CN109062296A - A kind of grain storage method based on temperature and humidity monitor - Google Patents
A kind of grain storage method based on temperature and humidity monitor Download PDFInfo
- Publication number
- CN109062296A CN109062296A CN201810978665.8A CN201810978665A CN109062296A CN 109062296 A CN109062296 A CN 109062296A CN 201810978665 A CN201810978665 A CN 201810978665A CN 109062296 A CN109062296 A CN 109062296A
- Authority
- CN
- China
- Prior art keywords
- grain
- interim
- temperature
- storehouse
- grain storehouse
- 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
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D27/00—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00
- G05D27/02—Simultaneous control of variables covered by two or more of main groups G05D1/00 - G05D25/00 characterised by the use of electric means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Storage Of Harvested Produce (AREA)
- Drying Of Solid Materials (AREA)
Abstract
The present invention discloses a kind of grain storage method based on temperature and humidity monitor, when progress grain is temporarily stored in a warehouse, the ventilation state in interim grain storehouse is determined based on BP neural network, specifically include step 1: according to the sampling period, pass through the humidity of grain in the interim grain storehouse of sensor measurement, temperature in interim grain storehouse, the height of grain face in interim grain storehouse；Step 2: determining the input layer vector of three layers of BP neural network；Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the neuron of middle layer are m；Step: 4: obtaining output layer neuron vector.It can be based on the grain storage method of temperature and humidity monitor, the ventilation state in interim grain storehouse can be controlled based on BP neural network, so that grain keeps dry and comfortable in interim grain storehouse, it is easily stored, the temperature of hot wind air can also be controlled, the drafting efficiency of silo is improved when interim grain storehouse is in ventilation state, it also can control the ventilation time in interim grain storehouse, further increase drafting efficiency.
Description
Technical field
The present invention relates to grain storage technical fields, and more particularly, the present invention relates to a kind of based on temperature and humidity monitor
Grain storage method.
Background technique
At present in the processing method of cereal, cereal enters finishing step after being directly over preliminary working process, causes cereal
Because temperature is excessively high, cause cereal percentage of damage higher, excellence rate decline, cereal finished product occurs mould after storing because of the excessively high packaging of temperature
The phenomenon that change.By taking rice is processed as an example, the temperature come out after rice preliminary working is at 45 DEG C, humidity 16%, if being directly entered essence
Manufacturing procedure (is polished and is screened out and crack rice), and rice is excessively high because of temperature, it is easy to broken；After fruit rice preliminary working, directly pack
Storage, since rice temperature is excessively high, humidity is too big, causes rice to occur mildew phenomena in storing process.
Grain is during storage, often because of the variation of the factors such as environment, weather, ventilation, causes grain rotten or worm occurs
Phenomena such as evil, so needing to keep good ventilation and heat condition during storage in grain.And when transporting grain, often
Grain is stored temporarily in a silo, in order to carrying of the grain between warehouse and harbour, and is temporarily stored in grain
Phenomena such as grain rots or insect pest occurs still easily occurs for period, therefore if not keeping good ventilation and heat condition
It is also required to be monitored grain during grain temporarily stores, convenient for being aerated processing at any time.
Summary of the invention
It is an object of the invention to design and develop a kind of grain storage method based on temperature and humidity monitor, can be based on
BP neural network controls the ventilation state in interim grain storehouse, so that grain keeps dry and comfortable in interim grain storehouse, it is easily stored.
The present invention can also control the temperature of hot wind air when interim grain storehouse is in ventilation state, improve the logical of silo
Wind efficiency.
The present invention also can control the ventilation time in interim grain storehouse, further increase drafting efficiency.
Technical solution provided by the invention are as follows:
A kind of grain storage method based on temperature and humidity monitor is based on BP neural network when progress grain is temporarily stored in a warehouse
It determines the ventilation state in interim grain storehouse, specifically comprises the following steps:
Step 1: according to the sampling period, by the humidity of grain in the interim grain storehouse of sensor measurement, in interim grain storehouse
Temperature, the height of grain face in interim grain storehouse；
Step 2: determining the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5}；Wherein, x1For
The humidity of grain, x in interim grain storehouse2For the temperature in interim grain storehouse, x3For the height of grain face in interim grain storehouse, x4For
The maximum storage time of interim grain storage, x5For the floor space of interim grain storehouse；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the neuron of middle layer are m；
Step: 4: obtaining output layer neuron vector o={ o1}；Wherein, o1For the ventilation state in interim grain storehouse, institute
Stating output layer neuron value isWork as o1When being 1, the ventilation state in interim grain storehouse is in the open state, works as o1It is 0
When, the ventilation state in interim grain storehouse is in close state.
Preferably, hot-air is passed through into interim place's silo when ventilation in the interim grain storehouse.
Preferably, the temperature of the hot-air meets:
Wherein, T is the temperature of hot-air, T0For the temperature in interim grain storehouse, s is the floor space of interim grain storehouse, and h is
The height of grain face, V in interim grain storehouse0For the volume of interim grain storehouse.
Preferably, the temperature of the hot-air also meets: T≤45 DEG C.
Preferably, the ventilation time in interim grain storehouse meets:
Wherein, η is the humidity of grain in interim grain storehouse, and e is the truth of a matter of natural logrithm, t0Maximum for interim grain storage is deposited
Store up the time.
Preferably, the neuron m of the middle layer meets:Wherein n is input layer
Number, p are output layer node number.
Preferably, the excitation function of the middle layer and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
Preferably, after by ventilation process, the humidity of grain meets in interim grain storehouse: η≤10%.
It is of the present invention the utility model has the advantages that
It (1), can it is an object of the invention to design and develop a kind of grain storage method based on temperature and humidity monitor
The ventilation state in interim grain storehouse is controlled based on BP neural network, so that grain keeps dry and comfortable in interim grain storehouse, is easy to store up
It deposits.
(2) present invention can also control the temperature of hot wind air, improve silo when interim grain storehouse is in ventilation state
Drafting efficiency, moreover it is possible to control the ventilation time in interim grain storehouse, further increase drafting efficiency.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text being capable of evidence
To implement.
The present invention provides a kind of grain storage method based on temperature and humidity monitor, when progress grain is temporarily stored in a warehouse, is based on
BP neural network determines the ventilation state in interim grain storehouse, specifically comprises the following steps:
Step 1: establishing BP neural network model.
Totally interconnected connection is formed on BP model between the neuron of each level, is not connected between the neuron in each level
It connects, the output of input layer is identical as input, i.e. oi=xi.The operating characteristic of the neuron of intermediate hidden layer and output layer
For
opj=fj(netpj)
Wherein p indicates current input sample, ωjiFor from neuron i to the connection weight of neuron j, opiFor neuron
The current input of j, opjIt is exported for it；fjFor it is non-linear can micro- non-decreasing function, be generally taken as S type function, i.e. fj(x)=1/ (1
+e-x)。
For the BP neural network architecture that the present invention uses by up of three-layer, first layer is input layer, and total n node is right
The n detection signal indicated in silo is answered, these signal parameters are provided by data preprocessing module；The second layer is hidden layer, total m
A node is determined in an adaptive way by the training process of network；Third layer is output layer, total p node, by system reality
It is needing to export in response to determining that.
The mathematical model of the network are as follows:
Input vector: x=(x1,x2,...,xn)T
Middle layer vector: y=(y1,y2,...,ym)T
Output vector: o=(o1,o2,...,op)T
In the present invention, input layer number is n=5, and output layer number of nodes is p=1, hidden layer number of nodes
5 parameters of input layer respectively indicate are as follows: x1For the humidity of grain in interim grain storehouse, x2For in interim grain storehouse
Temperature, x3For the height of grain face in interim grain storehouse, x4For the maximum storage time of interim grain storage, x5For the bottom of interim grain storehouse
Area；
1 parameter of output layer respectively indicates are as follows: o1For the ventilation state in interim grain storehouse, the output layer neuron value
ForWork as o1When being 1, the ventilation state in interim grain storehouse is in the open state, works as o1When being 0, in interim grain storehouse
Ventilation state be in close state.
Step 2: carrying out the training of BP neural network.
After establishing BP neural network nodal analysis method, the training of BP neural network can be carried out.It is passed through according to the history of product
Test the sample of data acquisition training, and the connection weight between given input node i and hidden layer node j, hidden node j and defeated
Connection weight between node layer k out.
(1) training method
Each subnet is using individually trained method；When training, first have to provide one group of training sample, each of these sample
This, to forming, when all reality outputs of network and its consistent ideal output, is shown to train by input sample and ideal output
Terminate；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；Output sample when the training of each subnet
As shown in table 1.
The output sample of 1 network training of table
(2) training algorithm
BP network is trained using error back propagation (Backward Propagation) algorithm, and step can be concluded
It is as follows:
Step 1: a selected structurally reasonable network, is arranged the initial value of all Node B thresholds and connection weight.
Step 2: making following calculate to each input sample:
(a) forward calculation: to l layers of j unit
In formula,L layers of j unit information weighted sum when being calculated for n-th,For l layers of j units with it is previous
Connection weight between the unit i of layer (i.e. l-1 layers),For preceding layer (i.e. l-1 layers, number of nodes nl-1) unit i give
The working signal come；When i=0, enableFor the threshold value of l layers of j unit.
If the activation primitive of unit j is sigmoid function,
And
If neuron j belongs to the first hidden layer (l=1), have
If neuron j belongs to output layer (l=L), have
And ej(n)=xj(n)-oj(n)；
(b) retrospectively calculate error:
For output unit
To hidden unit
(c) weight is corrected:
η is learning rate.
Step 3: new sample or a new periodic samples are inputted, and until network convergence, the sample in each period in training
Input sequence is again randomly ordered.
BP algorithm seeks nonlinear function extreme value using gradient descent method, exists and falls into local minimum and convergence rate is slow etc.
Problem.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time shorter,
Network can be effectively inhibited and sink into local minimum.Its weighed value adjusting rate is selected as
Δ ω=(JTJ+μI)-1JTe
Wherein J is error to Jacobi (Jacobian) matrix of weight differential, and I is input vector, and e is error vector,
Variable μ is the scalar adaptively adjusted, for determining that study is completed according to Newton method or gradient method.
In system design, system model is one merely through the network being initialized, and weight needs basis using
The data sample obtained in journey carries out study adjustment, devises the self-learning function of system thus.Specify learning sample and
In the case where quantity, system can carry out self study, to constantly improve network performance.
When divulging information in interim grain storehouse, hot-air is passed through into interim place's silo.
The temperature of the hot-air meets:
Wherein, T is the temperature of hot-air, T0For the temperature in interim grain storehouse, s is the floor space of interim grain storehouse, and h is
The height of grain face, V in interim grain storehouse0For the volume of interim grain storehouse.
And the temperature of the hot-air also meets: T≤45 DEG C.That is, when the temperature of hot-air is more than or equal to 45 DEG C,
The temperature of hot-air no longer with the temperature in interim grain storehouse, the floor space of interim grain storehouse, the height of grain face in interim grain storehouse
The volume of degree and interim grain storehouse is related, and be held in 45 degrees Celsius it is constant.
Ventilation time in interim grain storehouse meets:
Wherein, η is the humidity of grain in interim grain storehouse, and e is the truth of a matter of natural logrithm, t0Maximum for interim grain storage is deposited
Store up the time.
That is ventilation time maximum time for being up to interim grain storage, i.e., start to lead to when grain just starts temporarily to store
Wind, until grain is handled upside down away.
But the minimum value of ventilation time rule of thumb obtains, the humidity with grain in interim grain storehouse, interim grain storage
Maximum storage time, the temperature in interim grain storehouse, the floor space of interim grain storehouse, the height of grain face in interim grain storehouse,
The volume of interim grain storehouse and the temperature of hot-air are related.
After ventilation process, the humidity general satisfaction of grain in interim grain storehouse: η≤10%.
State further is passed through to inert gas in silo provided by the invention below with reference to specific embodiment
The method of determination is illustrated.
The floor space of interim grain storehouse used is 10m2, it is highly 5m, the volume of silo is 50m3, temporarily to store for 24 hours.
The grain for choosing 10 groups of different parameters is tested, and the parameters of grain are as shown in table 2.
The parameters of 2 grain of table
| Grouping | Humidity η/% | Temperature in silo/DEG C | Grain face height/m |
| 1 | 15 | 25 | 1 |
| 2 | 16 | 25 | 1 |
| 3 | 14 | 25 | 2 |
| 4 | 9 | 25 | 2 |
| 5 | 18 | 25 | 3 |
| 6 | 8 | 25 | 3 |
| 7 | 19 | 25 | 4 |
| 8 | 21 | 25 | 4 |
| 9 | 23 | 25 | 5 |
| 10 | 25 | 25 | 5 |
When the ventilation state of 10 groups of grain and the temperature (being determined according to formula (1)) and ventilation for being passed through hot-air
Between (according to formula (2) determine) as shown in table 3.
The ventilation state of 3 each group grain of table is passed through hot air temperature and ventilation time
Grain after acquiring 10 groups of ventilations is detected, and determines grain moisture, and the results are shown in Table 4.
4 grain moisture testing result of table
| Grouping | Humidity/% |
| 1 | 9.8 |
| 2 | 9.6 |
| 3 | 8.5 |
| 4 | 9.0 |
| 5 | 8.4 |
| 6 | 8.0 |
| 7 | 7.5 |
| 8 | 7.2 |
| 9 | 7.0 |
| 10 | 6.9 |
By above it was determined that the temperature of the ventilation state and hot-air that determine according to the present invention and ventilation time carry out
The humidity of grain after ventilation process is lower than 10%, is conducive to the storage of grain.
Grain storage method of the present invention based on temperature and humidity monitor, can be based on the interim storage of BP neural network control
Ventilation state in silo, so that grain keeps dry and comfortable in interim grain storehouse, it is easily stored.It can also be in logical in interim grain storehouse
When wind state, the temperature of hot wind air is controlled, improves the drafting efficiency of silo, moreover it is possible to when controlling the ventilation in interim grain storehouse
Between, further increase drafting efficiency.
Although the embodiments of the present invention have been disclosed as above, but its is not only in the description and the implementation listed
With it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can be easily
Realize other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously unlimited
In specific details.
Claims (8)
1. a kind of grain storage method based on temperature and humidity monitor, which is characterized in that when progress grain is temporarily stored in a warehouse, be based on BP
Neural network determines the ventilation state in interim grain storehouse, specifically comprises the following steps:
Step 1: the temperature according to the sampling period, by the humidity of grain in the interim grain storehouse of sensor measurement, in interim grain storehouse
It spends, the height of grain face in interim grain storehouse；
Step 2: determining the input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5}；Wherein, x1It is interim
The humidity of grain, x in grain storehouse2For the temperature in interim grain storehouse, x3For the height of grain face in interim grain storehouse, x4It is interim
The maximum storage time of grain storage, x5For the floor space of interim grain storehouse；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING to middle layer, the neuron of middle layer are m；
Step: 4: obtaining output layer neuron vector o={ o1}；Wherein, o1It is described defeated for the ventilation state in interim grain storehouse
Layer neuron value is outWork as o1When being 1, the ventilation state in interim grain storehouse is in the open state, works as o1When being 0,
Ventilation state in interim grain storehouse is in close state.
2. the grain storage method based on temperature and humidity monitor as described in claim 1, which is characterized in that the interim grain storehouse
When interior ventilation, hot-air is passed through into interim place's silo.
3. the grain storage method based on temperature and humidity monitor as claimed in claim 2, which is characterized in that the temperature of the hot-air
Degree meets:
Wherein, T is the temperature of hot-air, T0For the temperature in interim grain storehouse, s is the floor space of interim grain storehouse, and h is interim
The height of grain face, V in grain storehouse0For the volume of interim grain storehouse.
4. the grain storage method based on temperature and humidity monitor as claimed in claim 3, which is characterized in that the temperature of the hot-air
Degree also meets: T≤45 DEG C.
5. the grain storage method based on temperature and humidity monitor as claimed in claim 4, which is characterized in that in interim grain storehouse
Ventilation time meets:
Wherein, η is the humidity of grain in interim grain storehouse, and e is the truth of a matter of natural logrithm, t0For interim grain storage maximum storage when
Between.
6. the grain storage method based on temperature and humidity monitor as described in any one of claim 1-5, which is characterized in that institute
The neuron m for stating middle layer meets:Wherein n is input layer number, and p is output node layer
Number.
7. the grain storage method based on temperature and humidity monitor as described in any of claims 6, which is characterized in that in described
The excitation function of interbed and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
8. the grain storage method based on temperature and humidity monitor as claimed in claim 7, which is characterized in that pass through ventilation process
Afterwards, the humidity of grain meets in interim grain storehouse: η≤10%.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810978665.8A CN109062296B (en) | 2018-08-27 | 2018-08-27 | Grain storage method based on temperature and humidity monitoring |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810978665.8A CN109062296B (en) | 2018-08-27 | 2018-08-27 | Grain storage method based on temperature and humidity monitoring |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109062296A true CN109062296A (en) | 2018-12-21 |
| CN109062296B CN109062296B (en) | 2020-12-11 |
Family
ID=64757190
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810978665.8A Expired - Fee Related CN109062296B (en) | 2018-08-27 | 2018-08-27 | Grain storage method based on temperature and humidity monitoring |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109062296B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109755687A (en) * | 2019-01-16 | 2019-05-14 | 吉林大学 | It is a kind of battery accurately to be heated using graphene film and cooling system and its control method |
| CN110424207A (en) * | 2019-08-13 | 2019-11-08 | 吉林大学 | A kind of road heat collection underground energy-accumulation double temperature differential grade flow control system and control method |
| CN110458361A (en) * | 2019-08-14 | 2019-11-15 | 中储粮成都储藏研究院有限公司 | Grain quality index prediction technique based on BP neural network |
| CN110631640A (en) * | 2019-10-29 | 2019-12-31 | 云南师范大学 | Intelligent monitoring system and method for storage tobacco leaf mildew state based on Internet of things |
| CN110955289A (en) * | 2019-12-09 | 2020-04-03 | 王英 | Grain storage method based on temperature and humidity monitoring |
| CN115454182A (en) * | 2022-10-10 | 2022-12-09 | 中南林业科技大学 | Grain storage method, system, equipment and storage medium |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105075546A (en) * | 2015-09-22 | 2015-11-25 | 北京之云科技有限公司 | Barn ventilating system and method |
| CN105259064A (en) * | 2015-10-13 | 2016-01-20 | 辽宁工业大学 | Sliding abrasion testing device and calculation method with sample surface crystalline grains having preferred orientation |
| CN106352680A (en) * | 2016-10-28 | 2017-01-25 | 佳木斯大学 | Agaric double-section drying device and drying control method thereof |
| CN106818080A (en) * | 2017-01-15 | 2017-06-13 | 阜阳职业技术学院 | Huanghuai Area corn drying generator storehouse and its drying process method |
| CN107678410A (en) * | 2017-09-30 | 2018-02-09 | 中国农业大学 | It is a kind of towards the intelligent control method of greenhouse, system and controller |
| CN108022071A (en) * | 2017-12-05 | 2018-05-11 | 深圳春沐源控股有限公司 | Storage management method and Warehouse Management System |
| CN207505482U (en) * | 2017-10-19 | 2018-06-19 | 河南创卓仓储科技有限公司 | It is a kind of to prevent the silo that grain is affected by the external environment |
| CN108288123A (en) * | 2018-01-25 | 2018-07-17 | 武汉理工大学 | Distribution switchgear reliability estimation method based on BP neural network |
| CN108399763A (en) * | 2018-03-07 | 2018-08-14 | 辽宁工业大学 | A kind of intersection traffic Signalized control algorithm based on neural network |
-
2018
- 2018-08-27 CN CN201810978665.8A patent/CN109062296B/en not_active Expired - Fee Related
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105075546A (en) * | 2015-09-22 | 2015-11-25 | 北京之云科技有限公司 | Barn ventilating system and method |
| CN105259064A (en) * | 2015-10-13 | 2016-01-20 | 辽宁工业大学 | Sliding abrasion testing device and calculation method with sample surface crystalline grains having preferred orientation |
| CN106352680A (en) * | 2016-10-28 | 2017-01-25 | 佳木斯大学 | Agaric double-section drying device and drying control method thereof |
| CN106818080A (en) * | 2017-01-15 | 2017-06-13 | 阜阳职业技术学院 | Huanghuai Area corn drying generator storehouse and its drying process method |
| CN107678410A (en) * | 2017-09-30 | 2018-02-09 | 中国农业大学 | It is a kind of towards the intelligent control method of greenhouse, system and controller |
| CN207505482U (en) * | 2017-10-19 | 2018-06-19 | 河南创卓仓储科技有限公司 | It is a kind of to prevent the silo that grain is affected by the external environment |
| CN108022071A (en) * | 2017-12-05 | 2018-05-11 | 深圳春沐源控股有限公司 | Storage management method and Warehouse Management System |
| CN108288123A (en) * | 2018-01-25 | 2018-07-17 | 武汉理工大学 | Distribution switchgear reliability estimation method based on BP neural network |
| CN108399763A (en) * | 2018-03-07 | 2018-08-14 | 辽宁工业大学 | A kind of intersection traffic Signalized control algorithm based on neural network |
Non-Patent Citations (4)
| Title |
|---|
| 丁超: "储粮机械通风技术拓展研究", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
| 中华人民共和国国家质量监督检验检疫总局: "《中国人民共和国国家标准》", 26 July 2007 * |
| 国家粮食局: "《中国人民共和国粮食行业标准》", 13 May 2002 * |
| 孙彪瑞: "基于GA_BP神经网络的粮仓通风控制研究", 《河南工业大学学报（自然科学版）》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109755687A (en) * | 2019-01-16 | 2019-05-14 | 吉林大学 | It is a kind of battery accurately to be heated using graphene film and cooling system and its control method |
| CN109755687B (en) * | 2019-01-16 | 2023-10-27 | 吉林大学 | System for precisely heating and cooling battery by using graphene film and control method thereof |
| CN110424207A (en) * | 2019-08-13 | 2019-11-08 | 吉林大学 | A kind of road heat collection underground energy-accumulation double temperature differential grade flow control system and control method |
| CN110424207B (en) * | 2019-08-13 | 2020-06-12 | 吉林大学 | Road heat collection underground energy storage double-temperature-difference step flow control system and control method |
| CN110458361A (en) * | 2019-08-14 | 2019-11-15 | 中储粮成都储藏研究院有限公司 | Grain quality index prediction technique based on BP neural network |
| CN110631640A (en) * | 2019-10-29 | 2019-12-31 | 云南师范大学 | Intelligent monitoring system and method for storage tobacco leaf mildew state based on Internet of things |
| CN110955289A (en) * | 2019-12-09 | 2020-04-03 | 王英 | Grain storage method based on temperature and humidity monitoring |
| CN115454182A (en) * | 2022-10-10 | 2022-12-09 | 中南林业科技大学 | Grain storage method, system, equipment and storage medium |
| CN115454182B (en) * | 2022-10-10 | 2024-04-19 | 中南林业科技大学 | Grain storage method, system, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109062296B (en) | 2020-12-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109062296A (en) | A kind of grain storage method based on temperature and humidity monitor | |
| US20200279149A1 (en) | Method for reinforcement learning using virtual environment generated by deep learning | |
| CN110378435B (en) | Apple leaf disease identification method based on convolutional neural network | |
| Seginer | Some artificial neural network applications to greenhouse environmental control | |
| CN103105246A (en) | Greenhouse environment forecasting feedback method of back propagation (BP) neural network based on improvement of genetic algorithm | |
| Aghajani et al. | Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt | |
| CN109214579B (en) | BP neural network-based saline-alkali soil stability prediction method and system | |
| Çakır et al. | Yield prediction of wheat in south-east region of Turkey by using artificial neural networks | |
| CN110728349A (en) | Optimization method of mixed bat algorithm and optimization method of multilayer perceptron | |
| CN113777931B (en) | Icing wing type pneumatic model construction method, device, equipment and medium | |
| CN112255095A (en) | Soil stress-strain relation determining method | |
| CN108832663A (en) | The prediction technique and equipment of the generated output of micro-capacitance sensor photovoltaic generating system | |
| Enitan et al. | Food processing optimization using evolutionary algorithms | |
| CN110458361A (en) | Grain quality index prediction technique based on BP neural network | |
| CN109508498A (en) | Rubber shock absorber formula designing system and method based on BP artificial neural network | |
| CN107511823A (en) | The method of robot manipulating task track optimizing analysis | |
| Morimoto et al. | Optimization of a fuzzy controller for fruit storage using neural networks and genetic algorithms | |
| CN114117787A (en) | Short-term wind power prediction method based on SSA (simple sequence analysis) optimization BP (back propagation) neural network | |
| CN107016440B (en) | The multiresolution deep neural network intelligent diagnosing method of machine driving failure | |
| CN109781951B (en) | Fishpond water quality monitoring system and monitoring method | |
| CN110910263A (en) | Grain condition monitoring method based on GA-BP neural network | |
| Choi et al. | Comparison of artificial neural network and empirical models to determine daily reference evapotranspiration | |
| Fernández et al. | GMDH algorithm implemented in the intelligent identification of a bioprocess | |
| CN114202063A (en) | Fuzzy neural network greenhouse temperature prediction method based on genetic algorithm optimization | |
| Banakar et al. | Genetic algorithm optimizing approach in rosa petals hot air dryer |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20201026 Address after: 121001, 169 street, Guta District, Liaoning, Jinzhou Applicant after: LIAONING University OF TECHNOLOGY Address before: 121007 No. 5 Changbaishan Road Section, Jinzhou Economic and Technological Development Zone, Liaoning Province Applicant before: CHINA ETS INTERNATIONAL LOGISTICS Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20201211 Termination date: 20210827 |