Research On Intelligent Fishery Control System Based On WSN

The aquaculture water is highly complex, nonlinear, large delay system, the regulation of water quality, especially the dissolved oxygen has always been manually controlled. Human experts can make decisions just by observing the quality of aquaculture water, the experience of fish farming and meteorological condition. Artificial neural networks are used for water quality classification, meteorological conditions and control experience are represented by the knowledge and rules of expert system. This study solve the regulation problem of the dissolved oxygen and prediction problem of fish floating head or asphyxia by combining decision outcomes by artificial neural networks with expert knowledge.(1) The nature of water quality classification by the artificial neural networks is a multi-classification problem. A Comprehensive Water Quality Evaluation Neural Network (CWQNN) is constructed to make classification and this study develops an indicator to guide the trainning and demonstrate the effectiveness, reasonability of CWQNN according to the evaluation results.(2) This study implements’Expert Regulation Strategy of Dissolved Oxygen’ according to the related publications and the knowledge offered by the experts by Jess and achieves the control of the dissolved oxygen by combining the fuzzy expert control with’Expert Regulation Strategy of Dissolved Oxygen’. Experimental results show that this method can achieve the results as expected. Since high frequency of the occurrence of floating head or asphyxia and its serious impact on aquatic product, this study can predict the occurrence of fish floating head or asphyxia by combining the information acquired by CWQNN with meteorological factors and the experience of experts in the aquaculture water, and this is also implemented by Jess. Experimental results show that this method is feasible.(3) In order to solve the problem of data acquisition and remote control, a real-time monitoring and control system based on WSN is designed and developed which adopts the three-layer architecture of Internet of Things. The non-real-time and real-time data are handled respectively in order to solve the problem of real-time. Experimental results show great improvement of real-time.