CN109781951A - A kind of fishpond water quality monitoring system and monitoring method - Google Patents
A kind of fishpond water quality monitoring system and monitoring method Download PDFInfo
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 title claims abstract description 142
- 238000000034 method Methods 0.000 title claims abstract description 28
- 238000012544 monitoring process Methods 0.000 title claims abstract description 27
- 241000251468 Actinopterygii Species 0.000 claims abstract description 176
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims abstract description 69
- 229910052760 oxygen Inorganic materials 0.000 claims abstract description 69
- 239000001301 oxygen Substances 0.000 claims abstract description 69
- 238000001514 detection method Methods 0.000 claims abstract description 23
- 238000013528 artificial neural network Methods 0.000 claims abstract description 12
- 238000012360 testing method Methods 0.000 claims abstract description 11
- 230000008859 change Effects 0.000 claims description 20
- 210000002569 neuron Anatomy 0.000 claims description 19
- 238000005259 measurement Methods 0.000 claims description 5
- 230000009977 dual effect Effects 0.000 claims description 2
- 230000005284 excitation Effects 0.000 claims description 2
- 238000013507 mapping Methods 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims description 2
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 238000011897 real-time detection Methods 0.000 abstract description 7
- 238000012549 training Methods 0.000 description 11
- 230000006870 function Effects 0.000 description 7
- 230000008569 process Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000009360 aquaculture Methods 0.000 description 2
- 244000144974 aquaculture Species 0.000 description 2
- 238000009395 breeding Methods 0.000 description 2
- 230000001488 breeding effect Effects 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000002253 acid Substances 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000010923 batch production Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000011478 gradient descent method Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 239000011159 matrix material Substances 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
- 230000000737 periodic effect Effects 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000003908 quality control method Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
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Abstract
The present invention discloses a kind of fishpond water quality monitoring system, comprising: detection module is used to detect water temperature in fish pond, pH value, turbidity and real-time oxygen content；Microprocessor is connect with the detection module, for receiving the detection data of the detection module, and output test result；Control module is connect with the microprocessor, for receiving the testing result of microprocessor output, and is controlled fish pond and is changed water, fills the water and oxygen processed.The present invention also provides a kind of fishpond water quality monitoring method, can water temperature in real-time detection fish pond, pH value, turbidity and real-time oxygen content, and determine that water state, charging state and oxygen condition processed are changed in fish pond based on BP neural network, it is ensured that fishpond water quality is good, is easy to cultivate.
Description
Technical field
The present invention relates to fishpond water quality monitoring technical fields, and more particularly, the present invention relates to a kind of monitorings of fishpond water quality
System and monitoring method.
Background technique
To meet the needs of growing, along with the progress of science and technology, fishery cultivating is just towards extensive, batch production
Direction is developed, and carrying out water quality detection to breeding water body can be reasonably selection Culture style, monitoring industrial processes and improvement water
Matter environment provides the foundation of science, and carry out cultivation water evaluation, prediction and effective exploitation using water resource it is important before
It mentions.It in industrial aquaculture, is made of multiple breeding pools, and region is broad, in order to carry out comprehensive prison in real time to water quality
Control needs to dispose multiple observation points and carries out long-term continuous on-line checking and record.
Traditional water quality detection method be mainly artificial sample analysis, this mode sample frequency is low, large labor intensity without
Method realizes real time monitoring, cannot react the continuous dynamic change of water quality.
Summary of the invention
It, can be in real-time detection fish pond it is an object of the invention to design and develop a kind of fishpond water quality monitoring system
Water temperature, pH value, turbidity and real-time oxygen content, and control fish pond and be in a good cultivation state.
Another object of the present invention is to have designed and developed a kind of fishpond water quality monitoring method, being capable of real-time detection fish pond
Interior water temperature, pH value, turbidity and real-time oxygen content, and determine that water state, charging state are changed in fish pond based on BP neural network
And oxygen condition processed, it is ensured that fishpond water quality is good, is easy to cultivate.
The present invention can also be according to the pH value and turbidity in fish pond, the accurate quantity of exchanged water for controlling fish pond, it is ensured that fishpond water quality
Well, it is easy to cultivate.
Technical solution provided by the invention are as follows:
A kind of fishpond water quality monitoring system, comprising:
Detection module is used to detect water temperature in fish pond, pH value, turbidity and real-time oxygen content；
Microprocessor is connect with the detection module, for receiving the detection data of the detection module, and is exported
Testing result；
Control module is connect with the microprocessor, for receiving the testing result of the microprocessor output, and
Control fish pond is changed water, is filled the water and oxygen processed.
Preferably, the detection module includes:
Temperature sensor, for detecting water temperature in fish pond；
PH sensor, for detecting the pH value in fish pond；
Turbidity measurement instrument, for detecting the turbidity in fish pond；
Oxygen content testing instrument, for detecting the real-time oxygen content in fish pond.
A kind of fishpond water quality monitoring method acquires water temperature, pH value, turbidity and real-time oxygen content in fish pond, and is based on
BP neural network determines that water state, charging state and oxygen condition processed are changed in fish pond, specifically comprises the following steps:
Step 1: according to the sampling period, by water temperature in sensor measurement fish pond, pH value, turbidity and oxygen-containing in real time
Amount；
Step 2: determining input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5}；Wherein, x1
For water temperature T in fish pond, x2For the acidity-basicity ph in fish pond, x3For the turbidity ζ, x in fish pond4For real-time the oxygen content ω, x in fish pond5
For content n biological in fish pond；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING is to hidden layer, the neuron of hidden layer is m；
Step 4: obtaining output layer neuron vector o={ o1,o2,o3}；Wherein, o1Water state, o are changed for fish pond2For fish
Pool charging state, o3For fish pond oxygen condition, the output layer neuron value isK is output layer neuron sequence
Number, k={ 1,2,3 } works as okWhen=1, corresponding working state is opened, and works as okWhen=0, corresponding working state is closed.
Preferably,
As the turbidity ζ≤0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, o1=o2=0；
When any one of the acidity-basicity ph < 6.5 in fish pond, pH > 8.5 and the turbidity in fish pond ζ > 0.85mg/L at
Immediately, o1=o2=1.
Preferably,
As the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water, and change
Water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
As the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water, and change
Water meets:
Preferably,
As the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water, and change
Water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
As the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water, and change
Water meets:
Preferably,
As the turbidity ζ > 0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, fish pond is changed
Water, and quantity of exchanged water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm.
Preferably
When 15≤T≤30 DEG C,
As the real-time oxygen content ω < 2mg/L in fish pond, o3=1, start oxygen processed in fish pond, until ω >=3mg/L,
Stop oxygen processed；
When 30 DEG C of T >,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed；
When 15 DEG C of T <,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed.
Preferably
When fish pond horizontal plane is lower than floor level face, o1=0, o2=1, it is filled the water into fish pond, so that horizontal in fish pond
Face is lower than highest level face.
Preferably, the neuron of the hidden layer is 8, and the excitation function of the hidden layer and the output layer is all made of S
Type function fj(x)=1/ (1+e-x)。
It is of the present invention the utility model has the advantages that
(1) fishpond water quality provided by the invention monitors system, can water temperature in real-time detection fish pond, pH value is muddy
Degree and real-time oxygen content, and control fish pond and be in a good cultivation state.
(2) fishpond water quality monitoring method provided by the invention, can water temperature in real-time detection fish pond, pH value is muddy
Degree and real-time oxygen content, and determine that water state, charging state and oxygen condition processed are changed in fish pond based on BP neural network, it is ensured that fish
Pool water quality is good, is easy to cultivate.Can also be according to the pH value and turbidity in fish pond, the accurate quantity of exchanged water for controlling fish pond, it is ensured that fish
Pool water quality is good, is easy to cultivate.
Specific embodiment
The present invention is described in further detail below, to enable those skilled in the art's refer to the instruction text can
Implement accordingly.
The present invention provides a kind of fishpond water quality monitoring system, comprising: detection module is used to detect water temperature in fish pond, acid
Basicity, turbidity and real-time oxygen content；Microprocessor is connect with the detection module, for receiving the detection module
Detection data, and output test result；Control module is connect with the microprocessor, defeated for receiving the microprocessor
Testing result out, and control fish pond and change water, fill the water and oxygen processed.
The detection module includes: temperature sensor, for detecting water temperature in fish pond；PH sensor, for detecting fish
The pH value on the pool；Turbidity measurement instrument, for detecting the turbidity in fish pond；Oxygen content testing instrument, for detecting containing in real time for fish pond
Oxygen amount.
Fishpond water quality provided by the invention monitors system, can water temperature in real-time detection fish pond, pH value, turbidity
With real-time oxygen content, and controls fish pond and be in a good cultivation state.
The present invention also provides a kind of fishpond water quality monitoring method, water temperature in fish pond is acquired, pH value, turbidity and in real time
Oxygen content, and determine that water state, charging state and oxygen condition processed are changed in fish pond based on BP neural network, specifically include following step
It is rapid:
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 network architecture that the present invention uses by up of three-layer, first layer is input layer, total n node, corresponding
Indicate that the n detection signal in fish pond, these signal parameters are provided by data preprocessing module；The second layer is hidden layer, total m section
Point is determined in an adaptive way by the training process of network；Third layer is output layer, total p node, is needed by system is practical
It is being exported 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=3, hidden layer number of nodes m=8.
5 parameters of input layer respectively indicate are as follows: x1For water temperature T in fish pond, x2For the acidity-basicity ph in fish pond, x3For fish pond
Turbidity ζ, x4For real-time the oxygen content ω, x in fish pond5For content n biological in fish pond；
3 parameters of output layer respectively indicate are as follows: o1Water state, o are changed for fish pond2For fish pond charging state, o3For fish pond oxygen
State, the output layer neuron value areK is output layer neuron sequence number, and k={ 1,2,3 } works as okWhen=1,
Corresponding working state is opened, and o is worked askWhen=0, corresponding working state is closed.
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
Export the connection weight between node layer k.
(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, to forming, when all reality outputs of network and its consistent ideal output, is shown to instruct by input sample and ideal output
White silk terminates；Otherwise, by correcting weight, keep the ideal output of network consistent with reality output；Output sample when the training of each subnet
This is 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 return
It receives 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 it 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
The problems such as.A kind of more efficiently algorithm is Levenberg-Marquardt optimization algorithm, it makes the e-learning time more
It is short, 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.
(1) as the turbidity ζ≤0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, o1=o2=0；
When any one of the acidity-basicity ph < 6.5 in fish pond, pH > 8.5 and the turbidity in fish pond ζ > 0.85mg/L at
Immediately, o1=o2=1.
It specifically includes:
(1) as the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water,
And quantity of exchanged water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
(2) as the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water,
And quantity of exchanged water meets:
(3) as the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water,
And quantity of exchanged water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
(4) as the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water,
And quantity of exchanged water meets:
(5) as the turbidity ζ > 0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, fish pond is carried out
Water is changed, and quantity of exchanged water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm.
(2) it is carried out according to the oxygen content in fish pond control as follows:
(1) when 15≤T≤30 DEG C,
As the real-time oxygen content ω < 2mg/L in fish pond, o3=1, start oxygen processed in fish pond, until ω >=3mg/L,
Stop oxygen processed；
(2) when 30 DEG C of T >,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed；
(3) when 15 DEG C of T <,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed.
(3) when fish pond horizontal plane is lower than floor level face, o1=0, o2=1, it is filled the water into fish pond, so that in fish pond
Horizontal plane is lower than highest level face.
Further the method provided by the invention to engine technology state is carried out below with reference to specific embodiment
Explanation.
The present invention specifically chooses 10 groups of different qualities and carries out fish culture, and pond volume is 2m3, specific experiment data are such as
Shown in table 2.
2 different quality of table cultivates data
| Serial number | Water temperature (DEG C) | PH value | Turbidity (mg/L) | Real-time oxygen content (mg/L) | Fish quantity |
| 1 | 5 | 7 | 0.25 | 3.5 | 30 |
| 2 | 10 | 6.5 | 0.96 | 1.2 | 40 |
| 3 | 15 | 9.6 | 0.85 | 2.8 | 25 |
| 4 | 20 | 9.0 | 0.95 | 1.9 | 45 |
| 5 | 22 | 4.2 | 0.43 | 1.9 | 35 |
| 6 | 25 | 3.0 | 1.05 | 2.5 | 30 |
| 7 | 28 | 7.5 | 0.92 | 3.1 | 45 |
| 8 | 32 | 8.0 | 0.64 | 2.3 | 40 |
| 9 | 35 | 5.6 | 1.15 | 1.6 | 35 |
| 10 | 40 | 8.9 | 0.98 | 1.8 | 40 |
Water is not handled, 12 as a child observed the state of fish in pond, and concrete outcome is as shown in Table 3.
The state of fish when table 3 is without water process
When not handling pond, it may occur that fry is dead, is unfavorable for aquaculture.
Pond is handled according to the method described in the present invention, the state of specific processing data and fish is as shown in table 4
The state of table 2 water process data and fish
| Serial number | Quantity of exchanged water | Whether oxygen processed | Fish quantity |
| 1 | 0 | It is no | 30 |
| 2 | 0.12V0 | It is | 40 |
| 3 | 0.11V0 | It is no | 25 |
| 4 | 0.14V0 | It is | 45 |
| 5 | 0.25V0 | It is no | 35 |
| 6 | 0.34V0 | It is no | 30 |
| 7 | 0.07V0 | It is no | 45 |
| 8 | 0 | It is no | 40 |
| 9 | 0.33V0 | It is | 35 |
| 10 | 0.13V0 | It is | 40 |
From the above results, by the real-time adjusting to water quality of pond, it can guarantee fry normal growth, Bu Huifa
Life and death is died, and is easy to cultivate.
Fishpond water quality monitoring method provided by the invention, can water temperature in real-time detection fish pond, pH value, turbidity
With real-time oxygen content, and determine that water state, charging state and oxygen condition processed are changed in fish pond based on BP neural network, it is ensured that fish pond
Water quality is good, is easy to cultivate.Can also be according to the pH value and turbidity in fish pond, the accurate quantity of exchanged water for controlling fish pond, it is ensured that fish pond
Water quality is good, is easy to cultivate.
Although the embodiments of the present invention have been disclosed as above, but its institute not only in the description and the implementation
Column use, it can be fully applied to various fields suitable for the present invention, for those skilled in the art, can hold
It changes places and realizes other modification, therefore without departing from the general concept defined in the claims and the equivalent scope, the present invention is simultaneously
It is not limited to specific details.
Claims (10)
1. a kind of fishpond water quality monitors system characterized by comprising
Detection module is used to detect water temperature in fish pond, pH value, turbidity and real-time oxygen content；
Microprocessor is connect with the detection module, for receiving the detection data of the detection module, and exports detection knot
Fruit；
Control module is connect with the microprocessor, for receiving the testing result of the microprocessor output, and controls fish
The pool changes water, fills the water and oxygen processed.
2. fishpond water quality as described in claim 1 monitors system, which is characterized in that the detection module includes:
Temperature sensor, for detecting water temperature in fish pond；
PH sensor, for detecting the pH value in fish pond；
Turbidity measurement instrument, for detecting the turbidity in fish pond；
Oxygen content testing instrument, for detecting the real-time oxygen content in fish pond.
3. a kind of fishpond water quality monitoring method, which is characterized in that acquisition fish pond in water temperature, pH value, turbidity and in real time it is oxygen-containing
Amount, and determine that water state, charging state and oxygen condition processed are changed in fish pond based on BP neural network, specifically comprise the following steps:
Step 1: passing through water temperature in sensor measurement fish pond, pH value, turbidity and real-time oxygen content according to the sampling period；
Step 2: determining input layer vector x={ x of three layers of BP neural network1,x2,x3,x4,x5}；Wherein, x1For fish
Water temperature T in the pool, x2For the acidity-basicity ph in fish pond, x3For the turbidity ζ, x in fish pond4For real-time the oxygen content ω, x in fish pond5For fish pond
Interior biology content n；
Step 3: the input layer DUAL PROBLEMS OF VECTOR MAPPING is to hidden layer, the neuron of hidden layer is m；
Step 4: obtaining output layer neuron vector o={ o1,o2,o3}；Wherein, o1Water state, o are changed for fish pond2For fish pond water filling
State, o3For fish pond oxygen condition, the output layer neuron value isK be output layer neuron sequence number, k=1,
2,3 }, work as okWhen=1, corresponding working state is opened, and works as okWhen=0, corresponding working state is closed.
4. fishpond water quality monitoring method as claimed in claim 3, which is characterized in that
As the turbidity ζ≤0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, o1=o2=0；
When any one of the acidity-basicity ph < 6.5 in fish pond, pH > 8.5 and the turbidity in fish pond ζ > 0.85mg/L are set up,
o1=o2=1.
5. fishpond water quality monitoring method as claimed in claim 4, which is characterized in that
As the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water, and quantity of exchanged water
Meet:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
As the acidity-basicity ph < 6.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water, and quantity of exchanged water
Meet:
6. fishpond water quality monitoring method as claimed in claim 4, which is characterized in that
As the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ > 0.85mg/L in fish pond, fish pond is carried out to change water, and quantity of exchanged water
Meet:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm；
As the acidity-basicity ph > 8.5 in fish pond and the turbidity ζ≤0.85mg/L in fish pond, fish pond is carried out to change water, and quantity of exchanged water
Meet:
7. fishpond water quality monitoring method as claimed in claim 4, which is characterized in that
As the turbidity ζ > 0.85mg/L in pH value 6.5≤pH≤8.5 in fish pond and fish pond, fish pond is carried out to change water, and change
Water meets:
Wherein, VeFor quantity of exchanged water, V0For fish pond initial water content, e is the truth of a matter of natural logrithm.
8. fishpond water quality monitoring method as claimed in claim 3, which is characterized in that
When 15≤T≤30 DEG C,
As the real-time oxygen content ω < 2mg/L in fish pond, o3=1, start oxygen processed in fish pond, until ω >=3mg/L, stops system
Oxygen；
When 30 DEG C of T >,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed；
When 15 DEG C of T <,
When the real-time oxygen content in fish pondWhen, o3=1, start oxygen processed in fish pond, untilStop oxygen processed.
9. fishpond water quality monitoring method as claimed in claim 3, which is characterized in that
When fish pond horizontal plane is lower than floor level face, o1=0, o2=1, it is filled the water into fish pond, so that fish pond inner horizontal is lower than
Highest level face.
10. the fishpond water quality monitoring method as described in any one of claim 3-9, which is characterized in that the mind of the hidden layer
It is 8 through member, the excitation function of the hidden layer and the output layer is all made of S type function fj(x)=1/ (1+e-x)。
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| CN118275639A (en) * | 2024-05-30 | 2024-07-02 | 华南智能机器人创新研究院 | Cultivation area water circulation control method and device based on water quality monitoring |
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