Research And Implementation Of Online Monitoring,Prediction And Management Methods In Heating System

In winter,heating is indispensable in North China,especially in Northeast China,at present,heating stations in various regions supply the main heat for every household.In order to timely master the actual operation condition of heating equipment in heating stations,ensure on-demand heating and save energy,simultaneously facilitate the management of heating work in heating stations,an integrated online heating monitoring,prediction and management system is designed,developed and implemented.For monitoring the actual operation condition of heating equipment in heating stations,this paper studies the configuration design and development of monitoring the heating equipment working condition.Due to the different connections and operation structures of the heating equipment in each station,in order to quickly realize the reconstruction of the monitoring software interface and avoid the cumbersome development process,this paper develops the equipment operation condition interface by designing the configuration software architecture of the configuration module and the operation module,and monitors the equipment working condition in the form of animation to realize the real-time interaction of favorable man-machine interface.Through the real-time monitoring of the heating equipment in the browser of the heating monitoring,prediction and management system,the working condition and fault information of the heating equipment are displayed,helping the management personnel to gain insight into the heating situation in a timely manner.Ensuring the heating on demand and avoiding the imbalance between cold and hot are the guarantee of people’s comfort in winter,so it is necessary to predict the heat delivery of heating stations in advance.This paper studies the heating load prediction based on the improved BP neural network algorithm.The traditional BP neural network algorithm is easy to fall into the local optimal solution,leading the global optimal solution unsuccessful,at the same time the learning rate cannot be adjusted,leading the prediction results inaccurate,the improved BP neural network algorithm is proposed to avoid these limitations,which makes hourly prediction of heating load.The prediction model can be applied into the heating monitoring,prediction and management system.According to the prediction results,the heating managers can make reasonable arrangements for the heating amount in the future,so that people can work and live at the most suitable temperature.The heating monitoring,prediction and management system is designed,developed and implemented based on Python language,which has online monitoring of heating equipment operating condition,hourly heating load prediction,managing users,managing heating station,querying data statistics,recording operation logs and a series of functions such as controlling the heating equipment.The system has complete functions and can run smoothly,which provides convenience for the management of heating work and has practical guiding significance.