CN107633883A - A kind of health degree evaluation method based on big data analysis - Google Patents
A kind of health degree evaluation method based on big data analysis Download PDFInfo
- Publication number
- CN107633883A CN107633883A CN201711057623.2A CN201711057623A CN107633883A CN 107633883 A CN107633883 A CN 107633883A CN 201711057623 A CN201711057623 A CN 201711057623A CN 107633883 A CN107633883 A CN 107633883A
- Authority
- CN
- China
- Prior art keywords
- module
- index
- health degree
- health
- matrix
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Measuring And Recording Apparatus For Diagnosis (AREA)
Abstract
The invention discloses a kind of health degree evaluation method based on big data analysis：The device has bracelet, there is controller inside bracelet, alarm module, interface module, data memory module, wireless communication module, d GPS locating module, sleep monitor, temperature sensor, humidity sensor, PM2.5 sensors, pulse detection sensor, noise transducer, the device provides the real-time recording function of healthy data, real-time data record function is provided, the health degree of human body can continuously be monitored in real time for a long time, and combine historical data, health degree is evaluated, improves the evaluation mechanism of health degree.
Description
Technical field
The invention belongs to Intelligent worn device to monitor field, more particularly to a kind of health degree based on big data analysis
Evaluation method.
Background technology
In recent years, with the intelligent terminal such as the continuous development of terminal technology, various Intelligent bracelets, intelligent watch
Constantly flood the market, the terminal device experience for being greatly enriched user is enjoyed, and is set however, the existing intelligence of in the market is wearable
It is standby that there are still in place of some shortcomings：Current intelligent wearable device is still to use a kind of executive mode of passive type,
That is these intelligent wearable devices, only after the operational order of wearer is received, intelligent wearable device can just be gone
Perform the action of corresponding wearer's operational order, and can not automatically, hommization according to the condition and feelings of wearer
Not-ready status performs corresponding counter-measure on one's own initiative, with the help to wearer's offer much sooner, especially goes out in wearer
The urgent illness situation that now happens suddenly or it is depressed when, existing wearable device can not but be given.
The content of the invention
The purpose of the present invention is to overcome the shortcomings of prior art, proposes a kind of health degree based on big data analysis
Evaluation method.
Bracelet with the monitoring of health degree, bracelet inside have controller, and alarm module, interface module, data are deposited
Store up module, wireless communication module, d GPS locating module, sleep monitor, temperature sensor, humidity sensor, PM2.5 sensors,
Pulse detection sensor, noise transducer；Sleep monitor, temperature sensor, humidity sensor, PM2.5 sensors, pulse inspection
Survey sensor, noise transducer is all connected by interface module with data acquisition module；Alarm module, data acquisition module, number
Connected according to memory module, communication module, d GPS locating module and controller；It is characterized in that：Pulse detection sensor gathers pulse
Information, temperature sensor, humidity sensor, PM2.5 sensors, the noise transducer collection temperature of surrounding environment, humidity,
PM2.5, noise parameter；Sleep monitor can obtain breathing, heart rate and sleep quality information of the human body in sleep；Controller
Use processing is carried out to the Monitoring Data of data collecting module collected, and smart mobile phone is delivered to by wireless communication module
And Cloud Server, Cloud Server carry out big data analysis to the Monitoring Data, different mark parameter type are drawn, by contrasting expert
Early warning knowledge base, abnormity early warning information is drawn, be sent to smart mobile phone, d GPS locating module feeds back to smart mobile phone and positioned extremely
Information；The Cloud Server carries out big data analysis to the Monitoring Data：Analysis module is pre- using the expert pre-established
Alert knowledge base and health forecast model, the sleep in humidity, temperature, PM2.5, noise and health parameters in effect ambient parameter
Index, pulsation index, to judge whether health exception occurs, if there is abnormal, the abnormal positioning analysis of analysis module progress,
Draw anomaly analysis result；If health parameters are without exception, analysis module carries out intelligent Evaluation, the health to the health degree of human body
Spending the step of evaluating is：
1）, verification ambient parameter, the initial data of health parameters；
2）, establish stereochemical structure, using the sleep index in health parameters, pulsation index as primary element in stereochemical structure, with ring
Humidity, temperature, PM2.5, noise in the parameter of border are minor element；
3）, carry out single index calculating and scoring；
4）, structure discrimination matrix and stereochemical structure in weight coefficient；
5）, amendment weight coefficient；
6）, determine the health degree of human body.
Preferably, the calculating and scoring process for carrying out the implementation step 3) of intelligent Evaluation include：
(3-1) establishes scheme attribute decision table；
(3-2) forms judgment matrix：
(3-3) judgment matrix approach verifies；
(3-4) judgment matrix weight solves；
(3-5) comprehensive weight calculates and sequence.
Preferably, the process carried out in the implementation step 4) of intelligent Evaluation includes：
(4-1) establishes progressive stereoscopic level structure, clearly illustrates the relation between each level；
(4-2) is compared same layer element two-by-two by triple assessment method, establishes comparator matrix C；
(4-3) calculates the sequence index of comparator matrix C each element importance, and comparator matrix C is converted into judgment matrix；
(4-4) determines the relative weighting wi of evaluation index in each level；
(4-5) carries out consistency check.
Preferably, the process of the step (4-4) is：
Determine the eigenvalue of maximum λ max of the judgment matrix；
Determine characteristic vector W corresponding to eigenvalue of maximum λ max；
Determine weight vectors wi=(w1, w2 ... wn)；
Determine relative weighting of each evaluation index of certain one-level on its upper level index；
The step (4-5) carries out consistency check by following formula：
Wherein, DI is compatibility index, and b is weight phasor quantity；As DI ＜ 0.2, it is believed that receive the uniformity of judgment matrix；
As DI >=0.2, judgment matrix should be made an amendment again, then weight is recalculated to the matrix after correction and carries out uniformity inspection
Test until uniformity is received.
Preferably, the step（4-2）In comparator matrix be：
Wherein, triple assessment method is defined as：
Wherein, aiFor i-th of element in certain layer of index, cijFor i-th of element in certain layer of index and the comparison knot of j-th of element
Fruit, aiAnd ajFor same layer Index element.
Compared with prior art, its beneficial technique effect is the present invention：
For the demand of health degree monitoring, this method provides the real-time recording function of healthy data, there is provided number in real time
According to writing function, continuous for a long time the health degree of human body being monitored in real time, and combining historical data, health degree is evaluated, carried
The high evaluation mechanism of health degree.
Brief description of the drawings
Fig. 1 is the construction module figure of bracelet of the present invention.
Fig. 2 is the flow chart of human body health degree evaluation of the present invention.
Embodiment
Bracelet with the monitoring of health degree, bracelet inside have controller, and alarm module, interface module, data are deposited
Store up module, wireless communication module, d GPS locating module, sleep monitor, temperature sensor, humidity sensor, PM2.5 sensors,
Pulse detection sensor, noise transducer；Sleep monitor, temperature sensor, humidity sensor, PM2.5 sensors, pulse inspection
Survey sensor, noise transducer is all connected by interface module with data acquisition module；Alarm module, data acquisition module, number
Connected according to memory module, communication module, d GPS locating module and controller；It is characterized in that：Pulse detection sensor gathers pulse
Information, temperature sensor, humidity sensor, PM2.5 sensors, the noise transducer collection temperature of surrounding environment, humidity,
PM2.5, noise parameter；Sleep monitor can obtain breathing, heart rate and sleep quality information of the human body in sleep；Controller
Use processing is carried out to the Monitoring Data of data collecting module collected, and smart mobile phone is delivered to by wireless communication module
And Cloud Server, Cloud Server carry out big data analysis to the Monitoring Data, different mark parameter type are drawn, by contrasting expert
Early warning knowledge base, abnormity early warning information is drawn, be sent to smart mobile phone, d GPS locating module feeds back to smart mobile phone and positioned extremely
Information；The Cloud Server carries out big data analysis to the Monitoring Data：Analysis module is pre- using the expert pre-established
Alert knowledge base and health forecast model, the sleep in humidity, temperature, PM2.5, noise and health parameters in effect ambient parameter
Index, pulsation index, to judge whether health exception occurs, if there is abnormal, the abnormal positioning analysis of analysis module progress,
Draw anomaly analysis result；If health parameters are without exception, analysis module carries out intelligent Evaluation, the health to the health degree of human body
Spending the step of evaluating is：
1）, verification ambient parameter, the initial data of health parameters；
2）, establish stereochemical structure, using the sleep index in health parameters, pulsation index as primary element in stereochemical structure, with ring
Humidity, temperature, PM2.5, noise in the parameter of border are minor element；
3）, carry out single index calculating and scoring；
4）, structure discrimination matrix and stereochemical structure in weight coefficient；
5）, amendment weight coefficient；
6）, determine the health degree of human body.
Preferably, the calculating and scoring process for carrying out the implementation step 3) of intelligent Evaluation include：
(3-1) establishes scheme attribute decision table；
(3-2) forms judgment matrix：
(3-3) judgment matrix approach verifies；
(3-4) judgment matrix weight solves；
(3-5) comprehensive weight calculates and sequence.
Preferably, the process carried out in the implementation step 4) of intelligent Evaluation includes：
(4-1) establishes progressive stereoscopic level structure, clearly illustrates the relation between each level；
(4-2) is compared same layer element two-by-two by triple assessment method, establishes comparator matrix C；
(4-3) calculates the sequence index of comparator matrix C each element importance, and comparator matrix C is converted into judgment matrix；
(4-4) determines the relative weighting wi of evaluation index in each level；
(4-5) carries out consistency check.
Preferably, the process of the step (4-4) is：
Determine the eigenvalue of maximum λ max of the judgment matrix；
Determine characteristic vector W corresponding to eigenvalue of maximum λ max；
Determine weight vectors wi=(w1, w2 ... wn)；
Determine relative weighting of each evaluation index of certain one-level on its upper level index；
The step (4-5) carries out consistency check by following formula：
Wherein, DI is compatibility index, and b is weight phasor quantity；As DI ＜ 0.2, it is believed that receive the uniformity of judgment matrix；
As DI >=0.2, judgment matrix should be made an amendment again, then weight is recalculated to the matrix after correction and carries out uniformity inspection
Test until uniformity is received.
Preferably, step（4-2）In comparator matrix be：
Wherein, triple assessment method is defined as：
Wherein, aiFor i-th of element in certain layer of index, cijFor i-th of element in certain layer of index and the comparison knot of j-th of element
Fruit, aiAnd ajFor same layer index member.
The present invention is described in detail above, principle and embodiment party of the specific case used herein to the present invention
Formula is set forth, and the explanation of above example is only intended to help the method and its core concept for understanding the present invention；It is meanwhile right
In those of ordinary skill in the art, according to the thought of the present invention, change is had in specific embodiments and applications
Part, in summary, this specification content should not be construed as limiting the invention.
Claims (5)
1. a kind of health degree evaluation method based on big data analysis, there is the bracelet of health degree monitoring, in bracelet
Portion has controller, alarm module, interface module, data memory module, wireless communication module, d GPS locating module, sleep monitor
Instrument, temperature sensor, humidity sensor, PM2.5 sensors, pulse detection sensor, noise transducer；Sleep monitor, temperature
Degree sensor, humidity sensor, PM2.5 sensors, pulse detection sensor, noise transducer all pass through interface module and data
Acquisition module connects；Alarm module, data acquisition module, data memory module, communication module, d GPS locating module and controller
Connection；It is characterized in that：Pulse detection sensor gather pulse information, temperature sensor, humidity sensor, PM2.5 sensors,
The temperature of noise transducer collection surrounding environment, humidity, PM2.5, noise parameter；Sleep monitor can obtain human body and sleep
When breathing, heart rate and sleep quality information；Controller is carried out at information fusion to the Monitoring Data of data collecting module collected
Reason, and smart mobile phone and Cloud Server are delivered to by wireless communication module, Cloud Server carries out counting greatly to the Monitoring Data
According to analysis, different mark parameter type is drawn, by contrasting expert's early warning knowledge base, abnormity early warning information is drawn, is sent to intelligent hand
Machine, d GPS locating module feed back to smart mobile phone exception location information；The Cloud Server carries out big data to the Monitoring Data
Analyze and be：Analysis module is wet in effect ambient parameter using expert's early warning knowledge base for pre-establishing and health forecast model
Sleep index, pulsation index in degree, temperature, PM2.5, noise and health parameters, to judge whether health exception occurs,
If there is exception, analysis module carries out abnormal positioning analysis, draws anomaly analysis result；If health parameters are without exception, mould is analyzed
Block carries out intelligent Evaluation to the health degree of human body, is the step of the health degree evaluation：
1）, verification ambient parameter, the initial data of health parameters；
2）, establish stereochemical structure, using the sleep index in health parameters, pulsation index as primary element in stereochemical structure, with ring
Humidity, temperature, PM2.5, noise in the parameter of border are minor element；
3）, carry out single index calculating and scoring；
4）, structure discrimination matrix and stereochemical structure in weight coefficient；
5）, amendment weight coefficient；
6）, determine the health degree of human body.
A kind of 2. health degree monitoring method based on big data analysis according to claim 1, it is characterised in that：Enter
The calculating of the implementation step 3) of row intelligent Evaluation and scoring process include：
(3-1) establishes scheme attribute decision table；
(3-2) forms judgment matrix：
(3-3) judgment matrix approach verifies；
(3-4) judgment matrix weight solves；
(3-5) comprehensive weight calculates and sequence.
A kind of 3. health degree evaluation method based on big data analysis according to claim 1, it is characterised in that：Enter
Process in the implementation step 4) of row intelligent Evaluation includes：
(4-1) establishes progressive stereoscopic level structure, clearly illustrates the relation between each level；
(4-2) is compared same layer element two-by-two by triple assessment method, establishes comparator matrix C；
(4-3) calculates the sequence index of comparator matrix C each element importance, and comparator matrix C is converted into judgment matrix；
(4-4) determines the relative weighting wi of evaluation index in each level；
(4-5) carries out consistency check.
A kind of 4. health degree evaluation method based on big data analysis according to claim 3, it is characterised in that：Institute
The process for stating step (4-4) is：
Determine the eigenvalue of maximum λ max of the judgment matrix；
Determine characteristic vector W corresponding to eigenvalue of maximum λ max；
Determine weight vectors wi=(w1, w2 ... wn)；
Determine relative weighting of each evaluation index of certain one-level on its upper level index；
The step (4-5) carries out consistency check by following formula：
Wherein, DI is compatibility index, and b is weight phasor quantity；As DI ＜ 0.2, it is believed that receive the uniformity of judgment matrix；
As DI >=0.2, judgment matrix should be made an amendment again, then weight is recalculated to the matrix after correction and carries out uniformity inspection
Test until uniformity is received.
A kind of 5. health degree evaluation method based on big data analysis according to claim 3, it is characterised in that：Step
Suddenly（4-2）In comparator matrix be：
Wherein, triple assessment method is defined as：
Wherein, aiFor i-th of element in certain layer of index, cijFor i-th of element in certain layer of index and the comparison knot of j-th of element
Fruit, aiAnd ajFor same layer Index element.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711057623.2A CN107633883A (en) | 2017-11-01 | 2017-11-01 | A kind of health degree evaluation method based on big data analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711057623.2A CN107633883A (en) | 2017-11-01 | 2017-11-01 | A kind of health degree evaluation method based on big data analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107633883A true CN107633883A (en) | 2018-01-26 |
Family
ID=61106025
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711057623.2A Pending CN107633883A (en) | 2017-11-01 | 2017-11-01 | A kind of health degree evaluation method based on big data analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107633883A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108511064A (en) * | 2018-02-11 | 2018-09-07 | 河南工程学院 | The system for automatically analyzing healthy data based on deep learning |
| CN110750522A (en) * | 2018-07-23 | 2020-02-04 | 发那科株式会社 | Data management device, data management program, and data management method |
| CN110856653A (en) * | 2018-08-22 | 2020-03-03 | 北京医佳护健康医疗科技有限公司 | Health monitoring and early warning system based on vital sign data |
| CN111276208A (en) * | 2020-03-06 | 2020-06-12 | 中国人民解放军陆军军医大学第一附属医院 | Health analysis system based on big data |
| CN113643810A (en) * | 2021-08-10 | 2021-11-12 | 西北工业大学 | Cloud edge-end cooperative man-machine object QoS parameter reduction method |
-
2017
- 2017-11-01 CN CN201711057623.2A patent/CN107633883A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108511064A (en) * | 2018-02-11 | 2018-09-07 | 河南工程学院 | The system for automatically analyzing healthy data based on deep learning |
| CN110750522A (en) * | 2018-07-23 | 2020-02-04 | 发那科株式会社 | Data management device, data management program, and data management method |
| CN110750522B (en) * | 2018-07-23 | 2023-08-08 | 发那科株式会社 | Data management device, storage medium, and data management method |
| CN110856653A (en) * | 2018-08-22 | 2020-03-03 | 北京医佳护健康医疗科技有限公司 | Health monitoring and early warning system based on vital sign data |
| CN111276208A (en) * | 2020-03-06 | 2020-06-12 | 中国人民解放军陆军军医大学第一附属医院 | Health analysis system based on big data |
| CN113643810A (en) * | 2021-08-10 | 2021-11-12 | 西北工业大学 | Cloud edge-end cooperative man-machine object QoS parameter reduction method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107633883A (en) | A kind of health degree evaluation method based on big data analysis | |
| CN107669250A (en) | A kind of health degree monitoring method based on big data analysis | |
| CN103876711B (en) | Wearable electronic and measuring of human health management system | |
| CN105739575B (en) | A kind of data fusion method of facilities vegetable environmental parameter | |
| CN106937808A (en) | A kind of data collecting system of intelligent mattress | |
| CN104224147A (en) | Wireless portable human health and sleep quality monitor | |
| CN105913614A (en) | Method and device of monitoring tumbling, intelligent wearable device and tumbling alarm system | |
| CN103637782B (en) | A kind of health monitoring system based on intelligent terminal and method | |
| CN103845038B (en) | A kind of sign signal acquisition method and apparatus | |
| CN103462598A (en) | Human body information monitoring terminal and method based on smart phone | |
| CN109452728A (en) | A kind of Intelligent insole and its step size computation method based on step size computation | |
| US11272864B2 (en) | Respiratory disease monitoring wearable apparatus | |
| CN111855013A (en) | Body temperature detection device and method based on environment temperature and humidity compensation | |
| CN107909771A (en) | A kind of personnel's tumble alarm system and its implementation based on wireless sensor network | |
| CN105877719A (en) | Physical condition monitoring system for marathon race players | |
| CN108937867A (en) | A kind of dormant monitoring method and device | |
| CN105243285A (en) | Big data health forecast system | |
| CN109528163B (en) | Sleep monitoring method and equipment | |
| CN103445766A (en) | Historical physiological data and physical data comparison based status monitoring method and device | |
| CN112603327B (en) | Electrocardiosignal detection method, device, terminal and storage medium | |
| CN107019501B (en) | Remote tumble detection method and system based on genetic algorithm and probabilistic neural network | |
| CN105530581A (en) | Smart wearable device based on voice recognition and control method thereof | |
| CN204537237U (en) | Based on the multi-functional multi-parameter monitoring of Intelligent worn device and health management system arranged | |
| CN105549745A (en) | Monitoring method utilizing wearable device | |
| CN111768876A (en) | Intelligent family health data acquisition system, method, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20180126 |
|
| WD01 | Invention patent application deemed withdrawn after publication |