CN102550454A - Method for predicting cryptocaryoniosis in Larimichthys crocea - Google Patents
Method for predicting cryptocaryoniosis in Larimichthys crocea Download PDFInfo
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- 241001596950 Larimichthys crocea Species 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 title claims abstract description 14
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 59
- 201000010099 disease Diseases 0.000 claims abstract description 26
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 26
- 230000007613 environmental effect Effects 0.000 claims abstract description 12
- XKMRRTOUMJRJIA-UHFFFAOYSA-N ammonia nh3 Chemical compound N.N XKMRRTOUMJRJIA-UHFFFAOYSA-N 0.000 claims description 42
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 claims description 30
- 239000001301 oxygen Substances 0.000 claims description 30
- 229910052760 oxygen Inorganic materials 0.000 claims description 30
- 230000000694 effects Effects 0.000 claims description 10
- 230000002265 prevention Effects 0.000 claims description 6
- 230000000638 stimulation Effects 0.000 claims description 4
- 241001663425 Cryptocaryon Species 0.000 claims description 3
- ORIGEOXWTMPZQD-DUFGSWQCSA-N Cryptocaryon Natural products O[C@H]1C=C[C@H]2OC(=O)C[C@H]2[C@@H]1C(=O)C=Cc3ccccc3 ORIGEOXWTMPZQD-DUFGSWQCSA-N 0.000 claims description 3
- 238000013178 mathematical model Methods 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims description 2
- 238000011156 evaluation Methods 0.000 claims description 2
- NPPQSCRMBWNHMW-UHFFFAOYSA-N Meprobamate Chemical compound NC(=O)OCC(C)(CCC)COC(N)=O NPPQSCRMBWNHMW-UHFFFAOYSA-N 0.000 abstract 1
- 238000005516 engineering process Methods 0.000 description 13
- 238000009395 breeding Methods 0.000 description 8
- 230000001488 breeding effect Effects 0.000 description 8
- 238000009360 aquaculture Methods 0.000 description 4
- 244000144974 aquaculture Species 0.000 description 4
- 238000010586 diagram Methods 0.000 description 4
- 230000004936 stimulating effect Effects 0.000 description 4
- 238000004458 analytical method Methods 0.000 description 3
- 230000006806 disease prevention Effects 0.000 description 3
- 239000003814 drug Substances 0.000 description 3
- 229940079593 drug Drugs 0.000 description 3
- 230000003449 preventive effect Effects 0.000 description 3
- 208000031295 Animal disease Diseases 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000116 mitigating effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 239000000126 substance Substances 0.000 description 2
- 241000251468 Actinopterygii Species 0.000 description 1
- 241000894006 Bacteria Species 0.000 description 1
- 208000003322 Coinfection Diseases 0.000 description 1
- 241001663423 Cryptocaryon irritans Species 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 239000003623 enhancer Substances 0.000 description 1
- 210000002816 gill Anatomy 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 244000045947 parasite Species 0.000 description 1
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- 230000000246 remedial effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000033764 rhythmic process Effects 0.000 description 1
- 230000005945 translocation Effects 0.000 description 1
- 210000003812 trophozoite Anatomy 0.000 description 1
- 238000003911 water pollution Methods 0.000 description 1
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Abstract
Description
技术领域 technical field
本发明涉及一种大黄鱼,尤其是涉及一种大黄鱼刺激隐核虫病的预测方法。The invention relates to a large yellow croaker, in particular to a method for predicting cryptocytoniasis stimulated by large yellow croakers.
背景技术 Background technique
大黄鱼(Pseudosciaena crocea)是我国海水网箱养殖单一品种产量最大的鱼类,年产量约7万吨。但是,近年来刺激隐核虫(Cryptocaryon irritans)病的暴发、蔓延,严重威胁着大黄鱼养殖产业的可持续发展。据统计,2005年以来,福建宁德市三都澳养殖区大黄鱼因寄生虫及细菌性继发感染,每年造成的直接经济损失超过3亿元。2008年农业部在新版《一、二、三类动物疫病病种名录》中已将刺激隐核虫病列为二类动物疫病。Large yellow croaker (Pseudosciaena crocea) is the fish with the largest output of a single species in marine cage culture in my country, with an annual output of about 70,000 tons. However, in recent years, stimulating the outbreak and spread of Cryptocaryon irritans disease has seriously threatened the sustainable development of large yellow croaker aquaculture industry. According to statistics, since 2005, large yellow croakers in the Sanduao breeding area of Ningde City, Fujian Province have caused direct economic losses of more than 300 million yuan each year due to secondary infections caused by parasites and bacteria. In 2008, the Ministry of Agriculture has listed Cryptocaryoniasis as a second-class animal disease in the new version of "List of Types I, II, and III Animal Diseases".
大黄鱼刺激隐核虫病一旦发生暴发,使用药物防治的效果十分有限,尤其当刺激隐核虫侵入体表和鳃形成滋养体后,更难以用化学药物有效杀灭。此外,超量使用化学杀虫剂所引起的水质污染和永久残留等严重问题又带来了新的环境问题,最终影响水产品质量安全以及海水养殖持续健康发展。在长期的养殖实践中,养殖户已总结出一些预防性的有效防病措施,比如,在疾病暴发前期适时进行养殖密度分稀或者移排到水流较好的深水区,以及减少鲜杂饵料用量或者添加一些免疫增强剂等等,收到了较好的预防效果。可见,发病前适时、主动的预防措施比发病后药物防治的补救措施显得尤为重要。因此,建立一种大黄鱼刺激隐核虫疾病发生的提前预测技术,指导该病及早预防控制是非常有必要的。Once the cryptocystosis stimulated by large yellow croaker breaks out, the effect of using drugs to control it is very limited, especially when the stimulated Cryptocaryonia invades the body surface and gills to form trophozoites, it is even more difficult to effectively kill them with chemical drugs. In addition, serious problems such as water pollution and permanent residues caused by excessive use of chemical pesticides have brought new environmental problems, which ultimately affect the quality and safety of aquatic products and the sustainable and healthy development of marine aquaculture. In the long-term breeding practice, farmers have summed up some preventive and effective disease prevention measures, such as timely thinning the breeding density or moving them to deep water areas with better water flow in the early stage of disease outbreaks, and reducing the amount of fresh and miscellaneous baits Or add some immune enhancers, etc., and have received a better preventive effect. It can be seen that timely and active preventive measures before the onset are more important than remedial measures for drug prevention and treatment after the onset. Therefore, it is very necessary to establish an early prediction technology for the occurrence of Cryptocaryoniasis stimulated by large yellow croaker and to guide the early prevention and control of the disease.
我国已有依据透明度、温度和平均风速对大黄鱼养殖疾病(所有疾病)的预测方法(参见中国专利:200710068792.6)。但是,大黄鱼养殖疾病种类多样,流行特点各异,特别是同一时间点内多种疾病并发时所涉及的环境因子效应非常复杂,影响不同疾病的主效因子不完全相同,依据特定的水质因子对所有疾病实施预测难度较大。my country already has a prediction method for large yellow croaker breeding diseases (all diseases) based on transparency, temperature and average wind speed (see Chinese patent: 200710068792.6). However, there are various types of large yellow croaker breeding diseases, and their epidemic characteristics are different. In particular, the effects of environmental factors involved in the concurrent occurrence of multiple diseases at the same time point are very complex, and the main effect factors affecting different diseases are not completely the same. According to specific water quality factors It is difficult to predict all diseases.
大黄鱼刺激隐核虫病发生与我国大黄鱼主产区复杂的养殖生态系统密切相关,该病受养殖海域的水文、物理、化学及生物因子的多重影响明显,许多专家对刺激隐核虫病与水环境因子的关系进行了定性阐述。在此基础上,如果能建立基于关键水环境因子的预测技术,对特定的刺激隐核虫病实施预测判别,将对于这一恶性病害的预防减灾具有重要的意义。然而,我国尚没有与刺激隐核虫病关联的水环境因子预测技术的研究报道,更没有对大黄鱼刺激隐核虫病预测的成熟方法。The occurrence of cryptocystosis stimulated by large yellow croaker is closely related to the complex aquaculture ecosystem in the main production areas of large yellow croaker in my country. The relationship with water environment factors was qualitatively described. On this basis, if a prediction technology based on key water environment factors can be established to predict and discriminate specific Cryptocaryonia stimuli, it will be of great significance for the prevention and mitigation of this malignant disease. However, there is no research report on the prediction technology of water environment factors related to cryptocystosis in my country, and there is no mature method for predicting cryptocystosis in large yellow croaker.
发明内容 Contents of the invention
本发明的目的在于提供一种可准确预测将来某一时间点的水质环境因子,实现对大黄鱼刺激隐核虫病的提前预测的大黄鱼刺激隐核虫病的预测方法。The object of the present invention is to provide a method for predicting cryptocytosis of large yellow croaker that can accurately predict water quality environmental factors at a certain time point in the future and realize the prediction of cryptocystosis of large yellow croaker in advance.
本发明包括以下步骤:The present invention comprises the following steps:
1)水环境因子预测方程的构建1) Construction of water environment factor prediction equation
根据不同月份水环境因子的历史数据,运用多种时间序列模型分析水环境因子的变化规律与趋势,分别构建水温、溶解氧和氨氮值对月份的拟合曲线,比较拟合效果,从中筛选最好的模型建立拟合方程,其回归方程分别为:According to the historical data of water environmental factors in different months, a variety of time series models are used to analyze the changing laws and trends of water environmental factors, and the fitting curves of water temperature, dissolved oxygen and ammonia nitrogen values to months are respectively constructed to compare the fitting effects and select the best results from them. A good model establishes a fitting equation, and its regression equations are:
(1)水温:(1) Water temperature:
f(x)=20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)f(x)=20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)
其中f(x)为水温,x为月份;Where f(x) is the water temperature and x is the month;
(2)溶解氧:(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)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)
其中f(x)为溶解氧,x为月份;Where f(x) is dissolved oxygen and x is month;
(3)氨氮:(3) Ammonia nitrogen:
y(t)=0.182y(t-1)+0.778y(t-2)+e(t)y(t)=0.182y(t-1)+0.778y(t-2)+e(t)
其中y(t)为当期的氨氮值的实际值,y(t-1)为前一期的氨氮值,y(t-n)为前n期的氨氮值,e(t)为当期的误差值,则当期氨氮的实际值为当期氨氮的预测值加上误差值;Among them, y(t) is the actual value of the ammonia nitrogen value of the current period, y(t-1) is the ammonia nitrogen value of the previous period, y(t-n) is the ammonia nitrogen value of the previous n periods, and e(t) is the error value of the current period, Then the actual value of ammonia nitrogen in the current period is the predicted value of ammonia nitrogen in the current period plus the error value;
2)水环境因子的提前预测2) Prediction of water environment factors in advance
根据所建立的拟合方程,计算未来某一具体时间点的水温、溶解氧和氨氮的预测值;Calculate the predicted values of water temperature, dissolved oxygen and ammonia nitrogen at a specific time point in the future according to the established fitting equation;
3)刺激隐核虫发病情况的提前判别3) Stimulate early identification of Cryptocaryon morbidity
收集历史的月份、水温、溶解氧和氨氮4因子以及对应的刺激隐核虫病严重等级数据,利用R软件环境中加载的随机森林程序包对所述收集历史的月份、水温、溶解氧和氨氮4因子以及对应的刺激隐核虫病严重等级数据进行分析,建立月份、水温、溶解氧和氨氮4因子判别疾病等级的数学模型,利用水温、溶解氧和氨氮3个因子预测模型,对下一期的水温、溶解氧和氨氮值进行预测,然后将预测值代入已建好的疾病判别模型中,对大黄鱼刺激隐核虫病进行预测,并作相应解读,判断刺激隐核虫病的可能发生情况,据此采取适当的防控措施,减少因病损失;所得的预测值的准确性评估可与大黄鱼刺激隐核虫病的实际发生情况进行比较。Collect historical months, water temperature, dissolved oxygen and
本发明的技术方案是先将获得的水环境因子的预测值,然后代入基于水环境因子的刺激隐核虫病的数学判别模型,计算出疾病的预测值,形成大黄鱼刺激隐核虫病的提前预测技术,从而实现对大黄鱼刺激隐核虫病发生的提前预测。The technical scheme of the present invention is to firstly substitute the predicted value of the obtained water environment factor into the mathematical discriminant model of stimulating Cryptocaryoniasis based on water environment factors, calculate the predicted value of the disease, and form the prediction value of large yellow croaker stimulating Cryptocaryoniasis. Advanced prediction technology, so as to realize the early prediction of the occurrence of cryptocystosis stimulated by large yellow croaker.
与现有技术相比,本发明具有以下的优点:Compared with the prior art, the present invention has the following advantages:
1)简单方便。本发明基于历史数据库建立的水质预测技术,反映了周年变化规律,仅需要提供月份,就可以根据一系列拟合方程获得水环境因子的参数。并结合疾病判别模型进行运算,最终获得疾病的预测值,非常方便使用。1) Simple and convenient. The water quality prediction technology established by the invention based on the historical database reflects the annual change rule, and the parameters of the water environment factors can be obtained according to a series of fitting equations only by providing the month. Combined with the disease discrimination model for calculation, the predicted value of the disease is finally obtained, which is very convenient to use.
2)提前预测。本发明针对特定的刺激隐核虫病,首次提供了一种提前预测大黄鱼刺激隐核虫病的技术,方便养殖户及早采取措施进行疾病防控和预防减灾。2) Prediction in advance. The present invention provides a technology for predicting cryptocystosis of large yellow croaker in advance for the specific cryptocystosis irritating for the first time, which is convenient for farmers to take early measures for disease prevention and control and disaster prevention and mitigation.
附图说明 Description of drawings
图1为水温关于时间的趋势拟合图。在图1中,横坐标为时间(月),纵坐标为水温(℃);标记“◆”为实际值,“-”为预测曲线。Figure 1 is a trend fitting diagram of water temperature with respect to time. In Figure 1, the abscissa is time (month), and the ordinate is water temperature (°C); the mark "◆" is the actual value, and "-" is the forecast curve.
图2为溶解氧关于时间的趋势拟合图。在图2中,横坐标为时间(月),纵坐标为溶解氧(mg/l);标记“◆”为实际值,“-”为预测曲线。Figure 2 is a trend fitting graph of dissolved oxygen with respect to time. In Figure 2, the abscissa is time (month), and the ordinate is dissolved oxygen (mg/l); the mark "◆" is the actual value, and "-" is the forecast curve.
图3为氨氮值的自相关分析结果。在图3中,横坐标为滞后阶数Lag,纵坐标为样本相关度Sample Autocorrelation。Figure 3 is the autocorrelation analysis results of the ammonia nitrogen value. In Figure 3, the abscissa is the lag order Lag, and the ordinate is the sample correlation Sample Autocorrelation.
图4为氨氮关于时间的趋势拟合图。在图4中,横坐标为时间(月),纵坐标为NH4+-N(mg/l);标记“△”为实际值,“+”为预测值,“~”为内插线。Figure 4 is a trend fitting diagram of ammonia nitrogen with respect to time. In Figure 4, the abscissa is time (month), and the ordinate is NH4 + -N (mg/l); the mark "△" is the actual value, "+" is the predicted value, and "~" is the interpolation line.
具体实施方式 Detailed ways
以下实施例将结合附图对本发明作进一步说明。The following embodiments will further illustrate the present invention in conjunction with the accompanying drawings.
实施例1:水环境因子预测技术的建立。Example 1: Establishment of water environment factor prediction technology.
本发明首先收集福建宁德三都湾大黄鱼养殖海域的5个不同站位的水温、溶解氧和氨氮值的月监测数据(水质数据来自国家海洋局东海分局闽东海洋环境监测中心站,疾病数据来自福建省海洋水产技术推广总站病防科,数据采集、分析均按照国家规范标准),建立了反映其变化趋势和历史规律的回归方程。其中月份按2005年1月到2010年5月分别记成1-65;不同站位的水温、溶解氧、氨氮值分别求平均值,并比较多种方法的趋势及曲线的拟合效果,选择出最佳的拟合方程。The present invention first collects the monthly monitoring data of water temperature, dissolved oxygen and ammonia nitrogen value of 5 different stations in the large yellow croaker breeding sea area of Sandu Bay, Ningde, Fujian (the water quality data comes from the East China Sea Sub-bureau Fujian Ocean Environment Monitoring Center Station of the State Oceanic Administration, and the disease data From the Department of Disease Prevention and Control of Fujian Marine Aquaculture Technology Extension General Station, data collection and analysis are in accordance with national standards), and a regression equation reflecting its changing trend and historical law was established. Among them, the months are recorded as 1-65 according to January 2005 to May 2010; the water temperature, dissolved oxygen, and ammonia nitrogen values at different stations are respectively averaged, and the trends and curve fitting effects of various methods are compared. Get the best fit equation.
1)水温预测技术1) Water temperature prediction technology
水温是随季节变化而变化的,有着明显的周期性。本发明选择傅里叶函数对水温随月份变化趋势进行拟合,结果如下:The water temperature changes with the seasons and has obvious periodicity. The present invention selects the Fourier function to fit the water temperature with the monthly variation trend, and the results are as follows:
f(x)=20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)f(x)=20.93-2.835*cos(0.5276*x)-8.043*sin(0.5276*x)
其中f(x)为水温,x为月份。Where f(x) is the water temperature and x is the month.
该拟合曲线的R2=0.9528,说明用1阶傅里叶函数对水温随月份的变化拟合具有较好的效果,拟合图见图1。The R 2 of the fitting curve is 0.9528, which shows that the fitting effect of the first-order Fourier function on the variation of water temperature with the month is good, and the fitting diagram is shown in Fig. 1 .
2)溶解氧的预测技术2) Prediction technology of dissolved oxygen
溶解氧与大黄鱼的养殖模式、潮汐节律以及浮游动植物的周年变化等环境因子密切关,是影响刺激隐核虫病发生的重要因素。本发明选择傅里叶函数对溶解氧随月份变化趋势进行拟合,结果如下:Dissolved oxygen is closely related to environmental factors such as the breeding mode of large yellow croaker, tidal rhythm, and annual changes of phytoplankton, and is an important factor that stimulates the occurrence of cryptocystosis. The present invention selects the Fourier function to fit the trend of dissolved oxygen with the month, and the results are as follows:
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)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)
其中f(x)为溶解氧,x为月份。Where f(x) is dissolved oxygen and x is month.
该拟合曲线的R2=0.9369,说明用4阶傅里叶函数对溶解氧随月份的变化拟合具有较好的效果,拟合图见图2。The R 2 of the fitting curve is 0.9369, which shows that the 4th-order Fourier function has a good effect on fitting the change of dissolved oxygen with the month. The fitting diagram is shown in Fig. 2 .
3)氨氮的预测技术3) Prediction technology of ammonia nitrogen
氨氮是衡量水环境质量的重要因素,也是影响刺激隐核虫病发生的主效因子之一。本发明选择自回归模型模拟氨氮的变化趋势。通过ACF分析的结果(见图3),选择了2阶自回归模型,结果如下:Ammonia nitrogen is an important factor to measure the quality of water environment, and it is also one of the main factors that affect and stimulate the occurrence of Cryptocaryoniasis. The present invention selects an autoregressive model to simulate the changing trend of ammonia nitrogen. Through the results of ACF analysis (see Figure 3), a 2-order autoregressive model was selected, and the results are as follows:
y(t)=0.182y(t-1)+0.778y(t-2)+e(t)y(t)=0.182y(t-1)+0.778y(t-2)+e(t)
其中y(t)为当期的氨氮值的实际值,y(t-1)为前一期的氨氮值,y(t-n)为前n期的氨氮值,e(t)为当期的误差值,则当期氨氮的实际值为当期氨氮的预测值加上误差值。故当期氨氮的预测值y(t)=0.182y(t-1)+0.778y(t-2);Among them, y(t) is the actual value of the ammonia nitrogen value of the current period, y(t-1) is the ammonia nitrogen value of the previous period, y(t-n) is the ammonia nitrogen value of the previous n periods, and e(t) is the error value of the current period, Then the actual value of ammonia nitrogen in the current period is the predicted value of ammonia nitrogen in the current period plus the error value. Therefore, the predicted value of ammonia nitrogen in the current period y(t)=0.182y(t-1)+0.778y(t-2);
本模型的Loss function=0.000155492;FPE=0.000180371Loss function of this model=0.000155492; FPE=0.000180371
说明该自回归模型具有较好的模拟效果,预测模拟见图4。It shows that the autoregressive model has a good simulation effect, and the prediction simulation is shown in Figure 4.
实施例2 水环境因子预测技术的准确性分析Embodiment 2 Accuracy Analysis of Water Environment Factor Prediction Technology
根据实施例1中建立的拟合方程,预测了某年7月的水温、溶解氧和氨氮值。表1为预测值与实际值的比较。由表1可知,各项因子预测值与实际值相对误差值均值10%以内,说明水质预测效果较好。According to the fitting equation established in Example 1, the water temperature, dissolved oxygen and ammonia nitrogen values in July of a certain year were predicted. Table 1 compares the predicted value with the actual value. It can be seen from Table 1 that the average relative error between the predicted value and the actual value of each factor is within 10%, indicating that the water quality prediction effect is good.
表1 某年7月水质因子的预测值与实际值比较Table 1 Comparison of predicted and actual values of water quality factors in July of a certain year
注:相对误差=(预测值-实际值)/实际值*100%Note: relative error = (predicted value - actual value) / actual value * 100%
实施例3刺激隐核虫发病情况的提前预测Example 3 stimulates the advance prediction of Cryptocaryon morbidity
收集历史的月份、水温、溶解氧和氨氮4因子以及对应的刺激隐核虫病严重等级数据,利用R软件环境中加载的随机森林程序包对上述数据进行分析,建立月份、水温、溶解氧和氨氮4因子判别疾病等级的数学模型。Collect the historical month, water temperature, dissolved oxygen and
某年7月的水温预测值为28.6℃,溶解氧的预测值为6.04mg/l,氨氮的预测值为0.034mg/l,代入已建好的疾病判别模型中可以预测某年7月的刺激隐核虫病的发病情况。The predicted value of water temperature in July of a certain year is 28.6°C, the predicted value of dissolved oxygen is 6.04 mg/l, and the predicted value of ammonia nitrogen is 0.034 mg/l. Substituting it into the established disease discrimination model can predict the stimulus in July of a certain year. The incidence of cryptocystosis.
经过运算后,某年7月刺激隐核虫病发生的程度为2级,也就是说处于少量发病的状态,养殖密度较高区域可以适当采取移排分稀等手段以避免发病。After calculation, the degree of stimulating cryptocystosis in July of a certain year is level 2, that is to say, it is in a state of a small amount of disease, and the areas with high breeding density can take appropriate measures such as translocation and thinning to avoid the disease.
刺激隐核虫病的预测结果与与实际相符,说明该预警技术具有较高的准确性。The prediction results of Cryptocaryonia stimuli are in line with the actual situation, which shows that the early warning technology has high accuracy.
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