该系统是结合GIS技术和GSM通信技术,实施对农业灌溉用水的远程调度和管理同时合理的对农作物进行灌溉保证了农产品的质量。在系统中充分利用计算机技术和GIS技术对各个不同用水点的信息进行可视化的管理。同时利用GSM网络覆盖面积广、抗干扰强等特点,实现对数据的远程传输。利用Visual C++6.0和SQL Server 2000数据库技术来实现对实时数据进行处理。通过对系统中的灌溉和用水历史数据的科学分析,从而达到对灌溉用水的科学使用和管理。通过应用示范,为北京市水利系统提供一套功能完备齐全的用水计量管理系统,达到对北京市地下用水统一管理,统一收费和科学决策的目的。
The increasingly mature nonlinear technique can facilitate accurate forecasting of transient sap flow process of plant.In this paper,the dominated tree species,Pinus tabulaeformis and Platycladus orientalis in Beijing mountainous area were chosen for study.Their monitoring data range from June 18 th to September 9 th 2007 was derived to form the 1 985 sets of sample respectively.BP (back propagation) neural network models were established according to the theory of automaton network of discrete dynamic system,the target output of which was sap flow velocity and the inputs of which consisted of five influencing factors,ie,air temperature,relative humidity,light intensity,stem diameter growth and soil water potential.To improve the generalization quality of networks,Bayesian regularization and early stopping modes were involved in the training process.After training in two modes above,the linear regression between simulated outputs and the corresponding targets of test sample sets showed good fits (R>0.85),which indicated a high forecasting precision of the models established,specifically when 11 neurons in hidden layer.Models demonstrated fine generalization under the two training modes in that the fit of test sample was equivalent to that of training sample,which further indicated their availability in practice.
