CN109062296A - A kind of grain storage method based on temperature and humidity monitor - Google Patents
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- 238000003860 storage Methods 0.000 title claims abstract description 66
- 238000000034 method Methods 0.000 title claims abstract description 30
- 238000009423 ventilation Methods 0.000 claims abstract description 45
- 210000002569 neuron Anatomy 0.000 claims abstract description 21
- 238000013528 artificial neural network Methods 0.000 claims abstract description 15
- 238000005070 sampling Methods 0.000 claims abstract description 3
- 238000012544 monitoring process Methods 0.000 claims abstract 10
- 230000006870 function Effects 0.000 claims description 9
- 230000005284 excitation Effects 0.000 claims description 2
- 235000013339 cereals Nutrition 0.000 description 117
- 238000012549 training Methods 0.000 description 11
- 241000209094 Oryza Species 0.000 description 7
- 235000007164 Oryza sativa Nutrition 0.000 description 7
- 235000009566 rice Nutrition 0.000 description 7
- 230000008569 process Effects 0.000 description 6
- 238000013461 design Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 2
- 241000238631 Hexapoda Species 0.000 description 1
- 241000607479 Yersinia pestis Species 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
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- 238000001514 detection method Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 239000011261 inert gas Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004806 packaging method and process Methods 0.000 description 1
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
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- 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
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Abstract
本发明公开一种基于温湿度监控的粮食仓储方法，在进行粮食临时仓储时，基于BP神经网络确定临时储粮仓内的通风状态，具体包括步骤1：按照采样周期，通过传感器测量临时储粮仓内粮食的湿度，临时储粮仓内的温度，临时储粮仓内粮面的高度；步骤2：确定三层BP神经网络的输入层神经元向量；步骤3：所述输入层向量映射到中间层，中间层的神经元为m个；步骤:4：得到输出层神经元向量。能够基于温湿度监控的粮食仓储方法，能够基于BP神经网络控制临时储粮仓内的通风状态，使得临时储粮仓内粮食保持干爽，易于储存，还能在临时储粮仓处于通风状态时，控制热风空气的温度，提高粮仓的通风效率，还能控制临时储粮仓内的通风时间，进一步提高通风效率。The invention discloses a grain storage method based on temperature and humidity monitoring. When grain is temporarily stored, the ventilation state in the temporary grain storage bin is determined based on a BP neural network, which specifically includes step 1: measuring the temperature in the temporary grain storage bin through a sensor according to the sampling period. The humidity of the grain, the temperature in the temporary storage bin, the height of the grain surface in the temporary storage bin; step 2: determine the input layer neuron vector of the three-layer BP neural network; step 3: the input layer vector is mapped to the middle layer, the middle The number of neurons in the layer is m; Step: 4: Obtain the neuron vector of the output layer. The grain storage method based on temperature and humidity monitoring can control the ventilation state in the temporary grain storage bin based on the BP neural network, so that the grain in the temporary grain storage bin can be kept dry and easy to store. It can also control the hot air when the temporary grain storage bin is in a ventilated state. The temperature can improve the ventilation efficiency of the granary, and it can also control the ventilation time in the temporary storage silo to further improve the ventilation 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)
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| 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 |
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| 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 moldy state of stored tobacco leaves based on Internet of Things |
| CN110955289A (en) * | 2019-12-09 | 2020-04-03 | 王英 | Grain storage method based on temperature and humidity monitoring |
| CN114998823A (en) * | 2022-04-28 | 2022-09-02 | 河南科技大学 | Historical grain storage state judgment method based on temperature field cloud picture |
| 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 |
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