CN201830751U - System for forecasting influence of water quality parameters of aquaculture water body over growth state of aquaculture organisms - Google Patents
System for forecasting influence of water quality parameters of aquaculture water body over growth state of aquaculture organisms Download PDFInfo
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- CN201830751U CN201830751U CN2009202798877U CN200920279887U CN201830751U CN 201830751 U CN201830751 U CN 201830751U CN 2009202798877 U CN2009202798877 U CN 2009202798877U CN 200920279887 U CN200920279887 U CN 200920279887U CN 201830751 U CN201830751 U CN 201830751U
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Abstract
The utility model discloses a system for forecasting the influence of water quality parameters of an aquaculture water body over the growth state of aquaculture organisms. The system comprises a monitoring and recording unit, a BP (back propagation) neural network model generating unit, a BP neural network learning unit, a knowledge acquisition unit and a knowledge base unit; during detection, a water quality parameter input unit and a time input unit input corresponding water quality parameter information and time information to a neural network reasoning unit; then the neural network reasoning unit searches corresponding data from the knowledge base unit connected with the neural network reasoning unit; and lastly, the forecasting result of the growth state of the aquaculture organism is output through an aquaculture organism growth state output unit connected with the neural network reasoning unit. The forecasting system solves the problems of nonlinearity, fuzziness and uncertainty of knowledge in the field of aquaculture organism growth, which cannot be solved by the prior traditional simple system, achieves the detection of the growth state of the aquaculture organisms and the regulation and the control of the growth environment, and performs high practical guide and application functions over the practical production of organism aquaculture.
Description
Technical field
The utility model relates to a kind of aquaculture organisms growth conditions model and sets up system, relates in particular to a kind of prognoses system of the aquaculture organism growth conditions based on neural network expert system.
Background technology
At present, in the intensive aquaculture system, adopt the cultivating system of online water quality parameter detection technique, automatic bait throwing in control technology and part water quality parameter auto-control technology that practical application is all arranged both at home and abroad.Control technology that some are advanced such as artificial neural network technology, fuzzy control technology and expert system etc. all have the report of the aquaculture of being applied to.In intensive aquaculture, render to finished product results from seedling and look slightly difference of aquaculture organism kind different cycles length and process, influence factor is many during this time, and the surviving of aquaculture organism, growth conditions and the situation of gathering in the crops are at last influenced by many factors around it.Wherein the breeding water body water quality parameter is a wherein main part to the influence of aquaculture organism growth.
The breeding water body water quality parameter can be divided into three kinds of situations for the influence research of aquaculture organism growth conditions: single water quality parameter is to the research of the influence of aquaculture organism growth conditions; Two water quality parameters are to the influence of aquaculture organism growth conditions; Many water quality parameters are to the research of the influence of aquaculture organism growth conditions.
(1) single water quality parameter is to the research of the influence of biological growth state
By literature search, the single water quality parameter of breeding water body to the biological growth state to influence the researcher more, dependency relation is fairly simple and clear and definite, single water quality parameter is set up the model of biological growth state influence and is adopted traditional RBES, such as adopting production rule, IF ... THEN ... rule.It is in expert system, use comparatively general knowledge.What IF followed later is condition (former piece), and what THEN followed later is conclusion (consequent), and condition and conclusion all can be undertaken compound by logical operation AND, OR, NOT.Here, the understanding of production rule is very simple: if precondition is met, just produce corresponding action or conclusion.Comprised a large amount of rules in the knowledge base of production expert system, in other words, the knowledge base here is exactly a rule set.
(2) two water quality parameters are to the research of the influence of biological growth state
In addition, by literature search, two water quality parameters of breeding water body are less to the combined influence researcher of biological growth state, and dependency relation is also comparatively complicated, and two water quality parameters are set up the model of biological growth state influence and still adopted traditional RBES.
Wherein the single water quality parameter of breeding water body and two water quality parameter are clear and definite, simple to the influence surface co-relation of aquaculture organism growth conditions, comparatively convenient during to the influencing of aquaculture organism growth conditions to one or two specific water quality parameter of simple investigation, but owing to interact between each water quality parameter, the growth conditions of aquaculture organism is the overall water quality situation that depends on after every water quality parameter comprehensive function, so the single water quality parameter of breeding water body and two water quality parameter are also very limited to the degree of certainty of aquaculture organism growth conditions influence, the actual application value of research is also more limited.
(3) many water quality parameters are to the research of the influence of biological growth state
The domestic research that does not still have many water quality parameters to the biological growth state does not at present temporarily also utilize neural network expert system to carry out the example of aquaculture organism growth conditions research aspect.Therefore, when the model of aquaculture organism growth conditions influence being set up, mean that this will be an innovation during artificial intelligence technology is used in the aquaculture field based on water quality parameter.
Summary of the invention
The utility model can not satisfy the monitoring of aquaculture organism growth conditions all sidedly at the research of the biological growth state of single water quality parameter and two water quality parameters, can't solve non-linear, the ambiguity, uncertainty of aquaculture organism growth domain knowledge, the existence of problem such as not comprehensive, and develop the combined influence of a kind of many water quality parameters to the biological growth state, it no longer as one-parameter and two-parameter observe traditional expert system rule.Wherein, the correlation of each water quality parameter and intricate to the influence of biological growth state, at non-linear, the ambiguity of aquaculture organisms growth domain knowledge, uncertainty, problem such as not comprehensive, proposed to set up model based on the artificial neural network expert system, analyze the combined influence of many water quality parameters, and then instruct actual production the biological growth state.
Its concrete technological means that adopts is as follows:
A kind of breeding water body water quality parameter is characterized in that comprising to the prognoses system of aquaculture organism growth conditions influence:
The monitoring record unit, be used for putting in a suitable place to breed in the identical different breeding pond of cultivation density, adopt the identical bait of kind to feed, and the growth conditions of the same a collection of larva of the same biological variety of equal unanimity of daily ration, feeding quantity and bait throwing in time is monitored, randomly regulating is changed the numerical value of temperature, salinity, pH value, dissolved oxygen and ammonia nitrogen in the water quality parameter and their value is noted one by one, and the growth conditions that writes down aquaculture organism in each pond at one time is the long or body weight of body of aquaculture organism;
BP neural network model generation unit, be used for the BP neutral net is realized that the knowledge base of aquaculture organism growth conditions model represents, with the temperature in the water quality parameter of monitoring record unit record, salinity, pH value, dissolved oxygen and ammonia nitrogen input quantity as nerve network system, introduce time t another input quantity as neutral net simultaneously, long or body weight is as artificial neural network system's output quantity with the body of the aquaculture organism of the sign aquaculture organism growth conditions of monitoring record unit record;
BP neural network learning unit is used for the neural network model that BP neural network model generation unit makes up is carried out learning training;
The knowledge acquisition unit is used for the network model behind the BP neural network learning module training, comprises that network structure, network input variable, weight matrix, threshold matrix, iterations, output error value information are stored in the knowledge base unit;
The monitoring record unit is transferred to BP neural network model generation unit with the water quality parameter data of record and the body length or the weight data of aquaculture organism, after BP neural network model generation unit generation BP neutral net realizes aquaculture organism growth conditions model, be transferred in the BP neural network learning unit and carry out learning training, after carrying out the relevant information extraction by the knowledge acquisition unit that links to each other with BP neural network learning unit again, be transferred in the knowledge base unit; When carrying out actual prediction, import corresponding water quality parameter information and temporal information by water quality parameter input block and time input block neuralward network reasoning unit, from coupled knowledge base unit, search for corresponding data by the neutral net reasoning element again, export predicting the outcome of aquaculture organism growth conditions by the aquaculture organism growth conditions output unit that links to each other with the neutral net reasoning element at last.
Also comprise BP neural network learning adjustment unit in the BP neural network learning unit, be used for the model that BP neural network model generation unit is passed back is optimized adjustment.
Because it is conspicuous specific as follows that the breeding water body water quality parameter that has adopted technique scheme, the utility model to provide is compared its advantage to the prognoses system of aquaculture organism growth conditions influence with the pre existing examining system:
(1) solved the problem that single and water quality parameter can not comprehensively reflect, writes down and regulate and control the growth conditions of aquaculture organism based on the model of the aquaculture organism growth conditions of artificial neural network expert system, improve cultured output and quality to greatest extent, actual production is had higher actual guidance and application effect.
(2) problems such as non-linear, ambiguity that the simple expert system of tradition in the past can not solve aquaculture organisms growth domain knowledge, uncertainty have been solved.The accurate monitoring of aquaculture organism growth conditions and the regulation and control of growing environment have been realized.
(3) fill up the domestic blank of still not having many water quality parameters to the research of biological growth state, realized the breakthrough of intensive aquaculture technology.
Description of drawings
Fig. 1 is the major function structure chart of biological growing season management system;
Fig. 2 is the prognoses system structured flowchart of the utility model intensive culture breeding water body water quality parameter to the influence of aquaculture organism growth conditions;
Fig. 3 sets up the knowledge base structure figure of model for the utility model utilizes the BP neutral net;
Fig. 4 is the utility model embodiment Patinopecten yessoensis D type larval growth state and water quality parameter measured data table.
Embodiment
As shown in Figure 1, the breeding water body water quality parameter belongs to the growing season management subsystem of aquaculture computer management system to the model of aquaculture organism growth effect.Usually the growth conditions of aquaculture organism is represented with biological body length or body weight, select and the cultivation density setting at breed variety, and after food species, daily ration, feeding quantity and bait throwing in time are definite, aquaculture organism growing period, the variation of breeding water body water quality parameter directly affect biological growth conditions.If as input quantity, by the growth management system, the growth conditions of aquaculture organism is exactly the output quantity of system so the water quality parameter situation of change of breeding water body.The utility model is set up aquaculture organism growth conditions model based on the artificial neural network expert system, when other condition element certain, and the water quality parameter of breeding water body is when changing, and we can infer what kind of variation takes place the growth conditions of aquaculture organism by model.The actual result that obtains in result that reasoning draws and the aquaculture organism process of growth is approaching more, and the illustrative system error is more little, and it is accurate more that model is set up.Is that example is set up model with many water quality parameters to the influence of aquaculture organism growth conditions.Select the kind of same aquaculture organism for use, and be the same a collection of larva of same biological variety, they are put in a suitable place to breed in the identical different breeding pond of cultivation density, adopt the identical bait of kind to feed, and daily ration, feeding quantity and bait throwing in time are all consistent.Satisfying under the above-mentioned condition, randomly regulating changes multinomial water quality parameter numerical value and their value is noted one by one, and monitor at one time aquaculture organism in each pond growth conditions---the body of aquaculture organism is long, set up the model of this biological growth state as the input and output of the expert system of artificial neural network.
As Fig. 2, shown in Figure 3, for the process of setting up of vivid explanation aquaculture organism growth conditions model and the feasibility of model, the Patinopecten yessoensis of raising with certain cultivation base is as research object, with the scallop larval stage is main conceptual phase, study the influence of its growth water quality parameter in waters such as temperature, salinity, pH value, dissolved oxygen and ammonia-nitrogen content to its growth conditions, and utilizing artificial neural network and expert system knowledge to set up model, this model will be used to show the relation of the water quality parameter and the scallop growth conditions of scallop growing environment.Concrete operating process is as follows:
At first, the control of aquaculture organism growth conditions is the subsystem under the aquaculture organism growing season management system, along with aquaculture organism enters vegetative period, can realize the control of aquaculture organism growth conditions.Select the kind of same aquaculture organism for use, and be the same a collection of larva of same biological variety, they are put in a suitable place to breed in the identical different breeding pond of cultivation density, adopt the identical bait of kind to feed, and daily ration, feeding quantity and bait throwing in time are all consistent, monitoring record unit by system changes multinomial water quality parameter numerical value to randomly regulating and their value is noted one by one simultaneously, and the growth conditions of monitoring aquaculture organism in each pond at one time to be that the body of aquaculture organism is long also note it, Patinopecten yessoensis D type larval growth state and water quality parameter measured data table as shown in Figure 4.Wherein the monitoring record unit is mainly manual measurement, is input in the whole prognoses system by input system.
Owing to introduced hidden neuron in neutral net, neutral net just has the focus that ability, therefore corresponding learning algorithms such as better classification and memory have become research.EBP (the Error Back Propagation) algorithm that Rumelhart in 1985 etc. propose (being called for short BP) has systematically solved the problem concerning study of hidden unit layer connection weight in the multilayer neuroid, and provided complete derivation on mathematics.Because BP has overcome the indeterminable XOR of simple perceptron and some other problem, so the BP model has become one of important models of neutral net, and is extensive use of.Adopt the multilayer neural network model of BP algorithm to be commonly referred to as the BP network.Native system adopts the BP neutral net, but is not limited only to adopt this kind network.
The construction of knowledge base is a key of setting up expert system, use the BP neutral net and realize that the knowledge base of aquaculture organism growth conditions model represents, by BP neural network model generation unit with the monitoring record unit record with the temperature in the main breeding water body water quality parameter that influences aquaculture organism growth, salinity, the pH value, dissolved oxygen, ammonia nitrogen is as the input of nerve network system, consider that simultaneously what will set up is aquaculture organism growth conditions model, and aquaculture organism growth conditions and time are closely related, the body long (or body weight) that is aquaculture organism is becoming at any time, so here with the input quantity of time t as neutral net, all variablees that so just will influence the aquaculture organism growth conditions have all considered, and as the input quantity of neutral net; The body that characterizes the aquaculture organism of aquaculture organism growth conditions is grown (or body weight) output quantity as the artificial neural network system, and so far, the neural network model that makes up expert system knowledge base builds up (as shown in Figure 3).
By BP neural network learning unit the neural network model that BP neural network model generation unit makes up is carried out learning training then, in order to satisfy BP neutral net transfer function condition, before training, to do the sample standardization, form the standardized data of BP neural metwork training training sample.Because each columns in Fig. 4 table is according to not of uniform size, minimum is 0.350, and maximum is 237, so will carry out normalized to all data.During training, at first normalized training sample input data are loaded into the input of neural network model, are about to the temperature, salinity, pH value, dissolved oxygen, ammonia nitrogen in Fig. 4 table and are loaded into the input of neural network model after according to normalization with each columns such as time on corresponding date; Normalized training sample output data are corresponding with the output of neural network model, and last row shell long data that promptly characterizes the scallop growth conditions in Fig. 4 table is corresponding with the output of neural network model, carries out learning training.
Wherein by BP neural network learning adjustment unit the parameter that BP neural network learning unit forms is carried out the reasonability adjustment, wherein the learning process of BP network is made up of two parts: forward-propagating and backpropagation.When forward-propagating, network input information passes neuralward network model output layer from input layer after the hidden unit layer is handled, and the neuronic state of each layer only influences the neuron state of one deck down.If the output in that the neural network model output layer can not get wishing then changes backpropagation over to, error signal is returned along original neuron connecting path.Notice that this moment, the desired value of neural network model output layer was and the corresponding aquaculture organism growth conditions of the time variable t of neural network model input parameter measured value that promptly Shi Ce Patinopecten yessoensis body is long.With this desired value formation error signal of comparing with the neural network model real output value, the error signal backpropagation in the return course, is revised the weights that each layer neuron connects one by one.The continuous iteration of this process makes signal errors reach within the scope of permission at last, and the neural network learning training that makes up expert system knowledge base finishes.
Utilize the knowledge acquisition unit then, be used for, comprise that network structure, network input variable, weight matrix, threshold matrix, iterations, output error value information are stored in the knowledge base unit the network model behind the BP neural network learning module training.
When carrying out actual prediction, import corresponding water quality parameter information and temporal information by water quality parameter input block and time input block neuralward network reasoning unit, from coupled knowledge base unit, search for corresponding data by the neutral net reasoning element again, export predicting the outcome of aquaculture organism growth conditions by the aquaculture organism growth conditions output unit that links to each other with the neutral net reasoning element at last.With the Patinopecten yessoensis is example: when water quality parameter temperature, salinity, pH value, dissolved oxygen, the ammonia nitrogen of the reality of system's input measurement be respectively 14.1 (℃), 27.0 (g/l), 8.34,5.89 (mg/l), 0.351 (mg/l) and time parameter keep normal daily ration, feeding quantity and at interval when being 10 days, system will promptly realize according to the prediction of water quality parameter to the aquaculture organism growth conditions automatically according to through neural network model output Patinopecten yessoensis shell long value 191.50 (mm) so.
Usually in order to reach biological growth state model accurately, contrast the output valve of the output valve homologous ray of expecting to adjust by the BP neural network learning adjustment unit in the BP neural network learning unit.The aquaculture organism of the same kind that expected value provides for the aquaculture expert in native system is in the historical optimal value of different times, and it also is that the fine quality of certain aquaculture organism can be expected the ideal data that reaches at different times.After the output valve input system of expectation, the error of computing system real output value and desired output (being optimal value), if error can not meet the demands, then network returns by original route and carries out the weights adjustment, reaches requirement up to convergence error, training finishes.With Patinopecten yessoensis data instance in Fig. 4 table, be respectively 14.1 when system imports corresponding water quality parameter temperature, salinity, pH value, dissolved oxygen, ammonia nitrogen (℃), when 27.0 (g/l), 8.34,5.89 (mg/l), 0.351 (mg/l) and time parameter are 10 days, these data are added to the neural network model input after normalized.Neural network model begins learning training, at first forward-propagating, network input information passes neuralward network model output layer from input layer after the hidden unit layer is handled, the neuronic state of each layer only influences the neuron state of one deck down, the neural network model output valve is the predicted value of aquaculture organism growth conditions, i.e. the long predicted value of Patinopecten yessoensis shell.If the output in that the neural network model output layer can not get wishing then changes backpropagation over to, error signal is returned along original neuron connecting path.In this example the desired value of neural network model output layer be with Fig. 4 table in the 10th day actual measurement Patinopecten yessoensis shell long value 191.51 (mm) in No. 3 ponds, if the predicted value and the desired value of neural network model output there are differences, with this desired value formation error signal of comparing with the neural network model real output value, and according to the basic principle of BP network error anti-pass with the error signal backpropagation, in the return course, revise the weights that each layer neuron connects one by one.Suppose that the absolute error desired value after the anti-normalization of output valve is | Δ |≤0.01, when the actual prediction of output value of neural network model is 191.53 (mm), be Error Absolute Value | Δ |=191.53-191.51=0.02, error amount surpasses the scope that allows, the error anti-pass is revised the process of each layer neuron connection weights and is proceeded, the continuous iteration of this process reaches up to signal errors within the scope of permission.For example, when the actual prediction of output value of neural network model is 191.50 (mm), i.e. Error Absolute Value | Δ | during=191.51-191.50=0.01, signal errors reaches within the target range of permission, promptly | Δ |≤0.01, the neural network learning training finishes.
The above; it only is the preferable embodiment of the utility model; but protection domain of the present utility model is not limited thereto; anyly be familiar with those skilled in the art in the technical scope that the utility model discloses; be equal to replacement or change according to the technical solution of the utility model and inventive concept thereof, all should be encompassed within the protection domain of the present utility model.
Claims (2)
1. a breeding water body water quality parameter is characterized in that comprising: monitoring record unit, BP neural network model generation unit, BP neural network learning unit and knowledge acquisition unit to the prognoses system that the aquaculture organism growth conditions influences;
The monitoring record unit is transferred to BP neural network model generation unit with the water quality parameter data of record and the body length or the weight data of aquaculture organism, after BP neural network model generation unit generation BP neutral net realizes aquaculture organism growth conditions model, be transferred in the BP neural network learning unit and carry out learning training, after carrying out the relevant information extraction by the knowledge acquisition unit that links to each other with BP neural network learning unit again, be transferred in the knowledge base unit; When carrying out actual prediction, import corresponding water quality parameter information and temporal information by water quality parameter input block and time input block neuralward network reasoning unit, from coupled knowledge base unit, search for corresponding data by the neutral net reasoning element again, export predicting the outcome of aquaculture organism growth conditions by the aquaculture organism growth conditions output unit that links to each other with the neutral net reasoning element at last.
2. breeding water body water quality parameter according to claim 1 is to the prognoses system of aquaculture organism growth conditions influence, it is characterized in that also comprising BP neural network learning adjustment unit in the BP neural network learning unit, be used for the model that BP neural network model generation unit is passed back is optimized adjustment.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105446138A (en) * | 2015-12-16 | 2016-03-30 | 中国农业大学 | Water quality adjusting optimizing system and water quality adjusting optimizing method in aquatic organism cultivation environment |
| CN110476839A (en) * | 2019-07-24 | 2019-11-22 | 中国农业大学 | A kind of optimization regulating method and system based on fish growth |
| US20200285941A1 (en) * | 2019-03-06 | 2020-09-10 | Nec Corporation | Growth analysis system, growth analysis method, and growth analysis program |
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2009
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105446138A (en) * | 2015-12-16 | 2016-03-30 | 中国农业大学 | Water quality adjusting optimizing system and water quality adjusting optimizing method in aquatic organism cultivation environment |
| US20200285941A1 (en) * | 2019-03-06 | 2020-09-10 | Nec Corporation | Growth analysis system, growth analysis method, and growth analysis program |
| US11526730B2 (en) * | 2019-03-06 | 2022-12-13 | Nec Corporation | Growth analysis system, growth analysis method, and growth analysis program |
| CN110476839A (en) * | 2019-07-24 | 2019-11-22 | 中国农业大学 | A kind of optimization regulating method and system based on fish growth |
| CN110476839B (en) * | 2019-07-24 | 2020-07-31 | 中国农业大学 | Optimal regulation and control method and system based on fish growth |
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