CN109979568A - Mental health method for early warning, server, family members' terminal and system - Google Patents
Mental health method for early warning, server, family members' terminal and system Download PDFInfo
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Abstract
The present invention provides a kind of mental health method for early warning, which comprises receives the content of user terminal；Sentiment analysis is carried out to the content；Level in mental health is judged according to sentiment analysis result；Warning information is issued to family members' terminal according to level in mental health.The present invention also provides a kind of mental health Warning Service device, terminal and systems, solve the problems, such as that psychological health states can not be tracked in time in the prior art, can be realized by sentiment analysis and track and alert in time.
Description
Technical field
The present invention relates to Psychological Evaluation technical field, in particular to a kind of mental health method for early warning, server,
Family members' terminal and system.
Background technique
Due to life stress increase etc., mental diseases patient's sustainable growth such as depression.Traditional mental health water
It is flat to need to be helped face-to-face by seeking doctor to psychological hospital by patient, then assessed by doctor.And actually many trouble
Person does not go to see a doctor actively natively, it is difficult to track level in mental health in time.The people not good enough for psychological health states
Member, family's adjuvant treatment especially lack.Mental disease patient's major part rehabilitation life is tided in the family, scientific family
The level in mental health of patient can be improved in nursing, carries out psychological intervention to patient in time.However, general family is for depression
The level in mental health of patient is difficult to make professional judgement, nor has professional knowledge, can not be for patients with depression
Level in mental health takes psychological assisted care and intervention.
It is above-mentioned can not track psychological health states in time in the prior art aiming at the problem that, not yet propose effective solution at present
Certainly scheme.
Summary of the invention
The present invention is directed to solve above-mentioned technical problem at least to a certain extent.
The embodiment of the invention provides a kind of mental health method for early warning, server, family members' terminal and systems, existing to solve
There is the problem of technology can not track psychological health states in time.
According to an aspect of an embodiment of the present invention, a kind of mental health method for early warning is provided, which comprises
Receive the content of user terminal；
Sentiment analysis is carried out to the content；
Level in mental health is judged according to sentiment analysis result；
Warning information is issued to family members' terminal according to level in mental health.
According to an aspect of an embodiment of the present invention, a kind of mental health method for early warning is provided, which comprises
Server is received according to the issued warning information of level in mental health；
According to the warning information generate early warning interface, the early warning interface by text, image, audio or video one
Kind or a variety of combinations show warning information
Another aspect according to an embodiment of the present invention provides a kind of mental health Warning Service device, the server
Include:
Receiving module, for receiving the content of user terminal, the content is defeated in user terminal acquisition user's input method
The interior current browsed web content of perhaps user entered；
Analysis module, for carrying out sentiment analysis to the content；
Judgment module, for judging level in mental health according to sentiment analysis result；
Warning module, for issuing warning information to family members' terminal according to level in mental health.
Other side according to an embodiment of the present invention provides a kind of mental health early warning family members' terminal, the family members
Terminal includes:
Third receiving module, for receiving server according to the issued warning information of level in mental health；
Generation module, for according to the warning information generate early warning interface, the early warning interface by text, image,
One or more combinations of audio or video show warning information.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
Mental health method for early warning, server, family members' terminal and the system for tracking and alerting in time are realized in sense analysis.
Detailed description of the invention
Fig. 1 is a kind of mental health method for early warning flow chart of the embodiment of the present invention.
Fig. 2 is that a kind of mental health method for early warning of the embodiment of the present invention carries out sentiment analysis steps flow chart to the content
Figure.
Fig. 3 is to carry out sentiment analysis to the content described in a kind of mental health method for early warning of further embodiment of this invention
Flow chart of steps.
Fig. 4 is a kind of sentiment analysis of the corresponding character types of mental health method for early warning selection of the embodiment of the present invention
Tool analyze the emotion flow chart of steps of the content.
Fig. 5 is that a kind of mental health method for early warning of the embodiment of the present invention judges mental health water according to sentiment analysis result
Flat step flow chart.
Fig. 6 is that data synchronizing frequency flow chart of steps is arranged in a kind of mental health method for early warning of the embodiment of the present invention.
Fig. 7 is that number is arranged according to the level in mental health in a kind of mental health method for early warning of another embodiment of the present invention
According to synchronizing frequency flow chart of steps.
Fig. 8 is that a kind of mental health method for early warning of the embodiment of the present invention inquires matched family members' assisted care knowledge point
Flow chart of steps.
Fig. 9 is a kind of mental health method for early warning flow chart of yet another embodiment of the invention.
Figure 10 is a kind of mental health method for early warning flow chart of another embodiment of the present invention.
Figure 11 is a kind of mental health method for early warning flow chart of further embodiment of this invention.
Figure 12 is a kind of mental health method for early warning flow chart of yet another embodiment of the invention.
Figure 13 is a kind of mental health Warning Service device structural schematic diagram of the embodiment of the present invention.
Figure 14 is a kind of mental health early warning user terminal structural schematic diagram of the embodiment of the present invention.
Figure 15 is a kind of mental health early warning family members terminal structure schematic diagram of the embodiment of the present invention.
Figure 16 is a kind of mental health early warning system structural schematic diagram of the embodiment of the present invention.
Wherein: 100, server；101, the first receiving module；102, analysis module；103, judgment module；104, early warning mould
Block；105, selecting module；106, statistical module；107, computing module；108, setup module；109, the first sending module；110,
Enquiry module；200, user terminal；201, the second detecting module；202, module is obtained；203, the second sending module；204, second
Receiving module；300, family members' terminal；301, third receiving module；302, generation module；303, third detecting module；304, it shows
Module.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment 1
As shown in Figure 1, according to an aspect of an embodiment of the present invention, a kind of mental health method for early warning is provided, it is described
Method includes:
S110: receiving the content of user terminal, and the content is that user terminal acquires the content inputted in user's input method
Or the current browsed web content of user；
S120: sentiment analysis is carried out to the content；
S130: level in mental health is judged according to sentiment analysis result；
S140: warning information is issued to family members' terminal according to level in mental health.
In the specific implementation process, server receives the content of user terminal transmitted by user terminal, and the content is
User terminal collected user inputted in input method in the perhaps current browsed web content of user；Server is to described
Content carries out sentiment analysis；Server judges level in mental health according to sentiment analysis result；Server is according to mental health water
It puts down to family members' terminal and issues warning information.
The content that user inputs or browsed in the user terminal that it is held actively is acquired, then emotion is carried out to the content
Analysis, the level in mental health of user is judged according to sentiment analysis result, in the case where not needing user's interrogation at hospital,
It can be with the real-time tracking user psychology general level of the health.
It just will appreciate that user's heart due to by the way of active analysis user content, solving the one-to-one artificial interrogation of tradition
The reason state problem in short supply so as to cause psychological medical resource, so as to so that shrink services one-to-manyly.
Family members' terminal that server is held to family members according to level in mental health issues warning information, can make family members and
When psychological monitoring and intervention are carried out to user, be conducive to the negative emotions for alleviating user.
As shown in Fig. 2, in the specific implementation process, carrying out sentiment analysis step to the content includes:
S121: judge that the type of the content, the type include text, picture or voice；
S122: corresponding sentiment analysis mode is selected according to the type of the content, the sentiment analysis mode includes text
This analysis, picture analyzing and speech analysis.
Specifically, server judges that the type of the content, the type include text, picture or voice；Server root
Corresponding sentiment analysis mode is selected according to the type of the content, the sentiment analysis mode includes text analyzing, picture analyzing
And speech analysis.
Specifically, if the type is picture, server selects Image emotional semantic analytical technology to carry out the content
Affection recognition of image.Image emotional semantic analysis is to analyze and extract affective characteristics from image, use pattern identification and machine learning
Method calculating is executed to it, and then understand the emotion of people.Main mode identification technology includes: template in Image emotional semantic analysis
Match pattern identification, the pattern-recognition of Fuzzy Pattern Recognition, support vector machines, and the deep learning based on artificial neural network.
The specific method of Image emotional semantic analysis is the prior art, and this is not described in detail here.
Specifically, if the type is voice, server selects speech emotional analytical technology to carry out the content
Affection recognition of image.Speech emotional analysis is that voice signal is analyzed and handled, and obtains the affective state that people is in.It is main
The speech emotion recognition algorithm wanted includes gauss hybrid models, support vector machines, K arest neighbors, hidden Markov model, sonograph
+ convolution loop neural network, manual feature+convolution loop neural network.The specific method of speech emotional analysis is the prior art,
This is not described in detail here.
Some user's preferences select corresponding sentiment analysis mode in input voice or picture, according to the type of content, can
Such as to be equally able to carry out sentiment analysis when picture, voice content when user inputs rich-media content.
In the specific implementation process, according to the type of the content select corresponding sentiment analysis mode step include: as
Content described in fruit is text, then analyzes the emotion of the content.
Specifically, if the content is text, the emotion of content described in server analysis.Realize text emotion analysis
Method and algorithm include rule-based, automatic system and hybrid system.Rule-based method defines one group by script
Rule, for identification subjectivity, polarity or opinion main body.Various inputs can be used in rule.For example, classical NLP technology, such as
Stem, symbol, part-of-speech tagging and parsing.In addition, rule can also use dictionary (i.e. word and expression way list).Based on rule
The key step of algorithm then include: define two polarization word lists (for example, the negation words such as poor, worst, ugly become reconciled, most preferably,
The fronts such as beauty word)；Calculate the positive word number occurred in text in the content.Calculate the negative word number occurred in text.Such as
The quantity that fruit front occurs is greater than the quantity that negative word occurs and then returns to positive mood, on the contrary, returning to negative emotions.Otherwise,
It returns neutral.Automatic method depends on machine learning.Sentiment analysis task is usually modeled as classification problem, the content
Then text input returns to corresponding classification to classifier, for example, just, bearing or neutral (if carrying out polarity check).
In a kind of exemplary embodiment, if the content is text, the algorithm packet of the emotion of the content is analyzed
Include training unit and analytical unit.The realization of training unit are as follows: use development set；With machine learning classification algorithm training the inside
Training set obtains machine learning model classifier；Classified with machine learning model classifier to development set, finally obtains text point
The result of class；Manual intervention is carried out using corpus, data mark is carried out to text, provide the accurate of Machine learning classifiers
Accuracy；Obtain algorithm and characteristic dimension；It obtains test set, the machine learning of foundation is tested.The realization of analytical unit
Are as follows: calling trains Bayes model；Save final model；Load final Bayesian model；Segment and go stop words
Operation；Read in active text and passive text；What is called is Bayes model training method；It calls in Sentiment class
Handle method；Call the classify method in Bayes class；Call the classify method in Bayes.
As shown in figure 3, in the specific implementation process, it is described that emotion is carried out to the content if the content is text
Analytical procedure includes:
S123: judging that the character types of the content, the character types include Chinese, one in English and other characters
Kind or a variety of combinations；
S124: the sentiment analysis tool of the corresponding character types of selection analyze the emotion of the content.
Specifically, server judges that the character types of the content, the character types include Chinese, English and other words
One of symbol or a variety of combinations；The sentiment analysis tool of the corresponding character types of server selection analyze in described
The emotion of appearance.If the character types are English, server selects English text sentiment analysis tool, including Natural
Language Toolkit (NLTK), scikit-learn, SpaCy, Textacy, Tensorflow, Theano, fastText,
TextBlob.If if the character types are Chinese, server selects Chinese text sentiment analysis tool, including
SnowNLP, BosonNLP, Tencent's AI sentiment analysis.
By judging that character types select corresponding sentiment analysis tool, the use that can be applicable under multilingual environment.
As shown in figure 4, in the specific implementation process, the sentiment analysis tool of the corresponding character types of selection carries out
The emotion step for analyzing the content includes:
S124a: the character types in the content are counted；
S124b: if including two or more language in the character types, it is corresponding that each character types are counted
Number of characters；
S124c: the ratio of the total text character number of number of characters Zhan of each character types is calculated；
S124d: the sentiment analysis result of the content is calculated according to the accounting of each character types.
Specifically, the sentiment analysis tool of the corresponding character types of the selection analyze the emotion step of the content
It suddenly include: the character types in content described in server statistics；If in the server character types including two kinds or two kinds
Above language then counts the corresponding number of characters of each character types；Server calculates the total text of number of characters Zhan of each character types
The ratio of number of characters；Server calculates the sentiment analysis result of the content according to the accounting of each character types.
In the actual environment, some users like coming in same sentence using the sentence that multilingual, such as Chinese and English mix
Expression.It is mixed that how various language can be efficiently solved according to the sentiment analysis result that the accounting of each character types calculates the content
The miscellaneous sentiment analysis problem in same sentence.
In the specific implementation process, the accounting according to each character types calculates the sentiment analysis result step of the content
Suddenly include:
Calculate the sentiment analysis result formula of the content are as follows:
Wherein, PLiFor the sentiment analysis result of i-th kind of character；
KLiFor the ratio of the total text character number of i-th kind of number of characters Zhan
P is the sentiment analysis result of the content.
Specifically, number of the P between 0-1, more levels off to 1, then more positive, more approaches 0, then more passive.
As shown in figure 5, in the specific implementation process, described the step of level in mental health is judged according to sentiment analysis result
Further include:
S131: statistical history sentiment analysis result；
S132: according to the diversity judgement level in mental health of history sentiment analysis result and current sentiment analysis result.
Specifically, described the step of level in mental health is judged according to sentiment analysis result further include: server statistics are gone through
History sentiment analysis result；Server is according to the diversity judgement mental health of history sentiment analysis result and current sentiment analysis result
It is horizontal.Specifically, the history sentiment analysis result may include different cycles history sentiment analysis as a result, as the previous day,
The last week, preceding January, the preceding first quarter, the previous year or the history sentiment analysis result since use.
In the specific implementation process, the diversity judgement according to history sentiment analysis result and current sentiment analysis result
Level in mental health step includes:
The level in mental health calculation formula are as follows:
Wherein, β is level in mental health value；
For the average value of history sentiment analysis result；
For current sentiment analysis as a result, the average value of i.e. last m sentiment analysis result.
Specifically, the average value and current sentiment analysis for comparing history sentiment analysis result are as a result, emotional value can be monitored
The size variation of variable quantity.It is changed greatly in β emotional value, and when being positive value, β is bigger, then level in mental health is poorer, psychological shape
State is more passive.When β is negative value, show level in mental health height, psychological condition is more positive than history emotion.0 is more leveled off in β
When, show that psychology fluctuation is smaller.
In the specific implementation process, the level in mental health calculation formula are as follows:
Wherein, β is level in mental health value；
For the average value of history sentiment analysis result in i-th of period；
For current sentiment analysis as a result, the average value of i.e. last m sentiment analysis result.
KβiFor the time correlation coefficient in i-th of period, and it is described
Specifically, closer to the period of current time, time correlation coefficient is bigger.Time correlation coefficient and be 1.One
In kind specific embodiment, i-th of the cycle time related coefficient be can be set are as follows:
1st period was the previous day, the previous day time correlation coefficient Kβ1=0.4；
2nd period was the last week, the last week time correlation coefficient Kβ2=0.3；
3rd period was preceding January, previous month related coefficient Kβ3=0.15；
4th period was previous season, previous season time correlation coefficient Kβ4=0.10；
5th period was the previous year, the previous year time correlation coefficient Kβ5=0.05.
Specifically, apart from the more long history sentiment analysis of current time as a result, for current sentiment analysis result difference value
Point of reference it is smaller.Time correlation coefficient is added, can make the level in mental health value more can reflect the difference value of psychology variation.
It is changed greatly in β emotional value, and when being positive value, β is bigger, then level in mental health is poorer, and psychological condition is more passive.It is negative in β
When value, show level in mental health height, psychological condition is more positive than history emotion.β more level off to 0 when, show psychological fluctuation
It is smaller.
In a kind of exemplary embodiment, level in mental health can be divided into very actively, actively, in, passive, serious passiveness
Deng five grades, level in mental health value is equal to sentiment analysis result.According to sentiment analysis knot described in the level in mental health
Fruit judges that the step of level in mental health can be implemented as, and server judges whether the sentiment analysis result is lower than the first threshold
Value；If being lower than first threshold, the level in mental health is serious passive.Such as, the first threshold is preset as 0.2, such as
Sentiment analysis result described in fruit is 0.1, then the level in mental health is serious passive.
Level in mental health is judged by the absolute value of emotion variable quantity and sentiment analysis, and it is close can to have reacted comprehensively user
The general level of the health situation of phase.
As shown in fig. 6, in the specific implementation process, it is described that warning information is issued to family members' terminal according to level in mental health
After step further include:
S150: data synchronizing frequency is arranged according to the level in mental health, the data synchronizing frequency is the user
Terminal sends the user content to the frequency of the server.
Specifically, data synchronizing frequency, the data synchronizing band is arranged according to the level in mental health in the server
Rate is in current slot, and the user terminal sends the user content to the frequency of the server
As shown in fig. 7, in the specific implementation process, data synchronizing frequency step packet is arranged according to the level in mental health
It includes:
S151: data synchronization times are arranged according to the input frequency of user content, level in mental health, the data are synchronous
Number is the number that user content is submitted to server in this period；
S152: the user terminal is sent by the data synchronization times.
Specifically, data synchronization times formula is in this period
UT=FT×IT
UTFor data synchronization times, i.e., user content is submitted to the number of server in this period；
ITFor this period the number of user input；
FTIt is that user content is synchronized to number and the user's input of server in current slot for data synchronizing frequency
The ratio of number, its calculation formula is:
Wherein, FT-1For previous time period data synchronizing frequency.
Specifically, F0 is set as 1, i.e., when user inputs every time, and terminal all can be by the content synchronization of input to server.
β level off to 1 when, then psychology it is more passive, data synchronizing frequency is more frequent, increase monitoring dynamics, facilitate in time
It was found that the problem of terminal user.When β is less than or equal to 0, psychology is more positive, reduces Monitoring frequency, advantageously reduces mobile phone energy
Consumption reduces the consumption to handset capability.
As shown in figure 8, in the specific implementation process, it is described that warning information is issued to family members' terminal according to level in mental health
After step further include:
S160: according to the user information and level in mental health, matched family is inquired from family members' supplementary knowledge library
Belong to assisted care knowledge point, family members' supplementary knowledge library, which stores, helps the relevant knowledge point of family members' assisted care；
S170: the corresponding family members of the user are sent by family members' assisted care knowledge point.
In the specific implementation process, described that matched mental health knowledge step is inquired from family members' supplementary knowledge library also
Include:
Inquire the corresponding family members of the user and family members and the customer relationship；
Matched family members are inquired from family members' supplementary knowledge library according to the relationship of the family members and the user to assist protecting
Manage knowledge point.
In the specific implementation process, described that matched mental health knowledge step is inquired from family members' supplementary knowledge library also
Including
Calculate the knowledge point in family members' supplementary knowledge library and the knowledge degree of correlation of the user；
N knowledge points before being selected from big to small according to the knowledge degree of correlation；
Family members' terminal is sent by the knowledge point and its knowledge degree of correlation, with related according to knowledge by family members' terminal
Degree shows the knowledge point.
In the specific implementation process, the age of the knowledge degree of correlation and user, gender, level in mental health, psychological disease
Sick type and family members are related to the relationship of user.The attribute of knowledge point is set in family members' supplementary knowledge library, and the attribute includes
Age of user, gender, level in mental health, the relationship of mental disease type and family members and user.It calculates separately user and knows
Know the degree of correlation of the correspondence attribute of point, and all properties are added, is i.e. the knowledge degree of correlation of user and knowledge point.User and knowledge
The degree of correlation of the correspondence attribute of point can pass through Semantic Similarity Measurement.
The knowledge degree of correlation formula are as follows:
N is the knowledge point degree of correlation；
KyiIt is the weight of the i-th attribute；
Kc (i) is the i-th attribute of knowledge point；
User (i) is the i-th attribute of user；
Yu [kc (i), user (i)] refers to the semantic relevancy of kc (i) He user (i).
By the knowledge degree of correlation, find with the immediate document of user, be pushed to family members, family members can be made more have science,
Targetedly carry out family members' assisted care.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health method for early warning for tracking and alerting in time is realized in sense analysis.
Embodiment 2
As shown in figure 9, other side according to an embodiment of the present invention, provides a kind of mental health method for early warning, institute
The method of stating includes:
S210: the input state or browse state of user terminal are detected；
S220: the content for inputting or browsing is obtained；
S230: sending server for the content for inputting or browsing, by the server judgement and according to user
Level in mental health issue early warning.
In a kind of exemplary embodiment, the user terminal operations system is that android is obtained when the completion inputs
Take inputted content step to implement are as follows: InputMethodService function: the function starts for the first time in input method
When it is called, for doing the setting initialized；By calling onBindlnput interface function, in other client and
Input method connection；It calls InputMethodManager module as input method manager, manages the interaction of each section；Pass through
OnFinishlnputO function is used to obtain current end of input；Input method calls onDestroy () function when closing.
In the specific implementation process, described the step of sending server for the content for inputting or browsing, includes:
Receive data synchronizing frequency；
Server is sent by the content of the input according to the data synchronizing frequency.
Specifically, the step of sending server for the content for inputting or browsing realization are as follows: the user terminal
Receive server data synchronizing frequency or data synchronization times according to transmitted by level in mental health；The user terminal according to
The content of the input is sent server by the data synchronizing frequency or data synchronization times.
Different data synchronizing frequency or data synchronization times are set according to level in mental health, β level off to 1 when, then
Psychology is more passive, and data synchronizing frequency is more frequent, increases monitoring dynamics, facilitates the problem of finding terminal user in time.It is small in β
When 0, psychology is more positive, reduces Monitoring frequency, advantageously reduces mobile phone energy consumption, reduces the consumption to handset capability.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health method for early warning for tracking and alerting in time is realized in sense analysis.
Embodiment 3
As shown in Figure 10, another aspect according to an embodiment of the present invention provides a kind of mental health method for early warning, institute
The method of stating includes:
S310: server is received according to the issued warning information of level in mental health；
S320: early warning interface is generated according to the warning information, the early warning interface passes through text, image, audio or view
One or more combinations of frequency show warning information.
As shown in figure 11, in the specific implementation process, the early warning interface includes first information control, described according to
After warning information generates early warning interface step further include:
S330: the event in detecting and early warning interface；
S340: the selection event in response to being directed to the first information control shows User Detail, and the user is detailed
Thin information includes history level in mental health, current level in mental health.
As shown in figure 12, in the specific implementation process, the early warning interface includes the second information control, described according to
After warning information generates early warning interface step further include:
S330: the event in detecting and early warning interface；
S350: the selection event in response to being directed to second information control shows the knowledge list of family members' assisted care, institute
It states family members' assisted care knowledge list and shows the family members' assisted care knowledge point being adapted with current level in mental health.
In the specific implementation process, after the displaying family members assisted care knowledge listings step further include:
Detect the event in the knowledge list of family members' assisted care；
In response to the selection event for the knowledge point in family members' assisted care knowledge list, show that corresponding family members are auxiliary
Help nursing knowledge point.
In a kind of exemplary embodiment, the mental health method for early warning can be realized are as follows:
The input state or browse state of user terminal detecting user terminal；
User terminal obtains the content for inputting or browsing；Wherein, the step of obtaining inputted content realization are as follows: defeated
Enter when method starts for the first time and call InputMethodService function, does the setting initialized；Pass through calling
OnBindlnput interface function is connected with the input method；Call InputMethodManager module as input method manager,
Manage the interaction of each section；It is used to obtain current end of input by onFinishlnputO function；Input method close when
Call onDestroy () function；
The content for inputting or browsing is sent server by user terminal；
Server receives the content of user terminal, and the content is that user terminal acquires the content inputted in user's input method
Or the current browsed web content of user；
Server carries out sentiment analysis to the content；Wherein, server carries out sentiment analysis step packet to the content
It includes: judging that the type of the content, the type include text, picture or voice；It selects to correspond to according to the type of the content
Sentiment analysis mode, the sentiment analysis mode includes text analyzing, picture analyzing and speech analysis；If the type is
Text then judges that the character types of the content, the character types include Chinese, one of English and other characters or more
The combination of kind；The sentiment analysis tool of the corresponding character types of selection analyze the emotion of the content；In counting described
Character types in appearance；If including two or more language in the character types, each character types pair are counted
The number of characters answered；Calculate the ratio of the total text character number of number of characters Zhan of each character types；According to the accounting meter of each character types
Calculate the sentiment analysis result of the content；
Server judges level in mental health according to sentiment analysis result；It is described strong according to sentiment analysis result judgement psychology
The step of Kang Shuiping further include: statistical history sentiment analysis result；According to history sentiment analysis result and current sentiment analysis knot
The diversity judgement level in mental health of fruit；The history sentiment analysis result may include the history sentiment analysis knot of different cycles
Fruit；
Server issues warning information to family members' terminal according to level in mental health；
Family members' terminal receives server according to the issued warning information of level in mental health；
Family members' terminal according to the warning information generate early warning interface, the early warning interface by text, image, audio or
One or more combinations of video show warning information
Server inquires the corresponding family members of the user and family members and the customer relationship according to the user information；
Server inquires matched family members' assisted care knowledge point from family members' supplementary knowledge library, and family members' auxiliary is known
Know library and stores the relevant knowledge point of help family members' assisted care；Wherein, the age of the knowledge degree of correlation and user, gender,
Level in mental health, mental disease type and family members are related to the relationship of user；Knowledge point is set in family members' supplementary knowledge library
Attribute, the attribute includes the pass of age of user, gender, level in mental health, mental disease type and family members and user
System；The degree of correlation of user with the corresponding attribute of knowledge point are calculated separately, and all properties are added, is i.e. user and knowledge point knows
Know the degree of correlation；User can be by Semantic Similarity Measurement from family members' supplementary knowledge library with the degree of correlation of the corresponding attribute of knowledge point
Inquire matched family members' assisted care knowledge point step further include: the knowledge point in calculating family members' supplementary knowledge library and the user
The knowledge degree of correlation；N knowledge points before being selected from big to small according to the knowledge degree of correlation；The knowledge point and its knowledge is related
Degree is sent to family members' terminal, to show the knowledge point according to the knowledge degree of correlation by family members' terminal；
Family members' assisted care knowledge point is sent the corresponding family members of the user by server；
Event in family members' terminal detecting and early warning interface, the early warning interface include first information control；
Family members' terminal shows User Detail, the use in response to the selection event for the first information control
Family details include history level in mental health, current level in mental health；
The early warning interface further includes the second information control, and family members' terminal is in response to the choosing for second information control
Event is selected, shows that the knowledge list of family members' assisted care, family members' assisted care knowledge list are shown and current mental health water
Put down adaptable family members' assisted care knowledge point；
Family members' terminal detects the event in the knowledge list of family members' assisted care；
Family members' terminal is in response to the selection event for the knowledge point in family members' assisted care knowledge list, displaying pair
Answer family members' assisted care knowledge point
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health method for early warning for tracking and alerting in time is realized in sense analysis.
Embodiment 4
As shown in figure 13, according to an aspect of an embodiment of the present invention, a kind of mental health Warning Service device is provided
100, the server 100 includes:
Receiving module 101, for receiving the content of user terminal, the content is that user terminal acquires in user's input method
The interior current browsed web content of perhaps user of input；
Analysis module 102, for carrying out sentiment analysis to the content；
Judgment module 103, for judging level in mental health according to sentiment analysis result；
Warning module 104, for issuing warning information to family members' terminal according to level in mental health.
In the specific implementation process, server receives the content of user terminal transmitted by user terminal, and the content is
User terminal collected user inputted in input method in the content that perhaps browses in a browser；Server is to described
Content carries out sentiment analysis；Server judges level in mental health according to sentiment analysis result；Server is according to mental health water
It puts down to family members' terminal and issues warning information.
Actively acquire user inputted in the user terminal that it is held in perhaps browsing content mode, then to described interior
Hold and carry out sentiment analysis, the level in mental health of user is judged according to sentiment analysis result, is asked not needing user at hospital
It, can be with the real-time tracking user psychology general level of the health in the case where examining.
Due to by the way of active analysis user input content or browsing content, solving the one-to-one artificial interrogation of tradition
Just will appreciate that the user psychology problem in short supply so as to cause psychological medical resource, so as to so that shrink one-to-manyly
Service.
Family members' terminal that server is held to family members according to level in mental health issues warning information, can make family members and
When psychological monitoring and intervention are carried out to user, be conducive to the negative emotions for alleviating user.
In the specific implementation process, the server 100 further include:
The judgment module 103 is also used to judge that the type of the content, the type include text, picture or voice；
Selecting module 105, for selecting corresponding sentiment analysis mode, the sentiment analysis according to the type of the content
Mode includes text analyzing, picture analyzing and speech analysis.
Specifically, server judges that the type of the content, the type include text, picture or voice；Server root
Corresponding sentiment analysis mode is selected according to the type of the content, the sentiment analysis mode includes text analyzing, picture analyzing
And speech analysis.
Some user's preferences select corresponding sentiment analysis mode in input voice or picture, according to the type of content, can
Such as to be equally able to carry out sentiment analysis when picture, voice content when user inputs rich-media content.
In the specific implementation process, the server further include:
The analysis module 102 analyzes the emotion of the content if being also used to the content is text.
In the specific implementation process, the server 100 further include:
The judgment module 103 is also used to judge that the character types of the content, the character types include Chinese, English
One of text and other characters or a variety of combinations；
The selecting module 105, be also used to select to correspond to the character types sentiment analysis tool analyzed described in
The emotion of content.
By judging that character types select corresponding sentiment analysis tool, the use that can be applicable under multilingual environment.
In the specific implementation process, the server 100 further include:
Statistical module 106, for counting the character types in the content；
The statistical module 106 is united if being also used to the language in the character types comprising two or more
Count the corresponding number of characters of each character types；
Computing module 107, the ratio of the total text character number of number of characters Zhan for calculating each character types；
The computing module 107 is also used to calculate the sentiment analysis result of the content according to the accounting of each character types.
Specifically, the sentiment analysis tool of the corresponding character types of the selection analyze the emotion step of the content
It suddenly include: the character types in content described in server statistics；If in the server character types including two kinds or two kinds
Above language then counts the corresponding number of characters of each character types；Server calculates the total text of number of characters Zhan of each character types
The ratio of number of characters；Server calculates the sentiment analysis result of the content according to the accounting of each character types.
In the actual environment, some users like coming in same sentence using the sentence that multilingual, such as Chinese and English mix
Expression.It is mixed that how various language can be efficiently solved according to the sentiment analysis result that the accounting of each character types calculates the content
The miscellaneous sentiment analysis problem in same sentence.
In the specific implementation process, the sentiment analysis result formula of the content is calculated are as follows:
Wherein, PLiFor the sentiment analysis result of i-th kind of character；
KLiFor the ratio of the total text character number of i-th kind of number of characters Zhan
P is the sentiment analysis result of the content.
Specifically, number of the P between 0-1, more levels off to 1, then more positive, more approaches 0, then more passive.
In the specific implementation process, the server 100 further include:
The statistical module 106 is also used to statistical history sentiment analysis result；
The judgment module 103 is also used to be sentenced according to history sentiment analysis result and the difference of current sentiment analysis result
Disconnected level in mental health.
Specifically, the history sentiment analysis result may include the history sentiment analysis of different cycles as a result, as previous
It, the last week, preceding January, the preceding first quarter, the previous year or the history sentiment analysis result since use.
In the specific implementation process, the server 100 includes:
The level in mental health calculation formula are as follows:
Wherein, β is level in mental health value；
For the average value of history sentiment analysis result；
For current sentiment analysis as a result, the average value of i.e. last m sentiment analysis result.
Specifically, the average value and current sentiment analysis for comparing history sentiment analysis result are as a result, emotional value can be monitored
The size variation of variable quantity.It is changed greatly in β emotional value, and when being positive value, β is bigger, then level in mental health is poorer, psychological shape
State is more passive.When β is negative value, show level in mental health height, psychological condition is more positive than history emotion.0 is more leveled off in β
When, show that psychology fluctuation is smaller.
In the specific implementation process, the level in mental health calculation formula are as follows:
Wherein, β is level in mental health value；
For the average value of history sentiment analysis result in i-th of period；
For current sentiment analysis as a result, the average value of i.e. last m sentiment analysis result.
KβiFor the time correlation coefficient in i-th of period, and it is described
Specifically, closer to the period of current time, time correlation coefficient is bigger.Time correlation coefficient and be 1.One
In kind specific embodiment, i-th of the cycle time related coefficient be can be set are as follows:
1st period was the previous day, the previous day time correlation coefficient Kβ1=0.4；
2nd period was the last week, the last week time correlation coefficient Kβ2=0.3；
3rd period was preceding January, previous month related coefficient Kβ3=0.15；
4th period was previous season, previous season time correlation coefficient Kβ4=0.10；
5th period was the previous year, the previous year time correlation coefficient Kβ5=0.05.
Specifically, apart from the more long history sentiment analysis of current time as a result, for current sentiment analysis result difference value
Point of reference it is smaller.Time correlation coefficient is added, can make the level in mental health value more can reflect the difference value of psychology variation.
It is changed greatly in β emotional value, and when being positive value, β is bigger, then level in mental health is poorer, and psychological condition is more passive.It is negative in β
When value, show level in mental health height, psychological condition is more positive than history emotion.β more level off to 0 when, show psychological fluctuation
It is smaller.
In a kind of exemplary embodiment, level in mental health can be divided into very actively, actively, in, passive, serious passiveness
Deng five grades, level in mental health value is equal to sentiment analysis result.According to sentiment analysis knot described in the level in mental health
Fruit judges that the step of level in mental health can be implemented as, and server judges whether the sentiment analysis result is lower than the first threshold
Value；If being lower than first threshold, the level in mental health is serious passive.Such as, the first threshold is preset as 0.2, such as
Sentiment analysis result described in fruit is 0.1, then the level in mental health is serious passive.
In the specific implementation process, the server 100 further include:
Setup module 108, for data synchronizing frequency, the data synchronizing frequency to be arranged according to the level in mental health
The user content is sent to for the user terminal frequency of the server.
Specifically, data synchronizing frequency, the data synchronizing band is arranged according to the level in mental health in the server
Rate is in current slot, and the user terminal sends the user content to the frequency of the server
In the specific implementation process, data synchronizing frequency step is arranged according to the level in mental health includes:
It is synchronous to be also used to the input frequency according to user content, level in mental health setting data for the setup module 108
Number, the data synchronization times are the number that user content is submitted to server in this period；
First sending module 109, for sending the user terminal for the data synchronization times.
Specifically, data synchronization times formula is in this period
UT=FT×IT
UTFor data synchronization times, i.e., user content is submitted to the number of server in this period；
ITFor this period the number of user input；
FTIt is that user content is synchronized to number and the user's input of server in current slot for data synchronizing frequency
The ratio of number, its calculation formula is:
Wherein, FT-1For previous time period data synchronizing frequency.
Specifically, F0 is set as 1, i.e., when user inputs every time, and terminal all can be by the content synchronization of input to server.
β level off to 1 when, then psychology it is more passive, data synchronizing frequency is more frequent, increase monitoring dynamics, facilitate in time
It was found that the problem of terminal user.When β is less than or equal to 0, psychology is more positive, reduces Monitoring frequency, advantageously reduces mobile phone energy
Consumption reduces the consumption to handset capability.
In the specific implementation process, the server 100 further include:
Enquiry module 110, for being inquired from family members' supplementary knowledge library according to the user information and level in mental health
To matched family members' assisted care knowledge point, family members' supplementary knowledge library, which stores, helps the relevant knowledge of family members' assisted care
Point；
It is corresponding to be also used to send family members' assisted care knowledge point to the user for first sending module 109
Family members.
In the specific implementation process, the server 100 further include:
The enquiry module 110 is also used to inquire the corresponding family members of the user and family members and the customer relationship；
The enquiry module 110 is also used to according to the relationship of the family members and the user from family members' supplementary knowledge library
Inquire matched family members' assisted care knowledge point.
In the specific implementation process, the server 100 further include:
The computing module 107, the knowledge point for being also used to calculate family members' supplementary knowledge library are related to the knowledge of the user
Degree；
The selecting module 105 is also used to N before selecting from big to small according to knowledge degree of correlation knowledge points；
First sending module 109 is also used to send family members' terminal for the knowledge point and its knowledge degree of correlation, with
The knowledge point is shown according to the knowledge degree of correlation by family members' terminal.
In the specific implementation process, the age of the knowledge degree of correlation and user, gender, level in mental health, psychological disease
Sick type and family members are related to the relationship of user.The attribute of knowledge point is set in family members' supplementary knowledge library, and the attribute includes
Age of user, gender, level in mental health, the relationship of mental disease type and family members and user.It calculates separately user and knows
Know the degree of correlation of the correspondence attribute of point, and all properties are added, is i.e. the knowledge degree of correlation of user and knowledge point.User and knowledge
The degree of correlation of the correspondence attribute of point can pass through Semantic Similarity Measurement.
The knowledge degree of correlation formula are as follows:
N is the knowledge point degree of correlation；
KyiIt is the weight of the i-th attribute；
Kc (i) is the i-th attribute of knowledge point；
User (i) is the i-th attribute of user；
Yu [kc (i), user (i)] refers to the semantic relevancy of kc (i) He user (i).
By the knowledge degree of correlation, find with the immediate document of user, be pushed to family members, family members can be made more have science,
Targetedly carry out family members' assisted care.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health method for early warning for tracking and alerting in time is realized in sense analysis.
Embodiment 5
As shown in figure 14, it is whole to provide a kind of mental health early warning user for other side according to an embodiment of the present invention
End 200, the user terminal 200 include:
Second detecting module 201, for detecting the input state or browse state of user terminal；
Second obtains module 202, for obtaining the content for inputting or browsing；
Second sending module 203, for sending server for the content for inputting or browsing, by the server
Judge and early warning is issued according to the level in mental health of user.
In a kind of exemplary embodiment, the user terminal operations system is that android is obtained when the completion inputs
Take inputted content step to implement are as follows: InputMethodService function: the function starts for the first time in input method
When it is called, for doing the setting initialized；By calling onBindlnput interface function, in other client and
Input method connection；It calls InputMethodManager module as input method manager, manages the interaction of each section；Pass through
OnFinishlnputO function is used to obtain current end of input；Input method calls onDestroy () function when closing.
In the specific implementation process, the user terminal 200 further include:
Second receiving module 204, for receiving data synchronizing frequency；
Second sending module 203 is also used to be sent the content of the input to according to the data synchronizing frequency
Server.
Specifically, the step of sending server for the content for inputting or browsing realization are as follows: the user terminal
Receive server data synchronizing frequency or data synchronization times according to transmitted by level in mental health；The user terminal according to
The content of the input is sent server by the data synchronizing frequency or data synchronization times.
Different data synchronizing frequency or data synchronization times are set according to level in mental health, β level off to 1 when, then
Psychology is more passive, and data synchronizing frequency is more frequent, increases monitoring dynamics, facilitates the problem of finding terminal user in time.It is small in β
When 0, psychology is more positive, reduces Monitoring frequency, advantageously reduces mobile phone energy consumption, reduces the consumption to handset capability.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health early warning user terminal for tracking and alerting in time is realized in sense analysis.
Embodiment 6
As shown in figure 15, it is whole to provide a kind of mental health early warning family members for another aspect according to an embodiment of the present invention
End 300, family members' terminal 300 include:
Third receiving module 301, for receiving server according to the issued warning information of level in mental health；
Generation module 302, for generating early warning interface according to the warning information, the early warning interface passes through text, figure
One or more combinations of picture, audio or video show warning information.
In the specific implementation process, family members' terminal 300 further include:
Third detecting module 303, for the event in detecting and early warning interface；
Display module 304 shows that user believes in detail for the selection event in response to being directed to the first information control
Breath, the User Detail includes history level in mental health, current level in mental health.
In the specific implementation process, family members' terminal 300 further include:
The display module 304 is also used to the selection event in response to being directed to second information control, shows that family members are auxiliary
Nursing knowledge list is helped, family members' assisted care knowledge list shows that the family members being adapted with current level in mental health assist
Nursing knowledge point.
In the specific implementation process, family members' terminal 300 further include:
Third detecting module 303 is also used to detect the event in family members' assisted care knowledge list；
The display module 304 is also used in response to for the knowledge point in family members' assisted care knowledge list
Selection event shows corresponding family members' assisted care knowledge point.
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health early warning family members' terminal for tracking and alerting in time is realized in sense analysis.
Embodiment 7
As shown in figure 16, another aspect according to an embodiment of the present invention provides a kind of mental health early warning system, institute
The system of stating includes mental health Warning Service device 100, user terminal 200, family members' terminal 300.The user terminal 200 with it is described
Server 100 communicates to connect.Family members' terminal 300 is communicated to connect with the server 100.
The server 100 includes that memory and the processor for being coupled to the memory, processor are configured as being based on
Instruction stored in memory executes the mental health method for early warning in the disclosure in specific embodiment 1.Memory for example may be used
To include system storage, fixed non-volatile memory medium etc..System storage is for example stored with operating system, using journey
Sequence, Boot loader (Boot Loader), database and other programs etc..
The user terminal 200 includes that memory and the processor for being coupled to the memory, processor are configured as base
In instruction stored in memory, the mental health method for early warning in disclosure specific embodiment 2 is executed.Memory for example may be used
To include system storage, fixed non-volatile memory medium etc..System storage is for example stored with operating system, using journey
Sequence, Boot loader (Boot Loader), database and other programs etc..
Family members' terminal 300 includes that memory and the processor for being coupled to the memory, processor are configured as base
In instruction stored in memory, the mental health method for early warning in disclosure specific embodiment 3 is executed.Memory for example may be used
To include system storage, fixed non-volatile memory medium etc..System storage is for example stored with operating system, using journey
Sequence, Boot loader (Boot Loader), database and other programs etc..
The present invention solves the problems, such as that psychological health states can not be tracked in time in the prior art, and feelings can be passed through by providing one kind
The mental health early warning system for tracking and alerting in time is realized in sense analysis.
The disclosure also provides a kind of computer readable storage medium, is stored thereon with computer program, and the program is processed
The step of device realizes the mental health method for early warning of any one aforementioned embodiment when executing.
Those skilled in the art should be understood that embodiment of the disclosure can provide as method, system or computer journey
Sequence product.Therefore, complete hardware embodiment, complete software embodiment or combining software and hardware aspects can be used in the disclosure
The form of embodiment.Moreover, it wherein includes the calculating of computer usable program code that the disclosure, which can be used in one or more,
Machine can use the meter implemented in non-transient storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of calculation machine program product.
So far, be described in detail according to the mental health method for early warning, server, family members' terminal of the disclosure be
System.In order to avoid covering the design of the disclosure, some details known in the field are not described.Those skilled in the art according to
Above description, completely it can be appreciated how implementing technical solution disclosed herein.
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.There is no necessity and possibility to exhaust all the enbodiments.It is all this
Made any modifications, equivalent replacements, and improvements etc., should be included in the claims in the present invention within the spirit and principle of invention
Protection scope within.
Claims (10)
1. a kind of mental health method for early warning, which is characterized in that the described method includes:
Receive the content of user terminal；
Sentiment analysis is carried out to the content；
Level in mental health is judged according to sentiment analysis result；
Warning information is issued to family members' terminal according to level in mental health.
2. mental health method for early warning according to claim 1, which is characterized in that sent out according to level in mental health described
Out after warning information step further include:
According to the user information and level in mental health, matched family members' assisted care is inquired from family members' supplementary knowledge library
Knowledge point, family members' supplementary knowledge library, which stores, helps the relevant knowledge point of family members' assisted care；
The corresponding family members of the user are sent by family members' assisted care knowledge point.
3. mental health method for early warning according to claim 2, which is characterized in that described to be looked into from family members' supplementary knowledge library
Ask matched mental health knowledge step further include:
Inquire the corresponding family members of the user and family members and the customer relationship；
Matched family members' assisted care is inquired from family members' supplementary knowledge library according to the relationship of the family members and the user to know
Know point.
4. a kind of mental health method for early warning, which is characterized in that the described method includes:
Server is received according to the issued warning information of level in mental health；
According to the warning information generate early warning interface, the early warning interface by text, image, audio or video one kind or
A variety of combinations shows warning information.
5. mental health method for early warning according to claim 4, which is characterized in that the early warning interface includes the first information
Control, it is described according to the warning information generate early warning interface step after further include:
Event in detecting and early warning interface；
In response to being directed to the selection event of the first information control, User Detail, the User Detail packet are shown
Include history level in mental health, current level in mental health.
6. mental health method for early warning according to claim 5, which is characterized in that the early warning interface includes the first information
Control, it is described according to the warning information generate early warning interface step after further include:
Event in detecting and early warning interface；
In response to being directed to the selection event of second information control, the knowledge list of family members' assisted care is shown, the family members are auxiliary
Nursing knowledge list is helped to show the family members' assisted care knowledge point being adapted with current level in mental health.
7. a kind of mental health Warning Service device, which is characterized in that the server further include:
Receiving module, for receiving the content of user terminal, the content is to input in user terminal acquisition user's input method
The inside perhaps current browsed web content of user；
Analysis module, for carrying out sentiment analysis to the content；
Judgment module, for judging level in mental health according to sentiment analysis result；
Warning module, for issuing warning information to family members' terminal according to level in mental health.
8. a kind of mental health early warning family members' terminal, which is characterized in that family members' terminal includes:
Third receiving module, for receiving server according to the issued warning information of level in mental health；
Generation module, for generating early warning interface according to the warning information, the early warning interface passes through text, image, audio
Or one or more combinations of video show warning information.
9. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
Mental health method for early warning described in one of claim 1 to 6 is realized when row.
10. a kind of mental health early warning system, which is characterized in that the system comprises:
User terminal, family members' terminal according to claim 8, server according to claim 7；The user is whole
End is connect with the server communication；Family members' terminal is connect with the server communication；
Wherein, the user terminal includes: the second detecting module, for detecting the input state or browse state of user terminal；
Second obtains module, for obtaining the content for inputting or browsing；Second sending module, the content for will input or browse
It is sent to server, by the server judgement and to issue early warning according to the level in mental health of user.
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