CN110047575A - A kind of reaction type sleeping system based on Remote Decision-making - Google Patents
A kind of reaction type sleeping system based on Remote Decision-making Download PDFInfo
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- CN110047575A CN110047575A CN201910251604.6A CN201910251604A CN110047575A CN 110047575 A CN110047575 A CN 110047575A CN 201910251604 A CN201910251604 A CN 201910251604A CN 110047575 A CN110047575 A CN 110047575A
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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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- 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
- G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
Abstract
The reaction type sleeping system based on Remote Decision-making that the invention discloses a kind of, specifically includes: acquisition equipment (1), communication module (2), cloud platform (3), main control module (4), sleeping equipment (5), mobile terminal (6)；Acquisition equipment (1) is electrically connected with the input terminal of main control module (4), the sleeping equipment (5) is electrically connected with the output end of main control module (4), the communication module (2) is electrically connected with main control module (4), the main control module is connected by communication module and cloud platform (3) wireless communication, and the mobile terminal (6) and the cloud platform (3) wireless communication connect.The present invention is sent to cloud platform and is analyzed and processed by acquiring the sleep physiology signal message of user, obtains effective sleeping scheme in real time according to sleeping decision model, feeds back to sleeping equipment adjustment function for assisting sleep mode, user is helped to fall asleep, sleep well；Support the sleeping under two kinds of network states, system structure is perfect, efficient.
Description
Technical field
The present invention relates to medical electronics and healthy sleep field, more particularly, to a kind of feedback based on Remote Decision-making
Formula sleeping system.
Background technique
Sleep is most basic one of the vital movement of the mankind.About 1/3 time was spent in sleep among all one's life of people.
Sleep is the Physiological effect of human body and the guarantee in service life, and good sleep is the necessary condition of physiological health of regaining one's strength, maintain,
It does not sleep and does not just have lived continuity, importance is self-evident.However, with the acceleration of people's life rhythm, life pressure
The increase of power, the reasons such as shortage of movement, the ratio for the crowd that has a sleepless night are higher and higher.As contemporary people are to important sex consciousness of sleeping
Raising, more people gradually pay close attention to sleep health, many insomniacs wish by sleeping equipment improve sleep state.
As information technology is using more and more extensive, the development of mobile medical equipment and big data is in sleeping service
Constantly make progress, at the same cloud platform be capable of user provide large-scale calculations ability and huge resource pool.In sleeping system
Middle addition cloud platform can make system structure more perfect.Sleeping system can make full use of data resource and powerful meter in cloud platform
Calculation ability is excavated and sleeping scheme in conjunction with the technologies such as machine learning and big data excavation from the data case of flood tide complexity
The high sleep physiology feature of correlation, the accuracy rate for improving the decision model of sleeping scheme can be improved and help in conjunction with the communication technology
The real-time and validity of dormancy system.
Currently, the sleeping equipment of parts of traditional in the market, due to the limitation of underlying hardware arithmetic speed and processing capacity, one
Determine to limit the promotion of sleeping effect in degree.And a large amount of health datas that the wearable sleeping equipment of tradition generates user,
Lack the platform of an effective storage, management, inquiry and analysis, especially shortcoming analyzes valuable letter from big data
The ability of breath.In view of the above-mentioned problems, the present invention is directed to propose a kind of reaction type sleeping system based on Remote Decision-making, the system make
It is acquired in real time with physiological parameter of the acquisition module to human body, collected physiological parameter is transmitted to cloud platform and is carried out greatly
Data analysis, and the sleeping decision model come out using the classification algorithm training of optimization, obtain corresponding under current state help
Dormancy scheme, sleeping equipment family can be used to enter sleep state faster, improves and use according to sleeping project setting function for assisting sleep mode
Family sleep quality.
Summary of the invention
The present invention is to overcome above-mentioned sleeping system sleeping effect in the prior art undesirable, the physiology that sleeping equipment generates
Data can not effectively analysis and utilization defect, a kind of reaction type sleeping system based on Remote Decision-making is provided.
The present invention is directed to solve above-mentioned technical problem at least to a certain extent.
Primary and foremost purpose of the invention is in order to solve the above technical problems, technical scheme is as follows:
A kind of reaction type sleeping system based on Remote Decision-making, the sleeping system include: acquisition equipment, communication module,
Cloud platform, main control module, sleeping equipment, mobile terminal；
The acquisition equipment is electrically connected with the input terminal of main control module, the output end electricity of the sleeping equipment and main control module
Connection, the communication module are electrically connected with main control module, and the main control module is connected by communication module and cloud platform wireless communication
It connects, the mobile terminal and cloud platform wireless communication connect.In the present invention, sleep quality physiology is believed by acquisition equipment
Number is acquired in real time, is sent to main control module, can be emitted to cloud platform by communication module；When sleeping systems connection shape
When under state, sleep physiology signal data is sent to cloud platform, and cloud platform comes out according to the classification algorithm training using optimization
Sleeping decision model obtains corresponding sleeping Adjusted Option under user's current state, is passed back to main control module, and main control module again will
Sleeping Adjusted Option issues sleeping equipment；When in the state that sleeping system being in and do not network, sleep physiology signal data is passed
It send to main control module, main control module directly carries out algorithm operational analysis and obtains the current sleep state of user, and according to master control
The preset sleeping decision model of arithmetic system generates sleeping decision in real time and sleeping decision is sent to sleeping equipment in molding block.
Sleeping equipment adjusts function for assisting sleep mode according to real-time sleeping Adjusted Option or sleeping decision real-time optimization, so that more scientific have
Effect ground helps user to enter sleep state, increases user's sleeping time, improves sleep quality；Meanwhile the mobile terminal can visit
It asks user's dormant data library in cloud platform, carries out data access, and then show the Analysis of sleeping quality report of user, sleep feelings
Condition looks back, suggests reminding with sleep, to help user to realize more reasonable Sleep architecture, realizes healthy sleep.
Further, the acquisition equipment is one of electroencephalograph, ecg signal acquiring instrument, myoelectric apparatus or a variety of, institute
Acquisition equipment is stated for the sleep physiology signal data of acquisition user in real time and is sent to main control module, the sleep physiology signal
Data include: human body electroencephalogram or electrocardio or myoelectricity physiological signal.
Further, the communication module is wireless communication module, and the communication module is according to the instruction handle of main control module
User's sleep physiology signal data of acquisition equipment acquisition is sent to cloud platform, while receiving and being adjusted by the sleep that cloud platform is sent
Scheme is passed back to main control module.Convenience when user uses is improved using wireless communication module.
Further, the cloud platform includes data storage cell, Data Management Analysis unit, data display unit；
The user's sleep physiology signal data received is screened and constructs user's sleep by the data storage cell
Database, user's dormant data library include based on personal individual subscriber sample database, the individual subscriber sample
The effective sleeping Adjusted Option and individual subscriber sleep state data of individual subscriber are stored in database；User's dormant data
Library further includes the sleeping decision model for having training to finish and training sample；
The Data Management Analysis unit utilizes user's sleep physiology signal data and training in user's dormant data library
The sleeping decision model finished obtains the sleeping Adjusted Option of user；
The data display unit is for statistical analysis to dormant data in individual subscriber sample dormant data library, data are dug
Pick, and offer can be the port that mobile terminal accessing is checked.
Further, the construction step in user's dormant data library is as follows:
Step 1: user's sleep physiology signal number of acquisition equipment acquisition in the sleeping system where cloud platform acquisition user i
According to Sn is denoted as, the value of n is the positive integer more than or equal to 1, and user's sleep physiology signal data is multidimensional data；
Step 2: cloud platform Data Management Analysis unit carries out feature extraction, benefit to collected sleep physiology signal data
With sleeping decision model, obtains sleeping Adjusted Option, be recorded as Dn；
Step 3: sleeping Adjusted Option Dn is sent back main control module by communication module by cloud platform (3), and main control module will
Sleeping Adjusted Option Dn is sent to sleeping equipment, changes the function for assisting sleep mode of sleeping equipment；
Step 4: cloud platform data processing unit analyzes the current sleep physiological signal data Sn of user, obtains the last time
The function and effect of sleeping Adjusted Option Dn-1, the value of n is the positive integer more than or equal to 2, the sleeping Adjusted Option at this time
Whether the function and effect of Dn-1 are effectively determined as method are as follows: if the sleep physiology signal data Sn of user meets preset characterization
The brain activity of user, which tends to enter deeper dormant curve or meets the preset sleep cycle period, then to be determined
Sleeping Adjusted Option Dn-1 is effective, if otherwise the sleep physiology signal data Sn of user is unsatisfactory for the brain of preset characterization user
Activity tend to enter deeper dormant curve or do not meet the preset sleep cycle period then determine sleeping adjust
Scheme Dn-1 is invalid；The sleep cycle period determines according to the physiological status and the state of mind of user；
Step 5: when the effect of sleeping Adjusted Option is effective, the sleep physiology signal data Sn-1 of the currently active case
It is added in individual subscriber sample database together with corresponding sleeping Adjusted Option Dn-1；If sleeping Adjusted Option is invalid, directly
It connects and executes step 6；
Step 6: the sleep physiology signal data Sn of user is stored in user's dormant data library；
Step 7: waiting time T, by n plus 1；
Step 8: judging whether user's timing function for assisting sleep time or the time of getting up have arrived, if having not timed out, return to step
Rapid 1, continue to obtain active user's sleep physiology signal data and sleeping Adjusted Option is adjusted according to sleeping decision model in real time, is
User's sleeping；If having timed out, stop sleeping and data screening acquisition；
Step 9: adjustment is updated to user's dormant data library content, if user's sleep physiology data record away from it is current when
Between be more than that default months or data volume reach predetermined amount, then delete the data before default months or the data more than predetermined amount,
Retain data of the data of newest default months as personal sample database, provides reliable, Gao Shixiao for sleeping adjusting training
The data source of property.
Further, the sleeping decision model includes the sleeping decision model and individual's version sleeping decision model of universality
Type, the building of the sleeping decision model of the universality the following steps are included:
Step 1: according to the personal information of user i, the personal information includes age, insomnia situation, life stress and body
The relevant information of body, the state of mind extracts the other users that several are greater than preset threshold with user's i personal information correlation
Personal sample database randomly selects M effectively case samples then from the personal sample database of the other users of extraction
This, each case sample has a kind of sleeping scheme label, shares the different sleeping scheme of N kind in M effective case samples；
Step 2: using selected M effective case samples, first to the sleep physiology signal number in effective case sample
According to being pre-processed, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is given birth to from pretreated sleep
Reason signal data extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every using the method for supervised learning
The sleeping decision model of kind sleeping scheme, the sleeping decision model of the M effective raw N number of universalities of case sample common property, institute
Supervised learning method is stated using one-versus-rest SVMs algorithm, one-versus-rest SVMs algorithm is according to every
A kind of effectively case sample generates a support vector machines, and the different sleeping scheme of N kind has constructed N number of support vector machines
SVM generates the sleeping decision model of universality；
User i helps sleep scheme individual's version sleeping decision model building process as follows:
Step 1: according to chronological order extraction entry time close to current time from the personal sample database of user i
M effective sample case, the training set as sleeping decision model；
Step 2: using selected M effective case samples, first to the sleep physiology signal number in effective case sample
According to being pre-processed, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is given birth to from pretreated sleep
Reason signal data extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every using the method for supervised learning
The sleeping decision model of kind sleeping scheme, the M effective raw personal version sleeping decision models of case sample common property are described
Supervised learning method using one-versus-rest SVMs algorithm, one-versus-rest SVMs algorithm is according to every
A kind of effectively case sample generates a support vector machines, and the different sleeping scheme of N kind has constructed N number of support vector machines
SVM generates personal version sleeping decision model.
Further, the generation method of the sleeping Adjusted Option is as follows:
A. when user starts to enable sleeping equipment, the sleeping system first uses the sleeping decision model of universality, will use
The current sleep physiological signal data at family uploads to cloud platform, and the Data Management Analysis unit of the cloud platform extracts universality
Sleeping decision model required for feature, corresponding sleeping Adjusted Option is obtained according to the sleeping decision model of universality；
B. as user using sleeping equipment for a period of time after, sample size reaches preset value in personal sample database, this
Shi Zhumian system conversion enables personal version sleeping decision model, and the current sleep physiological signal data of user is uploaded to cloud and is put down
Platform, the Data Management Analysis unit of the cloud platform extracts feature required for personal version sleeping decision model, according to individual
Version sleeping decision model obtains corresponding sleeping Adjusted Option；
C. during using sleeping equipment, after exporting sleeping scheme every time, judge to reaction type the effect of sleeping scheme
Fruit then empties the user if in primary sleep, the ratio that the bad number of the function and effect of sleeping scheme accounts for is greater than the threshold value of setting
Personal sample database again pulls up new personal sample database using the sleeping decision model of universality, to realize and be
System is adaptively adjusted personal sample database, in order to construct more effective, accurate sleeping decision model.
Further, the main control module is local control decision module, including there are two types of operating modes:
It networks under operating mode, main control module receives user's sleep physiology signal data from acquisition equipment, and will use
Family sleep physiology signal data is emitted to cloud platform by communication module；Meanwhile main control module is come from by communication module reception
The sleeping Adjusted Option of cloud platform, and sleeping Adjusted Option is sent to sleeping equipment；
Under off-line mode, main control module uses Expert System Design, and to acquisition equipment, collected user sleeps in real time first
Dormancy physiological signal data is analyzed and processed, and extracts its characteristic value, obtains sleeping decision according to expert system, and by sleeping decision
It is sent to sleeping equipment, controls the output of sleeping equipment.
Further, the sleeping equipment is sleeping follower, and the sleeping follower is based on through cranium microcurrent stimulating
The sleeping electric stimulator or sleeping music player or sleeping light-regulator of therapy, the sleeping follower can be according to master
The requirement for the sleeping Adjusted Option that the sleeping decision or cloud platform for controlling module sleeping output return, adjustment export corresponding sleeping and make
With mode, helps user to rapidly enter sleep state, improve the sleep quality of user.
Further, the mobile terminal is used for the dormant data to cloud platform request user and visualizes,
The mobile terminal includes sleep stage display module, sleep suggestion reminding module, sleep tracking reporting modules, healthy sleep official communication
Ask module.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
The present invention acquires the sleep physiology signal message of user by acquiring equipment in real time, and is sent to cloud platform and is divided
Analysis processing obtains effective sleeping scheme in real time according to sleeping decision model, feeds back to sleeping equipment and adjust function for assisting sleep in real time
Mode work, effectively helps user to fall asleep, sleep well；The present invention can be to user's sleep physiology in the state of networking and off-network
Information carries out operational analysis, and the sleep state analysis preset sleeping decision model of result combination main control module according to user obtains
Real-time sleeping decision is sent to the real-time corrective action mode of sleeping equipment out, structurally consummate, efficient.
Detailed description of the invention
Fig. 1 is a kind of general structure schematic diagram of reaction type sleeping system based on Remote Decision-making of the invention.
Fig. 2 is the flow diagram that user's dormant data library is constructed in cloud platform of the invention.
Fig. 3 is the main flow schematic diagram of the sleeping decision model algorithm of the optimization in cloud platform of the invention.
Fig. 4 is mobile phone terminal illustrative view of functional configuration of the invention.
Appended drawing reference: 1- acquires equipment, 2- communication module, 3- cloud platform, 4- main control module, 5- sleeping equipment, 6- movement
Terminal.
Specific embodiment
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, a kind of reaction type sleeping system based on Remote Decision-making, the sleeping system includes: acquisition equipment
1, communication module 2, cloud platform 3, main control module 4, sleeping equipment 5, mobile terminal 6；
The acquisition equipment 1 is electrically connected with the input terminal of main control module 4, the output of the sleeping equipment 5 and main control module 4
End electrical connection, the communication module 2 are electrically connected with main control module 4, and the main control module is wireless by communication module and cloud platform 3
Communication connection, the mobile terminal 6 are connected with the cloud platform 3 wireless communication.In the present invention, by acquisition equipment 1 to human body
Sleep physiology signal data such as EEG signals or heart rate signal or electrocardiosignal or body movement signal etc. are acquired in real time, are sent to
Main control module 4 can be emitted to cloud platform 3 by communication module 2；When under sleeping systems connection state, sleep physiology signal number
According to cloud platform 3 is sent to, cloud platform 3 obtains user according to the sleeping decision model that the classification algorithm training using optimization comes out
Corresponding sleeping Adjusted Option under current state, is passed back to main control module 4, and sleeping Adjusted Option is issued help again by main control module 4
Dormancy equipment 5；It is main after sleep physiology signal data is transferred into main control module 4 when in the state that sleeping system being in and do not network
Control module 4 directly carries out algorithm operational analysis and obtains the current sleep state of user, and according to arithmetic system in main control module 4
In preset sleeping decision model (such as expert system rule library) generate sleeping decision and sleeping decision is sent to sleeping in real time
Equipment 5.Sleeping equipment 5 adjusts function for assisting sleep mode according to real-time sleeping Adjusted Option or sleeping decision real-time optimization, thus more
Add and scientificlly and effectively user is helped to enter sleep state, increase user's sleeping time, improves sleep quality；Meanwhile the movement
Terminal 6 may have access to user's dormant data library in cloud platform 3, carry out data access, and then show the Analysis of sleeping quality of user
Report, sleep quality look back, suggest reminding with sleep, to help user to realize more reasonable Sleep architecture, realize healthy sleep.
In the present embodiment, the acquisition equipment 1 be one of electroencephalograph, ecg signal acquiring instrument, myoelectric apparatus or a variety of,
The acquisition equipment 1 is for acquiring the sleep physiology signal data of user in real time and being sent to main control module 4, the sleep physiology
Signal data includes: human body electroencephalogram or electrocardio or myoelectricity physiological signal.
In the present embodiment, the communication module 2 is wireless communication module, and the communication module 2 is according to the finger of main control module 4
It enables and user's sleep physiology signal data that acquisition equipment 1 acquires is sent to cloud platform 3, while receiving and to be sent by cloud platform 3
Sleep Adjusted Option is passed back to main control module 4.Convenience when user uses is improved using wireless communication module.
In the present embodiment, the cloud platform 3 includes data storage cell, Data Management Analysis unit, data display unit；
The user's sleep physiology signal data received is screened and constructs user's sleep by the data storage cell
Database, user's dormant data library include based on personal individual subscriber sample database, the individual subscriber sample
The effective sleeping Adjusted Option and individual subscriber sleep state data of individual subscriber are stored in database；User's dormant data
Library further includes the sleeping decision model for having training to finish and training sample；
The Data Management Analysis unit utilizes user's sleep physiology signal data and training in user's dormant data library
The sleeping decision model finished obtains the sleeping Adjusted Option of user；
The data display unit is for statistical analysis to dormant data in individual subscriber sample dormant data library, data are dug
Pick, and provide and can access the port checked for mobile terminal 6.
In the present embodiment, as shown in Fig. 2, the construction step in user's dormant data library is as follows:
Step 1: user's sleep physiology signal of acquisition equipment acquisition in the sleeping system where the acquisition of cloud platform 3 user i
Data are denoted as Sn, and the value of n is the positive integer more than or equal to 1, and user's sleep physiology signal data is multidimensional data；
Step 2: 3 Data Management Analysis unit of cloud platform carries out feature extraction to collected sleep physiology signal data,
Using sleeping decision model, obtains sleeping Adjusted Option, be recorded as Dn；
Step 3: sleeping Adjusted Option Dn is sent back main control module 4 by communication module by cloud platform 3, and main control module 4 will
Sleeping Adjusted Option Dn is sent to sleeping equipment 5, changes the function for assisting sleep mode of sleeping equipment 5；
Step 4: 3 data processing unit of cloud platform analyzes the current sleep physiological signal data Sn of user, obtains the last time
Sleeping Adjusted Option Dn-1 function and effect, the value of n is positive integer more than or equal to 2, the sleeping adjustment side at this time
Whether the function and effect of case Dn-1 are effectively determined as method are as follows: if the sleep physiology signal data Sn of user meets preset table
The brain activity at requisition family tends to enter deeper dormant curve or meet the preset sleep cycle period then to sentence
Determine that sleeping Adjusted Option Dn-1 is effective, if otherwise the sleep physiology signal data Sn of user is unsatisfactory for the big of preset characterization user
Cerebration tends to enter deeper dormant curve or do not meet the preset sleep cycle period then to determine sleeping tune
Perfect square case Dn-1 is invalid；The sleep cycle period determines according to the physiological status and the state of mind of user；
Step 5: when the effect of sleeping Adjusted Option is effective, the sleep physiology signal data Sn-1 of the currently active case
It is added in individual subscriber sample database together with corresponding sleeping Adjusted Option Dn-1；If sleeping Adjusted Option is invalid, directly
It connects and executes step 6；
Step 6: the sleep physiology signal data Sn of user is stored in user's dormant data library；
Step 7: waiting time T, by n plus 1；
Step 8: judging whether user's timing function for assisting sleep time or the time of getting up have arrived, if having not timed out, return to step
Rapid 1, continue to obtain active user's sleep physiology signal data and sleeping Adjusted Option is adjusted according to sleeping decision model in real time, is
User's sleeping；If having timed out, stop sleeping and data screening acquisition；
Step 9: adjustment is updated to user's dormant data library content, if user's sleep physiology data record away from it is current when
Between be more than that preset 3 months or data volume reach predetermined amount, then the data before deleting 3 months or data more than predetermined amount,
Retain data of the newest 3 months data as personal sample database, is provided for sleeping adjusting training reliable, high-timeliness
Data source.
In the present embodiment, the sleeping decision model includes the sleeping decision model and individual's version sleeping decision model of universality
Type, as shown in figure 3, the building of the sleeping decision model of the universality the following steps are included:
Step 1: according to the personal information of user i, the personal information includes age, insomnia situation, life stress and body
The relevant information of body, the state of mind extracts the other users that several are greater than preset threshold with user's i personal information correlation
Personal sample database randomly selects M effectively case samples then from the personal sample database of the other users of extraction
This, each case sample has a kind of sleeping scheme label, shares the different sleeping scheme of N kind in M effective case samples；
Step 2: using selected M effective case samples, first to the sleep physiology signal number in effective case sample
According to being pre-processed, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is given birth to from pretreated sleep
Reason signal data extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every using the method for supervised learning
The sleeping decision model of kind sleeping scheme, the sleeping decision model of the M effective raw N number of universalities of case sample common property, institute
Supervised learning method is stated using one-versus-rest SVMs algorithm, one-versus-rest SVMs algorithm according to
Every a kind of effectively case sample generates a support vector machines, and the different sleeping scheme of N kind has constructed N number of supporting vector
Machine SVM generates the sleeping decision model of universality；
User i helps sleep scheme individual's version sleeping decision model building process as follows:
Step 1: according to chronological order extraction entry time close to current time from the personal sample database of user i
M effective sample case, the training set as sleeping decision model；
Step 2: using selected M effective case samples, first to the sleep physiology signal number in effective case sample
According to being pre-processed, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is given birth to from pretreated sleep
Reason signal data extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every using the method for supervised learning
The sleeping decision model of kind sleeping scheme, the M effective raw personal version sleeping decision models of case sample common property are described
Supervised learning method is using one-versus-rest SVMs algorithm, and one-versus-rest SVMs algorithm is according to each
The effective case sample of class generates a support vector machines, and the different sleeping scheme of N kind has constructed N number of support vector machines
SVM generates personal version sleeping decision model.
In the present embodiment, the generation method of the sleeping Adjusted Option is as follows:
A. when user starts to enable sleeping equipment, the sleeping system first uses the disaggregated model of universality, by user's
Current sleep physiological signal data is input to cloud platform 3, and the Data Management Analysis unit of the cloud platform 3 extracts universality
Feature required for disaggregated model obtains corresponding sleeping Adjusted Option according to the disaggregated model of universality；
B. after user's time preset using sleeping equipment, sample size reaches preset value in personal sample database,
Sleeping system conversion at this time enables personal version disaggregated model, and the current sleep physiological signal data of user is input to cloud platform,
The Data Management Analysis unit of the cloud platform extracts feature required for personal version disaggregated model, is determined according to personal version sleeping
Plan model obtains corresponding sleeping Adjusted Option；
C. during using sleeping equipment, after exporting sleeping scheme every time, judge to reaction type the effect of sleeping scheme
Fruit then empties the user if in primary sleep, the ratio that the bad number of the function and effect of sleeping scheme accounts for is greater than the threshold value of setting
Personal sample database again pulls up new personal sample database using the sleeping decision model of universality, to realize and be
System is adaptively adjusted personal sample database, in order to construct more effective, accurate sleeping decision model.
In the present embodiment, the main control module 4 is local control decision module, including there are two types of operating modes:
It networks under operating mode, main control module 4 receives user's sleep physiology signal data from acquisition equipment 1, and will
User's sleep physiology signal data is emitted to cloud platform 3 by communication module 2；Meanwhile main control module 4 is connect by communication module 2
The sleeping Adjusted Option from cloud platform 3 is received, and sleeping Adjusted Option is sent to sleeping equipment 5；
It does not network under operating mode, main control module 4 uses Expert System Design, collects in real time to acquisition equipment 1 first
User's sleep physiology signal data be analyzed and processed, extract its characteristic value, sleeping decision obtained according to expert system, and will
Sleeping decision is sent to sleeping equipment 5, controls the output of sleeping equipment 5.
In the present embodiment, the sleeping equipment 5 is sleeping follower, and the sleeping follower is based on piercing through cranium micro-current
The sleeping electric stimulator or sleeping music player or sleeping light-regulator of sharp therapy, the sleeping follower being capable of basis
The requirement for the sleeping Adjusted Option that the sleeping decision or cloud platform 3 of 4 sleeping of main control module output return, adjustment output is corresponding to be helped
The dormancy mode of action helps user to rapidly enter sleep state, improves the sleep quality of user.
In the present embodiment, the mobile terminal 6 is used for the dormant data to cloud platform request user and carries out visualization exhibition
Show, the mobile terminal 6 includes sleep stage display module, sleep suggestion reminding module, sleep tracking reporting modules, health sleeping
Dormancy consultation module.
In the present embodiment when specifically used sleeping system, user first will acquisition equipment 1 and sleeping equipment 5 and human body progress
Physical contact, opens its power supply respectively and works.Acquisition equipment 1 of the invention can acquire the heart rate signal or brain telecommunications of human body
Number etc., sleeping equipment 5 can be contacted with human body ear-lobe or head.It acquires equipment 1 and acquires the sleep physiology information of human body such as in real time
Heart rate signal or EEG signals etc., according to the real work mode of control module 4, control module 4 is directly to sleep physiology signal
Data handle or sleep physiology data are sent to cloud platform 3 by communication module 2, through cloud platform 3 or local decision-making into
Row algorithm analytic operation feeds back to main control module 4 after obtaining sleeping scheme, and sleeping Adjusted Option is sent to sleeping by main control module 4
Equipment 5, adjustment function for assisting sleep model function adjusts the sleep physiology structure of human body, to reach in human body to sleeping equipment 5 in real time
Improve user's wakefulness state, increases the effect of user's sleeping time.
In the present embodiment, using mobile phone as mobile terminal 6, it is illustrated in figure 4 the functional schematic of mobile phone terminal, is used
Family can check the recent sleep state of user by APP in mobile phone, and Analysis of sleeping quality report also can carry out health with online doctor
Consulting etc..
In the present embodiment, user can according to personal preference, by mobile terminal 6 or sleeping equipment 5 to the function for assisting sleep time,
The functions such as mode, prompting are configured.
The same or similar label correspond to the same or similar components；
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
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 reaction type sleeping system based on Remote Decision-making, which is characterized in that the sleeping system includes: acquisition equipment
(1), communication module (2), cloud platform (3), main control module (4), sleeping equipment (5), mobile terminal (6)；
Acquisition equipment (1) is electrically connected with the input terminal of main control module (4), the sleeping equipment (5) and main control module (4)
Output end electrical connection, the communication module (2) are electrically connected with main control module (4), and the main control module passes through communication module and cloud
Platform (3) wireless communication connection, the mobile terminal (6) and the cloud platform (3) wireless communication connect.
2. a kind of reaction type sleeping system based on Remote Decision-making according to claim 1, which is characterized in that the acquisition
Equipment (1) is one of electroencephalograph, ecg signal acquiring instrument, myoelectric apparatus or a variety of, and the acquisition equipment (1) for adopting in real time
Collect the sleep physiology signal data of user and be sent to main control module (4), the sleep physiology signal data includes: human body brain
Electricity or electrocardio or myoelectricity physiological signal.
3. a kind of reaction type sleeping system based on Remote Decision-making according to claim 1 to 2, which is characterized in that described logical
Believe that module (2) are wireless communication module, the communication module (2) acquires acquisition equipment (1) according to the instruction of main control module (4)
User's sleep physiology signal data be sent to cloud platform (3), while receiving the sleep Adjusted Option time sent by cloud platform (3)
Give main control module (4).
4. a kind of reaction type sleeping system based on Remote Decision-making according to claim 3, which is characterized in that the cloud is flat
Platform (3) includes data storage cell, Data Management Analysis unit, data display unit；
The user's sleep physiology signal data received is screened and constructs user's dormant data by the data storage cell
Library, user's dormant data library include based on personal individual subscriber sample database, the individual subscriber sample data
The effective sleeping Adjusted Option and individual subscriber sleep state data of individual subscriber are stored in library；User's dormant data library is also
It include the sleeping decision model and training sample that training finishes；
The Data Management Analysis unit is finished using user's sleep physiology signal data in user's dormant data library with training
Sleeping decision model obtain the sleeping Adjusted Option of user；
The data display unit is for statistical analysis to dormant data in individual subscriber sample dormant data library, data mining,
And it provides and the port checked can be accessed for mobile terminal (6).
5. a kind of reaction type sleeping system based on Remote Decision-making according to claim 4, which is characterized in that the user
The construction step in dormant data library is as follows:
Step 1: user's sleep physiology signal number of acquisition equipment acquisition in the sleeping system where cloud platform (3) acquisition user i
According to Sn is denoted as, the value of n is the positive integer more than or equal to 1, and user's sleep physiology signal data is multidimensional data；
Step 2: cloud platform (3) Data Management Analysis unit carries out feature extraction, benefit to collected sleep physiology signal data
With sleeping decision model, obtains sleeping Adjusted Option, be recorded as Dn；
Step 3: cloud platform (3) sends back sleeping Adjusted Option Dn main control module (4) by communication module, main control module (4)
Sleeping Adjusted Option Dn is sent to sleeping equipment (5), the function for assisting sleep mode of sleeping equipment (5) is changed；
Step 4: cloud platform (3) data processing unit analyzes the current sleep physiological signal data Sn of user, obtains the last time
The function and effect of sleeping Adjusted Option Dn-1, the value of n is the positive integer more than or equal to 2, the sleeping Adjusted Option at this time
The whether effective determination method of the function and effect of Dn-1 are as follows: if the sleep physiology signal data Sn of user meets preset characterization and uses
The brain activity smooth sequential at family tends to then determine sleeping Adjusted Option Dn-1 into deeper dormant curve
Effectively, if the sleep physiology signal data Sn of user on the contrary is unsatisfactory for the brain activity smooth sequential of preset characterization user or becomes
Then determine that sleeping Adjusted Option Dn-1 is invalid into deeper dormant curve；
Step 5: when the effect of sleeping Adjusted Option is effective, the sleep physiology signal data Sn-1 of the currently active case and right
The sleeping Adjusted Option Dn-1 answered is added to together in individual subscriber sample database；If sleeping Adjusted Option is invalid, directly hold
Row step 6；
Step 6: the sleep physiology signal data Sn of user is stored in user's dormant data library；
Step 7: waiting time T, by n plus 1；
Step 8: judge whether user's timing function for assisting sleep time or the time of getting up have arrived, if having not timed out, return step 1,
Continue to obtain active user's sleep physiology signal data and sleeping Adjusted Option is adjusted according to sleeping decision model in real time, is user
Sleeping；If having timed out, stop sleeping and data screening acquisition；
Step 9: adjustment being updated to user's dormant data library content, if user's sleep physiology data record is super away from current time
It crosses default months or data volume reaches predetermined amount, then delete the data before presetting months or the data more than predetermined amount, retain
Data of the data of newest default months as personal sample database, provide data source for sleeping adjusting training.
6. a kind of reaction type sleeping system based on Remote Decision-making according to claim 4, which is characterized in that the sleeping
Decision model includes the sleeping decision model and personal version sleeping decision model of universality, the sleeping decision model of the universality
Building the following steps are included:
Step 1: according to the personal information of user i, the personal information includes age, insomnia situation, life stress, is extracted several
The personal sample database of a other users for being greater than preset threshold with user's i personal information correlation, then from extraction its
In the personal sample database of his user, M effectively case samples are randomly selected, each case sample has a kind of sleeping scheme
Label shares the different sleeping scheme of N kind in M effectively case samples；
Step 2: using selected M effectively case samples, first to the sleep physiology signal data in effective case sample into
Row pretreatment, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is believed from pretreated sleep physiology
Number extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every kind and is helped using the method for supervised learning
The sleeping decision model of dormancy scheme, the sleeping decision model of the M effective raw N number of universalities of case sample common property, the prison
Educational inspector's learning method is using one-versus-rest SVMs algorithm, and one-versus-rest SVMs algorithm is according to every one kind
Effective case sample generates a support vector machines, and the different sleeping scheme of N kind has constructed N number of support vector machines,
Generate the sleeping decision model of universality；
User i helps sleep scheme individual's version sleeping decision model building process as follows:
Step 1: according to chronological order extraction entry time close to M of current time from the personal sample database of user i
Effective sample case, the training set as sleeping decision model；
Step 2: using selected M effectively case samples, first to the sleep physiology signal data in effective case sample into
Row pretreatment, the pretreatment includes being filtered, amplifying to sleep physiology signal data, is believed from pretreated sleep physiology
Number extracts every kind of corresponding feature of sleep physiology signal data；
Step 3: the feature extracted according to data in each effective case sample is generated every kind and is helped using the method for supervised learning
The sleeping decision model of dormancy scheme, the M effective raw personal version sleeping decision models of case sample common property, the supervision
Learning method has using one-versus-rest SVMs algorithm, one-versus-rest SVMs algorithm according to every one kind
It imitates case sample and generates a support vector machines, the different sleeping scheme of N kind has constructed N number of support vector machines, raw
At personal version sleeping decision model.
7. according to a kind of described in any item reaction type sleeping systems based on Remote Decision-making of claim 5-6, which is characterized in that
The generation method of the sleeping Adjusted Option is as follows:
A. when user starts to enable sleeping equipment, the sleeping system first uses the sleeping decision model of universality, by user's
Current sleep physiological signal data is input to cloud platform (3), and the Data Management Analysis unit of the cloud platform (3) extracts pervasive
Feature required for the sleeping decision model of property, obtains corresponding sleeping Adjusted Option according to the sleeping decision model of universality；
B. after user's time preset using sleeping equipment, sample size reaches preset value in personal sample database, at this time
Sleeping system conversion enables personal version sleeping decision model, and the current sleep physiological signal data of user is input to cloud platform,
The Data Management Analysis unit of the cloud platform extracts feature required for personal version sleeping decision model, is helped according to personal version
Dormancy decision model obtains corresponding sleeping Adjusted Option；
C. during using sleeping equipment, after exporting sleeping scheme every time, judge to reaction type the effect of sleeping scheme, if
In primary sleep, the ratio that the bad number of the function and effect of sleeping scheme accounts for is greater than the threshold value of setting, then empties the individual subscriber
Sample database again pulls up new personal sample database using the sleeping decision model of universality.
8. a kind of reaction type sleeping system based on Remote Decision-making according to claim 7, which is characterized in that the master control
Module (4) is local control decision module, including there are two types of operating modes:
It networks under operating mode, main control module (4) receives user's sleep physiology signal data from acquisition equipment (1), and will
User's sleep physiology signal data is emitted to cloud platform (3) by communication module (2)；Meanwhile main control module (4) passes through communication mould
Block (2) receives the sleeping Adjusted Option for coming from cloud platform (3), and sleeping Adjusted Option is sent to sleeping equipment (5)；
It works offline under mode, main control module (4) uses Expert System Design, collected in real time to acquisition equipment (1) first
User's sleep physiology signal data is analyzed and processed, and extracts its characteristic value, obtains sleeping decision according to expert system, and will help
Dormancy decision is sent to sleeping equipment (5), controls the output of sleeping equipment (5).
9. a kind of reaction type sleeping system based on Remote Decision-making according to claim 8, which is characterized in that the sleeping
Equipment (5) be sleeping follower, the sleeping follower be based on through cranium microcurrent stimulating therapy sleeping electric stimulator or
Sleeping music player or sleeping light-regulator, the sleeping follower can be helped according to what main control module (4) sleeping exported
The requirement for the sleeping Adjusted Option that dormancy decision or cloud platform (3) return, adjustment export corresponding function for assisting sleep mode, help user
Sleep state is rapidly entered, the sleep quality of user is improved.
10. a kind of reaction type sleeping system based on Remote Decision-making according to claim 9, which is characterized in that the shifting
Dynamic terminal (6) are used for the dormant data to cloud platform request user and visualize, and the mobile terminal (6) includes sleeping
Reminding module, sleep tracking reporting modules, healthy sleep consultation module are suggested in display module, sleep by stages for dormancy.
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