CN106779258A - A kind of predictablity rate big data health forecast system high - Google Patents
A kind of predictablity rate big data health forecast system high Download PDFInfo
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- 230000036541 health Effects 0.000 title claims abstract description 43
- 238000012216 screening Methods 0.000 claims abstract description 15
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- 238000006243 chemical reaction Methods 0.000 claims abstract description 9
- 238000001914 filtration Methods 0.000 claims abstract description 7
- 238000000034 method Methods 0.000 claims abstract description 4
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- 238000012544 monitoring process Methods 0.000 claims description 54
- 238000011156 evaluation Methods 0.000 claims description 13
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- 206010047924 Wheezing Diseases 0.000 description 1
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a kind of predictablity rate big data health forecast system high, including the sensor sample unit being sequentially connected, data screening unit, signal conversion unit and signal processing unit, sensor sample unit is used to be acquired healthy big data, the data screening unit is used to screen healthy big data, the signal conversion unit is that signal amplifies and filtration module, healthy big data after to screening is amplified and filtering process, healthy big data after amplified and filtered treatment is input into the signal processing unit, signal processing unit exports health forecast result after treatment.Beneficial effects of the present invention are：For user provides accurate health forecast, prevent trouble before it happens.
Description
Technical field
The present invention relates to big data technical field, and in particular to a kind of predictablity rate big data health forecast system high
System.
Background technology
Health is the wheezy topic of the mankind, and with urban development, various pollution problems immediately come, with white collar formula
Working clan is more and more, and amount of exercise is fewer and feweri, and as operating pressure is increasing, sleep quality problem immediately comes.It is all
These problems people is increasingly paid attention to health problem.
On the one hand, prior art can only be directed to the data for gathering and personal current state is estimated, and can not be to future
Health status be estimated.On the other hand, due to the large-scale data-handling capacity of shortage, the data analysis energy of various dimensions
Power, and deep data mining ability, even if containing a large amount of useful informations in the data collected, can also be flooded by mass data
Not yet.
Because the difference of information source is different with the angle for considering a problem, for same monitoring project or Contents for Monitoring often
A variety of monitoring models are had to exist, and every kind of model has respective advantage and disadvantage.Combinatorial theory is thought for same problem
For, it is combined the precision of prediction that can effectively improve model under certain condition by multiple difference monitoring models.So
And, traditional combined prediction has the following disadvantages：First, involved model is specific, it is also possible to suboptimum.Its
Two, built-up pattern is the optimal fitting to historical data, therefore cannot be ensured of optimum prediction.
The content of the invention
Regarding to the issue above, the present invention is intended to provide a kind of predictablity rate big data health forecast system high.
The purpose of the present invention is realized using following technical scheme：
There is provided a kind of predictablity rate big data health forecast system high, including the sensor sample list being sequentially connected
Unit, data screening unit, signal conversion unit and signal processing unit, sensor sample unit are used to carry out healthy big data
Collection, the data screening unit is used to screen healthy big data, and the signal conversion unit is that signal amplifies and filters
Ripple module, to screening after healthy big data be amplified and filtering process, the healthy big data after amplified and filtered treatment
The signal processing unit is input into, signal processing unit exports health forecast result after treatment.
Beneficial effects of the present invention are：For user provides accurate health forecast, prevent trouble before it happens.
Brief description of the drawings
Using accompanying drawing, the invention will be further described, but embodiment in accompanying drawing is not constituted to any limit of the invention
System, for one of ordinary skill in the art, on the premise of not paying creative work, can also obtain according to the following drawings
Other accompanying drawings.
Fig. 1 is structure connection diagram of the invention.
Reference：
Sensor sample unit 1, data screening unit 2, signal conversion unit 3, signal processing unit 4.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of predictablity rate of the present embodiment big data health forecast system high, including be sequentially connected
Sensor sample unit 1, data screening unit 2, signal conversion unit 3 and signal processing unit 4, sensor sample unit 1 are used
It is acquired in healthy big data, the data screening unit 2 is used to screen healthy big data, the signal conversion
Unit 3 be signal amplify and filtration module, to screening after healthy big data be amplified and filtering process, it is amplified and filtered
Healthy big data after treatment is input into the signal processing unit 4, and signal processing unit 4 exports health forecast knot after treatment
Really.
The present embodiment accurate health forecast for user provides, prevents trouble before it happens.
Preferably, the sensor sample unit 1 includes that temperature sensor, gas sensor, humidity sensor, pulse are passed
Sensor, acceleration transducer and step-counting sensor,
The health data that this preferred embodiment is obtained is more fully.
Preferably, the data screening unit 2 is screened using following steps to data：
Step 1：Big data is gathered, and the data of sensor collecting unit collection, the number are received using multiple databases
According to storehouse arrangement beyond the clouds；
Step 2：Big data imports/pretreatment, and the data from front end are imported into a large-scale distributed number for concentration
During according to storehouse, these mass datas are carried out with effective analysis, and healthy big data is pre-processed on the basis of importing；
Step 3：Big data statistical analysis, the mass data using distributed data base to storage in the inner carries out classification remittance
Always；
Step 4：Big data is screened, and underproof healthy big data after Classifying Sum is rejected.
This preferred embodiment is screened to data, is favorably improved health forecast efficiency.
Preferably, signal processing unit 4 is predicted using model combination to health, including the first model library sets up mould
Block, the second weight determination module, the 3rd combination determining module and the 4th evaluation module, first model library sets up module to be used for
Alternative model storehouse is set up, wherein comprising multiple monitoring models, second weight determination module is used to be the alternative model storehouse
In each monitoring model set weight, it is described 3rd combination determining module be used for according to the weight determine on it is described each
The optimal models combination of monitoring model, the 4th evaluation module is used to evaluate the optimal models composite behaviour.
The health forecast result that the present embodiment is obtained is more accurate, and the prediction for further increasing health forecast system is accurate
Rate.
Preferably, first model library sets up module for setting up alternative model storehouse, concretely comprises the following steps：The first step：Really
Determine alternative model storehouse, it is assumed that there are n kind monitoring models, alternative model storehouse availability vector QK is expressed as：QK=(QK1,QK2,…,
QKn), in above-mentioned formula, QKiI-th monitoring model is represented, i=1,2 ..., n, n represents monitoring model quantity in model library；The
Two steps：(2) predicted value of monitoring model is determined, the predicted value vector y of monitoring model can accordingly be expressed as：Y=(y1,y2,…,
yn), in above-mentioned formula, yiI-th predicted value of monitoring model is represented, i=1,2 ..., n, n represents monitoring model number in model library
Amount.Power of the monitoring model that second weight determination module is used to determine in the alternative model storehouse in model combination
Weight, concretely comprises the following steps：The first step：Determine model number of combinations, the monitoring model and monitoring model number of model combination are participated in every time
Amount is uncertain, and degree of participation is different, regards model combination as a chance event tested, and determines model number of combinations C
For：In above-mentioned formula, i is represented and is participated in model combination
The quantity of monitoring model, i=2 represents that at least two monitoring models participate in model combination, and β represents degree of participation complexity factor, β
∈ { 2,3 }；Then all of model combination of sets vector FN is represented by：FN=(FN1,FN2,…,FNC), in above-mentioned formula, FNjTable
Show that jth kind model is combined, j=1,2 ..., C, C represents all of model number of combinations；Second step：Determine that monitoring model participates in journey
Degree, model combination FNjAvailability vector is expressed as：FNj=(σ (QK1),σ(QK2),…,σ(QKn)), wherein, σ (QKi) represent monitoring
Model QKiDegree of participation, i=1,2 ..., n, if β=2,If β=3,3rd step：Monitoring model weights are determined, for i-th monitoring model QKi, use
In the following manner carries out tax power：In above-mentioned formula, NJiRepresent i-th
Individual monitoring model QKiRespective weights, yiRepresent i-th predicted value of monitoring model.
The first model library of this preferred embodiment big data health monitoring systems setting sets up module and the second weight determines mould
Block, overcome traditional model combined prediction exist involved model sample space not comprehensively, be easier because personal
Ability difference causes the problems such as ignoring more efficiently model, convenient next to carry out indifference selection to each monitoring model,
So as to obtain more structurally sound health forecast result.
Preferably, the 3rd combination determining module is used for the weight according to the monitoring model in model combination
Determine that optimal models is combined, specifically include following steps：The first step：If j=1, the combined prediction value of model combination is calculated
With measured value error AY：AY=BE-y ',In above-mentioned formula, y ' represents measured value；The
Two steps：J=j+1, works as j>C, all model groups are total to be finished, and finds out minimum of computation error combination, obtains optimal models combination,
Health is predicted using the combination；3rd step：The first step and second step are repeated every some cycles T, to ensure model group
Close and constantly update.
This preferred embodiment big data health monitoring systems set the 3rd and combine determining module, by calculating minimal error pair
Model combination is selected, and obtains optimum combination, is updated by being combined to model every some cycles, overcomes model
The consistency of combination and the limitation of historical data, it is ensured that instant optimum prediction, so as to ensure that the instantaneity of health forecast,
Can be preferably minimized for loss according to health forecast result instant hospitalizing by user.
Preferably, the 4th evaluation module is used to evaluate the optimal models composite behaviour.The evaluation letter
Number PJ is represented by：In above-mentioned formula, RU represents the secondary of predicated error minimum
Number, ZX represents the maximum number of times of predicated error, and MH represents that predicated error is in middle number of times, and evaluation function value is bigger, prediction
Precision is higher.
This preferred embodiment big data health monitoring systems set the 4th evaluation module, by setting up evaluation function, more
Objectively precision of prediction is evaluated, subjectivity and empirical evaluation method with relatively strong individual's preference is overcome, greatly
The big confidence level and science for strengthening health forecast.
Health is predicted using health monitoring systems of the present invention, when the monitoring model quantity difference in alternative model storehouse
For 20,25,30,35,40 when, health forecast result is counted, compared with the present invention is provided without, the beneficial effect of generation
It is really as shown in the table：
| Monitoring model quantity | The health forecast time shortens | Health forecast accuracy is lifted |
| 40 | 20% | 10% |
| 35 | 25% | 15% |
| 30 | 30% | 20% |
| 25 | 32% | 24% |
| 20 | 36% | 31% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of scope is protected, although being explained to the present invention with reference to preferred embodiment, one of ordinary skill in the art should
Work as understanding, technical scheme can be modified or equivalent, without deviating from the reality of technical solution of the present invention
Matter and scope.
Claims (7)
1. a kind of predictablity rate big data health forecast system high, it is characterized in that, including the sensor sample being sequentially connected
Unit, data screening unit, signal conversion unit and signal processing unit, sensor sample unit are used to enter healthy big data
Row collection, the data screening unit be used for healthy big data is screened, the signal conversion unit be signal amplify and
Filtration module, to screening after healthy big data be amplified and filtering process, the big number of the health after amplified and filtered treatment
According to the signal processing unit is input into, signal processing unit exports health forecast result after treatment.
2. a kind of predictablity rate according to claim 1 big data health forecast system high, it is characterized in that, the biography
Sensor sampling unit includes temperature sensor, gas sensor, humidity sensor, pulse transducer, acceleration transducer and meter
Step sensor.
3. a kind of predictablity rate according to claim 2 big data health forecast system high, it is characterized in that, the number
Data are screened using following steps according to screening unit：
Step 1：Big data is gathered, and the data of sensor collecting unit collection, the database are received using multiple databases
Arrangement is beyond the clouds；
Step 2：Big data imports/pretreatment, and the data from front end are imported into a large-scale distributed database for concentration
When, these mass datas are carried out with effective analysis, and healthy big data is pre-processed on the basis of importing；
Step 3：Big data statistical analysis, the mass data using distributed data base to storage in the inner carries out Classifying Sum；
Step 4：Big data is screened, and underproof healthy big data after Classifying Sum is rejected.
4. a kind of predictablity rate according to claim 3 big data health forecast system high, it is characterized in that, at signal
Reason unit is predicted using model combination to health, including the first model library sets up module, the second weight determination module, the 3rd
Combination determining module and the 4th evaluation module, first model library set up module for setting up alternative model storehouse, wherein including
Multiple monitoring models, second weight determination module is used to set power for each monitoring model in the alternative model storehouse
Weight, the 3rd combination determining module is used to determine the optimal models group on each monitoring model according to the weight
Close, the 4th evaluation module is used to evaluate the optimal models composite behaviour.
5. a kind of predictablity rate according to claim 4 big data health forecast system high, it is characterized in that, described
One model library sets up module for setting up alternative model storehouse, concretely comprises the following steps：The first step：Determine alternative model storehouse, it is assumed that there are n kinds
Monitoring model, alternative model storehouse availability vector QK is expressed as：QK=(QK1,QK2,…,QKn), in above-mentioned formula, QKiRepresent i-th
Individual monitoring model, i=1,2 ..., n, n represents monitoring model quantity in model library；Second step：(2) prediction of monitoring model is determined
Value, the predicted value vector y of monitoring model can accordingly be expressed as：Y=(y1,y2,…,yn), in above-mentioned formula, yiRepresent i-th prison
The predicted value of model is surveyed, i=1,2 ..., n, n represents monitoring model quantity in model library.Second weight determination module is used for
Determine weight of the monitoring model in the alternative model storehouse in model combination, concretely comprise the following steps：The first step：Determine mould
Type number of combinations, it is uncertain that the monitoring model and monitoring model quantity of model combination are participated in every time, and degree of participation is different,
Regard model combination as a chance event tested, determine that model number of combinations C is： In above-mentioned formula, i represent participate in model combination monitoring model quantity, i=2 represent to
Rare two monitoring models participate in model combination, and β represents degree of participation complexity factor, β ∈ { 2,3 }；Then all of model combination
Collection vector FN is represented by：FN=(FN1,FN2,…,FNC), in above-mentioned formula, FNjRepresent that jth kind model is combined, j=1,
2 ..., C, C represent all of model number of combinations；Second step：Determine monitoring model degree of participation, model combination FNjAvailability vector
It is expressed as：FNj=(σ (QK1),σ(QK2),…,σ(QKn)), wherein, σ (QKi) represent monitoring model QKiDegree of participation, i=1,
2 ..., n, ifIf β=3,The
Three steps：Monitoring model weights are determined, for i-th monitoring model QKi, tax power is carried out in the following ways： In above-mentioned formula, NJiRepresent i-th monitoring model QKiRespective weights, yiRepresent the
The i predicted value of monitoring model.
6. a kind of predictablity rate according to claim 5 big data health forecast system high, it is characterized in that, described
The weight that three combination determining modules are used for according to the monitoring model in model combination determines that optimal models is combined, specifically
Comprise the following steps：The first step：If j=1, the combined prediction value and measured value error AY of model combination are calculated：AY=BE-
Y ',In above-mentioned formula, y ' represents measured value；Second step：J=j+1, works as j>C,
All model groups are total to be finished, and finds out minimum of computation error combination, optimal models combination is obtained, using the combination to strong
Health is predicted；3rd step：The first step and second step are repeated every some cycles T, to ensure that model combination is constantly updated.
7. a kind of predictablity rate according to claim 6 big data health forecast system high, it is characterized in that, described
Four evaluation modules are used to evaluate the optimal models composite behaviour.The evaluation function PJ is represented by： In above-mentioned formula, RU represents the minimum number of times of predicated error, and ZX represents prediction
The maximum number of times of error, MH represents that predicated error is in middle number of times, and evaluation function value is bigger, and precision of prediction is higher.
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| CN108597605A (en) * | 2018-03-19 | 2018-09-28 | 特斯联（北京）科技有限公司 | A kind of life big data acquisition of personal health and analysis system |
| CN109493151A (en) * | 2019-01-10 | 2019-03-19 | 哈步数据科技(上海)有限公司 | Method for Sales Forecast method and system |
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| CN108597605A (en) * | 2018-03-19 | 2018-09-28 | 特斯联（北京）科技有限公司 | A kind of life big data acquisition of personal health and analysis system |
| CN109493151A (en) * | 2019-01-10 | 2019-03-19 | 哈步数据科技(上海)有限公司 | Method for Sales Forecast method and system |
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