Design And Implementation Of Decision-making Support System For Water Scheduling Consultation In Taihu Lake Basin

In order to support the scheduling decision of the Taihu Basin water conservancy project,on the basis of big data,artificial intelligence and other modern information technologies,a research on the Taihu Lake water level prediction technology based on artificial intelligence and statistical related methods was carried out,and a scheduling scheme generation method based on similarity analysis was proposed Based on the event-driven design concept,the design and implementation of the decision-making support system for the intelligent dispatch consultation in the Taihu Lake Basin was completed.At present,the system has been officially run by the Taihu Basin Administration,providing intelligent information support for the scheduling decision of the Taihu Basin water conservancy project.First,regarding the prediction of the Taihu Lake water level,considering the bottleneck of the prediction accuracy improvement based on the commonly used hydrodynamic calculation prediction method in the river network area,consider using artificial intelligence and statistical related methods to predict the future Taihu water level prediction.In order to grasp the trend of water regime changes in the Taihu Lake Basin in the next 3 days,on the basis of analyzing the long series of water regime data in the Taihu Lake Basin,the key factors that have the greatest impact on the future Taihu Lake water level were screened using the correlation coefficient method and based on multiple linear regression and BP neural network The algorithm constructs the water level prediction model for the next 1-7 days in the Taihu Lake Basin.The case study shows that the simulation results of the constructed model are good and can meet the actual engineering needs of decision-making support of the Taihu Lake Basin Dispatch.Secondly,in the aspect of scheduling decision support,starting from mining historical similar scheduling schemes,the research of historical similar scheduling process analysis is carried out.According to the traditional thinking model of relying on experience to find similar solutions,an intelligent similarity analysis model is constructed based on Euclidean distance,BORDA counting method and XGboost machine learning algorithm to find the most similar solutions in the past and current situation.The results show that the model has fast operation speed and good effect,and can actually support the scheduling decision of the Taihu Basin water conservancy project.Finally,in order to push the aforementioned predictions and scheduling decision conclusions to scheduling decision makers in a more intelligent manner,an event-driven scheduling decision support system was designed.The system can automatically determine the current scheduling scenario based on the prediction of the Taihu Lake water level.Based on the judged scenario-driven similarity analysis model,the conclusions presented in the analysis are presented to decision makers in a large-screen visualization,with a view to providing intelligent scheduling decision support for decision makers.