💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
健康饮食推荐系统的设计与实现介绍
健康饮食推荐系统是一个基于大数据技术的智能营养管理平台，采用Hadoop+Spark分布式计算框架作为核心技术架构，通过Python开发语言和Django后端框架构建稳定的服务端，前端采用Vue+ElementUI+Echarts技术栈实现友好的用户交互界面。系统集成了用户管理、营养成份管理、菜品类型管理、菜品信息管理、饮食记录管理等十二大功能模块，能够为用户提供个性化的饮食建议和营养搭配方案。平台运用Spark SQL进行海量营养数据的实时分析处理，结合Pandas和NumPy进行数据挖掘和统计计算，通过MySQL数据库存储用户饮食记录和营养信息，实现了从数据采集、处理、分析到可视化展示的完整数据流程。系统还提供社区交流功能，用户可以分享饮食心得，获取专业营养建议，同时支持反馈建议和举报记录管理，确保平台内容的健康性和可靠性，为现代人的科学饮食管理提供了技术支撑和数据保障。
健康饮食推荐系统的设计与实现演示视频
健康饮食推荐系统的设计与实现演示图片
健康饮食推荐系统的设计与实现代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum as spark_sum, avg, count, when
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("HealthyFoodRecommendation").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def analyze_nutrition_components(food_data_path, user_nutrition_requirements):
nutrition_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "nutrition_components").option("user", "root").option("password", "password").load()
food_nutrition_df = nutrition_df.select("food_id", "protein", "carbohydrate", "fat", "vitamin_a", "vitamin_c", "calcium", "iron").filter(col("protein") > 0)
avg_nutrition = food_nutrition_df.groupBy("food_id").agg(avg("protein").alias("avg_protein"), avg("carbohydrate").alias("avg_carb"), avg("fat").alias("avg_fat"), avg("vitamin_a").alias("avg_vita"), avg("vitamin_c").alias("avg_vitc"), avg("calcium").alias("avg_calcium"), avg("iron").alias("avg_iron"))
user_protein_need = user_nutrition_requirements.get("protein", 60)
user_carb_need = user_nutrition_requirements.get("carbohydrate", 250)
user_fat_need = user_nutrition_requirements.get("fat", 65)
nutrition_score_df = avg_nutrition.withColumn("nutrition_score", (col("avg_protein") / user_protein_need * 0.3 + col("avg_carb") / user_carb_need * 0.3 + col("avg_fat") / user_fat_need * 0.2 + col("avg_vita") / 800 * 0.1 + col("avg_vitc") / 100 * 0.1))
top_nutrition_foods = nutrition_score_df.orderBy(col("nutrition_score").desc()).limit(20)
nutrition_analysis_result = top_nutrition_foods.collect()
recommended_foods = []
for row in nutrition_analysis_result:
food_recommendation = {"food_id": row.food_id, "nutrition_score": round(row.nutrition_score, 3), "protein_content": round(row.avg_protein, 2), "carb_content": round(row.avg_carb, 2), "fat_content": round(row.avg_fat, 2)}
recommended_foods.append(food_recommendation)
return recommended_foods
def generate_personalized_diet_recommendations(user_id, diet_history_days=30):
user_diet_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "diet_records").option("user", "root").option("password", "password").load()
recent_diet_df = user_diet_df.filter(col("user_id") == user_id).filter(col("record_date") >= (datetime.now() - timedelta(days=diet_history_days)).strftime("%Y-%m-%d"))
food_frequency = recent_diet_df.groupBy("food_id").agg(count("*").alias("frequency"), avg("portion_size").alias("avg_portion"))
user_preferences = food_frequency.orderBy(col("frequency").desc()).limit(10)
food_info_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "food_info").option("user", "root").option("password", "password").load()
preference_with_info = user_preferences.join(food_info_df, "food_id").select("food_id", "food_name", "food_type", "frequency", "avg_portion", "calories_per_100g")
nutrition_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "nutrition_components").option("user", "root").option("password", "password").load()
daily_nutrition = recent_diet_df.join(nutrition_df, "food_id").groupBy("record_date").agg(spark_sum("protein").alias("daily_protein"), spark_sum("carbohydrate").alias("daily_carb"), spark_sum("fat").alias("daily_fat"), spark_sum("calories_per_100g").alias("daily_calories"))
avg_daily_nutrition = daily_nutrition.agg(avg("daily_protein").alias("avg_protein"), avg("daily_carb").alias("avg_carb"), avg("daily_fat").alias("avg_fat"), avg("daily_calories").alias("avg_calories")).collect()[0]
protein_gap = max(0, 60 - avg_daily_nutrition.avg_protein) if avg_daily_nutrition.avg_protein else 60
carb_gap = max(0, 250 - avg_daily_nutrition.avg_carb) if avg_daily_nutrition.avg_carb else 250
fat_gap = max(0, 65 - avg_daily_nutrition.avg_fat) if avg_daily_nutrition.avg_fat else 65
high_protein_foods = food_info_df.join(nutrition_df, "food_id").filter(col("protein") > 15).filter(~col("food_id").isin([row.food_id for row in user_preferences.collect()])).orderBy(col("protein").desc()).limit(5)
balanced_carb_foods = food_info_df.join(nutrition_df, "food_id").filter(col("carbohydrate") > 20).filter(col("carbohydrate") < 60).orderBy(col("carbohydrate").desc()).limit(3)
recommendations = {"protein_recommendations": [{"food_id": row.food_id, "food_name": row.food_name, "protein_content": row.protein} for row in high_protein_foods.collect()], "carb_recommendations": [{"food_id": row.food_id, "food_name": row.food_name, "carb_content": row.carbohydrate} for row in balanced_carb_foods.collect()], "nutrition_gaps": {"protein_gap": round(protein_gap, 2), "carb_gap": round(carb_gap, 2), "fat_gap": round(fat_gap, 2)}}
return recommendations
def intelligent_food_matching_algorithm(target_calories, target_protein, meal_type="lunch"):
food_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "food_info").option("user", "root").option("password", "password").load()
nutrition_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_food").option("dbtable", "nutrition_components").option("user", "root").option("password", "password").load()
complete_food_df = food_df.join(nutrition_df, "food_id").select("food_id", "food_name", "food_type", "calories_per_100g", "protein", "carbohydrate", "fat")
meal_suitable_foods = complete_food_df.filter(col("calories_per_100g") > 50).filter(col("calories_per_100g") < 400)
if meal_type == "breakfast":
meal_suitable_foods = meal_suitable_foods.filter(col("food_type").isin(["谷物类", "奶制品", "水果类"]))
elif meal_type == "lunch":
meal_suitable_foods = meal_suitable_foods.filter(col("food_type").isin(["肉类", "蔬菜类", "谷物类"]))
else:
meal_suitable_foods = meal_suitable_foods.filter(col("food_type").isin(["蔬菜类", "汤品类", "谷物类"]))
calorie_weight = 0.4
protein_weight = 0.35
variety_weight = 0.25
scored_foods = meal_suitable_foods.withColumn("calorie_score", when(col("calories_per_100g") <= target_calories * 1.2, (target_calories - abs(col("calories_per_100g") - target_calories)) / target_calories).otherwise(0.3))
scored_foods = scored_foods.withColumn("protein_score", when(col("protein") >= target_protein * 0.8, (col("protein") / target_protein).cast("double")).otherwise(col("protein") / target_protein * 0.5))
scored_foods = scored_foods.withColumn("final_score", col("calorie_score") * calorie_weight + col("protein_score") * protein_weight + variety_weight)
recommended_combinations = scored_foods.orderBy(col("final_score").desc()).limit(8)
main_dish = recommended_combinations.filter(col("food_type") == "肉类").first()
side_dishes = recommended_combinations.filter(col("food_type").isin(["蔬菜类", "谷物类"])).limit(3).collect()
combination_result = {"main_dish": {"food_id": main_dish.food_id, "food_name": main_dish.food_name, "calories": main_dish.calories_per_100g, "protein": main_dish.protein} if main_dish else None, "side_dishes": [{"food_id": dish.food_id, "food_name": dish.food_name, "calories": dish.calories_per_100g, "protein": dish.protein} for dish in side_dishes], "total_estimated_calories": sum([main_dish.calories_per_100g if main_dish else 0] + [dish.calories_per_100g for dish in side_dishes]), "total_estimated_protein": sum([main_dish.protein if main_dish else 0] + [dish.protein for dish in side_dishes])}
return combination_result
健康饮食推荐系统的设计与实现文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙个人简介：曾长期从事计算机专业培训教学，本人也热爱上课教学，语言擅长Java、微信小程序、Python、Golang、安卓Android等，开发项目包括大数据、深度学习、网站、小程序、安卓、算法。平常会做一些项目定制化开发、代码讲解、答辩教学、文档编写、也懂一些降重方面的技巧。平常喜欢分享一些自己开发中遇到的问题的解决办法，也喜欢交流技术，大家有技术代码这一块的问题可以问我！ 💛💛想说的话：感谢大家的关注与支持！ 💜💜 网站实战项目 安卓/小程序实战项目 大数据实战项目 深度学校实战项目 计算机毕业设计选题推荐
