Five-dimensional dynamic emotion visual chemotherapy healing method and system

Abstract

The invention discloses a five-dimensional dynamic emotion visual chemotherapy healing method and system, and relates to the technical field of emotion visual chemotherapy healing, and the method comprises the steps: employing a multi-sensor fusion method to collect physiological data, psychological assessment questionnaire results and historical emotion data of a user, and obtaining a basic emotion feature vector of the user; mapping the emotional state of the user to a five-dimensional emotional space based on the basic emotional feature vector, and initializing a particle set; collecting multi-mode biological signals of electroencephalogram, heart rate variability, electrodermal response, body temperature and voice emotion recognition of the user, and processing to obtain a feature vector reflecting the current emotion state of the user; the geometric morphology and kinetic parameters in the particle set are dynamically adjusted, a visualization engine is used for rendering particles, a two-way feedback adjustment mechanism is constructed, particle behaviors are adjusted according to the emotional state of the user, and the emotional adjustment ability of the user is enhanced; and designing an interactive emotion regulation game.

Description

Technical Field

[0001] This invention relates to the field of emotional visualization therapy technology, and in particular to a five-dimensional dynamic emotional visualization therapy method and system. Background Technology

[0002] Emotional visualization therapy is a comprehensive treatment method that combines modern psychology, computer graphics, biofeedback technology, and artificial intelligence. Its core idea is to transform abstract emotional states into intuitive visual representations through visualization, and to help users better understand and regulate their emotions through real-time feedback and interactive games. Therefore, how to utilize advanced technologies to improve the intelligence and safety of emotional visualization therapy has become one of the most pressing issues to be addressed.

[0003] In the field of emotional visualization therapy, traditional emotional treatment methods often adopt uniform standards, failing to fully consider individual differences, resulting in poor treatment outcomes. Moreover, most existing emotional regulation systems cannot provide real-time emotional feedback, causing users to be unable to adjust their emotional state in a timely manner. The lack of an effective immediate feedback mechanism makes the emotional regulation process passive and inefficient. At the same time, traditional mental health intervention methods are relatively dull and lack interactivity and fun, resulting in low user participation and affecting the treatment effect. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, this invention provides a five-dimensional dynamic emotion visualization therapy method to address the problems of traditional emotion therapy methods, which often adopt uniform standards and fail to fully consider individual differences, resulting in poor treatment effects. Furthermore, most existing emotion regulation systems cannot provide real-time emotion feedback, causing users to be unable to adjust their emotional state in a timely manner. The lack of an effective immediate feedback mechanism makes the emotion regulation process passive and inefficient. At the same time, traditional mental health intervention methods are relatively boring and lack interactivity and fun, resulting in low user participation and affecting the treatment effect.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] In a first aspect, the present invention provides a five-dimensional dynamic emotion visualization therapy method, which includes:

[0008] A multi-sensor fusion method was used to collect users' physiological data, psychological assessment questionnaire results, and historical emotion data to obtain the user's basic emotion feature vector;

[0009] Based on the aforementioned basic emotion feature vector, the user's emotional state is mapped to a five-dimensional emotion space, and the particle set is initialized.

[0010] The system collects multimodal biological signals from users, including electroencephalograms, heart rate variability, skin conductance, body temperature, and voice emotion recognition, and processes them to obtain feature vectors reflecting the user's current emotional state.

[0011] The geometric shape and dynamic parameters of the particle set are dynamically adjusted, and the particles are rendered using a visualization engine. A two-way feedback adjustment mechanism is constructed to adjust the particle behavior according to the user's emotional state, thereby enhancing the user's ability to regulate emotions.

[0012] Design an interactive emotion regulation game that allows users to participate in emotion regulation through gesture control. Record the data generated during the user's use, optimize the emotion-particle mapping model through deep learning algorithms, and generate a visual report containing emotion change curves, treatment effect scores, and suggestions after each use.

[0013] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method described in this invention, the method employs multi-sensor fusion to collect the user's physiological data, psychological assessment questionnaire results, and historical emotion data to obtain the user's basic emotion feature vector. The specific steps are as follows:

[0014] Standardized psychological scales were used to quantify users' depression and anxiety, obtaining specific scores S for each emotional dimension. i , where i represents the emotion dimension number;

[0015] The sentiment scores for each dimension are normalized so that the scores for each dimension range from 0 to 1. The normalization expression is as follows:

[0016]

[0017] Among them, S min and S max These are the minimum and maximum possible scores for that dimension, respectively.

[0018] Using the normalized sentiment score as weighting coefficients, the user's comprehensive sentiment feature vector is calculated, as expressed in the following expression:

[0019] E = [e1, e2, e3, e4, e5];

[0020] Where e1 = s i ′ Let i represent the emotion score of the i-th dimension.

[0021] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method of the present invention, the steps of mapping the user's emotional state to a five-dimensional emotion space based on the basic emotion feature vector and initializing the particle set are as follows:

[0022] Initialize the initial geometry of the particles based on the geometric shape corresponding to each emotion dimension;

[0023] The geometric shapes corresponding to each emotional dimension include: depression corresponding to drifting fog, anxiety corresponding to lightning bolts, anger corresponding to a blazing fireball, self-denial corresponding to a cluster of spikes, and giving up corresponding to loose meteors.

[0024] Initialize the particle's dynamic parameters based on the dynamic parameters corresponding to each emotion dimension;

[0025] The dynamic parameters corresponding to each emotion dimension include velocity, convergence, and rotation frequency;

[0026] Calculate the weight coefficients of particles in each dimension, and calculate the total particle set. The expression is as follows:

[0027] W = [w1, w2, w3, w4, w5];

[0028]

[0029] Among them, P i Let w represent the set of particles corresponding to the i-th dimension of emotion. i It can be dynamically adjusted based on the user's historical sentiment data.

[0030] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method described in this invention, the steps of collecting multimodal biological signals such as the user's electroencephalogram, heart rate variability, skin conductance response, body temperature, and voice emotion recognition, and processing them to obtain a feature vector reflecting the user's current emotional state, are as follows:

[0031] Kalman filters are used to preprocess the acquired multimodal biological signals to remove noise and improve the signal-to-noise ratio.

[0032] The feature vector is extracted using the following expression:

[0033] F = [f1, f2, ..., f n ];

[0034] Where f1 represents the α-band power of EEG, f2 represents the HRV value, f3 represents the GSR value, and so on;

[0035] The user's current emotional state score S is calculated based on the feature vector F. When the power of the α band of the EEG increases, it indicates that the user's emotions are becoming calmer. At this time, the particle motion speed corresponding to anxiety is reduced.

[0036] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method of the present invention, the specific steps of dynamically adjusting the geometric shape and dynamic parameters of the particle set and rendering the particles using a visualization engine are as follows:

[0037] Based on the user's current emotional state score S, dynamically adjust the particle's velocity v and rotation radius v;

[0038] Adjusting particle velocity, the expression is:

[0039] v new =v base +k v ·(SS baseline );

[0040] Among them, v base It is the base speed, k v It is the adjustment coefficient, S baseline It is the baseline emotional state score;

[0041] Similarly, the rotation radius of the particle is adjusted, as expressed by:

[0042] r new =r base +k r ·(SS baseline );

[0043] Where, r base It is the basic radius of rotation, k r It is the adjustment coefficient, S baseline It is the baseline emotional state score;

[0044] Use a visualization engine from Unity or Unreal Engine to render the adjusted particles and display them to the user.

[0045] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method of the present invention, the specific steps of constructing a two-way feedback regulation mechanism to adjust particle behavior according to the user's emotional state and enhance the user's emotion regulation ability are as follows:

[0046] When a user's emotions are detected to be stabilizing, a positive feedback mechanism is used to enhance the user's positive emotions.

[0047] As the user's emotions gradually calm down, the user's positive emotional experience is enhanced by changing the color and movement of the particles;

[0048] When emotions fluctuate drastically, a negative feedback mechanism prompts the user to make adjustments. When the user's emotions fluctuate drastically, the color and movement of the particles are changed to remind the user to pay attention to emotional regulation.

[0049] The changes in particle color and movement include softening the color, making the movement more orderly, darkening the color, and making the movement more chaotic;

[0050] Based on the user's emotional characteristics model, select the feedback method.

[0051] As a preferred embodiment of the five-dimensional dynamic emotion visualization therapy method described in this invention, the following steps are included: designing an interactive emotion regulation game that allows users to participate in emotion regulation through gesture control, recording data generated during user use, optimizing the emotion-particle mapping model through deep learning algorithms, and generating a visualization report containing emotion change curves, treatment effect scores, and suggestions after each use.

[0052] Design an interactive emotion regulation game that allows users to participate through gesture control and adjust the state of emotion particles;

[0053] The game is designed based on a personalized particle behavior feedback mechanism.

[0054] Record the data generated by the user during each use, including emotion change curves, treatment effect scores, and user interaction data;

[0055] The AutoEncoder deep learning algorithm is used to analyze the recorded data and extract users' emotional patterns.

[0056] Update the emotion-particle mapping model based on the extracted emotion patterns;

[0057] After each use, an updated emotion-particle mapping model is applied to provide a more personalized treatment plan;

[0058] The output includes a visual report containing mood change curves, treatment effectiveness scores, and recommendations.

[0059] Secondly, the present invention provides a five-dimensional dynamic emotion visualization healing system, comprising:

[0060] The module includes an emotion feature modeling module, a particle initialization module, a biological signal processing module, a particle visualization and regulation module, and an interactive intervention and feedback module.

[0061] The emotion feature modeling module is used to collect and integrate user physiological data, psychological assessment questionnaire results, and historical emotion data to extract the user's basic emotion feature vector.

[0062] The particle initialization module is used to map the user's emotional state to a five-dimensional emotional space based on the basic emotional feature vector output by the emotional feature modeling module, and initialize the particle set with corresponding geometric shape and dynamic parameters according to the emotional intensity of each dimension.

[0063] The biosignal processing module is used to collect multimodal biosignals from users, including electroencephalograms, heart rate variability, skin conductance, body temperature, and voice emotion recognition. Through signal preprocessing and feature extraction, it generates feature vectors that reflect the user's current emotional state.

[0064] The particle visualization and control module is used to dynamically adjust the geometric shape and dynamic parameters of the particle set according to the emotional state feature vector output by the biosignal processing module, and to render the particles in real time using a visualization engine. At the same time, it constructs a two-way feedback regulation mechanism to enhance the user's emotional regulation ability.

[0065] The interactive intervention and feedback module is used to provide interactive emotion regulation games, allowing users to participate in the emotion regulation process through gesture control, recording user interaction data and emotion change data, optimizing the emotion-particle mapping model based on deep learning algorithms, and generating a visual report containing emotion change curves, treatment effect scores, and suggestions after each use.

[0066] Thirdly, the present invention provides a computer device including a memory and a processor, wherein the memory stores a computer program, wherein when the computer program is executed by the processor, it implements any step of the five-dimensional dynamic emotion visualization healing method as described in the first aspect of the present invention.

[0067] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, it implements any step of the five-dimensional dynamic emotion visualization therapy method as described in the first aspect of the present invention.

[0068] The beneficial effects of this invention are as follows: By initializing the geometric shape corresponding to each emotional dimension, intuitive visualization of emotional states is achieved, helping users better understand and perceive their own emotional changes. The dynamic parameters of particles are initialized according to the dynamic parameters corresponding to each emotional dimension, making the particle's expression more diverse and enhancing the richness and dynamism of emotional expression. Preprocessing the collected multimodal biological signals using a Kalman filter removes noise, improves the signal-to-noise ratio, and ensures the accuracy of the emotional state feature vector. The user's current emotional state score is calculated based on the feature vector, and the particle movement speed corresponding to anxiety is reduced when the emotion tends to be calm, achieving dynamic feedback regulation of emotional states and improving the efficiency of emotional regulation. By dynamically adjusting the particle speed and rotation radius according to the user's current emotional state score, instantaneous response and dynamic display of emotional states are achieved, enhancing the user's emotional perception and self-regulation abilities. Attached Figure Description

[0069] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a flowchart of the five-dimensional dynamic emotion visualization therapy method in Example 1.

[0071] Figure 2 This is a schematic diagram of the five-dimensional dynamic emotion visualization healing system in Example 1. Detailed Implementation

[0072] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0073] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0074] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that is mutually exclusive with other embodiments.

[0075] Example, refer to Figure 1 and Figure 2 This embodiment of the invention provides a five-dimensional dynamic emotion visualization therapy method, comprising the following steps:

[0076] S1. A multi-sensor fusion method is used to collect users' physiological data, psychological assessment questionnaire results, and historical emotion data to obtain the user's basic emotion feature vector.

[0077] Furthermore, standardized psychological scales were used to quantify users' depression and anxiety, obtaining specific scores S for each emotional dimension. i , where i represents the emotion dimension number;

[0078] The sentiment scores for each dimension are normalized so that the scores for each dimension range from 0 to 1. The normalization expression is as follows:

[0079]

[0080] Among them, S min and S max These are the minimum and maximum possible scores for that dimension, respectively.

[0081] Using the normalized sentiment score as weighting coefficients, the user's comprehensive sentiment feature vector is calculated, as expressed in the following expression:

[0082] E = [e1, e2, e3, e4, e5];

[0083] Where e1 = s i ′ This represents the sentiment score for the i-th dimension;

[0084] It should be noted that standardized psychological scales include, but are not limited to, PHQ-9 and GAD-7. The emotion scores obtained through these scales have clinical validity and can provide a reliable quantitative basis for subsequent emotion modeling. The introduction of normalization aims to eliminate the problem of inconsistent scoring scales between different scales, making multi-dimensional emotion scores comparable, thereby improving the accuracy and consistency of emotion feature vectors. By using the normalized scores as weight coefficients to construct a comprehensive emotion feature vector, it is possible to achieve a multi-dimensional expression of the user's emotional state, providing a foundation for personalized particle initialization.

[0085] S2. Based on the basic emotion feature vector, map the user's emotional state to a five-dimensional emotion space and initialize the particle set;

[0086] Furthermore, the initial geometry of the particles is initialized based on the geometric shape corresponding to each emotional dimension.

[0087] The geometric shapes corresponding to each emotional dimension include: depression corresponding to a dense fog, anxiety corresponding to a lightning bolt, anger corresponding to a blazing fireball, self-denial corresponding to a cluster of spikes, and giving up corresponding to a loose meteor.

[0088] Initialize the particle's dynamic parameters based on the dynamic parameters corresponding to each emotion dimension;

[0089] The dynamic parameters corresponding to each emotion dimension include velocity, convergence, and rotation frequency;

[0090] Calculate the weight coefficients of particles in each dimension, and calculate the total particle set. The expression is as follows:

[0091] W = [w1, w2, w3, w4, w5];

[0092]

[0093] Among them, P i Let w represent the set of particles corresponding to the i-th dimension of emotion.i It can be dynamically adjusted based on the user's historical sentiment data;

[0094] It should be noted that the five dimensions in the five-dimensional emotional space correspond to emotional states such as depression, anxiety, anger, self-denial, and giving up. Each dimension is mapped through the geometric shape and dynamic parameters of particles, realizing a concrete expression of the emotional state. For example, the depressive emotion is represented by "drifting fog" particles, which show its diffuse and low-activity characteristics, while the anxious emotion is represented by "lightning-like rays," which show its high volatility and suddenness. Dynamic parameters such as velocity, aggregation and dispersion, and rotation frequency reflect the activity, concentration, and volatility of the emotion, respectively. The weighted synthesis method of the particle set enables the overall emotional state to dynamically adapt to individual differences and emotional changes, enhancing the flexibility and personalization of emotional expression.

[0095] S3. Collect multimodal biological signals from the user's electroencephalogram, heart rate variability, skin conductance response, body temperature, and voice emotion recognition, and process them to obtain a feature vector reflecting the user's current emotional state.

[0096] Furthermore, a Kalman filter is used to preprocess the acquired multimodal biological signals to remove noise and improve the signal-to-noise ratio;

[0097] The feature vector is extracted using the following expression:

[0098] F = [f1, f2, ..., f n ];

[0099] Where f1 represents the α-band power of EEG, f2 represents the HRV value, f3 represents the GSR value, and so on;

[0100] The user's current emotional state score S is calculated based on the feature vector F. When the power of the α band of the EEG increases, it indicates that the user's emotions tend to be calm. At this time, the particle motion speed corresponding to anxiety is reduced.

[0101] It should be noted that multimodal biosignal acquisition includes electroencephalography (EEG), heart rate variability (HRV), skin conductance response (GSR), body temperature measurement, and voice emotion recognition, covering multiple levels of emotional and physiological responses, including the central nervous system, autonomic nervous system, and behavioral expression. Kalman filtering is used for signal preprocessing, which effectively removes physiological noise and equipment interference, improving signal quality. The extracted feature vector serves as real-time input to the emotional state. Combined with indicators such as alpha band power changes in EEG, accurate judgments of emotional change trends can be made, thus providing a real-time feedback basis for the dynamic adjustment of the particle ensemble and enhancing the system's response speed and adjustment accuracy.

[0102] S4. Dynamically adjust the geometric shape and dynamic parameters of the particle collection, and use the visualization engine to render the particles, build a two-way feedback adjustment mechanism, adjust the particle behavior according to the user's emotional state, and enhance the user's emotional regulation ability.

[0103] Furthermore, based on the user's current emotional state score S, the particle's velocity v and rotation radius v are dynamically adjusted;

[0104] Adjusting particle velocity, the expression is:

[0105] v new =v base +k v ·(SS baseline );

[0106] Among them, v base It is the base speed, k v It is the adjustment coefficient, S baseline It is the baseline emotional state score;

[0107] Similarly, the rotation radius of the particle is adjusted, as expressed by:

[0108] r new =r base +k r ·(SS baseline );

[0109] Where, r base It is the basic radius of rotation, k r It is the adjustment coefficient, S baseline It is the baseline emotional state score;

[0110] Use a visualization engine from Unity or Unreal Engine to render the adjusted particles and display them to the user;

[0111] When a user's emotions are detected to be stabilizing, a positive feedback mechanism is used to enhance the user's positive emotions.

[0112] As the user's emotions gradually calm down, the user's positive emotional experience is enhanced by changing the color and movement of the particles;

[0113] When emotions fluctuate drastically, a negative feedback mechanism prompts the user to make adjustments. When the user's emotions fluctuate drastically, the color and movement of the particles are changed to remind the user to pay attention to emotional regulation.

[0114] Changing the color and movement of particles includes softening the color, making the movement more orderly, darkening the color, and making the movement more chaotic;

[0115] Select feedback methods based on user emotion feature models;

[0116] It should be noted that the dynamic adjustment mechanism of the particle ensemble is based on the user's current emotional state score. By setting adjustment formulas for speed and rotation radius, continuous and smooth control of particle behavior is achieved. For example, when emotions tend to be stable, the speed and rotation frequency of anxiety particles are reduced to make the image softer, thereby guiding the user into a relaxed state. When emotions fluctuate violently, the disorder of particle movement and color contrast are enhanced to visually stimulate the user to self-regulate. The design of the two-way feedback mechanism enables the system to not only actively intervene according to the user's state, but also to adaptively adjust according to the user's feedback, thereby improving the interactivity and personalization of emotional intervention.

[0117] S5. Design an interactive emotion regulation game that allows users to participate in emotion regulation through gesture control, record the data generated during the user's use, optimize the emotion-particle mapping model through deep learning algorithms, and generate a visual report containing emotion change curves, treatment effect scores and suggestions after each use.

[0118] Furthermore, we designed interactive emotion regulation games that allow users to participate through gesture control and adjust the state of emotion particles.

[0119] The game is designed based on a personalized particle behavior feedback mechanism;

[0120] Record the data generated by the user during each use, including emotion change curves, treatment effect scores, and user interaction data;

[0121] The AutoEncoder deep learning algorithm is used to analyze the recorded data and extract users' emotional patterns.

[0122] Update the emotion-particle mapping model based on the extracted emotion patterns;

[0123] After each use, an updated emotion-particle mapping model is applied to provide a more personalized treatment plan;

[0124] The output includes a visual report containing mood change curves, treatment effectiveness scores, and recommendations;

[0125] It should be noted that the interactive emotion regulation game is developed based on the Unity or Unreal Engine platform and combines gesture recognition technology (Leap Motion or Kinect) to enable users to participate in the emotion regulation process through natural interaction. The recorded user interaction data includes operational behavior, emotion change curves, and treatment response feedback. The data is modeled using the deep learning algorithm AutoEncoder to extract the user's emotional behavior patterns, thereby optimizing the emotion-particle mapping model and enabling the continuous evolution of the treatment plan. The visual report generated after each use not only presents the treatment process and results but also provides personalized suggestions to help users understand their own emotional change patterns and enhance the sustainability and scientific nature of the treatment.

[0126] This embodiment also provides a five-dimensional dynamic emotion visualization healing system, including:

[0127] The module includes an emotion feature modeling module, a particle initialization module, a biological signal processing module, a particle visualization and regulation module, and an interactive intervention and feedback module.

[0128] The emotion feature modeling module is used to collect and integrate user physiological data, psychological assessment questionnaire results, and historical emotion data to extract the user's basic emotion feature vector.

[0129] The particle initialization module is used to map the user's emotional state to a five-dimensional emotional space based on the basic emotional feature vector output by the emotional feature modeling module, and initialize the particle set with corresponding geometric shape and dynamic parameters according to the emotional intensity of each dimension.

[0130] The biosignal processing module is used to collect multimodal biosignals from users, including electroencephalograms, heart rate variability, skin conductance, body temperature, and voice emotion recognition. Through signal preprocessing and feature extraction, it generates feature vectors that reflect the user's current emotional state.

[0131] The particle visualization and control module is used to dynamically adjust the geometric shape and dynamic parameters of the particle set based on the emotional state feature vector output by the biosignal processing module, and uses a visualization engine to render the particles in real time. At the same time, it constructs a two-way feedback regulation mechanism to enhance the user's emotion regulation ability.

[0132] The interactive intervention and feedback module provides interactive emotion regulation games, allowing users to participate in the emotion regulation process through gesture control. It records user interaction data and emotion change data, optimizes the emotion-particle mapping model based on deep learning algorithms, and generates a visual report containing emotion change curves, treatment effect scores, and suggestions after each use.

[0133] This embodiment also provides a computer device applicable to the five-dimensional dynamic emotion visualization therapy method, including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to realize the five-dimensional dynamic emotion visualization therapy method proposed in the above embodiment.

[0134] The computer device can be a terminal, comprising a processor, memory, communication interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, carrier networks, NFC (Near Field Communication), or other technologies. The display screen can be an LCD screen or an e-ink screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad on the computer device's casing, or an external keyboard, touchpad, or mouse.

[0135] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements the five-dimensional dynamic emotion visualization healing method proposed in the above embodiments. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0136] In summary, this invention achieves intuitive visualization of emotional states by initializing the geometric shapes corresponding to each emotional dimension, helping users better understand and perceive their emotional changes. It initializes the particle dynamics parameters according to the dynamics parameters corresponding to each emotional dimension, making the particle's expression more diverse and enhancing the richness and dynamism of emotional expression. By using a Kalman filter to preprocess the collected multimodal biological signals, noise is removed, the signal-to-noise ratio is improved, and the accuracy of the emotional state feature vector is ensured. The user's current emotional state score is calculated based on the feature vector, and the particle movement speed corresponding to anxiety is reduced when the emotion tends to be calm, achieving dynamic feedback regulation of emotional states and improving the efficiency of emotional regulation. By dynamically adjusting the particle speed and rotation radius according to the user's current emotional state score, it achieves instant response and dynamic display of emotional states, enhancing the user's emotional perception and self-regulation capabilities.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A five-dimensional dynamic emotion visualization therapy method, characterized in that: include: A multi-sensor fusion method was used to collect users' physiological data, psychological assessment questionnaire results, and historical emotion data to obtain the user's basic emotion feature vector; Based on the aforementioned basic emotion feature vector, the user's emotional state is mapped to a five-dimensional emotion space, and the particle set is initialized. The system collects multimodal biological signals from users, including electroencephalograms, heart rate variability, skin conductance, body temperature, and voice emotion recognition, and processes them to obtain feature vectors reflecting the user's current emotional state. The geometric shape and dynamic parameters of the particle set are dynamically adjusted, and the particles are rendered using a visualization engine. A two-way feedback adjustment mechanism is constructed to adjust the particle behavior according to the user's emotional state, thereby enhancing the user's ability to regulate emotions. Design an interactive emotion regulation game that allows users to participate in emotion regulation through gesture control. Record the data generated during the user's use, optimize the emotion-particle mapping model through deep learning algorithms, and generate a visual report containing emotion change curves, treatment effect scores, and suggestions after each use.

2. The five-dimensional dynamic emotion visualization therapy method as described in claim 1, characterized in that: The method employs multi-sensor fusion to collect users' physiological data, psychological assessment questionnaire results, and historical emotional data to obtain the user's basic emotional feature vector. The specific steps are as follows: Standardized psychological scales were used to quantify users' depression and anxiety, obtaining specific scores S for each emotional dimension. i , where i represents the emotion dimension number; The sentiment scores for each dimension are normalized so that the scores for each dimension range from 0 to 1. The normalization expression is as follows: Among them, S min and S max These are the minimum and maximum possible scores for that dimension, respectively. Using the normalized sentiment score as weighting coefficients, the user's comprehensive sentiment feature vector is calculated, as expressed in the following expression: E = [e1, e2, e3, e4, e5]; Where e1 = s i ′ Let i represent the emotion score of the i-th dimension.

3. The five-dimensional dynamic emotion visualization therapy method as described in claim 2, characterized in that: The steps for mapping the user's emotional state to a five-dimensional emotional space based on the basic emotional feature vector and initializing the particle set are as follows: Initialize the initial geometry of the particles based on the geometric shape corresponding to each emotion dimension; The geometric shapes corresponding to each emotional dimension include: depression corresponding to drifting fog, anxiety corresponding to lightning-like rays, anger corresponding to a blazing fireball, self-denial corresponding to a cluster of spikes, and giving up corresponding to a loose meteor. The dynamic parameters of the particles are initialized based on the dynamic parameters corresponding to each emotional dimension. The dynamic parameters corresponding to each emotion dimension include velocity, convergence, and rotation frequency; Calculate the weight coefficients of particles in each dimension, and calculate the total particle set. The expression is as follows: W = [w1, w2, w3, w4, w5]; Among them, P i Let w represent the set of particles corresponding to the i-th dimension of emotion. i It can be dynamically adjusted based on the user's historical sentiment data.

4. The five-dimensional dynamic emotion visualization therapy method as described in claim 3, characterized in that: The process involves collecting multimodal biosignals from the user, including electroencephalogram (EEG), heart rate variability, skin conductance, body temperature, and voice emotion recognition, and processing these signals to obtain a feature vector reflecting the user's current emotional state. The specific steps are as follows: Kalman filters are used to preprocess the acquired multimodal biological signals to remove noise and improve the signal-to-noise ratio. The feature vector is extracted using the following expression: F=[f1,f2,...,f n ]; Where f1 represents the α-band power of EEG, f2 represents the HRV value, f3 represents the GSR value, and so on; The user's current emotional state score S is calculated based on the feature vector F. When the power of the α band of the EEG increases, it indicates that the user's emotions are becoming calmer. At this time, the particle motion speed corresponding to anxiety is reduced.

5. The five-dimensional dynamic emotion visualization therapy method as described in claim 4, characterized in that: The specific steps for dynamically adjusting the geometric shape and dynamic parameters of the particle set and rendering the particles using a visualization engine are as follows: Based on the user's current emotional state score S, dynamically adjust the particle's velocity v and rotation radius v; Adjusting particle velocity, the expression is: v new =v base +k v ·(SS baseline ); Among them, v base It is the base speed, k v It is the adjustment coefficient, S baseline It is the baseline emotional state score; Similarly, the rotation radius of the particle is adjusted, as expressed by: r new =r base +k r ·(S-S baseline ); Where, r base It is the basic radius of rotation, k r It is the adjustment coefficient, S baseline It is the baseline emotional state score; Use a visualization engine from Unity or Unreal Engine to render the adjusted particles and display them to the user.

6. The five-dimensional dynamic emotion visualization therapy method as described in claim 5, characterized in that: The specific steps for constructing a two-way feedback regulation mechanism to adjust particle behavior based on the user's emotional state and enhance the user's emotional regulation ability are as follows: When a user's emotions are detected to be stabilizing, a positive feedback mechanism is used to enhance the user's positive emotions. As the user's emotions gradually calm down, the user's positive emotional experience is enhanced by changing the color and movement of the particles; When emotions fluctuate drastically, a negative feedback mechanism prompts the user to make adjustments. When the user's emotions fluctuate drastically, the color and movement of the particles are changed to remind the user to pay attention to emotional regulation. The changes in particle color and movement include softening the color, making the movement more orderly, darkening the color, and making the movement more chaotic; Based on the user's emotional characteristics model, select the feedback method.

7. The five-dimensional dynamic emotion visualization therapy method as described in claim 6, characterized in that: The designed interactive emotion regulation game allows users to participate in emotion regulation through gesture control, records the data generated during the user's use, optimizes the emotion-particle mapping model through deep learning algorithms, and generates a visual report containing emotion change curves, treatment effect scores, and suggestions after each use. The specific steps are as follows: Design an interactive emotion regulation game that allows users to participate through gesture control and adjust the state of emotion particles; The game is designed based on a personalized particle behavior feedback mechanism. Record the data generated by the user during each use, including emotion change curves, treatment effect scores, and user interaction data; The AutoEncoder deep learning algorithm is used to analyze the recorded data and extract users' emotional patterns. Update the emotion-particle mapping model based on the extracted emotion patterns; After each use, an updated emotion-particle mapping model is applied to provide a more personalized treatment plan; The output includes a visual report containing mood change curves, treatment effectiveness scores, and recommendations.

8. A five-dimensional dynamic emotion visualization therapy system, based on the five-dimensional dynamic emotion visualization therapy method according to any one of claims 1 to 7, characterized in that: include: The module includes an emotion feature modeling module, a particle initialization module, a biological signal processing module, a particle visualization and regulation module, and an interactive intervention and feedback module. The emotion feature modeling module is used to collect and integrate user physiological data, psychological assessment questionnaire results, and historical emotion data to extract the user's basic emotion feature vector. The particle initialization module is used to map the user's emotional state to a five-dimensional emotional space based on the basic emotional feature vector output by the emotional feature modeling module, and initialize the particle set with corresponding geometric shape and dynamic parameters according to the emotional intensity of each dimension. The biosignal processing module is used to collect multimodal biosignals from users, including electroencephalograms, heart rate variability, skin conductance, body temperature, and voice emotion recognition. Through signal preprocessing and feature extraction, it generates feature vectors that reflect the user's current emotional state. The particle visualization and control module is used to dynamically adjust the geometric shape and dynamic parameters of the particle set according to the emotional state feature vector output by the biosignal processing module, and to render the particles in real time using a visualization engine. At the same time, it constructs a two-way feedback regulation mechanism to enhance the user's emotional regulation ability. The interactive intervention and feedback module is used to provide interactive emotion regulation games, allowing users to participate in the emotion regulation process through gesture control, recording user interaction data and emotion change data, optimizing the emotion-particle mapping model based on deep learning algorithms, and generating a visual report containing emotion change curves, treatment effect scores, and suggestions after each use.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, it implements the steps of the five-dimensional dynamic emotion visualization therapy method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the five-dimensional dynamic emotion visualization therapy method according to any one of claims 1 to 7.

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