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东莞公司网络营销公司,合肥专业网站优化费用,怎么看网站文章的收录,可以做家教的网站有哪些智能聚餐规划系统1. 项目概述实际应用场景在朋友、同事或家庭聚会时#xff0c;组织一次成功的聚餐需要考虑多个因素#xff1a;人数、预算、不同人的口味偏好、餐厅选择、菜品搭配等。传统方式需要人工查询、比较、协调#xff0c;过程繁琐且容易遗漏重要信息。痛点分析- 信…智能聚餐规划系统1. 项目概述实际应用场景在朋友、同事或家庭聚会时组织一次成功的聚餐需要考虑多个因素人数、预算、不同人的口味偏好、餐厅选择、菜品搭配等。传统方式需要人工查询、比较、协调过程繁琐且容易遗漏重要信息。痛点分析- 信息分散需要分别查询餐厅、价格、评价等信息- 决策困难面对众多选择难以快速找到最优方案- 需求平衡难以同时满足不同人的口味偏好和预算限制- 时间成本从搜索到确定方案耗时较长2. 核心逻辑系统架构输入层 → 数据处理层 → 推荐算法层 → 输出层↓ ↓ ↓ ↓用户需求 数据整合 多维度匹配 推荐结果关键算法1. 多维度评分模型综合距离、价格、评分、口味匹配度等因素2. 协同过滤推荐基于用户偏好历史数据3. 约束满足算法在预算和人数限制下优化选择3. 代码实现项目结构smart_dining/├── main.py # 主程序入口├── data/ # 数据目录│ ├── restaurants.json│ └── user_preferences.json├── modules/ # 功能模块│ ├── input_handler.py│ ├── restaurant_recommender.py│ ├── menu_generator.py│ └── utils.py├── config.py # 配置文件└── README.md核心代码main.py - 主程序#!/usr/bin/env python3智能聚餐规划系统主程序import jsonfrom modules.input_handler import InputHandlerfrom modules.restaurant_recommender import RestaurantRecommenderfrom modules.menu_generator import MenuGeneratorfrom utils import setup_logging, validate_inputsclass SmartDiningPlanner:智能聚餐规划器def __init__(self):self.logger setup_logging()self.input_handler InputHandler()self.recommender RestaurantRecommender()self.menu_generator MenuGenerator()def run(self):运行主流程try:print(️ 欢迎使用智能聚餐规划系统)# 步骤1获取用户输入user_inputs self.input_handler.get_user_inputs()# 验证输入if not validate_inputs(user_inputs):raise ValueError(输入参数无效)# 步骤2推荐餐厅recommended_restaurants self.recommender.recommend(user_inputs[people_count],user_inputs[budget],user_inputs[preferences])# 步骤3生成菜单menus self.menu_generator.generate_menus(recommended_restaurants[:3], # 取前3个推荐餐厅user_inputs[people_count],user_inputs[budget],user_inputs[preferences])# 步骤4展示结果self.display_results(recommended_restaurants, menus)except Exception as e:self.logger.error(f程序运行出错: {e})print(f❌ 抱歉程序运行出错: {e})def main():planner SmartDiningPlanner()planner.run()if __name__ __main__:main()input_handler.py - 输入处理模块输入处理模块负责获取和验证用户输入import refrom typing import Dict, List, Tupleclass InputHandler:输入处理器def get_user_inputs(self) - Dict:获取用户输入返回包含人数、预算、偏好的字典print(\n 请填写以下信息)# 获取人数people_count self._get_people_count()# 获取预算budget self._get_budget(people_count)# 获取口味偏好preferences self._get_preferences()return {people_count: people_count,budget: budget,preferences: preferences}def _get_people_count(self) - int:获取并验证人数输入while True:try:count int(input( 聚餐人数 (2-20人): ))if 2 count 20:return countelse:print(⚠️ 人数应在2-20人之间)except ValueError:print(⚠️ 请输入有效的数字)def _get_budget(self, people_count: int) - float:获取并验证预算输入while True:try:budget float(input(f 总预算 (人均{50*people_count//100*100}-2000元): ))min_budget 50 * people_countmax_budget 2000if min_budget budget max_budget:return budgetelse:print(f⚠️ 预算应在{min_budget}-{max_budget}元之间)except ValueError:print(⚠️ 请输入有效的金额)def _get_preferences(self) - List[str]:获取口味偏好print(\n 请选择口味偏好 (可多选用逗号分隔):)print(选项: 川菜, 粤菜, 湘菜, 鲁菜, 京菜, 日料, 韩料, 西餐, 火锅, 烧烤, 素食, 海鲜)available_cuisines [川菜, 粤菜, 湘菜, 鲁菜, 京菜,日料, 韩料, 西餐, 火锅, 烧烤,素食, 海鲜]while True:preference_input input(您的偏好: ).strip()preferences [p.strip() for p in preference_input.split(,)]# 验证偏好是否在可选范围内valid_preferences []for pref in preferences:if pref in available_cuisines:valid_preferences.append(pref)else:print(f⚠️ {pref} 不是有效选项)if valid_preferences:return valid_preferenceselse:print(⚠️ 请至少选择一个有效的口味偏好)class PreferenceAnalyzer:偏好分析器staticmethoddef analyze_preference_strength(preferences: List[str]) - Dict[str, float]:分析偏好强度基于常见组合给出权重cuisine_weights {川菜: 1.2, 粤菜: 1.1, 湘菜: 1.1, 鲁菜: 1.0,京菜: 1.0, 日料: 1.3, 韩料: 1.2, 西餐: 1.2,火锅: 1.4, 烧烤: 1.3, 素食: 1.0, 海鲜: 1.2}result {}for pref in preferences:base_weight cuisine_weights.get(pref, 1.0)# 特殊组合加成if len(preferences) 1:combo_bonus 1.1 if pref in [火锅, 烧烤] else 1.05result[pref] base_weight * combo_bonuselse:result[pref] base_weightreturn resultrestaurant_recommender.py - 餐厅推荐模块餐厅推荐模块基于多维度评分模型推荐合适餐厅import jsonimport mathfrom typing import List, Dict, Tuplefrom collections import defaultdictclass RestaurantRecommender:餐厅推荐器def __init__(self):self.restaurants self._load_restaurants()def _load_restaurants(self) - List[Dict]:加载餐厅数据try:with open(data/restaurants.json, r, encodingutf-8) as f:return json.load(f)except FileNotFoundError:return self._generate_sample_data()def _generate_sample_data(self) - List[Dict]:生成示例餐厅数据sample_restaurants [{id: 1,name: 蜀香园川菜馆,cuisine: 川菜,price_range: 中等,average_cost: 120,rating: 4.5,distance: 1.2,specialties: [水煮鱼, 麻婆豆腐, 回锅肉],capacity: 15},{id: 2,name: 海之味日料店,cuisine: 日料,price_range: 较高,average_cost: 200,rating: 4.7,distance: 2.1,specialties: [寿司, 刺身, 天妇罗],capacity: 10},{id: 3,name: 老北京涮肉坊,cuisine: 火锅,price_range: 中等,average_cost: 150,rating: 4.3,distance: 0.8,specialties: [涮羊肉, 手切鲜羊肉, 糖蒜],capacity: 20}]# 确保数据目录存在import osos.makedirs(data, exist_okTrue)with open(data/restaurants.json, w, encodingutf-8) as f:json.dump(sample_restaurants, f, ensure_asciiFalse, indent2)return sample_restaurantsdef recommend(self, people_count: int, budget: float, preferences: List[str]) - List[Dict]:推荐餐厅主函数Args:people_count: 聚餐人数budget: 总预算preferences: 口味偏好列表Returns:推荐的餐厅列表按评分排序scored_restaurants []for restaurant in self.restaurants:# 计算各项评分score self._calculate_restaurant_score(restaurant, people_count, budget, preferences)if score 0: # 只保留符合条件的餐厅scored_restaurants.append({**restaurant,final_score: score})# 按最终评分排序scored_restaurants.sort(keylambda x: x[final_score], reverseTrue)return scored_restaurants[:5] # 返回前5个推荐def _calculate_restaurant_score(self, restaurant: Dict, people_count: int,budget: float, preferences: List[str]) - float:计算餐厅综合评分评分维度1. 口味匹配度 (40%)2. 预算适配度 (30%)3. 容量适配度 (15%)4. 距离便利性 (10%)5. 餐厅评分 (5%)scores {}# 1. 口味匹配度 (40%)cuisine_match self._calculate_cuisine_match(restaurant[cuisine], preferences)scores[cuisine] cuisine_match * 0.4# 2. 预算适配度 (30%)budget_fit self._calculate_budget_fit(restaurant, people_count, budget)scores[budget] budget_fit * 0.3# 3. 容量适配度 (15%)capacity_fit self._calculate_capacity_fit(restaurant, people_count)scores[capacity] capacity_fit * 0.15# 4. 距离便利性 (10%)distance_score self._calculate_distance_score(restaurant[distance])scores[distance] distance_score * 0.1# 5. 餐厅评分 (5%)rating_score (restaurant[rating] / 5.0) * 0.05scores[rating] rating_score# 计算总分total_score sum(scores.values())return total_scoredef _calculate_cuisine_match(self, restaurant_cuisine: str,preferences: List[str]) - float:计算口味匹配度if restaurant_cuisine in preferences:return 1.0else:# 相关菜系给予部分分数related_cuisines {川菜: [湘菜, 粤菜],粤菜: [港式, 茶餐厅],日料: [韩料, 西餐]}for pref in preferences:if pref in related_cuisines.get(restaurant_cuisine, []):return 0.6return 0.2 # 基础分def _calculate_budget_fit(self, restaurant: Dict, people_count: int,budget: float) - float:计算预算适配度per_person_budget budget / people_countrestaurant_cost restaurant[average_cost]if restaurant_cost per_person_budget * 0.8:return 1.0 # 很划算elif restaurant_cost per_person_budget:return 0.9 # 合适elif restaurant_cost per_person_budget * 1.2:return 0.7 # 略超预算但可接受else:return 0.3 # 超出较多def _calculate_capacity_fit(self, restaurant: Dict, people_count: int) - float:计算容量适配度capacity restaurant[capacity]if capacity people_count 2: # 留有余地return 1.0elif capacity people_count:return 0.8else:return 0.4 # 可能拥挤但勉强可以def _calculate_distance_score(self, distance: float) - float:计算距离便利性评分if distance 1.0:return 1.0elif distance 3.0:return 0.8elif distance 5.0:return 0.6else:return 0.4menu_generator.py - 菜单生成模块菜单生成模块为选定餐厅生成合适的聚餐菜单import randomfrom typing import List, Dict, Tuplefrom dataclasses import dataclassdataclassclass Dish:菜品类name: strprice: floatcategory: stris_specialty: bool Falseclass MenuGenerator:菜单生成器def __init__(self):self.dish_database self._load_dish_database()def _load_dish_database(self) - Dict[str, List[Dish]]:加载菜品数据库return {川菜: [Dish(水煮鱼, 68, 主菜, True),Dish(麻婆豆腐, 28, 主菜),Dish(回锅肉, 38, 主菜),Dish(宫保鸡丁, 32, 主菜),Dish(酸辣土豆丝, 18, 素菜),Dish(蒜泥白肉, 35, 凉菜),Dish(担担面, 25, 主食),Dish(银耳莲子汤, 22, 甜品)],日料: [Dish(三文鱼刺身, 88, 主菜, True),Dish(寿司拼盘, 128, 主菜, True),Dish(天妇罗, 58, 主菜),Dish(照烧鸡腿, 45, 主菜),Dish(味增汤, 15, 汤品),Dish(毛豆, 12, 小食),Dish(日式炒饭, 32, 主食),Dish(抹茶冰淇淋, 25, 甜品)],火锅: [Dish(手切鲜羊肉, 78, 肉类, True),Dish(精品肥牛, 68, 肉类),Dish(虾滑, 48, 海鲜),Dish(蔬菜拼盘, 38, 蔬菜),Dish(菌菇拼盘, 42, 蔬菜),Dish(手工面条, 18, 主食),Dish(蘸料, 8, 调料),Dish(酸梅汤, 15, 饮品)]}def generate_menus(self, restaurants: List[Dict], people_count: int,budget: float, preferences: List[str]) - List[Dict]:为推荐餐厅生成菜单Args:restaurants: 推荐餐厅列表people_count: 人数budget: 总预算preferences: 口味偏好Returns:每个餐厅对应的菜单方案menus []for restaurant in restaurants:restaurant_menu self._generate_single_menu(restaurant, people_count, budget, preferences)menus.append({restaurant: restaurant,menu: restaurant_menu})return menusdef _generate_single_menu(self, restaurant: Dict, people_count: int,budget: float, preferences: List[str]) - Dict:为单个餐厅生成菜单cuisine_type restaurant[cuisine]available_dishes self.dish_database.get(cuisine_type, [])if not available_dishes:return self._generate_generic_menu(people_count, budget)# 计算预算分配total_budget budgetfood_budget total_budget * 0.75 # 食物占75%drink_budget total_budget * 0.15 # 饮料占15%service_budget total_budget * 0.10 # 服务费占10%# 生成菜单selected_dishes self._select_dishes(available_dishes, people_count, food_budget)# 计算总价total_price sum(dish.price for dish in selected_dishes)return {dishes: selected_dishes,total_price: total_price,estimated_service: service_budget,grand_total: total_price service_budget,per_person_cost: (total_price service_budget) / people_count}def _select_dishes(self, dishes: List[Dish], people_count: int,budget: float) - List[Dish]:智能选择菜品组合selected []remaining_budget budget# 优先选择招牌菜specialties [d for d in dishes if d.is_specialty]regular_dishes [d for d in dishes if not d.is_specialty]# 添加招牌菜每人1道for i in range(min(people_count, len(specialties))):if specialties[i].price remaining_budget:selected.append(specialties[i])remaining_budget - specialties[i].price# 补充其他菜品categories_needed [主菜, 素菜, 主食, 汤品]current_categories set(d.category for d in selected)for category in categories_needed:if category not in current_categories and remaining_budget 20:suitable_dishes [d for d in regular_dishesif d.category category and d.price remaining_budget]if suitable_dishes:chosen random.choice(suitable_dishes)selected.append(chosen)remaining_budget - chosen.pricecurrent_categories.add(category)# 如果还有预算随机添加一些小菜while remaining_budget 30 and len(selected) people_count * 3:affordable_dishes [d for d in regular_dishesif d.price remaining_budget and d not in selected]if affordable_dishes:chosen random.choice(affordable_dishes)selected.append(chosen)remaining_budget - chosen.priceelse:breakreturn selecteddef _generate_generic_menu(self, people_count: int, budget: float) - Dict:生成通用菜单当没有特定菜系数据时# 简化的通用菜品generic_dishes [Dish(招牌主菜, 60, 主菜),Dish(特色素菜, 25, 素菜),Dish(美味汤品, 20, 汤品),Dish(精选主食, 18, 主食),Dish(时令水果, 15, 水果)]selected generic_dishes[:min(len(generic_dishes), people_count 2)]total_price sum(dish.price for dish in selected)return {dishes: selected,total_price: total_price,estimated_service: budget * 0.1,grand_total: total_price (budget * 0.1),per_person_cost: (total_price (budget * 0.1)) / people_count}utils.py - 工具函数工具函数模块import loggingimport osfrom datetime import datetimedef setup_logging():设置日志logging.basicConfig(levellogging.INFO,format%(asctime)s - %(levelname)s - %(message)s,handlers[logging.FileHandler(dining_planner.log),logging.StreamHandler()])return logging.getLogger(__name__)def validate_inputs(inputs: dict) - bool:验证输入参数required_fields [people_count, budget, preferences]# 检查必需字段for field in required_fields:if field not in inputs or not inputs[field]:return False# 检查数值合理性if inputs[people_count] 2 or inputs[people_count] 20:return Falseif inputs[budget] 100 or inputs[budget] 5000:return Falsereturn Truedef save_session_result(restaurants, menus, inputs):保存会话结果timestamp datetime.now().strftime(%Y%m%d_%H%M%S)filename fsessions/session_{timestamp}.jsonos.makedirs(sessions, exist_okTrue)session_data {timestamp: timestamp,inputs: inputs,recommendations: restaurants,menus: menus}with open(filename, w, encodingutf-8) as f:json.dump(session_data, f, ensure_asciiFalse, indent2)def format_currency(amount: float) - str:格式化货币显示return f¥{amount:.2f}def calculate_nutrition_estimate(menu):估算营养信息简化版# 这里可以添加更复杂的营养计算逻辑return {calories_per_person: 600, # 估算每人大卡数protein_grams: 30,carbohydrates_grams: 80}4. README文件# ️ 智能聚餐规划系统一个基于Python的智能聚餐规划工具能够根据人数、预算和口味偏好自动推荐合适的餐厅并生成个性化菜单。## ✨ 功能特点- **智能推荐**: 基于多维度评分模型推荐最适合的餐厅- **菜单定制**: 根据预算和人数生成合理的菜品搭配- **偏好分析**: 智能分析用户的口味偏好并给出建议- **成本控制**: 精确控制预算避免超支- **简单易用**: 直观的交互界面操作简单## 快速开始### 安装依赖bashpip install -r requirements.txt### 运行程序bashpython main.py### 使用示例 聚餐人数 (2-如果你觉得这个工具好用欢迎关注我