配方计算器2.0

feed_formula_calculator.py

@@ -1,36 +1,240 @@
-import optimize_feed
+import pulp
+import pandas as pd
+from tabulate import tabulate
+import os
 
-# 示例调用
+
-if __name__ == "__main__":
+def calculate_nutrients_from_formula(formula_proportions, ingredients):
-    # 原料数据（加工后，单位：%或MJ/kg，价格元/吨）
+    """
-    ingredients = {
+    计算市面配方营养总量，作为需求校准
-        '构树叶': {'蛋白': 15, '能量': 9, '纤维': 10, '赖氨酸': 0.5, '蛋氨酸': 0.1, '钙': 1.5, '磷': 0.2, '价格': 600},
+    :param formula_proportions: 配方比例字典，如 {'玉米': 0.59, '豆粕': 0.15}
-        '玉米胚芽': {'蛋白': 11, '能量': 13, '纤维': 7, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 0.3,
+    :param ingredients: 原料数据字典，含营养值/价格
-                     '价格': 1700},
+    :return: 营养总量字典，如 {'蛋白': 17.76, '能量': 11.56}
-        '麦麸': {'蛋白': 15, '能量': 10, '纤维': 12, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 1.0, '价格': 1300},
+    """
-        '米糠': {'蛋白': 13, '能量': 11, '纤维': 10, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 1.5, '价格': 1100},
+    nutrients = list(set(key for ing in ingredients.values() for key in ing if key not in ['价格']))
-        '牧草': {'蛋白': 17, '能量': 8.5, '纤维': 20, '赖氨酸': 0.6, '蛋氨酸': 0.2, '钙': 1.2, '磷': 0.3, '价格': 800},
+    total_nutrients = {}
-        '玉米': {'蛋白': 9, '能量': 13.5, '纤维': 2.5, '赖氨酸': 0.3, '蛋氨酸': 0.17, '钙': 0.02, '磷': 0.3,
+    for nut in nutrients:
-                 '价格': 2367},
+        total = sum(formula_proportions.get(i, 0) * ingredients.get(i, {}).get(nut, 0) for i in formula_proportions)
-        '豆粕': {'蛋白': 46, '能量': 10.5, '纤维': 4, '赖氨酸': 2.8, '蛋氨酸': 0.6, '钙': 0.3, '磷': 0.7, '价格': 3300},
+        total_nutrients[nut] = round(total, 2)
-        '植物油': {'蛋白': 0, '能量': 36, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 0, '磷': 0, '价格': 7500},
+    return total_nutrients
-        '赖氨酸': {'蛋白': 78, '能量': 0, '纤维': 0, '赖氨酸': 78, '蛋氨酸': 0, '钙': 0, '磷': 0, '价格': 15000},
+
-        '蛋氨酸': {'蛋白': 99, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 99, '钙': 0, '磷': 0, '价格': 25000},
+
-        '预混料': {'蛋白': 0, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 10, '磷': 5, '价格': 3000}
+def calculate_cost_from_formula(formula_proportions, ingredients):
+    """
+    计算市面配方成本，作为参考
+    :param formula_proportions: 配方比例字典，如 {'玉米': 0.59}
+    :param ingredients: 原料数据字典，含价格
+    :return: 成本字典，含总价格和一斤价格
+    """
+    total_price = sum(
+        formula_proportions.get(i, 0) * ingredients.get(i, {}).get('价格', 0) for i in formula_proportions)
+    return {
+        '总价格': round(total_price, 2),
+        '一斤饲料价格': round(total_price / 2000, 2)  # 吨=2000斤
     }
 
+
+def optimize_feed(requirements, ingredients, result_fields, optimization_type='min'):
+    """
+    优化饲料配方，计算最低/最高目标值的原料比例
+    :param requirements: 猪营养需求字典，如 {'蛋白_下限': 17, '能量_下限': 12.5, '纤维_上限': 6}
+    :param ingredients: 原料数据字典，如 {'构树叶_发酵': {'蛋白': 15, '能量': 9, '纤维': 15, '价格': 600}}
+    :param result_fields: 结果字段列表，如 ['比例', '成本']
+    :param optimization_type: 'min'（最小化，如成本）或 'max'（最大化，如消化率）
+    :return: 字典，含结果字段和值，如 {'构树叶_发酵': {'比例': 14.29, '成本': 85.74}, '总价格': 1989.12}
+    """
+    sense = pulp.LpMinimize if optimization_type == 'min' else pulp.LpMaximize
+    model = pulp.LpProblem("Feed_Optimization", sense)
+
+    # 决策变量：每种原料比例（0-1，单位：比例）
+    x = pulp.LpVariable.dicts("比例", ingredients, lowBound=0, upBound=1)
+
+    # 目标函数：最小化总价格（纯价格，无额外权重）
+    model += pulp.lpSum([x[i] * ingredients[i]['价格'] for i in ingredients]), "总价格"
+
+    # 约束1：总比例=100%
+    model += pulp.lpSum([x[i] for i in ingredients]) == 1, "总比例"
+
+    # 约束2：营养需求（下限/上限）
+    for req, value in requirements.items():
+        if '下限' in req:
+            nutrient = req.replace('_下限', '')
+            # 营养总和≥下限（如蛋白≥17%）
+            model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) >= value, req
+        elif '上限' in req:
+            nutrient = req.replace('_上限', '')
+            # 营养总和≤上限（如纤维≤6%）
+            model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) <= value, req
+
+    # 求解：用CBC极限精度，深度优先，限时10秒
+    try:
+        solver = pulp.PULP_CBC_CMD(msg=True,
+                                   options=['primalT', '1e-12', 'dualT', '1e-12', 'maxIt', '10000000', 'presolve', 'on',
+                                            'strategy', '2', 'randomC', '123', 'sec', '10'])
+        model.solve(solver)
+    except AttributeError:
+        print("警告：CBC不可用，使用默认求解器，精度可能稍低")
+        model.solve()
+
+    # 检查是否找到最优解
+    if pulp.LpStatus[model.status] != 'Optimal':
+        print("约束值（调试用）：")
+        for name, constraint in model.constraints.items():
+            print(f"{name}: {constraint.value()}")
+        return {"错误": "无解！检查数据（纤维/单宁超？蛋白/氨基酸不足？）"}
+
+    # 构建结果
+    result = {}
+    total_price = pulp.value(model.objective)
+    result['总价格'] = round(total_price, 2)
+    result['一斤饲料价格'] = round(total_price / 2000, 2)  # 吨=2000斤
+
+    # 每种原料的比例（%）和成本（元/吨）
+    for i in ingredients:
+        result[i] = {field: round(pulp.value(x[i]) * 100, 2) if field == '比例' else round(
+            pulp.value(x[i]) * ingredients[i]['价格'], 2) for field in result_fields}
+
+    # 计算营养总量
+    nutrients = list(set(key for ing in ingredients.values() for key in ing if key not in ['价格']))
+    result['营养总量'] = {}
+    for nut in nutrients:
+        total = sum(pulp.value(x[i]) * ingredients[i].get(nut, 0) for i in ingredients)
+        result['营养总量'][nut] = round(total, 2)
+
+    # 检查超标（宁可无解也不超）
+    for req, value in requirements.items():
+        if '上限' in req:
+            nut = req.replace('_上限', '')
+            if result['营养总量'][nut] > value + 0.01:  # 容忍小误差
+                return {"错误": f"{nut}超上限: {result['营养总量'][nut]} > {value} (可能浮点误差)"}
+
+    # 存CSV到results/
+    os.makedirs('results', exist_ok=True)
+    df_formula = pd.DataFrame(
+        [(k, v['比例'], v['成本']) for k, v in result.items() if k not in ['总价格', '一斤饲料价格', '营养总量']],
+        columns=['原料', '比例', '成本'])
+    df_formula.to_csv('results/优化配方结果.csv', index=False)
+
+    # 营养偏差表
+    nutrient_table = []
+    for nut, value in result['营养总量'].items():
+        req_key = f"{nut}_下限" if f"{nut}_下限" in requirements else f"{nut}_上限"
+        req_type = '下限' if f"{nut}_下限" in requirements else '上限'
+        req_value = requirements.get(req_key, None)
+        deviation = value - req_value if req_key.endswith('_下限') else req_value - value
+        deviation_pct = (deviation / req_value * 100) if req_value != 0 else 0
+        status = '达标' if (req_key.endswith('_下限') and value >= req_value) or (
+                req_key.endswith('_上限') and value <= req_value) else '不达标'
+        nutrient_table.append([nut, value, req_value, req_type, deviation, deviation_pct, status])
+    df_nutrients = pd.DataFrame(nutrient_table,
+                                columns=['营养', '实际值', '需求值', '需求类型', '偏差', '偏差比例(%)', '状态'])
+    df_nutrients.to_csv('results/营养总量与需求偏差.csv', index=False)
+
+    return result
+
+
+if __name__ == "__main__":
+    # 原料数据（单位：%或MJ/kg，价格元/吨，2025年9月农业农村部/饲料市场信息网）
+    ingredients = {
+        '构树叶_未加工': {'蛋白': 13, '能量': 8.5, '纤维': 25, '赖氨酸': 0.4, '蛋氨酸': 0.08, '钙': 1.8, '磷': 0.15,
+                          '单宁': 2.5, '价格': 500},
+        '构树叶_发酵': {'蛋白': 15, '能量': 9, '纤维': 15, '赖氨酸': 0.5, '蛋氨酸': 0.1, '钙': 1.5, '磷': 0.2,
+                        '单宁': 1.5, '价格': 600},
+        '玉米胚芽_压榨': {'蛋白': 11, '能量': 13, '纤维': 7, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 0.3,
+                          '单宁': 0, '价格': 1700},
+        '麦麸': {'蛋白': 15, '能量': 10, '纤维': 12, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 1.0, '单宁': 0,
+                 '价格': 1300},
+        '米糠': {'蛋白': 13, '能量': 11, '纤维': 10, '赖氨酸': 0.4, '蛋氨酸': 0.2, '钙': 0.1, '磷': 1.5, '单宁': 0,
+                 '价格': 1100},
+        '牧草_苜蓿干': {'蛋白': 17, '能量': 8.5, '纤维': 20, '赖氨酸': 0.6, '蛋氨酸': 0.2, '钙': 1.2, '磷': 0.3,
+                        '单宁': 0.5, '价格': 800},
+        '玉米': {'蛋白': 9, '能量': 13.5, '纤维': 2.5, '赖氨酸': 0.3, '蛋氨酸': 0.17, '钙': 0.02, '磷': 0.3, '单宁': 0,
+                 '价格': 2367},
+        '豆粕': {'蛋白': 46, '能量': 10.5, '纤维': 4, '赖氨酸': 2.8, '蛋氨酸': 0.6, '钙': 0.3, '磷': 0.7, '单宁': 0,
+                 '价格': 3300},
+        '植物油_豆油': {'蛋白': 0, '能量': 36, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 0, '磷': 0, '单宁': 0,
+                        '价格': 7500},
+        '赖氨酸_L盐': {'蛋白': 78, '能量': 0, '纤维': 0, '赖氨酸': 78, '蛋氨酸': 0, '钙': 0, '磷': 0, '单宁': 0,
+                       '价格': 15000},
+        '蛋氨酸_DL': {'蛋白': 99, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 99, '钙': 0, '磷': 0, '单宁': 0,
+                      '价格': 25000},
+        '预混料_维生素微量元素': {'蛋白': 0, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 10, '磷': 5,
+                                  '单宁': 0, '价格': 3000},
+        '鱼粉': {'蛋白': 60, '能量': 12, '纤维': 1, '赖氨酸': 3.5, '蛋氨酸': 1.5, '钙': 5, '磷': 3, '单宁': 0,
+                 '价格': 5000},
+        '骨粉': {'蛋白': 0, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 30, '磷': 15, '单宁': 0,
+                 '价格': 1500},
+        '食盐': {'蛋白': 0, '能量': 0, '纤维': 0, '赖氨酸': 0, '蛋氨酸': 0, '钙': 0, '磷': 0, '单宁': 0,
+                 '价格': 500}
+    }
+
+    # 育肥猪需求（30-115kg，NY/T 65-2004+丹麦标准，单位：%或MJ/kg）
     requirements = {
         '蛋白_下限': 17,
-        '能量_下限': 13,
+        '能量_下限': 12.5,
-        '纤维_上限': 5,
+        '纤维_上限': 6,
-        '赖氨酸_下限': 0.75,
+        '赖氨酸_下限': 0.8,
         '蛋氨酸_下限': 0.25,
         '钙_下限': 0.5,
-        '磷_下限': 0.4
+        '磷_下限': 0.4,
+        '单宁_上限': 0.5
     }
 
-    # 运行优化
+    # 优化配方
-    result = optimize_feed.optimize_feed(requirements, ingredients, ['比例', '成本'], 'min')
+    result = optimize_feed(requirements, ingredients, ['比例', '成本'], 'min')
-    print("优化结果:")
+
-    for key, value in result.items():
+    # 美化输出：优化配方
-        print(f"{key}: {value}")
+    print("\n=== 优化配方结果 ===")
+    table_data = [(k, v['比例'], v['成本']) for k, v in result.items() if
+                  k not in ['总价格', '一斤饲料价格', '营养总量']]
+    headers = ['原料', '比例 (%)', '成本 (元/吨)']
+    print(tabulate(table_data, headers=headers, tablefmt='grid', floatfmt='.2f'))
+    print(f"总价格: {result['总价格']:.2f} 元/吨")
+    print(f"一斤饲料价格: {result['一斤饲料价格']:.2f} 元")
+
+    # 营养总量与需求偏差
+    print("\n=== 营养总量与需求偏差 ===")
+    nutrient_table = []
+    for nut, value in result['营养总量'].items():
+        req_key = f"{nut}_下限" if f"{nut}_下限" in requirements else f"{nut}_上限"
+        req_type = '下限' if f"{nut}_下限" in requirements else '上限'
+        req_value = requirements.get(req_key, None)
+        deviation = value - req_value if req_key.endswith('_下限') else req_value - value
+        deviation_pct = (deviation / req_value * 100) if req_value != 0 else 0
+        status = '达标' if (req_key.endswith('_下限') and value >= req_value) or (
+                req_key.endswith('_上限') and value <= req_value) else '不达标'
+        nutrient_table.append([nut, value, req_value, req_type, deviation, deviation_pct, status])
+    print(tabulate(nutrient_table, headers=['营养', '实际值', '需求值', '需求类型', '偏差', '偏差比例(%)', '状态'],
+                   tablefmt='grid', floatfmt='.2f'))
+
+    # 市面配方（2025年养猪网，21-35kg调整至30-115kg）
+    market_formula = {
+        '玉米': 0.59,
+        '麦麸': 0.13,
+        '豆粕': 0.15,
+        '鱼粉': 0.06,
+        '骨粉': 0.015,
+        '食盐': 0.005
+    }
+
+    # 市面配方营养和成本
+    market_nutrients = calculate_nutrients_from_formula(market_formula, ingredients)
+    market_cost = calculate_cost_from_formula(market_formula, ingredients)
+
+    print("\n=== 市面配方营养总量 ===")
+    print(tabulate([(k, v) for k, v in market_nutrients.items()], headers=['营养', '值'], tablefmt='grid',
+                   floatfmt='.2f'))
+    pd.DataFrame([(k, v) for k, v in market_nutrients.items()], columns=['营养', '值']).to_csv(
+        'results/市面配方营养总量.csv', index=False)
+
+    print("\n=== 市面配方成本与偏差 ===")
+    cost_table = [
+        ['总价格 (元/吨)', market_cost['总价格'], result['总价格'], market_cost['总价格'] - result['总价格']],
+        ['一斤饲料价格 (元)', market_cost['一斤饲料价格'], result['一斤饲料价格'],
+         market_cost['一斤饲料价格'] - result['一斤饲料价格']]
+    ]
+    print(tabulate(cost_table, headers=['项', '市面配方', '优化配方', '偏差'], tablefmt='grid', floatfmt='.2f'))
+    pd.DataFrame(cost_table, columns=['项', '市面配方', '优化配方', '偏差']).to_csv('results/市面配方成本与偏差.csv',
+                                                                                    index=False)
+
+    print("\n所有结果已存至 results/ 文件夹")

optimize_feed.py

@@ -1,48 +0,0 @@
-import pulp
-def optimize_feed(requirements, ingredients, result_fields, optimization_type='min'):
-    """
-    优化饲料配方，计算最低/最高目标值的原料比例
-    :param requirements: 猪营养需求字典，如 {'蛋白_下限': 16, '能量_下限': 12, '纤维_上限': 8}
-    :param ingredients: 原料数据字典，如 {'构树叶': {'蛋白': 18, '能量': 10, '纤维': 5, '价格': 500}}
-    :param result_fields: 结果字段列表，如 ['比例', '成本']
-    :param optimization_type: 'min'（最小化，如成本）或 'max'（最大化，如消化率）
-    :return: 字典，含结果字段和值，如 {'构树叶': {'比例': 0.2, '成本': 100}, '总成本': 1475}
-    """
-    # 创建模型
-    sense = pulp.LpMinimize if optimization_type == 'min' else pulp.LpMaximize
-    model = pulp.LpProblem("Feed_Optimization", sense)
-
-    # 决策变量：原料比例（0-1）
-    x = pulp.LpVariable.dicts("比例", ingredients, lowBound=0, upBound=1)
-
-    # 目标函数：优化指定字段（如价格）
-    target_field = next(f for f in ingredients[list(ingredients.keys())[0]] if '价格' in f or '成本' in f)  # 默认优化价格
-    model += pulp.lpSum([x[i] * ingredients[i][target_field] for i in ingredients]), "目标"
-
-    # 约束1：总比例=100%
-    model += pulp.lpSum([x[i] for i in ingredients]) == 1, "总比例"
-
-    # 约束2：营养需求（下限/上限）
-    for req, value in requirements.items():
-        if '下限' in req:
-            nutrient = req.replace('_下限', '')
-            model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) >= value, req
-        elif '上限' in req:
-            nutrient = req.replace('_上限', '')
-            model += pulp.lpSum([x[i] * ingredients[i][nutrient] for i in ingredients]) <= value, req
-
-    # 求解
-    model.solve()
-
-    # 构建结果
-    if pulp.LpStatus[model.status] != 'Optimal':
-        return {"错误": "无解！检查原料数据或需求（比如纤维全超上限？）"}
-
-    result = {}
-    total_target = pulp.value(model.objective)
-    result['总' + target_field] = round(total_target, 2)
-    for i in ingredients:
-        result[i] = {field: round(pulp.value(x[i]) * 100, 2) if field == '比例' else round(
-            pulp.value(x[i]) * ingredients[i][target_field], 2) for field in result_fields}
-    return result
-

results/优化配方结果.csv

@@ -0,0 +1,16 @@
+原料,比例,成本
+构树叶_未加工,0.0,0.0
+构树叶_发酵,0.0,0.0
+玉米胚芽_压榨,73.58,1250.87
+麦麸,0.0,0.0
+米糠,0.0,0.0
+牧草_苜蓿干,0.0,0.0
+玉米,8.1,191.8
+豆粕,15.79,521.05
+植物油_豆油,0.0,0.0
+赖氨酸_L盐,0.0,0.0
+蛋氨酸_DL,0.0,0.0
+预混料_维生素微量元素,0.0,0.0
+鱼粉,1.52,76.15
+骨粉,1.0,15.06
+食盐,0.0,0.0

results/市面配方成本与偏差.csv

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+项,市面配方,优化配方,偏差
+总价格 (元/吨),2385.53,2054.93,330.60000000000036
+一斤饲料价格 (元),1.19,1.03,0.15999999999999992

results/市面配方营养总量.csv

@@ -0,0 +1,9 @@
+营养,值
+蛋氨酸,0.31
+能量,11.56
+赖氨酸,0.86
+钙,0.82
+磷,0.82
+蛋白,17.76
+纤维,3.7
+单宁,0.0

results/营养总量与需求偏差.csv

@@ -0,0 +1,9 @@
+营养,实际值,需求值,需求类型,偏差,偏差比例(%),状态
+蛋氨酸,0.28,0.25,下限,0.030000000000000027,12.00000000000001,达标
+能量,12.5,12.5,下限,0.0,0.0,达标
+赖氨酸,0.81,0.8,下限,0.010000000000000009,1.250000000000001,达标
+钙,0.5,0.5,下限,0.0,0.0,达标
+磷,0.55,0.4,下限,0.15000000000000002,37.50000000000001,达标
+蛋白,17.0,17.0,下限,0.0,0.0,达标
+纤维,6.0,6.0,上限,0.0,0.0,达标
+单宁,0.0,0.5,上限,0.5,100.0,达标