Based on Improved Artificial Neural Network Sewage Monitoring Alarm System Method

Sewage discharge has become a key issue affecting the quality of the water environment, and how to effectively monitor and manage sewage discharge behavior has become a key factor to avoid water pollution and improve water quality. However, the current domestic sewage discharge monitoring system is not perfect, resulting in the lack of effective monitoring of enterprise sewage discharge by regulatory authorities, which provides an opportunity for enterprises to steal discharge. In the background of sewage treatment plant, the comprehensive design of sewage monitoring and alarm system is carried out based on the idea of physical information fusion. The design adopts a four-layer information physical architecture, which is divided into four parts: perception communication, fusion processing, push, and execution. In the fusion treatment part, the neural network intelligent algorithm is used to predict the dissolved oxygen, and the oxygen delivery is adjusted according to the predicted value to achieve accurate aeration and optimize the effluent quality. The push and execution parts adopt multiparameter monitoring to realize the smooth operation of equipment and ensure the system security. A new optimal control strategy of dissolved oxygen based on neural network is proposed. Through a large number of experiments and historical data, the intake index and dissolved oxygen value of the aeration tank under the condition of optimal outlet water are obtained as samples. According to the sample training, the BP neural network optimized by particle swarm optimization algorithm is adopted to achieve accurate prediction of dissolved oxygen under different inlet water conditions. The smooth operation of sewage treatment equipment is accomplished by the lower machine and the upper machine. In sewage treatment, each process section collects the equipment status in strict accordance with the order of sewage monitoring facilities. Then the communication network between the upper computer and the lower computer and the sensor is designed. The lower machine adopts PLC as the core, programming PLC through STEP7, and uses PID algorithm to control dissolved oxygen. The PC is developed in C language, so as to realize user login, real-time data display, over-limit fault alarm, report query, user management, etc. The PC integrates MATLAB neural network on the platform to predict dissolved oxygen through mixed programming quantity. The sewage alarm system based on improved artificial neural network is sensitive and has excellent performance. It provides a new idea for intelligent sewage detection and real-time monitoring.