DIT psychological regulation system

Abstract

The invention discloses a DIT psychological regulation system, which relates to the technical field of psychological motivation and interpersonal oriented therapy, and comprises a surface layer detection module, a deep layer detection module, a psychological classification module, a motivation evaluation module and a guide regulation module, the surface layer detection module is used for carrying out professional emotion label judgment on a user after the user fills in a psychological evaluation questionnaire, and the deep layer detection module is used for carrying out deep brain wave detection on the user by utilizing a brain wave detection instrument to obtain a deep emotion label of the user; the system accurately and detailedly analyzes all emotion tags of the user through targeted emotion problem tests, automatically evaluates the psychological state of the user, reasonably formulates a personalized adjustment scheme based on a DIT psychological treatment scheme, records emotion changes of the user, provides suggestions after analysis to encourage the user to perform self-reflection and adjustment, and improves the user experience. The user can obtain real-time feedback and guidance in the using process, and the user is helped to apply psychological regulation skills in an actual scene.

Description

Technical Field

[0001] The present invention relates to the technical field of psychodynamic and interpersonal oriented therapy, and in particular to a DIT psychological adjustment system. Background Art

[0002] With the rapid development of society and the accelerated pace of life, mental health issues are receiving increasing attention. Anxiety, depression and other problems are prevalent, which has promoted the demand for psychological adjustment tools. The development of information technology and artificial intelligence has provided new possibilities for psychological adjustment, making psychological intervention through digital platforms a reality. The DIT psychological adjustment system is a system designed to help individuals regulate their emotions and improve their mental health.

[0003] DIT systems are typically based on the principles of cognitive behavioral therapy, emphasizing the relationship between thinking, emotions, and behavior, helping users identify and change negative thinking patterns, focusing on how individuals identify and manage emotions, and providing specific tools and strategies to improve emotional management capabilities.

[0004] At present, the Chinese invention patent with application number CN202410899952.5 discloses a psychological anxiety regulation system, including a physiological parameter monitoring module, a psychological anxiety analysis module, a psychological anxiety regulation module and a user interface. The collected physiological parameters are processed and analyzed through a long short-term memory network to generate an evaluation value of the solitude anxiety index, and based on the collected parameters and the constructed model, a regulation intensity recommendation is provided, which solves the problem that the existing technology cannot comprehensively monitor multiple physiological parameters and provide real-time regulation recommendations; however, the existing technology cannot accurately and in detail analyze all the user's emotional labels through targeted emotional problem tests, automatically evaluate the user's psychological state, and cannot reasonably formulate personalized regulation plans based on the DIT psychological treatment plan. The system cannot record the user's emotional changes, cannot provide suggestions after analysis to encourage users to reflect on themselves and adjust, and users cannot obtain immediate feedback and guidance during use to help them apply psychological regulation techniques in actual scenarios. Summary of the invention

[0005] The technical problem solved by the present invention is that the existing technology cannot accurately and in detail analyze all the user's emotional labels through targeted emotional problem tests, automatically evaluate the user's psychological state, and cannot reasonably formulate personalized adjustment plans based on the DIT psychological treatment plan. The system cannot record the user's emotional changes, and cannot provide suggestions after analysis to encourage users to reflect on themselves and make adjustments. Users cannot obtain instant feedback and guidance during use to help them apply psychological adjustment techniques in actual scenarios.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a DIT psychological adjustment system, including a surface detection module, a deep detection module, a psychological classification module, a motivation assessment module and a guidance adjustment module:

[0007] The surface detection module is used to determine the user's professional emotion label after the user fills in the psychological assessment questionnaire, and classify the emotion label to obtain the user's current surface emotion disorder type;

[0008] The deep detection module includes using a brain wave detection instrument to perform deep brain wave detection on the user, and obtaining the user's current deep emotional disorder type after processing the detected brain waves, and obtaining the user's deep emotional label;

[0009] The psychological classification module is used to compare and analyze the deep emotional disorder with the current surface emotional disorder type, obtain the detailed emotional label type group of the user, deeply classify the user according to the emotional label type group, and determine whether the user is suitable for the psychological adjustment model of DIT;

[0010] The motivation assessment module is used to assess the user's emotional motivation, obtain the user's new emotional label and new label set, and calculate the user's emotional trend for the DIT therapist to judge and identify the source of the user's emotions;

[0011] The guidance and regulation module is used to monitor the user's emotions throughout the process and record the user's emotional changes, use the pre-trained GPT4 to guide the user's daily speech and regulate his mood, and record the daily speech guidance effect and mood guidance and regulation effect in real time, and combine the daily speech guidance effect and mood guidance and regulation effect with the user's emotional change process to draw the user's emotional disorder change curve.

[0012] Preferably, the surface detection module includes a first testing unit and a surface determination unit:

[0013] The first testing unit includes testing the user's emotional problems according to psychoanalytic theories, wherein the psychoanalytic theories include attachment theory, object relations theory, interpersonal psychoanalysis and mentalization theory;

[0014] Scoring the emotional problem test conducted by the user to obtain regional scores and total scores classified according to the psychological symptom types, comparing the regional scores and total scores with preset corresponding international normal standard score intervals, evaluating the severity of the psychological symptom type of the user according to the score value exceeding the standard score interval, and classifying the psychological symptom type into the emotional label set of the user;

[0015] Obtaining the user's current surface emotional disorder type according to international standards and the user's emotional tag set;

[0016] The types of psychological symptoms described include somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, attachment, and paranoia.

[0017] Preferably, the deep detection module includes a second test unit, a brain wave detection unit and a deep label association unit, the second test unit and the brain wave detection unit are performed synchronously, when the user is answering the personality self-assessment scale given by the second test unit, the brain wave detection unit simultaneously detects the first resting state brain wave signal, the second resting state brain wave signal, the third brain wave signal and the fourth brain wave signal of the user;

[0018] Calculating the average electric power of the first resting-state EEG signal, the average electric power includes calculating the average value after obtaining the real-time electric power according to the Fourier transform formula, and using the average electric power as a covariate to calculate the covariance of the difference between the third EEG signal and the fourth EEG signal. If the covariance is higher than a preset stability threshold, the cognitive load of the user exceeds the standard proportion, and an emotional label with heavy cognitive load is added to the user;

[0019] Calculate the curve expression of the power change of the third EEG signal and the fourth EEG signal band along with the feedback of the answering process of the second test unit by using function calculation software; if the curve expression shows a downward trend, the user's cognitive processing method is abnormal, and add an emotional label of abnormal cognitive processing method to the user;

[0020] If the curve expression shows an upward trend, the user is cognitively normal;

[0021] If the curve expression is not positively correlated, the user has potential schizophrenia, and an emotion label of schizophrenia is added to the user;

[0022] calculating the electric power of the second resting-state EEG signal in real time, and if the electric power of the first resting-state EEG signal and the electric power of the second resting-state EEG signal are enhanced, the user has a strong self-perception ability;

[0023] If the electrical power of the first resting-state EEG signal and the electrical power of the second resting-state EEG signal are weakened, the user's self-perception ability is weak, and an emotional label of self-crisis is added to the user.

[0024] Preferably, the deep label contact unit comprises:

[0025] All emotion tags obtained by the user through the surface detection module and the deep detection module are stored in the user's information storage address in the form of a dictionary, and duplicate tags are removed;

[0026] Collecting the corresponding label standard thresholds of normal people and the corresponding label standard thresholds of people with mental illness, performing pairwise comparisons among the corresponding label thresholds of the user, normal people and people with mental illness by a leave-one-out cross-validation method, and obtaining the overall emotional disorder assessment of the user, wherein the overall emotional disorder assessment includes the difference values ​​between the user and normal people and the difference values ​​between the user and people with mental illness;

[0027] The first resting-state EEG signal, the second resting-state EEG signal, the third EEG signal and the fourth EEG signal are subjected to a bandpass filter to remove noise, and the independent component analysis method is used to remove eye movement and electromyographic artifacts to obtain a preprocessed continuous signal, and the continuous signal is divided into multiple time periods to obtain a discrete signal, and the mean, standard deviation, maximum value and minimum value of the discrete signal are calculated, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to Fourier transform to extract spectral features, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to wavelet transform to extract time-frequency features, and the difference value is subjected to Huffman coding to generate a corresponding difference feature vector, and the time domain and frequency domain of the discrete signal are subjected to the ... remove noise, and the eye movement and electromyography artifacts are removed by using the independent component analysis method to remove eye movement and electromyography artifacts. , spectral features, time-frequency features and difference feature vectors are fused through tensor decomposition technology to generate a multidimensional feature space, the LASSO regression model is used to screen out the features that have the greatest impact on the construction efficiency of the multidimensional feature space, the multidimensional feature space is input into the support vector machine model to construct a multidimensional model, the multidimensional feature spaces of several users are counted, a multidimensional feature space set is established, the multidimensional model is trained using the multidimensional feature space set, the parameters of the trained multidimensional model are tuned using a cross-validation method, and the influence of the user's deep brain waves on the emotion label is obtained, the influence is graded from level 1 to level n, and the grade division is input as a label into the information storage address of the corresponding user.

[0028] Preferably, the psychological classification module includes a label statistics unit and a label classification unit:

[0029] The tag statistics unit includes classifying all tags in the user's information storage address into major emotional categories, and the major emotional categories include depression, mania, PTSD post-traumatic stress disorder and bipolar disorder.

[0030] Preferably, the label classification unit comprises:

[0031] The emotion tags classified into emotion categories are standardized, wherein the standardized operation includes numbering the emotion categories by one-hot encoding, deeply classifying all emotion tags into the numbers of the emotion categories by using a TF-IDF model, and marking the user with a secondary tag set, wherein the tag set is the number of the emotion category included in the user;

[0032] Collecting the major emotion categories corresponding to and opposite to the DIT psychological adjustment method, comparing the user's tag set with the major emotion categories corresponding to the DIT psychological adjustment method, and identifying and screening the user includes:

[0033] If the user's tag set does not include the major emotion category corresponding to the DIT psychological adjustment method or the user's tag set includes the major emotion category corresponding to the DIT psychological adjustment method and includes the major emotion category that is opposite to the DIT psychological adjustment method, all the user's information is input into the non-DIT psychological adjustment queue, and the emotion tags of the users in the non-DIT psychological adjustment queue are counted to obtain an emotion tag combination that is definitely not suitable for the DIT psychological adjustment model, and the emotion tag combination is recorded. When the tag statistics unit detects a user carrying the emotion tag combination during the analysis, all the corresponding user's information is input into the non-DIT psychological adjustment queue, and the emotion tag combination that is definitely not suitable for the DIT psychological adjustment model is updated in real time;

[0034] The entire information includes all contents included in the user's information storage address and the address itself;

[0035] If the tag set of the user includes the major emotion category corresponding to the DIT psychological adjustment method and does not have the major emotion category opposite to the DIT psychological adjustment method, all the information of the user is input into the motivation evaluation module.

[0036] Preferably, the motivation assessment module comprises a language analysis unit and an assessment unit:

[0037] The language analysis unit includes collecting the user's language, inputting the language into a bag-of-words model to obtain the user's language keywords, constructing the user's language framework, inputting the language framework into a gating system to obtain emotional language features represented by emotional keywords in the user's language, inputting the emotional language features into the deep detection module and the psychological classification module, obtaining a label classification of the emotional language features, and inputting the label classification of the emotional language features as a new emotional label into the user's information storage address;

[0038] The user is asked questions from different psychological perspectives, where the psychological perspectives are based on the psychoanalytic theory. The user's answers to the questions from different psychological perspectives are input into the language analysis unit to obtain new emotion labels. The input operation of the language analysis unit is recursively repeated to obtain the user's new emotion label and a new label set.

[0039] Preferably, the evaluation unit comprises:

[0040] Input the new emotion label and new label set of the user into the PCA principal component analysis model, use the target keywords crawled from the network as identification parameters, obtain the evaluation purpose of the user, input the evaluation purpose into the emotion dictionary AFINN, map the evaluation purpose to the emotion score, and directly calculate the emotion trend including:

[0041] The sentiment scores obtained by mapping all evaluation purposes are accumulated. If there is no corresponding word in the sentiment dictionary AFINN, it is ignored. The accumulated sentiment score represents the trend of the overall sentiment;

[0042] If the accumulated emotion score is positive, it means that the user is in a positive emotion;

[0043] If the accumulated emotion score is negative, it means that the user is in a negative emotion;

[0044] If the accumulated emotion score is close to zero, it means that the user is emotionally neutral;

[0045] The results obtained by the evaluation unit are used by the DIT therapist to judge and identify the source of the user's emotions.

[0046] Preferably, the guidance and adjustment module includes:

[0047] The daily speech guidance effect and mood counseling and regulation effect included in the daily language of the user are collected and input into the psychological classification module and the motivation evaluation module to obtain the emotional labels, label sets and the trend of the user's overall emotions corresponding to the daily speech guidance effect and the mood counseling and regulation effect, perform Huffman coding on the emotional labels, label sets and the trend of the user's overall emotions, and use the deep label connection unit to classify the Huffman codes into emotional impact levels, calculate the weighted average of the emotional impact level divisions, and draw a user emotional disorder change curve according to time.

[0048] Preferably, the DIT therapist is a trained psychiatrist or psychologist.

[0049] The beneficial effects of the present invention are as follows: through targeted emotional problem tests, all the user's emotional labels can be accurately and detailedly analyzed, the user's psychological state can be automatically evaluated, and a personalized adjustment plan can be reasonably formulated based on the DIT psychological treatment plan. The system can record the user's emotional changes, and provide suggestions after analysis to encourage the user to reflect on and adjust themselves. The user can obtain instant feedback and guidance during use to help them apply psychological adjustment techniques in actual scenarios. The system aims to provide users with convenient psychological adjustment services and promote emotional health and psychological resilience. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A basic flow chart of a DIT psychological adjustment system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0052] Reference Figure 1 , which is an embodiment of the present invention, provides a DIT psychological adjustment system, including a surface detection module, a deep detection module, a psychological classification module, a motivation assessment module and a guidance adjustment module:

[0053] The surface detection module is used to determine the user's professional emotion label after the user fills in the psychological assessment questionnaire, and classify the emotion label to obtain the user's current surface emotion disorder type;

[0054] The deep detection module includes using a brain wave detection instrument to perform deep brain wave detection on the user, and obtaining the user's current deep emotional disorder type after processing the detected brain waves, and obtaining the user's deep emotional label;

[0055] The psychological classification module is used to compare and analyze the deep emotional disorder with the current surface emotional disorder type, obtain the detailed emotional label type group of the user, deeply classify the user according to the emotional label type group, and determine whether the user is suitable for the psychological adjustment model of DIT;

[0056] The motivation assessment module is used to assess the user's emotional motivation, obtain the user's new emotional label and new label set, and calculate the user's emotional trend for the DIT therapist to judge and identify the source of the user's emotions;

[0057] The guidance and regulation module is used to monitor the user's emotions throughout the process and record the user's emotional changes, use the pre-trained GPT4 to guide the user's daily speech and regulate his mood, and record the daily speech guidance effect and mood guidance and regulation effect in real time, and combine the daily speech guidance effect and mood guidance and regulation effect with the user's emotional change process to draw the user's emotional disorder change curve.

[0058] This system can accurately and in detail analyze all the user's emotional labels through targeted emotional problem tests, automatically evaluate the user's mental state, and reasonably formulate personalized adjustment plans based on the DIT psychotherapy plan. The system can record the user's emotional changes, and after analysis, provide suggestions to encourage users to reflect on themselves and make adjustments.

[0059] The surface detection module includes a first test unit and a surface determination unit:

[0060] The first testing unit includes conducting targeted emotional problem tests on users according to psychoanalytic theories, wherein the psychoanalytic theories include attachment theory, object relations theory, interpersonal psychoanalysis and mentalization theory;

[0061] Scoring the emotional problem test conducted by the user to obtain regional scores and total scores classified according to the psychological symptom types, comparing the regional scores and total scores with preset corresponding international normal standard score intervals, evaluating the severity of the psychological symptom type of the user according to the score value exceeding the standard score interval, and classifying the psychological symptom type into the emotional label set of the user;

[0062] Obtaining the user's current surface emotional disorder type according to international standards and the user's emotional tag set;

[0063] The types of psychological symptoms described include somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, attachment, and paranoia.

[0064] DIT psychodynamic and interpersonally oriented therapy believes that the symptoms of "depression and anxiety" are a reflection of interpersonal relationship difficulties and difficulties in internal object relationships, which will affect the loss and separation of underlying attachment relationships and pose a threat to the self and the self.

[0065] The deep detection module includes a second test unit, a brain wave detection unit and a deep label association unit. The second test unit and the brain wave detection unit are performed synchronously. When the user is answering the personality self-assessment scale given by the second test unit, the brain wave detection unit simultaneously detects the first resting state brain wave signal, the second resting state brain wave signal, the third brain wave signal and the fourth brain wave signal of the user;

[0066] During the test, the EEG laboratory remained quiet. After cleaning the scalp, the test user sat in front of the computer in a comfortable posture, with the eyes about 70 cm away from the stimulation screen. After the test user was ready, the instructions were read and the resting EEG test began. First, the resting EEG test with eyes open was performed. The test subject was required to open his eyes and blink naturally, keep his mind relaxed and not think about anything, and stare at the plus sign in the center of the screen for 3 minutes. The plus sign was about 35×35 pixels in size and gray, and the screen background was black to reduce visual stimulation to the test subject. After 3 minutes, the screen appeared a reminder to close your eyes, and the test subject was supervised to close his eyes and keep his mind relaxed.

[0067] 3min, pay attention to the whole process of resting state and remind the subjects not to fall asleep.

[0068] Calculating the average electric power of the first resting-state EEG signal, the average electric power includes calculating the average value after obtaining the real-time electric power according to the Fourier transform formula, and using the average electric power as a covariate to calculate the covariance of the difference between the third EEG signal and the fourth EEG signal. If the covariance is higher than a preset stability threshold, the cognitive load of the user exceeds the standard proportion, and an emotional label with heavy cognitive load is added to the user;

[0069] Utilizing function calculation software to calculate the curve expression of the power change of the third EEG signal and the fourth EEG signal band along with the feedback of the answering process of the second test unit. Under normal circumstances, the curve expression should show a univariate linear regression trend. If the curve expression shows a downward trend, the user's cognitive processing method is abnormal, and an emotional label of abnormal cognitive processing method is added to the user.

[0070] If the curve expression shows an upward trend, the user is cognitively normal;

[0071] If the curve expression is not positively correlated, the user has potential schizophrenia, and an emotion label of schizophrenia is added to the user;

[0072] calculating the electric power of the second resting-state EEG signal in real time, and if the electric power of the first resting-state EEG signal and the electric power of the second resting-state EEG signal are enhanced, the user has a strong self-perception ability;

[0073] If the electrical power of the first resting-state EEG signal and the electrical power of the second resting-state EEG signal are weakened, the user's self-perception ability is weak, and an emotional label of self-crisis is added to the user.

[0074] The increased power of the first and second resting-state EEG signals is associated with decreased excitability of the visual cortex. At this time, the activity of brain areas related to external directed attention is reduced. The reduced excitability of the visual cortex helps prevent external interference from visual input and disturb the individual's introspective state. Conversely, the decreased power of the first and second resting-state EEG signals is associated with increased excitability of the visual cortex, which helps process external visual stimuli.

[0075] The deep label contact unit comprises:

[0076] All emotion tags obtained by the user through the surface detection module and the deep detection module are stored in the user's information storage address in the form of a dictionary, and duplicate tags are removed;

[0077] Collecting the corresponding label standard thresholds of normal people and the corresponding label standard thresholds of people with mental illness, performing pairwise comparisons among the corresponding label thresholds of the user, normal people and people with mental illness by a leave-one-out cross-validation method, and obtaining the overall emotional disorder assessment of the user, wherein the overall emotional disorder assessment includes the difference values ​​between the user and normal people and the difference values ​​between the user and people with mental illness;

[0078] The first resting-state EEG signal, the second resting-state EEG signal, the third EEG signal and the fourth EEG signal are subjected to a bandpass filter to remove noise, and the independent component analysis method is used to remove artifacts of eye movement and electromyography to obtain a preprocessed continuous signal, and the continuous signal is divided into multiple time periods to obtain a discrete signal, each time period is 1 or 2 seconds, and the mean, standard deviation, maximum value and minimum value of the discrete signal are calculated, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to Fourier transform to extract spectral features, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to wavelet transform to extract time-frequency features, and the difference value is Huffman encoded to generate a corresponding difference feature vector, and the time and frequency characteristics of the discrete signal are converted into the time and frequency characteristics of the discrete signal. The domain, frequency domain, spectrum features, time-frequency features and difference feature vectors are fused through tensor decomposition technology to generate a multidimensional feature space, the LASSO regression model is used to screen out the features that have the greatest impact on the construction efficiency of the multidimensional feature space, the multidimensional feature space is input into the support vector machine model to construct a multidimensional model, the multidimensional feature spaces of several users are counted, a multidimensional feature space set is established, the multidimensional model is trained using the multidimensional feature space set, the parameters of the trained multidimensional model are tuned using a cross-validation method, and the accurate influence of the user's deep brain waves on the emotion label is obtained, the influence is graded from the 1st level to the nth level, and the grade division is input as a label into the information storage address of the corresponding user.

[0079] As a method of EEG feature selection, support vector machine can restore a large number of data sequences such as scalp position, band, time window, etc. to the data most relevant to the classification problem. It can be used to build classifiers for known diagnoses based on potential patterns of EEG activity, and apply the verified classifiers to new clinical samples through cross-validation iterative refinement. Support vector machine provides classification at the individual level, so it can answer questions related to diagnosis, prediction of treatment response and disease progression based on preclinical indicators.

[0080] The psychological classification module includes a label statistics unit and a label classification unit:

[0081] The tag statistics unit includes classifying all tags in the user's information storage address into major emotional categories, and the major emotional categories include depression, mania, PTSD post-traumatic stress disorder and bipolar disorder.

[0082] The label classification unit comprises:

[0083] The emotion tags classified into emotion categories are standardized, wherein the standardized operation includes numbering the emotion categories by one-hot encoding, deeply classifying all emotion tags into the numbers of the emotion categories by using a TF-IDF model, and marking the user with a secondary tag set, wherein the tag set is the number of the emotion category included in the user;

[0084] Collecting the major emotion categories corresponding to and opposite to the DIT psychological adjustment method, comparing the user's tag set with the major emotion categories corresponding to the DIT psychological adjustment method, and identifying and screening the user includes:

[0085] If the user's tag set does not include the major emotion category corresponding to the DIT psychological adjustment method or the user's tag set includes the major emotion category corresponding to the DIT psychological adjustment method and includes the major emotion category that is opposite to the DIT psychological adjustment method, all the user's information is input into the non-DIT psychological adjustment queue, and the emotion tags of the users in the non-DIT psychological adjustment queue are counted to obtain an emotion tag combination that is definitely not suitable for the DIT psychological adjustment model, and the emotion tag combination is recorded. When the tag statistics unit detects a user carrying the emotion tag combination during the analysis, all the corresponding user's information is input into the non-DIT psychological adjustment queue, and the emotion tag combination that is definitely not suitable for the DIT psychological adjustment model is updated in real time;

[0086] The entire information includes all contents included in the user's information storage address and the address itself;

[0087] If the tag set of the user includes the major emotion category corresponding to the DIT psychological adjustment method and does not have the major emotion category opposite to the DIT psychological adjustment method, all the information of the user is input into the motivation evaluation module.

[0088] The motivation assessment module includes a language analysis unit and an assessment unit:

[0089] The language analysis unit includes collecting the user's language, inputting the language into a bag-of-words model to obtain the user's language keywords, constructing the user's language framework, inputting the language framework into a gating system to obtain emotional language features represented by emotional keywords in the user's language, inputting the emotional language features into the deep detection module and the psychological classification module, obtaining a label classification of the emotional language features, and inputting the label classification of the emotional language features as a new emotional label into the user's information storage address;

[0090] The user is asked questions from different psychological perspectives, where the psychological perspectives are based on the psychoanalytic theory. The user's answers to the questions from different psychological perspectives are input into the language analysis unit to obtain new emotion labels. The input operation of the language analysis unit is recursively repeated to obtain the user's new emotion label and a new label set.

[0091] Often the assessment will provide the user with an opportunity to try out different narratives to present the problem; the user may find the DIT's interpersonal and emotional emphasis meaningful and thus shift focus from his or her previous biochemical explanations to more psychological ones.

[0092] The evaluation unit comprises:

[0093] Input the new emotion label and new label set of the user into the PCA principal component analysis model, use the target keywords crawled from the network as identification parameters, obtain the evaluation purpose of the user, input the evaluation purpose into the emotion dictionary AFINN, map the evaluation purpose to the emotion score, and directly calculate the emotion trend including:

[0094] The sentiment scores obtained by mapping all evaluation purposes are accumulated. If there is no corresponding word in the sentiment dictionary AFINN, it is ignored. The accumulated sentiment score represents the trend of the overall sentiment;

[0095] If the accumulated emotion score is positive, it means that the user is in a positive emotion and does not need treatment;

[0096] If the accumulated emotion score is negative, it means that the user is in a negative emotion and needs intervention from a DIT therapist;

[0097] If the accumulated emotion score is close to zero, it means that the user is emotionally neutral, and the guidance and adjustment module can be used to adjust the user's emotions;

[0098] The results obtained by the evaluation unit are used by the DIT therapist to judge and identify the source of the user's emotions.

[0099] The judgment and identification process includes:

[0100] The DIT therapist rates the unconscious satisfaction of the user's symptoms to assess the relative strength of the user's motivation for change;

[0101] The DIT therapist scores the psychological expectations of the user to assess the motivation intensity of the user;

[0102] The DIT therapist scores the user's interpersonal relationships, passivity, initiative, external influences, psychological balance and shame, and takes the maximum score to get the user's emotional state.

[0103] If the DIT therapist finds a match and balance between his / her own psychology and the user's emotional source, the DIT therapist can maximize the consistency with the user's own thinking mode or life philosophy, so as to deal with the user's emotional source in the best way. DIT focuses very clearly on the user's current life and interpersonal function in the therapeutic interaction, as well as the user's emotional experience. Given this therapeutic focus, it is important for the DIT therapist to assess the user's interest in dealing with these topics as early as possible. Sometimes, this requires the therapist to connect the user's current difficulties or feelings or symptoms with their past and present interpersonal relationships and behaviors through a series of explanations to encourage the user to have a stronger interest in DIT. It won't be long before the user realizes that his / her various narratives of victimization and sacrifice will gradually be challenged by the therapist, inviting the user to reflect on his / her role in the repetitive pattern. This repetitive interpersonal pattern is precisely the focus of DIT treatment, which requires the user to have sufficient motivation to change, especially when the symptoms will bring great benefits to the user.

[0104] Motivation is not meant to be a static psychological state; users often go through different phases during treatment, with some days being highly motivated and others being so powerful that the motivation to change is reduced.

[0105] The guidance and adjustment module comprises:

[0106] The daily discourse guidance effect and mood counseling and regulation effect included in the daily language of the user are collected and input into the psychological classification module and the motivation assessment module to obtain the emotional labels, label sets and the trend of the user's overall emotions corresponding to the daily discourse guidance effect and the mood counseling and regulation effect, perform Huffman encoding on the emotional labels, label sets and the trend of the user's overall emotions, and use the deep label connection unit to classify the Huffman codes into emotional impact levels, calculate the weighted average of the emotional impact level classifications, draw a user emotional disorder change curve according to time, and the DIT therapist adjusts the user treatment according to the user emotional disorder change curve.

[0107] Patients suitable for DIT treatment were screened and assigned to appropriate DIT therapists who were trained psychiatrists or psychologists for a one-on-one structured interview.

[0108] This system uses targeted emotional problem tests to accurately and in detail analyze all of the user's emotional labels, automatically evaluate the user's psychological state, and reasonably develop personalized adjustment plans based on the DIT psychotherapy plan. The system records the user's emotional changes and provides suggestions after analysis to encourage the user to reflect on themselves and make adjustments. Users can obtain instant feedback and guidance during use to help them apply psychological adjustment techniques in actual scenarios. This system aims to provide users with convenient psychological adjustment services and promote emotional health and psychological resilience.

[0109] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, 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 (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0110] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A DIT psychological adjustment system, characterized in that: It includes surface detection module, deep detection module, psychological classification module, motivation assessment module and guidance and regulation module: The surface detection module is used to determine the user's professional emotion label after the user fills in the psychological assessment questionnaire, and classify the emotion label to obtain the user's current surface emotion disorder type; The deep detection module includes using a brain wave detection instrument to perform deep brain wave detection on the user, and obtaining the user's current deep emotional disorder type after processing the detected brain waves, and obtaining the user's deep emotional label; The psychological classification module is used to compare and analyze the deep emotional disorder with the current surface emotional disorder type, obtain the detailed emotional label type group of the user, deeply classify the user according to the emotional label type group, and determine whether the user is suitable for the psychological adjustment model of DIT; The motivation assessment module is used to assess the user's emotional motivation, obtain the user's new emotional label and new label set, and calculate the user's emotional trend for the DIT therapist to judge and identify the source of the user's emotions; The guidance and regulation module is used to monitor the user's emotions throughout the process and record the user's emotional changes, use the pre-trained GPT4 to guide the user's daily speech and regulate his mood, and record the daily speech guidance effect and mood guidance and regulation effect in real time, and combine the daily speech guidance effect and mood guidance and regulation effect with the user's emotional change process to draw the user's emotional disorder change curve.

2. The DIT psychological adjustment system according to claim 1, characterized in that: The surface detection module includes a first test unit and a surface determination unit: The first testing unit includes testing the user's emotional problems according to psychoanalytic theories, wherein the psychoanalytic theories include attachment theory, object relations theory, interpersonal psychoanalysis and mentalization theory; Scoring the emotional problem test conducted by the user to obtain regional scores and total scores classified according to the psychological symptom types, comparing the regional scores and total scores with preset corresponding international normal standard score intervals, evaluating the severity of the psychological symptom type of the user according to the score value exceeding the standard score interval, and classifying the psychological symptom type into the emotional label set of the user; Obtaining the user's current surface emotional disorder type according to international standards and the user's emotional tag set; The types of psychological symptoms described include somatization, obsessive-compulsive symptoms, interpersonal sensitivity, depression, anxiety, hostility, phobia, attachment, and paranoia.

3. The DIT psychological adjustment system according to claim 1, characterized in that: The deep detection module includes a second test unit, a brain wave detection unit and a deep label association unit. The second test unit and the brain wave detection unit are performed synchronously. When the user is answering the personality self-assessment scale given by the second test unit, the brain wave detection unit simultaneously detects the first resting state brain wave signal, the second resting state brain wave signal, the third brain wave signal and the fourth brain wave signal of the user; Calculating the average electric power of the first resting-state EEG signal, the average electric power includes calculating the average value after obtaining the real-time electric power according to the Fourier transform formula, and using the average electric power as a covariate to calculate the covariance of the difference between the third EEG signal and the fourth EEG signal. If the covariance is higher than a preset stability threshold, the cognitive load of the user exceeds the standard proportion, and an emotional label with heavy cognitive load is added to the user; Calculate the curve expression of the power change of the third EEG signal and the fourth EEG signal band along with the feedback of the answering process of the second test unit by using function calculation software; if the curve expression shows a downward trend, the user's cognitive processing method is abnormal, and add an emotional label of abnormal cognitive processing method to the user; If the curve expression shows an upward trend, the user is cognitively normal; If the curve expression is not positively correlated, the user has potential schizophrenia, and an emotion label of schizophrenia is added to the user; calculating the electric power of the second resting-state EEG signal in real time, and if the electric power of the first resting-state EEG signal and the electric power of the second resting-state EEG signal are enhanced, the user has a strong self-perception ability; If the electrical power of the first resting-state EEG signal and the electrical power of the second resting-state EEG signal are weakened, the user's self-perception ability is weak, and an emotional label of self-crisis is added to the user.

4. The DIT psychological adjustment system according to claim 3, characterized in that: The deep label contact unit comprises: All emotion tags obtained by the user through the surface detection module and the deep detection module are stored in the user's information storage address in the form of a dictionary, and duplicate tags are removed; Collecting the corresponding label standard thresholds of normal people and the corresponding label standard thresholds of people with mental illness, performing pairwise comparisons among the corresponding label thresholds of the user, normal people and people with mental illness by a leave-one-out cross-validation method, and obtaining the overall emotional disorder assessment of the user, wherein the overall emotional disorder assessment includes the difference values ​​between the user and normal people and the difference values ​​between the user and people with mental illness; The first resting-state EEG signal, the second resting-state EEG signal, the third EEG signal and the fourth EEG signal are subjected to a bandpass filter to remove noise, and the independent component analysis method is used to remove eye movement and electromyographic artifacts to obtain a preprocessed continuous signal, and the continuous signal is divided into multiple time periods to obtain a discrete signal, and the mean, standard deviation, maximum value and minimum value of the discrete signal are calculated, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to Fourier transform to extract spectral features, and the mean, standard deviation, maximum value and minimum value of the discrete signal are subjected to wavelet transform to extract time-frequency features, and the difference value is subjected to Huffman coding to generate a corresponding difference feature vector, and the time domain and frequency domain of the discrete signal are subjected to the ... remove noise, and the eye movement and electromyography artifacts are removed by using the independent component analysis method to remove eye movement and electromyography artifacts. , spectral features, time-frequency features and difference feature vectors are fused through tensor decomposition technology to generate a multidimensional feature space, the LASSO regression model is used to screen out the features that have the greatest impact on the construction efficiency of the multidimensional feature space, the multidimensional feature space is input into the support vector machine model to construct a multidimensional model, the multidimensional feature spaces of several users are counted, a multidimensional feature space set is established, the multidimensional model is trained using the multidimensional feature space set, the parameters of the trained multidimensional model are tuned using a cross-validation method, and the influence of the user's deep brain waves on the emotion label is obtained, the influence is graded from level 1 to level n, and the grade division is input as a label into the information storage address of the corresponding user.

5. The DIT psychological adjustment system according to claim 4, characterized in that: The psychological classification module includes a label statistics unit and a label classification unit: The tag statistics unit includes classifying all tags in the user's information storage address into major emotional categories, and the major emotional categories include depression, mania, PTSD post-traumatic stress disorder and bipolar disorder.

6. The DIT psychological adjustment system according to claim 5, characterized in that: The label classification unit comprises: The emotion tags classified into emotion categories are standardized, wherein the standardized operation includes numbering the emotion categories by one-hot encoding, deeply classifying all emotion tags into the numbers of the emotion categories by using a TF-IDF model, and marking the user with a secondary tag set, wherein the tag set is the number of the emotion category included in the user; Collecting the major emotion categories corresponding to and opposite to the DIT psychological adjustment method, comparing the user's tag set with the major emotion categories corresponding to the DIT psychological adjustment method, and identifying and screening the user includes: If the user's tag set does not include the major emotion category corresponding to the DIT psychological adjustment method or the user's tag set includes the major emotion category corresponding to the DIT psychological adjustment method and includes the major emotion category that is opposite to the DIT psychological adjustment method, all the user's information is input into the non-DIT psychological adjustment queue, and the emotion tags of the users in the non-DIT psychological adjustment queue are counted to obtain an emotion tag combination that is definitely not suitable for the DIT psychological adjustment model, and the emotion tag combination is recorded. When the tag statistics unit detects a user carrying the emotion tag combination during the analysis, all the corresponding user's information is input into the non-DIT psychological adjustment queue, and the emotion tag combination that is definitely not suitable for the DIT psychological adjustment model is updated in real time; The entire information includes all contents included in the user's information storage address and the address itself; If the tag set of the user includes the major emotion category corresponding to the DIT psychological adjustment method and does not have the major emotion category opposite to the DIT psychological adjustment method, all the information of the user is input into the motivation evaluation module.

7. The DIT psychological adjustment system according to claim 6, characterized in that: The motivation assessment module includes a language analysis unit and an assessment unit: The language analysis unit includes collecting the user's language, inputting the language into a bag-of-words model to obtain the user's language keywords, constructing the user's language framework, inputting the language framework into a gating system to obtain emotional language features represented by emotional keywords in the user's language, inputting the emotional language features into the deep detection module and the psychological classification module, obtaining a label classification of the emotional language features, and inputting the label classification of the emotional language features as a new emotional label into the user's information storage address; The user is asked questions from different psychological perspectives, where the psychological perspectives are based on the psychoanalytic theory. The user's answers to the questions from different psychological perspectives are input into the language analysis unit to obtain new emotion labels. The input operation of the language analysis unit is recursively repeated to obtain the user's new emotion label and a new label set.

8. The DIT psychological adjustment system according to claim 7, characterized in that: The evaluation unit comprises: Input the new emotion label and new label set of the user into the PCA principal component analysis model, use the target keywords crawled from the network as identification parameters, obtain the evaluation purpose of the user, input the evaluation purpose into the emotion dictionary AFINN, map the evaluation purpose to the emotion score, and directly calculate the emotion trend including: The sentiment scores obtained by mapping all evaluation purposes are accumulated. If there is no corresponding word in the sentiment dictionary AFINN, it is ignored. The accumulated sentiment score represents the trend of the overall sentiment; If the accumulated emotion score is positive, it means that the user is in a positive emotion; If the accumulated emotion score is negative, it means that the user is in a negative emotion; If the accumulated emotion score is close to zero, it means that the user is emotionally neutral; The results obtained by the evaluation unit are used by the DIT therapist to judge and identify the source of the user's emotions.

9. The DIT psychological adjustment system according to claim 8, characterized in that: The guidance and adjustment module comprises: The daily speech guidance effect and mood counseling and regulation effect included in the daily language of the user are collected and input into the psychological classification module and the motivation evaluation module to obtain the emotional labels, label sets and the trend of the user's overall emotions corresponding to the daily speech guidance effect and the mood counseling and regulation effect, perform Huffman coding on the emotional labels, label sets and the trend of the user's overall emotions, and use the deep label connection unit to classify the Huffman codes into emotional impact levels, calculate the weighted average of the emotional impact level divisions, and draw a user emotional disorder change curve according to time.

10. The DIT psychological adjustment system according to claim 9, characterized in that: The DIT therapist is a trained psychiatrist or psychologist.

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