Development Of Water Quality Online Monitoring System Based On Node.js And Associated Analysis Technology

With the increasing shortage of water resources,sewage treatment plants are increasing,and how to quickly and efficiently achieve water quality monitoring has become a major problem to be solved.With the rapid development of mobile communication technology,remote monitoring based on mobile Internet infrastructure is no longer a problem.This article starts with the underlying hardware and designs and develops a complete set of B/S architecture based on the node.js server water quality online monitoring system.Because the chemical oxygen demand of the key parameters of the reaction wastewater quality is difficult to measure,although there are many on-line monitors for measurement in the market,there are widespread disadvantages such as high price and high maintenance cost.Therefore,this article relies on the large amount of water obtained by the online monitoring system.Data,based on the development of support vector machines,realizes real-time online prediction of chemical oxygen demand in sewage water.According to the analysis,the prediction accuracy meets the design requirements and can be put into the actual production environment.This article takes the actual project as the background,and the developed online monitoring system considers all aspects of the actual production environment.The on-site PC was developed using the Siemens WinCC configuration software.The on-site main controller was a domestic LangPuFeng RPC 2400 programmable communication converter.The entire low-level hardware detection cabinet adopts a modular design,which is easy to transport and assembled in the field.The water quality monitoring system monitoring center is developed based on node.js,and is divided into bottom communication service,Restful Api service,and page service according to functions.The three services are independent and perform well in managing multiple water quality monitoring sites.Based on the large amount of water quality data acquired by the online monitoring system,this paper uses the scikit-learn machine learning library to develop a chemical oxygen demand prediction model based on a support vector machine.The measured mean square correlation coefficient of the model is 0.825,which can accurately predict the chemical oxygen demand parameters of sewage water.According to the actual production environment operating conditions,the water quality monitoring system designed and developed in this paper is in good operating condition.The user can conveniently manage the field equipment under its jurisdiction and obtain on-site measurement data in time,which provides decision-makers with good data support and machine learning.The introduction of algorithms also provides new possibilities for water quality measurement methods.