// 创建一个 Stopwatch 实例
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using System.Collections.Concurrent;
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using System.Diagnostics;
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using System.Text;
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Console.WriteLine("开始模拟...");
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Stopwatch stopwatch = new Stopwatch();
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stopwatch.Start();
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var file_path = Path.Combine(AppDomain.CurrentDomain.BaseDirectory, "1897563622226399232.inp");
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var pressure = 0d;
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var minDemand = 50.0; // 最小总需水量(m³/h)
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var maxDemand = 500.0; // 最大总需水量(m³/h)
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var calcCount = 20000; // 计算次数
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var list = new List<SimulationResult>(calcCount);
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using (var helper = new Yw.Epanet.InteropXHelper())
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{
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var err = helper.Open(file_path, "", "");
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err = helper.GetCount(Yw.Epanet.eCountType.Node, out int nodeCount);
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err = helper.OpenH();
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var allDemands = GenerateDemandMatrix(minDemand, maxDemand, nodeCount, calcCount);
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for (int i = 0; i < calcCount; i++)
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{
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helper.InitH(false);
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var demands = allDemands[i];
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for (int nodeIdx = 1; nodeIdx <= nodeCount; nodeIdx++)
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{
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double demand = demands[nodeIdx - 1];
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err = helper.SetNodeValue(nodeIdx, Yw.Epanet.eNodeProperty.BaseDemand, demand);
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}
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helper.RunH(out long t);
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var result = new SimulationResult();
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for (int nodeIdx = 1; nodeIdx <= nodeCount; nodeIdx++)
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{
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pressure = 0;
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helper.GetNodeValue(nodeIdx, Yw.Epanet.eNodeProperty.Pressure, out pressure);
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result.UpdateMaxPressure(nodeIdx, pressure);
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}
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helper.NextH(out long tstep);
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list.Add(result);
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}
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helper.Close();
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}
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stopwatch.Stop();
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Console.WriteLine("所有模拟已完成。");
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Console.WriteLine($"总时间:{stopwatch.Elapsed.TotalSeconds}");
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var strBuild = new StringBuilder();
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foreach (var item in list)
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{
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var pressureList = item.MaxPressures.Values.ToList();
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strBuild.AppendLine($"{pressureList.Min()},{pressureList.Max()}");
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}
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Console.WriteLine(strBuild.ToString());
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Console.ReadLine();
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Console.ReadKey();
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/// <summary>
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/// 生成需水量矩阵（基于遗传算法的随机分配策略[2](@ref)）
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/// </summary>
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double[][] GenerateDemandMatrix(double minDemand, double maxDemand, int nodeCount, int totalSteps)
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{
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var demandMatrix = new double[totalSteps][];
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double stepSize = (maxDemand - minDemand) / (totalSteps - 1);
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var rand = new Random();
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for (int step = 0; step < totalSteps; step++)
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{
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double totalDemand = minDemand + step * stepSize;
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demandMatrix[step] = GenerateRandomDemands(rand, nodeCount, totalDemand);
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}
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return demandMatrix;
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}
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/// <summary>
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/// 随机分配总需水量到各节点（符合案例中的用水量分配逻辑[1](@ref)）
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/// </summary>
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double[] GenerateRandomDemands(Random rand, int nodeCount, double totalDemand)
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{
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double[] demands = new double[nodeCount];
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double remaining = totalDemand;
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// 加权随机分配算法
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for (int i = 0; i < nodeCount - 1; i++)
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{
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double maxPossible = remaining / (nodeCount - i);
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demands[i] = rand.NextDouble() * maxPossible;
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remaining -= demands[i];
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}
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demands[nodeCount - 1] = remaining; // 确保总和准确
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return demands;
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}
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double[,] GenerateAllDemands(int nodeCount, int totalIterations, double[] weValues)
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{
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double[,] demands = new double[totalIterations, nodeCount];
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Parallel.For(0, totalIterations, iter =>
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{
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var rand = new Random();
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for (int node = 0; node < nodeCount; node++)
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{
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// 遗传算法式选择
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int idx = rand.Next(weValues.Length);
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// 20%概率变异
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if (rand.NextDouble() < 0.2)
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idx = (idx + 1) % weValues.Length;
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demands[iter, node] = weValues[idx];
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}
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});
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return demands;
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}
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public class SimulationResult
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{
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public ConcurrentDictionary<int, double> MaxPressures { get; } = new();
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public void UpdateMaxPressure(int nodeId, double pressure)
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{
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MaxPressures.AddOrUpdate(nodeId, pressure,
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(id, old) => Math.Max(old, pressure));
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}
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}
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