Research On Water Quality Monitoring System Of Fishing Grounds Based On Internet Of Things

The traditional management methods in the fisheries industry heavily rely on manual observation and management,making it difficult to achieve precision management.There are limitations and errors in the accuracy of monitored data.In today’s fisheries sector,utilizing intelligent approaches to achieve precision farming in fishery ponds has become an important direction for industry development.This article primarily focuses on developing a fishery water quality monitoring system based on NB-IoT communication technology,utilizing a "cloud-edge-device" development model.First,research on system "end" development will be conducted,developing device-side applications on IoT development boards.This system will be equipped with a DS18B20 temperature sensor,PH sensor,and total dissolved solid TDS sensor,which will collect water quality data.Code will be written on the microcontroller on the Arduino board to process the data.The water quality data will then be transmitted to the cloud platform through the MQTT message queue telemetry transmission protocol.Secondly,the design of software nodes for the "cloud" will be researched.An Internet of Things cloud platform will be built,allowing users to access the platform and control the underlying devices and local data processing through terminals.The cloud platform will interact with sensors to collect various water quality parameters for analysis,providing secure and reliable connection communication capabilities for the equipment.In addition,research on the "edge" side design of the system will be conducted.The edge side is mainly focused on pre-processing and stream data processing,using the Kalman filter algorithm to denoise the collected data,and using the particle swarm optimization algorithm in collective intelligence algorithms to iteratively optimize the value of the objective function.The effectiveness of the algorithm will be verified through experimental validation.Finally,system testing and experimental analysis will be conducted.The testing will mainly be divided into two parts:system power consumption testing and cloud testing.We will test and experiment the water temperature,PH value,and TDS value,and analyze the comparative test data and error values.The results show that the system can meet the design requirements and help improve the digital level and efficiency of fishing farms,which can contribute to the sustainable development of fisheries.