Python微信校园智能食堂与餐饮管理系统

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功能概述

本系统基于Python开发，实现微信生态下的校园食堂及餐饮全流程智能化管理，包括以下核心功能：

1. 智能菜品推荐与营养分析
2. 无人结算与支付系统
3. 后厨智能管理与供应链优化
4. 食品安全监控与预警
5. 就餐人流预测与座位引导
6. 个性化饮食计划与健康管理
7. 餐饮大数据分析与决策支持

核心代码实现

1. 配置文件 (config/dining_config.py)

   import os
   from datetime import timedelta
   from typing import Dict, List, Any, Tuple
   import numpy as np
   
   class DiningConfig:
       """餐饮管理系统配置"""
       
       # 数据库配置
       DATABASE = {
           'host': os.getenv('DB_HOST', 'localhost'),
           'port': int(os.getenv('DB_PORT', 5432)),
           'user': os.getenv('DB_USER'),
           'password': os.getenv('DB_PASSWORD'),
           'name': os.getenv('DB_NAME', 'campus_dining'),
           'pool_size': 30,
           'max_overflow': 40
       }
       
       # 微信配置
       WECHAT = {
           'mini_program': {
               'app_id': os.getenv('WX_MINI_APP_ID'),
               'secret': os.getenv('WX_MINI_SECRET'),
           },
           'payment': {
               'mch_id': os.getenv('WX_PAY_MCH_ID'),
               'mch_key': os.getenv('WX_PAY_MCH_KEY'),
           },
           'template_ids': {
               'order_confirmed': 'TEMPLATE_ORDER_CONFIRMED',
               'nutrition_report': 'TEMPLATE_NUTRITION_REPORT',
               'food_safety_alert': 'TEMPLATE_FOOD_SAFETY_ALERT',
               'meal_reminder': 'TEMPLATE_MEAL_REMINDER',
               'special_offer': 'TEMPLATE_SPECIAL_OFFER',
               'diet_plan_update': 'TEMPLATE_DIET_PLAN_UPDATE'
           }
       }
       
       # 智能推荐配置
       INTELLIGENT_RECOMMENDATION = {
           'recommendation_engines': {
               'collaborative_filtering': {
                   'similarity_metric': 'cosine',
                   'neighborhood_size': 50,
                   'diversity_factor': 0.2
               },
               'content_based': {
                   'feature_weights': {
                       'nutrition': 0.3,
                       'taste_preference': 0.25,
                       'allergy_restrictions': 0.2,
                       'price_sensitivity': 0.15,
                       'preparation_time': 0.1
                   }
               },
               'context_aware': {
                   'time_of_day': True,
                   'weather_conditions': True,
                   'physical_activity': True,
                   'health_condition': True
               }
           },
           'nutrition_standards': {
               'daily_calorie_intake': {
                   'male': {'sedentary': 2200, 'moderate': 2600, 'active': 3000},
                   'female': {'sedentary': 1800, 'moderate': 2200, 'active': 2500}
               },
               'macronutrient_ratios': {
                   'carbohydrates': {'min': 0.45, 'max': 0.65},
                   'proteins': {'min': 0.1, 'max': 0.35},
                   'fats': {'min': 0.2, 'max': 0.35}
               },
               'micronutrient_requirements': {
                   'vitamins': ['A', 'B', 'C', 'D', 'E'],
                   'minerals': ['calcium', 'iron', 'zinc', 'magnesium']
               }
           }
       }
       
       # 智能结算配置
       INTELLIGENT_CHECKOUT = {
           'recognition_technologies': {
               'computer_vision': {
                   'dish_recognition_accuracy': 0.98,
                   'portion_size_detection': True,
                   'multiple_dish_handling': True
               },
               'rfid_technology': {
                   'smart_tray_identification': True,
                   'real_time_tracking': True
               },
               'sensor_fusion': {
                   'weight_sensors': True,
                   'temperature_sensors': True
               }
           },
           'payment_methods': {
               'wechat_pay': {'processing_time': '2s', 'success_rate': 0.995},
               'campus_card': {'processing_time': '1s', 'success_rate': 0.999},
               'face_payment': {'processing_time': '3s', 'success_rate': 0.985}
           },
           'fraud_detection': {
               'anomaly_detection': True,
               'pattern_recognition': True,
               'real_time_alerts': True
           }
       }
       
       # 后厨管理配置
       KITCHEN_MANAGEMENT = {
           'inventory_management': {
               'real_time_tracking': True,
               'expiration_alerts': True,
               'auto_replenishment': True,
               'waste_reduction': True
           },
           'production_planning': {
               'demand_forecasting': {
                   'algorithm': 'lstm',
                   'prediction_horizon': 7,  # 天
                   'update_frequency': 'daily'
               },
               'batch_optimization': {
                   'minimize_waste': True,
                   'maximize_freshness': True,
                   'optimize_labor': True
               }
           },
           'quality_control': {
               'ingredient_inspection': {
                   'freshness_standards': True,
                   'safety_checks': True,
                   'quality_grading': True
               },
               'cooking_process': {
                   'temperature_monitoring': True,
                   'time_control': True,
                   'consistency_checks': True
               }
           }
       }
       
       # 食品安全配置
       FOOD_SAFETY = {
           'monitoring_systems': {
               'temperature_control': {
                   'storage_temperature': {'refrigerator': '0-4°C', 'freezer': '-18°C'},
                   'cooking_temperature': {'meat': '75°C', 'poultry': '82°C', 'seafood': '63°C'}
               },
               'hygiene_monitoring': {
                   'hand_washing_compliance': True,
                   'surface_cleanliness': True,
                   'utensil_sanitization': True
               },
               'allergen_management': {
                   'cross_contamination_prevention': True,
                   'allergen_labeling': True,
                   'emergency_response': True
               }
           },
           'traceability_system': {
               'supply_chain_tracking': True,
               'batch_identification': True,
               'recall_management': True
           },
           'compliance_standards': {
               'haccp_principles': True,
               'local_regulations': True,
               'international_standards': True
           }
       }
       
       # 就餐管理配置
       DINING_MANAGEMENT = {
           'crowd_prediction': {
               'prediction_models': {
                   'time_series': True,
                   'machine_learning': True,
                   'external_factors': True
               },
               'peak_hours_management': {
                   'staff_allocation': True,
                   'resource_optimization': True,
                   'queue_management': True
               }
           },
           'seat_guidance': {
               'real_time_occupancy': True,
               'group_size_matching': True,
               'preference_based': True,
               'accessibility_consideration': True
           },
           'environment_optimization': {
               'temperature_control': True,
               'ventilation_management': True,
               'noise_level_monitoring': True,
               'lighting_optimization': True
           }
       }
       
       # 健康管理配置
       HEALTH_MANAGEMENT = {
           'personalized_diet_plans': {
               'health_goal_alignment': True,
               'medical_condition_adaptation': True,
               'cultural_preference_respect': True,
               'budget_constraints': True
           },
           'nutrition_tracking': {
               'calorie_counting': True,
               'nutrient_balance': True,
               'intake_monitoring': True,
               'progress_tracking': True
           },
           'behavioral_coaching': {
               'habit_formation': True,
               'portion_control': True,
               'mindful_eating': True,
               'goal_setting': True
           }
       }
       
       # 供应链优化配置
       SUPPLY_CHAIN_OPTIMIZATION = {
           'procurement_optimization': {
               'supplier_selection': {
                   'quality_scoring': True,
                   'reliability_assessment': True,
                   'cost_effectiveness': True
               },
               'order_optimization': {
                   'economic_order_quantity': True,
                   'safety_stock_calculation': True,
                   'lead_time_consideration': True
               }
           },
           'logistics_management': {
               'delivery_scheduling': True,
               'route_optimization': True,
               'cold_chain_management': True
           },
           'sustainability_initiatives': {
               'local_sourcing': True,
               'seasonal_ingredients': True,
               'food_waste_reduction': True,
               'packaging_optimization': True
           }
       }

2. 餐饮管理服务 (services/dining_service.py)

   import json
   import logging
   import asyncio
   from datetime import datetime, timedelta
   from typing import Dict, List, Optional, Tuple, Set
   import numpy as np
   import pandas as pd
   from sqlalchemy import create_engine, text
   from sqlalchemy.orm import sessionmaker
   import redis
   import cv2
   from sklearn.metrics.pairwise import cosine_similarity
   from tensorflow import keras
   import pulp  # 线性规划优化
   
   from config.dining_config import DiningConfig
   from utils.wechat_util import WeChatUtil
   from utils.nutrition_analyzer import NutritionAnalyzer
   from utils.dish_recommender import DishRecommender
   from utils.kitchen_optimizer import KitchenOptimizer
   from utils.safety_monitor import SafetyMonitor
   from exceptions.dining_exception import DiningException
   
   class DiningService:
       """餐饮管理服务"""
       
       def __init__(self):
           self.config = DiningConfig()
           self.engine = create_engine(
               f"postgresql://{self.config.DATABASE['user']}:{self.config.DATABASE['password']}"
               f"@{self.config.DATABASE['host']}:{self.config.DATABASE['port']}/{self.config.DATABASE['name']}",
               pool_size=self.config.DATABASE['pool_size'],
               max_overflow=self.config.DATABASE['max_overflow']
           )
           self.Session = sessionmaker(bind=self.engine)
           self.redis = redis.Redis(**self.config.REDIS)
           self.logger = logging.getLogger(__name__)
           
           # 初始化组件
           self.nutrition_analyzer = NutritionAnalyzer(self.config.INTELLIGENT_RECOMMENDATION['nutrition_standards'])
           self.dish_recommender = DishRecommender(self.config.INTELLIGENT_RECOMMENDATION)
           self.kitchen_optimizer = KitchenOptimizer(self.config.KITCHEN_MANAGEMENT)
           self.safety_monitor = SafetyMonitor(self.config.FOOD_SAFETY)
       
       def recommend_personalized_meals(self, user_id: str, meal_context: Dict) -> Dict:
           """推荐个性化餐品"""
           self._validate_meal_context(meal_context)
           
           session = self.Session()
           try:
               # 获取用户健康档案
               health_profile = self._get_user_health_profile(user_id)
               
               # 分析营养需求
               nutritional_needs = self.nutrition_analyzer.calculate_daily_requirements(
                   health_profile,
                   meal_context['meal_type'],
                   meal_context['physical_activity']
               )
               
               # 获取饮食偏好和限制
               dietary_preferences = self._get_dietary_preferences(user_id)
               allergy_restrictions = self._get_allergy_restrictions(user_id)
               
               # 多算法推荐
               recommendation_results = self.dish_recommender.generate_recommendations(
                   nutritional_needs,
                   dietary_preferences,
                   allergy_restrictions,
                   meal_context
               )
               
               # 营养平衡优化
               balanced_combinations = self._optimize_nutrition_balance(
                   recommendation_results['recommended_dishes'],
                   nutritional_needs
               )
               
               # 价格敏感性调整
               price_optimized_options = self._adjust_for_price_sensitivity(
                   balanced_combinations,
                   meal_context['budget_constraint']
               )
               
               # 生成最终推荐
               final_recommendations = self._generate_final_recommendations(
                   price_optimized_options,
                   meal_context['time_constraint']
               )
               
               # 保存推荐记录
               recommendation_id = self._save_recommendation_record(
                   session,
                   user_id,
                   final_recommendations,
                   nutritional_needs
               )
               
               session.commit()
               
               # 发送微信推荐
               self._send_meal_recommendation(user_id, final_recommendations, nutritional_needs)
               
               return {
                   'recommendation_id': recommendation_id,
                   'nutritional_needs': nutritional_needs,
                   'recommendation_algorithms': recommendation_results['algorithms_used'],
                   'balanced_combinations': balanced_combinations,
                   'final_recommendations': final_recommendations,
                   'health_considerations': self._explain_health_considerations(final_recommendations),
                   'alternative_options': self._generate_alternative_options(final_recommendations),
                   'generated_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"餐品推荐失败: {str(e)}")
               raise DiningException(f"餐品推荐失败: {str(e)}")
           finally:
               session.close()
       
       def process_smart_checkout(self, checkout_data: Dict) -> Dict:
           """处理智能结算"""
           self._validate_checkout_data(checkout_data)
           
           session = self.Session()
           try:
               # 图像识别菜品
               dish_recognition = self._recognize_dishes_from_image(checkout_data['tray_image'])
               
               # 重量传感器验证
               weight_verification = self._verify_portion_sizes(
                   dish_recognition['identified_dishes'],
                   checkout_data['weight_measurements']
               )
               
               # RFID托盘识别
               tray_identification = self._identify_tray(checkout_data['rfid_tag'])
               
               # 多模态数据融合
               fused_dish_data = self._fuse_multimodal_data(
                   dish_recognition,
                   weight_verification,
                   tray_identification
               )
               
               # 价格计算
               pricing_calculation = self._calculate_total_price(fused_dish_data)
               
               # 欺诈检测
               fraud_detection = self._detect_potential_fraud(
                   fused_dish_data,
                   checkout_data['user_behavior']
               )
               
               if fraud_detection['suspicious']:
                   raise DiningException(f"检测到异常行为: {fraud_detection['reasons']}")
               
               # 多种支付方式处理
               payment_processing = self._process_payment(
                   pricing_calculation['total_amount'],
                   checkout_data['payment_method'],
                   checkout_data['user_id']
               )
               
               # 生成营养报告
               nutrition_report = self._generate_nutrition_report(fused_dish_data['dishes'])
               
               # 保存交易记录
               transaction_id = self._save_checkout_transaction(
                   session,
                   checkout_data['user_id'],
                   fused_dish_data,
                   pricing_calculation,
                   payment_processing
               )
               
               session.commit()
               
               # 发送电子收据
               self._send_electronic_receipt(
                   checkout_data['user_id'],
                   transaction_id,
                   fused_dish_data,
                   pricing_calculation,
                   nutrition_report
               )
               
               return {
                   'transaction_id': transaction_id,
                   'dish_recognition': dish_recognition,
                   'weight_verification': weight_verification,
                   'pricing_calculation': pricing_calculation,
                   'fraud_detection': fraud_detection,
                   'payment_processing': payment_processing,
                   'nutrition_report': nutrition_report,
                   'processing_time': self._calculate_processing_time(checkout_data['start_time']),
                   'checkout_completed_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"智能结算失败: {str(e)}")
               raise DiningException(f"智能结算失败: {str(e)}")
           finally:
               session.close()
       
       def optimize_kitchen_operations(self, kitchen_data: Dict) -> Dict:
           """优化后厨运营"""
           self._validate_kitchen_data(kitchen_data)
           
           session = self.Session()
           try:
               # 需求预测
               demand_forecast = self.kitchen_optimizer.forecast_demand(
                   kitchen_data['historical_sales'],
                   kitchen_data['external_factors']
               )
               
               # 库存优化
               inventory_optimization = self._optimize_inventory_levels(
                   demand_forecast,
                   kitchen_data['current_stock'],
                   kitchen_data['supplier_lead_times']
               )
               
               # 生产计划优化
               production_planning = self.kitchen_optimizer.optimize_production_schedule(
                   demand_forecast,
                   kitchen_data['kitchen_capacity'],
                   kitchen_data['staff_availability']
               )
               
               # 菜单工程优化
               menu_engineering = self._optimize_menu_engineering(
                   kitchen_data['menu_items'],
                   demand_forecast,
                   kitchen_data['profit_margins']
               )
               
               # 供应链协调
               supply_chain_coordination = self._coordinate_supply_chain(
                   inventory_optimization,
                   production_planning,
                   kitchen_data['supplier_network']
               )
               
               # 浪费减少策略
               waste_reduction = self._implement_waste_reduction_strategies(
                   production_planning,
                   inventory_optimization
               )
               
               # 保存优化方案
               optimization_id = self._save_kitchen_optimization(
                   session,
                   demand_forecast,
                   inventory_optimization,
                   production_planning,
                   menu_engineering,
                   supply_chain_coordination,
                   waste_reduction
               )
               
               session.commit()
               
               # 部署优化方案
               deployment_results = self._deploy_optimization_solutions(
                   inventory_optimization,
                   production_planning,
                   menu_engineering
               )
               
               return {
                   'optimization_id': optimization_id,
                   'demand_forecast': demand_forecast,
                   'inventory_optimization': inventory_optimization,
                   'production_planning': production_planning,
                   'menu_engineering': menu_engineering,
                   'supply_chain_coordination': supply_chain_coordination,
                   'waste_reduction': waste_reduction,
                   'deployment_results': deployment_results,
                   'expected_benefits': self._calculate_expected_benefits(optimization_id),
                   'optimized_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"后厨优化失败: {str(e)}")
               raise DiningException(f"后厨优化失败: {str(e)}")
           finally:
               session.close()
       
       def monitor_food_safety(self, safety_data: Dict) -> Dict:
           """监控食品安全"""
           self._validate_safety_data(safety_data)
           
           session = self.Session()
           try:
               # 温度监控
               temperature_monitoring = self.safety_monitor.monitor_temperature_compliance(
                   safety_data['temperature_readings']
               )
               
               # 卫生检查
               hygiene_inspection = self.safety_monitor.perform_hygiene_checks(
                   safety_data['hygiene_sensors']
               )
               
               # 过敏原管理
               allergen_management = self._manage_allergen_controls(
                   safety_data['ingredient_tracking'],
                   safety_data['preparation_processes']
               )
               
               # 供应链追溯
               supply_chain_traceability = self._track_supply_chain(
                   safety_data['ingredient_batches'],
                   safety_data['supplier_certifications']
               )
               
               # 风险评估
               risk_assessment = self.safety_monitor.assess_food_safety_risks(
                   temperature_monitoring,
                   hygiene_inspection,
                   allergen_management,
                   supply_chain_traceability
               )
               
               # 预警系统
               early_warnings = self._generate_safety_alerts(risk_assessment)
               
               # 合规性检查
               compliance_checking = self._verify_regulatory_compliance(
                   safety_data['regulatory_standards']
               )
               
               # 保存安全记录
               safety_report_id = self._save_safety_monitoring_report(
                   session,
                   temperature_monitoring,
                   hygiene_inspection,
                   allergen_management,
                   supply_chain_traceability,
                   risk_assessment,
                   early_warnings,
                   compliance_checking
               )
               
               session.commit()
               
               # 发送安全警报
               if early_warnings['critical_alerts']:
                   self._issue_safety_alerts(early_warnings['critical_alerts'])
               
               return {
                   'safety_report_id': safety_report_id,
                   'temperature_monitoring': temperature_monitoring,
                   'hygiene_inspection': hygiene_inspection,
                   'allergen_management': allergen_management,
                   'supply_chain_traceability': supply_chain_traceability,
                   'risk_assessment': risk_assessment,
                   'early_warnings': early_warnings,
                   'compliance_checking': compliance_checking,
                   'safety_score': self._calculate_overall_safety_score(risk_assessment),
                   'improvement_recommendations': self._generate_safety_improvements(risk_assessment),
                   'monitored_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"安全监控失败: {str(e)}")
               raise DiningException(f"安全监控失败: {str(e)}")
           finally:
               session.close()
       
       def manage_dining_crowd(self, crowd_data: Dict) -> Dict:
           """管理就餐人流"""
           self._validate_crowd_data(crowd_data)
           
           session = self.Session()
           try:
               # 实时人流监测
               real_time_monitoring = self._monitor_real_time_crowd(
                   crowd_data['sensor_readings'],
                   crowd_data['camera_feeds']
               )
               
               # 人流预测
               crowd_prediction = self._predict_crowd_flow(
                   real_time_monitoring,
                   crowd_data['historical_patterns'],
                   crowd_data['external_factors']
               )
               
               # 座位引导优化
               seat_guidance = self._optimize_seat_guidance(
                   real_time_monitoring['current_occupancy'],
                   crowd_prediction['expected_arrivals']
               )
               
               # 队列管理
               queue_management = self._manage_queues(
                   real_time_monitoring['queue_lengths'],
                   crowd_prediction['service_demand']
               )
               
               # 资源分配优化
               resource_allocation = self._optimize_resource_allocation(
                   crowd_prediction,
                   crowd_data['available_resources']
               )
               
               # 环境优化
               environment_optimization = self._optimize_dining_environment(
                   real_time_monitoring['environmental_data']
               )
               
               # 保存管理记录
               management_id = self._save_crowd_management_record(
                   session,
                   real_time_monitoring,
                   crowd_prediction,
                   seat_guidance,
                   queue_management,
                   resource_allocation,
                   environment_optimization
               )
               
               session.commit()
               
               # 实时调整实施
               real_time_adjustments = self._implement_real_time_adjustments(
                   seat_guidance,
                   queue_management,
                   resource_allocation
               )
               
               return {
                   'management_id': management_id,
                   'real_time_monitoring': real_time_monitoring,
                   'crowd_prediction': crowd_prediction,
                   'seat_guidance': seat_guidance,
                   'queue_management': queue_management,
                   'resource_allocation': resource_allocation,
                   'environment_optimization': environment_optimization,
                   'real_time_adjustments': real_time_adjustments,
                   'customer_satisfaction_metrics': self._calculate_satisfaction_metrics(real_time_adjustments),
                   'efficiency_improvements': self._calculate_efficiency_improvements(real_time_adjustments),
                   'managed_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"人流管理失败: {str(e)}")
               raise DiningException(f"人流管理失败: {str(e)}")
           finally:
               session.close()
       
       def create_personalized_diet_plan(self, user_id: str, health_goals: Dict) -> Dict:
           """创建个性化饮食计划"""
           self._validate_health_goals(health_goals)
           
           session = self.Session()
           try:
               # 健康评估
               health_assessment = self._assess_user_health(user_id)
               
               # 目标设定
               goal_setting = self._set_realistic_goals(health_assessment, health_goals)
               
               # 营养需求计算
               nutritional_requirements = self.nutrition_analyzer.calculate_personalized_requirements(
                   health_assessment,
                   goal_setting
               )
               
               # 饮食计划生成
               diet_plan = self._generate_comprehensive_diet_plan(
                   nutritional_requirements,
                   health_goals['preferences'],
                   health_goals['constraints']
               )
               
               # 行为习惯整合
               behavior_integration = self._integrate_behavioral_strategies(
                   diet_plan,
                   health_assessment['current_habits']
               )
               
               # 进度跟踪系统
               progress_tracking = self._setup_progress_tracking_system(
                   diet_plan,
                   goal_setting
               )
               
               # 保存饮食计划
               diet_plan_id = self._save_personalized_diet_plan(
                   session,
                   user_id,
                   diet_plan,
                   nutritional_requirements,
                   goal_setting,
                   progress_tracking
               )
               
               session.commit()
               
               # 发送饮食计划
               self._send_diet_plan_notification(user_id, diet_plan, goal_setting)
               
               return {
                   'diet_plan_id': diet_plan_id,
                   'health_assessment': health_assessment,
                   'goal_setting': goal_setting,
                   'nutritional_requirements': nutritional_requirements,
                   'diet_plan': diet_plan,
                   'behavior_integration': behavior_integration,
                   'progress_tracking': progress_tracking,
                   'compliance_predictions': self._predict_plan_compliance(diet_plan, health_assessment),
                   'expected_outcomes': self._project_health_outcomes(goal_setting, diet_plan),
                   'created_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"饮食计划创建失败: {str(e)}")
               raise DiningException(f"饮食计划创建失败: {str(e)}")
           finally:
               session.close()
       
       def analyze_dining_analytics(self, analytics_request: Dict) -> Dict:
           """分析餐饮数据"""
           self._validate_analytics_request(analytics_request)
           
           session = self.Session()
           try:
               # 多源数据收集
               multi_source_data = self._collect_multi_source_dining_data(
                   analytics_request['time_period'],
                   analytics_request['data_sources']
               )
               
               # 关键绩效指标计算
               kpi_analysis = self._calculate_dining_kpis(multi_source_data)
               
               # 趋势分析
               trend_analysis = self._analyze_dining_trends(multi_source_data)
               
               # 预测分析
               predictive_insights = self._generate_predictive_insights(
                   multi_source_data,
                   analytics_request['prediction_horizon']
               )
               
               # 优化建议生成
               optimization_recommendations = self._generate_optimization_recommendations(
                   kpi_analysis,
                   trend_analysis,
                   predictive_insights
               )
               
               # 可视化数据准备
               visualization_data = self._prepare_analytics_visualization(
                   kpi_analysis,
                   trend_analysis,
                   predictive_insights
               )
               
               # 保存分析报告
               analytics_report_id = self._save_analytics_report(
                   session,
                   kpi_analysis,
                   trend_analysis,
                   predictive_insights,
                   optimization_recommendations
               )
               
               session.commit()
               
               return {
                   'analytics_report_id': analytics_report_id,
                   'kpi_analysis': kpi_analysis,
                   'trend_analysis': trend_analysis,
                   'predictive_insights': predictive_insights,
                   'optimization_recommendations': optimization_recommendations,
                   'visualization_data': visualization_data,
                   'data_quality_metrics': self._assess_data_quality(multi_source_data),
                   'implementation_roadmap': self._create_implementation_roadmap(optimization_recommendations),
                   'generated_at': datetime.now().isoformat()
               }
               
           except Exception as e:
               session.rollback()
               self.logger.error(f"餐饮分析失败: {str(e)}")
               raise DiningException(f"餐饮分析失败: {str(e)}")
           finally:
               session.close()
   
   # 工具类实现
   class NutritionAnalyzer:
       """营养分析器"""
       
       def __init__(self, config: Dict):
           self.config = config
           self.nutrient_database = self._load_nutrient_database()
           self.requirement_models = self._build_requirement_models()
       
       def calculate_personalized_requirements(self, health_profile: Dict, goals: Dict) -> Dict:
           """计算个性化营养需求"""
           try:
               # 基础代谢率计算
               bmr = self._calculate_bmr(health_profile)
               
               # 活动水平调整
               tdee = self._adjust_for_activity_level(bmr, health_profile['activity_level'])
               
               # 目标导向调整
               goal_adjusted = self._adjust_for_goals(tdee, goals)
               
               # 宏量营养素分配
               macronutrient_distribution = self._distribute_macronutrients(goal_adjusted, goals)
               
               # 微量营养素需求
               micronutrient_requirements = self._calculate_micronutrient_needs(health_profile, goals)
               
               # 膳食纤维需求
               fiber_requirements = self._calculate_fiber_needs(health_profile)
               
               return {
                   'total_calories': goal_adjusted,
                   'macronutrients': macronutrient_distribution,
                   'micronutrients': micronutrient_requirements,
                   'fiber': fiber_requirements,
                   'hydration_needs': self._calculate_hydration_needs(health_profile),
                   'meal_timing_recommendations': self._recommend_meal_timing(goals),
                   'special_considerations': self._identify_special_considerations(health_profile)
               }
           except Exception as e:
               raise DiningException(f"营养需求计算失败: {str(e)}")
   
   class DishRecommender:
       """菜品推荐器"""
       
       def __init__(self, config: Dict):
           self.config = config
           self.recommendation_engines = self._initialize_recommendation_engines()
           self.dish_database = self._load_dish_database()
       
       def generate_recommendations(self, nutritional_needs: Dict, preferences: Dict, 
                                  restrictions: Dict, context: Dict) -> Dict:
           """生成菜品推荐"""
           try:
               recommendations = {}
               
               # 协同过滤推荐
               if 'collaborative_filtering' in self.config['recommendation_engines']:
                   cf_recommendations = self._collaborative_filtering_recommend(
                       preferences['user_id'],
                       context['meal_type']
                   )
                   recommendations['collaborative'] = cf_recommendations
               
               # 基于内容推荐
               if 'content_based' in self.config['recommendation_engines']:
                   cb_recommendations = self._content_based_recommend(
                       nutritional_needs,
                       preferences,
                       restrictions
                   )
                   recommendations['content_based'] = cb_recommendations
               
               # 上下文感知推荐
               if 'context_aware' in self.config['recommendation_engines']:
                   context_recommendations = self._context_aware_recommend(
                       context,
                       nutritional_needs
                   )
                   recommendations['context_aware'] = context_recommendations
               
               # 混合推荐
               hybrid_recommendations = self._hybrid_recommendation(
                   recommendations,
                   self.config['recommendation_engines']
               )
               
               return {
                   'algorithms_used': list(recommendations.keys()),
                   'recommended_dishes': hybrid_recommendations,
                   'explanation': self._explain_recommendations(hybrid_recommendations),
                   'diversity_score': self._calculate_diversity_score(hybrid_recommendations)
               }
           except Exception as e:
               raise DiningException(f"菜品推荐失败: {str(e)}")
   
   class KitchenOptimizer:
       """后厨优化器"""
       
       def __init__(self, config: Dict):
           self.config = config
           self.optimization_models = self._initialize_optimization_models()
           self.forecasting_engine = self._build_forecasting_engine()
       
       def forecast_demand(self, historical_data: pd.DataFrame, external_factors: Dict) -> Dict:
           """预测需求"""
           try:
               # 时间序列分析
               time_series_forecast = self._time_series_analysis(historical_data)
               
               # 机器学习预测
               ml_forecast = self._machine_learning_prediction(historical_data, external_factors)
               
               # 集成预测
               ensemble_forecast = self._ensemble_forecasting(time_series_forecast, ml_forecast)
               
               # 不确定性量化
               uncertainty_quantification = self._quantify_forecast_uncertainty(ensemble_forecast)
               
               return {
                   'time_series_forecast': time_series_forecast,
                   'machine_learning_forecast': ml_forecast,
                   'ensemble_forecast': ensemble_forecast,
                   'uncertainty_quantification': uncertainty_quantification,
                   'confidence_intervals': self._calculate_confidence_intervals(ensemble_forecast),
                   'peak_demand_periods': self._identify_peak_periods(ensemble_forecast)
               }
           except Exception as e:
               raise DiningException(f"需求预测失败: {str(e)}")

3. Flask API接口 (app/main.py)

   from flask import Flask, request, jsonify
   from services.dining_service import DiningService
   from config.dining_config import DiningConfig
   import logging
   import threading
   
   app = Flask(__name__)
   config = DiningConfig()
   dining_service = DiningService()
   
   # 配置日志
   logging.basicConfig(level=logging.INFO)
   logger = logging.getLogger(__name__)
   
   @app.route('/api/dining/meals/recommend', methods=['POST'])
   def recommend_meals():
       """推荐个性化餐品"""
       try:
           data = request.get_json()
           user_id = request.headers.get('X-User-Id')
           
           if not user_id:
               return jsonify({'code': 1, 'msg': '用户ID不能为空'}), 400
           
           result = dining_service.recommend_personalized_meals(user_id, data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"餐品推荐失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/checkout/process', methods=['POST'])
   def process_checkout():
       """处理智能结算"""
       try:
           data = request.get_json()
           user_id = request.headers.get('X-User-Id')
           
           if not user_id:
               return jsonify({'code': 1, 'msg': '用户ID不能为空'}), 400
           
           result = dining_service.process_smart_checkout(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"智能结算失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/kitchen/optimize', methods=['POST'])
   def optimize_kitchen():
       """优化后厨运营"""
       try:
           data = request.get_json()
           
           result = dining_service.optimize_kitchen_operations(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"后厨优化失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/safety/monitor', methods=['POST'])
   def monitor_safety():
       """监控食品安全"""
       try:
           data = request.get_json()
           
           result = dining_service.monitor_food_safety(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"安全监控失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/crowd/manage', methods=['POST'])
   def manage_crowd():
       """管理就餐人流"""
       try:
           data = request.get_json()
           
           result = dining_service.manage_dining_crowd(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"人流管理失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/diet-plan/create', methods=['POST'])
   def create_diet_plan():
       """创建个性化饮食计划"""
       try:
           data = request.get_json()
           user_id = request.headers.get('X-User-Id')
           
           if not user_id:
               return jsonify({'code': 1, 'msg': '用户ID不能为空'}), 400
           
           result = dining_service.create_personalized_diet_plan(user_id, data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"饮食计划创建失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/analytics/generate', methods=['POST'])
   def generate_analytics():
       """生成餐饮分析"""
       try:
           data = request.get_json()
           
           result = dining_service.analyze_dining_analytics(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"餐饮分析失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/menu/today', methods=['GET'])
   def get_today_menu():
       """获取今日菜单"""
       try:
           canteen_id = request.args.get('canteen_id')
           meal_type = request.args.get('meal_type', 'lunch')
           
           result = dining_service.get_today_menu(canteen_id, meal_type)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"菜单获取失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/nutrition/analyze', methods=['POST'])
   def analyze_nutrition():
       """分析营养信息"""
       try:
           data = request.get_json()
           
           result = dining_service.analyze_nutritional_content(data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"营养分析失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/order/preorder', methods=['POST'])
   def preorder_meal():
       """预点餐"""
       try:
           data = request.get_json()
           user_id = request.headers.get('X-User-Id')
           
           if not user_id:
               return jsonify({'code': 1, 'msg': '用户ID不能为空'}), 400
           
           result = dining_service.process_preorder(user_id, data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"预点餐失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   @app.route('/api/dining/feedback/submit', methods=['POST'])
   def submit_feedback():
       """提交餐饮反馈"""
       try:
           data = request.get_json()
           user_id = request.headers.get('X-User-Id')
           
           if not user_id:
               return jsonify({'code': 1, 'msg': '用户ID不能为空'}), 400
           
           result = dining_service.process_feedback(user_id, data)
           return jsonify({'code': 0, 'data': result})
           
       except Exception as e:
           logger.error(f"反馈提交失败: {str(e)}")
           return jsonify({'code': 2, 'msg': str(e)}), 500
   
   if __name__ == '__main__':
       # 启动后台餐饮监控服务
       monitoring_thread = threading.Thread(
           target=dining_service.start_dining_monitoring,
           daemon=True
       )
       monitoring_thread.start()
       
       app.run(host='0.0.0.0', port=5000, debug=False)

使用说明

1. 系统部署

1. 安装Python 3.8+
2. 安装依赖包：

   pip install flask pandas numpy scikit-learn tensorflow opencv-python redis sqlalchemy pulp ortools

3. 配置环境变量：

   export DB_HOST=localhost
   export DB_PORT=5432
   export DB_USER=postgres
   export DB_PASSWORD=yourpassword
   export DB_NAME=campus_dining
   export REDIS_HOST=localhost
   export REDIS_PORT=6379
   export WX_MINI_APP_ID=your_appid
   export WX_MINI_SECRET=your_secret
   export WX_PAY_MCH_ID=your_mch_id
   export WX_PAY_MCH_KEY=your_mch_key

4. 创建数据库并导入SQL结构
5. 启动服务：

   python app/main.py

2. 功能使用

2.1 智能菜品推荐

1. 个性化推荐：
   • 基于健康档案的营养需求分析
   • 考虑口味偏好和过敏限制
   • 多算法融合推荐
   • 实时上下文感知
2. 营养优化：
   • 宏量营养素平衡
   • 微量营养素满足
   • 膳食纤维摄入
   • 水分需求计算

2.2 智能结算系统

1. 多模态识别：
   • 计算机视觉菜品识别
   • RFID托盘追踪
   • 重量传感器验证
   • 数据融合技术
2. 无感支付：
   • 微信支付集成
   • 校园卡支付
   • 刷脸支付
   • 防欺诈检测

2.3 后厨智能管理

1. 需求预测：
   • 时间序列分析
   • 机器学习预测
   • 外部因素考虑
   • 不确定性量化
2. 运营优化：
   • 库存管理优化
   • 生产计划调度
   • 菜单工程优化
   • 供应链协调

2.4 食品安全监控

1. 实时监控：
   • 温度合规监测
   • 卫生标准检查
   • 过敏原管理
   • 供应链追溯
2. 风险预警：
   • 风险评估系统
   • 早期预警机制
   • 合规性检查
   • 改进建议生成

2.5 就餐人流管理

1. 智能预测：
   • 实时人流监测
   • 人流流量预测
   • 高峰时段管理
   • 资源优化分配
2. 体验优化：
   • 智能座位引导
   • 队列管理优化
   • 环境舒适度调节
   • 满意度提升

API接口

• POST /api/dining/meals/recommend

• 推荐个性化餐品

  POST /api/dining/checkout/process

• 处理智能结算

  POST /api/dining/kitchen/optimize

• 优化后厨运营

  POST /api/dining/safety/monitor

• 监控食品安全

  POST /api/dining/crowd/manage

• 管理就餐人流

  POST /api/dining/diet-plan/create

• 创建饮食计划

  POST /api/dining/analytics/generate

• 生成餐饮分析

  GET /api/dining/menu/today

• 获取今日菜单

  POST /api/dining/nutrition/analyze

• 分析营养信息

  POST /api/dining/order/preorder

• 预点餐

  POST /api/dining/feedback/submit

• 提交反馈

系统特点

1. 智能化程度高：AI驱动的全流程优化
2. 个性化服务：基于用户特征的定制化推荐
3. 实时响应：毫秒级数据处理和决策
4. 安全可靠：多重安全监控和预警机制
5. 用户体验好：无感支付、智能引导等便捷功能
6. 数据驱动：基于大数据的持续优化
7. 生态集成：与微信生态深度整合

这个系统适合校园餐饮场景，能够显著提升餐饮服务质量和管理效率，实现餐饮服务的智能化、个性化管理。系统可根据实际需求进行定制扩展。

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关键词：python 管理系统 餐饮管理 管理系 Optimization

相关内容：餐饮管理系统