CN106472412A - Pet feeding method and system based on internet of things - Google Patents
Pet feeding method and system based on internet of things Download PDFInfo
- Publication number
- CN106472412A CN106472412A CN201610883571.3A CN201610883571A CN106472412A CN 106472412 A CN106472412 A CN 106472412A CN 201610883571 A CN201610883571 A CN 201610883571A CN 106472412 A CN106472412 A CN 106472412A
- Authority
- CN
- China
- Prior art keywords
- pet
- feeding
- particle
- house pet
- decision
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 24
- 230000036541 health Effects 0.000 claims abstract description 18
- 239000011159 matrix material Substances 0.000 claims abstract description 16
- 230000000694 effects Effects 0.000 claims abstract description 14
- 230000036760 body temperature Effects 0.000 claims abstract description 13
- NUFBIAUZAMHTSP-UHFFFAOYSA-N 3-(n-morpholino)-2-hydroxypropanesulfonic acid Chemical compound OS(=O)(=O)CC(O)CN1CCOCC1 NUFBIAUZAMHTSP-UHFFFAOYSA-N 0.000 claims abstract description 12
- 238000013528 artificial neural network Methods 0.000 claims abstract description 11
- 230000036391 respiratory frequency Effects 0.000 claims abstract description 9
- 230000035611 feeding Effects 0.000 claims abstract description 7
- 239000002245 particle Substances 0.000 claims description 25
- 230000003044 adaptive effect Effects 0.000 claims description 9
- 230000007935 neutral effect Effects 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 6
- 210000002569 neuron Anatomy 0.000 claims description 6
- 230000000241 respiratory effect Effects 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 4
- 235000019789 appetite Nutrition 0.000 claims description 4
- 230000036528 appetite Effects 0.000 claims description 4
- 238000012549 training Methods 0.000 claims description 4
- 230000036772 blood pressure Effects 0.000 claims description 3
- 230000001815 facial effect Effects 0.000 claims description 2
- 230000009191 jumping Effects 0.000 claims description 2
- 230000007246 mechanism Effects 0.000 claims description 2
- 238000010845 search algorithm Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 6
- 238000005457 optimization Methods 0.000 description 4
- 241000894007 species Species 0.000 description 4
- 230000000474 nursing effect Effects 0.000 description 3
- 238000013461 design Methods 0.000 description 2
- 241000894006 Bacteria Species 0.000 description 1
- 208000008589 Obesity Diseases 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000000844 anti-bacterial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000037213 diet Effects 0.000 description 1
- 235000005911 diet Nutrition 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
- 239000003337 fertilizer Substances 0.000 description 1
- 230000002431 foraging effect Effects 0.000 description 1
- 230000001121 heart beat frequency Effects 0.000 description 1
- 230000002262 irrigation Effects 0.000 description 1
- 238000003973 irrigation Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 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
- 230000004044 response Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01K—ANIMAL HUSBANDRY; CARE OF BIRDS, FISHES, INSECTS; FISHING; REARING OR BREEDING ANIMALS, NOT OTHERWISE PROVIDED FOR; NEW BREEDS OF ANIMALS
- A01K67/00—Rearing or breeding animals, not otherwise provided for; New breeds of animals
- A01K67/02—Breeding vertebrates
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
Abstract
The invention provides a kind of Pet feeding method and system based on Internet of Things, inclusion therein：The species of collection house pet, sex, age, palmic rate, respiratory frequency, body temperature, activity, feeding type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding type and feeding amount constitute decision variable；Utilize the complex nonlinear relation between Elman neural network influence factor matrix X and pet health index in server, obtain Pet feeding model；Using MOPSO algorithm, Pet feeding model is optimized, obtains one group of optimal solution of decision variable；Using this group optimal solution of decision variable as house pet recommendation decision-making X*Shown by the terminal unit that server is issued to user；The recommendation decision-making X that user shows according to terminal unit*Feeding house pet.Can determine the Pet feeding scheme of optimum using the present invention, be that house pet has built more preferable living environment.
Description
Technical field
The present invention relates to house pet intelligence nursing field is and in particular to a kind of Pet feeding method based on Internet of Things and be
System.
Background technology
With the fast development of national economy, house pet increasingly becomes the one of the pleased selection of people as a kind of sustenance of emotion
The mode of kind.But if the personal experience simply using shortage scientific basis feeds to house pet, its irrational nursing scheme
House pet may be made to lack nutrition and to lead to disease or eutrophication so that obesity, all not reach the target of our anticipations, indirectly make
Substantial amounts of energy loss and money is become to waste.
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 user can in time feeding pet scheme be adjusted.Each factor of impact pet health degree
Between often embody the complexity of height and non-linear, certain difficulty is had using conventional prediction, analysis method.
Content of the invention
The present invention passes through to provide a kind of Pet feeding method and system based on Internet of Things, to solve existing Pet feeding mistake
Due to a lack of nursing experience it is impossible to controlling optimum feeding scheme and leading to house pet hungry or unsound problem in journey.
For solving the above problems, the present invention employs the following technical solutions and is achieved：
On the one hand, the Pet feeding method based on Internet of Things that the present invention provides, including：
Step S1：The species of collection house pet, sex, age, palmic rate, respiratory frequency, body temperature, activity, feeding class
Type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding type and feed
Appetite constitutes decision variable；
Step S2：Utilize between Elman neural network influence factor matrix X and pet health index in server
Complex nonlinear relation, obtain Pet feeding model；
Step S3：Using MOPSO algorithm, Pet feeding model is optimized, obtains one group of optimal solution of decision variable；
Step S4：Using this group 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 based on Internet of Things that the present invention provides, 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；
Unit set up by Pet feeding model, for utilizing Elman neural network influence factor matrix X in server
Complex nonlinear relation and pet health index between, obtains Pet feeding model；
Decision variable optimal solution acquiring unit, for being optimized to Pet feeding model using MOPSO algorithm, acquisition is determined
One group of optimal solution of plan variable, and the recommendation decision-making X as house pet*；
Recommend decision-making issuance unit, for by 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 advantage of the Pet feeding method and system based on Internet of Things that the present invention provides is：Profit
With Elman neural network Pet feeding model, recycle MOPSO algorithm optimization Pet feeding model it is determined that house pet feeds
Appetite, the optimal value of food type, and the optimal value of feeding pet amount, food type is constituted feeding pet scheme immediate feedback
To user, allow user can understand the present situation of house pet whenever and wherever possible, be that house pet has built more preferable living environment.
Brief description
Fig. 1 is the schematic flow sheet according to the embodiment of the present invention based on the Pet feeding method of Internet of Things；
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 based on Internet of Things according to embodiments of the present invention.
As shown in figure 1, the Pet feeding method based on Internet of Things of the present invention, including：
Step S1：The species of collection house pet, sex, age, palmic rate, respiratory frequency, body temperature, activity, feeding class
Type, feeding amount, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, feeding type and feed
Appetite constitutes decision variable.
Health degree y to house pet is obtained by statistics1Affecting maximum variable is：Pet breeds x1, age x2, heart beating
Frequency x3, respiratory frequency 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, respiratory frequency x4, activity x5, body temperature x6Number is measured by corresponding sensor
According to；Present image 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 pet6Gathered by temperature sensor and obtain；Palmic rate x of house pet3Gathered by heart rate sensor
Obtain；Respiratory frequency x of house pet4Gathered by respiratory frequen and obtain；The activity x of house pet5Obtained by pedometer collection
?；It is attached with temperature sensor, heart rate sensor, respiratory frequen, pedometer respectively using sample circuit, and will
The body temperature of the house pet that temperature sensor, heart rate sensor, respiratory frequen, pedometer collect respectively, palmic rate, exhale
Inhale frequency, activity is converted into digital signal.
House pet gathers acquisition in the facial characteristics of current time by photographic head, and photographic head converts image information into numeral
Signal.
In the present invention, server is preferably Cloud Server.
Step S2：Utilize between Elman neural network influence factor matrix X and pet health index in server
Complex nonlinear relation, obtain Pet feeding model.
Setting Xk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input vector, N be training sample number,
For during the g time iteration between input layer M and hidden layer I
Weighted vector, WJPG () is weighted vector during the g time iteration 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.
The complexity between Elman neural network influence factor matrix X and pet health index is utilized in server
Non-linear relation, obtains the process of Pet feeding model, including：
Step S21：Initialization, if iterationses g initial value is 0, is assigned to W respectivelyMI(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 forward calculation Elman every layer of neuron of 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, enter step S26,
If it is less, entering step S29；
Step S26：Judge whether iterationses g+1 is more than maximum iteration time, if it does, entering step S29, no
Then, enter step S27；
Step S27：To input sample XkThe partial gradient δ of backwards calculation Elman every layer of neuron of neutral net；
Step S28：Calculate modified weight amount Δ W, and revise weights；Make g=g+1, jump to step S23；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether to complete the training of all samples；If it is, completing to model；If not, jumping to step
S22.
Elman neutral net design in, the number of hidden nodes number be determine Elman neural network model quality pass
Key, is also the difficult point in the design of Elman neutral net, to be determined the nodes of hidden layer here using trial and error procedure.
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 net is as shown in table 2 below.
Table 2 Elman neutral net arrange parameter
By said process, can get Elman neural network prediction effect 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 to Fig. 2-3, the pre- maximum survey of health index
Error is 2.0%, and model prediction accuracy is high, meets modeling demand.
Step S3：Using MOPSO algorithm, (Modified Bacteria Foraging Optimization improves antibacterial
Look for food optimized algorithm) 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.
Included using the step that MOPSO algorithm is optimized to Pet feeding model：
Step S31：Initialization Pet feeding model parameter, this parameter includes population scale R, maximum iteration time T, random
Generate n particle x1,x2, L, xn, accelerated factor c1And c2；Wherein, c1For acceleration weight from particle to individual extreme value movement, c2For
To the acceleration weight of global optimum movement, it is sky that order achieves collection Q to particle.
Step S32：Calculate the fitness p of each particle in populationiWith individual adaptive optimal control degreeWeigh particle current
The degree of optimization of position.
The fitness value of particle is less, and its degree of optimization is higher.
Step S33：Fitness p by each particleiWith individual adaptive optimal control degreeIt is compared, if fitness pi?
Join individual adaptive optimal control degreeBy fitness piReplace individual adaptive optimal control degreeOtherwise, retain original individual optimum suitable
Response
Step S34：The particle of non-dominant all in population is added and achieves collection Q, and delete the particle arranged in population.
Step S35：Select a particle by the use of press mechanism and Tabu search algorithm as global optimum in archive collection Q.
Step S36：The speed of more new particle itself and position；
Wherein, the speed of particle more new formula is：
The location updating formula of particle is：
Step S37：Judge whether iterationses reach maximum iteration time T, if it is, exporting current global optimum
Solution, otherwise, circulation step S32- step S36, till iterationses reach maximum iteration time T.
Each position of particle is a variable, all corresponding solution of each variable.
Step S4：Using this group optimal solution of decision variable as house pet recommendation decision-making X*User is issued to by server
Terminal unit shown.
Every 2 hours collection one secondary data of various kinds of sensors upload onto the server, and server connects data and passes through Pet feeding
Model provides the current feeding amount recommended of house pet and food type.
Step S5：The recommendation decision-making feeding of pets that user shows according to terminal unit.
User can open intelligent pet on the terminal device and feed interface (as shown in Figure 4), this house pet of interface display
Brief information, the brief information of house pet includes the image of house pet, current health index, and user can arrange the ideal of house pet at interface
Health index, is issued feeding type and the feeding amount of recommendation by server.
The current health index of house pet is obtained by being optimized to Pet feeding model based on MOPSO algorithm, the working as of house pet
Front health index is corresponding with one group of optimal solution of decision variable.
The Pet feeding method based on Internet of Things that the present invention provides, first, is adopted using sensor, the first-class hardware of shooting
The physical signs parameter of collection house pet, house pet image, feeding amount, food type；Then, the data collecting is uploaded onto the server
Stored, utilized the complexity between Elman neural network influence factor matrix X and pet health index in server
Non-linear relation, is obtained Pet feeding model, using MOPSO algorithm, Pet feeding model is optimized, and obtains each decision-making and becomes
One group of optimal solution of amount, and using this group optimal solution as PC the or APP terminal recommending decision-making to be issued to user, finally, Yong Huke
According to feeding amount and the food type of recommending decision-making to determine house pet.The method can determine the Pet feeding scheme of optimum, for doting on
Thing has built more preferable living environment.
Corresponding with said method, the present invention also provides a kind of Pet feeding system based on Internet of Things.
The Pet feeding system based on Internet of Things for the present invention, 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 is with reference to above-mentioned steps S1.
Unit set up by Pet feeding model, for utilizing Elman neural network influence factor matrix X in server
Complex nonlinear relation and pet health index between, obtains Pet feeding model.Pet feeding model is set up unit and is set up
The detailed process of Pet feeding model is with reference to above-mentioned steps S2.
Decision variable optimal solution acquiring unit, for being optimized to Pet feeding model using MOPSO algorithm, acquisition is determined
One group of optimal solution of plan variable, and the recommendation decision-making X as house pet*.Decision variable optimal solution acquiring unit obtains decision variable
Optimal solution detailed process with reference to above-mentioned steps S3.
Recommend decision-making issuance unit, for by 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, modification, interpolation 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. a kind of Pet feeding method based on Internet of Things is it is characterised in that comprise the steps：
Step S1：The collection species of house pet, sex, age, palmic rate, respiratory frequency, body temperature, activity, feeding type, feed
Appetite, present image, Current body mass constitute influence factor matrix X, and upload onto the server；Wherein, described feeding type and institute
State feeding amount and constitute decision variable；
Step S2：Utilize between Elman neural network influence factor matrix X and pet health index in described server
Complex nonlinear relation, obtain Pet feeding model；
Step S3：Using MOPSO algorithm, described Pet feeding model is optimized, one group that obtains described decision variable optimum
Solution；
Step S4：Using this group optimal solution of described decision variable as described house pet recommendation decision-making X*By under described server
The terminal unit being sent to user is shown；
Step S5：The recommendation decision-making X that described user shows according to described terminal unit*House pet described in feeding.
2. the Pet feeding method based on Internet of Things according to claim 1 is it is characterised in that described Pet feeding model
Middle Xk=[xk1,xk2,L,xkM] (k=1,2, L, S) be input sample, S be training sample number, WMIG () is the g time iteration
When weighted vector between input layer M and hidden layer I, WJPG () is weights arrow during the g time iteration between hidden layer J and output layer P
Amount, WJCG () is weighted vector during the g time iteration between hidden layer J and undertaking layer C, Yk(g)=[yk1(g),yk2(g),L,ykP
(g)] (k=1,2, L, S) be reality output during the g time iteration, dk=[dk1,dk2,L,dkP] (k=1,2, L, S) be expectation
Output；And,
The step setting up described Pet feeding model includes：
Step S21：Initialization, if iterationses g initial value is 0, is assigned to W respectivelyMI(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 net 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, enter step S26, if
It is less than, then enter step S29；
Step S26：Judging whether iterationses g+1 is more than maximum iteration time, if it does, entering step S29, otherwise, entering
Enter step S27；
Step S27：To input sample XkThe partial gradient δ of every layer of neuron of Elman neutral net described in backwards calculation；
Step S28：Calculate modified weight amount Δ W, and revise weights；Make g=g+1, jump to step S23；
Wherein, Δ Wij=η δij, η is the learning efficiency；Wij(g+1)=Wij(g)+ΔWij(g)；
Step S29：Judge whether to complete the training of all samples；If it is, completing to model；If not, jumping to step S22.
3. the Pet feeding method based on Internet of Things according to claim 1 is it is characterised in that utilize MOPSO algorithm pair
The step that described Pet feeding model is optimized, including：
Step S31：Initialize described Pet feeding model parameter, described parameter include population scale R, maximum iteration time T, with
Machine generates n particle x1,x2, L, xn, accelerated factor c1And c2；Wherein c1, it is the acceleration weight to individual extreme value movement for the particle, c2
For particle to the acceleration weight of global optimum movement, make and achieve collection Q for sky；
Step S32：Calculate the fitness p of each particle in described populationiWith individual adaptive optimal control degree
Step S33：Fitness p by each particle in described populationiWith individual adaptive optimal control degreeIt is compared, if described
Fitness piDomination described individuality adaptive optimal control degreeBy described fitness piReplace described individuality adaptive optimal control degreeOtherwise,
Retain original individuality adaptive optimal control degree
Step S34：By the particle of non-dominant all in population add described achieve collection Q, and delete and arranged in described population
Particle；
Step S35：Select a particle by the use of press mechanism and Tabu search algorithm in described archive in collection Q as global optimum；
Step S36：The speed of more new particle itself and position；
Wherein, the speed of particle more new formula is：
The location updating formula of particle is：
Step S37：Judge whether iterationses reach described maximum iteration time T, if it is, exporting current global optimum
Solution, otherwise, circulation step S32- step S36, till iterationses reach described maximum iteration time T.
4. the Pet feeding method based on Internet of Things according to any one of claim 1-3 it is characterised in that
Gather the body temperature of described house pet using temperature sensor；
Gather the palmic rate of described house pet using heart rate sensor；
Gather the respiratory frequency of described house pet using respiratory frequen；
Gather the activity of described house pet using pedometer；
Gather the facial characteristics in current time for the described house pet using photographic head, and convert image information into digital signal；With
And,
Using sample circuit respectively with described temperature sensor, described heart rate sensor, described respiratory frequen, described meter
Step device is attached, and by described temperature sensor, described heart rate sensor, described respiratory frequen, described pedometer
The body temperature of the house pet collecting respectively, palmic rate, respiratory frequency, activity are converted into digital signal.
5. a kind of Pet feeding system based on Internet of Things is it is characterised in that include：
Data acquisition unit, for gathering the species of house pet, sex, 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, described feeding class
Type and described feeding amount constitute decision variable；
Unit set up by Pet feeding model, for utilizing Elman neural network influence factor matrix X in described server
Complex nonlinear relation and pet health index between, obtains Pet feeding model；
Decision variable optimal solution acquiring unit, for being optimized to described Pet feeding model using MOPSO algorithm, obtains institute
State one group of optimal solution of decision variable, and the recommendation decision-making X as described house pet*；
Recommend decision-making issuance unit, for by described server by the recommendation decision-making X of described house pet*It is issued to the terminal of user
Equipment is shown.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610883571.3A CN106472412B (en) | 2016-10-10 | 2016-10-10 | pet feeding method and system based on internet of things |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610883571.3A CN106472412B (en) | 2016-10-10 | 2016-10-10 | pet feeding method and system based on internet of things |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106472412A true CN106472412A (en) | 2017-03-08 |
| CN106472412B CN106472412B (en) | 2018-03-27 |
Family
ID=58269291
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610883571.3A Active CN106472412B (en) | 2016-10-10 | 2016-10-10 | pet feeding method and system based on internet of things |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106472412B (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108399617A (en) * | 2018-02-14 | 2018-08-14 | 中国农业大学 | A kind of detection method and device of animal health condition |
| 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 |
| CN110506709A (en) * | 2019-08-12 | 2019-11-29 | 南京大学（溧水）生态环境研究院 | A kind of fly maggot breeding intelligence cloth feeding-system and method |
| 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 |
| CN111374070A (en) * | 2020-03-20 | 2020-07-07 | 深圳市帅鸽美羽科技有限公司 | Carrier pigeon feeding system |
| CN111758113A (en) * | 2018-01-16 | 2020-10-09 | 哈比有限公司 | Method and system for a pet health platform |
| WO2020238548A1 (en) * | 2019-05-28 | 2020-12-03 | 北京猫猫狗狗科技有限公司 | Client, feeder and method for generating pet feeding list |
| CN115328242A (en) * | 2022-10-11 | 2022-11-11 | 山东华邦农牧机械股份有限公司 | Culture environment intelligent regulation system based on remote control |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164742A (en) * | 2013-04-02 | 2013-06-19 | 南京邮电大学 | Server performance prediction method based on particle swarm optimization nerve network |
| CN103535288A (en) * | 2013-08-05 | 2014-01-29 | 无锡科尤艾信息科技有限公司 | Remote intelligent monitoring pet feeder system |
| US20150066496A1 (en) * | 2013-09-02 | 2015-03-05 | Microsoft Corporation | Assignment of semantic labels to a sequence of words using neural network architectures |
| CN104963691A (en) * | 2015-06-03 | 2015-10-07 | 华中科技大学 | Stability prediction control method for soil pressure shield excavation surface under complex stratum condition |
-
2016
- 2016-10-10 CN CN201610883571.3A patent/CN106472412B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103164742A (en) * | 2013-04-02 | 2013-06-19 | 南京邮电大学 | Server performance prediction method based on particle swarm optimization nerve network |
| CN103535288A (en) * | 2013-08-05 | 2014-01-29 | 无锡科尤艾信息科技有限公司 | Remote intelligent monitoring pet feeder system |
| US20150066496A1 (en) * | 2013-09-02 | 2015-03-05 | Microsoft Corporation | Assignment of semantic labels to a sequence of words using neural network architectures |
| CN104963691A (en) * | 2015-06-03 | 2015-10-07 | 华中科技大学 | Stability prediction control method for soil pressure shield excavation surface under complex stratum condition |
Non-Patent Citations (2)
| Title |
|---|
| 张俊玲等: "基于粒子群优化的Elman神经网络无模型控制", 《智能系统学报》 * |
| 王晓霞等: "进化Elman神经网络在实时数据预测中的应用", 《电力自动化设备》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111758113A (en) * | 2018-01-16 | 2020-10-09 | 哈比有限公司 | Method and system for a pet health platform |
| CN108399617A (en) * | 2018-02-14 | 2018-08-14 | 中国农业大学 | A kind of detection method and device of animal health condition |
| 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 |
| WO2020238548A1 (en) * | 2019-05-28 | 2020-12-03 | 北京猫猫狗狗科技有限公司 | Client, feeder and method for generating pet feeding list |
| CN110506709A (en) * | 2019-08-12 | 2019-11-29 | 南京大学（溧水）生态环境研究院 | A kind of fly maggot breeding intelligence cloth feeding-system and method |
| 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 |
| CN111374070A (en) * | 2020-03-20 | 2020-07-07 | 深圳市帅鸽美羽科技有限公司 | Carrier pigeon feeding system |
| CN115328242A (en) * | 2022-10-11 | 2022-11-11 | 山东华邦农牧机械股份有限公司 | Culture environment intelligent regulation system based on remote control |
| CN115328242B (en) * | 2022-10-11 | 2022-12-27 | 山东华邦农牧机械股份有限公司 | Culture environment intelligent regulation system based on remote control |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106472412B (en) | 2018-03-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106472412A (en) | Pet feeding method and system based on internet of things | |
| CN106472332B (en) | Pet feeding method and system based on dynamic intelligent algorithm | |
| CN106407711A (en) | Recommendation method and recommendation system of pet feeding based on cloud data | |
| CN106614273B (en) | Pet feeding method and system based on Internet of Things big data analysis | |
| Zambrano et al. | Sparse computation in adaptive spiking neural networks | |
| CN104573359B (en) | A kind of mass-rent labeled data integration method of task based access control difficulty and mark person's ability | |
| CN106482502B (en) | The intelligence drying long-range control method and system recommended based on cloud platform big data | |
| CN106447117B (en) | Pet feeding method and system based on the daily data analysis of pet | |
| Razmjooy et al. | A study on metaheuristic-based neural networks for image segmentation purposes | |
| CN108694444A (en) | A kind of plant cultivating method based on intelligent data acquisition Yu cloud service technology | |
| CN106444379A (en) | Intelligent drying remote control method and system based on internet of things recommendation | |
| CN106650212A (en) | Intelligent plant breeding method and system based on data analysis | |
| CN106444378A (en) | Plant culture method and system based on IoT (Internet of things) big data analysis | |
| CN109961142A (en) | A kind of Neural network optimization and device based on meta learning | |
| CN109087706A (en) | Human health appraisal procedure and assessment system based on sleep big data | |
| CN111061959B (en) | Group intelligent software task recommendation method based on developer characteristics | |
| CN108507700A (en) | A kind of pig house multi-point temperature prediction technique and system | |
| CN101615223A (en) | Health data integrated system and method thereof | |
| CN113012811B (en) | Traditional Chinese medicine syndrome diagnosis and health evaluation method combining deep convolutional network and graph neural network | |
| CN110110372A (en) | A kind of user's timing behavior automatic segmentation prediction technique | |
| CN109567313B (en) | Intelligent insole with biological characteristic recognition function | |
| CN107945534A (en) | A kind of special bus method for predicting based on GMDH neutral nets | |
| CN107145723A (en) | Hospital process management system based on artificial neural network | |
| CN108614422A (en) | Dissolved oxygen optimal control method, apparatus and system in the cultivation of continental rise plant circulation water | |
| CN108962349A (en) | A kind of sleep massage method and system based on cloud data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20200630 Address after: 401331 Yi Fu Road, Chongqing University of Science and Technology, 20 East Road, University Town, Shapingba, Chongqing, I324 Patentee after: CHONGQING YIKETONG TECHNOLOGY Co.,Ltd. Address before: 401331 Shapingba University District, Chongqing City Road, No. 20 Patentee before: Chongqing University of Science & Technology |
|
| PE01 | Entry into force of the registration of the contract for pledge of patent right | ||
| PE01 | Entry into force of the registration of the contract for pledge of patent right |
Denomination of invention: Pet feeding methods and systems based on the Internet of Things Granted publication date: 20180327 Pledgee: Guangzhou Jierui Investment Co.,Ltd. Pledgor: CHONGQING YIKETONG TECHNOLOGY CO.,LTD. Registration number: Y2024980005331 |