CN109256192A - A kind of undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based - Google Patents
A kind of undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based Download PDFInfo
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- CN109256192A CN109256192A CN201810816957.1A CN201810816957A CN109256192A CN 109256192 A CN109256192 A CN 109256192A CN 201810816957 A CN201810816957 A CN 201810816957A CN 109256192 A CN109256192 A CN 109256192A
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
- G16H20/70—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to mental therapies, e.g. psychological therapy or autogenous training
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- G06N3/00—Computing arrangements based on biological models
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Abstract
The present invention provides a kind of undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming methods neural network based, comprising: 1) obtains the multiple information sources data of student；The Psychology and behavior data of student are obtained using preprocessing algorithms by the multiple information sources data；The Mental Depression for obtaining student tests self-appraisal table；By the psychological test self-appraisal table as a result, carrying out psychological health states label label to student；By the Psychology and behavior data, feature related with psychological health states is extracted；By the feature, the main feature component of data is extracted using PCA algorithm；2) after obtaining students psychology behavioral data and extracting main feature component, Mental Depression Early-warning Model is established and trained using neural network algorithm；3) new student's multiple information sources data are obtained, the depressive state of new individual students is assessed according to the Mental Depression Early-warning Model.
Description
Technical field
The present invention relates to a kind of monitoring and pre-alarming method more particularly to a kind of undergraduate psychological behavior unusual fluctuation monitoring and warning sides
Method belongs to big data application field.
Background technique
According to WHO Report, depression is in whole world disease incidence about 11%, it has also become endangers the of human health
Four big diseases are likely to become to the year two thousand twenty and are only second to the cardiopathic second largest disease.In China, incidence of depression is up to 7%,
And because it is found that insufficient with understanding not in time, treatment rate is only 20%.Because committed suicide event caused by depression takes place frequently.Colleges and universities
Student is as specific group active and sensitive in social life, when all experiencing the youth of great variety in physiology, psychology
Phase, Psychological Health Problem are more prominent compared with other crowds, the school students ' psychological health research of University of California Berkeley in 2014
Report points out have the people of 43%-46% to suffer from spleen in bioscience postgraduate, and the report of University of Arizona in 2015 is pointed out
Most of doctors bear the pressure of " be higher than average level ", and it is most important that school and education related matters be included in pressure
Source.
Currently, the mental health services of college student also rest on " passive " mode, it is main to pass through traditional questionnaire issuer
Formula or student, which seek advice from or go to see a doctor to Mental Health Counseling center, finds psychological unusual fluctuation individual.But by manpower and material resources institute
Limit, psychological study person can not obtain the data of covering research object entirety for a long time, and also inconvenience is to individual mind health shape
State variation carries out follow-up study, it is difficult to carry out timely pro-active intervention to Psychology and behavior unusual fluctuation individual.
With the development of the technologies such as sensor, high speed network, mobile interchange, cloud computing, artificial intelligence, as support
The research in big data technology this ancient and complicated field to psychology is also gradually unfolded.
Patent " a kind of Mental health evaluation method based on internet cloud client database " (number:
CN201610808888.0 a kind of Mental health evaluation method based on internet cloud client database is proposed in).In method, cloud
Client database is used to store the factor score of known center of a sample's reason test scale；The heart is established using RBF neural network algorithm
Manage health evaluation model.After RBF neural network model assesses new individual psychological health states, assessment result is uploaded to
Cloud.Advantage is that RBF neural and internet cloud client database memory technology are conjointly employed in Mental health evaluation,
Efficiency and accuracy are improved, the disadvantage is that this method is still based on traditional psychological test table as a result, can not be to mental health
State is tracked research.
In patent " a kind of psychological health states appraisal procedure " (number: CN201210576344.8), Chinese Academy of Sciences's psychology is ground
Study carefully institute Zhu Ting and encourage etc. and proposes a kind of method for carrying out psychological health states assessment using machine learning.The realization of the appraisal procedure
Step are as follows: firstly, establishing based on individual networks behavioural characteristic and Demographics in known sample and training being based on network
The psychological health states assessment models of behavioural characteristic；Secondly the network behavior feature and Demographics of new individual are obtained；
Preferably, according to the assessment models of above-mentioned foundation, the psychological health states of the new individual are obtained.Advantage is a cancellation subjective factor pair
The influence of psychological health states assessment, the disadvantage is that behavioral data source is single, it is not thorough enough to excavate, psychological health states assessment
Accuracy not can guarantee.
Existing to exert in conclusion although the study on psychological health based on big data technology has obtained some concerns
Description human psychology-behavior theoretical model is set up in power and research not yet, there are data sources single, theory analysis less than
The problems such as position, research contents be not comprehensive, data mining is not thorough, inherently sees or rests on the static state of psychological health states
Evaluation stage.
It not yet finds to establish Psychology and behavior unusual fluctuation monitoring and warning model using education big data and machine learning algorithm at present
Patent.
Summary of the invention
The purpose of the present invention is overcome defect existing for above-mentioned existing system and provide a kind of data are comprehensive, accuracy is high,
Good, objective, the scientific undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method of dynamic property.
To achieve the above object, the present invention takes following technical scheme, the specific steps are as follows:
A kind of undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based, which is characterized in that including as follows
Step:
S1, student is obtained in school Psychology and behavior data；By in real time/quasi real time mode will come from each information system, such as learn
Table relevant to Psychology and behavior in school education administration system, card system, work system, library system, input-output control system etc.
It is synchronized to this early warning system.
S2, the field of the correlation table from above- mentioned information system is analyzed, select required field and extracted extremely
In a single table.Data prediction is carried out to the table data, such as fills up missing values, normalization, hough transformation；Obtain student
Psychological test self-appraisal table；By the psychological test self-appraisal table as a result, to student carry out psychological health states label label,
It will be classified as one kind with slight, moderate, severe student, no depression is classified as another kind of；
S3, dimensionality reduction is carried out to features described above using PCA algorithm, is obtaining students psychology behavioral data and is extracting main spy
After levying component, using neural network algorithm, Psychology and behavior unusual fluctuation monitoring and warning model is established and trained；
S4, new student's multiple information sources data are obtained, is carried out according to the Psychology and behavior unusual fluctuation monitoring and warning model pre-
It surveys, obtains the psychological health states of new individual students.
Wherein, table relevant to Psychology and behavior is undergraduate's Basic Information Table, postgraduate's Basic Information Table, all-purpose card in S1
Consumption schedule, library's request form, library enter and leave record sheet, list of results, curriculum schedule, scholarship table etc..
In the step S1, the students psychology behavioral data includes students' genders, the prize-winning situation of student, student's punishment
Situation, student's school work situation, student's all-purpose card consumption, student pass in and out dormitory gate inhibition situation, student pass in and out library's situation,
Student checks out situation, student's breakfast, lunch and dinner situation, student's social activity situation.
Wherein, the student wins a prize situation including obtaining scholarship number and the amount of money.
Wherein, student's punishment situation includes that student punishes number and grade.
Wherein, student's school work situation include each subject excellent rate of student, it is rate of good, pass rate, rate of failing, outstanding
Door number, good door number and lattice door number, too late lattice door number.
Wherein, student's all-purpose card consumption includes consumption total value, week consumption total value, all average consumption amount of money.
Wherein, the student passes in and out brush gate inhibition's number that dormitory gate inhibition situation is each hour one day.
Wherein, it includes the number into library that the student, which passes in and out library's situation,.
Wherein, the student check out situation include check out in term total amount, the number that checks out in total, each type of borrow distribution
Wherein, student's breakfast, lunch and dinner situation include breakfast, lunch, dinner dining ratio, variance and average value, pass through institute
Three values stated are it can be seen that whether student has dinner in school.
Wherein, student's social activity situation will be swiped the card together in 5 minutes and be eaten by student's breakfast, lunch and dinner situation
The student of meal is considered that a Social behaviors once occur, finds with highest 5 students of specified student's social activity number together
It swipes the card social characteristics of the number as student.
Wherein, in the step S2, the preprocessing algorithms include the following steps:
Step2.1: to from multiple information sources data carry out data scrubbing, by fill in missing values, smooth noise data,
The inconsistency that outlier solves data is deleted in identification；
Step2.2: carrying out data integration, and all data acquisition systems from multiple data sources for same individual are got up
And take measures to avoid redundancy when data integration, if it is necessary, Step2.1 can also be carried out again；
Step2.3: carrying out hough transformation, carries out simplifying expression to data set.
In the step S2, the students psychology test self-appraisal table is that beck self rating depression surveys the evaluation and test certainly of table, sas anxiety
Table, sds self rating depression survey table, are added according to certain weight ratio, obtain final depressed rating.
In the step S3, the PCA algorithm is also known as principal component analysis technology or principal component analysis, it is intended to utilize dimensionality reduction
Thought, the dimension of data is reduced under the premise of guaranteeing that data are essential.The PCA algorithm is a kind of relatively conventional dimensionality reduction
Algorithm.
In the step S3, the neural network algorithm is multi-layer perception (MLP), and structure is classical M-P neuron knot
Structure, activation primitive are Logistic function, optimizer lbfgs.The network is full Connection Neural Network, is inputted as using PCA
Student characteristics data after dimensionality reduction, export for two classification assessment results, i.e., it is depressed or not depressed.
Detailed description of the invention
Fig. 1 is the flow chart of the Psychology and behavior unusual fluctuation monitoring and pre-alarming method of embodiment；
Specific embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.The present embodiment is with technical solution of the present invention
Premised on implemented, the detailed implementation method and specific operation process are given, but protection scope of the present invention is not limited to
Following embodiments.
In order to make it easy to understand, briefly introducing the principles of science based on the present invention first.
The development and progress of big data technology is changing the every aspect of human society from depth and range, in science
The scientific research of " fourth normal form (4nf "-based on big data after experimental science, induction and conclusion, Computer Simulation is expedited the emergence of in field
Normal form.Under this background, the method that scientists attempt maintenance data science explains that the collapse of the economy, financial bubble etc. are recognized in the past
For the thing for being " dance of god ".Such as social physics researchers pass through various aspects in comprehensive collection human lives
Data crumbs analyze human behavior rule；Human behavior kineticist is attempted deep using quantification Spatiotemporal Statistical Analysis and modeling
Level understands public sentiment and transmission principle, optimization traffic programme, information recommendation etc..
Further in psychometrics, the psychological health states assessment to student is all the psychological row by individual students
It is measured indirectly for characterization.The over behavior performance of individual is dominated and is influenced by its psychological condition, and the difference of behavior can
To detect the difference of psychologic status, it is possible to scientific by mental health progress of the Psychology and behavior characterize data to student,
Accurately assessment.
It, not only can be with all of full-time comprehensive trace recording individual further under the support of modern information technologies fast development
External presentation, and either society or virtual society can use electronic data to realize whole seamless recording, this
Outside, big data storage management and cloud computing also provide the service of data efficient storage and analysis for Mental health evaluation.
Based on the above principles, according to one embodiment of present invention, it is supervised as shown in Figure 1 for the Psychology and behavior unusual fluctuation of embodiment
Method for early warning flow chart is surveyed, is included the following steps:
S1: the multiple information sources data of student are obtained.
Specifically, the student's essential information for being stored in school information platform is acquired, the school information platform
Student is had recorded in the basic studies living condition in school, including school's education administration system, card system, work system, library
System, input-output control system etc..
S2: data prediction is carried out to data, therefrom extracts feature relevant to Psychology and behavior.
Specifically, above-mentioned relation data is done data preprocessing operation, to its data scrubbing, the purpose is to self-confident in the future
The a variety of data for ceasing platform carry out missing values processing, smooth noise data, identification or delete outlier, solve the different of data
Cause property problem, missing values processing can by ignore tuple, be filled in manually missing values, using attribute center measurement fill in missing values
Etc. modes, noise can by branch mailbox, return, the methods of the point analysis that peels off remove；Data integration, the purpose is to merge from multiple
The data of data storage help to reduce the redundancy and inconsistent in data set, help to improve the accurate of mining process thereafter
Property and speed can eliminate redundant data by correlation analysis during data integration；Hough transformation, the purpose is to be used to
Specification to data set indicates, reduces the size of data, but still close to the integrality for keeping initial data.Located in advance according to this data
Integrated framework is managed, realizes the mental representation data fusion of Virtual Space and entity space.
Specifically, pretreated data should include gender, student prize-winning situation, student's punishment situation, student's school work feelings
Condition, student's all-purpose card consumption, student pass in and out dormitory gate inhibition situation, student pass in and out library's situation, student check out situation, learn
Raw breakfast, lunch and dinner situation, student's social activity situation.
S3: being combined the pretreated data of step S2, dimensionality reduction, utilizes the data training neural network after dimensionality reduction.
Specifically, training data is obtained X=[X by column arrangement first1, X2..., Xn], wherein n is training dataset
Number, and conclusion is stored in vector y, y=[y1, y2..., yn], yi∈ { 0,1 }, wherein 0 represents without depression, 1 representative has
Depression.
Specifically, training data is carried out ten folding cross validations, takes and wherein 90% training set is used as to be used to train network,
10%, as verifying collection, verifies and optimizes network structure.
Specifically, carrying out PCA dimensionality reduction to training set first, retaining the data capacity of k%, (k is needed to be optimized
Amount) and whitening processing is carried out, training set X ' and transformation matrix M after obtaining dimensionality reduction；
Further, validation data set is projected to matrix M, obtains the feature vector Q of verifying collection.
Further, by X ' by being sent into the full connection multilayer perceptron neural network classifier in batches, and Q is collected with verifying
The F1 value of classification results is that majorized function carries out network iteration, debugs hidden layers numbers, the dimensionality reduction dimension, network parameter of most effective fruit
Decaying weight stops the parameters such as the number of iterations, batch amount of training data ahead of time, to obtain optimal neural network model W.
Specific F1 is defined as:
Wherein TP is the quantity that depression is determined as depression, and FP is not depressed to be determined as depressed quantity, and FN is determined as depression
Not depressed quantity.
S4, for unknown student, execute S1-S2 step, obtain student characteristics vector s, and throw to Metzler matrix described in S3
Shadow obtains feature x ' after dimensionality reduction.
Further, the students psychology behavioural characteristic x ' newly obtained is input to the neural network model W
In, obtain prediction result Y, Y ∈ { 0,1 }.
Based on big data technology, this implementation Psychology and behavior unusual fluctuation monitoring and pre-alarming method obtained has analysis result quasi-
Really, objective, the advantages of analytic process is efficient, broad covered area.
Claims (9)
1. a kind of undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based, which is characterized in that including walking as follows
It is rapid:
S1, student is obtained in school Psychology and behavior data；By in real time/quasi real time mode will come from each information system, such as school teach
Table relevant to Psychology and behavior is synchronous in business system, card system, work system, library system, input-output control system etc.
To this early warning system.
S2, the field of the correlation table from above- mentioned information system is analyzed, select required field and extracted to single
In one table.Data prediction is carried out to the table data, such as fills up missing values, normalization, hough transformation；Obtain the heart of student
Reason test self-appraisal table；By the psychological test self-appraisal table as a result, to student's progress psychological health states label label, will suffer from
There are slight, moderate, severe student to be classified as one kind, no depression is classified as another kind of；
S3, dimensionality reduction is carried out to features described above using PCA algorithm, is obtaining students psychology behavioral data and is extracting main feature point
After amount, using neural network algorithm, Psychology and behavior unusual fluctuation monitoring and warning model is established and trained；
S4, new student's multiple information sources data are obtained, is predicted, is obtained according to the Psychology and behavior unusual fluctuation monitoring and warning model
To the psychological health states of new individual students.
2. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, wherein table relevant to Psychology and behavior is undergraduate's Basic Information Table, postgraduate's Basic Information Table, all-purpose card consumption in S1
Table, library's request form, library enter and leave record sheet, list of results, curriculum schedule, scholarship table etc..
3. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, in the step S1, the students psychology behavioral data include students' genders, student win a prize situation, student's punishment situation,
Student's school work situation, student's all-purpose card consumption, student passes in and out dormitory gate inhibition situation, student passes in and out library's situation, student
Check out situation, student's breakfast, lunch and dinner situation, student's social activity situation.
4. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, wherein the student wins a prize situation including obtaining scholarship number and the amount of money；Student's punishment situation includes student's punishment
Number and grade；Student's school work situation includes each subject excellent rate of student, rate of good, pass rate, rate of failing, outstanding door
Several, good door number and lattice door number, too late lattice door number.
5. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, wherein student's all-purpose card consumption includes consumption total value, week consumption total value, all average consumption amount of money.
6. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In the student passes in and out brush gate inhibition's number that dormitory gate inhibition situation is each hour one day；The student passes in and out library's situation
Number including entering library；The student situation that checks out includes the total amount, number that checks out in total, each type of of checking out in term
Borrow distribution；Student's breakfast, lunch and dinner situation include breakfast, lunch, dinner dining ratio, variance and average value, by described
Three values are it can be seen that whether student has dinner in school.
7. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, wherein student's social activity situation, by student's breakfast, lunch and dinner situation, by have a meal of swiping the card together in 5 minutes
Life is considered that a Social behaviors once occur, finds secondary with swiping the card together for highest 5 students of specified student's social activity number
Social characteristics of the number as student.
8. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In, wherein in the step S2, the preprocessing algorithms include the following steps:
Step2.1: data scrubbing is carried out to the data from multiple information sources, by filling in missing values, smooth noise data, identification
Or delete the inconsistency that outlier solves data；
Step2.2: carrying out data integration, will get up and adopts for all data acquisition systems of same individual from multiple data sources
Redundancy when measure being taken to avoid data integration, if it is necessary, Step2.1 can also be carried out again；
Step2.3: carrying out hough transformation, carries out simplifying expression to data set.
9. undergraduate psychological behavior unusual fluctuation monitoring and pre-alarming method neural network based as described in claim 1, feature exist
In in the step S3, the neural network algorithm is multi-layer perception (MLP), and structure is classical M-P neuronal structure, activation
Function is Logistic function, optimizer lbfgs.The network is full Connection Neural Network, is inputted as after using PCA dimensionality reduction
Student characteristics data, export for two classification assessment results, i.e., it is depressed or not depressed.
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