CN115472264A - Non-contact psychological state prediction method - Google Patents
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Abstract
The invention discloses a non-contact psychological state prediction method, which particularly relates to the technical field of psychological prediction, and is characterized in that different monitored personnel are classified and processed, a deep learning network model is trained to obtain a prediction model, and then video images of the monitored personnel obtained through monitoring data are subjected to feature extraction and then input into the prediction model for prediction, so that the emotional tendency of the monitored personnel is analyzed, the non-contact psychological state prediction of the monitored personnel is realized, the prediction reliability is improved, the time for the monitored personnel and questionnaire issuing and checking personnel is not influenced, and unnecessary psychological burden is not caused to the monitored personnel; in addition, the invention can simultaneously carry out psychological monitoring on a large amount of monitored personnel through monitoring data, is suitable for large-scale places with more personnel, such as training camps, factories, companies, schools and the like, and plays an important role in monitoring the psychological states of a large number of personnel.
Description
Technical Field
The invention relates to the technical field of psychological prediction, in particular to a non-contact type psychological state prediction method.
Background
Mental state is one of the basic forms of mental activity, and refers to the complete characteristics of mental activity over a certain period of time. For example, attention, fatigue, tension, relaxation, worry, joy and the like, the device has the characteristics of psychological process and individual psychological characteristics, has both temporary property and stability, is a link for mediating the psychological process and the individual psychological characteristics, and forms a background for developing all psychological activities.
At present, in some colleges and universities or employment environments, due to the fact that learning difficulty is high, achievement is nervous or pressure is too high, problems easily occur to the psychological states of people, when the psychological states of people have problems, if the psychological states of people cannot be found and intervened in time, the psychological problems are likely to be larger and larger, not only do academic work and work affected, but also serious people can hurt the physical health of people.
Disclosure of Invention
In order to overcome the above defects in the prior art, the present invention provides a non-contact psychological state prediction method, and the technical problem to be solved by the present invention is: the method for detecting the psychological state in the prior art has the problems of low reliability, personnel time waste and incapability of monitoring and processing by self.
In order to achieve the purpose, the invention provides the following technical scheme: a non-contact psychological state prediction method comprises the following steps:
s1, acquiring basic information of monitored personnel, and acquiring social data by using data through a social platform.
And S2, classifying the monitored personnel according to the similarity to obtain different categories.
And S3, obtaining precursor behaviors and expression changes of different classes of people with psychological problems through big data, and training the deep learning network model to obtain a prediction model.
And S4, acquiring and collecting the current video image of the monitored person through the monitoring data, and extracting the facial image information and the behavior information.
And S5, carrying out feature extraction on the video data of the monitored personnel to obtain behavior information, identifying the facial image information, extracting facial image features, and judging the expression of the monitored personnel.
And S6, inputting the behavior information and the expression change into a prediction model, judging whether corresponding precursor behaviors appear or not by the prediction model, judging the current psychological state of the monitored personnel, analyzing the emotional tendency of the monitored personnel, and outputting the emotional tendency, so that the psychological state of the monitored personnel is predicted, and when the emotional tendency of the monitored personnel is found to be in a problem, early warning is carried out on related personnel, and the related personnel are informed to intervene in the psychology of the monitored personnel.
As a further scheme of the invention: the basic information includes the name, gender, age, academic calendar, work, family composition and social situation of the monitored person.
As a further scheme of the invention: the deep learning network model comprises but is not limited to a deep convolutional neural network model built in a tensoflow environment, and the deep learning network model comprises an input layer, three convolutional layers, two full-connection layers and an output layer, wherein corresponding pooling layers are arranged behind the first two convolutional layers.
As a further scheme of the invention: the specific method for training the deep learning network model comprises the following steps:
and S31, cleaning and then performing dimension reduction processing on the specific information of the precursor behaviors and the expression changes when psychological problems occur to different classes of people acquired through big data.
And S32, dividing the multiple groups of data into training data and testing data, wherein the training data is 80%, the testing data is 20%, and the training data is used as the input of the deep learning network model to train the deep learning network model.
And S33, after the training is finished, testing through the test data, finishing the training when the testing accuracy is higher than 99%, and predicting the corresponding psychological state through different behaviors and expression changes by taking the trained deep learning network model as a prediction model.
As a further scheme of the invention: the method for classifying the monitored personnel according to the similarity comprises the following specific steps:
s21, arranging the basic information of the monitored personnel, screening text pictures and videos in the social data, selecting data related to emotion, and performing theme analysis.
S22, dividing different monitored personnel into five types, namely ABCDE, according to the five-personality theory, wherein ABCDE respectively represents five types, namely Keron type, responsibility type, camber type, pleasant type and neural type, and quantizing the types of the different monitored personnel into a vector by adopting the five-personality scale principle, namely:
P＝<A score ,B score ,C score ,D score ,E score >。
s23, each type is more specifically represented with some characteristics, the characteristics are classified to obtain x sub-dimensions, and the score condition of each sub-dimension of the monitored person on each personality is further calculated, namely the score condition of each sub-dimension of the monitored person on each personality is calculated
Wherein e is i Sub-dimension, k, representing camber i The weight of the influence of each dimension on the personality type is initialized to 1/n, and then optimized and adjusted according to the feedback of monitored personnel.
And S24, expressing the characteristics expressed by each type of personality on the emotion and behavior, constructing the logical relationship among the personality, the emotion and the behavior, performing logical association, and constructing a series of element structures for calculating the sub-dimension score of each personality type of the monitored person from the infection, expression, intervention and embodying of three relationship types, so as to obtain the tendency score of the personality type of the monitored person, thereby judging the current psychological state of the monitored person.
As a further scheme of the invention: the psychological states are divided into four types, namely a healthy state, an adverse state, a psychological disorder and a psychological crisis.
As a further scheme of the invention: the calculation formula of each personality type sub-dimension score is as follows:
wherein n is m Number of emotion types, n b Number of types of behaviors, M = (M1, M2, \8230;, M nm )，B＝(b1,b2,…，b nb )，I nm ×n b The relation matrix is a relation matrix of emotion types and behavior types, the relation matrix is 1 if the relation matrix has a link relation, and otherwise the relation matrix is 0.
As a further scheme of the invention: the specific method for screening the text pictures and videos in the social data and selecting the data related to the emotion comprises the following steps: and performing text embedding and Kmeans clustering on the acquired massive text data through a Bert model to identify sentences close to the centroid for automatic abstract selection, so as to select specific data relevant to emotion.
As a further scheme of the invention: the steps of identifying the facial image information, extracting the facial image characteristics and judging the expression of the monitored person specifically comprise the following steps:
s51, extracting a face part in the monitoring data, performing gray processing to obtain an expression image, and performing preprocessing, wherein the preprocessing specifically comprises the following steps: cutting and size normalization are carried out according to the characteristics of the three eyes and the five eyes of the face and the set model of the face, so that the regions irrelevant to the expression are removed, and the interference of irrelevant information on the expression recognition is reduced;
and S52, multi-feature extraction is carried out on the preprocessed expression image, the three extracted features are LDP, DWT and Sobel respectively, the three feature graphs are input into a convolutional neural network in a three-channel mode to carry out self-adaptive fusion, and finally expression classification is carried out on the fused features through a Softmax classifier.
The invention has the beneficial effects that:
1. according to the invention, different monitored personnel are classified, precursor behavior and expression change when psychological problems occur to different classes of personnel are obtained through big data, a deep learning network model is trained to obtain a prediction model, and then video images of the monitored personnel obtained through monitoring data are subjected to feature extraction and then input into the prediction model for prediction, so that the current psychological state of the monitored personnel is judged, the emotional tendency of the monitored personnel is analyzed, the non-contact psychological state prediction of the monitored personnel is realized, the prediction reliability is improved, the time for the monitored personnel and questionnaires to be issued and checked is not influenced, and unnecessary psychological burden is not caused to the monitored personnel;
2. according to the invention, when psychological problem precursors appear in the psychological states and emotional tendencies of monitored personnel, early warning processing is carried out on related personnel, and the psychological intervention is informed to the tested personnel, so that the bad emotions of the personnel can be timely dredged;
3. the invention can simultaneously carry out psychological monitoring on a large amount of monitored personnel through monitoring data, is suitable for large-scale places with more personnel such as training camps, factories, companies, schools and the like, and plays an important role in monitoring the psychological states of a large number of personnel.
Drawings
FIG. 1 is a schematic flow chart of the psychological state prediction according to the present invention;
FIG. 2 is a schematic flow chart of a deep learning network model training method according to the present invention;
FIG. 3 is a flow chart illustrating the person classification according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example (b):
as shown in fig. 1 to 3, a non-contact mental state prediction method includes the following steps:
s1, basic information such as the name, gender, age, academic history, work, family composition and social situation of a monitored person is obtained through a manager, and meanwhile, massive release data are crawled from a platform for expressing emotion with high probability such as a microblog, broad shadow film comment, a friend circle, a network and Yiyun music comment and the like.
S2, arranging basic information of monitored personnel, screening text pictures and videos in social data, selecting data related to emotion, performing theme analysis, performing text embedding and Kmeans clustering on a large amount of acquired text data through a Bert model to identify sentences close to a centroid for automatic abstract selection, selecting specific data related to emotion, performing text embedding and Kmeans clustering through the Bert model to identify sentences close to the centroid for automatic abstract selection, automatically screening important sentences from a large amount of social texts of the monitored personnel, solving the problems of information fragmentation and useless information interference better, and improving the efficiency and accuracy of the model.
According to the five-personality theory, different monitored persons are divided into five types of ABCDE, wherein ABCDE respectively represents five types of Kairan type, responsibility type, camber type, pleasure type and neural type, and the five-personality scale principle is adopted to quantize the types of the different monitored persons into a vector, namely:
P＝<A score ,B score ,C score ,D score ,E score >。
then each type more specifically shows some characteristics, the characteristics are classified to obtain x sub-dimensions, and the score condition of each sub-dimension of the monitored person on each type of personality is further calculated, namely the score condition of each sub-dimension of the monitored person on each type of personality is calculated
Wherein e is i Sub-dimension, k, representing camber i The weight of the influence of each dimension on the personality type is initialized to 1/n, and then optimized and adjusted according to the feedback of monitored personnel.
Because the emotion can influence the behavior to a certain extent, and the behavior is often in expression of emotion, the characteristics expressed by each personality are expressed in emotion and behavior, the logical relationship among personality, emotion and behavior is constructed, and logical association is carried out; for example, the Kaliman form represents the number and density of interpersonal interactions, the need for excitement to achieve pleasurable ability, a dimension that contrasts social, active, personally directed individuals with silent, serious, 33148j agalma and calm people, which can be measured by two qualities, the level of interpersonal involvement, which assesses how well an individual likes others to accompany, and the level of vitality, which reflects the individual's personal rhythm and vitality; for example, the distraction is an important emotional expression of the camber, the demand company is an important behavioral expression of the camber, the distraction and the seeking company both show the camber personality tendencies of the monitored person, and the behaviors and the emotions generally influence each other, but the influence is influenced by the personality type, the emotions and the behavioral relations of the persons with different personality tendencies are different, the distraction points to the distraction for the expression intervention of the seeking company behavior, because the camber generally likes to contact with the person and is enthusiastic, and when the person is in a group, a positive emotion is generally expressed, such as distraction; and on the contrary, the behavior is directed from the emotion, the same reason is also provided, and a series of element structures are constructed from infection, expression, intervention and three relation types for calculating the sub-dimension scores of each personality type of the monitored personnel so as to obtain the tendency score of the personality type of the monitored personnel, so that the current psychological state of the monitored personnel is judged.
And S3, acquiring precursor behavior and expression change of different classes of people with psychological problems through the big data, and cleaning and then reducing the dimension of the concrete information of the precursor behavior and the expression change of different classes of people with psychological problems, which is acquired through the big data.
And dividing the multiple groups of data into training data and testing data, wherein the training data is 80%, the testing data is 20%, and the training data is used as the input of the deep learning network model to train the deep learning network model.
And after the training is finished, testing through the test data, finishing the training when the testing accuracy is higher than 99%, and predicting the corresponding psychological state through different behaviors and expression changes by taking the trained deep learning network model as a prediction model.
And S4, acquiring and collecting the current video image of the monitored person through the monitoring data, and extracting the facial image information and the behavior information.
S5, carry out the feature extraction to monitored personnel 'S video data, obtain the behavior information, discern facial image information simultaneously, extract facial image characteristic, judge monitored personnel' S expression, specifically do: cutting and size normalization are carried out according to the characteristics of the three eyes and the five eyes of the face and the set model of the face, so that the regions irrelevant to the expression are removed, and the interference of irrelevant information on expression recognition is reduced;
and then, multi-feature extraction is carried out on the preprocessed expression images, the three extracted features are LDP, DWT and Sobel respectively, the three feature images are input into a convolutional neural network in a three-channel mode for self-adaptive fusion, and finally expression classification is carried out on the fused features through a Softmax classifier.
And S6, inputting the behavior information and the expression change into a prediction model, judging whether corresponding precursor behaviors appear or not by the prediction model, judging the current psychological state of the monitored personnel, analyzing the emotional tendency of the monitored personnel, and outputting the emotional tendency, so that the psychological state of the monitored personnel is predicted, and when the emotional tendency of the monitored personnel is found to be in a problem, early warning is carried out on related personnel, and the related personnel are informed to intervene in the psychology of the monitored personnel.
The deep learning network model comprises but is not limited to a deep convolutional neural network model built in a tensoflow environment, and the deep convolutional neural network model comprises an input layer, three convolutional layers, two full-connection layers and an output layer, wherein corresponding pooling layers are arranged behind the first two convolutional layers.
The psychological states are divided into four types, namely healthy states, bad states, psychological disorders and psychological crisis.
The calculation formula of each personality type sub-dimension score is as follows:
wherein n is m Number of emotion types, n b Number of types of behaviors, M = (M1, M2, \8230;, M nm )，B＝(b1,b2,…，b nb )，I nm ×n b The relation matrix is a relation matrix of emotion types and behavior types, the relation matrix is 1 if the relation matrix has a link relation, and otherwise the relation matrix is 0.
In the embodiment of the invention, the psychological state prediction method comprises the steps of classifying different monitored personnel, acquiring precursor behavior and expression change when psychological problems occur to different classes of personnel through big data, training a deep learning network model to obtain a prediction model, performing feature extraction on video images of the monitored personnel obtained through monitoring data, and inputting the video images into the prediction model for prediction, so that the current psychological state of the monitored personnel is judged, the emotional tendency of the monitored personnel is analyzed, and the non-contact psychological state prediction of the monitored personnel is realized.
The points to be finally explained are: although the present invention has been described in detail with reference to the general description and the specific embodiments, on the basis of the present invention, the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the spirit of the corresponding technical solutions of the embodiments of the present invention.
Claims (9)
1. A non-contact psychological state prediction method is characterized by comprising the following steps:
s1, acquiring basic information of monitored personnel, and acquiring social data by using data through a social platform;
s2, classifying the monitored personnel according to the similarity to obtain different classes;
s3, obtaining precursor behaviors and expression changes of different classes of people with psychological problems through big data, and training the deep learning network model to obtain a prediction model;
s4, acquiring and collecting a current video image of a monitored person through monitoring data, and extracting facial image information and behavior information;
s5, extracting characteristics of video data of the monitored personnel to obtain behavior information, identifying facial image information, extracting facial image characteristics, and judging expressions of the monitored personnel;
and S6, inputting the behavior information and the expression change into a prediction model, judging whether corresponding precursor behaviors appear or not by the prediction model, judging the current psychological state of the monitored personnel, analyzing the emotional tendency of the monitored personnel, and outputting, so that the psychological state of the monitored personnel is predicted, and when the emotional tendency of the monitored personnel is found to be in a problem, early warning is carried out on related personnel, and the related personnel is informed to intervene in the psychology of the monitored personnel.
2. A method of predicting mental state in a non-contact manner according to claim 1, wherein: the basic information includes the name, sex, age, academic calendar, work, family composition and social situation of the monitored person.
3. A method of predicting mental state in a non-contact manner according to claim 1, wherein: the deep learning network model comprises but is not limited to a deep convolutional neural network model built in a tensoflow environment, and the deep convolutional neural network model comprises an input layer, three convolutional layers, two full-connection layers and an output layer, wherein corresponding pooling layers are arranged behind the first two convolutional layers.
4. A method according to claim 3, wherein the method further comprises: the specific method for training the deep learning network model comprises the following steps:
s31, cleaning and then reducing dimensions of specific information of precursor behaviors and expression changes when psychological problems occur to different classes of people acquired through big data;
s32, dividing the multiple groups of data into training data and testing data, wherein the training data is 80%, the testing data is 20%, and the training data is used as the input of a deep learning network model to train the deep learning network model;
and S33, testing through the test data after the training is finished, finishing the training when the testing accuracy is higher than 99%, and predicting the corresponding psychological state through different behaviors and expression changes by taking the trained deep learning network model as a prediction model.
5. A method of predicting mental state in a non-contact manner according to claim 1, wherein: the method for classifying the monitored personnel according to the similarity comprises the following specific steps:
s21, arranging basic information of monitored personnel, screening text pictures and videos in social data, selecting data related to emotion, and performing theme analysis;
s22, dividing different monitored personnel into five types of ABCDE according to a five-personality theory, wherein the ABCDE respectively represents five types of Kairan type, responsibility type, camber type, pleasure type and neural type, and quantizing the types of the different monitored personnel into a vector by adopting a five-personality scale principle, namely:
P＝<A score ,B score ,C score ,D score ,E score >；
s23, each type is more specifically represented with some characteristics, the characteristics are classified to obtain x sub-dimensions, and the score condition of each sub-dimension of the monitored person on each personality is further calculated, namely the score condition of each sub-dimension of the monitored person on each personality is calculated
Wherein e is i Sub-dimension, k, representing camber i Initializing the influence weight of each dimension on the personality type to 1/n, and then carrying out optimization adjustment on the influence weight according to the feedback of monitored personnel;
s24, expressing the characteristics expressed by each personality to the emotion and behavior, constructing the logical relationship among the personality, the emotion and the behavior, performing logical association, constructing a series of element structures for calculating the sub-dimension score of each personality type of the monitored personnel from infection, expression, intervention and embodying three relationship types, and further obtaining the tendency score of the personality type of the monitored personnel, thereby judging the current psychological state of the monitored personnel.
6. A method according to claim 5, characterized in that: the psychological states are divided into four types, namely a healthy state, a bad state, a psychological disorder and a psychological crisis.
7. A method according to claim 5, wherein the method further comprises: the calculation formula of each personality type sub-dimension score is as follows:
wherein n is m Number of emotion types, n b Number of types of behaviors, M = (M1, M2, \8230;, M nm )，B＝(b1,b2,…，b nb )，I nm ×n b The relation matrix is a relation matrix of emotion types and behavior types, the relation matrix is 1 if the relation matrix has a link, and the relation matrix is 0 if the relation matrix has the link.
8. A method according to claim 5, characterized in that: the specific method for screening the text pictures and videos in the social data and selecting the data related to the emotion comprises the following steps: and performing text embedding and Kmeans clustering on the acquired massive text data through a Bert model to identify sentences close to the centroid for automatic abstract selection, so as to select specific data relevant to emotion.
9. A method of predicting mental state in a non-contact manner according to claim 1, wherein: the steps of identifying the facial image information, extracting facial image features and judging the expression of the monitored person specifically comprise the following steps:
s51, extracting a face part in the monitoring data, performing gray processing to obtain an expression image, and performing preprocessing, wherein the preprocessing specifically comprises the following steps: cutting and size normalization are carried out according to the characteristics of the three eyes and the five eyes of the face and the set model of the face, so that the regions irrelevant to the expression are removed, and the interference of irrelevant information on the expression recognition is reduced;
and S52, extracting multiple features of the preprocessed expression image, inputting the three feature images into a convolutional neural network in a three-channel mode to perform self-adaptive fusion, and finally performing expression classification on the fused features through a Softmax classifier, wherein the three extracted features are LDP, DWT and Sobel respectively.
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