Research On Energy Saving And Monitoring Of Intelligent Pump Based On Edge Computing

In recent years,with the continuous development of industrial automation technology,more and more intelligent control systems have been applied in various industrial fields.In the control of water pumps,due to the complexity of the water pump system,traditional manual control methods can no longer meet the needs of energy conservation and intelligent control.The energy-saving and monitoring system of water pumps is receiving increasing attention.Edge computing platform can effectively solve this problem.Therefore,based on the edge computing platform,this paper conducts research on the energy conservation and monitoring of pumps,and proposes two algorithms as follows:(1)A model was established with the optimization objective of minimizing the daily power consumption cost of the pumping station in sewage treatment plants to address the issues of low operating efficiency and high operating costs.The improved particle swarm optimization algorithm is used as the model solving algorithm.The idea of simulated annealing is introduced into the elementary particle swarm optimization algorithm to solve the problem that particles are affected by local minimum.Through the practical application in the sewage lift pump station,it is found that the simulated annealing particle swarm optimization(SA-PSO)is far better than the elementary particle swarm optimization algorithm in algorithm stability.Under the improved particle swarm optimization algorithm planning,the daily power consumption of the sewage pump station optimization scheme is lower than that of the elementary particle swarm optimization algorithm and the scheme formulated by experience.(2)Back Propagation(BP)neural network is an artificial neural network with strong approximation and prediction capabilities.In intelligent pump systems,BP neural networks can be used to predict the future operating status and energy consumption of pumps.By training historical data,the BP neural network can provide accurate pump state prediction,helping control systems adjust and optimize.During the training process,it is necessary to set the input data and target output,as well as error indicators and training times.Then,based on the node outputs of the hidden layer and output layer,the actual output and target output errors are calculated,and the connection weights and thresholds are adjusted through backpropagation algorithms to ultimately obtain the trained model.In order to better apply the trained model,it is necessary to deploy the model.Edge computing technology is used to deploy models,deploy the trained models to the edge cloud,and realize real-time monitoring and prediction of pump station energy consumption.During the deployment process,it is necessary to configure and install relevant software and environment,and use appropriate language for programming.Edge computing is a computing model that allocates computing power to devices and edge nodes.By deploying computing and storage capabilities on Edge device close to data sources,data transmission delay and bandwidth costs can be greatly reduced,and response speed and data security can be improved.Specifically,the workflow of the intelligent pump energy-saving and monitoring system is as follows: Firstly,sensors are used to obtain the operating status information of the pump,including parameters such as flow rate,head,voltage,etc.Then,input these data into the SA-PSO algorithm for processing,optimize the pump control strategy,and reduce energy consumption.Next,input the processed data into the BP neural network for training to predict the future state and energy consumption of the pump.During this process,the Azure IoT Edge platform can be used to deploy and run the SA-PSO algorithm and BP neural network model,achieving localized data processing and analysis,reducing the pressure of data transmission and cloud computing,and improving the system’s response speed and accuracy.Finally,deploy the trained model to the edge cloud for real-time monitoring and control.In this paper,the pump station of Yunchen Huayuan Domestic Sewage Treatment Plant is taken as the experimental object.Through the application of edge computing technology,the intelligent pump system realizes the data processing and analysis,optimizes the operation scheme of the pump set,predicts the future energy consumption,and realizes the purpose of intelligent control and energy conservation.At the same time,the application of edge computing technology also provides a more flexible and efficient solution for the deployment and management of intelligent pump systems.