Immersive art therapy auxiliary system and application method thereof

Abstract

The present invention discloses an immersive art therapy auxiliary system and an application method thereof, which relates to the field of psychotherapy technology and includes an information collection module, an environment construction module, a psychological analysis module, a program generation module, a model training module and a blockchain recording module. The advantages of the present invention are: generating a quantitative psychological assessment report through multimodal data analysis and a machine learning algorithm, making up for the deficiency of relying on subjective judgment in traditional art therapy; patients can create art in an immersive virtual environment, gain an immersive experience, enhance the sense of participation and effect of treatment, and can dynamically generate personalized treatment plans according to the patient's psychological state and creative behavior, thereby improving the accuracy and effect of treatment. The model training module helps to continuously optimize the psychological analysis model, improve the accuracy and reliability of the model, and enable it to more accurately analyze the patient's psychology in practical applications.

Description

Technical Field

[0001] The present invention relates to the technical field of psychotherapy, and in particular to an immersive art therapy auxiliary system and an application method thereof. Background Art

[0002] Mental health refers to an individual's good state of cognition, emotion, behavior, etc., which is specifically manifested in intact personality, strong adaptability, appropriate behavior, etc. Mental health is influenced by both genetics and environment, especially the parenting style of the original family in childhood, which has a great impact on the development of mental health. With the increase of social pressure, mental health problems are increasing. Art therapy, as a non-drug treatment method, has gradually received widespread attention. Traditional art therapy mainly relies on the subjective judgment of psychologists and lacks objective quantitative evaluation tools, making it difficult to accurately measure the treatment effect.

[0003] Existing art therapy tools cannot provide an immersive creative environment. Patients find it difficult to obtain sufficient interaction and feedback during the creative process, which affects their sense of participation and effectiveness in the treatment. It is also impossible to accurately analyze the patient's psychological state through their creative behavior and works, making it difficult to objectively evaluate the treatment effect. Although VR / AR technology has been applied in the medical field, its application in art therapy is still in its early stages. In particular, there is no mature solution for psychological assessment combined with data analysis. To this end, we propose an immersive art therapy auxiliary system and its application method. Summary of the Invention

[0004] The object of the present invention is to provide an immersive art therapy auxiliary system and an application method thereof.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a treatment assistance system includes:

[0006] An information collection module that collects large-scale datasets covering a variety of artistic styles and mental health status;

[0007] The environment construction module uses VR technology to create a virtual space to obtain a treatment space;

[0008] The psychological analysis module collects the patient's behavioral data and biological signal data in real time and obtains the convolutional neural network (CNN). Based on the convolutional neural network (CNN), it combines the patient's behavioral data and biological signal data to build a psychological analysis model, and uses the psychological analysis model to analyze the patient's psychological state and psychological state change trends;

[0009] The treatment plan generation module generates treatment plans based on the patient's psychological state and creative behavior, as well as the rate of change of psychological state collected in real time. It also deploys a molecular dynamics simulator based on a diffusion model to collect patient saliva, analyze the patient's saliva metabolome data, and map it to virtual receptor protein targets. It then uses a three-dimensional graph convolutional network to predict the distribution of neurotransmitter concentration fields and, combined with a reinforcement learning algorithm, generates dynamic regulation strategies for dopamine and serotonin.

[0010] Neurotransmitters are chemical substances that transmit information between neurons. The neurotransmitter concentration field distribution refers to the distribution of neurotransmitter concentration in three-dimensional space within a specific spatial region of the nervous system (such as the brain). It describes the changes in neurotransmitter content at different locations and reflects the uneven distribution characteristics of neurotransmitters in the nervous system.

[0011] Studying the distribution of neurotransmitter concentrations typically requires the use of a variety of techniques, such as immunohistochemistry, microdialysis, and positron emission tomography (PET). Immunohistochemistry can label neurotransmitters to observe their distribution in tissue sections. Microdialysis can monitor changes in neurotransmitter concentrations in specific brain regions in real time. PET can perform non-invasive neurotransmitter concentration imaging in living animals or humans, thereby obtaining three-dimensional distribution information of neurotransmitter concentrations throughout the brain.

[0012] Reinforcement learning is a type of machine learning algorithm that learns optimal behavior strategies based on environmental feedback;

[0013] The model training module extracts information recorded in a large-scale data set, obtains verification cases, and records them in a verification table. It analyzes the types of behavioral data in the verification cases, extracts corresponding numerical values ​​of different types, calculates the distance index of different verification cases using the Euclidean distance algorithm, numbers the verification cases, extracts two verification cases with similar distance indexes from the verification table, deletes them from the verification table, establishes a comparison group, records the two verification cases in the comparison group, obtains X comparison groups, and the X comparison groups constitute the verification data set. At this time, the psychological analysis model is trained and verified for X-1 rounds. In each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained using the training set and verified using the verification set, and the verification result information is recorded until the X comparison groups are used as the verification set once.

[0014] As a further solution of the present invention: the treatment assistance system further includes:

[0015] The blockchain recording module uses private chain technology based on Ethereum to record all hash values ​​of the data processing process through smart contracts, and uses the proof-of-work consensus mechanism to ensure that the data cannot be tampered with.

[0016] As a further solution of the present invention: after the large-scale data set in the information acquisition module is obtained, the data in the large-scale data set will be preprocessed using image segmentation technology and style classification technology, and the preprocessed data will be labeled using image segmentation technology and style classification technology.

[0017] As a further solution of the present invention: when the treatment space in the environment construction module is obtained, the patient's text description of color, layout, and lighting will be obtained to obtain ideal space information, and then the treatment space will be constructed according to the ideal space information. At the same time, the treatment space will be stored to form a treatment set, and the color characteristics and layout characteristics of the treatment space will be analyzed, and the color characteristics and layout characteristics will be marked in the corresponding treatment space. After the color characteristics and layout characteristics in the environment construction module are marked in the treatment space, a retrieval establishment threshold will be established synchronously. When the number of treatment spaces in the treatment set is higher than the retrieval establishment threshold, a retrieval unit will be established, wherein the retrieval unit is used to collect the patient's description of the ideal space, obtain description information, analyze the color characteristics and layout characteristics in the description information, obtain retrieval characteristics, and then match the treatment space according to the retrieval characteristics, and display the matched treatment space to the patient. At the same time, the patient's modification opinions are collected and obtained, the modification opinions are matched and the space parameters are adjusted through the preset rule library, and the modified treatment space is stored in the treatment set.

[0018] As a further solution of the present invention: after obtaining the treatment plan in the plan generation module, the behavioral tasks, creative tasks and psychological intervention measures corresponding to the treatment plan will be analyzed, and they will be published to the user, and the user's feedback will be collected in real time, and the treatment space will be adjusted according to the user's feedback information.

[0019] As a further solution of the present invention: during training, the psychological analysis model in the model training module will obtain X-1 verification result information. At this time, the X-1 verification result information will constitute a verification result table, and the obtained verification result table will be displayed to Y professionals, who will then score and evaluate it, and then calculate the average value of the comments on different verification result information.

[0020] As a further solution of the present invention: when calculating the average value of the comments in the model training module, let the number of professionals be P and the different point evaluations be D F , let the average value of the reviews be D J :

[0021]

[0022] The average value of the reviews is calculated using the above formula.

[0023] As a further solution of the present invention: After the average value of the comments in the model training module is calculated, a display threshold is established, wherein the display threshold is set by the staff independently, and the display threshold is set to Z Y , when D J ≥Z Y When D J <Z Y , the corresponding verification results of the review average will not be displayed.

[0024] In addition, an application method of the immersive art therapy auxiliary system is provided, comprising the following steps:

[0025] S100, collects a large-scale dataset covering a variety of art styles and mental health conditions, and uses VR technology to create a virtual space to obtain a therapeutic space;

[0026] S200, collecting the patient's behavioral data and biosignal data in real time, building a psychological analysis model based on a convolutional neural network (CNN) and combining the patient's behavioral data and biosignal data, and using the psychological analysis model to analyze the patient's psychological state and psychological state change trends;

[0027] S300: Generate a personalized treatment plan, obtain verification cases, generate a dynamic regulation strategy for dopamine and serotonin, and record it in a verification table; analyze the types of behavioral data in the verification cases, extract corresponding values ​​for different types, and calculate distance indices for different verification cases using a Euclidean distance algorithm;

[0028] S400: Number the verification cases, extract two verification cases with similar distance indices from the verification table, delete them from the verification table, establish a comparison group, record the two verification cases in the comparison group, obtain X comparison groups, and the X comparison groups constitute the verification data set;

[0029] S500. Perform X-1 rounds of training and verification on the psychological analysis model. In each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained using the training set, and the psychological analysis model is verified using the verification set. The verification result information is recorded, and the entire data processing process is recorded using blockchain.

[0030] By adopting the above technical solution, compared with the prior art, the beneficial effects of the present invention are:

[0031] 1. This invention generates quantitative psychological assessment reports through multimodal data analysis and machine learning algorithms, making up for the deficiency of traditional art therapy that relies on subjective judgment. Patients can create art in an immersive virtual environment, gain an immersive experience, enhance the sense of participation and effectiveness of treatment, and dynamically generate personalized treatment plans based on the patient's psychological state and creative behavior, improving the accuracy and effectiveness of treatment. The model training module helps to continuously optimize the psychological analysis model, improve the accuracy and reliability of the model, and enable it to more accurately analyze the patient's psychology in practical applications;

[0032] 2. The present invention helps improve the quality and availability of data through the information collection module, making subsequent analysis and model training based on this data more accurate and efficient, facilitating subsequent targeted analysis and treatment plan formulation based on different styles. The use of the environment construction module can fully meet the personalized needs of patients and create a treatment environment that better meets their psychological expectations, thereby improving patient participation and satisfaction, increasing the speed of generating treatment space, and improving the utilization rate of previous treatment space;

[0033] 3. The present invention makes the evaluation more comprehensive and scientific through the model training module, can integrate the opinions of multiple professionals, more objectively reflect the pros and cons of the verification results, avoid ambiguity and subjectivity in the calculation process, ensure the accuracy and reliability of the evaluation results, flexibly control the display content, improve work efficiency, and also focus more on high-quality verification results, promoting the optimization of the psychological analysis model. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 A schematic diagram of a system flow in an embodiment of the present invention;

[0035] Figure 2 Schematic diagram of the method steps in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings. It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention.

[0037] In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0038] Please see the attached Figure 1 -Attached Figure 2 , the immersive art therapy auxiliary system and its application method of the present invention, the therapy auxiliary system includes;

[0039] An information collection module that collects large-scale datasets covering a variety of artistic styles and mental health status;

[0040] The environment construction module uses VR technology to create a virtual space to obtain a treatment space;

[0041] The psychological analysis module collects the patient's behavioral data and biological signal data in real time and obtains the convolutional neural network (CNN). Based on the convolutional neural network (CNN), it combines the patient's behavioral data and biological signal data to build a psychological analysis model, and uses the psychological analysis model to analyze the patient's psychological state and psychological state change trends;

[0042] The treatment plan generation module generates treatment plans based on the patient's psychological state and creative behavior, as well as the rate of change of psychological state collected in real time. It also deploys a molecular dynamics simulator based on a diffusion model to collect patient saliva, analyze the patient's saliva metabolome data, and map it to virtual receptor protein targets. It then uses a three-dimensional graph convolutional network to predict the distribution of neurotransmitter concentration fields and combines it with a reinforcement learning algorithm to generate dynamic regulation strategies for dopamine and serotonin.

[0043] The diffusion model is a generative model that generates new samples by modeling the data distribution and learning the diffusion process of the data. In molecular dynamics simulations, the diffusion model treats the movement of molecules as a diffusion process and simulates the dynamic behavior of molecules by modeling their positions and states at different times. For example, the model first encodes the initial state of the molecular system and then gradually introduces noise through a series of diffusion steps to simulate the state changes of the molecules under the influence of factors such as thermal motion. In the reverse process, the model learns to recover from the noisy state to the initial state, thereby simulating the molecular dynamics process.

[0044] Patient saliva metabolome data refers to a series of information obtained after qualitative and quantitative analysis of various metabolites in the patient's saliva. Various analytical techniques are used to detect and analyze metabolites in saliva. Commonly used techniques include nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), and liquid chromatography-mass spectrometry (LC-MS). These techniques can separate, identify, and quantify metabolites in saliva, thereby obtaining metabolome data.

[0045] Virtual receptor protein targets are generated by analyzing the three-dimensional structure of known proteins using computer software to identify sites that may bind to ligands. These sites are usually pockets or crevices on the protein surface, with specific amino acid compositions and spatial structures, and can bind to ligands through hydrogen bonds, van der Waals forces, electrostatic interactions, and other methods. For example, when designing anticancer drugs, virtual targets can be constructed for the active sites of specific proteins overexpressed in tumor cells (such as the epidermal growth factor receptor);

[0046] The 3D Graph Convolutional Network (GCN) is a deep learning-based neural network architecture designed and optimized for 3D data based on traditional GCNs. By defining convolution operations on 3D graphs, it extends the concept of convolution kernels in traditional GCNs to 3D graph structures. By leveraging the information of graph nodes and edges, the GCN can automatically learn the spatial features and structural information of the data. Specifically, it aggregates the features of nodes and their neighbors, and through multiple layers of nonlinear transformations and convolution operations, extracts higher-level feature representations, enabling effective modeling and analysis of 3D data.

[0047] The model training module extracts information recorded in a large-scale data set, obtains verification cases, and records them in a verification table. It analyzes the types of behavioral data in the verification cases, extracts corresponding numerical values ​​of different types, calculates the distance index of different verification cases using the Euclidean distance algorithm, numbers the verification cases, extracts two verification cases with similar distance indexes from the verification table, deletes them from the verification table, establishes a comparison group, records the two verification cases in the comparison group, obtains X comparison groups, and the X comparison groups constitute the verification data set. At this time, the psychological analysis model is trained and verified for X-1 rounds. In each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained using the training set and verified using the verification set, and the verification result information is recorded until X comparison groups are used as the verification set once.

[0048] The treatment assistance system also includes;

[0049] The blockchain recording module uses Ethereum-based private chain technology to record all hash values ​​of the data processing process through smart contracts and uses the proof-of-work consensus mechanism to ensure that the data cannot be tampered with;

[0050] In one embodiment of the present invention: after the large-scale data set in the information acquisition module is obtained, the data in the large-scale data set will be preprocessed using image segmentation technology and style classification technology, and the preprocessed data will be labeled using image segmentation technology and style classification technology.

[0051] In one embodiment of the present invention: when the treatment space in the environment construction module is obtained, the patient's text description of color, layout, and lighting will be obtained to obtain ideal space information, and then the treatment space will be constructed according to the ideal space information. At the same time, the treatment space will be stored to form a treatment set, and the color characteristics and layout characteristics of the treatment space will be analyzed, and the color characteristics and layout characteristics will be marked in the corresponding treatment space. After the color characteristics and layout characteristics in the environment construction module are marked in the treatment space, a retrieval establishment threshold will be established synchronously. When the number of treatment spaces in the treatment set is higher than the retrieval establishment threshold, a retrieval unit will be established, wherein the retrieval unit is used to collect the patient's description of the ideal space, obtain description information, analyze the color characteristics and layout characteristics in the description information, obtain retrieval characteristics, and then match the treatment space according to the retrieval characteristics, and display the matched treatment space to the patient. At the same time, the patient's modification opinions are collected and obtained, the modification opinions are matched and the space parameters are adjusted through the preset rule library, and the modified treatment space is stored in the treatment set.

[0052] In one embodiment of the present invention: after the treatment plan in the plan generation module is obtained, the behavioral tasks, creative tasks and psychological intervention measures corresponding to the treatment plan will be analyzed, and they will be published to the user, and the user's feedback will be collected in real time, and the treatment space will be adjusted according to the user's feedback information.

[0053] In one embodiment of the present invention: during training, the psychological analysis model in the model training module will obtain X-1 verification result information. At this time, the X-1 verification result information will constitute a verification result table, and the obtained verification result table will be presented to Y professionals, who will then score and evaluate it, and then calculate the average value of the comments on different verification result information.

[0054] In one embodiment of the present invention, when calculating the average value of the comments in the model training module, let the number of professionals be P and the different point evaluations be D. F , let the average value of the reviews be D J :

[0055]

[0056] The average review value is calculated using the above formula.

[0057] In one embodiment of the present invention, after the average value of the reviews in the model training module is calculated, a display threshold is established, wherein the display threshold is set by the staff independently, and the display threshold is set to Z Y , when D J ≥Z Y When D J <Z Y, the corresponding verification results of the review average will not be displayed.

[0058] Example 1, please refer to the attached Figure 1 -Attached Figure 2 , collect large-scale data sets covering various artistic styles and mental health states, use VR technology to create a virtual space, obtain a treatment space, collect patients' behavioral data and biosignal data in real time, build a psychological analysis model based on convolutional neural network CNN combined with patients' behavioral data and biosignal data, and use the psychological analysis model to analyze patients' psychological states and psychological state change trends, generate personalized treatment plans, obtain verification cases, generate dopamine and serotonin dynamic regulation strategies, and record them in the verification table, analyze the types of behavioral data in the verification cases, extract the corresponding values ​​of different types, and calculate the different verification cases through the Euclidean distance algorithm. The distance index of the verification case is calculated, and the verification cases are numbered. Two verification cases with similar distance indexes are extracted from the verification table and deleted from the verification table. A comparison group is established, and the two verification cases are recorded in the comparison group to obtain X comparison groups, which constitute the verification data set. The psychological analysis model is trained and verified for X-1 rounds. In each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained with the training set, and the psychological analysis model is verified with the verification set. The verification result information is recorded, and the entire data processing process is recorded using the blockchain.

[0059] Example 2, please refer to the attached Figure 1 -Attached Figure 2 , collect large-scale data sets covering a variety of artistic styles and mental health states, use image segmentation technology and style classification technology to preprocess the data in the large-scale data set, and use image segmentation technology and style classification technology to annotate the preprocessed data, use VR technology to create a virtual space, obtain the patient's text description of the ideal space, construct a treatment space based on the ideal space information, and store the treatment space to form a treatment set, and analyze the color characteristics and layout characteristics of the treatment space. When the number of treatment spaces in the treatment set is higher than the retrieval establishment threshold, use the retrieval unit to collect the patient's description of the ideal space, analyze the color characteristics and layout characteristics in the description information, match the treatment space according to the retrieval characteristics, and display the matched treatment space to the patient. Collect and obtain the patient's modification opinions, automatically modify the treatment space according to the modification opinions, generate a personalized treatment plan, analyze the behavioral tasks, creative tasks and psychological intervention measures corresponding to the treatment plan, and publish it to users, collect user feedback in real time, and adjust the treatment space according to user feedback information.

[0060] Example 3, please refer to the attached Figure 1 -Attached Figure 2 , extract the information recorded in the large-scale data set, obtain the verification cases, and record them in the verification table, analyze the types of behavioral data in the verification cases, extract the corresponding values ​​of different types, calculate the distance index of different verification cases through the Euclidean distance algorithm, number the verification cases, extract two verification cases with similar distance indexes from the verification table, delete them from the verification table, establish a comparison group, record the two verification cases in the comparison group, obtain X comparison groups, and then use X comparison groups to train the psychological analysis model to obtain X-1 verification result information. At this time, X-1 verification result information will constitute a verification result table, and the obtained verification result table will be displayed to Y professionals, who will then score and evaluate it, and then calculate the average value of the comments on different verification result information, establish a display threshold, where the display threshold is set by the staff, and the display threshold is set to Z Y , when D J ≥Z Y When D J <Z Y , the corresponding verification results of the review average will not be displayed.

[0061] Specifically, when using the Euclidean distance formula to calculate the distance index of different verification cases, the values ​​of the same information type in different verification cases are extracted. Let the different values ​​of the first verification case be B S , let the different values ​​of the second verification case be E S , let the number of different values ​​be M, let the distance index be J Z :

[0062]

[0063] The distance index is calculated using the above formula, and the information type distribution order of the first verification case and the second verification case is the same.

[0064] Specifically, when determining the display threshold, the highest and lowest point-score evaluations can be extracted, and then the average of the highest and lowest point-score evaluations is calculated to obtain the mean of the range. The mean of the range can then be used as the display threshold.

[0065] Specifically, convolutional neural networks are a deep learning model specifically designed to process data with grid structures. In large-scale mental health surveys, convolutional neural networks can quickly analyze various psychological assessment data, such as questionnaire results, behavioral test data, etc., to generate mental health assessment reports for each person and screen out people who may have psychological problems. Biosignal data refers to data contained in various signals that can reflect the physiological and pathological states of organisms. These signals can be collected and analyzed through different technical means.

[0066] Working principle:

[0067] First, a large-scale data set covering a variety of artistic styles and mental health states is collected, and the data in the large-scale data set is preprocessed using image segmentation technology and style classification technology. At the same time, the preprocessed data is annotated using image segmentation technology and style classification technology, and a virtual space is created using VR technology to obtain the patient's textual description of the ideal space. A treatment space is constructed based on the ideal space information, and the treatment space is stored to form a treatment set. The color features and layout features of the treatment space are analyzed. When the number of treatment spaces in the treatment set exceeds the retrieval establishment threshold, a retrieval unit will be established, and the retrieval unit will be used to collect the patient's description of the ideal space, analyze the color features and layout features in the description information, match the treatment space according to the retrieval features, and display the matched treatment space to the patient. The patient's modification opinions are collected and obtained, and the treatment space is automatically modified according to the modification opinions to generate a personalized treatment plan, generate a dopamine and serotonin dynamic regulation strategy, and analyze the treatment plan. Corresponding behavioral tasks, creative tasks and psychological intervention measures are published to users, user feedback is collected in real time, the treatment space is adjusted according to user feedback information, verification cases are obtained, and recorded in the verification table, the types of behavioral data in the verification cases are analyzed, the corresponding values ​​of different types are extracted, the distance index of different verification cases is calculated by the Euclidean distance algorithm, the verification cases are numbered, two verification cases with similar distance indexes are extracted from the verification table, and they are deleted from the verification table, a comparison group is established, the two verification cases are recorded in the comparison group, X comparison groups are obtained, and the X comparison groups constitute the verification data set, the psychological analysis model is trained and verified for X-1 rounds, and in each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained with the training set, and the psychological analysis model is verified with the verification set, and the verification result information is recorded. The entire data processing process is recorded using the blockchain to obtain Verification result information, then X-1 verification result information will form a verification result table, and the obtained verification result table will be displayed to Y professionals, and then the professionals will point and evaluate, and then calculate the average value of the comments on different verification result information, and establish a display threshold, where the display threshold is set by the staff independently, and the display threshold is set to Z Y , when D J ≥Z Y When D J <Z Y When the verification result of the average review value is displayed, the entire data processing process is recorded using the blockchain, and the entire workflow ends here.

[0068] Although the present invention is disclosed above with reference to preferred embodiments, this is not intended to limit the present invention. Any person skilled in the art may make possible changes and modifications without departing from the spirit and scope of the present invention. Therefore, any modifications, equivalent variations, and modifications made to the above embodiments in accordance with the technical essence of the present invention without departing from the content of the technical solution of the present invention shall fall within the scope of protection defined by the claims of the present invention.

Claims

1. Immersive art therapy auxiliary system, characterized by: Therapeutic assistance systems include; An information collection module that collects large-scale datasets covering a variety of artistic styles and mental health status; The environment construction module uses VR technology to create a virtual space to obtain a treatment space; The psychological analysis module collects the patient's behavioral data and biological signal data in real time and obtains the convolutional neural network (CNN). Based on the convolutional neural network (CNN), it combines the patient's behavioral data and biological signal data to build a psychological analysis model, and uses the psychological analysis model to analyze the patient's psychological state and psychological state change trends; The treatment plan generation module generates treatment plans based on the patient's psychological state and creative behavior, as well as the rate of change of psychological state collected in real time. It also deploys a molecular dynamics simulator based on a diffusion model to collect patient saliva, analyze the patient's saliva metabolome data, and map it to virtual receptor protein targets. It then uses a three-dimensional graph convolutional network to predict the distribution of neurotransmitter concentration fields and, combined with a reinforcement learning algorithm, generates dynamic regulation strategies for dopamine and serotonin. The model training module extracts information recorded in a large-scale data set, obtains verification cases, and records them in a verification table. It analyzes the types of behavioral data in the verification cases, extracts corresponding numerical values ​​of different types, calculates the distance index of different verification cases using the Euclidean distance algorithm, numbers the verification cases, extracts two verification cases with similar distance indexes from the verification table, deletes them from the verification table, establishes a comparison group, records the two verification cases in the comparison group, obtains X comparison groups, and the X comparison groups constitute the verification data set. At this time, the psychological analysis model is trained and verified for X-1 rounds. In each round of training, one of the comparison groups is extracted as the verification set, and the remaining X-1 comparison groups are used as the training set. The psychological analysis model is trained using the training set and verified using the verification set, and the verification result information is recorded until the X comparison groups are used as the verification set once.

2. The immersive art therapy auxiliary system according to claim 1, characterized in that: The treatment assistance system further comprises: The blockchain recording module uses private chain technology based on Ethereum to record all hash values ​​of the data processing process through smart contracts, and uses the proof-of-work consensus mechanism to ensure that the data cannot be tampered with.

3. The immersive art therapy auxiliary system according to claim 1, characterized in that: After the large-scale data set in the information acquisition module is obtained, the image segmentation technology and the style classification technology are used to pre-process the data in the large-scale data set, and the image segmentation technology and the style classification technology are used to label the pre-processed data.

4. The immersive art therapy auxiliary system according to claim 1, characterized in that: When the treatment space in the environment construction module is obtained, the patient's text description of color, layout, and lighting will be obtained to obtain ideal space information, and then the treatment space will be constructed according to the ideal space information. At the same time, the treatment space will be stored to form a treatment set, and the color characteristics and layout characteristics of the treatment space will be analyzed, and the color characteristics and layout characteristics will be marked in the corresponding treatment space. After the color characteristics and layout characteristics in the environment construction module are marked in the treatment space, a retrieval establishment threshold will be established simultaneously. When the number of treatment spaces in the treatment set is higher than the retrieval establishment threshold, a retrieval unit will be established, wherein the retrieval unit is used to collect the patient's description of the ideal space, obtain description information, analyze the color characteristics and layout characteristics in the description information, obtain retrieval characteristics, and then match the treatment space according to the retrieval characteristics, and display the matched treatment space to the patient. At the same time, the patient's modification opinions are collected and obtained, the modification opinions are matched and the space parameters are adjusted through the preset rule library, and the modified treatment space is stored in the treatment set.

5. The immersive art therapy auxiliary system according to claim 1, characterized in that: After obtaining the treatment plan in the plan generation module, the behavioral tasks, creative tasks and psychological intervention measures corresponding to the treatment plan will be analyzed, and the plan will be released to the user, and the user's feedback will be collected in real time, and the treatment space will be adjusted according to the user's feedback information.

6. The immersive art therapy auxiliary system according to claim 1, characterized in that: During training, the psychological analysis model in the model training module will obtain X-1 verification result information. At this time, the X-1 verification result information will constitute a verification result table, and the obtained verification result table will be presented to Y professionals, who will then score and evaluate it, and then calculate the average value of the comments on different verification result information.

7. The immersive art therapy auxiliary system according to claim 6, characterized in that: When calculating the average value of the reviews in the model training module, let the number of professionals be P and the different point evaluations be D. F , let the average value of the reviews be D J : The average review value is calculated using the above formula.

8. The immersive art therapy auxiliary system according to claim 7, characterized in that: After the average value of the reviews in the model training module is calculated, a display threshold will be established, where the display threshold is set by the staff. Set the display threshold as Z Y , when D J ≥Z Y When D J <Z Y , the corresponding verification results of the review average will not be displayed.

Images