Research And Design Of A Remote Monitoring System For Cyanobacteria In Chaohu Partial Lake Based On IoT Technology

The continuous advancement of industrialization and urbanization,different kinds of wastewater such as industrial wastewater,domestic wastewater and agricultural receding water converge into the lake through the river.The nutrient composition of the lake water rich in nitrogen,phosphorus and other elements provides sufficient nutrients for the growth of various algae groups,causing a large number of algae to proliferate and form blooms covering the surface of the lake.The dominant algal bloom in Chaohu Lake is cyanobacteria,and every summer cyanobacterial blooms lead to deterioration of water quality,Cause the water resources of the Chaohu Lake Basin to be negatively affected.Therefore,it is very important to establish long-term continuous monitoring and early warning for the cyanobacteria blooms in Chaohu Lake.This paper introduces the research and design of Chaohu Lake local remote cyanobacteria monitoring and early warning system based on IoT technology.The monitoring of local cyanobacteria blooms in Chaohu Lake is transmitted remotely through wireless 4G communication transmission,and the image recognition algorithm is used to process the monitored cyanobacterial images and determine the level of outbreak to realize the monitoring of remote cyanobacterial water bloom..The system is structured according to the three-layer model of the Internet of Things,as followed,design of image acquisition device for perception layer,the content of the transport layer is to study the 4G wireless communication transmission protocols,and the design of the system remote management platform for the application layer.The image acquisition device of the remote monitoring terminal is a microprocessor development board of the S5PV210 model,the image sensor uses a CMOS module model OV3640,the power supply method uses a combination of solar power generation and lithium battery storage.The hardware implementation of the transmission layer uses a wireless transmission module based on 4G communication,and the remote communication protocol is RTP/RTCP real-time transmission protocol based on UDP communication protocol.The system management platform of application layer uses Python development environment,web design based on B/S architecture,JS and HTML interface design language,real-time display of cyanobacteria outbreak image and outbreak level using web browser.For the acquired cyanobacterial water bloom images using the color features of cyanobacterial water bloom,the RGB three-channel components in image processing are used to extract the color feature values,and the cyanobacterial bloom levels are classified by constructing the correspondence between the algal density and the RGB feature component values of cyanobacterial images.In this paper,IOT technology is applied to the monitoring of cyanobacterial bloom in environmental monitoring,using image acquisition device to monitor the growth of cyanobacterial bloom in the local area of Chaohu Lake in real time,transmitting cyanobacterial bloom images remotely through wireless communication,then processing the collected cyanobacterial images and determining the level of the bloom,and finally displaying the real-time monitoring results through the web management platform,realizing the remote monitoring of local cyanobacterial bloom in Chaohu Lake.This is a remote monitoring of the local cyanobacteria outbreak in Chaohu Lake,with the advantages of timeliness and intelligence.In this paper,a 4G transmission module is used to establish data communication between the remote image acquisition device and the laboratory management platform to realize the monitoring of cyanobacterial water bloom outbreak in the local area of Chaohu Lake by web browser observation.Based on the color feature recognition of image processing,this paper establishes the correspondence between algae density and RGB channel component values of cyanobacteria images,realizes the classification of five levels of cyanobacteria outbreaks,provides outbreak warning alerts when the recognition results reach the set conditions,provides timely feedback on cyanobacteria outbreaks,and helps to control the further aggravation of water pollution by cyanobacteria outbreaks.