virtual social interactions such as anxiety, loneliness, and emotional indifference, and automatically categorizes individuals into left-behind children, empty-nest elderly, long-distance couples, and other special groups through questionnaires and behavioral data, assigning them differentiated weight coefficients.
[0010] Preferably, the emotional intelligence analysis layer receives data collected by the data perception layer and calculates the user's personalized emotional need index by combining questionnaire and behavioral data. The calculation formula is as follows: ; In the formula, This represents the index of users' personalized emotional needs. This indicates abnormal values in heart rate variability. Indicates the intensity of skin electrical conductivity response. Indicates the degree of deviation from skin temperature. Indicates physiological weight, Indicates the weight of the population type. Indicates social distancing weight. Represents the user type coefficient. This indicates the social distancing index.
[0011] Preferably, the tactile interaction layer incorporates a multimodal tactile intensity synthesis formula to dynamically generate personalized tactile parameters. The synthesis formula is as follows: ; In the formula, , , These represent the actual temperature, actual pressure, and actual vibration frequency, respectively. , , These represent the base temperature, base pressure, and base vibration frequency, respectively. , , These represent the gain coefficients for temperature, pressure, and frequency-based emotional intensity, respectively. , , These represent the temperature, pressure, and frequency affinity adjustment coefficients, respectively. This indicates the level of intimacy in the interaction.
[0012] Preferably, the user context adaptation layer is classified into a user feature adaptation sublayer, an emotion type adaptation sublayer, and a preference learning sublayer to meet the differentiated needs of different groups and emotions. The social scene fusion layer deeply integrates haptic feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression through sub-layers. These sub-layers are designed as follows: (1) The scene pattern recognition sub-layer automatically recognizes WeChat video, Zoom meeting and voice call and triggers the corresponding haptic protocol; (2) The intimacy modeling sublayer constructs an intimacy index based on the social object, interaction frequency and emotional value of the communication content, and adjusts the upper limit of tactile intensity; (3) When the motion synchronization engine sublayer detects voice keywords or video gestures, it triggers a hug tactile feedback with a delay of <200ms to achieve touch-speech synchronization.
[0013] Preferably, the intelligent operation and maintenance management layer has a built-in feature to improve the satisfaction level of the user. The calculation formula is as follows: ; In the formula, , , These represent the weighting of physiological improvement, subjective pleasure, and changes in social behavior, respectively. , These represent the scores on the Self-Rating Anxiety Scale before and after the intervention, respectively. This represents the change in the pleasure dimension of the Russell model. , This indicates the social initiative index before and after the intervention.
[0014] Compared with existing technologies, the present invention provides a tactile interaction intelligent management system for virtual social scenarios, which has the following beneficial effects: 1. This invention constructs a basic framework comprising a data perception layer, an emotional intelligence analysis layer, a knowledge base layer, a tactile interaction layer, an execution feedback layer, a user context adaptation layer, a social scene integration layer, and an intelligent operation and maintenance management layer. Furthermore, it embeds an emotional demand index into the emotional intelligence analysis layer, the tactile interaction layer, and the intelligent operation and maintenance management layer. Multimodal tactile intensity synthesis formula and placebo effect satisfaction Three computational models realize full-link quantitative driving from physiological signal acquisition → emotional quantitative assessment → dynamic synthesis of tactile parameters → closed-loop optimization of execution effect, enabling the system to achieve the beneficial effects of fast tactile semantic transmission, fast response and relief of user anxiety.
[0015] 2. This invention, through the collaborative work of the user context adaptation layer's population characteristic adaptation sublayer and emotion type adaptation sublayer, sets differentiated safety thresholds for three special groups: left-behind children, empty-nest elderly, and long-distance couples, respectively, for pressure <8kPa / temperature <38℃, low pressure with high warmth, and high pressure with high frequency. Based on emotional states such as anxiety, loneliness, and emotional numbness, it automatically matches a tactile combination strategy of pressure + vibration, temperature + low frequency, and progressively increasing tactile sensations. Combined with the preference learning sublayer, it continuously optimizes the parameter baseline using historical interaction data, achieving the beneficial effects of personalized intervention and an average monthly increase in user satisfaction.
[0016] 3. This invention achieves deep integration of the scene pattern recognition sublayer, intimacy modeling sublayer, and motion synchronization engine sublayer of the social scene fusion layer. It automatically identifies multiple mainstream platforms such as WeChat video and Zoom meetings and triggers corresponding protocols. Based on NLP analysis, it constructs an intimacy index to dynamically adjust the upper limit of tactile intensity. When keywords such as "hug" or open arms gestures are detected, it achieves tactile-verbal synchronization of less than 200ms. It accurately aligns physical touch with virtual social behavior in the spatiotemporal dimension, ultimately achieving the beneficial effects of enhanced emotional immersion and improved user social initiative index. Attached Figure Description
[0017] Figure 1 This is a system flowchart of the present invention. Detailed Implementation
[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0019] Please see Figure 1 A haptic interaction intelligent management system for virtual social scenarios, comprising a data perception layer, an emotional intelligence analysis layer, a knowledge base layer, a haptic interaction layer, an execution feedback layer, a user context adaptation layer, a social scenario fusion layer, and an intelligent operation and maintenance management layer. The data perception layer collects physiological states, tactile features, and contextual data in virtual social scenarios in real time, providing a multimodal data source for emotion computing; The emotional intelligence analysis layer, based on physiological, psychological, and behavioral data, accurately identifies users' emotional states and demographic types, generating a personalized emotional need index for each user. ; The knowledge base layer is responsible for storing tactile semantics, user profiles, and scenario strategies, providing decision-making support for multimodal tactile encoding. Specifically, it includes: (1) Define multiple standard tactile corpora such as “warm hug” = 40℃ + 8kPa + 3Hz pulse and “soothing pat” = 36℃ + 5kPa + 2Hz fluctuation, and support remote parsing and calling; (2) Dynamically maintain the body size, tactile sensitivity preference, and psychological acceptance threshold of three special groups (e.g., elderly people's stress tolerance <10kPa, children's temperature limit <40℃). (3) Preset tactile triggering rules for virtual social scenarios such as video call active hug mode, voice chat tap synchronization mode and nighttime loneliness automatic intervention mode; The tactile interaction layer will index the user's personalized emotional needs. It is converted into executable tactile commands, and simultaneously adopts a multimodal tactile intensity synthesis formula to dynamically generate personalized tactile parameters, realizing temperature-pressure-vibration triaxial coordination and remote real-time control. The execution feedback layer executes tactile commands to ensure the realism of the touch and the comfort of wearing, and provides real-time feedback on the hardware execution status, as well as changes in the user's physiological response, wearing comfort and safety risk warning information. This information is then uploaded to the intelligent operation and maintenance management layer and the user scenario adaptation layer via a real-time data stream of 80-100ms. The user context adaptation layer enables intelligent matching and dynamic optimization of tactile patterns to meet the differentiated needs of different groups and emotions. The social scene integration layer deeply integrates haptic feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression; The intelligent operation and maintenance management system calculates the satisfaction level of the comfort effect based on feedback information. By employing a multi-dimensional closed-loop feedback mechanism, the system is monitored, evaluated, and optimized throughout the entire process, ensuring its safety, reliability, and continuous evolution.
[0020] The data perception layer includes a physiological signal acquisition sublayer, a tactile parameter acquisition sublayer, and a social context perception sublayer.
[0021] The physiological signal acquisition sublayer collects users' physiological signals in real time through wearable sensors, including blood flow, blood pressure, heart rate, skin temperature, skin electrical activity (such as electrooculography), skin conductivity (EDA), respiratory rate (RR), and electromyography (EMG), which are used to monitor users' emotional state in real time.
[0022] The tactile parameter acquisition sublayer collects mechanical parameters of the user's skin contact through pressure sensors and motion sensors, including contact position, contact area, pressure change, contact duration, pressure rise / fall rate and vibration frequency (tactile texture), etc., to quantify the actual contact action.
[0023] The social context awareness sublayer uses multi-source data fusion and analysis technology to identify the strength of emotional connections and the quality of interaction in virtual social interactions, including the type of virtual social interaction (video / voice / text), the interaction object (family / partner / friend), the duration and frequency of interaction, the emotional tendency of the interaction content (positive / neutral / negative), the duration of interaction response delay, the stability of periodic interaction (such as whether the contact is at a fixed time), and the initiative of interaction initiation (one-sided initiation / two-way interaction), etc., to assess the level of social alienation risk.
[0024] The advantages are: it achieves objective quantitative monitoring of users' emotional state through the physiological signal acquisition sublayer, achieves accurate digital translation of real contact actions through the tactile parameter acquisition sublayer, achieves comprehensive evaluation of the quality of virtual social interaction through the social context perception sublayer, and finally achieves the goal of multi-dimensional data collaborative support and eliminating the limitations of a single data source, providing a comprehensive and reliable raw data foundation for subsequent emotion analysis and tactile adaptation.
[0025] The emotional intelligence analysis layer incorporates the Russell emotional loop model, which maps physiological signals to a two-dimensional coordinate system of pleasure and arousal. It identifies typical negative emotions in virtual social interactions, such as anxiety, loneliness, and emotional indifference. At the same time, it automatically categorizes individuals into special groups, such as left-behind children, empty-nest elderly, and long-distance couples, based on questionnaire and behavioral data, and assigns them differentiated weight coefficients.
[0026] The emotional intelligence analysis layer receives data collected by the data perception layer and combines it with questionnaire and behavioral data to calculate the user's personalized emotional need index. The formula for quantifying the intensity of a user's need for immediate comfort is as follows: ; In the formula, This represents the index of users' personalized emotional needs. This indicates abnormal heart rate variability (normalized difference from baseline). Indicates the intensity of skin electrical conductivity response (unit: μS). This indicates the degree of deviation of skin temperature (difference from normal value). This represents the physiological weight (0.5). This represents the weight of the population type (0.3). This indicates a social distancing weight (0.2). This represents the user type coefficient: left-behind children = 1.2, empty-nest elderly = 1.3, long-distance couples = 1.0. It represents the social distancing index, which is the average daily virtual social time / offline social time, and is dimensionless.
[0027] The advantages are: by fusing Russell's emotional circle model with multi-dimensional data, it achieves accurate identification of user emotional states and user types; and by calculating a personalized emotional need index for each user. To assess the current level of emotional deprivation and the intensity of the user's need for comfort: when When ∈[0,0.3), it is determined to be low demand, triggering mild companion-type tactile sensation (such as low-frequency tapping); when When ∈ [0.3, 0.7), it is determined to be a medium need, triggering a moderate comforting touch (such as a standard hug); when When the value is ∈[0.7,1.0], it is determined to be a high demand, triggering enhanced comforting tactile sensations (such as high-pressure long hugs + high-temperature warmth), ultimately achieving a precise match between "emotion - people - needs", providing a decision-making core for personalized tactile services.
[0028] The knowledge base layer is responsible for storing tactile semantics, user profiles, and scenario strategies, providing decision-making support for multimodal tactile encoding, including: (1) Standard tactile corpus (e.g., warm hug = 40℃ + 8kPa + 3Hz pulse); (2) Special population characteristic database (body size, tactile sensitivity, psychological threshold); (3) Virtual social scene trigger rule base (video call / voice chat / night intervention mode).
[0029] The advantages are: a standardized tactile corpus enables unified parsing and efficient invocation of remote tactile commands; a special population feature database avoids the risk of tactile parameters exceeding safety thresholds; and a scene trigger rule base enables natural adaptation of tactile feedback to social scenarios. These three elements work together to build a three-in-one knowledge support system integrating semantics, population, and scenario, significantly improving system decision-making efficiency and the security of tactile services.
[0030] The haptic interaction layer incorporates a multimodal haptic intensity synthesis formula to dynamically generate personalized haptic parameters. The synthesis formula is as follows: ; In the formula, , , These represent the actual temperature, actual pressure, and actual vibration frequency, respectively. , , These represent the base temperature, base pressure, and base vibration frequency, respectively (based on the tactile semantic library, such as the hugging mode: 40℃, 8kPa, 3Hz). , , These represent the gain coefficients for emotional intensity based on temperature, pressure, and frequency (temperature 0.5℃ / ENI unit, pressure 0.8kPa / ENI unit, frequency 0.2Hz / ENI unit). , , These represent the temperature, pressure, and frequency intimacy adjustment coefficients (0.1~0.3, dynamically adjusted according to the intimacy level of the social object). This represents the interaction intimacy index (ranging from 0.5 to 1.0, calculated by the social context perception sublayer).
[0031] The advantage is: through calculation (Actual temperature) assesses the user's immediate need for warmth and dynamically matches temperature supply to different emotional states; through calculation... (Actual pressure) Assess the user's need for tactile support to achieve personalized adaptation of pressure parameters; through calculation (Actual vibration frequency) assesses the differences in users' needs for interaction, accurately reproduces the texture features of real contact, and finally achieves the beneficial effect of triaxial synergistic optimization of temperature, pressure and vibration, and tactile experience that highly matches the user's immediate needs, while realizing low-latency and accurate execution of remote commands.
[0032] The user context adaptation layer is designed with sub-layers to address the differentiated needs of different groups and emotions, specifically: (1) Sub-layers adapted to population characteristics: Children's mode (pressure limit 8kPa, temperature limit 38℃, gentle vibration), elderly mode (low pressure, high warmth, extended response delay), couples mode (high pressure, high frequency, instant response). (2) Emotion type matching sub-layer: Anxiety emotion preferentially activates the stress + vibration combination, loneliness emotion preferentially activates the temperature + low frequency vibration, and emotional numbness adopts a gradual intensity increase strategy; (3) Preference learning sub-layer: Built-in personalized parameter evolution formula, continuously optimizes the baseline of haptic parameters through historical interaction data.
[0033] The advantages are: by adapting the sub-layer to meet the safety and comfort needs of special groups, by adapting the sub-layer to the emotion type, it achieves precise matching of emotions and touch, and by continuously optimizing the parameter baseline and conforming to users' long-term habits through the preference learning sub-layer, the three work together to achieve the beneficial effect of full-dimensional adaptation of groups, emotions and preferences, thereby greatly improving user satisfaction and loyalty.
[0034] The social scene integration layer deeply integrates haptic feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression through sub-layers. These sub-layers are designed as follows: (1) The scene pattern recognition sub-layer automatically recognizes various mainstream virtual social platforms such as WeChat video, Zoom meeting, and voice call, and triggers the corresponding tactile protocol; (2) The intimacy modeling sublayer constructs an intimacy index (0-1.0) based on social objects, interaction frequency, and emotional value of communication content (NLP analysis), and adjusts the upper limit of tactile intensity; (3) When the motion synchronization engine sublayer detects voice keywords (such as "hug") or video gestures (such as opening arms), it triggers tactile feedback of hugging with a delay of <200ms to achieve tactile-verbal synchronization.
[0035] The advantages are: seamless multi-platform adaptation is achieved through the scene pattern recognition sub-layer, breaking down the compatibility barriers of social software; the intimacy modeling sub-layer ensures that the intensity of touch matches the social relationship, avoiding over-intervention or insufficient comfort; the motion synchronization engine sub-layer achieves real-time linkage between language / gesture and touch, enhancing the emotional immersion of virtual social interaction, and finally achieving the beneficial effect of natural integration of tactile feedback and virtual social interaction, and more three-dimensional and realistic emotional expression.
[0036] The intelligent operation and maintenance management system has a built-in comfort effect to improve satisfaction. The calculation formula evaluates the intervention effect from three dimensions: physiological, subjective, and behavioral, driving the iteration of the knowledge base. The calculation formula is as follows: ; In the formula, , , The weights represent the physiological improvement, subjective pleasure, and social behavior change, respectively (physiological improvement 0.4, subjective pleasure 0.4, social behavior change 0.2). , The scores on the anxiety self-rating scale before and after the intervention are respectively (the material requires a reduction of ≥30%). This represents the change in the pleasure dimension of the Russell model (+ indicates improved mood). , This represents the social initiative index before and after the intervention (number of times actively initiated virtual social interactions / total number of interactions).
[0037] The advantage is that by incorporating a placebo effect into the intelligent operation and maintenance management layer, satisfaction levels can be improved. The calculation formula enables a quantitative assessment of the placebo effect. It comprehensively judges the effectiveness of the intervention by using data from three dimensions: physiological, subjective, and behavioral. Through a closed-loop feedback mechanism, it drives the continuous iteration of the knowledge base, tactile parameters, and adaptation strategies. Ultimately, it achieves the beneficial effects of system self-optimization, maintaining service quality and adaptation accuracy in the long term, and ensuring the system's stability, reliability, and continuous evolution.
[0038] In summary, this invention system achieves personalized, precise, and natural tactile experiences in virtual social scenarios through the collaborative linkage of eight layers: data perception, emotion analysis, knowledge support, tactile interaction, feedback optimization, context adaptation, scene integration, and operation and maintenance support. It deeply integrates the principles of tactile comfort with intelligent technology, ultimately solving the problems of emotional alienation, increased loneliness, and intergenerational communication barriers caused by the lack of physical contact in virtual social interactions. It provides a brand-new emotional communication platform for special groups such as left-behind children, empty-nest elderly, and long-distance couples.
[0039] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.
Claims
1. A tactile interactive intelligent management system for virtual social scenarios, characterized in that, It includes a data perception layer, an emotional intelligence analysis layer, a knowledge base layer, a tactile interaction layer, an execution feedback layer, a user context adaptation layer, a social scene integration layer, and an intelligent operation and maintenance management layer; The data perception layer collects physiological states, tactile features, and contextual data in virtual social scenarios in real time, providing a multimodal data source for emotion computing; The emotional intelligence analysis layer identifies users' emotional states and demographic types based on multimodal data sources, and generates a personalized emotional need index for each user. ; The knowledge base layer is responsible for storing tactile semantics, user profiles, and scenario strategies, providing a basis for decision-making in multimodal tactile coding; The tactile interaction layer will index the user's personalized emotional needs. It is converted into tactile commands, and simultaneously adopts a multimodal tactile intensity synthesis formula to dynamically generate personalized tactile parameters, realizing temperature-pressure-vibration triaxial coordination and remote real-time control; The execution feedback layer executes tactile commands and provides real-time feedback on hardware execution status, changes in user physiological response, wearing comfort, and safety risk warning information. This information is then uploaded to the intelligent operation and maintenance management layer and the user scenario adaptation layer via a real-time data stream of 80-100ms. The user context adaptation layer enables intelligent matching and dynamic optimization of tactile patterns to meet the differentiated needs of different groups and emotions. The social scene fusion layer deeply integrates tactile feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression; The intelligent operation and maintenance management layer calculates the satisfaction level of the comfort effect based on feedback information. The system is monitored, evaluated, and optimized throughout the entire process through a multi-dimensional closed-loop feedback mechanism.
2. The haptic interaction intelligent management system for virtual social scenarios according to claim 1, characterized in that: The data perception layer includes a physiological signal acquisition sublayer, a tactile parameter acquisition sublayer, and a social context perception sublayer.
3. The haptic interaction intelligent management system for virtual social scenarios according to claim 2, characterized in that: The physiological signal acquisition sublayer collects the user's physiological signals in real time through wearable sensors.
4. The haptic interaction intelligent management system for virtual social scenarios according to claim 2, characterized in that: The tactile parameter acquisition sublayer collects the mechanical parameters of the user's skin contact through pressure sensors and motion sensors.
5. The haptic interaction intelligent management system for virtual social scenarios according to claim 2, characterized in that: The social context perception sublayer identifies the strength of emotional connections and the quality of interaction in virtual social interactions through multi-source data fusion and analysis technology.
6. The haptic interaction intelligent management system for virtual social scenarios according to claim 1, characterized in that: The emotional intelligence analysis layer incorporates the Russell emotional loop model, which maps physiological signals to a two-dimensional coordinate system of pleasure and arousal. It identifies typical negative emotions in virtual social interactions, such as anxiety, loneliness, and emotional indifference. Simultaneously, through questionnaires and behavioral data, it automatically categorizes individuals into left-behind children, empty-nest elderly, long-distance couples, and other special groups, assigning them differentiated weight coefficients.
7. The haptic interaction intelligent management system for virtual social scenarios according to claim 6, characterized in that: The emotional intelligence analysis layer receives data collected by the data perception layer and calculates the user's personalized emotional need index by combining questionnaire and behavioral data. The calculation formula is as follows: ； In the formula, This represents the index of users' personalized emotional needs. This indicates abnormal values in heart rate variability. Indicates the intensity of skin electrical conductivity response. Indicates the degree of deviation from skin temperature. Indicates physiological weight, Indicates the weight of the population type. Indicates social distancing weight. Represents the user type coefficient. This indicates the social distancing index.
8. The haptic interaction intelligent management system for virtual social scenarios according to claim 1, characterized in that: The tactile interaction layer incorporates a multimodal tactile intensity synthesis formula to dynamically generate personalized tactile parameters. The synthesis formula is as follows: ； In the formula, , , These represent the actual temperature, actual pressure, and actual vibration frequency, respectively. , , These represent the base temperature, base pressure, and base vibration frequency, respectively. , , These represent the gain coefficients for temperature, pressure, and frequency-based emotional intensity, respectively. , , These represent the temperature, pressure, and frequency affinity adjustment coefficients, respectively. This indicates the level of intimacy in the interaction.
9. The haptic interaction intelligent management system for virtual social scenarios according to claim 1, characterized in that: The user context adaptation layer is classified into a user feature adaptation sublayer, an emotion type adaptation sublayer, and a preference learning sublayer to meet the differentiated needs of different groups and emotions. The social scene fusion layer deeply integrates haptic feedback with virtual social behavior, achieving natural synchronization and enhancement of emotional expression through sub-layers. These sub-layers are designed as follows: (1) The scene pattern recognition sub-layer automatically recognizes WeChat video, Zoom meeting and voice call and triggers the corresponding haptic protocol; (2) The intimacy modeling sublayer constructs an intimacy index based on the social object, interaction frequency and emotional value of the communication content, and adjusts the upper limit of tactile intensity; (3) When the motion synchronization engine sublayer detects voice keywords or video gestures, it triggers a hug tactile feedback with a delay of <200ms to achieve touch-speech synchronization.
10. The haptic interaction intelligent management system for virtual social scenarios according to claim 1, characterized in that: The intelligent operation and maintenance management layer has a built-in comfort effect satisfaction level. The calculation formula is as follows: ； In the formula, , , These represent the weighting of physiological improvement, subjective pleasure, and changes in social behavior, respectively. , These represent the scores on the Self-Rating Anxiety Scale before and after the intervention, respectively. This represents the change in the pleasure dimension of the Russell model. , This indicates the social initiative index before and after the intervention.