| With the rapid development of global economy, Industrial and agricultural pollution aggravate which makes lakes and reservoirs algal blooms growing phenomenon. In recent years, Prevention of water bloom become a hot issue. Scholars, at home and abroad, have started to launch a study from mathematical mechanism and intelligence technology, etc. On the basis of in-depth study blooms to predict structures, water quality parameters of automatic acquisition and analysis system will be one of the focus of future research,Then the.issue is unfolding around this area.Firstly, this paper discusses the research background and significance and The research status both at home and abroad and then presents the research contents. Refine the research according issue needs analysis, then the overall design idea and diagram of water quality monitoring system is proposed, simply introducing the acquisition terminal and monitoring system. And then focusing on bloom prediction methods. By combining the strong points of Grey-model and BP Neural network, building the mechanism model of bloom prediction has overcome the weakness that the low prediction accuracy of grey model and many training data that the BP neural network required, solving the problem of algae bloom forecasting in the limited condition of monitoring information. Then, this paper expounds the software and hardware design of acquisition terminals. Acquisition terminals adopt YSI, a multi-probe sensor, which gathers water quality parameters, AVR which processes water quality parameters and GPRS which transmits parameters to monitoring center. Finally, this paper accounts for monitoring system design. The system was designed by using VC++, which can display the receiving water quality parameters in real-time and also can store these data into the database, which to facilitate the query. At the same time the system can synthetically analyze and govern these data, Achieve the prediction function. |
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