CN106614273A - Pet feeding method and system based on big data analysis of Internet of Things - Google Patents
Pet feeding method and system based on big data analysis of Internet of Things Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000007405 data analysis Methods 0.000 title abstract 2
- 230000036772 blood pressure Effects 0.000 claims abstract description 19
- 230000036541 health Effects 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims abstract description 17
- 230000000694 effects Effects 0.000 claims abstract description 14
- 230000036760 body temperature Effects 0.000 claims abstract description 13
- 238000013528 artificial neural network Methods 0.000 claims abstract description 11
- 230000035611 feeding Effects 0.000 claims abstract description 6
- 230000007935 neutral effect Effects 0.000 claims description 8
- 230000008569 process Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 6
- 230000035772 mutation Effects 0.000 claims description 5
- 238000012549 training Methods 0.000 claims description 4
- 238000012217 deletion Methods 0.000 claims description 2
- 230000037430 deletion Effects 0.000 claims description 2
- 235000013399 edible fruits Nutrition 0.000 claims description 2
- 230000007613 environmental effect Effects 0.000 claims description 2
- 230000009191 jumping Effects 0.000 claims description 2
- 230000001121 heart beat frequency Effects 0.000 abstract description 3
- 235000013305 food Nutrition 0.000 description 7
- 235000019789 appetite Nutrition 0.000 description 2
- 230000036528 appetite Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000474 nursing effect Effects 0.000 description 2
- 208000008589 Obesity Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000005911 diet Nutrition 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000008451 emotion Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012851 eutrophication Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000001815 facial effect Effects 0.000 description 1
- 239000003337 fertilizer Substances 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 235000016709 nutrition Nutrition 0.000 description 1
- 230000035764 nutrition Effects 0.000 description 1
- 235000020824 obesity Nutrition 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; AVICULTURE; APICULTURE; PISCICULTURE; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K67/00—Rearing or breeding animals, not otherwise provided for; New or modified breeds of animals
- A01K67/02—Breeding vertebrates
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Abstract
The invention provides a pet feeding method and system based on big data analysis of Internet of Things. The method comprises the steps that the kind, gender, age, heartbeat frequency, blood pressure, body temperature, activity, feeding type and feeding amount of a pet are acquired, a current image and current weight form an influence factor matrix X and are uploaded to a server, wherein the feeding type and the feeding amount form decision variables; an Elman neural network is utilized to establish the complicated nonlinear relation between the influence factor matrix X and pet health indexes in a server, and a pet feeding model is obtained; an SPEA-II algorithm is utilized to optimize the pet feeding model, and a group of optimal solutions of the decision variables is obtained; the group of optimal solutions of the decision variables serves as a recommended decision X* of the pet to be issued to a terminal device of a user through the server for display; the user feeds the pet according to the recommended decision X* displayed by the terminal device. By utilizing the pet feeding method and the pet feeding system, an optimal pet feeding scheme can be determined, and a good living environment is created for the pet.
Description
Technical field
The present invention relates to house pet intelligently feeds field, and in particular to a kind of Pet feeding analyzed based on Internet of Things big data
Method and system.
Background technology
With the fast development of national economy, house pet increasingly becomes people gladly select one as a kind of sustenance of emotion
The mode of kind.But if the personal experience for simply using shortage scientific basis feeds to house pet, its irrational nursing scheme
House pet may be made to lack nutrition causes disease or eutrophication so that obesity, does not all reach the target that we envision, and makes indirectly
Waste into the loss of substantial amounts of energy and money.
At present, the problem of urgent need to resolve is to set up a set of comprehensive Pet feeding model, and by house pet physical signs, diet
Situation feeds back to user, and feeding pet scheme can be adjusted in time for user.Affect each factor of pet health degree
Between often embody the complexity of height and non-linear, there is certain difficulty using conventional prediction, analysis method.
The content of the invention
The present invention is existing to solve by providing a kind of Pet feeding method and system analyzed based on Internet of Things big data
Due to a lack of the experience of nursing during Pet feeding, it is impossible to control optimum feeding scheme and cause house pet hungry or unsound ask
Topic.
To solve the above problems, the present invention is employed the following technical solutions and is achieved：
On the one hand, the Pet feeding method analyzed based on Internet of Things big data that the present invention is provided, including：
Step S1：The collection species of house pet, sex, the age, palmic rate, blood pressure, body temperature, activity, feeding type, feed
Appetite, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding type and feeding amount
Constitute decision variable；
Step S2：Utilize between Elman neural networks influence factor's matrix X and pet health index in server
Complex nonlinear relation, obtain Pet feeding model；
Step S3：Pet feeding model is optimized using SPEA-II algorithms, obtain decision variable one group is optimum
Solution；
Step S4：Using this group of optimal solution of decision variable as house pet recommendation decision-making X*User is issued to by server
Terminal unit shown；
Step S5：The recommendation decision-making X that user shows according to terminal unit*Feeding house pet.
On the other hand, the Pet feeding system analyzed based on Internet of Things big data that the present invention is provided, including：
Data acquisition unit, for gather the species of house pet, sex, the age, palmic rate, blood pressure, body temperature, activity,
Feeding type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding class
Type and feeding amount constitute decision variable；
Pet feeding model sets up unit, for utilizing Elman neural network influence factor matrix X in server
With the complex nonlinear relation between pet health index, Pet feeding model is obtained；
Decision variable optimal solution acquiring unit, for being optimized to Pet feeding model using SPEA-II algorithms, is obtained
One group of optimal solution of decision variable, and as the recommendation decision-making X of house pet*；
Recommend decision-making issuance unit, for passing through server by the recommendation decision-making X of house pet*It is issued to the terminal unit of user
Shown.
Compared with prior art, the Pet feeding method and system analyzed based on Internet of Things big data that the present invention is provided
Advantage is：Using complicated non-between Elman neural networks influence factor's matrix X and pet health index in server
Linear relationship, obtains the dynamic model of Pet feeding, recycles SPEA-II algorithm optimization Pet feeding models, it is determined that house pet
Feeding amount, the optimal value of food type, and feeding pet amount, the optimal value of food type composition feeding pet scheme is immediately anti-
Feed user, allows user to understand the present situation of house pet whenever and wherever possible, is that house pet has built more preferable living environment.
Description of the drawings
Fig. 1 is that the flow process of the Pet feeding method analyzed based on Internet of Things big data according to the embodiment of the present invention is illustrated
Figure；
Fig. 2 is to be predicted the outcome figure according to the health indicator of the embodiment of the present invention；
Fig. 3 is the health indicator prediction-error image according to the embodiment of the present invention；
Fig. 4 is the user interface schematic diagram according to the embodiment of the present invention.
Specific embodiment
Fig. 1 shows the flow process of the Pet feeding method analyzed based on Internet of Things big data according to embodiments of the present invention.
As shown in figure 1, the Pet feeding method analyzed based on Internet of Things big data of the present invention, including：
Step S1：The collection species of house pet, sex, the age, palmic rate, blood pressure, body temperature, activity, feeding type, feed
Appetite, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding type and feeding amount
Constitute decision variable.
Health degree y to house pet is obtained by statistics1The maximum variable is affected to be：Pet breeds x1, age x2, heart beating
Frequency x3, blood pressure x4, activity x5, body temperature x6, present image x7, sex x8, Current body mass x9, feeding amount x10, food type x11,
Totally 11 variables；Wherein, palmic rate x3, blood pressure x4, activity x5, body temperature x6By corresponding sensor measurement data；Current figure
As x7Gathered by photographic head, pet breeds x1, age x2, sex x8, Current body mass x9For build-in attribute, by user input；Feeding
Amount x10, food type x11Constitute decision variable.
The body temperature x of house pet6Obtained by temperature sensor collection；Palmic rate x of house pet3Gathered by heart rate sensor
Obtain；The blood pressure x of house pet4Obtained by blood pressure sensor collection；The activity x of house pet5Obtained by pedometer collection；Utilize
Sample circuit is attached respectively with temperature sensor, heart rate sensor, blood pressure sensor, pedometer, weight sensor, and will
The body temperature of the house pet that temperature sensor, heart rate sensor, blood pressure sensor, pedometer, weight sensor are collected respectively, heart beating
Frequency, blood pressure, activity, Current body mass are converted into digital signal.
Facial characteristics of the house pet at current time are obtained by photographic head collection, and photographic head converts image information into numeral
Signal.
In the present invention, server is preferably Cloud Server.
Step S2：Utilize between Elman neural networks influence factor's matrix X and pet health index in server
Complex nonlinear relation, obtain Pet feeding model.
X is setk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input vector, N be training sample number,
For the g time iteration when between input layer M and hidden layer I
Weighted vector, WJPWeighted vector when () is the g time iteration g between hidden layer J and output layer P, WJCWhen () is the g time iteration g
Weighted vector Y between hidden layer J and undertaking layer Ck(g)=[yk1(g),yk2(g),L,ykP(g)] (k=1,2, L, S) be the g time
The reality output of network, d during iterationk=[dk1,dk2,L,dkP] (k=1,2, L, S) be desired output, iterationses g be 500.
Using the complexity between Elman neural networks influence factor's matrix X and pet health index in server
Non-linear relation, obtains the process of Pet feeding model, including：
Step S21：Initialization, if iterationses g initial values are 0, is assigned to respectively WMI(0)、WJP(0)、WJC(0) one (0,1)
Interval random value；
Step S22：Stochastic inputs sample Xk；
Step S23：To input sample Xk, reality output Y of every layer of neuron of forward calculation Elman neutral netk(g)；
Step S24：According to desired output dkWith reality output Yk(g), calculation error E (g)；
Step S25：Whether error in judgement E (g) is less than default error amount；If greater than or be equal to, into step S26,
It is less than as crossed, then into step S29；
Step S26：Whether iterationses g+1 is judged more than maximum iteration time, if it is greater, then into step S29, it is no
Then, into step S27；
Step S27：To input sample XkThe partial gradient δ of every layer of neuron of backwards calculation Elman neutral net；
Step S28：Modified weight amount Δ W is calculated, and corrects weights；G=g+1 is made, step S23 is jumped to；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether the training for completing all samples；If it is, completing modeling；If not, jumping to step
S22。
In the design of Elman neutral nets, the number of hidden nodes number be the pass for determining Elman neural network models quality
Key, is also the difficult point in the design of Elman neutral nets, determines the nodes of hidden layer using trial and error procedure here.
In formula, p is hidden neuron nodes, and n is input layer number, and m is output layer neuron number, and k is 1-10
Between constant.The arrange parameter of Elman neutral nets is as shown in table 2 below.
Table 2Elman neutral net arrange parameters
By said process, Elman neural network predictions effect is obtained as Figure 2-3.The base that intelligent pet is fed
Plinth is the foundation of model, and model accuracy directly affects output result.By analyzing Fig. 2-3, the pre- maximum survey of health index
Error is -3.9%, and model prediction accuracy is high, meets modeling demand.
Step S3：Using SPEA-II algorithms (Strength Pareto EvolutioanryAlgorithm-II, intensity
Evolution algorithm-II) Pet feeding model is optimized, obtain one group of optimal solution of decision variable.
Obtain one group of optimal solution of decision variable, that is, obtain the irrigation amount of house pet, dose, one group of Fertilizer Type
Optimal value.
The step of being optimized to Pet feeding model using SPEA-II algorithms is included：
Step S31：Each individual fitness F (i)=R (i)+D (i) in population in calculating Pet feeding model, wherein,
R (i) and D (i) are two factors for affecting F (i) size.
Concept is arranged according to Pareto, R (i) is obtained：
In formula, Q is filing collection, and P is evolution colony, the population that iteration updates, initial population P (0) is selected,
Intersect, mutation operation obtains second filial generation population P (1), second filial generation population P (1) is selected, intersected, mutation operation obtains the
Three generations population P (2), by that analogy.
D (i) is to affect F (i) while considering that domination is individual and is arranged individual information, and F (i) is obtained using neighbour's mechanism
Obtain more scientific evaluation：
In formula, M is the individual amount of filing collection Q,It is individual i to the Euclidean distance between its k-th adjacent body；
In order to calculateNeed to calculate individuality i to evolution colony P and file to collect the distance between other all individualities in Q, and according to
Increasing is arranged.
Step S32：Environmental selection is carried out to the individuality of population, new filing collection Q is obtainedt+1, t is current iteration number of times；Its
In, step S32 includes：
Step S321：Individuality of the fitness less than 1 is chosen in population and is put into filing collection Qt+1In：
Step S322：If filing collection Qt+1In individual amount and M it is equal, i.e., | Qt+1|=M, directly using filing collection
Qt+1；If filing collection Qt+1In individual amount be less than M, i.e., | Qt+1| ＜ M, then in previous generation population PtAnd QtMiddle selection (M- | Q
|) individual fitness less than 1 individuality be put into it is described filing collection Qt+1In；If filing collection Qt+1In individual amount be more than M, i.e., |
Qt+1| ＞ M, then individual i is selected successively from filing collection Q according to construction processt+1Middle deletion：
Wherein,Represent individuality i with filing collection Qt+1In k-th individuality Euclidean distance, exist when there is at least one individuality
It is neighbouring with its front l it is individual there is identical minimum euclidean distance, and it is neighbouring from its k-th it is individual with it is different apart from when,
Delete an individuality with minimum euclidean distance.
Step S323：Judge whether iterationses reach the default upper limit；If reached, output filing collection Qt+1Value；Such as
Fruit is not up to, and carries out step S33；
Step S33：Initial population is selected, is intersected, mutation operation updated after new population Pt+1；
Step S34：By population Pt+1With filing collection Qt+1Step S31, circulation step S31 and step S32 are substituted into, until iteration
Till number of times reaches the default upper limit, and export the value of current Q.
For initial population P (0), filing integrate Q (0) as sky, individuality of the fitness less than 1 is selected from initial population P (0)
In being put into filing collection Q (0), judge whether iterationses reach the default upper limit, if reached, directly output filing collects Q's (0)
Value, if not up to, is selected, is intersected, mutation operation to initial population P (0), new population P (1) is obtained, from population P
(1) choose individuality of the fitness less than 1 in be put in filing collection Q (1), by that analogy, until iterationses are reached on default
It is limited to stop, the value of the current filing collection Q of output.It should be noted that P and Q are the set of continuous renewal.
Step S4：Using this group of optimal solution of decision variable as house pet recommendation decision-making X*User is issued to by server
Terminal unit shown.
Various kinds of sensors gathered a secondary data per 2 hours and uploads onto the server, and server connects data and by Pet feeding
Model provides feeding amount and the food type that house pet is currently recommended.
Step S5：The recommendation decision-making feeding of pets that user shows according to terminal unit.
User can on the terminal device open intelligent pet and feed interface (as shown in Figure 4), the interface display house pet
Brief information, the brief information of house pet includes image, the current health index of house pet, and user can arrange the ideal of house pet at interface
Health index, by server the feeding type and feeding amount of recommendation are issued.
The current health index of house pet is obtained by being optimized to Pet feeding model based on SPEA-II algorithms, house pet
Current health index is corresponding with the one of decision variable group of optimal solution.
The Pet feeding method analyzed based on Internet of Things big data that the present invention is provided, first, using sensor, photographic head
Physical signs parameter, house pet image, feeding amount, the food type of house pet are gathered Deng hardware；Then, the data for collecting are uploaded
Stored to server, in server using Elman neural networks influence factor's matrix X and pet health index it
Between complex nonlinear relation, obtain Pet feeding model, Pet feeding model is optimized using SPEA-II algorithms, obtain
To one group of optimal value of each decision variable, and this group of optimal solution is issued to into PC the or APP terminals of user as recommendation decision-making, most
Afterwards, user can be according to the feeding amount and food type for recommending decision-making to determine house pet.The method can determine the Pet feeding of optimum
Scheme, is that house pet has built more preferable living environment.
Corresponding with said method, the present invention also provides a kind of Pet feeding system analyzed based on Internet of Things big data.
The Pet feeding system analyzed based on Internet of Things big data that the present invention is provided, including：
Data acquisition unit, for gather the species of house pet, sex, the age, palmic rate, blood pressure, body temperature, activity,
Feeding type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding class
Type and feeding amount constitute decision variable.The detailed process of data acquisition unit gathered data refers to above-mentioned steps S1.
Pet feeding model sets up unit, for utilizing Elman neural network influence factor matrix X in server
With the complex nonlinear relation between pet health index, Pet feeding model is obtained.Pet feeding model sets up unit foundation
The detailed process of Pet feeding model refers to above-mentioned steps S2.
Decision variable optimal solution acquiring unit, for being optimized to Pet feeding model using SPEA-II algorithms, is obtained
One group of optimal solution of decision variable, and as the recommendation decision-making X of house pet*.Decision variable optimal solution acquiring unit obtains decision-making and becomes
The optimal solution detailed process of amount refers to above-mentioned steps S3.
Recommend decision-making issuance unit, for passing through server by the recommendation decision-making X of house pet*It is issued to the terminal unit of user
Shown.
The recommendation decision-making X that user shows according to terminal unit*Feeding is carried out to house pet.
It should be pointed out that described above is not limitation of the present invention, the present invention is also not limited to the example above,
Change, modified, addition or replacement that those skilled in the art are made in the essential scope of the present invention, also should
Belong to protection scope of the present invention.
Claims (5)
1. it is a kind of based on Internet of Things big data analyze Pet feeding method, it is characterised in that comprise the steps：
Step S1：Species, sex, age, palmic rate, blood pressure, body temperature, activity, feeding type, the feeding of collection house pet
Amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, the feeding type and described
Feeding amount constitutes decision variable；
Step S2：Answering between Elman neural networks influence factor's matrix X and pet health index is utilized in server
Miscellaneous non-linear relation, obtains Pet feeding model；
Step S3：The Pet feeding model is optimized using SPEA-II algorithms, obtains one group of the decision variable most
Excellent solution；
Step S4：Using this group of optimal solution of the decision variable as the house pet recommendation decision-making X*By under the server
The terminal unit for being sent to user is shown；
Step S5：The recommendation decision-making X that the user shows according to the terminal unit*House pet described in feeding.
2. it is according to claim 1 based on Internet of Things big data analyze Pet feeding method, it is characterised in that it is described to dote on
Thing feeds X in modelk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input sample, S for training sample number, WMIG () is
Weighted vector during the g time iteration between input layer M and hidden layer I, WJPWhen () is the g time iteration g between hidden layer J and output layer P
Weighted vector, WJCWeighted vector when () is the g time iteration g between hidden layer J and undertaking layer C, Yk(g)=[yk1(g),yk2
(g),L,ykP(g)] reality output of (k=1,2, L, S) when being the g time iteration, dk=[dk1,dk2,L,dkP] (k=1,2, L, S)
For desired output；And,
The step of setting up the Pet feeding model includes：
Step S21：Initialization, if iterationses g initial values are 0, is assigned to respectively WMI(0)、WJP(0)、WJC(0) one (0,1) is interval
Random value；
Step S22：Stochastic inputs sample Xk；
Step S23：To input sample Xk, reality output Y of every layer of neuron of Elman neutral nets described in forward calculationk(g)；
Step S24：According to desired output dkWith reality output Yk(g), calculation error E (g)；
Step S25：Whether error in judgement E (g) is less than default error amount, if greater than or be equal to, into step S26, if
It is less than, then into step S29；
Step S26：Whether iterationses g+1 is judged more than maximum iteration time, if it does, into step S29, otherwise, entering
Enter step S27；
Step S27：To input sample XkThe partial gradient δ of every layer of neuron of Elman neutral nets described in backwards calculation；
Step S28：Modified weight amount Δ W is calculated, and corrects weights；G=g+1 is made, step S23 is jumped to；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether the training for completing all samples；If it is, completing modeling；If not, jumping to step S22.
3. it is according to claim 1 based on Internet of Things big data analyze Pet feeding method, it is characterised in that utilize
The step of SPEA-II algorithms are optimized to the Pet feeding model, including：
Step S31：Each individual fitness F (i)=R (i)+D (i) in population is calculated in the Pet feeding model, wherein,
S (j)=| j | j ∈ P+Q ∧ i ＞ j } |；
In formula, P is evolution colony, and Q is filing collection；
In formula, M is the individual amount of filing collection Q,It is individual i to the Euclidean distance between its k-th adjacent body；
Step S32：Environmental selection is carried out to the individuality of the population, new filing collection Q is obtainedt+1, t is current iteration number of times；Its
In, step S32 includes：
Step S321：Individuality of the fitness less than 1 is chosen in the population and is put into the filing collection Qt+1In：
Qt+1=i | i ∈ Pt+Qt∧ F (i) ＜ 1 }；
Step S322：If the filing collection Qt+1In individual amount be less than M, then in previous generation population PtAnd QtMiddle selection (M- |
Q |) individual fitness less than 1 individuality be put into it is described filing collection Qt+1In；If Qt+1In individual amount be more than M, then according to building
Process selects successively individual i from filing collection Qt+1Middle deletion：
Wherein,Represent individuality i with filing collection Qt+1In k-th individuality Euclidean distance, when having at least one individuality before with it
L it is neighbouring it is individual there is identical minimum euclidean distance, and it is neighbouring from its k-th it is individual with it is different apart from when, delete one
The individual individuality with minimum euclidean distance；
Step S323：Judge whether iterationses reach the default upper limit；If reached, the filing collection Q is exportedt+1Value；Such as
Fruit is not up to, and carries out step S33；
Step S33：Initial population is selected, is intersected, mutation operation updated after new population Pt+1；
Step S34：By the population Pt+1With the filing collection Qt+1Step S31, circulation step S31 and step S32 are substituted into, until
Till iterationses reach the default upper limit, and export the value of current Q.
4. according to any one of claim 1-3 based on Internet of Things big data analyze Pet feeding method, its feature
It is,
The body temperature of the house pet is gathered using temperature sensor；
The palmic rate of the house pet is gathered using heart rate sensor；
The blood pressure of the house pet is gathered using blood pressure sensor；
The activity of the house pet is gathered using pedometer；
Image information of the house pet at current time is gathered using photographic head, and converts image information into digital signal；With
And,
Walked with the temperature sensor, the heart rate sensor, the blood pressure sensor, the meter respectively using sample circuit
Device, the weight sensor are attached, and by the temperature sensor, the heart rate sensor, the blood pressure sensor, institute
State the body temperature of the house pet that pedometer is collected respectively, palmic rate, blood pressure, activity, Current body mass and be converted into digital signal.
5. it is a kind of based on Internet of Things big data analyze Pet feeding system, it is characterised in that include：
Data acquisition unit, for gathering species, sex, age, palmic rate, blood pressure, body temperature, activity, the feeding of house pet
Type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, the feeding class
Type and the feeding amount constitute decision variable；
Pet feeding model sets up unit, for utilizing Elman neural network influence factor matrix X in the server
With the complex nonlinear relation between pet health index, Pet feeding model is obtained；
Decision variable optimal solution acquiring unit, for being optimized to the Pet feeding model using SPEA-II algorithms, is obtained
One group of optimal solution of the decision variable, and as the recommendation decision-making X of the house pet*；
Recommend decision-making issuance unit, for by the server by the recommendation decision-making X of the house pet*It is issued to the terminal of user
Equipment is shown.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108207683A (en) * | 2018-01-31 | 2018-06-29 | 四川思诺创意科技有限公司 | A kind of pet health analysis and management system |
| CN108694444A (en) * | 2018-05-15 | 2018-10-23 | 重庆科技学院 | A kind of plant cultivating method based on intelligent data acquisition Yu cloud service technology |
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| CN111758113A (en) * | 2018-01-16 | 2020-10-09 | 哈比有限公司 | Method and system for a pet health platform |
| CN108207683A (en) * | 2018-01-31 | 2018-06-29 | 四川思诺创意科技有限公司 | A kind of pet health analysis and management system |
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| CN109348443A (en) * | 2018-09-12 | 2019-02-15 | 广州粤创富科技有限公司 | A kind of pet information management system |
| CN110516719A (en) * | 2019-08-13 | 2019-11-29 | 四川康佳智能终端科技有限公司 | A kind of pet data processing method, system and storage medium based on Internet of Things |
| CN114097645A (en) * | 2020-08-26 | 2022-03-01 | 中移动信息技术有限公司 | Training method, device, equipment and storage medium of pet health model |
| CN112331303A (en) * | 2020-11-26 | 2021-02-05 | 小佩网络科技（上海）有限公司 | Pet feeding plan generation method and device, electronic equipment and storage medium |
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