CN102550454A - Method for predicting cryptocaryoniosis in Larimichthys crocea - Google Patents
Method for predicting cryptocaryoniosis in Larimichthys crocea Download PDFInfo
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Abstract
A method for predicting cryptocaryoniosis in Larimichthys crocea relates to Larimichthys crocea, and belongs to a method of accurately predicting a water environmental factor at a certain future point-in-time to achieve advanced prediction on cryptocaryoniosis in Larimichthys crocea. The method includes: plugging a predicted value of the obtained water environmental factor in a mathematical discriminatory model for cryptocaryoniosis based on the water environmental factor; calculating a predicted value of the cryptocaryoniosis to generate the advanced prediction on the cryptocaryoniosis in Larimichthys crocea; and obtaining advanced prediction on the cryptocaryoniosis in Larimichthys crocea. The method is simple and convenient and capable of reflecting anural variation, parameters of the water environmental factor can be obtained according to a series of fitting equations only by providing month, the parameters can be calculated with a disease discriminatory model to finally obtain the predicted value of the cryptocaryoniosis, and accordingly, the method is quite convenient in use. For the specific cryptocaryoniosis, the method is convenient for farmers to timely take measures to control the disease and prevent and alleviate disasters.
Description
Technical field
The present invention relates to a kind of large yellow Crocker, especially relate to the Forecasting Methodology that a kind of large yellow Crocker stimulates cryptocaryoniosis.
Background technology
Large yellow Crocker (Pseudosciaena crocea) is the maximum fish of China's seawater cage culture single variety output, about 70,000 tons of annual production.But, stimulating sick the breaking out, spread of cryptonucleus insect (Cryptocaryon irritans) in recent years, the sustainable development of large yellow Crocker aquaculture industry in serious threat.According to statistics, since 2005, all Australia culture zone large yellow Crocker is because of parasite and bacillary secondary infection for Fujian Ningde City three, and the direct economic loss that causes every year is above 300,000,000 yuan.The Ministry of Agriculture will stimulate cryptocaryoniosis to classify two types of animal epidemics as in new edition " one, two, three type of sick register of planting of animal epidemic " in 2008.
In a single day large yellow Crocker stimulates cryptocaryoniosis to break out, use the effect of medical treatment very limited, especially after stimulating cryptonucleus insect to invade body surface and gill formation trophozoite, more is difficult to effectively kill with chemicals.In addition, excess uses caused water pollution of chemical insecticide and permanent serious problems such as residual to bring new environmental problem again, finally influences aquatic product quality safety and mariculture sustainable and healthy development.In long-term breed practice; The raiser has summed up some preventative effective prophylactic measures; Such as; Carry out cultivation density in outbreak of disease early stage in good time and divide rare or move and be discharged to current profundal zone preferably, and reduce bright assorted bait consumption or add some immunopotentiators or the like, received preventive effect preferably.It is thus clear that premorbid is in good time, preventive measure initiatively seem particularly important than the remedial measure of morbidity back medical treatment.Therefore, set up the Predicting Technique in advance that a kind of large yellow Crocker stimulates the cryptonucleus insect disease to take place, guidance should be necessary in disease prevention and control early.
China existing according to transparency, temperature and mean wind speed to the Forecasting Methodology of large yellow Crocker culture diseases (all diseases) (referring to Chinese patent: 200710068792.6).But; Large yellow Crocker culture diseases kind is various; Epidemic characteristic is different; Envirment factor effect related when multiple disease is concurrent in the particularly same time point is very complicated, and the main factor of imitating that influences various disease is incomplete same, and it is bigger according to specific water quality factor all diseases to be implemented the prediction difficulty.
Large yellow Crocker stimulates the breeding ecological system that cryptocaryoniosis takes place and China large yellow Crocker main producing region is complicated closely related; The multiple of the hydrology, physics, chemistry and biotic factor that this disease is cultured the marine site influences obviously, and many experts have carried out qualitative elaboration to the relation that stimulates the cryptocaryoniosis and the water environment factor.On this basis,, specific stimulation cryptocaryoniosis is implemented prediction differentiate, will have great importance for the prevention mitigation of this pernicious disease if can set up Predicting Technique based on the key water envirment factor.Yet China is the research report of not related with stimulating cryptocaryoniosis water environment factor Predicting Technique still, more large yellow Crocker is not stimulated the maturation method of cryptocaryoniosis prediction.
Summary of the invention
The object of the present invention is to provide a kind of water quality environment factor that can accurately predict in the future sometime, realize large yellow Crocker is stimulated the Forecasting Methodology of large yellow Crocker stimulation cryptocaryoniosis of the prediction in advance of cryptocaryoniosis.
The present invention includes following steps:
1) structure of water environment factor predictive equation
Historical data according to the difference month water environment factor; Use multiple time series models to analyze the Changing Pattern and the trend of the water environment factor; Make up the matched curve of water temperature, dissolved oxygen and ammonia nitrogen value respectively to month; Relatively fitting effect is therefrom screened best model and is set up fit equation, and its regression equation is respectively:
(1) water temperature:
f(x)＝20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)
Wherein f (x) is a water temperature, and x is month;
(2) dissolved oxygen:
f(x)＝7.083+-0.2234*cos(0.02547*x)+0.179*sin(0.02547*x)+0.9994*cos(2*0.02547*x)+0.9807*sin(2*0.02547*x)+0.6179*cos(3*0.02547*x)+0.1761*sin(3*0.02547*x)+0.2184*cos(4*0.02547*x)+0.002575*sin(4*0.02547*x)
Wherein f (x) is a dissolved oxygen, and x is month;
(3) ammonia nitrogen:
y(t)＝0.182y(t-1)+0.778y(t-2)+e(t)
Wherein y (t) is the real value of current ammonia nitrogen value, and y (t-1) is the ammonia nitrogen value of preceding first phase, and y (t-n) is the ammonia nitrogen value of preceding n phase, and e (t) is current error amount, and then the real value of current ammonia nitrogen is that the predicted value of current ammonia nitrogen adds error amount;
2) prediction in advance of the water environment factor
According to the fit equation of being set up, calculate the predicted value of water temperature, dissolved oxygen and the ammonia nitrogen of following a certain concrete time point;
3) differentiation in advance of stimulation cryptonucleus insect incidence
Collect historical month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and the corresponding serious level data of stimulation cryptocaryoniosis; Utilize the random forest program package that loads in the R software environment that the said serious level data of stimulation cryptocaryoniosis of collecting historical month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and correspondence is analyzed; Set up month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and differentiate the mathematical model of disease grade, utilize water temperature, dissolved oxygen and 3 factor forecast models of ammonia nitrogen, to water temperature, dissolved oxygen and the ammonia nitrogen value of first phase are predicted down; In the disease discrimination model of then the predicted value substitution having been built up; Stimulate cryptocaryoniosis to predict to large yellow Crocker, and do corresponding deciphering, a situation arises to judge the possibility that stimulates cryptocaryoniosis; Take suitable prevention and control measure in view of the above, reduce due to illness loss; The assessment of the accuracy of the predicted value of gained can stimulate the reality of cryptocaryoniosis a situation arises and compares with large yellow Crocker.
Technical scheme of the present invention is the predicted value of elder generation with the water environment factor that obtains; Substitution is based on the mathematics discrimination model of the stimulation cryptocaryoniosis of the water environment factor then; Calculate the predicted value of disease; Forming large yellow Crocker stimulates the Predicting Technique in advance of cryptocaryoniosis, thereby realizes large yellow Crocker is stimulated the pathogenetic prediction in advance of cryptonucleus insect.
Compared with prior art, the present invention has following advantage:
1) simple and convenient.The present invention is based on the water quality prediction technology that historical data base is set up, reflected the anniversary Changing Pattern, only need be provided month, just can obtain the parameter of the water environment factor according to a series of fit equation.And combine the disease discrimination model to carry out computing, finally obtain the predicted value of disease, very convenient use.
2) prediction in advance.The present invention is directed to specific stimulation cryptocaryoniosis, provide a kind of first and predicted that in advance large yellow Crocker stimulates the technology of cryptocaryoniosis, make things convenient for the raiser to take measures to carry out disease prevention and control and prevention mitigation early.
Description of drawings
Fig. 1 is the trend fitting figure of water temperature about the time.In Fig. 1, abscissa is time (moon), ordinate be water temperature (℃); Mark " ◆ " is a real value, and "-" is prediction curve.
Fig. 2 is the trend fitting figure of dissolved oxygen about the time.In Fig. 2, abscissa is time (moon), and ordinate is dissolved oxygen (mg/l); Mark " ◆ " is a real value, and "-" is prediction curve.
Fig. 3 is the autocorrelation analysis result of ammonia nitrogen value.In Fig. 3, abscissa is hysteresis exponent number Lag, and ordinate is sample degree of correlation Sample Autocorrelation.
Fig. 4 is the trend fitting figure of ammonia nitrogen about the time.In Fig. 4, abscissa is time (moon), and ordinate is NH4
+-N (mg/l); Mark " △ " is a real value, and "+" is predicted value, and "～" is interpolated line.
Embodiment
Following examples will combine accompanying drawing that the present invention is described further.
Embodiment 1: the foundation of water environment factor Predicting Technique.
The present invention at first collects moon Monitoring Data of water temperature, dissolved oxygen and ammonia nitrogen value of 5 different erect-positions that Ningde, Fujian San Douwan large yellow Crocker cultures the marine site, and (water quality data is from the foreign environmental monitoring central station in the East Sea, National Bureau of Oceanography East Sea branch office Fujian; The disease data are from sick anti-section of Fujian Province marine fishery technology popularization master station; Data acquisition, analysis are all according to the national regulation standard), set up the regression equation that reflects its variation tendency and historical law.Wherein month is by remembering into 1-65 year May in January, 2005 to 2010 respectively; Water temperature, dissolved oxygen, the ammonia nitrogen value of different erect-positions are averaged respectively, and compare the trend of several different methods and the fitting effect of curve, select best fit equation.
1) water temperature Predicting Technique
Water temperature changes with seasonal variation, and obvious periodic property is arranged.The present invention select Fourier's function to water temperature with month variation tendency carry out match, the result is following:
f(x)＝20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)
Wherein f (x) is a water temperature, and x is month.
The R of this matched curve
2=0.9528, explain that with 1 rank Fourier's function water temperature is had effect preferably with the variation match in month, fitted figure is seen Fig. 1.
2) Predicting Technique of dissolved oxygen
Envirment factors such as the aquaculture model of dissolved oxygen and large yellow Crocker, the morning and evening tides rhythm and pace of moving things and the vegeto-animal all annual variation of swimming are closed closely, are that influence stimulates the pathogenetic key factor of cryptonucleus insect.The present invention select Fourier's function to dissolved oxygen with month variation tendency carry out match, the result is following:
f(x)＝7.083+-0.2234*cos(0.02547*x)+0.179*sin(0.02547*x)+0.9994*cos(2*0.02547*x)+0.9807*sin(2*0.02547*x)+0.6179*cos(3*0.02547*x)+0.1761*sin(3*0.02547*x)+0.2184*cos(4*0.02547*x)+0.002575*sin(4*0.02547*x)
Wherein f (x) is a dissolved oxygen, and x is month.
The R of this matched curve
2=0.9369, explain that with 4 rank Fourier's functions dissolved oxygen is had effect preferably with the variation match in month, fitted figure is seen Fig. 2.
3) Predicting Technique of ammonia nitrogen
Ammonia nitrogen is a key factor of weighing quality of water environment, also is that influence stimulates pathogenetic main one of the factor of imitating of cryptonucleus insect.The present invention selects the variation tendency of autoregression model simulation ammonia nitrogen.Through the (see figure 3) as a result that ACF analyzes, selected 2 rank autoregression models, the result is following:
y(t)＝0.182y(t-1)+0.778y(t-2)+e(t)
Wherein y (t) is the real value of current ammonia nitrogen value, and y (t-1) is the ammonia nitrogen value of preceding first phase, and y (t-n) is the ammonia nitrogen value of preceding n phase, and e (t) is current error amount, and then the real value of current ammonia nitrogen is that the predicted value of current ammonia nitrogen adds error amount.So predicted value y (t)=0.182y (t-1)+0.778y (t-2) of current ammonia nitrogen;
The Loss function=0.000155492 of this model; FPE=0.000180371
Explain that this autoregression model has simulate effect preferably, prognosis modelling is seen Fig. 4.
The analysis of the accuracy of embodiment 2 water environment factor Predicting Techniques
According to the fit equation of setting up among the embodiment 1, the water temperature of having predicted certain year July, dissolved oxygen and ammonia nitrogen value.Table 1 is the comparison of predicted value and real value.Can know by table 1, in each item factor predicted value and the real value relative error value average 10%, explain that the water quality prediction effect is better.
The predicted value of table 1 water quality factor in certain year July and real value are relatively
| The factor | Real value | Predicted value | Relative error (%) |
| Water temperature (℃) | 28.0 | 28.6 | 2.1 |
| Dissolved oxygen (mg/l) | 5.86 | 6.04 | 3.1 |
| Ammonia nitrogen (mg/l) | 0.030 | 0.034 | 9.7 |
Annotate: relative error=(predicted value-real value)/real value * 100%
Embodiment 3 stimulates the prediction in advance of cryptonucleus insect incidence
Collect historical month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and the corresponding serious level data of stimulation cryptocaryoniosis; Utilize the random forest program package that loads in the R software environment that above-mentioned data are analyzed, set up the mathematical model that month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors are differentiated the disease grade.
The water temperature predicted value in certain year July is 28.6 ℃, and the predicted value of dissolved oxygen is 6.04mg/l, and the predicted value of ammonia nitrogen is 0.034mg/l, can predict the incidence of the stimulation cryptocaryoniosis in certain year July in the disease discrimination model that substitution has been built up.
Through after the computing, stimulating the pathogenetic degree of cryptonucleus insect certain year July is 2 grades, that is to say the state that is in a small amount of morbidity, and the cultivation density upper zone can suitably take means such as the row's of moving branch is rare to avoid morbidity.
Stimulate cryptocaryoniosis predict the outcome with and met in practice, explain that this early warning technology has higher accuracy.
Claims (1)
1. a large yellow Crocker stimulates the Forecasting Methodology of cryptocaryoniosis, it is characterized in that may further comprise the steps:
1) structure of water environment factor predictive equation
Historical data according to the difference month water environment factor; Use multiple time series models to analyze the Changing Pattern and the trend of the water environment factor; Make up the matched curve of water temperature, dissolved oxygen and ammonia nitrogen value respectively to month; Relatively fitting effect is therefrom screened best model and is set up fit equation, and its regression equation is respectively:
(1) water temperature:
f(x)＝20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)
Wherein f (x) is a water temperature, and x is month;
(2) dissolved oxygen:
f(x)＝7.083+-0.2234*cos(0.02547*x)+0.179*sin(0.02547*x)+0.9994*cos(2*0.02547*x)+0.9807*sin(2*0.02547*x)+0.6179*cos(3*0.02547*x)+0.1761*sin(3*0.02547*x)+0.2184*cos(4*0.02547*x)+0.002575*sin(4*0.02547*x)
Wherein f (x) is a dissolved oxygen, and x is month;
(3) ammonia nitrogen:
y(t)＝0.182y(t-1)+0.778y(t-2)+e(t)
Wherein y (t) is the real value of current ammonia nitrogen value, and y (t-1) is the ammonia nitrogen value of preceding first phase, and y (t-n) is the ammonia nitrogen value of preceding n phase, and e (t) is current error amount, and then the real value of current ammonia nitrogen is that the predicted value of current ammonia nitrogen adds error amount;
2) prediction in advance of the water environment factor
According to the fit equation of being set up, calculate the predicted value of water temperature, dissolved oxygen and the ammonia nitrogen of following a certain concrete time point;
3) differentiation in advance of stimulation cryptonucleus insect incidence
Collect historical month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and the corresponding serious level data of stimulation cryptocaryoniosis; Utilize the random forest program package that loads in the R software environment that the said serious level data of stimulation cryptocaryoniosis of collecting historical month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and correspondence is analyzed; Set up month, water temperature, dissolved oxygen and ammonia nitrogen 4 factors and differentiate the mathematical model of disease grade, utilize water temperature, dissolved oxygen and 3 factor forecast models of ammonia nitrogen, to water temperature, dissolved oxygen and the ammonia nitrogen value of first phase are predicted down; In the disease discrimination model of then the predicted value substitution having been built up; Stimulate cryptocaryoniosis to predict to large yellow Crocker, and do corresponding deciphering, a situation arises to judge the possibility that stimulates cryptocaryoniosis; Take suitable prevention and control measure in view of the above, reduce due to illness loss; The assessment of the accuracy of the predicted value of gained and large yellow Crocker stimulate the reality of cryptocaryoniosis a situation arises and compare.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103229737A (en) * | 2013-04-26 | 2013-08-07 | 宁波大学 | Method for forecasting large-scale bacteriosis occurrence time of cage-cultured pseudosciaena crocea |
| CN105660462A (en) * | 2015-12-18 | 2016-06-15 | 中山大学 | Cyst inactivation method for controlling cryptocaryon irritans disease of fishes |
| CN107180152A (en) * | 2016-03-09 | 2017-09-19 | 日本电气株式会社 | Disease forecasting system and method |
| CN108244003A (en) * | 2018-01-18 | 2018-07-06 | 中国农业大学 | A kind of aquatic products plant disease epidemic trend prediction and methods of exhibiting and system |
| CN110100764A (en) * | 2019-06-06 | 2019-08-09 | 江西省水产技术推广站 | A kind of aquaculture method |
| CN112889717A (en) * | 2021-02-24 | 2021-06-04 | 中山大学 | Method for biologically preventing and controlling cryptocaryon irritans infection by using tilapia |
| CN116562469A (en) * | 2023-07-10 | 2023-08-08 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
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2012
- 2012-01-13 CN CN2012100098528A patent/CN102550454A/en active Pending
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103229737A (en) * | 2013-04-26 | 2013-08-07 | 宁波大学 | Method for forecasting large-scale bacteriosis occurrence time of cage-cultured pseudosciaena crocea |
| CN103229737B (en) * | 2013-04-26 | 2015-08-05 | 宁波大学 | The forecast of the extensive time of origin of Pseudosciaena crocea bacteriosis |
| CN105660462A (en) * | 2015-12-18 | 2016-06-15 | 中山大学 | Cyst inactivation method for controlling cryptocaryon irritans disease of fishes |
| CN107180152A (en) * | 2016-03-09 | 2017-09-19 | 日本电气株式会社 | Disease forecasting system and method |
| CN108244003A (en) * | 2018-01-18 | 2018-07-06 | 中国农业大学 | A kind of aquatic products plant disease epidemic trend prediction and methods of exhibiting and system |
| CN108244003B (en) * | 2018-01-18 | 2019-09-13 | 中国农业大学 | A kind of aquatic products plant disease epidemic trend prediction and methods of exhibiting and system |
| CN110100764A (en) * | 2019-06-06 | 2019-08-09 | 江西省水产技术推广站 | A kind of aquaculture method |
| CN112889717A (en) * | 2021-02-24 | 2021-06-04 | 中山大学 | Method for biologically preventing and controlling cryptocaryon irritans infection by using tilapia |
| CN112889717B (en) * | 2021-02-24 | 2022-03-15 | 中山大学 | Method for biologically preventing and controlling cryptocaryon irritans infection by using tilapia |
| CN116562469A (en) * | 2023-07-10 | 2023-08-08 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
| CN116562469B (en) * | 2023-07-10 | 2023-09-19 | 湖南师范大学 | Fresh water fish pathogen transmission prediction method, device, equipment and storage medium |
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Application publication date: 20120711 |