703 lines
22 KiB
Plaintext
703 lines
22 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "e19ec4ae5347c678",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"\n",
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"pd.options.display.max_columns = 100"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "initial_id",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"df_crop_details = pd.read_excel('./data/2.xlsx', sheet_name=1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1d2a51b3414be94",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 去除空格\n",
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"df_crop_details['cropName'] = df_crop_details['cropName'].apply(lambda x: x.strip())\n",
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"# CropType = [x.strip() for x in CropType]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "514cd9136d9ca341",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_crop_planting = pd.read_excel('./data/2.xlsx', sheet_name=0)\n",
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"# 照例去除一下空格\n",
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"df_crop_planting['cropName'] = df_crop_planting['cropName'].apply(lambda x: x.strip())\n",
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"# ffill\n",
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"df_crop_planting['landName'] = df_crop_planting['landName'].ffill()\n",
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"# df_crop_planting"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "1503f8b642c842db",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_land = pd.read_excel('./data/1.xlsx', sheet_name=0)\n",
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"# 去除landType和landName的空格\n",
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"df_land['landType'] = df_land['landType'].apply(lambda x: x.strip())\n",
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"df_land['landName'] = df_land['landName'].apply(lambda x: x.strip())\n",
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"# df_land"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"id": "3cdf51a9a9d4d30f",
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"metadata": {},
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"outputs": [],
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"source": [
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"\n",
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"unit_profit_lsc = []\n",
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"for line in df_crop_details.values:\n",
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" s = str(line[7]).split('-')\n",
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" unit_profit_lsc.append((float(s[0]) + float(s[1])) / 2 * line[5] - line[6])\n",
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"df_crop_details['unitProfit'] = unit_profit_lsc\n",
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"# df_crop_details"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"id": "569016a9b90f841b",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_crop_type_land = pd.read_excel('./data/1.xlsx', sheet_name=1)\n",
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"# 老规矩，去掉cropName和cropType的空格\n",
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"df_crop_type_land['cropType'] = df_crop_type_land['cropType'].apply(lambda x: x.strip())\n",
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"df_crop_type_land['cropName'] = df_crop_type_land['cropName'].apply(lambda x: x.strip())\n",
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"# df_crop_type_land"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "a7661d84217b578c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 搞一下季节和年份的集合\n",
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"SeasonType = [\"单季\", \"第一季\", \"第二季\"]\n",
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"SeasonDict = {\"单季\": 1, \"第一季\": 1, \"第二季\": 2}\n",
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"SeasonNum = [1, 2]\n",
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"years = [2024, 2025, 2026, 2027, 2028, 2029, 2030]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "4a4e06a1f2d4d8ab",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 枚举地块类型\n",
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"LandType = {\"A\": \"平旱地\", \"B\": \"梯田\", \"C\": \"山坡地\", \"D\": \"水浇地\", \"E\": \"普通大棚\", \"F\": \"智慧大棚\"}\n",
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"# LandType"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "cc938565a63a8129",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 枚举地块\n",
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"LandName = df_crop_planting['landName'].unique()\n",
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"# LandName"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "dae53a6215cea525",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 枚举地块面积，取df_land的landName为key，landArea为value\n",
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"LandArea = {x: df_land[df_land['landName'] == x]['landArea'].values[0] for x in LandName}\n",
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"# LandArea"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "7a0e6cc209d93b56",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 读入作物名称\n",
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"CropName = df_crop_details['cropName'].unique()\n",
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"# CropName"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "8a280f918bb3139a",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'粮食（豆类）': array(['黄豆', '黑豆', '红豆', '绿豆', '爬豆'], dtype=object),\n",
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" '粮食': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦', '水稻'],\n",
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" dtype=object),\n",
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" '蔬菜（豆类）': array(['豇豆', '刀豆', '芸豆'], dtype=object),\n",
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" '蔬菜': array(['土豆', '西红柿', '茄子', '菠菜', '青椒', '菜花', '包菜', '油麦菜', '小青菜', '黄瓜',\n",
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" '生菜', '辣椒', '空心菜', '黄心菜', '芹菜', '大白菜', '白萝卜', '红萝卜'], dtype=object),\n",
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" '食用菌': array(['榆黄菇', '香菇', '白灵菇', '羊肚菌'], dtype=object),\n",
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" '粮食（除了水稻）': array(['小麦', '玉米', '谷子', '高粱', '黍子', '荞麦', '南瓜', '红薯', '莜麦', '大麦'],\n",
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" dtype=object),\n",
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" '豆类': array(['黄豆', '黑豆', '红豆', '绿豆', '爬豆', '豇豆', '刀豆', '芸豆'], dtype=object)}"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 作物分类\n",
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"CropType = {x: df_crop_type_land[df_crop_type_land['cropType'] == x]['cropName'].values for x in\n",
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" df_crop_type_land['cropType'].values}\n",
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"CropType['粮食（除了水稻）'] = CropType['粮食'][:-1] # 这样不太好，但能用\n",
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"CropType['豆类'] = np.array(list(CropType['粮食（豆类）']) + list(CropType['蔬菜（豆类）']), dtype=object)\n",
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"CropType"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "bf6d61d66f06bdeb",
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"metadata": {},
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"outputs": [],
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"source": [
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"# any([x[-1] == ' ' for x in CropName])\n",
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"# 居然有空格\n",
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"# 现在去掉了"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"id": "22f9730e3d9c1016",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 亩产量\n",
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"unit_yield_lsc = {\n",
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" l: {\n",
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" s: {\n",
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" c: df_crop_details[\n",
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" (df_crop_details['cropName'] == c) &\n",
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" (df_crop_details['cropLandType'] == l) &\n",
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" (df_crop_details['season'] == s)\n",
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" ]['unitYield'].values[0] if df_crop_details[\n",
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" (df_crop_details['cropName'] == c) &\n",
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" (df_crop_details['cropLandType'] == l) &\n",
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" (df_crop_details['season'] == s)\n",
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" ]['unitYield'].values.size > 0 else 0\n",
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" for c in CropName\n",
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" } for s in SeasonType\n",
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" }\n",
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" for l in LandType.values()\n",
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"}\n",
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"\n",
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"# unit_yield"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"id": "99dbe4ca6c54db0b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 亩利润\n",
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"unit_profit_lsc = {\n",
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" l: {\n",
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" s: {\n",
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" c: df_crop_details[\n",
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" (df_crop_details['cropName'] == c) &\n",
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" (df_crop_details['cropLandType'] == l) &\n",
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" (df_crop_details['season'] == s)\n",
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" ]['unitProfit'].values[0]\n",
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" if df_crop_details[\n",
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" (df_crop_details['cropName'] == c) &\n",
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" (df_crop_details['cropLandType'] == l) &\n",
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" (df_crop_details['season'] == s)\n",
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" ]['unitYield'].values.size > 0 else 0\n",
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" for c in CropName\n",
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" } for s in SeasonType\n",
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" }\n",
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" for l in LandType.values()\n",
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"}\n",
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"\n",
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"# unit_profit"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "13f2ccc7aa215d4c",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 每种作物的总需求\n",
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"crop_demand = {\n",
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" c: 0\n",
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" for c in CropName\n",
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"}\n",
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"\n",
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"# 这里需要另一张表\n",
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"# 代码独立出来移到上面去了\n",
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"\n",
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"for line in df_crop_planting.values:\n",
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" # 面积*该土地类型的亩产量 面积 地块类型字典 地块类型 季节 作物名称\n",
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" crop_demand[line[2]] += line[4] * unit_yield_lsc[LandType[line[0][0]]][line[5]][line[2]]\n",
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"\n",
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"# crop_demand"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"id": "5d09872708d40da4",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 准备开搞\n",
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"from pulp import LpMaximize, LpProblem, LpVariable, lpSum, value, LpInteger, PULP_CBC_CMD, LpContinuous, LpBinary\n",
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"import pulp"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "b6eff87c7a762769",
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"metadata": {},
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"outputs": [],
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"source": [
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"model = LpProblem(\"Crop_Planting_Optimization_with_Specific_Rules\", LpMaximize)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 20,
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"id": "7bfdb9425c3e9683",
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"metadata": {},
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"outputs": [],
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"source": [
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"# %timeit X = LpVariable.dicts(\"X\", (CropName, LandName, years, seasons), lowBound=0, cat=LpContinuous)\n",
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"# %timeit XX = LpVariable.dicts(\"X\", [(c, l, y, s) for c in CropName for l in LandName for y in years for s in seasons],lowBound=0, cat=LpContinuous)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "4cb129e20385a7ee",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 地块l在y年的s季的种植c的量\n",
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"# X = LpVariable.dicts(\"crop_plant_area\", (CropName, LandName, years, SeasonType), lowBound=0, cat=LpContinuous)\n",
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"X = LpVariable.dicts(\"X\", [(c, l, y, s)\n",
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" for c in CropName\n",
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" for l in LandName\n",
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" for y in years\n",
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" for s in SeasonType],\n",
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" lowBound=0,\n",
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" cat=LpContinuous\n",
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" )\n",
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"# X"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 22,
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"id": "9dab983ca7b3b607",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 地块l在y年的s季是否种植了c\n",
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"# Y = LpVariable.dicts(\"crop_plant_bool\", (CropName, LandName, years, SeasonType), cat=LpBinary)\n",
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"Y = LpVariable.dicts(\"Y\", [(c, l, y, s) for c in CropName for l in LandName for y in years for s in SeasonType],\n",
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" cat=LpBinary)\n",
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"\n",
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"# Y"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 23,
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"id": "603175d36624172f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 确保与X进行01约束\n",
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"for c in CropName:\n",
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" for l in LandName:\n",
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" for y in years:\n",
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" for s in SeasonType:\n",
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" # 如果种植了作物，则种植面积大于0\n",
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" model += X[c, l, y, s] <= 10000000 * Y[c, l, y, s], f\"PlantingConstraint1_{c}_{l}_{y}_{s}\"\n",
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" # 如果未种植作物，则种植面积为0\n",
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" model += X[c, l, y, s] >= 0.000001 * Y[c, l, y, s], f\"PlantingConstraint2_{c}_{l}_{y}_{s}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "4505b0f2fe6bf93d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 目标函数：总利润 = sum(x[i]*profit[c,l,s])\n",
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"model += lpSum( # 地块类型 季节 作物类型\n",
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" X[c, l, y, s] * unit_profit_lsc[LandType[l[0]]][s][c]\n",
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" for c in CropName\n",
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" for l in LandName\n",
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" for y in years\n",
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" for s in SeasonType\n",
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"), \"total_profit\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"id": "1f185c0856fbcf27",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 约束，启动！\n",
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"# 条件1：每年的所有季节中每种作物的种植量必须小于需求量\n",
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"for c in CropName:\n",
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" for y in years:\n",
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" model += lpSum(X[c, l, y, s] * unit_yield_lsc[LandType[l[0]]][s][c] for l in LandName for s in SeasonType) <= \\\n",
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" crop_demand[c], f\"CropDemandConstraint_{c}_{y}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"id": "7e13de7700d38f19",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 条件2： 每年每季每块地的种植面积不能超过该地块的总面积\n",
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"for l in LandName:\n",
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" for y in years:\n",
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" for s in SeasonType:\n",
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" model += lpSum(X[c, l, y, s] for c in CropName) <= LandArea[l], f\"LandAreaConstraint_{l}_{y}_{s}\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 27,
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"id": "1f6ac4323825f28b",
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"metadata": {},
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"outputs": [],
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"source": [
|
||
"# 条件3： 平旱地、梯田和山坡地每年适宜单季种植[粮食类作物（水稻除外）]。因为除了[粮食类作物（水稻除外）]以外的作物似乎已经被上面的产量和利润的0给约束了，所以大概不需要做限制了吧\n",
|
||
"for l in LandName:\n",
|
||
" if l[0] not in [\"A\", \"B\", \"C\"]: # [\"平旱地\", \"梯田\", \"山坡地\"]\n",
|
||
" continue\n",
|
||
" for s in SeasonType:\n",
|
||
" if s != \"单季\":\n",
|
||
" model += lpSum(Y[c, l, y, s] for c in CropName for y in years) == 0, f\"SingleSeasonConstraint_{l}_{s}\"\n",
|
||
" # model += lpSum(X[c, l, y, s] for c in CropName for y in years) == 0"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 28,
|
||
"id": "c0e9b6b37092f0e7",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件4： 水浇地每年可以单季种植水稻或两季种植蔬菜作物。\n",
|
||
"for l in LandName:\n",
|
||
" if l[0] != \"D\": # [\"水浇地\"]\n",
|
||
" continue\n",
|
||
" for y in years:\n",
|
||
" model += (\n",
|
||
" (lpSum(Y[\"水稻\", l, y, \"单季\"]) != 0) and\n",
|
||
" (lpSum(\n",
|
||
" Y[c, l, y, s] if c != \"水稻\" else 0\n",
|
||
" for c in CropName\n",
|
||
" for s in [\"第一季\", \"第二季\"]\n",
|
||
" ) == 0)\n",
|
||
" ) or (\n",
|
||
" (lpSum(Y[\"水稻\", l, y, \"单季\"]) == 0) and\n",
|
||
" (lpSum(\n",
|
||
" Y[c, l, y, s] if c != \"水稻\" else 0\n",
|
||
" for c in CropName\n",
|
||
" for s in [\"第一季\", \"第二季\"]\n",
|
||
" ) != 0)\n",
|
||
" ), f\"irrigatedConstraint_{l}_{y}\"\n",
|
||
"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "8f8da502641f32fa",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件5： 若在某块水浇地种植两季蔬菜，第一季可种植多种蔬菜（大白菜、白萝卜和红萝卜除外）；第二季只能种植大白菜、白萝卜和红萝卜中的一种（便于管理）。\n",
|
||
"# 感觉没必要，因为前面有field和profit为0的惩罚\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "df2d7e8918382a0",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件6： 根据季节性要求，大白菜、白萝卜和红萝卜只能在水浇地的第二季种植。\n",
|
||
"# 感觉也没必要，同理\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "1d402d3df13f6732",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件7： 普通大棚每年种植两季作物，第一季可种植多种蔬菜（大白菜、白萝卜和红萝卜除外），第二季只能种植食用菌。\n",
|
||
"# 同理\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "930ab378623172ec",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件8： 食用菌类只能在普通大棚第二季的时候种植\n",
|
||
"# 同理\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 33,
|
||
"id": "bda8b4c538a097bc",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件9： 智慧大棚每年都可种植两季蔬菜（大白菜、白萝卜和红萝卜除外）\n",
|
||
"# 同理"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 29,
|
||
"id": "e8fb2dea1326a05b",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件10： 别种得太碎了，啥意思？ 每种作物不超过3块地吧。\n",
|
||
"for c in CropName:\n",
|
||
" for y in years:\n",
|
||
" for s in SeasonType:\n",
|
||
" model += lpSum(Y[c, l, y, s] for l in LandName) <= 3, f\"MaxCropTypeConstraint_{c}_{y}_{s}\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 30,
|
||
"id": "49f1490823c6af3c",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件11： 最小种植面积：0.2吧\n",
|
||
"for l in LandName:\n",
|
||
" for y in years:\n",
|
||
" for s in SeasonType:\n",
|
||
" for c in CropName:\n",
|
||
" model += X[c, l, y, s] >= 0.2 * (1 if Y[c, l, y, s] else 0), f\"MinCropAreaConstraint_{c}_{l}_{y}_{s}\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 31,
|
||
"id": "bdf1048b83723f91",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件12 ： 最重要的，不能重复种植同一块地同一年同一季的同一作物\n",
|
||
"# 每种作物在同一地块（含大棚）都不能连续重茬种植\n",
|
||
"\n",
|
||
"for l in LandName:\n",
|
||
" for c in CropName:\n",
|
||
" for y in years: # 单季不能连年种植 同一年不能连续种植 跨年也不能连续种植\n",
|
||
" if y < years[-1]:\n",
|
||
" model += Y[c, l, y, \"单季\"] + Y[c, l, y + 1, \"单季\"] <= 1, f\"SingleSeasonConstraint1_{c}_{l}_{y}\"\n",
|
||
" model += Y[c, l, y, \"第一季\"] + Y[c, l, y + 1, \"第二季\"] <= 1, f\"DoubleSeasonConstraint1_{c}_{l}_{y}\"\n",
|
||
" model += Y[c, l, y, \"第一季\"] + Y[c, l, y, \"第二季\"] <= 1, f\"DoubleSeasonConstraint2_{c}_{l}_{y}\"\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 37,
|
||
"id": "50da0aff9c68ae24",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": [
|
||
"# 条件13： 最重要的，从 2023 年开始要求每个地块（含大棚） 的所有土地三年内至少种植一次豆类作物\n",
|
||
"# CropType['豆类']\n",
|
||
"for l in LandName:\n",
|
||
" for y in years[:-2]:\n",
|
||
" model += lpSum(Y[c,l,y_r,s] for c in CropType['豆类'] for y_r in range(y,y+2) for s in SeasonType)>=1, f\"BeanConstraint_{l}_{y}\"\n",
|
||
" # model += lpSum(Y[c, l, y_r, \"单季\"] for c in CropType['豆类'] for y_r in\n",
|
||
" # range(y - 2, y + 1)) >= 1, f\"BeanConstraint1_{l}_{y}\"\n",
|
||
" # model += lpSum(Y[c, l, y_r, s] for c in CropType['豆类'] for y_r in range(y - 2, y + 1) for s in\n",
|
||
" # [\"第一季\", \"第二季\"]) >= 1, f\"BeanConstraint2_{l}_{y}\""
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 32,
|
||
"id": "d4792a1210afca3b",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": [
|
||
"1"
|
||
]
|
||
},
|
||
"execution_count": 32,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"# model.solve()\n",
|
||
"\n",
|
||
"# 创建求解器实例\n",
|
||
"solver = pulp.PULP_CBC_CMD(\n",
|
||
" timeLimit=120,\n",
|
||
" threads=4,\n",
|
||
")\n",
|
||
"solver.solve(model)\n",
|
||
"\n",
|
||
"\n",
|
||
"# print(Y)\n",
|
||
"# for c in CropName:\n",
|
||
"# for l in LandName:\n",
|
||
"# for y in years:\n",
|
||
"# for s in SeasonType:\n",
|
||
"# print(f\"Y[({c}, {l}, {y}, {s})] = {pulp.value(Y[(c, l, y, s)])}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 40,
|
||
"id": "cb3f4e95111e3292",
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"结果已保存到文件：planting_plan_2024-2030.xlsx\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"\n",
|
||
"# 输出文件名，包含起始年份和结束年份\n",
|
||
"output_filename = f\"planting_plan_{years[0]}-{years[-1]}.xlsx\"\n",
|
||
"\n",
|
||
"# 使用 pd.ExcelWriter 创建 Excel 文件\n",
|
||
"with pd.ExcelWriter(output_filename) as writer:\n",
|
||
" # 遍历每一年和每个季节\n",
|
||
" for y in years:\n",
|
||
" for s in SeasonType:\n",
|
||
" # 创建一个空的 DataFrame，横轴为作物名称，纵轴为地块名称\n",
|
||
" result_table = pd.DataFrame(columns=CropName, index=LandName)\n",
|
||
"\n",
|
||
" # 填充表格数据\n",
|
||
" for l in LandName:\n",
|
||
" for c in CropName:\n",
|
||
" # 获取变量 X[c, l, y, s] 的值\n",
|
||
" crop_value = value(X[c, l, y, s])\n",
|
||
" if crop_value is not None and crop_value > 0:\n",
|
||
" result_table.at[l, c] = crop_value # 填入结果\n",
|
||
"\n",
|
||
" # 用 0 填充空值\n",
|
||
" result_table.fillna(0, inplace=True)\n",
|
||
"\n",
|
||
" # 定义每个 sheet 的名称为 年份_季节\n",
|
||
" sheet_name = f\"{y}_Season_{s}\"\n",
|
||
"\n",
|
||
" # 将每个 result_table 写入到 Excel 的不同 sheet 中\n",
|
||
" result_table.to_excel(writer, sheet_name=sheet_name)\n",
|
||
"\n",
|
||
"print(f\"结果已保存到文件：{output_filename}\")\n"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": null,
|
||
"id": "93b4b3b64fd941aa",
|
||
"metadata": {},
|
||
"outputs": [],
|
||
"source": []
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3 (ipykernel)",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 3
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython3",
|
||
"version": "3.9.17"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|