Water Quality Monitoring System Based on Improved Kalman Filter Algorithm

For the processing of data in water quality detection in fishing grounds, a water quality monitoring system based on cloud edge integration is established. For the noise problem encountered in data collection, an improved Kalman filter data fusion algorithm based on sparrow search algorithm is proposed, and a mathematical model in the filtering process is established, making the system highly robust. This system can accurately obtain environmental data without supervision and upload it to the cloud platform, making contributions to improving the digitalization level and breeding efficiency of fishing grounds and the sustainable development of fisheries.