本文提出了一个全面而灵活的液压系统标定框架 网络模型。可以为配水系统指定校准任务 根据数据可用性和模型应用要求。它允许用户: 灵活选择模型参数的任意组合，例如管道粗糙度， 连接需求和连接（管道、阀门和泵）运行状态；容易地 聚合模型参数，以减少问题维度，以便快速计算； 并且，始终指定边界条件和交叉点需求荷载 与现场数据收集相对应。然后将模型校准定义为隐式校准 非线性优化问题，采用强大的遗传算法求解 算法（GA），一种基于自然进化原理的通用搜索范式 以及生物繁殖。校准溶液是通过最小化 模型预测值与现场观测值之间的差异 压力和管道流量。通过这种方法，建模人员可以在建模过程中得到充分的帮助 校准过程，因此有可能实现高水平的良好模型校准 自信。因此，可以为传导系统开发校准模型 分析和运营管理。最后给出了一个应用实例 基于遗传的水校准方法的有效性和稳健性 分布模型。包括11个参考文献、图表。

This paper proposes a comprehensive and flexible framework for calibrating a hydraulic network model. Calibration tasks can be specified for a water distribution system according to data availability and model application requirements. It allows a user to: flexibly choose any combination of the model parameters such as pipe roughness, junction demand and link (pipes, valves and pumps) operational status; easily aggregate model parameters to reduce the problem dimension for expeditious calculation; and, consistently specify boundary conditions and junction demand loadings that are corresponding to field data collection. A model calibration is then defined as an implicit nonlinear optimization problem, which is solved by employing a powerful genetic algorithm (GA), a generic search paradigm based on the principles of natural evolution and biological reproduction. Calibration solutions are obtained by minimizing the discrepancy between the model predicted and the field observed values of junction pressures and pipe flows. With this methodology, a modeler can be fully assisted during a calibration process, thus it is possible to achieve a good model calibration with high level of confidence. As a result, calibrated models can be developed for conducting system analysis and operational management. An example application is presented to demonstrate the efficacy and robustness of the genetic-based methodology for calibrating a water distribution model. Includes 11 references, figures.