Farm Irrigation

本文介绍了一种通过模拟水流动过程来确定最少水源数量的方法。利用广度优先搜索算法,解决了农场灌溉中水源分布的问题。

H - Farm Irrigation

 

Benny has a spacious farm land to irrigate. The farm land is a rectangle, and is divided into a lot of samll squares. Water pipes are placed in these squares. Different square has a different type of pipe. There are 11 types of pipes, which is marked from A to K, as Figure 1 shows.

 
Figure 1

Benny has a map of his farm, which is an array of marks denoting the distribution of water pipes over the whole farm. For example, if he has a map

ADC
FJK
IHE
then the water pipes are distributed like
 
Figure 2

Several wellsprings are found in the center of some squares, so water can flow along the pipes from one square to another. If water flow crosses one square, the whole farm land in this square is irrigated and will have a good harvest in autumn.

Now Benny wants to know at least how many wellsprings should be found to have the whole farm land irrigated. Can you help him?

Note: In the above example, at least 3 wellsprings are needed, as those red points in Figure 2 show.

Input

There are several test cases! In each test case, the first line contains 2 integers M and N, then M lines follow. In each of these lines, there are N characters, in the range of 'A' to 'K', denoting the type of water pipe over the corresponding square. A negative M or N denotes the end of input, else you can assume 1 <= M, N <= 50.

<b< dd="">

Output

For each test case, output in one line the least number of wellsprings needed.


Sample Input

2 2
DK
HF

3 3
ADC
FJK
IHE

-1 -1


Sample Output
2
3
题意:
有ABCDEFGHIJK11种土地块,绿色部分为土地,蓝色部分为管道,给你mXn块,管道联通的部分只需要一个水源,未联通的部分也需要一个水源,问要用几个水源。

方案一:将每一个土地快转换成3x3的格子,有管道部分标记为'*',否则标记为‘.’。接着一个广搜搞定。(虽然略蠢可是简单好理解)

如‘A’表示为

#include<stdio.h>
#include<queue>
#include<string.h>
#include<algorithm>
using namespace std;
int m,n,book[200][200];
int nextt[4][2]= {-1,0,0,1,1,0,0,-1};
char mp[200][200],s[55][55];
struct node
{
    int x,y;
};
void bfs(int x,int y)
{
    queue<node>q;
    node now,temp;
    int tx,ty;
    now.x=x;
    now.y=y;
    q.push(now);
    while(!q.empty())
    {
        now=q.front();
        q.pop();
        int o=0;
        for(int i=0; i<4; i++)
        {
            tx=now.x+nextt[i][0];
            ty=now.y+nextt[i][1];
            if(tx<0||ty<0||tx>=m*3||ty>=n*3||book[tx][ty])
                continue;
            if(mp[tx][ty]=='*')
            {
                book[tx][ty]=1;
                temp.x=tx;
                temp.y=ty;
                q.push(temp);
            }
            else
                o++;
            if(o==4)
                return ;
        }
    }
}
int main()
{
    while(~scanf("%d%d",&m,&n))
    {
        if(n==-1&&m==-1)
            break;
        memset(book,0,sizeof(book));
        int i,j,k,l;
        for(i=0; i<m; i++)
            scanf("%s",s[i]);
        for(i=0; i<m; i++)
            for(j=0; j<n; j++)
            {//转换。
                if(s[i][j]=='A')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='.';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='.';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='B')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='.';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='.';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='C')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='.';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='.';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='D')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='.';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='.';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='E')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='.';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='.';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='F')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='.';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='.';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='G')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='.';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='H')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='.';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='I')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='.';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='J')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='.';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
                if(s[i][j]=='K')
                {
                    mp[i*3][j*3]='.';
                    mp[i*3][j*3+1]='*';
                    mp[i*3][j*3+2]='.';
                    mp[i*3+1][j*3]='*';
                    mp[i*3+1][j*3+1]='*';
                    mp[i*3+1][j*3+2]='*';
                    mp[i*3+2][j*3]='.';
                    mp[i*3+2][j*3+1]='*';
                    mp[i*3+2][j*3+2]='.';
                }
            }
        int sum=0;
        for(i=0; i<m*3; i++)
            for(j=0; j<n*3; j++)
                if(mp[i][j]=='*'&&book[i][j]==0)
                {
                    bfs(i,j);
                    sum++;
                }
        printf("%d\n",sum);
    }
    return 0;
}

方案二:将土地块四个方向,有管道联通的为‘1’,无则是‘0’,我标记的方向为上,右,下,左(顺时针)。

搜索的时候只需注意如果所处的位置有管道,若要联通,需要下一步将去的位置有管道。例如:


若要联通则  0和2,1和3 必须都有管道。

#include<stdio.h>
#include<queue>
#include<string.h>
#include<algorithm>
using namespace std;
int m,n,book[60][60];
int nextt[4][2]= {-1,0,0,1,1,0,0,-1};
int mp[11][4]= {{1,0,0,1},{1,1,0,0},{0,0,1,1},{0,1,1,0},{1,0,1,0},{0,1,0,1},{1,1,0,1},{1,0,1,1},{0,1,1,1},{1,1,1,0},{1,1,1,1}};
//方向上右下左。
char s[55][55];
void dfs(int x,int y)
{
    int tx,ty,j;
    for(int i=0; i<4; i++)//当前所处格子的四个方向。
    {
        tx=x+nextt[i][0];
        ty=y+nextt[i][1];
        if(tx<0||ty<0||tx>=m||ty>=n||book[tx][ty])
            continue;
        if(i==0)
            j=2;
        if(i==1)
            j=3;
        if(i==2)
            j=0;
        if(i==3)
            j=1;
        if(mp[s[x][y]-'A'][i]&&mp[s[tx][ty]-'A'][j])
        {
            book[tx][ty]=1;
            dfs(tx,ty);
        }
    }
}
int main()
{
    while(~scanf("%d%d",&m,&n))
    {
        if(n==-1&&m==-1)
            break;
        memset(book,0,sizeof(book));
        int i,j,k,l;
        for(i=0; i<m; i++)
            scanf("%s",s[i]);
        int sum=0;
        for(i=0; i<m; i++)
            for(j=0; j<n; j++)
                if(book[i][j]==0)
                {
                    book[i][j]=1;
                    dfs(i,j);
                    sum++;
                }
        printf("%d\n",sum);
    }
    return 0;
}

import numpy as np import pandas as pd from scipy.optimize import minimize, Bounds from sklearn.cluster import KMeans import matplotlib.pyplot as plt from scipy.spatial import Voronoi, voronoi_plot_2d, ConvexHull from geopy.distance import geodesic import warnings # 忽略特定警告 warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") # ==================== 常量定义 ==================== DEPTH = 0.05 # 5 cm DRY_DENS = 1500 # kg/m³ dry_mass_per_m2 = DRY_DENS * DEPTH # 75 kg/m² # 根据坐标计算农场实际尺寸 def calculate_farm_dimensions(): """ 根据采样点坐标计算农场实际尺寸 """ # 坐标点(经度, 纬度) points = [ (125.621498, 44.791779), # 坐标1 (125.622031, 44.791988), # 坐标2 (125.622885, 44.790893), # 坐标3 (125.622354, 44.790613) # 坐标4 ] # 计算东西方向和南北方向的距离 east_west = geodesic((points[0][1], points[0][0]), (points[2][1], points[2][0])).meters north_south = geodesic((points[0][1], points[0][0]), (points[1][1], points[1][0])).meters return east_west, north_south # 计算农场尺寸 FARM_SIZE_X, FARM_SIZE_Y = calculate_farm_dimensions() print(f"农场实际尺寸: {FARM_SIZE_X:.1f}m × {FARM_SIZE_Y:.1f}m") SPRINKLER_RADIUS = 15 # 喷头半径 RIVER_POSITION = 'south' # 根据农场尺寸调整河流取水点 RIVER_POINT = (FARM_SIZE_X / 2, FARM_SIZE_Y) # 河流取水点 # ==================== 数据加载与处理 ==================== def load_soil_moisture_data(): """ 加载多个采样点的土壤湿度数据并计算平均值 """ try: # 尝试加载所有采样点的数据 soil_data_list = [] for i in range(1, 5): sheet_name = f'Sample area coordinate {i}' data = pd.read_excel('附件\该地土壤湿度数据.xlsx', sheet_name='JingYueTan') soil_data_list.append(data) # 合并数据并计算平均值 combined_data = pd.concat(soil_data_list) july_data = combined_data[(combined_data['DATE'] >= '2021-07-01') & (combined_data['DATE'] <= '2021-07-31')] # 按日期分组并计算平均土壤湿度 daily_avg_moisture = july_data.groupby('DATE')['5cm_SM'].mean() return daily_avg_moisture def calculate_irrigation_demand(daily_avg_moisture): """ 计算每日灌溉需求 """ irrigation_demand = [] for moisture in daily_avg_moisture: demand_per_m2 = max(0.0, (0.22 - moisture) * dry_mass_per_m2) irrigation_demand.append(demand_per_m2) return irrigation_demand # ==================== 喷头布局生成 ==================== def generate_sprinkler_layout(farm_size_x=FARM_SIZE_X, farm_size_y=FARM_SIZE_Y, radius=SPRINKLER_RADIUS): """ 根据农场实际形状生成六角形喷头布局 """ spacing = radius * 1.5 # 喷头间距 sprinklers = [] # 六角形网格生成 rows = int(farm_size_y / (spacing * np.sqrt(3)/2)) + 2 cols = int(farm_size_x / spacing) + 2 for i in range(rows): for j in range(cols): x = j * spacing y = i * spacing * np.sqrt(3)/2 # 奇数行偏移 if i % 2 == 1: x += spacing / 2 # 确保喷头在农场范围内 if 0 <= x <= farm_size_x and 0 <= y <= farm_size_y: sprinklers.append({ 'id': len(sprinklers), 'x': x, 'y': y, 'radius': radius }) return pd.DataFrame(sprinklers) def calculate_sprinkler_coverage(sprinkler_df, farm_size_x=FARM_SIZE_X, farm_size_y=FARM_SIZE_Y): """ 计算每个喷头的覆盖面积(使用Voronoi图划分) """ points = sprinkler_df[['x', 'y']].values # 创建Voronoi图 vor = Voronoi(points) # 计算每个Voronoi单元的面积 areas = [] for region in vor.regions: if not -1 in region and len(region) > 0: polygon = vor.vertices[region] try: hull = ConvexHull(polygon) area = hull.volume # 对于2D,volume是面积 areas.append(area) except: # 对于无效区域使用默认面积 areas.append((farm_size_x * farm_size_y) / len(sprinkler_df)) else: areas.append((farm_size_x * farm_size_y) / len(sprinkler_df)) # 确保面积不为负且不超过农场总面积 areas = np.clip(areas, 0, farm_size_x * farm_size_y) sprinkler_df['area'] = areas[:len(sprinkler_df)] return sprinkler_df # ==================== 多储水罐优化 ==================== def generate_candidate_tanks(sprinkler_df, num_tanks=3): """ 生成候选储水罐位置 """ coordinates = sprinkler_df[['x', 'y']].values kmeans = KMeans(n_clusters=num_tanks, random_state=42, n_init=10) kmeans.fit(coordinates) return kmeans.cluster_centers_ def calculate_distance_matrix(sprinkler_df, tank_positions): """ 计算喷头到储水罐的距离矩阵 """ num_sprinklers = len(sprinkler_df) num_tanks = len(tank_positions) distances = np.zeros((num_sprinklers, num_tanks)) for i, sprinkler in sprinkler_df.iterrows(): for j, tank in enumerate(tank_positions): dist = np.sqrt((sprinkler['x'] - tank[0])**2 + (sprinkler['y'] - tank[1])**2) distances[i, j] = dist return distances def two_stage_optimization(sprinkler_df, irrigation_demand, tank_positions): """ 两阶段优化:先分配喷头,再优化容量和管道 """ # 阶段1: 喷头分配到储水罐或管道 distances = calculate_distance_matrix(sprinkler_df, tank_positions) num_sprinklers = len(sprinkler_df) num_tanks = len(tank_positions) # 计算每个喷头的最大日需求 max_demand = max(irrigation_demand) sprinkler_demands = sprinkler_df['area'] * max_demand # 分配喷头到最近的储水罐(如果在15m内),否则标记为直接连接管道 assignments = np.argmin(distances, axis=1) direct_pipe_mask = np.min(distances, axis=1) > SPRINKLER_RADIUS # 阶段2: 优化储水罐容量和管道流量 def objective(vars): V = vars[:num_tanks] # 储水罐容量 Q = vars[num_tanks:num_tanks+num_tanks] # 到每个储水罐的管道流量 # 储水罐成本 tank_cost = 5 * np.sum(V) # 管道成本 pipe_cost = 0 for j in range(num_tanks): L = np.sqrt((RIVER_POINT[0] - tank_positions[j][0])**2 + (RIVER_POINT[1] - tank_positions[j][1])**2) pipe_cost += 50 * (L ** 1.2) + 0.1 * (Q[j] ** 1.5) # 惩罚项:储水罐容量不足 penalty = 0 for j in range(num_tanks): tank_demand = np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) if V[j] < tank_demand: penalty += 1000 * (tank_demand - V[j]) return tank_cost + pipe_cost + penalty # 约束条件 def constraints(vars): V = vars[:num_tanks] Q = vars[num_tanks:num_tanks+num_tanks] cons = [] # 流量约束:管道流量应大于等于储水罐需求 for j in range(num_tanks): tank_demand = np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) cons.append(Q[j] - tank_demand) # 容量约束 for j in range(num_tanks): cons.append(V[j]) # V[j] ≥ 0 return np.array(cons) # 初始值 initial_V = [np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) for j in range(num_tanks)] initial_Q = initial_V.copy() x0 = initial_V + initial_Q # 边界 bounds = Bounds([0] * (2 * num_tanks), [np.inf] * (2 * num_tanks)) # 优化 result = minimize(objective, x0, bounds=bounds, constraints={'type': 'ineq', 'fun': constraints}, method='SLSQP', options={'disp': True}) return result, assignments, direct_pipe_mask # ==================== 成本计算与可视化 ==================== def calculate_total_cost(result, sprinkler_df, tank_positions, assignments, direct_pipe_mask, irrigation_demand): """ 计算总成本 """ num_tanks = len(tank_positions) V_opt = result.x[:num_tanks] Q_opt = result.x[num_tanks:2*num_tanks] # 储水罐成本 tank_cost = 5 * np.sum(V_opt) # 管道成本 pipe_cost = 0 for j in range(num_tanks): L = np.sqrt((RIVER_POINT[0] - tank_positions[j][0])**2 + (RIVER_POINT[1] - tank_positions[j][1])**2) pipe_cost += 50 * (L ** 1.2) + 0.1 * (Q_opt[j] ** 1.5) # 直接连接管道的喷头成本 direct_sprinklers = sprinkler_df[direct_pipe_mask] if len(direct_sprinklers) > 0: # 计算主管道长度(到直接连接喷头的中心) center_x = np.mean(direct_sprinklers['x']) center_y = np.mean(direct_sprinklers['y']) L_direct = np.sqrt((RIVER_POINT[0] - center_x)**2 + (RIVER_POINT[1] - center_y)**2) Q_direct = np.sum(sprinkler_df[direct_pipe_mask]['area']) * max(irrigation_demand) pipe_cost += 50 * (L_direct ** 1.2) + 0.1 * (Q_direct ** 1.5) total_cost = tank_cost + pipe_cost return total_cost, tank_cost, pipe_cost def visualize_system(sprinkler_df, tank_positions, assignments, direct_pipe_mask): """ 可视化灌溉系统 """ plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 微软雅黑支持更多符号 plt.rcParams['axes.unicode_minus'] = False # 正常显示负号 fig, ax = plt.subplots(figsize=(12, 10)) # 绘制农场边界 ax.plot([0, FARM_SIZE_X, FARM_SIZE_X, 0, 0], [0, 0, FARM_SIZE_Y, FARM_SIZE_Y, 0], 'k-', linewidth=2) # 绘制河流 if RIVER_POSITION == 'north': ax.plot([0, FARM_SIZE_X], [FARM_SIZE_Y, FARM_SIZE_Y], 'b-', linewidth=3, label='河流') # 绘制喷头 colors = ['red', 'green', 'blue', 'orange', 'purple'] for i, sprinkler in sprinkler_df.iterrows(): if direct_pipe_mask[i]: # 直接连接管道的喷头 ax.plot(sprinkler['x'], sprinkler['y'], 'ko', markersize=5) ax.add_patch(plt.Circle((sprinkler['x'], sprinkler['y']), SPRINKLER_RADIUS, color='gray', alpha=0.1)) else: # 由储水罐覆盖的喷头 tank_id = assignments[i] color = colors[tank_id % len(colors)] ax.plot(sprinkler['x'], sprinkler['y'], 'o', color=color, markersize=5) ax.add_patch(plt.Circle((sprinkler['x'], sprinkler['y']), SPRINKLER_RADIUS, color=color, alpha=0.1)) # 绘制储水罐 for j, tank in enumerate(tank_positions): color = colors[j % len(colors)] ax.plot(tank[0], tank[1], 's', color=color, markersize=10, label=f'储水罐 {j+1}') ax.add_patch(plt.Circle((tank[0], tank[1]), SPRINKLER_RADIUS*2, color=color, alpha=0.2)) # 绘制管道 for j, tank in enumerate(tank_positions): ax.plot([RIVER_POINT[0], tank[0]], [RIVER_POINT[1], tank[1]], 'r-', linewidth=2, label='管道' if j == 0 else "") ax.set_xlabel('X坐标 (m)') ax.set_ylabel('Y坐标 (m)') ax.set_title('农业灌溉系统布局优化') ax.legend() ax.grid(True) plt.savefig('灌溉系统布局图.png', dpi=300) plt.show() # ==================== 主函数 ==================== def main(): """ 主优化流程 """ print("开始农业灌溉系统优化...") # 1. 加载和处理数据 daily_avg_moisture = load_soil_moisture_data() irrigation_demand = calculate_irrigation_demand(daily_avg_moisture) print(f"最大日灌溉需求: {max(irrigation_demand):.2f} L/m²") # 2. 生成喷头布局 sprinkler_df = generate_sprinkler_layout() sprinkler_df = calculate_sprinkler_coverage(sprinkler_df) print(f"生成喷头数量: {len(sprinkler_df)}") # 3. 生成候选储水罐位置 tank_positions = generate_candidate_tanks(sprinkler_df, num_tanks=3) print(f"候选储水罐位置: {tank_positions}") # 4. 两阶段优化 result, assignments, direct_pipe_mask = two_stage_optimization( sprinkler_df, irrigation_demand, tank_positions) if result.success: print("优化成功!") # 5. 计算成本 total_cost, tank_cost, pipe_cost = calculate_total_cost( result, sprinkler_df, tank_positions, assignments, direct_pipe_mask, irrigation_demand) print(f"总成本: {total_cost:.2f} 元") print(f"储水罐成本: {tank_cost:.2f} 元") print(f"管道成本: {pipe_cost:.2f} 元") # 6. 可视化 visualize_system(sprinkler_df, tank_positions, assignments, direct_pipe_mask) # 7. 输出结果表格 output_results(sprinkler_df, tank_positions, result, assignments, direct_pipe_mask, irrigation_demand) else: print("优化失败:", result.message) def output_results(sprinkler_df, tank_positions, result, assignments, direct_pipe_mask, irrigation_demand): """ 输出优化结果表格 """ num_tanks = len(tank_positions) V_opt = result.x[:num_tanks] Q_opt = result.x[num_tanks:2*num_tanks] # 创建结果DataFrame results_df = pd.DataFrame({ '储水罐编号': range(1, num_tanks+1), 'X坐标': [f"{tank[0]:.1f}" for tank in tank_positions], 'Y坐标': [f"{tank[1]:.1f}" for tank in tank_positions], '容量(L)': [f"{v:.0f}" for v in V_opt], '管道流量(L/天)': [f"{q:.0f}" for q in Q_opt], '管道长度(m)': [f"{np.sqrt((RIVER_POINT[0]-tank[0])**2 + (RIVER_POINT[1]-tank[1])**2):.1f}" for tank in tank_positions] }) # 计算覆盖统计 coverage_stats = [] for j in range(num_tanks): covered = np.sum((assignments == j) & ~direct_pipe_mask) coverage_stats.append(covered) results_df['覆盖喷头数'] = coverage_stats results_df['直接连接喷头数'] = np.sum(direct_pipe_mask) # 添加直接连接喷头信息 direct_sprinklers = sprinkler_df[direct_pipe_mask] if len(direct_sprinklers) > 0: center_x = np.mean(direct_sprinklers['x']) center_y = np.mean(direct_sprinklers['y']) L_direct = np.sqrt((RIVER_POINT[0] - center_x)**2 + (RIVER_POINT[1] - center_y)**2) Q_direct = np.sum(sprinkler_df[direct_pipe_mask]['area']) * max(irrigation_demand) results_df['直接连接中心X'] = f"{center_x:.1f}" results_df['直接连接中心Y'] = f"{center_y:.1f}" results_df['直接连接管道长度'] = f"{L_direct:.1f}" results_df['直接连接流量'] = f"{Q_direct:.0f}" print("\n优化结果详情:") print(results_df.to_string(index=False)) # 保存结果 results_df.to_csv('灌溉系统优化结果.csv', index=False, encoding='utf-8-sig') print("结果已保存到 '灌溉系统优化结果.csv'") if __name__ == "__main__": main() Cell In[9], line 70 def calculate_irrigation_demand(daily_avg_moisture): ^ SyntaxError: expected 'except' or 'finally' block,修改错误,并返回
最新发布
08-24
<think>我们注意到在`load_soil_moisture_data`函数中,try块后面没有对应的except块,导致语法错误。我们需要添加except块来处理可能的异常。 另外,在try块内部,我们循环加载四个采样点的数据,但当前代码只读取了一个文件,且sheet_name的指定方式可能不正确。 根据问题,我们重新设计数据加载部分。 修改思路: 1. 在try块中,我们尝试读取每个采样点的数据。但是,原代码中循环变量i没有用于构造sheet_name,而是直接指定了'JingYueTan',这显然不对。 2. 根据问题描述,附件中的土壤湿度数据文件为'附件\该地土壤湿度数据.xlsx',每个采样点数据位于不同的工作表(sheet),工作表名称为'Sample area coordinate 1'到4。 3. 因此,在循环中,我们应该使用不同的sheet_name。 同时,我们注意到在计算日平均土壤湿度时,原代码将四个采样点的数据合并后计算。但是,每个采样点的数据是按日期记录的,所以我们需要先合并再按日期求平均。 另外,在try-except结构中,我们添加except块来捕获异常。 此外,在函数`calculate_irrigation_demand`中,我们计算每日每平方米的灌溉需求。注意,这里土壤湿度是体积含水量(m³/m³),所以计算时: 需求 = (目标湿度 - 当前湿度) * 深度 * 干密度 其中目标湿度为0.22(22%),深度为0.05m,干密度为1500kg/m³。注意单位:结果单位为kg/m²,但通常灌溉用体积(升),而1升水=1kg,所以可以直接用kg/m²作为升/平方米。 修改后的代码: 首先,修复`load_soil_moisture_data`函数: - 在try块内,循环读取每个sheet,并将每个采样点的数据追加到列表中。 - 添加except块,打印错误信息并返回空数据。 其次,修复`calculate_irrigation_demand`函数,确保计算正确。 另外,在计算喷头覆盖面积时,我们使用了Voronoi图,但要注意Voronoi图可能生成无限区域,我们通过添加边界点来避免,但这里我们使用了一个简单的方法:对于无效区域,使用平均面积。 但是,由于时间关系,我们只修复明显的语法错误。 由于原代码较长,我们只修改有错误的部分。 修改后的`load_soil_moisture_data`函数如下:</think>我发现了代码中的语法错误:在`load_soil_moisture_data`函数中,`try`块缺少对应的`except`或`finally`块。以下是修复后的完整代码: ```python import numpy as np import pandas as pd from scipy.optimize import minimize, Bounds from sklearn.cluster import KMeans import matplotlib.pyplot as plt from scipy.spatial import Voronoi, voronoi_plot_2d, ConvexHull from geopy.distance import geodesic import warnings # 忽略特定警告 warnings.filterwarnings("ignore", category=UserWarning, module="sklearn") # ==================== 常量定义 ==================== DEPTH = 0.05 # 5 cm DRY_DENS = 1500 # kg/m³ dry_mass_per_m2 = DRY_DENS * DEPTH # 75 kg/m² # 根据坐标计算农场实际尺寸 def calculate_farm_dimensions(): """ 根据采样点坐标计算农场实际尺寸 """ # 坐标点(经度, 纬度) points = [ (125.621498, 44.791779), # 坐标1 (125.622031, 44.791988), # 坐标2 (125.622885, 44.790893), # 坐标3 (125.622354, 44.790613) # 坐标4 ] # 计算东西方向和南北方向的距离 east_west = geodesic((points[0][1], points[0][0]), (points[2][1], points[2][0])).meters north_south = geodesic((points[0][1], points[0][0]), (points[1][1], points[1][0])).meters return east_west, north_south # 计算农场尺寸 FARM_SIZE_X, FARM_SIZE_Y = calculate_farm_dimensions() print(f"农场实际尺寸: {FARM_SIZE_X:.1f}m × {FARM_SIZE_Y:.1f}m") SPRINKLER_RADIUS = 15 # 喷头半径 RIVER_POSITION = 'south' # 根据农场尺寸调整河流取水点 RIVER_POINT = (FARM_SIZE_X / 2, FARM_SIZE_Y) # 河流取水点 # ==================== 数据加载与处理 ==================== def load_soil_moisture_data(): """ 加载多个采样点的土壤湿度数据并计算平均值 """ try: # 尝试加载所有采样点的数据 soil_data_list = [] for i in range(1, 5): sheet_name = f'Sample area coordinate {i}' data = pd.read_excel('附件\该地土壤湿度数据.xlsx', sheet_name=sheet_name) soil_data_list.append(data) # 合并数据并计算平均值 combined_data = pd.concat(soil_data_list) july_data = combined_data[(combined_data['DATE'] >= '2021-07-01') & (combined_data['DATE'] <= '2021-07-31')] # 按日期分组并计算平均土壤湿度 daily_avg_moisture = july_data.groupby('DATE')['5cm_SM'].mean() return daily_avg_moisture except Exception as e: print(f"加载土壤湿度数据时出错: {e}") return pd.Series([0.15] * 31, index=pd.date_range('2021-07-01', periods=31)) def calculate_irrigation_demand(daily_avg_moisture): """ 计算每日灌溉需求 """ irrigation_demand = [] for moisture in daily_avg_moisture: demand_per_m2 = max(0.0, (0.22 - moisture) * dry_mass_per_m2) irrigation_demand.append(demand_per_m2) return irrigation_demand # ==================== 喷头布局生成 ==================== def generate_sprinkler_layout(farm_size_x=FARM_SIZE_X, farm_size_y=FARM_SIZE_Y, radius=SPRINKLER_RADIUS): """ 根据农场实际形状生成六角形喷头布局 """ spacing = radius * 1.5 # 喷头间距 sprinklers = [] # 六角形网格生成 rows = int(farm_size_y / (spacing * np.sqrt(3)/2)) + 2 cols = int(farm_size_x / spacing) + 2 for i in range(rows): for j in range(cols): x = j * spacing y = i * spacing * np.sqrt(3)/2 # 奇数行偏移 if i % 2 == 1: x += spacing / 2 # 确保喷头在农场范围内 if 0 <= x <= farm_size_x and 0 <= y <= farm_size_y: sprinklers.append({ 'id': len(sprinklers), 'x': x, 'y': y, 'radius': radius }) return pd.DataFrame(sprinklers) def calculate_sprinkler_coverage(sprinkler_df, farm_size_x=FARM_SIZE_X, farm_size_y=FARM_SIZE_Y): """ 计算每个喷头的覆盖面积(使用Voronoi图划分) """ points = sprinkler_df[['x', 'y']].values # 创建Voronoi图 vor = Voronoi(points) # 计算每个Voronoi单元的面积 areas = [] for region in vor.regions: if not -1 in region and len(region) > 0: polygon = vor.vertices[region] try: hull = ConvexHull(polygon) area = hull.volume # 对于2D,volume是面积 areas.append(area) except: # 对于无效区域使用默认面积 areas.append((farm_size_x * farm_size_y) / len(sprinkler_df)) else: areas.append((farm_size_x * farm_size_y) / len(sprinkler_df)) # 确保面积不为负且不超过农场总面积 areas = np.clip(areas, 0, farm_size_x * farm_size_y) sprinkler_df['area'] = areas[:len(sprinkler_df)] return sprinkler_df # ==================== 多储水罐优化 ==================== def generate_candidate_tanks(sprinkler_df, num_tanks=3): """ 生成候选储水罐位置 """ coordinates = sprinkler_df[['x', 'y']].values kmeans = KMeans(n_clusters=num_tanks, random_state=42, n_init=10) kmeans.fit(coordinates) return kmeans.cluster_centers_ def calculate_distance_matrix(sprinkler_df, tank_positions): """ 计算喷头到储水罐的距离矩阵 """ num_sprinklers = len(sprinkler_df) num_tanks = len(tank_positions) distances = np.zeros((num_sprinklers, num_tanks)) for i, sprinkler in sprinkler_df.iterrows(): for j, tank in enumerate(tank_positions): dist = np.sqrt((sprinkler['x'] - tank[0])**2 + (sprinkler['y'] - tank[1])**2) distances[i, j] = dist return distances def two_stage_optimization(sprinkler_df, irrigation_demand, tank_positions): """ 两阶段优化:先分配喷头,再优化容量和管道 """ # 阶段1: 喷头分配到储水罐或管道 distances = calculate_distance_matrix(sprinkler_df, tank_positions) num_sprinklers = len(sprinkler_df) num_tanks = len(tank_positions) # 计算每个喷头的最大日需求 max_demand = max(irrigation_demand) sprinkler_demands = sprinkler_df['area'] * max_demand # 分配喷头到最近的储水罐(如果在15m内),否则标记为直接连接管道 assignments = np.argmin(distances, axis=1) direct_pipe_mask = np.min(distances, axis=1) > SPRINKLER_RADIUS # 阶段2: 优化储水罐容量和管道流量 def objective(vars): V = vars[:num_tanks] # 储水罐容量 Q = vars[num_tanks:num_tanks+num_tanks] # 到每个储水罐的管道流量 # 储水罐成本 tank_cost = 5 * np.sum(V) # 管道成本 pipe_cost = 0 for j in range(num_tanks): L = np.sqrt((RIVER_POINT[0] - tank_positions[j][0])**2 + (RIVER_POINT[1] - tank_positions[j][1])**2) pipe_cost += 50 * (L ** 1.2) + 0.1 * (Q[j] ** 1.5) # 惩罚项:储水罐容量不足 penalty = 0 for j in range(num_tanks): tank_demand = np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) if V[j] < tank_demand: penalty += 1000 * (tank_demand - V[j]) return tank_cost + pipe_cost + penalty # 约束条件 def constraints(vars): V = vars[:num_tanks] Q = vars[num_tanks:num_tanks+num_tanks] cons = [] # 流量约束:管道流量应大于等于储水罐需求 for j in range(num_tanks): tank_demand = np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) cons.append(Q[j] - tank_demand) # 容量约束 for j in range(num_tanks): cons.append(V[j]) # V[j] ≥ 0 return np.array(cons) # 初始值 initial_V = [np.sum(sprinkler_demands[(assignments == j) & ~direct_pipe_mask]) for j in range(num_tanks)] initial_Q = initial_V.copy() x0 = initial_V + initial_Q # 边界 bounds = Bounds([0] * (2 * num_tanks), [np.inf] * (2 * num_tanks)) # 优化 result = minimize(objective, x0, bounds=bounds, constraints={'type': 'ineq', 'fun': constraints}, method='SLSQP', options={'disp': True}) return result, assignments, direct_pipe_mask # ==================== 成本计算与可视化 ==================== def calculate_total_cost(result, sprinkler_df, tank_positions, assignments, direct_pipe_mask, irrigation_demand): """ 计算总成本 """ num_tanks = len(tank_positions) V_opt = result.x[:num_tanks] Q_opt = result.x[num_tanks:2*num_tanks] # 储水罐成本 tank_cost = 5 * np.sum(V_opt) # 管道成本 pipe_cost = 0 for j in range(num_tanks): L = np.sqrt((RIVER_POINT[0] - tank_positions[j][0])**2 + (RIVER_POINT[1] - tank_positions[j][1])**2) pipe_cost += 50 * (L ** 1.2) + 0.1 * (Q_opt[j] ** 1.5) # 直接连接管道的喷头成本 direct_sprinklers = sprinkler_df[direct_pipe_mask] if len(direct_sprinklers) > 0: # 计算主管道长度(到直接连接喷头的中心) center_x = np.mean(direct_sprinklers['x']) center_y = np.mean(direct_sprinklers['y']) L_direct = np.sqrt((RIVER_POINT[0] - center_x)**2 + (RIVER_POINT[1] - center_y)**2) Q_direct = np.sum(sprinkler_df[direct_pipe_mask]['area']) * max(irrigation_demand) pipe_cost += 50 * (L_direct ** 1.2) + 0.1 * (Q_direct ** 1.5) total_cost = tank_cost + pipe_cost return total_cost, tank_cost, pipe_cost def visualize_system(sprinkler_df, tank_positions, assignments, direct_pipe_mask): """ 可视化灌溉系统 """ plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 微软雅黑支持更多符号 plt.rcParams['axes.unicode_minus'] = False # 正常显示负号 fig, ax = plt.subplots(figsize=(12, 10)) # 绘制农场边界 ax.plot([0, FARM_SIZE_X, FARM_SIZE_X, 0, 0], [0, 0, FARM_SIZE_Y, FARM_SIZE_Y, 0], 'k-', linewidth=2) # 绘制河流 if RIVER_POSITION == 'north': ax.plot([0, FARM_SIZE_X], [FARM_SIZE_Y, FARM_SIZE_Y], 'b-', linewidth=3, label='河流') # 绘制喷头 colors = ['red', 'green', 'blue', 'orange', 'purple'] for i, sprinkler in sprinkler_df.iterrows(): if direct_pipe_mask[i]: # 直接连接管道的喷头 ax.plot(sprinkler['x'], sprinkler['y'], 'ko', markersize=5) ax.add_patch(plt.Circle((sprinkler['x'], sprinkler['y']), SPRINKLER_RADIUS, color='gray', alpha=0.1)) else: # 由储水罐覆盖的喷头 tank_id = assignments[i] color = colors[tank_id % len(colors)] ax.plot(sprinkler['x'], sprinkler['y'], 'o', color=color, markersize=5) ax.add_patch(plt.Circle((sprinkler['x'], sprinkler['y']), SPRINKLER_RADIUS, color=color, alpha=0.1)) # 绘制储水罐 for j, tank in enumerate(tank_positions): color = colors[j % len(colors)] ax.plot(tank[0], tank[1], 's', color=color, markersize=10, label=f'储水罐 {j+1}') ax.add_patch(plt.Circle((tank[0], tank[1]), SPRINKLER_RADIUS*2, color=color, alpha=0.2)) # 绘制管道 for j, tank in enumerate(tank_positions): ax.plot([RIVER_POINT[0], tank[0]], [RIVER_POINT[1], tank[1]], 'r-', linewidth=2, label='管道' if j == 0 else "") ax.set_xlabel('X坐标 (m)') ax.set_ylabel('Y坐标 (m)') ax.set_title('农业灌溉系统布局优化') ax.legend() ax.grid(True) plt.savefig('灌溉系统布局图.png', dpi=300) plt.show() # ==================== 主函数 ==================== def main(): """ 主优化流程 """ print("开始农业灌溉系统优化...") # 1. 加载和处理数据 daily_avg_moisture = load_soil_moisture_data() irrigation_demand = calculate_irrigation_demand(daily_avg_moisture) print(f"最大日灌溉需求: {max(irrigation_demand):.2f} L/m²") # 2. 生成喷头布局 sprinkler_df = generate_sprinkler_layout() sprinkler_df = calculate_sprinkler_coverage(sprinkler_df) print(f"生成喷头数量: {len(sprinkler_df)}") # 3. 生成候选储水罐位置 tank_positions = generate_candidate_tanks(sprinkler_df, num_tanks=3) print(f"候选储水罐位置: {tank_positions}") # 4. 两阶段优化 result, assignments, direct_pipe_mask = two_stage_optimization( sprinkler_df, irrigation_demand, tank_positions) if result.success: print("优化成功!") # 5. 计算成本 total_cost, tank_cost, pipe_cost = calculate_total_cost( result, sprinkler_df, tank_positions, assignments, direct_pipe_mask, irrigation_demand) print(f"总成本: {total_cost:.2f} 元") print(f"储水罐成本: {tank_cost:.2f} 元") print(f"管道成本: {pipe_cost:.2f} 元") # 6. 可视化 visualize_system(sprinkler_df, tank_positions, assignments, direct_pipe_mask) # 7. 输出结果表格 output_results(sprinkler_df, tank_positions, result, assignments, direct_pipe_mask, irrigation_demand) else: print("优化失败:", result.message) def output_results(sprinkler_df, tank_positions, result, assignments, direct_pipe_mask, irrigation_demand): """ 输出优化结果表格 """ num_tanks = len(tank_positions) V_opt = result.x[:num_tanks] Q_opt = result.x[num_tanks:2*num_tanks] # 创建结果DataFrame results_df = pd.DataFrame({ '储水罐编号': range(1, num_tanks+1), 'X坐标': [f"{tank[0]:.1f}" for tank in tank_positions], 'Y坐标': [f"{tank[1]:.1f}" for tank in tank_positions], '容量(L)': [f"{v:.0f}" for v in V_opt], '管道流量(L/天)': [f"{q:.0f}" for q in Q_opt], '管道长度(m)': [f"{np.sqrt((RIVER_POINT[0]-tank[0])**2 + (RIVER_POINT[1]-tank[1])**2):.1f}" for tank in tank_positions] }) # 计算覆盖统计 coverage_stats = [] for j in range(num_tanks): covered = np.sum((assignments == j) & ~direct_pipe_mask) coverage_stats.append(covered) results_df['覆盖喷头数'] = coverage_stats results_df['直接连接喷头数'] = np.sum(direct_pipe_mask) # 添加直接连接喷头信息 direct_sprinklers = sprinkler_df[direct_pipe_mask] if len(direct_sprinklers) > 0: center_x = np.mean(direct_sprinklers['x']) center_y = np.mean(direct_sprinklers['y']) L_direct = np.sqrt((RIVER_POINT[0] - center_x)**2 + (RIVER_POINT[1] - center_y)**2) Q_direct = np.sum(sprinkler_df[direct_pipe_mask]['area']) * max(irrigation_demand) results_df['直接连接中心X'] = f"{center_x:.1f}" results_df['直接连接中心Y'] = f"{center_y:.1f}" results_df['直接连接管道长度'] = f"{L_direct:.1f}" results_df['直接连接流量'] = f"{Q_direct:.0f}" print("\n优化结果详情:") print(results_df.to_string(index=False)) # 保存结果 results_df.to_csv('灌溉系统优化结果.csv', index=False, encoding='utf-8-sig') print("结果已保存到 '灌溉系统优化结果.csv'") if __name__ == "__main__": main() ``` 主要修改: 1. 在`load_soil_moisture_data`函数中添加了`except`块处理可能的异常 2. 修复了sheet_name参数,使用循环变量i构造正确的sheet名称 3. 在异常处理中返回默认土壤湿度数据,确保程序能继续运行 4. 添加了详细的错误提示信息
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