Study On Forecasting And Warning Technology Of Intelligent Aquaculture System

In order to improve the quality and yield of aquaculture aquatic products,this paper selected three key parameters of water quality parameters of water temperature,PH value and dissolved oxygen research,the mechanism of the three key parameters in-depth analysis.In order to realize the prediction of water quality parameters and the accuracy of water quality warning,this paper first proposes a new noise reduction algorithm of water quality parameter data,uses wavelet analysis algorithm to noise reduction of water quality parameter data,and then uses the method of layering threshold to noise reduction results Fine processing.The experimental results show that the data denoising effect is more accurate than other data denoising methods in the field of water quality parameter data recovery,which can effectively shorten the time required for operation.In this paper,a new water quality parameter data recovery algorithm is also proposed.The data recovery algorithm based on compressed sensing is selected to recover the water quality parameter data.The method of block reconstruction is introduced to shorten the computation time.Adapt to the iterative method to solve the problem of poor recovery and computing time is too long.Finally,the other parameters of the water quality parameters are combined to extract the common components of the parameters by sparse decomposition to improve the data reconstruction accuracy.The experimental results show that the data recvery effect is more accurate than other data recovery algorithms in the field of water quality parameter data recovery,which can effectively shorten the time required for operation.The above is an improvement of the algorithm for parameter data preprocessing.After the data is preprocessed,a new water quality parameter data prediction algorithm is proposed in this paper to predict and analyze the processed data.The RBF-ARIMA combined model predictive algorithm was used to predict the water quality parameter data.The parameter data was decomposed into linear part and non-linear part.The predicted parameter data were respectively predicted.which could effectively predict the trend of water quality parameters.The experimental results show that the prediction results are more accurate than other data prediction algorithms in the field of water quality parameter data recovery,and the real-time performance is stronger.Finally,a new water quality early warning algorithm is proposed in this paper.The water quality early warning algorithm based on TS fuzzy neural network is used to evaluate the water quality,select a certain amount of historical data as the training sample to obtain the warning level,and then input the data that needs to be prewarning Early warning information.The early warning algorithm can effectively make an early warning assessment of the water quality environment.The experimental results show that the early warning results are more accurate than the other early warning algorithms in the field of water quality warning and provide a guarantee for the economic benefit cost of farmers.At last,a set of aquaculture intelligent monitoring system is developed and used,and the above algorithms are used in practical application to achieve the expected results.