CN106559486A - A kind of endowment nurse method and system based on big data - Google Patents
A kind of endowment nurse method and system based on big data Download PDFInfo
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Abstract
Maintaining method is seen in a kind of endowment based on big data, comprises the steps：Multiple data of physiological index by nurse object are gathered by intelligent hardware devices；Preliminary normal range value of the different sexes in the physical signs number of different age group is set；Generate each final normal range value for being nursed object；The data of physiological index collected in this step is sent to into cloud computing platform；Cloud computing platform generates data of physiological index curve map in platform；And real-time update is carried out to data of physiological index curve map；Contrasted with final normal range value according to data of physiological index curve map, judge fluctuation range whether more than the first default early warning value or the second default early warning value；Nurse schemes generation server obtains the instruction of caregiver, and all data of physiological index of preset time period are recalled according to instruction and forward together with the data genaration nurse scheme more than the first default early warning value or the second default early warning value.
Description
Technical field
The present invention relates to Smart Home technical field, more particularly to a kind of endowment based on big data is seen maintaining method and is
System.
Background technology
With social life level, the raising of medical level and the decline of fertility-rate, many areas in the whole world are all stepped into
Aging society.The decline of decline and physical function of the old man due to reagency, it is easy to occur in life unexpected.And by
Working is needed in the children of old man, therefore in addition to the family of the nurse that has the ability to engage, ordinary people is difficult to take into account work at ordinary times
Make and the treatment to old man, therefore many old men select apartment for elderly people to enjoy one's declining years.
Apartment for elderly people have the nursing staff of specialty, and the old men have the room of oneself, but are only unable to self-care ability
Old man has special nursing staff constantly to cultivate and look after, and other old men when wanting help press aloud and utter a sound bell, in the on duty of nurse station
The room number that personnel are shown according to correspondence, is made house calls.However, when there is emergency case, old man cannot press aloud and utter a sound bell
When, nursing staff just timely can not provide help to old man, it is likely that the event that some can be caused serious occurs.
With the development of big data technology, valuable information can be obtained from a large amount of basic datas by cloud computing,
And big data technology is rarely seen for intelligence endowment nurse field.
The content of the invention
In view of this, the present invention proposes a kind of to see maintaining method based on the endowment of big data with big data cloud computing technology
And system.
Maintaining method is seen in a kind of endowment based on big data, and which comprises the steps：
S1, by intelligent hardware devices gather it is multiple by nurse object data of physiological index；
S2, by collect it is multiple by nurse object data of physiological index divided according to sex, age bracket；And root
According to the sex, age bracket that divide, preliminary normal range value of the different sexes in the physical signs number of different age group is set；
S3, according to each nurse object personalized difference and preliminary normal range value generate each and nursed object
Final normal range value；
S4, continue through intelligent hardware devices to it is multiple by nurse object data of physiological index be monitored；This is walked
The data of physiological index collected in rapid is sent to cloud computing platform；
S5, cloud computing platform generate data of physiological index curve map in platform；And data of physiological index curve map is entered
Row real-time update；
S6, contrasted with final normal range value according to data of physiological index curve map, judged whether fluctuation range surpasses
The first default early warning value is crossed, when early warning value being preset more than first and not less than the second default early warning value, by this more than first
The data of physiological index of default early warning value is added in observed data buffer unit, and is counted；Judge that the numerical value for counting is
It is no more than default early warning time numerical value, when more than default early warning time numerical value, jump to step S9；Not less than the first default early warning
During value, step S4 is jumped to；When more than the second default early warning value, step S7 is jumped to；
S7, the corresponding time point of data more than the second default early warning value is recalled into forward all of the first preset time period
Data of physiological index sends jointly to nurse schemes generation server together with the data more than the second default early warning value；
S8, nurse schemes generation server obtain the instruction of caregiver, and during according to instructing and recall default forward
Between section all data of physiological index together with the data genaration nurse side more than the first default early warning value or the second default early warning value
Case；
S9, the corresponding time point of data by first time more than the first default early warning value recall forward the second preset time period
All data of physiological index send jointly to nurse schemes generation server together with the data more than the first default early warning value, and
Jump to step S8.
See in maintaining method in the endowment based on big data of the present invention,
In the step S6 when more than the first default early warning value, continuation judges whether data of physiological index velocity of wave motion surpasses
Default velocity of wave motion threshold value is crossed, when more than default velocity of wave motion threshold value, step S9 is jumped to.
See in maintaining method in the endowment based on big data of the present invention,
Encryption data is generated according to the physiology identification feature of each nurse object, and by encryption data to adopting in step S4
The data of physiological index for collecting is sent to cloud computing platform after being encrypted；
Step S5 is also decrypted to the data of physiological index after encryption including cloud computing platform, and raw in platform
Into data of physiological index curve map；The key of the decryption is the physiology identification feature of corresponding nurse object, will be nursed
The physiology identification feature of object is stored in cloud computing platform after being encrypted in itself.
The present invention also provides a kind of endowment nursing system based on big data, and which is included such as lower unit：
Data of physiological index collecting unit, for gathering multiple physical signs by nurse object by intelligent hardware devices
Data；
Dtex levies data dividing unit, for by collect it is multiple by nurse object data of physiological index according to property
Not, age bracket is divided；And physical signs of the different sexes in different age group is arranged according to the sex, age bracket for dividing
Several preliminary normal range values；
Normal range value determining unit, for personalized difference and preliminary normal range value according to each nurse object
Generate each final normal range value for being nursed object；
Data acquisition transmitting element, for continuing through intelligent hardware devices to multiple physical signs numbers by nurse object
According to being monitored；Notebook data is gathered the data of physiological index collected in transmitting element and is sent to cloud computing platform；
Data of physiological index curve map signal generating unit, for generating data of physiological index by cloud computing platform in platform
Curve map；And real-time update is carried out to data of physiological index curve map；
Data judge jump-transfer unit, for being contrasted with final normal range value according to data of physiological index curve map,
Whether fluctuation range is judged more than the first default early warning value, more than the first default early warning value and not less than the second default early warning value
When, this data of physiological index more than the first default early warning value is added in observed data buffer unit, and is counted；
Judge whether the numerical value for counting exceedes default early warning time numerical value, when more than default early warning time numerical value, jump to data backtracking and jump
Turn unit；When not less than the first default early warning value, data acquisition transmitting element is jumped to；More than the second default early warning value
When, jump to data backtracking transmitting element；
Data recall transmitting element, for the corresponding time point of data more than the second default early warning value is recalled forward the
All data of physiological index of one preset time period send jointly to nurse scheme together with the data more than the second default early warning value
Generate server；
Nurse scheme acquiring unit, for the instruction by nursing schemes generation server acquisition caregiver, and according to
Instruction and forward all data of physiological index of backtracking preset time period are together with default more than the first default early warning value or second
The data genaration nurse scheme of early warning value；
Data recall jump-transfer unit, for by first time more than the first default early warning value the corresponding time point of data forward
The all data of physiological index for recalling the second preset time period send jointly to see together with the data more than the first default early warning value
Shield schemes generation server, and jump to nurse scheme acquiring unit.
In the endowment nursing system based on big data of the present invention,
The data judge that in jump-transfer unit when more than the first default early warning value continuation judges data of physiological index fluctuation
Whether speed exceedes default velocity of wave motion threshold value, when more than default velocity of wave motion threshold value, jumps to data backtracking jump-transfer unit.
In the endowment nursing system based on big data of the present invention,
Encryption data is generated according to the physiology identification feature of each nurse object, and data acquisition is sent out by encryption data
The data of physiological index collected in sending unit is sent to cloud computing platform after being encrypted；
The data of physiological index curve map signal generating unit also includes cloud computing platform to the data of physiological index after encryption
Be decrypted, and data of physiological index curve map is generated in platform；The key of the decryption is corresponding nurse object
Physiology identification feature, is stored in cloud computing platform after being encrypted by the physiology identification feature of nurse object in itself.
Implement the endowment nursing system based on big data and method of present invention offer compared with prior art with following
Beneficial effect：The present invention is judged fluctuation model by contrasting with final normal range value according to data of physiological index curve map
Enclose whether more than the first default early warning value, when early warning value being preset more than first and not less than the second default early warning value, by this
It is added in observed data buffer unit more than the data of physiological index of the first default early warning value, and is counted；Judge to count
Numerical value whether exceed default early warning time numerical value, when more than default early warning time numerical value, jump to step S9；Not less than first
During default early warning value, step S4 is jumped to；When more than the second default early warning value, step S7 is jumped to；S7, will be pre- more than second
If the corresponding time point of the data of early warning value recalls forward all data of physiological index of the first preset time period together with more than
The data of two default early warning values send jointly to nurse schemes generation server.According to the technology with big data cloud computing, root
Different countermeasures are taken according to different data cases.
Description of the drawings
Fig. 1 is the improved endowment nursing system structured flowchart based on big data of the embodiment of the present invention.
Specific embodiment
The embodiment of the present invention provides a kind of endowment based on big data and sees maintaining method, and which comprises the steps：
S1, by intelligent hardware devices gather it is multiple by nurse object data of physiological index；
S2, by collect it is multiple by nurse object data of physiological index divided according to sex, age bracket；And root
According to the sex, age bracket that divide, preliminary normal range value of the different sexes in the physical signs number of different age group is set；
S3, according to each nurse object personalized difference and preliminary normal range value generate each and nursed object
Final normal range value；
S4, continue through intelligent hardware devices to it is multiple by nurse object data of physiological index be monitored；This is walked
The data of physiological index collected in rapid is sent to cloud computing platform；
S5, cloud computing platform generate data of physiological index curve map in platform；And data of physiological index curve map is entered
Row real-time update；
S6, contrasted with final normal range value according to data of physiological index curve map, judged whether fluctuation range surpasses
The first default early warning value is crossed, when early warning value being preset more than first and not less than the second default early warning value, by this more than first
The data of physiological index of default early warning value is added in observed data buffer unit, and is counted；Judge that the numerical value for counting is
It is no more than default early warning time numerical value, when more than default early warning time numerical value, jump to step S9；Not less than the first default early warning
During value, step S4 is jumped to；When more than the second default early warning value, step S7 is jumped to；
S7, the corresponding time point of data more than the second default early warning value is recalled into forward all of the first preset time period
Data of physiological index sends jointly to nurse schemes generation server together with the data more than the second default early warning value；
S8, nurse schemes generation server obtain the instruction of caregiver, and during according to instructing and recall default forward
Between section all data of physiological index together with the data genaration nurse side more than the first default early warning value or the second default early warning value
Case；
S9, the corresponding time point of data by first time more than the first default early warning value recall forward the second preset time period
All data of physiological index send jointly to nurse schemes generation server together with the data more than the first default early warning value, and
Jump to step S8.
See in maintaining method in the endowment based on big data of the present invention,
In the step S6 when more than the first default early warning value, continuation judges whether data of physiological index velocity of wave motion surpasses
Default velocity of wave motion threshold value is crossed, when more than default velocity of wave motion threshold value, step S9 is jumped to.
See in maintaining method in the endowment based on big data of the present invention,
Encryption data is generated according to the physiology identification feature of each nurse object, and by encryption data to adopting in step S4
The data of physiological index for collecting is sent to cloud computing platform after being encrypted；
Step S5 is also decrypted to the data of physiological index after encryption including cloud computing platform, and raw in platform
Into data of physiological index curve map；The key of the decryption is the physiology identification feature of corresponding nurse object, will be nursed
The physiology identification feature of object is stored in cloud computing platform after being encrypted in itself.
As shown in figure 1, the present invention also provides a kind of endowment nursing system based on big data, which is included such as lower unit：
Data of physiological index collecting unit, for gathering multiple physical signs by nurse object by intelligent hardware devices
Data；
Dtex levies data dividing unit, for by collect it is multiple by nurse object data of physiological index according to property
Not, age bracket is divided；And physical signs of the different sexes in different age group is arranged according to the sex, age bracket for dividing
Several preliminary normal range values；
Normal range value determining unit, for personalized difference and preliminary normal range value according to each nurse object
Generate each final normal range value for being nursed object；
Data acquisition transmitting element, for continuing through intelligent hardware devices to multiple physical signs numbers by nurse object
According to being monitored；Notebook data is gathered the data of physiological index collected in transmitting element and is sent to cloud computing platform；
Data of physiological index curve map signal generating unit, for generating data of physiological index by cloud computing platform in platform
Curve map；And real-time update is carried out to data of physiological index curve map；
Data judge jump-transfer unit, for being contrasted with final normal range value according to data of physiological index curve map,
Whether fluctuation range is judged more than the first default early warning value, more than the first default early warning value and not less than the second default early warning value
When, this data of physiological index more than the first default early warning value is added in observed data buffer unit, and is counted；
Judge whether the numerical value for counting exceedes default early warning time numerical value, when more than default early warning time numerical value, jump to data backtracking and jump
Turn unit；When not less than the first default early warning value, data acquisition transmitting element is jumped to；More than the second default early warning value
When, jump to data backtracking transmitting element；
Data recall transmitting element, for the corresponding time point of data more than the second default early warning value is recalled forward the
All data of physiological index of one preset time period send jointly to nurse scheme together with the data more than the second default early warning value
Generate server；
Nurse scheme acquiring unit, for the instruction by nursing schemes generation server acquisition caregiver, and according to
Instruction and forward all data of physiological index of backtracking preset time period are together with default more than the first default early warning value or second
The data genaration nurse scheme of early warning value；
Data recall jump-transfer unit, for by first time more than the first default early warning value the corresponding time point of data forward
The all data of physiological index for recalling the second preset time period send jointly to see together with the data more than the first default early warning value
Shield schemes generation server, and jump to nurse scheme acquiring unit.
In the endowment nursing system based on big data of the present invention,
The data judge that in jump-transfer unit when more than the first default early warning value continuation judges data of physiological index fluctuation
Whether speed exceedes default velocity of wave motion threshold value, when more than default velocity of wave motion threshold value, jumps to data backtracking jump-transfer unit.
In the endowment nursing system based on big data of the present invention,
Encryption data is generated according to the physiology identification feature of each nurse object, and data acquisition is sent out by encryption data
The data of physiological index collected in sending unit is sent to cloud computing platform after being encrypted；
The data of physiological index curve map signal generating unit also includes cloud computing platform to the data of physiological index after encryption
Be decrypted, and data of physiological index curve map is generated in platform；The key of the decryption is corresponding nurse object
Physiology identification feature, is stored in cloud computing platform after being encrypted by the physiology identification feature of nurse object in itself.
Implement the endowment nursing system based on big data and method of present invention offer compared with prior art with following
Beneficial effect：The present invention is judged fluctuation model by contrasting with final normal range value according to data of physiological index curve map
Enclose whether more than the first default early warning value, when early warning value being preset more than first and not less than the second default early warning value, by this
It is added in observed data buffer unit more than the data of physiological index of the first default early warning value, and is counted；Judge to count
Numerical value whether exceed default early warning time numerical value, when more than default early warning time numerical value, jump to step S9；Not less than first
During default early warning value, step S4 is jumped to；When more than the second default early warning value, step S7 is jumped to；S7, will be pre- more than second
If the corresponding time point of the data of early warning value recalls forward all data of physiological index of the first preset time period together with more than
The data of two default early warning values send jointly to nurse schemes generation server.According to the technology with big data cloud computing, root
Different countermeasures are taken according to different data cases.
The step of method described with reference to the embodiments described herein or algorithm, directly can be held with hardware, processor
Capable software module, or the combination of the two is implementing.Software module can be placed in random access memory, internal memory, read-only storage,
In electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field, institute is public
In the storage medium of any other forms known.
It is understood that for the person of ordinary skill of the art, can be done with technology according to the present invention design
Go out other various corresponding changes and deformation, and all these changes and deformation should all belong to the protection model of the claims in the present invention
Enclose.
Claims (6)
1. maintaining method is seen in a kind of endowment based on big data, and which comprises the steps：
S1, by intelligent hardware devices gather it is multiple by nurse object data of physiological index；
S2, by collect it is multiple by nurse object data of physiological index divided according to sex, age bracket；And according to draw
Preliminary normal range value of the sex, age bracket setting different sexes divided in the physical signs number of different age group；
S3, according to each nurse object personalized difference and preliminary normal range value generate each and nursed the final of object
Normal range value；
S4, continue through intelligent hardware devices to it is multiple by nurse object data of physiological index be monitored；By in this step
The data of physiological index for collecting is sent to cloud computing platform；
S5, cloud computing platform generate data of physiological index curve map in platform；And reality is carried out to data of physiological index curve map
Shi Gengxin；
S6, contrasted with final normal range value according to data of physiological index curve map, judge fluctuation range whether more than
One default early warning value, when early warning value being preset more than first and not less than the second default early warning value, this is preset more than first
The data of physiological index of early warning value is added in observed data buffer unit, and is counted；Judge whether the numerical value for counting surpasses
Default early warning time numerical value is crossed, when more than default early warning time numerical value, step S9 is jumped to；Not less than the first default early warning value
When, jump to step S4；When more than the second default early warning value, step S7 is jumped to；
S7, all physiology that the corresponding time point of data more than the second default early warning value is recalled forward the first preset time period
Achievement data sends jointly to nurse schemes generation server together with the data more than the second default early warning value；
S8, nurse schemes generation server obtain the instruction of caregiver, and recall according to instruction and forward preset time period
All data of physiological index together with the data genaration nurse scheme more than the first default early warning value or the second default early warning value；
S9, the corresponding time point of data by first time more than the first default early warning value recall forward the institute of the second preset time period
There is data of physiological index to send jointly to nurse schemes generation server together with the data more than the first default early warning value, and redirect
To step S8.
2. maintaining method is seen based on the endowment of big data as claimed in claim 1, it is characterised in that
In the step S6 when more than the first default early warning value, it is pre- that continuation judges whether data of physiological index velocity of wave motion exceedes
If velocity of wave motion threshold value, when more than default velocity of wave motion threshold value, step S9 is jumped to.
3. maintaining method is seen based on the endowment of big data as claimed in claim 2, it is characterised in that
Encryption data is generated according to the physiology identification feature of each nurse object, and by encryption data to collecting in step S4
Data of physiological index be encrypted after be sent to cloud computing platform；
Step S5 is also decrypted to the data of physiological index after encryption including cloud computing platform, and in platform generates life
Reason achievement data curve map；The key of the decryption is the physiology identification feature of corresponding nurse object, will be by nurse object
Physiology identification feature be encrypted in itself after be stored in cloud computing platform.
4. a kind of endowment nursing system based on big data, which is included such as lower unit：
Data of physiological index collecting unit, for gathering multiple physical signs numbers by nurse object by intelligent hardware devices
According to；
Dtex levies data dividing unit, for by collect it is multiple by nurse object data of physiological index according to sex, year
Age section is divided；And different sexes are arranged in the physical signs number of different age group according to the sex, age bracket for dividing
Preliminary normal range value；
Normal range value determining unit, generates for the personalized difference and preliminary normal range value according to each nurse object
Each is nursed the final normal range value of object；
Multiple data of physiological index by nurse object are entered by data acquisition transmitting element for continuing through intelligent hardware devices
Row monitoring；Notebook data is gathered the data of physiological index collected in transmitting element and is sent to cloud computing platform；
Data of physiological index curve map signal generating unit, for generating data of physiological index curve by cloud computing platform in platform
Figure；And real-time update is carried out to data of physiological index curve map；
Data judge jump-transfer unit, for being contrasted with final normal range value according to data of physiological index curve map, judge
Whether fluctuation range presets early warning value more than first, when early warning value being preset more than first and not less than the second default early warning value,
This data of physiological index more than the first default early warning value is added in observed data buffer unit, and is counted；Sentence
Whether the disconnected numerical value for counting exceedes default early warning time numerical value, when more than default early warning time numerical value, jumps to data backtracking and redirects
Unit；When not less than the first default early warning value, data acquisition transmitting element is jumped to；When more than the second default early warning value,
Jump to data backtracking transmitting element；
Data recall transmitting element, pre- for the corresponding time point of data more than the second default early warning value is recalled forward first
If all data of physiological index of time period send jointly to nurse schemes generation together with the data more than the second default early warning value
Server；
Nurse scheme acquiring unit, for the instruction of caregiver is obtained by nursing schemes generation server, and according to instruction
And all data of physiological index of preset time period are recalled forward together with more than the first default early warning value or the second default early warning
The data genaration nurse scheme of value；
Data recall jump-transfer unit, are recalled for the corresponding time point of data by first time more than the first default early warning value forward
All data of physiological index of the second preset time period send jointly to nurse side together with the data more than the first default early warning value
Case generates server, and jumps to nurse scheme acquiring unit.
5. the endowment nursing system based on big data as claimed in claim 4, it is characterised in that
The data judge that continuation judges data of physiological index velocity of wave motion in jump-transfer unit when more than the first default early warning value
Whether exceed default velocity of wave motion threshold value, when more than default velocity of wave motion threshold value, jump to data backtracking jump-transfer unit.
6. the endowment nursing system based on big data as claimed in claim 5, it is characterised in that
Encryption data is generated according to the physiology identification feature of each nurse object, and single is sent to data acquisition by encryption data
The data of physiological index collected in unit is sent to cloud computing platform after being encrypted；
The data of physiological index curve map signal generating unit is also carried out to the data of physiological index after encryption including cloud computing platform
Decryption, and data of physiological index curve map is generated in platform；The key of the decryption is the physiology of corresponding nurse object
Identification feature, is stored in cloud computing platform after being encrypted by the physiology identification feature of nurse object in itself.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341752A (en) * | 2017-07-19 | 2017-11-10 | 合肥盛东信息科技有限公司 | It is a kind of based on by the intelligent endowment platform service system of nurse old man's physical signs |
| CN108375909A (en) * | 2017-12-19 | 2018-08-07 | 海尔优家智能科技（北京）有限公司 | A kind of parameters analysis method, device, equipment and computer readable storage medium |
| CN110493331A (en) * | 2019-08-14 | 2019-11-22 | 广州市巨硅信息科技有限公司 | A kind of intelligently support parents nurses system of having no mishap |
| CN112133430A (en) * | 2020-10-15 | 2020-12-25 | 丁玉 | Clinical biochemical dynamic monitoring system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140210627A1 (en) * | 2013-01-31 | 2014-07-31 | KHN Solutions, Inc. | Method and system for monitoring intoxication |
| CN105342562A (en) * | 2015-10-19 | 2016-02-24 | 上海斐讯数据通信技术有限公司 | Monitoring method and monitoring system |
| CN105725975A (en) * | 2016-02-04 | 2016-07-06 | 中国人民解放军总医院 | Selection method of remote real-time psychological and physiological information collection data transmission channel |
-
2016
- 2016-11-12 CN CN201611040305.0A patent/CN106559486B/en not_active Expired - Fee Related
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140210627A1 (en) * | 2013-01-31 | 2014-07-31 | KHN Solutions, Inc. | Method and system for monitoring intoxication |
| CN105342562A (en) * | 2015-10-19 | 2016-02-24 | 上海斐讯数据通信技术有限公司 | Monitoring method and monitoring system |
| CN105725975A (en) * | 2016-02-04 | 2016-07-06 | 中国人民解放军总医院 | Selection method of remote real-time psychological and physiological information collection data transmission channel |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107341752A (en) * | 2017-07-19 | 2017-11-10 | 合肥盛东信息科技有限公司 | It is a kind of based on by the intelligent endowment platform service system of nurse old man's physical signs |
| CN108375909A (en) * | 2017-12-19 | 2018-08-07 | 海尔优家智能科技（北京）有限公司 | A kind of parameters analysis method, device, equipment and computer readable storage medium |
| CN110493331A (en) * | 2019-08-14 | 2019-11-22 | 广州市巨硅信息科技有限公司 | A kind of intelligently support parents nurses system of having no mishap |
| CN112133430A (en) * | 2020-10-15 | 2020-12-25 | 丁玉 | Clinical biochemical dynamic monitoring system |
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