CN106614234B - A kind of energy-saving and environment-friendly cultivating system - Google Patents
A kind of energy-saving and environment-friendly cultivating system Download PDFInfo
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- CN106614234B CN106614234B CN201710028103.2A CN201710028103A CN106614234B CN 106614234 B CN106614234 B CN 106614234B CN 201710028103 A CN201710028103 A CN 201710028103A CN 106614234 B CN106614234 B CN 106614234B
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- aquaculture pond
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- 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
- A01K63/00—Receptacles for live fish, e.g. aquaria; Terraria
- A01K63/003—Aquaria; Terraria
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- 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
- A01K63/00—Receptacles for live fish, e.g. aquaria; Terraria
- A01K63/04—Arrangements for treating water specially adapted to receptacles for live fish
- A01K63/042—Introducing gases into the water, e.g. aerators, air pumps
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- Life Sciences & Earth Sciences (AREA)
- Environmental Sciences (AREA)
- Marine Sciences & Fisheries (AREA)
- Animal Husbandry (AREA)
- Biodiversity & Conservation Biology (AREA)
- Apparatus Associated With Microorganisms And Enzymes (AREA)
- Medicines Containing Material From Animals Or Micro-Organisms (AREA)
Abstract
The present invention provides a kind of cultivating systems of high-efficient energy-saving environment friendly, including aquaculture pond ontology, the Shrimp waste aquaculture pond and fish culture pond that useful partition separates is arranged in aquaculture pond body upper part, the lower part of aquaculture pond ontology is settling zone, settling zone is communicated with dredge pump, and the sewage of dredge pump discharge sends the top of Shrimp waste aquaculture pond back to after effluent treatment plant purifies；The side in fish culture pond is communicated with the oxygenation blower for being sent into oxygen to aquaculture pond ontology.The invention has the benefit that will be re-fed into the Shrimp waste aquaculture pond relatively low to water quality requirement by purified sewage by separately cultivating Shrimp waste and fish, improve cultivation production capacity, effectively recycling culture resources, energy conservation and environmental protection.
Description
Technical field
The present invention relates to aquaculture fields, and in particular to a kind of energy-saving and environment-friendly cultivating system.
Background technique
With the raising of mariculture technology level and the expansion of the market demand, I crosses aquaculture technology and has obtained rapidly
Development.In aquaculture process, a large amount of breeding wastewater can be generated, most of aquaculture is useless by these cultivation at present
Water outlet, rather than the method recycled is used, biggish waste is caused, cultivation production capacity is limited.
Summary of the invention
In view of the above-mentioned problems, the present invention provides a kind of energy-saving and environment-friendly cultivating system.
The purpose of the present invention is realized using following technical scheme:
A kind of energy-saving and environment-friendly cultivating system, including aquaculture pond ontology, aquaculture pond body upper part are arranged useful partition and separate
Shrimp waste aquaculture pond and fish culture pond, the lower part of aquaculture pond ontology be settling zone, settling zone is communicated with dredge pump, dredge pump
The sewage of discharge sends the top of Shrimp waste aquaculture pond back to after effluent treatment plant purifies；The side in fish culture pond is communicated with
For being sent into the oxygenation blower of oxygen to aquaculture pond ontology.
The invention has the benefit that by separately cultivating Shrimp waste and fish, it will be by purified sewage again
It is sent into the Shrimp waste aquaculture pond relatively low to water quality requirement, improves cultivation production capacity, effectively recycling culture resources, energy-saving ring
It protects.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is overall structure diagram of the invention；
Fig. 2 is the structural block diagram of fail analysis device.
Appended drawing reference:
Partition -1；Shrimp waste aquaculture pond -2；Fish culture pond -3；Settling zone -4；Dredge pump -5；Effluent treatment plant -6；
Oxygenation blower -7；Ionic formula air cleaning unit -8；Strainer -9；Fail analysis device -10；Sample data acquisition module -11；Vibration
Dynamic signal data preprocessing module -12；Historical failure characteristic extracting module -13；Real-time fault diagnosis feature vector acquisition module-
14；Fault diagnosis model establishes module -15；Fault diagnosis identification module -16.
Specific embodiment
The invention will be further described with the following Examples.
A kind of energy-saving and environment-friendly cultivating system as shown in Figure 1, including aquaculture pond ontology, aquaculture pond body upper part are provided with
The Shrimp waste aquaculture pond 2 separated with partition 1 and fish culture pond 3, the lower part of aquaculture pond ontology are settling zone 4, and settling zone 4 is connected to
There is dredge pump 5, the sewage that dredge pump 5 is discharged sends the top of Shrimp waste aquaculture pond 2 back to after the purification of effluent treatment plant 6；Fish
The side of class aquaculture pond 3 is communicated with the oxygenation blower 7 for being sent into oxygen to aquaculture pond ontology.
Preferably, oxygenation blower 7 is connected to ionic formula air cleaning unit 8, between the top and lower part of aquaculture pond ontology
It is separated with strainer 9, effluent treatment plant 6 is filtering ponds or microbial reaction pond.
The above embodiment of the present invention will be re-fed by separately cultivating Shrimp waste and fish by purified sewage
In the Shrimp waste aquaculture pond relatively low to water quality requirement, cultivation production capacity is improved, effectively recycling culture resources, energy conservation and environmental protection.
Preferably, oxygenation blower 7 further includes the fail analysis device 10 for diagnosing 7 failure of oxygenation blower.
Preferably, the fail analysis device 10 includes sequentially connected sample data acquisition module 11, vibration signal number
Data preprocess module 12, historical failure characteristic extracting module 13, real-time fault diagnosis feature vector acquisition module 14, fault diagnosis
Model building module 15 and fault diagnosis identification module 16.
Preferably, the sample data acquisition module 11 is used to acquire the oxygenation blower 7 in normal condition by sensor
The historical vibration signal data of multiple measuring points when being run under lower and various malfunctions.
Preferably, the vibration signal data preprocessing module 12 is used for collected original historical vibration signal data
It is pre-processed, specifically:
Assuming that collected original historical vibration signal data integrates as X ', X ' is filtered out as the following formula using Finite Impulse Response filter
Out of band components:
Wherein, X is the historical vibration signal data obtained after filtering, and c is the number of measuring point, o=1,2,3 ... c-1；H is
Finite Impulse Response filter combines the filtration coefficient of sensor used, and h=τ/2f0, wherein τ is by digital filter self-characteristic
The constant of decision, f0For the intrinsic frequency acquisition of sensor used.
In this preferred embodiment, vibration signal filtering is carried out by FIR filter, it being capable of adaptive different vibration letter
Number, the time domain waveform distortion in original historical vibration signal data is eliminated, output has filtered partial noise and distorted without time domain
Vibration signal, improve the precision that is handled the data for diagnosing 7 failure of oxygenation blower.
Preferably, which is used to filter from by vibration signal data preprocessing module 12
Wavelet packet singular value features are extracted in historical vibration signal data afterwards constitutes fault diagnosis feature vector sample；Preferably, should
Sensor is current vortex sensor.
Preferably, described from by being mentioned in the filtered historical vibration signal data of vibration signal data preprocessing module 12
Wavelet packet singular value features are taken to constitute fault diagnosis feature vector sample, specifically:
(1) the historical vibration signal at the moment measured when the oxygenation blower 7 is in state θ from measuring point μ is set as θμ
(X), μ=1 ..., c, c are the number of measuring point, to θμ(X) K layer scattering WAVELET PACKET DECOMPOSITION is carried out, 2 in K layers are extractedKA decomposition
Coefficient is reconstructed all decomposition coefficients, with Xj(j=0,1 ..., 2K- 1) reconstruction signal of K layers of each node, structure are indicated
Build eigenmatrixWherein the value of K is combined according to historical experience and actual conditions and is determined, to eigenmatrix TKInto
Row singular value decomposition obtains the feature vector Y of the matrixX=(η1,η2,…,ηv), wherein η1,η2,…,ηvFor by eigenmatrix TK
The singular value of decomposition, v are by eigenmatrix TKThe number of the singular value of decomposition defines historical vibration signal θμ(X) corresponding failure
Diagnostic characteristic vectorAre as follows:
In formula, max (YX) indicate feature vector YXIn maximum singular value, min (YX) indicate feature vector YXIn minimum
Singular value；
(2) the fault diagnosis feature vector being calculated is screened, excludes underproof fault diagnosis feature vector,
If the quantity of the underproof fault diagnosis feature vector excluded is c ', then at the moment when oxygenation blower 7 is in state θ
Fault diagnosis feature vector sample are as follows:
In this preferred embodiment, wavelet packet singular value features are extracted as fault diagnosis feature vector, and define failure
The characteristic parameter of diagnostic characteristic vector improves the fault-tolerance diagnosed to oxygenation blower 7, effectively reduces noise data
It influences, accuracy rate is high and the calculating time is short.
Preferably, the described pair of fault diagnosis feature vector being calculated is screened, and excludes underproof fault diagnosis
Feature vector, specifically: in all fault diagnosis features being calculated at the moment when oxygenation blower 7 is in state θ
Feature vector Screening Samples collection of the vector as the moment calculates the standard deviation sigma and desired value of this feature vector Screening Samples collection
μ, then set when the oxygenation blower 7 is in state θ the data screening threshold value at the moment asWhereinFor desired value μ
Maximal possibility estimation,For the maximal possibility estimation of standard deviation sigma, if the fault diagnosis feature vector being calculatedNo
Meet following equation, then reject the fault diagnosis feature vector:
In this preferred embodiment, the fault diagnosis feature vector being calculated is screened using aforesaid way, is excluded
Underproof fault diagnosis feature vector, objective science improve the essence that the oxygenation blower 7 to cultivating system carries out fault diagnosis
Exactness.
Preferably, the historical failure characteristic extracting module 13 also stores up the underproof fault diagnosis feature vector of rejecting
It is stored in an ephemeral data reservoir, works as satisfactionWhen, in historical failure characteristic extracting module 13
K value is further corrected, specific as follows: ifThen the value of K is according to original historical experience
It is combined with actual conditions and is revised as K+1 on the basis of determining；IfThen the value of K is according to original history
Experience and actual conditions combination are revised as K+2 on the basis of determining；Wherein, c is the number of measuring point, and c ' is that underproof failure is examined
The quantity of disconnected feature vector, N are the integer threshold values being manually set.
In this preferred embodiment, the ratio of measuring point number can be accounted for according to underproof fault diagnosis feature vector, automatically
K value is adjusted, the influence that underproof fault diagnosis feature vector carries out fault diagnosis to oxygenation blower 7 is further reduced, mentions
The high accuracy of fault diagnosis further ensures that cultivating system so as to the on-call maintenance when oxygenation blower 7 breaks down
Normal operation.
Preferably, the real-time fault diagnosis feature vector acquisition module 14 is used to obtain the real-time event of the oxygenation blower 7
Hinder diagnostic characteristic vector.
Preferably, the fault diagnosis model is established module 15 and is examined for establishing the failure based on improved support vector machines
Disconnected model, and fault diagnosis model is trained using fault diagnosis feature vector sample, calculate fault diagnosis model ginseng
Several optimal solutions obtains the fault diagnosis model of training completion；Wherein, the failure of the foundation based on improved support vector machines
Diagnostic model, comprising:
(1) using radial basis function as kernel function, using the kernel function by the fault diagnosis feature vector sample from original
Space reflection realizes fault diagnosis feature vector sample classification, structure to higher dimensional space, in higher dimensional space construction optimal decision function
Make optimal decision function are as follows:
In formula, x is the fault diagnosis feature vector sample of input, and ZY (x) is the fault diagnosis feature vector sample of input
Corresponding output, J (x) indicate radial basis function, and q is weight vectors, and a is deviation, exp (- q2-a2) it is to introduce about q and a
Potential energy majorized function, ∈ be manually set potential energy majorized function parameter,By in historical failure characteristic extracting module 13
The data of ephemeral data reservoir are calculated, and wherein c is the number of measuring point, and c ' is underproof fault diagnosis feature vector
Quantity；
(2) objective function of support vector machines is defined are as follows:
The constraint condition of support vector machines are as follows:
S.t yi(qxi+a)≥1-λi,λi>=0, i=1 ..., M
In formula, minY (q, a, λi) be support vector machines objective function, C*For the penalty factor after optimization, M examines for failure
The quantity of disconnected feature vector sample；xiFor i-th of fault diagnosis feature vector sample of input, yi(qxi+ a) it is the i-th of input
The corresponding output of a fault diagnosis feature vector sample, q is weight vectors, and a is deviation, λiFor the error variance of introducing；
(3) objective function for solving the support vector machines obtains weight vectors q and deviation a；
(4) substituting into optimal decision function with obtained weight vectors q and deviation a is established fault diagnosis model.
In this preferred embodiment, pass through introducingThat is fault diagnosis feature vector disqualification rate, and the gesture about q and a
Can majorized function, further improve the actual accuracy of the optimal decision function, for fault diagnosis model foundation provide it is good
Good functional foundations, so that the more accurate fault diagnosis model of building, improves the essence for carrying out fault diagnosis to oxygenation blower 7
Degree.
The optimization of the value of the radius parameter of penalty factor and the kernel function is wherein carried out by following manner:
A, all fault diagnosis feature vector sample means are divided into the subset not included mutually；
B, the value range for setting the value of the radius parameter of penalty factor and the kernel function, to the position of each particle to
Amount carries out two-dimensional encoded, generation primary group；
C, training set is selected to the corresponding parameter of each particle and carries out cross validation, obtained prediction model classification accuracy is made
For the corresponding target function value of particle；
D, the particle in population is iterated；
E, all particles are evaluated with target function value, when the Evaluation: Current value of some particle is better than its history evaluation value,
As the optimal history evaluation of the particle, current particle optimal location vector is recorded；
F, globally optimal solution is found, if its value is better than current history optimal solution, updates, reaches the stop criterion of setting
When, then it stops search, exports the value of the radius parameter of optimal penalty factor and the kernel function, otherwise return to search again
Rope.
The present embodiment optimizes the value of the radius parameter of penalty factor and the kernel function using aforesaid way, optimizes
Time is relatively short, and effect of optimization is good, so as to obtain the support vector machines of better performances, further increases to oxygenation blower
7 carry out the precision of fault diagnosis.
Preferably, the fault diagnosis identification module 16 is used for the real-time fault diagnosis feature vector of the oxygenation blower 7
It is input in the fault diagnosis model of training completion, completes the diagnosis identification of failure.
According to above-described embodiment, inventor has carried out a series of tests, is the experimental data tested below, should
Experimental data shows that the present invention can effectively purify water, recycle culture resources, energy conservation and environmental protection, and can accurately and fast
Fault detection and maintenance are carried out to the oxygenation blower 7 in cultivating system, it can be seen that, the present invention is being applied to having for cultivating system
Close fault detection when produce highly significant the utility model has the advantages that
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation of range is protected, although explaining in detail referring to preferred embodiment to the present invention, those skilled in the art are answered
Work as understanding, it can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from the reality of technical solution of the present invention
Matter and range.
Claims (2)
1. a kind of energy-saving and environment-friendly cultivating system, including aquaculture pond ontology, characterized in that aquaculture pond body upper part setting it is useful every
The lower part of the Shrimp waste aquaculture pond and fish culture pond that plate separates, aquaculture pond ontology is settling zone, and settling zone is communicated with dredge pump,
The sewage of dredge pump discharge sends the top of Shrimp waste aquaculture pond back to after effluent treatment plant purifies；The side in fish culture pond
It is communicated with the oxygenation blower for being sent into oxygen to aquaculture pond ontology；The oxygenation blower further includes for diagnosing the event of oxygenation blower
The fail analysis device of barrier；The fail analysis device includes sequentially connected sample data acquisition module, vibration signal data
Preprocessing module, historical failure characteristic extracting module, real-time fault diagnosis feature vector acquisition module, fault diagnosis model are established
Module and fault diagnosis identification module；The vibration signal data preprocessing module is used to believe collected original historical vibration
Number is pre-processed, specifically:
Assuming that collected original historical vibration signal data integrates as X ', the band of X ' is filtered out as the following formula using Finite Impulse Response filter
Outer component:
Wherein, X is the historical vibration signal data obtained after filtering, and c is the number of measuring point, o=1,2,3 ... c-1；H is FIR number
Word filter combines the filtration coefficient of sensor used, and h=τ/2f0, wherein τ is determined by digital filter self-characteristic
Constant, f0For the intrinsic frequency acquisition of sensor used.
2. a kind of energy-saving and environment-friendly cultivating system according to claim 1, characterized in that the oxygenation blower and ionic formula
Air cleaning unit connection, is separated between the top and lower part of aquaculture pond ontology with strainer, and the effluent treatment plant is filtering
Pond or microbial reaction pond.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101766138A (en) * | 2010-01-13 | 2010-07-07 | 苏州大学 | Circulating-water aquaculture system and application thereof |
| CN103081843A (en) * | 2011-11-04 | 2013-05-08 | 喃嵘水产(上海)有限公司 | Centralization indoor constant temperature circulation aquaculture system |
| CN205865654U (en) * | 2016-07-29 | 2017-01-11 | 中国水产科学研究院珠江水产研究所 | Weight -reducing farming systems of fish |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101766138A (en) * | 2010-01-13 | 2010-07-07 | 苏州大学 | Circulating-water aquaculture system and application thereof |
| CN103081843A (en) * | 2011-11-04 | 2013-05-08 | 喃嵘水产(上海)有限公司 | Centralization indoor constant temperature circulation aquaculture system |
| CN205865654U (en) * | 2016-07-29 | 2017-01-11 | 中国水产科学研究院珠江水产研究所 | Weight -reducing farming systems of fish |
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