CN113793687A - Mental health dynamic management system and method - Google Patents

Abstract

The invention provides a mental health dynamic management system and a method thereof, wherein the system comprises: the evaluation module is used for dynamically acquiring the behavior information of the user and evaluating the current mental health state of the user based on the behavior information; the determining module is used for determining a first intervention mode corresponding to the first exception type based on a preset exception type-intervention mode library when the evaluation result comprises at least one first exception type; and the intervention module is used for performing corresponding intervention based on the first intervention mode. The mental health dynamic management system evaluates the current mental health state of the user based on the behavior information of the user, determines a corresponding first intervention mode to intervene in time when the evaluation result contains a first abnormal type, helps people with mental disorder to rescue in time when the people generate an overstimulation behavior, and does not need to monitor by people all the time.

Description

Mental health dynamic management system and method

Technical Field

The invention relates to the technical field of mental health management, in particular to a mental health dynamic management system and a mental health dynamic management method.

Background

At present, more and more people pay attention to self psychological health, and most people regularly go to professional institutions (such as psychological hospitals and the like) to perform psychological physical examination;

people who are diagnosed by professional institutions as having psychological and mental disorder problems may have overstimulation behaviors, and need to be monitored and carry out professional rescue measures, but the implementation is troublesome;

therefore, a solution is needed.

Disclosure of Invention

One of the objectives of the present invention is to provide a system and a method for dynamically managing mental health, wherein a current mental health status of a user is evaluated based on behavior information of the user, and when an evaluation result includes a first abnormal type, a corresponding first intervention mode is determined to intervene in time, so as to help a person with mental disorder to generate an overstimulation behavior, help the person in time, and avoid the need of monitoring the person at any time.

The embodiment of the invention provides a mental health dynamic management system, which comprises:

the evaluation module is used for dynamically acquiring the behavior information of the user and evaluating the current mental health state of the user based on the behavior information;

the determining module is used for determining a first intervention mode corresponding to the first exception type based on a preset exception type-intervention mode library when the evaluation result comprises at least one first exception type;

and the intervention module is used for performing corresponding intervention based on the first intervention mode.

Preferably, the evaluation module performs the following operations:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capture strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first features and second features in the risk feature library, if the first features are matched with the second features, taking the second features matched with the first features as third features, and taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type comprises: individual risks and combined risks;

acquiring at least one first captured scene involved in a second process;

when the risk type of the risk corresponding to the third feature is an individual risk, acquiring the reliability of the captured scene, and if the reliability is less than or equal to a preset reliability threshold, rejecting the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first processes before and/or after the second process from the stream program list, and taking the first processes as third processes;

acquiring at least one second capturing scene related to a third process;

obtaining at least one wagering relationship between a first captured scene and a second captured scene, determining a wager type for the wagering relationship, the wager type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

performing feature matching on the fourth features and the combined risk features;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, removing the corresponding first capture node;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of a user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting information to be predicted into the prediction model, and acquiring a prediction result;

obtaining a prediction process of generating a prediction result by a prediction model;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking a corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting a first process after a second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting a second process and a third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

rejecting first capture nodes except the second capture node in the first capture nodes;

when first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

acquiring at least one latest behavior information item through a third capture node;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

Preferably, the intervention module performs the following operations:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing a virtual space;

after initialization is finished, judging whether a user enters a first initial area in a virtual space through VR equipment or not;

if yes, acquiring a first direction faced by a user in real time;

determining a virtual display corresponding to a first direction in a virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether a user enters a second starting area through VR equipment or not;

if so, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to a second direction in the virtual scene, and triggering and displaying a second psychology education material corresponding to the trigger point;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and a second psychology education material triggered previously in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention concentration of the user viewing a second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of a user watching a second psychological education material and a corresponding second concentration overall degree into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

Preferably, the mental health dynamic management system further comprises:

the psychological consultation module is used for the user to perform psychological consultation to a psychologist;

the psychological consultation module performs the following operations:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by a user from a psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

Preferably, the mental health dynamic management system further comprises:

and the early warning module is used for sending the first abnormal type to the monitoring terminal bound by the user when the evaluation result contains at least one first abnormal type.

The embodiment of the invention provides a mental health dynamic management method, which comprises the following steps:

step S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information;

step S2: when the evaluation result contains at least one first abnormal type, determining a first intervention mode corresponding to the first abnormal type based on a preset abnormal type-intervention mode library;

step S3: based on the first intervention mode, corresponding intervention is carried out.

Preferably, step S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information, wherein the evaluation comprises the following steps:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capture strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first features and second features in the risk feature library, if the first features are matched with the second features, taking the second features matched with the first features as third features, and taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type comprises: individual risks and combined risks;

acquiring at least one first captured scene involved in a second process;

when the risk type of the risk corresponding to the third feature is an individual risk, acquiring the reliability of the captured scene, and if the reliability is less than or equal to a preset reliability threshold, rejecting the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first processes before and/or after the second process from the stream program list, and taking the first processes as third processes;

acquiring at least one second capturing scene related to a third process;

obtaining at least one wagering relationship between a first captured scene and a second captured scene, determining a wager type for the wagering relationship, the wager type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

performing feature matching on the fourth features and the combined risk features;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, removing the corresponding first capture node;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of a user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting information to be predicted into the prediction model, and acquiring a prediction result;

obtaining a prediction process of generating a prediction result by a prediction model;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking a corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting a first process after a second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting a second process and a third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

rejecting first capture nodes except the second capture node in the first capture nodes;

when first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

acquiring at least one latest behavior information item through a third capture node;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

Preferably, step S3: based on the first intervention mode, performing corresponding intervention, including:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing a virtual space;

after initialization is finished, judging whether a user enters a first initial area in a virtual space through VR equipment or not;

if yes, acquiring a first direction faced by a user in real time;

determining a virtual display corresponding to a first direction in a virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether a user enters a second starting area through VR equipment or not;

if so, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to a second direction in the virtual scene, and triggering and displaying a second psychology education material corresponding to the trigger point;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and a second psychology education material triggered previously in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention concentration of the user viewing a second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of a user watching a second psychological education material and a corresponding second concentration overall degree into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

Preferably, the mental health dynamic management method further includes:

the user can make psychological consultation with a psychologist;

wherein, supply the user to carry out psychological consultation to psychologist, include:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by a user from a psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

Preferably, the mental health dynamic management method further includes:

and when the evaluation result contains at least one first abnormal type, sending the first abnormal type to the monitoring terminal bound by the user.

Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:

FIG. 1 is a diagram of a mental health dynamics management system according to an embodiment of the present invention;

FIG. 2 is a diagram of another mental health dynamics management system according to an embodiment of the present invention;

fig. 3 is a schematic diagram of a mental health dynamic management method according to an embodiment of the present invention.

Detailed Description

The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.

An embodiment of the present invention provides a mental health dynamic management system, as shown in fig. 1, including:

the evaluation module 1 is used for dynamically acquiring behavior information of a user and evaluating the current mental health state of the user based on the behavior information;

the determining module 2 is configured to determine, when the evaluation result includes at least one first exception type, a first intervention mode corresponding to the first exception type based on a preset exception type-intervention mode library;

and the intervention module 3 is used for performing corresponding intervention based on the first intervention mode.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset exception type-intervention mode library is specifically as follows: a database containing first intervention modes (such as guiding the user to relax) corresponding to different first abnormal types (such as excessive anxiety);

acquiring behavior information of a user (for example, what action is generated in what environment, the three-dimensional outline scanning of the environment where the user is located can be realized based on the millimeter wave radar sensor, the user can wear wearing equipment provided with the millimeter wave radar sensor, and what webpage the user accesses can be realized); evaluating the current mental health state of the user based on the behavior information; if the evaluation result contains the first abnormal type, determining a first intervention mode, and performing intervention in time (when intervention is performed, the intervention can be performed based on the wearable equipment);

the embodiment of the invention evaluates the current mental health state of the user based on the behavior information of the user, determines a corresponding first intervention mode when the evaluation result contains a first abnormal type, intervenes in time, helps people with mental disorder to rescue in time when the people generate an overstimulation behavior, and does not need to monitor the situation all the time.

The embodiment of the invention provides a mental health dynamic management system, and an evaluation module 1 executes the following operations:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capture strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first features and second features in the risk feature library, if the first features are matched with the second features, taking the second features matched with the first features as third features, and taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type comprises: individual risks and combined risks;

acquiring at least one first captured scene involved in a second process;

when the risk type of the risk corresponding to the third feature is an individual risk, acquiring the reliability of the captured scene, and if the reliability is less than or equal to a preset reliability threshold, rejecting the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first processes before and/or after the second process from the stream program list, and taking the first processes as third processes;

acquiring at least one second capturing scene related to a third process;

obtaining at least one wagering relationship between a first captured scene and a second captured scene, determining a wager type for the wagering relationship, the wager type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

performing feature matching on the fourth features and the combined risk features;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, removing the corresponding first capture node;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of a user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting information to be predicted into the prediction model, and acquiring a prediction result;

obtaining a prediction process of generating a prediction result by a prediction model;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking a corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting a first process after a second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting a second process and a third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

rejecting first capture nodes except the second capture node in the first capture nodes;

when first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

acquiring at least one latest behavior information item through a third capture node;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset risk characteristic library specifically comprises the following steps: a database containing a plurality of risk features in the process of capturing network behavior (e.g., lowering security levels, accessing hyperlinks not belonging to on-site resources); the preset confidence threshold specifically comprises: for example, 98; the preset risk feature-combined risk feature library specifically comprises the following steps: combined risk features corresponding to different risk features are included (for example, the security level is continuously reduced, and a hyperlink in a webpage corresponding to the hyperlink is accessed); the preset number is specifically as follows: for example, 3; the preset first guarantee value threshold specifically is: for example, 85; the preset second guarantee value threshold specifically is: for example, 90; the preset prediction model specifically comprises: the model is generated after a large amount of records of manual behaviors predicted based on historical behavior information are learned by a machine learning algorithm, and the model can predict the behaviors; the preset non-standard feature library specifically comprises the following steps: the method comprises the following steps of including non-standard characteristics of a plurality of prediction models in the prediction process (for example, when certain type of behavior data is discontinuous, self-completing is carried out); the preset feature-judgment value library specifically comprises: a database (e.g., 50) containing decision values corresponding to different non-canonical features; the preset influence analysis model specifically comprises the following steps: the model is generated after learning a large number of records of influences of irregular features of the manual analysis prediction model prediction process on the subsequent process by using a machine learning algorithm, the influence can be analyzed by the model, and an influence value is obtained, wherein the larger the influence value is, the larger the influence value is; the preset judgment value threshold specifically comprises the following steps: for example, 75; the preset exception type-capturing node library is specifically as follows: a database containing capture nodes corresponding to different first exception types; the preset mental health assessment model specifically comprises the following steps: a model generated after learning a record of a large number of manual (for example: psychologists) psychophysical evaluations by using a machine learning algorithm, wherein the model can perform the psychophysical evaluations;

the capture objects of the first capture node (behavior capture data acquisition node) are divided into network behaviors (e.g., what web pages to access) and real-world behaviors (e.g., what actions to generate); when the capture object is a network behavior, capturing by adopting different capture strategies, performing strategy decomposition and flow analysis (executing sequence analysis) on the capture strategies to obtain a flow sequence (for example, flow 1, accessing a current webpage browsed by a user, flow 2, acquiring webpage content, extracting keywords, flow 3, if a hyperlink exists, reducing the security level, accessing the hyperlink, flow 4, if a new hyperlink exists in the webpage corresponding to the hyperlink, continuously reducing the security level, and accessing the new hyperlink); matching the first characteristic and the second characteristic of the first process, if the first characteristic and the second characteristic are matched, determining a third characteristic (for example, reducing the security level and accessing the hyperlink) in the process 3, determining a risk type, wherein the risk type is divided into an individual risk (for example, Trojan horse exists in the hyperlink) and a combined risk (potential Trojan horse exists in the hyperlink, and a code for activating the password exists in the hyperlink in the webpage corresponding to the hyperlink); when the risk is an independent risk, acquiring the credibility of a capturing scene (such as a webpage), and if the credibility is lower, removing a corresponding first capturing node; if the combined risk is the combined risk, determining the combined risk characteristics (for example, 'continuously reducing the security level, accessing a new hyperlink'), ensuring the security of the hyperlink to form a guarantee relationship when the hyperlink is set in a general website, wherein the guarantee types are an active guarantee (guaranteeing the hyperlink webpage by a webpage per se) and a passive guarantee (belonging to the same network company), and the guarantee value is larger, and the guarantee strength is larger; if the first guarantee value is too small and/or the second guarantee value is too small and/or the fourth feature (namely 'continuously reducing the security level and accessing a new hyperlink' in the process 4) is matched and matched with the combined risk feature, indicating that the capturing process has serious risk and is not feasible, and removing a corresponding first capturing node; when the capture object is a real behavior, acquiring behavior information of the user acquired before, integrating and inputting the behavior information into a prediction model, predicting the behavior of the user which possibly occurs next, and removing irrelevant first capture nodes; however, the degree of automation of the prediction model is high, and manual work cannot intervene in the prediction process, so that whether non-standard features exist in the prediction process of the prediction model needs to be judged, influences are analyzed, and whether irrelevant first capture nodes are removed is determined; after all the first capture nodes needing to be removed are removed, behavior information is obtained through the remaining third capture nodes, and the behavior information is input into a mental health assessment model for assessment;

the embodiment of the invention is provided with a plurality of first capture nodes, different verifications are carried out based on different capture objects, when the capture objects are network behaviors, the feasibility of a capture strategy is verified, the security and the accuracy of network behavior capture are ensured, when the capture objects are real behaviors, the prediction process of a prediction model is checked, the efficiency of a system is improved (irrelevant first capture nodes are removed), and meanwhile, the accuracy of behavior prediction is ensured; and finally, the mental health assessment model is used for assessing, so that the mental health assessment efficiency is improved.

The embodiment of the invention provides a mental health dynamic management system, wherein an intervention module 3 executes the following operations:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing a virtual space;

after initialization is finished, judging whether a user enters a first initial area in a virtual space through VR equipment or not;

if yes, acquiring a first direction faced by a user in real time;

determining a virtual display corresponding to a first direction in a virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether a user enters a second starting area through VR equipment or not;

if so, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to a second direction in the virtual scene, and triggering and displaying a second psychology education material corresponding to the trigger point;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and a second psychology education material triggered previously in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention concentration of the user viewing a second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of a user watching a second psychological education material and a corresponding second concentration overall degree into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset intervention mode-virtual space library specifically comprises the following steps: virtual spaces (VR spaces, for example, with relaxed subjects in the VR spaces) corresponding to different first intervention modes (for example, guiding the user to relax); the preset threshold of the degree of attention concentration is specifically: for example, 95; the preset first time period specifically comprises the following steps: for example, 100 seconds; the preset time threshold specifically comprises: for example, 3; the preset psychology education material association library specifically comprises the following steps: including associations between different psychoeducational materials (e.g., mental relaxation music and mental relaxation exercises); the preset progress judgment model specifically comprises the following steps: a model generated after learning a large number of records of manual progress judgment by using a machine learning algorithm, wherein the model can judge whether the user can exit the virtual space or not based on the progress (such as playing to when, switching of the user) of the user watching psycho-educational materials and the overall degree of attention concentration;

when a first abnormal type is generated, prompting a user to wear VR equipment, initializing a virtual space, when the user is judged to enter a first initial area (the user enters the initial area of the virtual space), acquiring a first direction facing the user, controlling a corresponding virtual display (the display faces the user) to play a first mental education material (such as a relaxing tutorial), and acquiring a first overall degree of attention concentration (such as analyzing whether the user always watches, whether the user follows to do an action and the like) based on an attention analysis technology; if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold, the user needs to continue to conduct; if the first direction changes too much, the user dislikes the psychological education mode and actively switches; triggering a second starting area (an area in a virtual space) to guide a user to enter the area, triggering and constructing a virtual scene (for example, a forest with beautiful scenery) when judging that the user enters the area, acquiring a second direction, determining a trigger point (for example, suspension display 'relax operation') corresponding to the second direction (the user is triggered when looking at the second direction), determining whether a second psychological education material of a new team member is related to a previously triggered psychological education material when the second direction is changed, and if so, displaying screens in a split mode (for example, displaying a relax sound source MV and a relax operation teaching course video, wherein the user can do relax operation in the forest and is more beneficial to relax); judging whether the user can exit the virtual space or not based on the progress judging model, and if so, guiding the user to exit;

according to the embodiment of the invention, the virtual space corresponding to the first intervention mode can be determined, and a user can enter the virtual space through VR equipment to receive targeted psychological education; when the user is determined to dislike the former psychological education mode, the user does not need to actively switch (the user is not vexed due to the need of active switching), the method is more reasonable, and the method can effectively help the user to psychologically dredge.

The embodiment of the invention provides a mental health dynamic management system, which further comprises:

the reforming module is used for reforming the abnormal type-intervention mode library at regular time;

wherein the reforming module performs the following operations:

extracting a plurality of corresponding combinations in the anomaly type-intervention mode library, wherein the corresponding combinations comprise: a third anomaly type and a second intervention mode;

determining at least one alternative third intervention mode corresponding to the third anomaly type based on a preset anomaly type-alternative intervention mode library;

acquiring at least one first effect value of the third intervention mode, and outputting an acquisition source of the first effect value;

determining a source type of the acquisition source, wherein the source type comprises: local and network;

when the source type of the acquisition source is local, acquiring an experience value of the acquisition source, and adjusting the corresponding first effect value based on the experience value, wherein an adjustment formula is as follows:

wherein α' is the first effect value after adjustment, α is the first effect value before adjustment, γ_1,0For a preset empirical threshold, gamma₁Is the empirical value;

when the source type of the acquisition source is a network, acquiring a credit evaluation value of the acquisition source, and adjusting the corresponding first effect value based on the credit evaluation value, wherein an adjustment formula is as follows:

wherein β' is the first effect value after adjustment, β is the first effect value before adjustment, γ_2,0Is a preset credit rating threshold, gamma₂The credit evaluation value is the credit evaluation value;

after adjustment, taking the adjusted first effect value as a second effect value, and taking the rest first effect values as third effect values;

calculating a ranking index based on the second effect value and the third effect value, the calculation formula being as follows:

where σ is the ranking index, p_tIs the t-th second effect value, l₁Is the total number of the second effect values, o_tIs the t-th said third effect value, l₂Is the total number of the third effect values, epsilon is a preset constant, n₁Is the total number of the second effect values greater than or equal to a preset first effect value threshold, n₂Is the total number of the third effect values greater than or equal to a preset second effect value threshold, mu₁And mu₂The weight value is a preset weight value;

selecting a third intervention mode corresponding to the maximum value of the ranking index and taking the third intervention mode as a fourth intervention mode;

determining whether the fourth intervention mode is the same as the second intervention mode, if not, replacing the fourth intervention mode with the second intervention mode;

and finishing reforming after the second intervention modes needing replacing are replaced.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset exception type-alternative intervention mode library is specifically as follows: a database containing alternative intervention modes corresponding to different exception types; the preset empirical value threshold specifically comprises: for example, 75; the preset credit evaluation value threshold specifically includes: for example, 68; the preset constants are specifically: for example, 2; the preset first effect value is specifically as follows: for example, 90; the preset second effect value threshold specifically includes: 88;

acquiring a first effect value of a third intervention mode, wherein the acquisition source of the first effect value is divided into a local place (a local tester continuously performs experiments on the effect of the intervention mode) and a network (testers of other mechanisms continuously perform experiments on the effect of the intervention mode); when the source type is local, acquiring an experience value (which can be determined by the seniority of experimenters and the like); when the source type is network, obtaining credit evaluation value (which can be determined by the reliability of the data provided in the past); adjusting the first effect value based on the experience value and the credit evaluation value, wherein in the adjustment formula, the size of the experience value and the size of the credit evaluation value are in positive correlation with the adjusted first effect value, namely the smaller the experience value or the smaller the credit evaluation value is, the smaller the adjusted first effect value is; calculating a sorting index based on the adjusted first effect value, namely the second effect value, and the first effect value, namely the third effect value which is not adjusted, wherein in the formula, the size of the second effect value and the size of the third effect value are in positive correlation with the size of the sorting index;

the embodiment of the invention regularly reforms the abnormal type-intervention mode library, ensures that the optimal intervention mode can be determined, and improves the intervention effect; meanwhile, the optimal intervention mode is quickly determined through the formula, and the working efficiency of the system is greatly improved.

An embodiment of the present invention provides a mental health dynamic management system, as shown in fig. 2, further including:

the psychological consultation module 4 is used for the user to perform psychological consultation on a psychologist;

psychological consultation module 4 performs the following operations:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by a user from a psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

The working principle and the beneficial effects of the technical scheme are as follows:

can cooperate with psychologists, and the user can select the psychologist who wants to consult to perform online psychological consultation.

The embodiment of the invention provides a mental health dynamic management system, which further comprises:

and the early warning module is used for sending the first abnormal type to the monitoring terminal bound by the user when the evaluation result contains at least one first abnormal type.

The working principle and the beneficial effects of the technical scheme are as follows:

and when the evaluation result contains the first abnormal type, sending the first abnormal type to a monitoring terminal (such as a parent mobile phone) bound by the user for prompting and early warning.

An embodiment of the present invention provides a mental health dynamic management method, as shown in fig. 3, including:

step S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information;

step S2: when the evaluation result contains at least one first abnormal type, determining a first intervention mode corresponding to the first abnormal type based on a preset abnormal type-intervention mode library;

step S3: based on the first intervention mode, corresponding intervention is carried out.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset exception type-intervention mode library is specifically as follows: a database containing first intervention modes (such as guiding the user to relax) corresponding to different first abnormal types (such as excessive anxiety);

acquiring behavior information of a user (for example, what action is generated in what environment, the three-dimensional outline scanning of the environment where the user is located can be realized based on the millimeter wave radar sensor, the user can wear wearing equipment provided with the millimeter wave radar sensor, and what webpage the user accesses can be realized); evaluating the current mental health state of the user based on the behavior information; if the evaluation result contains the first abnormal type, determining a first intervention mode, and performing intervention in time (when intervention is performed, the intervention can be performed based on the wearable equipment);

the embodiment of the invention evaluates the current mental health state of the user based on the behavior information of the user, determines a corresponding first intervention mode when the evaluation result contains a first abnormal type, intervenes in time, helps people with mental disorder to rescue in time when the people generate an overstimulation behavior, and does not need to monitor the situation all the time.

The embodiment of the invention provides a mental health dynamic management method, comprising the following steps of S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information, wherein the evaluation comprises the following steps:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capture strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first features and second features in the risk feature library, if the first features are matched with the second features, taking the second features matched with the first features as third features, and taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type comprises: individual risks and combined risks;

acquiring at least one first captured scene involved in a second process;

when the risk type of the risk corresponding to the third feature is an individual risk, acquiring the reliability of the captured scene, and if the reliability is less than or equal to a preset reliability threshold, rejecting the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first processes before and/or after the second process from the stream program list, and taking the first processes as third processes;

acquiring at least one second capturing scene related to a third process;

obtaining at least one wagering relationship between a first captured scene and a second captured scene, determining a wager type for the wagering relationship, the wager type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

performing feature matching on the fourth features and the combined risk features;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, removing the corresponding first capture node;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of a user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting information to be predicted into the prediction model, and acquiring a prediction result;

obtaining a prediction process of generating a prediction result by a prediction model;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking a corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting a first process after a second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting a second process and a third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

rejecting first capture nodes except the second capture node in the first capture nodes;

when first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

acquiring at least one latest behavior information item through a third capture node;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset risk characteristic library specifically comprises the following steps: a database containing a plurality of risk features in the process of capturing network behavior (e.g., lowering security levels, accessing hyperlinks not belonging to on-site resources); the preset confidence threshold specifically comprises: for example, 98; the preset risk feature-combined risk feature library specifically comprises the following steps: combined risk features corresponding to different risk features are included (for example, the security level is continuously reduced, and a hyperlink in a webpage corresponding to the hyperlink is accessed); the preset number is specifically as follows: for example, 3; the preset first guarantee value threshold specifically is: for example, 85; the preset second guarantee value threshold specifically is: for example, 90; the preset prediction model specifically comprises: the model is generated after a large amount of records of manual behaviors predicted based on historical behavior information are learned by a machine learning algorithm, and the model can predict the behaviors; the preset non-standard feature library specifically comprises the following steps: the method comprises the following steps of including non-standard characteristics of a plurality of prediction models in the prediction process (for example, when certain type of behavior data is discontinuous, self-completing is carried out); the preset feature-judgment value library specifically comprises: a database (e.g., 50) containing decision values corresponding to different non-canonical features; the preset influence analysis model specifically comprises the following steps: the model is generated after learning a large number of records of influences of irregular features of the manual analysis prediction model prediction process on the subsequent process by using a machine learning algorithm, the influence can be analyzed by the model, and an influence value is obtained, wherein the larger the influence value is, the larger the influence value is; the preset judgment value threshold specifically comprises the following steps: for example, 75; the preset exception type-capturing node library is specifically as follows: a database containing capture nodes corresponding to different first exception types; the preset mental health assessment model specifically comprises the following steps: a model generated after learning a record of a large number of manual (for example: psychologists) psychophysical evaluations by using a machine learning algorithm, wherein the model can perform the psychophysical evaluations;

the capture objects of the first capture node (behavior capture data acquisition node) are divided into network behaviors (e.g., what web pages to access) and real-world behaviors (e.g., what actions to generate); when the capture object is a network behavior, capturing by adopting different capture strategies, performing strategy decomposition and flow analysis (executing sequence analysis) on the capture strategies to obtain a flow sequence (for example, flow 1, accessing a current webpage browsed by a user, flow 2, acquiring webpage content, extracting keywords, flow 3, if a hyperlink exists, reducing the security level, accessing the hyperlink, flow 4, if a new hyperlink exists in the webpage corresponding to the hyperlink, continuously reducing the security level, and accessing the new hyperlink); matching the first characteristic and the second characteristic of the first process, if the first characteristic and the second characteristic are matched, determining a third characteristic (for example, reducing the security level and accessing the hyperlink) in the process 3, determining a risk type, wherein the risk type is divided into an individual risk (for example, Trojan horse exists in the hyperlink) and a combined risk (potential Trojan horse exists in the hyperlink, and a code for activating the password exists in the hyperlink in the webpage corresponding to the hyperlink); when the risk is an independent risk, acquiring the credibility of a capturing scene (such as a webpage), and if the credibility is lower, removing a corresponding first capturing node; if the combined risk is the combined risk, determining the combined risk characteristics (for example, 'continuously reducing the security level, accessing a new hyperlink'), ensuring the security of the hyperlink to form a guarantee relationship when the hyperlink is set in a general website, wherein the guarantee types are an active guarantee (guaranteeing the hyperlink webpage by a webpage per se) and a passive guarantee (belonging to the same network company), and the guarantee value is larger, and the guarantee strength is larger; if the first guarantee value is too small and/or the second guarantee value is too small and/or the fourth feature (namely 'continuously reducing the security level and accessing a new hyperlink' in the process 4) is matched and matched with the combined risk feature, indicating that the capturing process has serious risk and is not feasible, and removing a corresponding first capturing node; when the capture object is a real behavior, acquiring behavior information of the user acquired before, integrating and inputting the behavior information into a prediction model, predicting the behavior of the user which possibly occurs next, and removing irrelevant first capture nodes; however, the degree of automation of the prediction model is high, and manual work cannot intervene in the prediction process, so that whether non-standard features exist in the prediction process of the prediction model needs to be judged, influences are analyzed, and whether irrelevant first capture nodes are removed is determined; after all the first capture nodes needing to be removed are removed, behavior information is obtained through the remaining third capture nodes, and the behavior information is input into a mental health assessment model for assessment;

the embodiment of the invention is provided with a plurality of first capture nodes, different verifications are carried out based on different capture objects, when the capture objects are network behaviors, the feasibility of a capture strategy is verified, the security and the accuracy of network behavior capture are ensured, when the capture objects are real behaviors, the prediction process of a prediction model is checked, the efficiency of a system is improved (irrelevant first capture nodes are removed), and meanwhile, the accuracy of behavior prediction is ensured; and finally, the mental health assessment model is used for assessing, so that the mental health assessment efficiency is improved.

The embodiment of the invention provides a mental health dynamic management method, comprising the following steps of S3: based on the first intervention mode, performing corresponding intervention, including:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing a virtual space;

after initialization is finished, judging whether a user enters a first initial area in a virtual space through VR equipment or not;

if yes, acquiring a first direction faced by a user in real time;

determining a virtual display corresponding to a first direction in a virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether a user enters a second starting area through VR equipment or not;

if so, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to a second direction in the virtual scene, and triggering and displaying a second psychology education material corresponding to the trigger point;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and a second psychology education material triggered previously in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention concentration of the user viewing a second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of a user watching a second psychological education material and a corresponding second concentration overall degree into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset intervention mode-virtual space library specifically comprises the following steps: virtual spaces (VR spaces, for example, with relaxed subjects in the VR spaces) corresponding to different first intervention modes (for example, guiding the user to relax); the preset threshold of the degree of attention concentration is specifically: for example, 95; the preset first time period specifically comprises the following steps: for example, 100 seconds; the preset time threshold specifically comprises: for example, 3; the preset psychology education material association library specifically comprises the following steps: including associations between different psychoeducational materials (e.g., mental relaxation music and mental relaxation exercises); the preset progress judgment model specifically comprises the following steps: a model generated after learning a large number of records of manual progress judgment by using a machine learning algorithm, wherein the model can judge whether the user can exit the virtual space or not based on the progress (such as playing to when, switching of the user) of the user watching psycho-educational materials and the overall degree of attention concentration;

when a first abnormal type is generated, prompting a user to wear VR equipment, initializing a virtual space, when the user is judged to enter a first initial area (the user enters the initial area of the virtual space), acquiring a first direction facing the user, controlling a corresponding virtual display (the display faces the user) to play a first mental education material (such as a relaxing tutorial), and acquiring a first overall degree of attention concentration (such as analyzing whether the user always watches, whether the user follows to do an action and the like) based on an attention analysis technology; if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold, the user needs to continue to conduct; if the first direction changes too much, the user dislikes the psychological education mode and actively switches; triggering a second starting area (an area in a virtual space) to guide a user to enter the area, triggering and constructing a virtual scene (for example, a forest with beautiful scenery) when judging that the user enters the area, acquiring a second direction, determining a trigger point (for example, suspension display 'relax operation') corresponding to the second direction (the user is triggered when looking at the second direction), determining whether a second psychological education material of a new team member is related to a previously triggered psychological education material when the second direction is changed, and if so, displaying screens in a split mode (for example, displaying a relax sound source MV and a relax operation teaching course video, wherein the user can do relax operation in the forest and is more beneficial to relax); judging whether the user can exit the virtual space or not based on the progress judging model, and if so, guiding the user to exit;

according to the embodiment of the invention, the virtual space corresponding to the first intervention mode can be determined, and a user can enter the virtual space through VR equipment to receive targeted psychological education; when the user is determined to dislike the former psychological education mode, the user does not need to actively switch (the user is not vexed due to the need of active switching), the method is more reasonable, and the method can effectively help the user to psychologically dredge.

The embodiment of the invention provides a mental health dynamic management method, which further comprises the following steps:

regularly reforming the abnormal type-intervention mode library;

wherein periodically reforming the exception type-intervention mode library comprises:

extracting a plurality of corresponding combinations in the anomaly type-intervention mode library, wherein the corresponding combinations comprise: a third anomaly type and a second intervention mode;

determining at least one alternative third intervention mode corresponding to the third anomaly type based on a preset anomaly type-alternative intervention mode library;

acquiring at least one first effect value of the third intervention mode, and outputting an acquisition source of the first effect value;

determining a source type of the acquisition source, wherein the source type comprises: local and network;

when the source type of the acquisition source is local, acquiring an experience value of the acquisition source, and adjusting the corresponding first effect value based on the experience value, wherein an adjustment formula is as follows:

wherein α' is the first effect value after adjustment, α is the first effect value before adjustment, γ_1,0For a preset empirical threshold, gamma₁Is the empirical value;

when the source type of the acquisition source is a network, acquiring a credit evaluation value of the acquisition source, and adjusting the corresponding first effect value based on the credit evaluation value, wherein an adjustment formula is as follows:

wherein β' is the first effect value after adjustment, β is the first effect value before adjustment, γ_2,0Is a preset credit rating threshold, gamma₂The credit evaluation value is the credit evaluation value;

after adjustment, taking the adjusted first effect value as a second effect value, and taking the rest first effect values as third effect values;

calculating a ranking index based on the second effect value and the third effect value, the calculation formula being as follows:

where σ is the ranking index, p_tIs the t-th second effect value, l₁Is the total number of the second effect values, o_tIs the t-th said third effect value, l₂Is the total number of the third effect values, epsilon is a preset constant, n₁Is the total number of the second effect values greater than or equal to a preset first effect value threshold, n₂Is the total number of the third effect values greater than or equal to a preset second effect value threshold, mu₁And mu₂The weight value is a preset weight value;

selecting a third intervention mode corresponding to the maximum value of the ranking index and taking the third intervention mode as a fourth intervention mode;

determining whether the fourth intervention mode is the same as the second intervention mode, if not, replacing the fourth intervention mode with the second intervention mode;

and finishing reforming after the second intervention modes needing replacing are replaced.

The working principle and the beneficial effects of the technical scheme are as follows:

the preset exception type-alternative intervention mode library is specifically as follows: a database containing alternative intervention modes corresponding to different exception types; the preset empirical value threshold specifically comprises: for example, 75; the preset credit evaluation value threshold specifically includes: for example, 68; the preset constants are specifically: for example, 2; the preset first effect value is specifically as follows: for example, 90; the preset second effect value threshold specifically includes: 88;

acquiring a first effect value of a third intervention mode, wherein the acquisition source of the first effect value is divided into a local place (a local tester continuously performs experiments on the effect of the intervention mode) and a network (testers of other mechanisms continuously perform experiments on the effect of the intervention mode); when the source type is local, acquiring an experience value (which can be determined by the seniority of experimenters and the like); when the source type is network, obtaining credit evaluation value (which can be determined by the reliability of the data provided in the past); adjusting the first effect value based on the experience value and the credit evaluation value, wherein in the adjustment formula, the size of the experience value and the size of the credit evaluation value are in positive correlation with the adjusted first effect value, namely the smaller the experience value or the smaller the credit evaluation value is, the smaller the adjusted first effect value is; calculating a sorting index based on the adjusted first effect value, namely the second effect value, and the first effect value, namely the third effect value which is not adjusted, wherein in the formula, the size of the second effect value and the size of the third effect value are in positive correlation with the size of the sorting index;

the embodiment of the invention regularly reforms the abnormal type-intervention mode library, ensures that the optimal intervention mode can be determined, and improves the intervention effect; meanwhile, the optimal intervention mode is quickly determined through the formula, and the working efficiency of the system is greatly improved.

The embodiment of the invention provides a mental health dynamic management method, which further comprises the following steps:

the user can make psychological consultation with a psychologist;

wherein, supply the user to carry out psychological consultation to psychologist, include:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by a user from a psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

The working principle and the beneficial effects of the technical scheme are as follows:

can cooperate with psychologists, and the user can select the psychologist who wants to consult to perform online psychological consultation.

The embodiment of the invention provides a mental health dynamic management method, which is used for sending a first abnormal type to a monitoring terminal bound by a user when an evaluation result contains at least one first abnormal type.

The working principle and the beneficial effects of the technical scheme are as follows:

and when the evaluation result contains the first abnormal type, sending the first abnormal type to a monitoring terminal (such as a parent mobile phone) bound by the user for prompting and early warning.

It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A mental health dynamics management system, comprising:

the evaluation module is used for dynamically acquiring the behavior information of the user and evaluating the current mental health state of the user based on the behavior information;

the determining module is used for determining a first intervention mode corresponding to the first exception type based on a preset exception type-intervention mode library when the evaluation result comprises at least one first exception type;

and the intervention module is used for performing corresponding intervention based on the first intervention mode.

2. The mental health dynamics management system of claim 1, wherein the assessment module performs the following operations:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capturing strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first feature and a second feature in the risk feature library, if the first feature is matched and matched with the second feature, taking the second feature matched and matched as a third feature, and simultaneously taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type includes: individual risks and combined risks;

acquiring at least one first captured scene involved in the second process;

when the risk type of the risk corresponding to the third feature is an individual risk, obtaining the reliability of the capture scene, and if the reliability is less than or equal to a preset reliability threshold, removing the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first flows before and/or after the second flow from the flow sequence, and taking the first flows as third flows;

acquiring at least one second capturing scene related to the third flow;

obtaining at least one guaranty relationship between the first capture scenario and a second capture scenario, determining a guaranty type for the guaranty relationship, the guaranty type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

feature matching the fourth feature with the combined risk feature;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, the corresponding first capture node is removed;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of the user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting the information to be predicted into the prediction model, and acquiring a prediction result;

acquiring a prediction process of the prediction model for generating the prediction result;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking the corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting the first process after the second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting the second process and the third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

culling the first capture nodes except the second capture node from the first capture nodes;

when the first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

obtaining, by the third capture node, the latest at least one behavior information item;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

3. The mental health dynamics management system of claim 1, wherein the intervention module performs the following operations:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing the virtual space;

after initialization is finished, judging whether the user enters a first initial area in the virtual space through VR equipment or not;

if yes, acquiring a first direction faced by the user in real time;

determining a virtual display corresponding to the first direction in the virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value, and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether the user enters the second starting area through VR equipment or not;

if yes, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to the second direction in the virtual scene, triggering a second psychology education material corresponding to the trigger point and displaying the second psychology education material;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and the second psychology education material triggered before in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention focus of the user on viewing the second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of the user watching the second psychological education material and the overall degree corresponding to the second attention concentration into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

4. The mental health dynamics management system of claim 1, further comprising:

the psychological consultation module is used for the user to perform psychological consultation on a psychologist;

the psychological consultation module performs the following operations:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by the user from the psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

5. The mental health dynamics management system of claim 1, further comprising:

and the early warning module is used for sending the first abnormal type to the monitoring terminal bound by the user when the evaluation result contains at least one first abnormal type.

6. A method for managing mental health dynamics, comprising:

step S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information;

step S2: when the evaluation result contains at least one first abnormal type, determining a first intervention mode corresponding to the first abnormal type based on a preset abnormal type-intervention mode library;

step S3: and performing corresponding intervention based on the first intervention mode.

7. The mental health dynamics management method according to claim 6, wherein the step S1: dynamically acquiring behavior information of a user, and evaluating the current mental health state of the user based on the behavior information, wherein the evaluation comprises the following steps:

acquiring a preset capture node set, wherein the capture node set comprises: a plurality of first capture nodes;

acquiring a capture object corresponding to the first capture node, wherein the capture object comprises: network behavior and reality behavior;

when a capture object corresponding to the first capture node is a network behavior, acquiring a capture strategy corresponding to the first capture node;

carrying out strategy decomposition on the capturing strategy to obtain a plurality of decomposition strategies;

carrying out flow analysis on the decomposition strategy to obtain a flow sequence;

traversing a first flow in the flow sequence according to the flow sequence;

performing feature extraction on the traversed first flow to obtain at least one first feature;

acquiring a preset risk feature library, performing feature matching on the first feature and a second feature in the risk feature library, if the first feature is matched and matched with the second feature, taking the second feature matched and matched as a third feature, and simultaneously taking the corresponding first process as a second process;

obtaining a risk type of a risk corresponding to the third feature, wherein the risk type includes: individual risks and combined risks;

acquiring at least one first captured scene involved in the second process;

when the risk type of the risk corresponding to the third feature is an individual risk, obtaining the reliability of the capture scene, and if the reliability is less than or equal to a preset reliability threshold, removing the corresponding first capture node;

when the risk type of the risk corresponding to the third feature is a combined risk, determining at least one combined risk feature corresponding to the third feature based on a preset risk feature-combined risk feature library;

selecting a preset number of first flows before and/or after the second flow from the flow sequence, and taking the first flows as third flows;

acquiring at least one second capturing scene related to the third flow;

obtaining at least one guaranty relationship between the first capture scenario and a second capture scenario, determining a guaranty type for the guaranty relationship, the guaranty type comprising: active and passive warranties;

when the guarantee type of the guarantee relationship is active guarantee, analyzing the guarantee relationship to obtain a first guarantee value;

when the guarantee type of the guarantee relationship is passive guarantee, analyzing the guarantee relationship to obtain a second guarantee value;

performing feature extraction on the third flow to obtain at least one fourth feature;

feature matching the fourth feature with the combined risk feature;

if the fourth characteristic is matched and matched with the combined risk characteristic, and/or the first guarantee value is smaller than or equal to a preset first guarantee value threshold value, and/or the second guarantee value is smaller than or equal to a preset second guarantee value threshold value, the corresponding first capture node is removed;

when a capture object corresponding to the first capture node is a real behavior, acquiring behavior information of the user acquired before and integrating the behavior information to obtain information to be predicted;

acquiring a preset prediction model, inputting the information to be predicted into the prediction model, and acquiring a prediction result;

acquiring a prediction process of the prediction model for generating the prediction result;

performing process analysis on the prediction process to obtain a process sequence;

traversing a first process in the process sequence according to the process sequence;

performing feature extraction on the traversed first process to obtain a plurality of fifth features;

acquiring a preset non-standard feature library, performing feature matching on the fifth feature and a sixth feature in the non-standard feature library, if the matching is in accordance with the sixth feature, taking the sixth feature in accordance with the matching as a seventh feature, and simultaneously taking the corresponding first process as a second process;

inquiring a preset feature-judgment value library, and determining a judgment value corresponding to the seventh feature;

selecting the first process after the second process in the process sequence as a third process;

acquiring a preset influence analysis model, inputting the second process and the third process into the influence analysis model, and acquiring an influence value;

after traversing is finished, when the judgment values are all smaller than or equal to a preset judgment value threshold and the influence values are all smaller than or equal to a preset influence value threshold, trying to extract at least one second abnormal type contained in the prediction result, and if the extraction is successful, determining at least one second capture node corresponding to the second abnormal type based on a preset abnormal type-capture node library;

culling the first capture nodes except the second capture node from the first capture nodes;

when the first capture nodes needing to be removed in the first capture nodes are all removed, taking the remaining first capture nodes as third capture nodes;

obtaining, by the third capture node, the latest at least one behavior information item;

integrating the acquired behavior information items to acquire the behavior information of the user, and finishing the acquisition;

and acquiring a preset mental health assessment model, inputting the behavior information into the mental health assessment model, acquiring an assessment result and finishing assessment.

8. The mental health dynamics management method according to claim 6, wherein the step S3: based on the first intervention mode, performing corresponding intervention, including:

determining a virtual space corresponding to the first intervention mode based on a preset intervention mode-virtual space library;

initializing the virtual space;

after initialization is finished, judging whether the user enters a first initial area in the virtual space through VR equipment or not;

if yes, acquiring a first direction faced by the user in real time;

determining a virtual display corresponding to the first direction in the virtual space, and controlling the first virtual display to play a first psychological education material;

if the virtual display corresponding to the first direction changes, controlling the changed virtual display to relay and play the first mental education material;

after the first mental education material is played, analyzing the total degree of first attention concentration of the user watching the first mental education material based on an attention analysis technology;

if the first attention focusing overall degree is larger than or equal to a preset attention focusing overall degree threshold value, guiding the user to exit the virtual space;

if the first attention focusing overall degree is smaller than the attention focusing overall degree threshold value, and/or the number of times of changes of the virtual display corresponding to the first direction in a preset first time period is larger than or equal to a preset number threshold value, controlling the virtual display to stop playing, and simultaneously triggering a second starting area in the virtual space;

judging whether the user enters the second starting area through VR equipment or not;

if yes, triggering the virtual space to construct a virtual scene, and guiding the user to move in the virtual scene;

acquiring a second direction faced by the user in real time in the moving process of the user;

determining a trigger point corresponding to the second direction in the virtual scene, triggering a second psychology education material corresponding to the trigger point and displaying the second psychology education material;

if the trigger point corresponding to the second direction changes, determining whether a second psychology education material corresponding to the new trigger point is associated with a previously triggered second psychology education material or not based on a preset psychology education material association library;

if yes, displaying a second psychology education material corresponding to the new trigger point and the second psychology education material triggered before in a split screen mode;

if not, only displaying a second psychological education material corresponding to the new trigger point;

acquiring progress information of the user watching the second mental education material;

analyzing a second overall degree of attention focus of the user on viewing the second mental education material based on an attention analysis technique;

acquiring a preset progress judgment model, inputting progress information of the user watching the second psychological education material and the overall degree corresponding to the second attention concentration into the progress judgment model, and acquiring a judgment result;

and if the judgment result is that the progress is qualified, guiding the user to exit the virtual space.

9. The mental health dynamics management method of claim 6, further comprising:

providing the user with psychological consultation to a psychologist;

wherein, supply the user to carry out psychological consultation to psychologist, include:

acquiring and displaying a preset psychologist list;

receiving a psychologist selected by the user from the psychologist list;

and establishing a chat window, and accessing the user and the psychologist into the chat window.

10. The mental health dynamics management method of claim 6, further comprising:

and when the evaluation result contains at least one first abnormal type, sending the first abnormal type to the monitoring terminal bound by the user.

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