KR20180098840A - Real-time system of psychotherapy, and method thereof - Google Patents

Abstract

The present invention relates to a real-time psychotherapy system and a method thereof. The real-time psychotherapy system comprises: a sensor device for recognizing biometric information of a psychotherapy subject and transmitting the same to a user device; a user device for receiving content address information from a chatbot server and displaying corresponding content address information to the psychotherapy subject; a central server; and a chatbot server. According to the present invention, it is possible to manage mental health of the psychotherapy subject.

Description

[0001] REAL-TIME SYSTEM OF PSYCHOTHERAPY, AND METHOD THEREOF [0002]

The present invention relates to a real-time psychotherapy system and method thereof, and more particularly, to a real-time psychotherapy system and method for analyzing emotions of a psychotherapist using real-time information of a psychotherapist including a biometric information of a subject, The present invention relates to a real-time psychotherapy system and a method for guiding psychotherapy contents corresponding to an emotion of an analyzed psychotherapy subject.

Natural Language Processing is a technology that computer recognizes text data composed of natural language used by humans and extracts specific information. It is composed of automatic summary, machine translation, entity name recognition, relationship extraction, emotion analysis and topic subdivision And the like.

Sentimental Analysis, which can apply natural language processing, is an area of text mining technology that extracts the meaning of specific emotional expressions in text composed of natural language, , And how the author has feelings about the subject. For example, when the emotion analysis technique is applied to the movie review of the creator entered online and the emotion of the author is extracted and a score corresponding to the extracted emotion is given to recommend a movie suitable for the author, And the analysis of the feelings of the author of the answers obtained through the questionnaire. However, it is not easy to draw accurate emotions because it does not reflect the situation of the author in real time.

In addition, studies are underway to analyze emotions by extracting features from biological signals. Biological signals include bioelectrical signals, bioimpedance signals, bio-acoustic signals, biomagnetic signals, biomechanical signals, and biochemical signals. More specifically, the electrocardiogram (ECG), which is measured in the form of voltage associated with the heartbeat, is a signal that changes according to the activity state of the cerebrum. It is a variety of brain such as cognitive function, sensory function, motor function, emotional state and sleep state Electroencephalogram (EEG) that selectively shows the activity state according to the region, EMG (electromyogram) indicating the shrinkage strength and the heart rate variability (HRV) indicating the periodic change of the heartbeat .

In vivo signals, emotions can be distinguished from normal mind, joy, depression, sadness, and anger. Electrocardiograms, electroencephalograms, and heartbeat variations are highly related to changes in emotions in vital signs. However, inconsistencies between expressed emotions and actual emotions, The limitation of emotional analysis is pointed out due to the difference in expression due to the difference in expression, and it is not easy to generalize emotional signals of a person who suppresses actual emotions.

A worker who suppresses actual emotions and expresses emotions in order to match the emotions of others is defined as emotional workers. Emotional workers perform occupations such as telemegetor, hotel manager, credit counselor, customer counselor, and addictive therapist. As the level of customer's demand for services increases, work stress is increased due to poor treatment such as personality ignorance, rhetoric and sexual harassment There is a possibility that problems such as depression, hypertension, sleeping disorder, alcohol addiction and gambling addiction are likely to occur, so that a solution is needed to respond to emotional feelings of the emotionally recognized workers.

 Choi, A Young, Woon - Taek. (2005). Extraction of Biological Signal Feature for Emotion Analysis. Proceedings of the Korea HCI Society Conference,, 624-629.

The present invention extracts a psychological analysis questionnaire suitable for the subject of psychotherapy using real-time information of a subject of psychotherapy including biometric information of the subject of psychotherapy and chat information of the subject of psychotherapy, and based on the extracted psychological analysis questionnaire The purpose of this study is to manage mental health of psychotherapy subjects by measuring emotional information of psychotherapy subjects and providing emotional therapy contents to psychotherapy subjects.

In order to achieve the above object, a real-time psychotherapy system including a sensor device, a user device, a central server, and a chatt bot server according to the present invention includes a sensor device for recognizing and transmitting biometric information of a psychotherapist to a user device, And transmits the content address information from the chat server to the central server, receives the reply sentence of the psychotherapy subject corresponding to the psychological analysis query sent from the chat server, A user device for displaying the corresponding content address information to the psychotherapy object, a psychological analysis inquiry message to the chat server, receiving and accumulating the corresponding chat emotion information, accumulating and storing the chat emotion information and the user information The risk of individual stress is calculated, A central server for determining content information corresponding to the risk of personal stress and delivering the content address information corresponding to the content information to the chatbot server, the psychological analysis query sent from the central server to the user device, A chatter server for generating the chat emotion information based on an emotion dictionary based on a reply sentence of the psychotherapy subject received from the user device and delivering the chat emotion information to the central server and delivering the content address information received from the central server to the user device And the psychological analysis query is extracted from the psychological analysis query list based on the user information and the chat emotion information accumulated in the central server.

In order to attain the above object, a server apparatus for real-time psychotherapy having a user information storage unit for storing user information and a chat emotion information storage unit for storing chat emotion information according to the present invention includes user information including biometric information, A psychological analysis query extracted from a psychological analysis query list based on real-time information of a psychotherapist who includes user information stored in the user information storage unit and previously stored chat feeling information, A chat emotion storage unit for accumulating and storing the chat emotion information received from the chat inquiry processing unit for each user, a chat emotion storage unit for storing the chat emotion information received by the chatting unit, The information stored in the information storage unit A stress risk calculation unit for calculating a stress risk based on the user information and the chat feeling information stored in the chat feeling storage unit; a content risk information calculation unit for determining content information corresponding to the calculated stress risk, And a content recommendation unit for delivering the content to the chatbot server.

In order to attain the above object, there is provided a method of managing a psychotherapy target, comprising the steps of: (a) receiving user information including biometric information from a user device and storing the user information; (b) Receiving the chat emotion information corresponding to the psychological analysis query sent from the chat server by transmitting a psychological analysis query extracted from the psychological analysis query list to the chat server based on real-time information of the chat chat server, (c) Accumulating the emotion information for each user; (d) calculating a stress risk based on the stored user information and the stored chat emotion information; And (e) determining content information corresponding to the calculated stress risk and delivering content address information corresponding to the content information to the chat server.

In order to attain the above object, a real-time psychological emotional therapy chatbot server apparatus having an emotion dictionary according to the present invention transmits a psychological analysis query sent from a central server to a user device, A chat emotion information calculation and transmission unit for calculating chat sentiment information based on an emotion dictionary based on the received reply sentence and transmitting the calculated sentiment information to the central server; And a content delivery unit for delivering the content address information received from the central server to the user equipment.

According to various aspects of the present invention, it is possible to measure the emotion of the subject of psychotherapy using the real-time situation information of the subject of the psychotherapy including the subject's biological information and the chat information of the subject of the psychotherapy, And the mental health of the subject to be psychotherapy can be managed by providing the contents of the emotional therapy corresponding to the feelings of the subject.

FIG. 1 is a block diagram of a real-time psychotherapy system for emotional workers according to an embodiment of the present invention.
FIG. 2 is a configuration diagram of the central server of FIG. 1;
FIG. 3 is a configuration diagram of the chatbot server of FIG. 1,
Figure 4 is a flow chart of a real-time psychotherapy method for emotional workers in accordance with an embodiment of the present invention.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

Hereinafter, embodiments of the present invention will be described in order to facilitate understanding of the present invention, and the present invention is not limited to these embodiments. In particular, the present invention can be configured by combining at least any one of individual components, individual functions, or individual steps included in each embodiment.

The present invention can be applied to a general public or anyone who needs psychotherapy, but in the following description of an embodiment according to the present invention, an emotional worker is exemplified.

FIG. 1 is a block diagram of a real-time psychotherapy system for emotional workers according to an embodiment of the present invention. The real-time psychotherapy system includes a communication network 1, a sensor device 10, a user device 20, And a chatbot server (40).

The communication network 1 may be a single network or a complex network that allows the user device 20, the central server 30, and the chat server 40 to communicate with each other via a network. 1, the sensor device 10 may be further connected to the communication network 1 so that the sensor device 10 directly communicates with the central server 30. [

The sensor device 10 is a device for recognizing and measuring a living body signal as a sensor. The biological signal to be measured includes an ECG (Electrocardiogram) measured in the form of a voltage related to the heartbeat, a signal Electroencephalogram (EEG) that selectively displays various activity states of the brain such as cognitive function, sensory function, exercise function, emotional state, and sleep state, and EMG (Electromyogram) And the like.

In addition, the sensor device 10 may be a wearable device such as an accessory type including a smart watch, a band, glasses and a necklace, a fabric / garment integrated type including a shirt, a sock and shoes, a patch, a headset, And the like, and a bio-implantable type including a chip implantation in a pill and a body part.

The sensor device 10 can transmit a bio-signal to the user device 20 via Bluetooth or Wi-Fi. The sensor device 10 is provided with a small / low-power sensor network communication module for low power, low noise, The biometric signal may be transmitted to the biometric sensor 20.

The real-time psychotherapy system may be configured to include at least one sensor device 10.

The user device 20 is a device used by emotional workers. The user device 20 periodically receives biometric information from the sensor device 10 and receives real-time information including user characteristic information, user situation information, and biometric information transmitted from the sensor device 10, And periodically collects the user information and transmits the collected information to the central server 30. The user device 20 may include a PC, a notebook, and a smart phone.

The user device 20 may include an application that is an intermediary for communicating with the chat server 40. The user device 20 may receive the psychological analysis query sent from the chat server 40 through the application, The reply sentence of the emotional worker corresponding to the query is input from the emotional worker through the application and transmitted to the chatbot server 40. The process of receiving the psychological analysis query from the chat server 40 and delivering the reply of the emotion worker to the chat server 40 may be performed at least once.

Here, emotional workers are workers who routinely control and control their emotions in order to match the emotions of others, and carry out occupations such as telemegetor, hotel manager, credit counselor, customer consultant, and addictive therapist.

The user information may include user characteristic information, user biometric information, and user context information. The user characteristic information may include gender information, age information, occupation information, and momentum information as information indicating the characteristics of the emotional worker. The user biometric information may include skin temperature information, pulse information, speech amount information, tone information, volume information, brain wave information, pulse wave information, and heart rate variability information as biometric information of the emotional worker. The user situation information may include time, position information of the emotion worker measured by the GPS sensor, weather information based on the position information of the emotional worker, and location information based traffic information of the emotional worker.

The central server 30 receives and stores user information including biometric information, user characteristic information, user situation information, and the like from the user device 20. The central server 30 extracts biometric feature information by analyzing biometric information among the user information, and extracts biometric feature information based on the biometric feature information. The central server 30 may store the biometric information as it is, and may store the biometric characteristic information extracted from the biometric information. Further, the biometric information and the extracted biometric characteristic information can be stored together.

The stored user information may be updated by dividing information by user, or the user information may be accumulated and stored for each user. In addition, the central server 30 may update only some of the user information, or may accumulate and store some information. For example, the age information is updated and stored, and biometric feature information is accumulated and stored. The central server 30 may also store user information in a block chain.

In addition, the central server 30 extracts biometric feature information expressing emotions by analyzing the biometric information, and extracts biometric emotion information by deriving emotion inference rules for each biometric information.

The central server 30 cumulatively stores chat emotion information for each user, extracts a psychological analysis query based on the stored user information and the stored chat emotion information, and delivers the psychological analysis query message to the chat server 40. [ In addition, the central server 30 receives and stores the chat emotion information from the chat server 40, and may store the chat emotion information in the block chain. Then, the central server 30 calculates the stress risk of each user corresponding to the user information and the chat feeling information, and determines the content information corresponding to the calculated stress risk. And also transmits the content address information corresponding to the content information to the user device 20 through the chatbot server 40. [

Here, the psychological analysis query is extracted from the psychological analysis query list stored in the central server 30 based on the information of the emotional workers stored in real time, and includes at least one query statement. And an additional question for inducing conversation. Also, the psychological analysis query list can be updated, and the psychological analysis query list can be updated even on the basis of cloud sourcing.

Here, the stress risk can be calculated based on the vector value according to the grade, and it can be divided into specific grades within each grade. For example, stress risk is classified into upper, middle, and lower grades, and the upper and lower grades may be divided into upper and lower imaginings. Also, stress risk can be classified into upper, middle, and lower grades depending on the type of stress. Types of stress can be classified into positive stress, acute stress, and chronic stress. Stress types can be classified into stress types. For example, a stress level may be identified for each stressor based on the stressor, such as person, work, work, or individual. In addition, the vector reference value for dividing the grades may be changed based on information such as the type of occupation of the emotion worker and the nationality of the emotion worker.

Since the central server 30 has content information corresponding to the risk of stress, at least one content information corresponding to the calculated stress risk level can be determined and recommended, and the method of recommending the content information can be performed by directly A method of providing content address information that can be viewed, a method of providing category information of content information, and the like. The category of the content information to be provided can be optimized based on individual information per user. For example, if the content information category includes " Listen to music ", " View movies ", and " The information will be "Watch movies" and in detail "Watch movies".

The chatbot server 40 receives the psychological analysis query sent to the central server 30, and communicates with the user device 20 on the basis of the psychological analysis inquiry statement to transmit a reply corresponding to the psychological analysis query to the user device 20 And receives chat messages and generates chat sentiment information through the sentiment dictionary. The chatbot server 40 delivers the generated chat emotion information to the central server 30 and receives the content address information from the central server 30 and transmits the received content address information to the user device 20.

Here, the emotion dictionary is a dictionary storing emotion words expressing emotions, and consists of emotion words and emotion vector values of each emotion word in pairs, and the emotion word is composed of general nouns, proper nouns, adjectives, adverbs, . The emotion dictionary assigns emotion classification scores to the collected learning paragraphs, and calculates emotion vector values for all words appearing in the learning paragraphs. The collected learning paragraph may be a text document used in an early stage for machine learning in advance, or may be an answer sent from the user device 20. [

The score of the emotion classification of each learning paragraph is a real value between 0 and 1, and the emotion classification score of each learning paragraph is expressed as Score (P) = <Se1, Se2, ..., Sek> .

Also, the emotion vector value for each word is calculated using a TF-IDF (Term Frequency-Inverse Document Frequency) technique. The emotion vector value EV (W) of the emotion word W is calculated by the following equation (1) for the set of learning paragraphs P in which W appears,

.

As shown in equation (2)

The IDF (W) is the inverse document frequency (DF), which is the number of documents in which the word W appears in all the learning paragraphs. DF (W, P) (W) is a reciprocal value of a word, meaning a word that does not appear well in other learning documents.

Here, the chat emotion information is an emotion vector value corresponding to an emotion word belonging to the sent reply sentence, and the emotion vector value includes at least one vector value calculated based on the TF-IDF technique. Emotion vector values can be applied and extended based on the TF-IDF technique.

Here, the content address information is preferably a URL address for viewing the extracted content information.

Fig. 2 shows an example of a configuration diagram of the central server of Fig.

2, the central server 30 includes a user information storing unit 31, a chatting query processing unit 33, a chatting feeling storing unit 35, a stress risk calculating unit 37, and a content recommending unit 39. [ .

The user information storage unit 31 receives and stores user information including biometric information, user characteristic information, user situation information, and the like from the user device 20. The user information storage unit 31 extracts the biometric feature information by analyzing the biometric information among the user information, and extracts the biometric feature information based on the biometric feature information. The user information storage unit 31 may store the biometric information as it is, and may store the biometric feature information extracted from the biometric information. Further, the biometric information and the extracted biometric characteristic information can be stored together. The user information storage unit 31 may store user information in a block chain.

The stored user information may be updated for each user, or the user information may be accumulated for each user. In addition, some of the user information may be updated, or some information may be accumulated and stored. For example, the age information is updated and stored, and biometric feature information can be accumulated and stored.

The chat query processing unit 33 extracts the psychological analysis query sentence based on the user-cumulated chat sentiment information stored in the user information, delivers the sentence to the chat server 40, Emotion information is received.

Here, the psychological analysis query is extracted from the psychological analysis query list stored in the central server 30 based on the information of the emotional workers stored in real time, and includes at least one query statement. And an additional question for inducing conversation. Also, the psychological analysis query list can be updated, and the psychological analysis query list can be updated even on the basis of cloud sourcing.

The chat emotion storage unit 35 accumulates and stores the chat emotion information received from the chat query processing unit 33 for each user. The chat emotion information is an emotion vector value corresponding to all the emotion words belonging to the received sentence, and the emotion vector value is a vector value calculated based on the TF-IDF technique.

The stress risk calculator 37 calculates the user risk information stored in the user information storage unit 31 and the stress risk per user corresponding to the chat feeling information received from the chat feeling storage unit 35. [ The stress risk can be calculated based on the vector value according to the grade, and it can be divided into specific grades within each grade. For example, stress risk is classified into upper, middle, and lower grades, and the upper and lower grades may be divided into upper and lower imaginings. In addition, stress risk can be divided into upper, middle, and lower grades depending on the type of stress. Types of stress can be classified into positive stress, acute stress, and chronic stress. Stress types can also be classified into stress types. For example, a stress level may be identified for each stressor based on the stressor, such as person, work, work, or individual. In addition, the vector reference value for dividing the grades may be changed based on information such as the type of occupation of the emotion worker and the nationality of the emotion worker.

The content recommendation unit 39 determines the content information corresponding to the stress risk calculated in the stress risk calculation 37 and delivers the content address information corresponding to the content information to the user device 20 through the chattob server 40 I recommend you. The content information may include 2D content information, exercise information, reading information, music information, connection information for professional psychological counseling, professional recommendation information, and the like. The method of recommending the content information may include a method of providing the content address information in which the content information can be directly viewed, a method of providing the category information of the content information, and the like. The category of the content information to be provided can be optimized based on individual information per user.

FIG. 3 shows an example of the configuration of the chat server in FIG.

3, the chatbot server 40 includes a chat section 41, a chat emotion information calculation and transmission section 43, and a contents processing section 45, and has an emotion dictionary.

The chatting unit 41 receives the psychological analysis inquiry statement from the central server 30 and delivers a psychological analysis inquiry statement to the user device 20 to perform a chat for psychological analysis and transmits a reply sentence of the emotional worker to the user device 20 . Emotional workers may have at least one reply.

The chat emotion information calculation and transmission unit 43 generates the chat emotion information based on the emotion dictionary based on the reply sent from the chat unit 41 and transmits the chat emotion information to the central server 30.

Here, the emotion dictionary is a dictionary storing emotion words expressing emotions, and consists of emotion words and emotion vector values of each emotion word in pairs, and the emotion word is composed of general nouns, proper nouns, adjectives, adverbs, . The emotion dictionary assigns emotion classification scores to the collected learning paragraphs, and calculates emotion vector values for all words appearing in the learning paragraphs. The collected learning paragraph may be a text document used in an early stage for machine learning in advance, or may be an answer sent from the user device 20. [ Also, when the emotion word not included in the emotion dictionary is included in the reply, the emotion word not in the emotion dictionary can be added to the emotion dictionary and stored.

Here, the chat emotion information may be an emotion vector value corresponding to all the emotion words belonging to the received reply sentence, and the emotion vector value may be a vector value calculated based on the TF-IDF technique.

The content delivery unit 45 delivers the content address information received from the central server 30 to the user device 20 to recommend the content. Content address information Here, the content address information is preferably a URL address for viewing the extracted content information. In addition, category information of the content information may be received from the central server 30 and transmitted to the user device 20 to recommend the content.

FIG. 4 illustrates an example of a flow chart of a real-time psychotherapy method for emotion workers according to an embodiment of the present invention. Hereinafter, a real-time psychotherapy method for emotional workers according to an embodiment of the present invention will be described step by step with reference to FIG.

The sensor device 10 periodically transmits the biometric information to the user device 20 (S0). The biometric information may include skin temperature information, pulse information, speech amount information, tone information, volume information, brain wave information, and the like as biometric information of the emotional worker. The sensor device 10 periodically transmits all the measured biometric information to the user device 20 and may transmit only the changed biometric information to the previously transmitted biometric information.

The user device 20 is a device for collecting user information in real time. The user device 20 is a device that collects user information on the basis of user's location information, traffic situation information according to user location information, And weather information according to the location of the user, and also collects the biometric information transmitted from the sensor device 10 (S1).

The user device 20 periodically transmits the collected user information to the central server 30 (S3). Also, the user device 20 may transmit the collected user information to the central server 30 at the request of the central server 30. Also, the user device 20 can transmit only the changed user information to the central server 30, thereby reducing the communication overhead of the central server 30.

The central server 30 stores the received user information (S5). The central server 30 extracts biometric feature information by analyzing biometric information among the user information, and extracts biometric feature information based on the biometric feature information. The central server 30 may store the biometric information as it is, and may store the biometric characteristic information extracted from the biometric information. Further, the biometric information and the extracted biometric characteristic information can be stored together. The stored user information can update information for each user, and the user information can be accumulated and stored for each user. In addition, some of the user information may be updated, or some information may be stored in a cumulative manner. For example, the age information may be updated and stored, and biometric feature information may be accumulated and stored.

The central server 30 cumulatively stores chat emotion information for each user, and extracts a psychological analysis query based on the stored user information and the stored chat emotion information (S7). Here, the psychological analysis query is extracted from the psychological analysis query list stored in the central server 30 based on the information of the emotional workers stored in real time, and includes at least one query statement. As shown in FIG. Also, the psychological analysis query list can be updated, and the psychological analysis query list can be updated even on the basis of cloud sourcing.

The central server 30 delivers the psychological analysis query to the chat server 40 (S9).

The chatbot server 40 receives the sentence corresponding to the psychoanalysis query sentence from the user device 20 (S13) by delivering the received psychoanalysis query sentence to the user device 20 (S11). The process of transmitting the psychological analysis query message to the user device 20 (S11) and receiving the reply message corresponding to the psychological analysis query message from the user device 20 may be performed at least once.

The chatbot server 40 generates the chat emotion information based on the emotion dictionary based on the received reply sentence (S15) and transmits it to the central server 30 (S17). Here, the chat emotion information is an emotion vector value corresponding to all the emotion words belonging to the received sentence, and the emotion vector value is a vector value calculated based on the TF-IDF technique. Emotion vector values can also be applied and extended based on the TF-IDF technique.

The central server 30 receives the chat emotion information from the chat server 40, and calculates a user-specific stress risk corresponding to the user information and the chat emotion information (S19).

The central server 30 determines the content information corresponding to the calculated stress risk level and transmits the content address information corresponding to the content information to the user device 20 through the chat server 40 to recommend it (S23) . In addition, the central server 30 may transmit the category information of the content information to the user device 20 via the chat server 40 and recommend it.

Although not shown in the drawings, the real-time psychotherapy method for emotional workers according to another embodiment of the present invention will be described with reference to FIG.

The chopper server 40 may perform the step S7 of extracting the psychological analysis query sentence from the central server 30. [ In order to extract the psychological analysis query message from the chat server 40, the central server 30 transmits the user information and the chat emotion information to the chat server 40. The chat server 40 transmits the received chat information Based on the information, the psychological analysis query can be extracted from the list of existing psychological analysis questionnaires. Based on the extracted psychological analysis query, the chat server 40 communicates with the user device 20 to receive the reply sentence corresponding to the psychological analysis query from the user device 20.

The foregoing description is merely illustrative of the technical idea of the present invention, and various changes and modifications may be made by those skilled in the art without departing from the essential characteristics of the present invention. Therefore, the embodiments disclosed in the present invention are not intended to limit the scope of the present invention but to limit the scope of the technical idea of the present invention. It is intended that the present invention cover the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents.

1: communication network 10: sensor device
20: user equipment 30: central server
40: Chattb server 31: User information storage unit
33: a chatting query processing unit 35: a chatting feeling storing unit
37: stress risk calculation unit 39: content recommendation unit
41: chat section 43: chat emotion information calculation transmission section
45:

Claims (13)

A real-time psychotherapy system including a sensor device, a user device, a central server and a chat server,
A sensor device for recognizing the biometric information of the subject of psychotherapy and transmitting the biometric information to a user device;
And transmits the user information including the biometric information to the central server, receives the reply sentence of the psychotherapy subject corresponding to the psychological analysis query sent from the chat server, and transmits the received reply to the chat server, A user device that receives address information and displays corresponding content address information to a psychotherapy subject;
And transmits the psychoanalytic query message to the chat server to accumulate and store the corresponding chat emotion information and to calculate the personal stress risk level corresponding to the chat emotion information and the user information, And transmitting the content address information corresponding to the content information to the chat server;
Transmitting the psychological analysis query sent from the central server to the user device, generating the chat emotion information based on emotion dictionary information from the reply sentences of the psychotherapy recipient received from the user device, And a chat server for transmitting the content address information received from the central server to the user device,
Wherein the psychological analysis query is extracted from at least one psychological analysis query list based on the user information and chat emotion information accumulated in the central server. A server apparatus for real-time psychotherapy having a user information storage unit for storing user information and a chat emotion information storage unit for storing chat emotion information,
A user information storage unit for receiving and storing user information including biometric information from a user device;
The psychological analysis query sentence extracted from the psychological analysis question list based on the real-time information of the subject of psychotherapy including the user information stored in the user information storage unit and the pre-stored chat feeling information is transmitted to the chat server, A chatting processor for receiving the chatting feeling information corresponding to the chatting feeling information;
A chat emotion storage unit for accumulating and storing the chat emotion information received from the chatting unit for each user;
A stress risk calculation unit for calculating a stress risk based on the user information stored in the user information storage unit and the chat feeling information stored in the chat feeling storage unit;
And a content recommendation unit for determining content information corresponding to the calculated stress risk and delivering content address information corresponding to the content information to the chatbot server. 3. The method of claim 2,
The user information includes at least one of a biological signal including at least one of electroencephalogram, pulse wave and heart beat, user's location information, traffic situation information according to user location information, season information according to user's location, The user status information including at least one of the user status information,
Wherein the user information storage unit extracts and stores a biometric signal characteristic from the bio-signal. 3. The method of claim 2,
Wherein the user information and the chat emotion information are stored in a block chain. 3. The method of claim 2,
And the content address information is a URL. (a) receiving user information including biometric information from a user device and storing the user information;
(b) a psychological analysis query sent from a psychological analysis query list based on real-time information of a psychotherapist who includes stored user information and pre-stored chat emotion information is transmitted to the chat server, and the psychological analysis query Receiving chat emotion information corresponding to the door;
(c) cumulatively storing the received chat feeling information for each user;
(d) calculating a stress risk based on the stored user information and the stored chat feeling information; And
(e) determining content information corresponding to the calculated stress risk and delivering content address information corresponding to the content information to the chat server. The method according to claim 6,
The user information includes biometric signals including at least one of brain waves, pulse waves and heart beat variability. The user information includes at least one of a position information of the user, traffic situation information according to user position information, season information according to the user's position, Weather information, and the like, and user condition information including at least one of weather information, weather information, and the like. The method according to claim 6,
Wherein the user information and the chat emotion information are stored in a block chain. The method according to claim 6,
Wherein the content address information is a URL. A real-time psychological emotional therapy chatbot server apparatus having an emotion dictionary,
A chat section for delivering a psychological analysis query sent from a central server to a user device and receiving a reply sentence of a psychotherapy subject using the user device;
A chat emotion information calculation and transmission unit for calculating chat sentiment information based on an emotion dictionary based on the reply sentence received and transmitting the calculated chat sentiment information to the central server; And
And a content delivery unit for delivering the content address information received from the central server to the user equipment. 11. The method of claim 10,
Wherein the chat emotion information includes at least one emotion vector value corresponding to an emotion word belonging to the received reply sentence. 12. The method of claim 11,
Wherein the emotion vector value is a vector value calculated based on a TF-IDF technique. 11. The method of claim 10,
Wherein the emotional dictionary adds the emotional word to the wordbook and stores the emotional word in the wordbook when the emoticon word is not included in the preliminarily stored emotional wordbook of the emotional dictionary.

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