Data method for depressive state monitoring

Abstract

The invention discloses a data method for depressive state monitoring, which comprises the following steps: acquiring personal basic information of a depressive disorder tester, and matching corresponding test content data based on the personal basic information; obtaining response content data of the depression tester, extracting first response emotion feature data, a corresponding emotion type and an emotion score based on a preset deep learning model, and obtaining second response emotion feature data, a corresponding emotion type and an emotion score based on a preset emotion driving model; establishing an emotion matrix of the depression testers according to the emotion types and the emotion scores, and calling a depression emotion matrix in a depression database, and calculating a first depression state evaluation value and a second depression state evaluation value corresponding to the first emotion matrix, the second emotion matrix and the depression emotion matrix, comparing the first depression state evaluation value and the second depression state evaluation value with a preset depression emotion threshold value to obtain a depression comparison result, and judging the mental health state of the depression tester according to the depression comparison result.

Description

Technical Field

[0001] The present invention relates to the technical field of mental health detection, and in particular to a data method for monitoring depression status. Background Art

[0002] Health is not merely the absence of disease and physical weakness, but a state of perfect physical, mental and social adaptation. Mental health is as important as physical health. A good and complete mental health intervention system is an indispensable part of the national public health system and preventive medicine.

[0003] At present, in the Chinese invention patent with the publication number CN115607159B, a method and device for identifying depression based on the analysis of the spatiotemporal characteristics of eye movement sequences are disclosed, including obtaining the eye movement data of the test subject to be identified when viewing a combination of positive and negative emotional pictures; inputting the eye movement data of the test subject to be identified into a depression identification network based on the spatiotemporal characteristics of eye movement sequences; obtaining the depression identification result of the test subject to be identified according to the eye movement data of the test subject to be identified and the depression identification network based on the spatiotemporal characteristics of eye movement sequences. However, the related art does not comprehensively characterize the emotions initially judged and the emotions after subsequent driving in the emotional judgment of the depressive state. The current judgment of the depressive state lacks dynamism and continuity, and the emotional judgment is relatively simple and general. There is no subsequent inductive verification based on the emotions initially expressed by the test subject, which easily leads to the detection of accidental emotions and abnormal emotions in the emotional judgment process. Summary of the invention

[0004] The technical problem solved by the present invention is: there is no comprehensive representation of the emotions initially judged and the subsequent driven emotions in the emotional judgment of the depressive state, the judgment of the current depressive state lacks dynamism and continuity, the emotional judgment is relatively simple and general, and there is no subsequent inductive verification based on the emotions initially expressed by the test subject, which easily leads to incomplete detection of accidental emotions and abnormal emotions in the emotional judgment process.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a data method for monitoring depression status, specifically comprising the following steps:

[0006] Step S100, collecting basic personal information of the depression test subject, and matching corresponding test content data based on the basic personal information;

[0007] Step S200, the depression tester responds to the test content data to obtain corresponding response content data, extracts first response emotional feature data of the response content data based on a preset deep learning model, obtains the emotional type and emotional score corresponding to the first response emotional feature data, inputs the first response emotional feature data into a preset emotion-driven model, and obtains second response emotional feature data and the corresponding emotional type and emotional score;

[0008] Step S300, establishing an emotion matrix of a depression tester according to the emotion types and emotion scores, wherein the emotion matrix includes a first emotion matrix and a second emotion matrix, wherein the rows of the emotion matrix are emotion types, and the columns of the emotion matrix are emotion scores corresponding to the response emotion feature data;

[0009] Step S400, retrieve the depression emotion matrix in the depression database, calculate the first emotion matrix, the second emotion matrix and the first depression state assessment value and the second depression state assessment value corresponding to the depression emotion matrix, compare the first depression state assessment value and the second depression state assessment value with a preset depression emotion threshold to obtain a depression comparison result, and judge the mental health state of the depression tester according to the depression comparison result.

[0010] As a preferred solution of the data method for monitoring depression status described in the present invention, wherein: the test content data includes test text data, test image data, test video data and test question data;

[0011] The response content data includes response text data, response image data, response video data and response question data;

[0012] The response emotion feature data includes voice emotion features, eye movement emotion features, facial emotion features and body emotion features.

[0013] As a preferred solution of the data method for monitoring depression state described in the present invention, step S100 specifically includes:

[0014] Step S101, collecting basic personal information of a depression test subject, the basic personal information including the age, gender and region of the depression test subject, inputting the age and gender into a test content database, and the test content database outputting first test content data corresponding to the depression test subject;

[0015] Step S102, inputting the region into the test content database, the test content database acquiring cultural information data of the region, and outputting second test content data corresponding to the depression tester according to the cultural information data;

[0016] Step S103: merge and splice the first test content data and the second test content data to match corresponding test content data.

[0017] As a preferred solution of the data method for monitoring depression state described in the present invention, step S200 specifically includes:

[0018] Step S201, obtaining test text data, test image data, test video data and test question data corresponding to the depression test subject;

[0019] Step S202, the depression test subject responds to the test text data, test image data, test video data and test question data to obtain corresponding response text data, response image data, response video data and response question data;

[0020] Step S203, inputting the response text data, response image data, response video data and response question data into a preset deep learning model to extract the first response emotion feature data, and obtaining the emotion type and emotion score corresponding to the first response emotion feature data;

[0021] Step S204: input the first response emotion characteristic data into a preset emotion-driven model to obtain the second response emotion characteristic data and the corresponding emotion type and emotion score.

[0022] As a preferred solution of the data method for monitoring depression state described in the present invention, step S300 specifically includes:

[0023] Step S301, using the emotion types corresponding to the first response emotion feature data output by the deep learning model as the rows of the first emotion matrix, and using the emotion scores corresponding to the first response emotion feature data as the columns of the first emotion matrix;

[0024] Step S302, using the emotion types corresponding to the second response emotion feature data output by the deep learning model as rows of the second emotion matrix, and using the emotion scores corresponding to the second response emotion feature data as columns of the second emotion matrix;

[0025] Step S303, filling the first emotion matrix and the second emotion matrix, if the corresponding emotion scores and emotion types do not exist, fill in 0 operation.

[0026] As a preferred solution of the data method for monitoring depression state described in the present invention, step S400 specifically includes:

[0027] Step S401, input the basic personal information of the depression tester into the retrieved depression database to obtain the corresponding depression emotion matrix. The expression of the depression emotion matrix is: i ranges from 1 to 4;

[0028] Step S402: Calculate the first emotion matrix Second Emotion Matrix The first depression state evaluation value and the second depression state evaluation value corresponding to the depression mood matrix, the mathematical expression of the first depression state evaluation value is as follows:

[0029]

[0030] Wherein, P1 represents the first depression state assessment value, β j represents the weight of the emotion type of the first emotion matrix, d 1i represents the emotion score of the emotion category in the first emotion matrix, v 1i The emotion scores representing the emotion categories of the depression emotion matrix;

[0031] The mathematical expression of the first depression state assessment value is as follows:

[0032]

[0033] Wherein, P2 represents the second depressive state assessment value, γ j represents the weight of the emotion category of the second emotion matrix, and r1i represents the emotion score of the emotion category of the second emotion matrix;

[0034] Step S403, the depression mood threshold includes a first depression mood threshold and a second depression mood threshold, the first depression state assessment value and the first depression mood threshold T1 are used to obtain a first depression comparison result, and the second depression state assessment value and the second depression mood threshold T2 are used to obtain a second depression comparison result; the mental health state of the depression tester is judged according to the first depression comparison result and the second depression comparison result.

[0035] As a preferred solution of the data method for monitoring depression status described in the present invention, the emotion types include fear, sadness, disgust, anger, happiness, calmness and surprise; and the emotion score ranges from 0 to 1.

[0036] As a preferred solution of the data method for monitoring depression state described in the present invention, the specific method of obtaining the voice emotion characteristics, eye movement emotion characteristics, facial emotion characteristics and body emotion characteristics includes:

[0037] Collect the response data and response video data generated by depression testers answering multiple test questions in real time, put the collected response data and response video data into a pre-trained deep learning model, and extract voice emotion features and facial emotion features;

[0038] Real-time depression collection of response video data generated by testers when watching multiple test video data, the collected response video data is put into a pre-trained deep learning model to extract facial emotion features and body emotion features;

[0039] The response image data generated by the depression tester when viewing multiple test image data is collected in real time, and the collected response image data is put into the pre-trained deep learning model to extract the eye movement emotion features;

[0040] The response text data generated by depression testers when reading multiple test text data are collected in real time, and the collected response text data are put into a pre-trained deep learning model to extract voice emotion features.

[0041] As a preferred solution of the data method for monitoring depression state described in the present invention, the specific method for obtaining the weight of the emotion type includes: calculating the sub-attention allocation result of the response text data, response image data, response video data and response question data to depression emotion based on the attention mechanism;

[0042] The emotion weights of the fear, sadness, disgust, anger, happiness, calmness and surprise to the depressive emotion are calculated based on the sub-attention allocation results.

[0043] As a preferred solution of the data method for monitoring depression status described in the present invention, the specific method for judging the mental health status of the depression tester according to the first depression comparison result and the second depression comparison result includes:

[0044] When P1 is greater than T1 and P2 is greater than T2, it is severe depression and requires immediate psychological intervention;

[0045] When P1 is greater than T1 and P2 is less than T2, it is moderate depression and needs to be communicated with a psychology teacher;

[0046] When P1 is less than T1 and P2 is greater than T2, it is mild depression and the depressed test-taker needs to adjust his / her mood.

[0047] When P1 is less than T1 and P2 is less than T2, the person is mentally healthy and no intervention is needed.

[0048] The beneficial effects of the present invention are as follows: by matching the corresponding test content data with the personal basic information (including age, gender and region) of the depression tester, the depression tester can be provided with targeted and personalized test content data, thereby avoiding the erroneous judgment of depression caused by the single test content data and individual differences, and improving the accuracy and precision of the judgment of the depression state; based on the test text data, test image data, test video data and test question data, a plurality of methods are used to preliminarily judge the emotion type and the corresponding emotion score of the depression tester; based on the first response emotion feature data of the depression tester to the test content data, the emotion-driven model matches the corresponding driving emotion question according to the response emotion feature data, obtains the second response emotion feature data of the depression tester to the driving emotion question, compares the first response emotion feature data and the second response emotion feature data with the preset first depression emotion threshold and the first depression emotion threshold, and can mobilize the emotion development trend of the depression tester, thereby avoiding the one-sided judgment of emotion when diagnosing depression, and improving the anti-interference of emotion judgment, so as to obtain the data that best reflects the real depression state of the depression tester, and play a multimodal dual judgment of depression emotion, thereby improving the judgment of the mental health state of the depression tester. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A basic flow chart of a data method for monitoring depression status provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0051] Example, see Figure 1 , is an embodiment of the present invention, and provides a data method for monitoring depression status, which specifically includes the following steps:

[0052] Step S100, collecting basic personal information of the depression test subject, and matching corresponding test content data based on the basic personal information;

[0053] Step S200, the depression tester responds to the test content data to obtain corresponding response content data, extracts first response emotional feature data of the response content data based on a preset deep learning model, obtains the emotional type and emotional score corresponding to the first response emotional feature data, inputs the first response emotional feature data into a preset emotion-driven model, and obtains second response emotional feature data and the corresponding emotional type and emotional score;

[0054] Step S300, establishing an emotion matrix of a depression tester according to the emotion types and emotion scores, wherein the emotion matrix includes a first emotion matrix and a second emotion matrix, wherein the rows of the emotion matrix are emotion types, and the columns of the emotion matrix are emotion scores corresponding to the response emotion feature data;

[0055] Step S400, retrieve the depression emotion matrix in the depression database, calculate the first emotion matrix, the second emotion matrix and the first depression state assessment value and the second depression state assessment value corresponding to the depression emotion matrix, compare the first depression state assessment value and the second depression state assessment value with a preset depression emotion threshold to obtain a depression comparison result, and judge the mental health state of the depression tester according to the depression comparison result.

[0056] The test content data includes test text data, test image data, test video data and test question data;

[0057] The response content data includes response text data, response image data, response video data and response question data;

[0058] The response emotion feature data includes voice emotion features, eye movement emotion features, facial emotion features and body emotion features.

[0059] In this embodiment, the test content data covers a wide range of aspects and diverse forms, which can realize multimodal emotional judgment of depression test subjects, making the judgment of the mental health status of depression test subjects more comprehensive and convincing.

[0060] Step S100 specifically includes:

[0061] Step S101, collecting basic personal information of a depression test subject, the basic personal information including the age, gender and region of the depression test subject, inputting the age and gender into a test content database, and the test content database outputting first test content data corresponding to the depression test subject;

[0062] Step S102, inputting the region into the test content database, the test content database acquiring cultural information data of the region, and outputting second test content data corresponding to the depression tester according to the cultural information data;

[0063] Step S103: merge and splice the first test content data and the second test content data to match corresponding test content data.

[0064] In this embodiment, by matching the corresponding test content data with the basic personal information (including age, gender and region) of the depression test subject, targeted and personalized test content data can be provided to the depression test subject, thereby avoiding the erroneous judgment of depressive emotions caused by the single test content data and individual differences, and improving the accuracy and precision of the judgment of the depressive state.

[0065] Step S200 specifically includes:

[0066] Step S201, obtaining test text data, test image data, test video data and test question data corresponding to the depression test subject;

[0067] Step S202, the depression test subject responds to the test text data, test image data, test video data and test question data to obtain corresponding response text data, response image data, response video data and response question data;

[0068] Step S203, inputting the response text data, response image data, response video data and response question data into a preset deep learning model to extract the first response emotion feature data, and obtaining the emotion type and emotion score corresponding to the first response emotion feature data;

[0069] Step S204: input the first response emotion characteristic data into a preset emotion-driven model to obtain the second response emotion characteristic data and the corresponding emotion type and emotion score.

[0070] In this embodiment, the deep learning model can analyze the response text data, response image data, response video data and response question data of the depression test subject to obtain the corresponding emotion type and emotion score. The emotion score represents the intensity of the emotion type. For example, fear is divided into mild fear, moderate fear and strong fear. The emotion-driven model matches the corresponding driving emotion question according to the response emotion feature data. The depression test subject responds to the corresponding driving emotion question to obtain the second response emotion feature data of the depression test subject to the driving emotion question. The driving question is committed to reversing the emotion type and emotion score corresponding to the first response emotion feature data, thereby achieving double verification of the test subject's emotion type and avoiding the erroneous indicators of emotion judgment caused by the accidental appearance of a certain emotion type.

[0071] Step S300 specifically includes:

[0072] Step S301, using the emotion types corresponding to the first response emotion feature data output by the deep learning model as the rows of the first emotion matrix, and using the emotion scores corresponding to the first response emotion feature data as the columns of the first emotion matrix;

[0073] Step S302, using the emotion types corresponding to the second response emotion feature data output by the deep learning model as rows of the second emotion matrix, and using the emotion scores corresponding to the second response emotion feature data as columns of the second emotion matrix;

[0074] Step S303, filling the first emotion matrix and the second emotion matrix, if the corresponding emotion scores and emotion types do not exist, fill in 0 operation.

[0075] In this embodiment, the number of rows of the first emotion matrix is ​​7, the number of columns of the first emotion matrix is ​​5, the number of rows of the second emotion matrix is ​​7, and the number of columns of the second emotion matrix is ​​5. An emotion matrix is ​​made for the emotion types and the corresponding emotion scores, so that mathematical operations and weighting can be performed on the emotion matrix, making the emotion judgment operational and digital, and being able to intuitively express the emotion form.

[0076] Step S400 specifically includes:

[0077] Step S401, input the basic personal information of the depression tester into the retrieved depression database to obtain the corresponding depression emotion matrix. The expression of the depression emotion matrix is: i ranges from 1 to 4;

[0078] Step S402: Calculate the first emotion matrix Second Emotion Matrix The first depression state evaluation value and the second depression state evaluation value corresponding to the depression mood matrix, the mathematical expression of the first depression state evaluation value is as follows:

[0079]

[0080] Wherein, P1 represents the first depression state assessment value, β j represents the weight of the emotion type of the first emotion matrix, d 1i represents the emotion score of the emotion category in the first emotion matrix, v 1i The emotion scores representing the emotion categories of the depression emotion matrix;

[0081] The mathematical expression of the first depression state assessment value is as follows:

[0082]

[0083] Wherein, P2 represents the second depressive state assessment value, γ j represents the weight of the emotion category of the second emotion matrix, and r1i represents the emotion score of the emotion category of the second emotion matrix;

[0084] Step S403, the depression mood threshold includes a first depression mood threshold and a second depression mood threshold, the first depression state assessment value and the first depression mood threshold T1 are used to obtain a first depression comparison result, and the second depression state assessment value and the second depression mood threshold T2 are used to obtain a second depression comparison result; the mental health state of the depression tester is judged according to the first depression comparison result and the second depression comparison result.

[0085] In this embodiment, based on the first response emotional characteristic data of the depression tester to the test content data, the emotion-driven model matches the corresponding driving emotional question according to the response emotional characteristic data, and obtains the second response emotional characteristic data of the depression tester to the driving emotional question. This can eliminate abnormal emotions during the initial judgment, so that the emotional judgment process has a feedback correction effect, thereby improving the comprehensiveness of emotional judgment and avoiding the one-sidedness of emotional judgment.

[0086] The emotion types include fear, sadness, disgust, anger, happiness, calmness and surprise; the emotion score ranges from 0 to 1.

[0087] In this embodiment, the range of emotion scores is 0 to 1, and the size of the emotion score represents the emotion intensity of the corresponding emotion type. When the emotion score is less than 0.5, the emotion intensity of the corresponding emotion type is mild. When the emotion score is greater than 0.5 and less than 0.8, the emotion intensity of the corresponding emotion type is moderate. When the emotion score is greater than 0.8, the emotion intensity of the corresponding emotion type is strong. Segmenting the emotions of depression testers can increase the accuracy of emotion judgment.

[0088] The specific method of obtaining the voice emotion features, eye movement emotion features, facial emotion features and body emotion features includes:

[0089] Collect the response data and response video data generated by depression testers answering multiple test questions in real time, put the collected response data and response video data into a pre-trained deep learning model, and extract voice emotion features and facial emotion features;

[0090] Real-time depression collection of response video data generated by testers when watching multiple test video data, the collected response video data is put into a pre-trained deep learning model to extract facial emotion features and body emotion features;

[0091] The response image data generated by the depression tester when viewing multiple test image data is collected in real time, and the collected response image data is put into the pre-trained deep learning model to extract the eye movement emotion features;

[0092] The response text data generated by depression testers when reading multiple test text data are collected in real time, and the collected response text data are put into a pre-trained deep learning model to extract voice emotion features.

[0093] The specific method for obtaining the weight of the emotion type includes: calculating the sub-attention allocation result of the response text data, response image data, response video data and response question data to the depression emotion based on the attention mechanism;

[0094] The emotion weights of the fear, sadness, disgust, anger, happiness, calmness and surprise to the depressive emotion are calculated based on the sub-attention allocation results.

[0095] In this embodiment, even if the emotion types judged by different methods are the same, the persuasiveness of the corresponding emotion types is different. Calculating the persuasiveness of response text data, response image data, response video data, and response question data based on the attention mechanism can improve the comprehensiveness and authenticity of emotion judgment.

[0096] The specific method for judging the mental health status of the depression tester according to the first depression comparison result and the second depression comparison result includes:

[0097] When P1 is greater than T1 and P2 is greater than T2, it is severe depression and requires immediate psychological intervention;

[0098] When P1 is greater than T1 and P2 is less than T2, it is moderate depression and needs to be communicated with a psychology teacher;

[0099] When P1 is less than T1 and P2 is greater than T2, it is mild depression and the depressed test-taker needs to adjust his / her mood.

[0100] When P1 is less than T1 and P2 is less than T2, the person is mentally healthy and no intervention is needed.

[0101] In this embodiment, based on the preliminary first depression comparison result of the depression tester and the emotion-driven model, the corresponding driving emotional question is matched according to the first response emotional feature data, the second response emotional feature data of the depression tester to the driving emotional question is obtained, and the corresponding second depression state assessment value is calculated, and the second depression state assessment value is compared with the second depression emotional threshold to obtain the second depression comparison result. The second depression comparison result can reflect the extent to which the depression tester can be mobilized to have positive emotions. Diagnosing the mental health state according to the first depression comparison result and the second depression comparison result can improve the anti-interference ability of emotional judgment.

[0102] By matching the corresponding test content data with the basic personal information (including age, gender and region) of the depression tester, the depression tester can be provided with targeted and personalized test content data, avoiding the wrong judgment of depression caused by the single test content data and individual differences, and improving the accuracy and precision of the judgment of the depression state. Based on the test text data, test image data, test video data and test question data, a variety of methods are used to preliminarily judge the emotion type and the corresponding emotion score of the depression tester. Based on the first response emotion feature data of the depression tester to the test content data, the emotion-driven model matches the corresponding driving emotion question according to the response emotion feature data to obtain the second response emotion feature data of the depression tester to the driving emotion question. The first response emotion feature data and the second response emotion feature data are compared with the preset first depression emotion threshold and the first depression emotion threshold, which can mobilize the emotional development trend of the depression tester, thereby avoiding the one-sided judgment of emotion when diagnosing depression, and improving the anti-interference of emotion judgment, so as to obtain the data that best reflects the real depression state of the depression tester, and play a multimodal dual judgment of depression, thereby improving the judgment of the mental health state of the depression tester.

[0103] It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Among them, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0104] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A data method for monitoring depression status, characterized in that: include: Step S100, collecting basic personal information of the depression test subject, and matching corresponding test content data based on the basic personal information; Step S200, the depression tester responds to the test content data to obtain corresponding response content data, extracts first response emotional feature data of the response content data based on a preset deep learning model, obtains the emotional type and emotional score corresponding to the first response emotional feature data, inputs the first response emotional feature data into a preset emotion-driven model, and obtains second response emotional feature data and the corresponding emotional type and emotional score; Step S300, establishing an emotion matrix of a depression tester according to the emotion types and emotion scores, wherein the emotion matrix includes a first emotion matrix and a second emotion matrix, wherein the rows of the emotion matrix are emotion types, and the columns of the emotion matrix are emotion scores corresponding to the response emotion feature data; Step S400, retrieve the depression emotion matrix in the depression database, calculate the first emotion matrix, the second emotion matrix and the first depression state assessment value and the second depression state assessment value corresponding to the depression emotion matrix, compare the first depression state assessment value and the second depression state assessment value with a preset depression emotion threshold to obtain a depression comparison result, and judge the mental health state of the depression tester according to the depression comparison result.

2. A method for monitoring depression status according to claim 1, characterized in that: The test content data includes test text data, test image data, test video data and test question data; The response content data includes response text data, response image data, response video data and response question data; The response emotion feature data includes voice emotion features, eye movement emotion features, facial emotion features and body emotion features.

3. A method for monitoring depression status according to claim 2, characterized in that: Step S100 specifically includes: Step S101, collecting basic personal information of a depression test subject, the basic personal information including the age, gender and region of the depression test subject, inputting the age and gender into a test content database, and the test content database outputting first test content data corresponding to the depression test subject; Step S102, inputting the region into the test content database, the test content database acquiring cultural information data of the region, and outputting second test content data corresponding to the depression tester according to the cultural information data; Step S103: merge and splice the first test content data and the second test content data to match corresponding test content data.

4. A data method for monitoring depression status as claimed in claim 3, characterized in that: Step S200 specifically includes: Step S201, obtaining test text data, test image data, test video data and test question data corresponding to the depression test subject; Step S202, the depression test subject responds to the test text data, test image data, test video data and test question data to obtain corresponding response text data, response image data, response video data and response question data; Step S203, inputting the response text data, response image data, response video data and response question data into a preset deep learning model to extract the first response emotion feature data, and obtaining the emotion type and emotion score corresponding to the first response emotion feature data; Step S204: input the first response emotion characteristic data into a preset emotion-driven model to obtain the second response emotion characteristic data and the corresponding emotion type and emotion score.

5. A data method for monitoring depression status as claimed in claim 4, characterized in that: Step S300 specifically includes: Step S301, using the emotion types corresponding to the first response emotion feature data output by the deep learning model as rows of the first emotion matrix, and using the emotion scores corresponding to the first response emotion feature data as columns of the first emotion matrix; Step S302, using the emotion types corresponding to the second response emotion feature data output by the deep learning model as rows of the second emotion matrix, and using the emotion scores corresponding to the second response emotion feature data as columns of the second emotion matrix; Step S303, filling the first emotion matrix and the second emotion matrix, if the corresponding emotion scores and emotion types do not exist, fill in 0 operation.

6. A data method for monitoring depression status as claimed in claim 5, characterized in that: Step S400 specifically includes: Step S401, input the basic personal information of the depression test subject into the retrieved depression database to obtain the corresponding depression emotion matrix. The expression of the depression emotion matrix is: i ranges from 1 to 4; Step S402: Calculate the first emotion matrix Second Emotion Matrix The first depression state evaluation value and the second depression state evaluation value corresponding to the depression mood matrix, the mathematical expression of the first depression state evaluation value is as follows: Wherein, P1 represents the first depression state assessment value, β j represents the weight of the emotion type of the first emotion matrix, d 1i represents the emotion score of the emotion category in the first emotion matrix, v 1i The emotion scores representing the emotion categories of the depression emotion matrix; The mathematical expression of the first depression state assessment value is as follows: Wherein, P2 represents the second depressive state assessment value, γ j represents the weight of the emotion category of the second emotion matrix, and r1i represents the emotion score of the emotion category of the second emotion matrix; Step S403, the depression mood threshold includes a first depression mood threshold and a second depression mood threshold, the first depression state assessment value and the first depression mood threshold T1 are used to obtain a first depression comparison result, and the second depression state assessment value and the second depression mood threshold T2 are used to obtain a second depression comparison result; the mental health state of the depression tester is judged according to the first depression comparison result and the second depression comparison result.

7. A data method for monitoring depression status according to claim 6, characterized in that: The emotion types include fear, sadness, disgust, anger, happiness, calmness and surprise; the emotion score ranges from 0 to 1.

8. A data method for monitoring depression status according to claim 7, characterized in that: The specific method of obtaining the voice emotion features, eye movement emotion features, facial emotion features and body emotion features includes: Collect the response data and response video data generated by depression testers answering multiple test questions in real time, put the collected response data and response video data into a pre-trained deep learning model, and extract voice emotion features and facial emotion features; Real-time depression collection of response video data generated by testers when watching multiple test video data, and the collected response video data are put into a pre-trained deep learning model to extract facial emotion features and body emotion features; Collect the response image data generated by depression testers when they watch multiple test image data in real time, and put the collected response image data into a pre-trained deep learning model to extract eye movement emotion features; The response text data generated by depression testers when reading multiple test text data are collected in real time, and the collected response text data are put into a pre-trained deep learning model to extract voice emotion features.

9. A data method for monitoring depression status as claimed in claim 8, characterized in that: The specific method for obtaining the weight of the emotion type includes: calculating the sub-attention allocation result of the response text data, response image data, response video data and response question data to the depression emotion based on the attention mechanism; The emotion weights of the fear, sadness, disgust, anger, happiness, calmness and surprise to the depressive emotion are calculated based on the sub-attention allocation results.

10. A data method for monitoring depression status according to claim 9, characterized in that: The specific method for judging the mental health status of the depression tester according to the first depression comparison result and the second depression comparison result includes: When P1 is greater than T1 and P2 is greater than T2, it is severe depression and requires immediate psychological intervention; When P1 is greater than T1 and P2 is less than T2, it is moderate depression and needs to be communicated with a psychology teacher; When P1 is less than T1 and P2 is greater than T2, it is mild depression and the depressed test-taker needs to adjust his / her mood. When P1 is less than T1 and P2 is less than T2, the person is mentally healthy and no intervention is needed.

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