Research On Data Fusion Technology Of Water Environment Monitoring System Based On WSN

Water is the source of life,and everything is inseparable from water.But people are at the expense of the environment at the same time as they are developing at a high speed.General Secretary Water environment monitoring is beneficial to the adjustment of the overall industrial structure by national policies on a macro level,and is conducive to the improvement of the environment and the improvement of the quality of life of residents.Wireless sensor network(WSN)combines communication technology,computer technology and Internet of Things to integrate it with its low cost,wide distribution and flexible network construction,making it more suitable and leading the new innovation direction of water environment monitoring system.However,the current monitoring results are often one-sided,not timely and have a large deviation from the real situation,which has a great impact on the subsequent prevention of water pollution,the determination of pollution location and effective response measures,while a large amount of raw data is transmitted to Monitoring and processing centers are bound to cause an increase in transmission volume and network congestion to increase energy consumption.Therefore,it is very important and urgent to process data using Data Fusion technology.Based on the WSN(Wireless Sensor Network)water environment monitoring as the research background,this paper proposes an implementation plan for the water quality monitoring system based on the above issues,and combines the characteristics of the water quality monitoring parameters of the Liao River Basin to select the PH value,temperature,dissolved oxygen,ammonia nitrogen and total Five parameters of phosphorus were used as measurement parameters of the water environment monitoring system,and these data were processed by fusion.This paper proposes a two-level data fusion mechanism,in which the data level uses an adaptive weighted data fusion algorithm to reduce the amount of data transmitted,thereby reducing network energy consumption and extending the network life cycle.Aiming at the problems of insufficient fusion accuracy and poor anti-interference performance of the adaptive weighting algorithm,a stepwise adaptive weighted data fusion algorithm is proposed.By adding a virtual sensor to increase the number of fusions to improve the anti-interference performance,the traditional adaptive weighted data fusion algorithm is easily affected by external factors and the fusion result is not accurate enough.The decision-making level uses a BP neural network-based data fusion algorithm and optimizes it.It introduces dynamic factors to reduce the number of iterations,optimizes the weight initialization when setting the network construction parameters,and then selects a sample to train the network and perform the analysis on the water environment.Classification judgement determines the water quality situation to test the network.The simulation results of MATLAB show that the two-level data fusion mechanism can save the sensor energy and improve the accuracy of the data,and effectively judge the water quality to improve the credibility,and provide a basis for the subsequent further strategy.