poj 1273

Every time it rains on Farmer John's fields, a pond forms over Bessie's favorite clover patch. This means that the clover is covered by water for awhile and takes quite a long time to regrow. Thus, Farmer John has built a set of drainage ditches so that Bessie's clover patch is never covered in water. Instead, the water is drained to a nearby stream. Being an ace engineer, Farmer John has also installed regulators at the beginning of each ditch, so he can control at what rate water flows into that ditch.        
Farmer John knows not only how many gallons of water each ditch can transport per minute but also the exact layout of the ditches, which feed out of the pond and into each other and stream in a potentially complex network.        
Given all this information, determine the maximum rate at which water can be transported out of the pond and into the stream. For any given ditch, water flows in only one direction, but there might be a way that water can flow in a circle.        

Input

The input includes several cases. For each case, the first line contains two space-separated integers, N (0 <= N <= 200) and M (2 <= M <= 200). N is the number of ditches that Farmer John has dug. M is the number of intersections points for those ditches. Intersection 1 is the pond. Intersection point M is the stream. Each of the following N lines contains three integers, Si, Ei, and Ci. Si and Ei (1 <= Si, Ei <= M) designate the intersections between which this ditch flows. Water will flow through this ditch from Si to Ei. Ci (0 <= Ci <= 10,000,000) is the maximum rate at which water will flow through the ditch.      

Output

For each case, output a single integer, the maximum rate at which water may emptied from the pond.       

Sample Input

5 4
1 2 40
1 4 20
2 4 20
2 3 30
3 4 10

Sample Output

50
标准的最大流模板题
#include <iostream>
#include <stdio.h>
using namespace std;
const int oo=1e9;
const int mm=111111;
const int mn=999;
int node,scr,dest,edge;
int ver[mm],flow[mm],next[mm];
int head[mn],work[mn],dis[mn],q[mn];
void prepare(int _node,int _scr,int _dest)
{
 node=_node,scr=_scr,dest=_dest;
 for(int i=0;i<node;++i)
  head[i]=-1;
 edge=0;
}
void addedge(int u,int v,int c)
{
 ver[edge]=v,flow[edge]=c,next[edge]=head[u],head[u]=edge++;
 ver[edge]=u,flow[edge]=0,next[edge]=head[v],head[v]=edge++;
}
bool Dinic_bfs()
{
 int i,u,v,l,r=0;
 for(i=0;i<node;i++)
   dis[i]=-1;
 dis[q[r++]=scr]=0;
 for(l=0; l<r; ++l)
 {
   for(i=head[u=q[l]];i>=0;i=next[i])
   {
     if(flow[i]&&dis[v=ver[i]]<0)
     {
      dis[q[r++]=v]=dis[u]+1;
      if(v==dest)
      return 1;
     }
   }
 }
  return 0;
}
int Dinic_dfs(int u,int exp)
{
 if(u==dest) return exp;
 for(int &i=work[u],v,tmp;i>=0;i=next[i])
  if(flow[i]&&dis[v=ver[i]]==dis[u]+1&&(tmp=Dinic_dfs(v,min(exp,flow[i])))>0)
  {
   flow[i]-=tmp;
   flow[i^1]+=tmp;
   return tmp;
  }
  return 0;
}
int Dinic_flow()
{
 int i,ret=0,delta;
 while(Dinic_bfs())
 {
  for(i=0;i<node;++i) work[i]=head[i];
  while(delta=Dinic_dfs(scr,oo)) ret+=delta;
 }
 return ret;
}
int main()
{
    int n,m,u,v,c,hehe;
    while(cin>>n>>m)
    {
     prepare(m+1,1,m);
     while(n--)
     {
      cin>>u>>v>>c;
      addedge(u,v,c);
     }
     hehe=Dinic_flow();
    cout<<hehe<< endl;
    }
    return 0;
}

内容概要:该论文研究增程式电动汽车(REEV)的能量管理策略,针对现有优化策略实时性差的问题,提出基于工况识别的自适应等效燃油消耗最小策略(A-ECMS)。首先建立整车Simulink模型和基于规则的策略;然后研究动态规划(DP)算法和等效燃油最小策略;接着通过聚类分析将道路工况分为四类,并设计工况识别算法;最后开发基于工况识别的A-ECMS,通过高德地图预判工况类型并自适应调整SOC分配。仿真显示该策略比规则策略节油8%,比简单SOC规划策略节油2%,并通过硬件在环实验验证了实时可行性。 适合人群:具备一定编程基础,特别是对电动汽车能量管理策略有兴趣的研发人员和技术爱好者。 使用场景及目标:①理解增程式电动汽车能量管理策略的基本原理;②掌握动态规划算法和等效燃油消耗最小策略的应用;③学习工况识别算法的设计和实现;④了解基于工况识别的A-ECMS策略的具体实现及其优化效果。 其他说明:此资源不仅提供了详细的MATLAB/Simulink代码实现,还深入分析了各算法的原理和应用场景,适合用于学术研究和工业实践。在学习过程中,建议结合代码调试和实际数据进行实践,以便更好地理解策略的优化效果。此外,论文还探讨了未来的研究方向,如深度学习替代聚类、多目标优化以及V2X集成等,为后续研究提供了思路。
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