一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
基于大数据的食物口味差异数据分析与可视化系统是一套运用现代大数据技术构建的综合性分析平台，该系统采用Hadoop+Spark分布式计算框架作为核心技术架构，结合Django后端框架和Vue前端技术栈，实现了对大规模食物口味数据的深度挖掘与智能分析。系统通过HDFS分布式文件系统存储海量口味数据，利用Spark SQL进行高效的数据查询与处理，配合Pandas和NumPy进行精细化的数据分析操作。系统提供了包括用户管理、口味差异数据管理、宏观数据分布分析、生活习惯差异分析、地理文化口味分析、多维交叉因素分析、用户聚类特征分析等十一个核心功能模块，通过Echarts可视化组件将复杂的数据分析结果以直观的图表形式展现，帮助用户深入理解不同地域、文化背景下的食物口味偏好差异，为食品行业的市场调研、产品开发提供数据支撑，同时也为相关学术研究提供了实用的分析工具。
三、基于大数据的食物口味差异数据分析与可视化系统-视频解说
Hadoop+Spark+Django毕设选题：食物口味差异数据分析系统技术实现详解
四、基于大数据的食物口味差异数据分析与可视化系统-功能展示
五、基于大数据的食物口味差异数据分析与可视化系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, avg, sum, max, min, when, desc, asc
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler, StringIndexer
from django.http import JsonResponse
from django.views import View
import pandas as pd
import numpy as np
import json
spark = SparkSession.builder.appName("FoodTasteAnalysis").master("local[*]").getOrCreate()
class MacroDataAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
region = data.get('region', '')
taste_type = data.get('taste_type', '')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/food_taste").option("dbtable", "taste_data").option("user", "root").option("password", "password").load()
if region:
df = df.filter(col("region") == region)
if taste_type:
df = df.filter(col("taste_type") == taste_type)
macro_stats = df.groupBy("region", "taste_category").agg(count("*").alias("total_count"), avg("preference_score").alias("avg_score"), sum("consumption_frequency").alias("total_frequency"))
regional_distribution = df.groupBy("region").agg(count("*").alias("region_count")).orderBy(desc("region_count"))
taste_popularity = df.groupBy("taste_type").agg(count("*").alias("popularity"), avg("rating").alias("avg_rating")).orderBy(desc("popularity"))
age_taste_correlation = df.groupBy("age_group", "taste_preference").agg(count("*").alias("count")).orderBy("age_group", desc("count"))
seasonal_trends = df.groupBy("season", "taste_category").agg(avg("preference_intensity").alias("intensity"), count("*").alias("frequency"))
gender_preferences = df.groupBy("gender", "flavor_type").agg(count("*").alias("preference_count"), avg("satisfaction_score").alias("satisfaction"))
macro_result = macro_stats.collect()
regional_result = regional_distribution.collect()
taste_result = taste_popularity.collect()
age_result = age_taste_correlation.collect()
seasonal_result = seasonal_trends.collect()
gender_result = gender_preferences.collect()
response_data = {"macro_distribution": [{"region": row["region"], "category": row["taste_category"], "count": row["total_count"], "avg_score": float(row["avg_score"]), "frequency": row["total_frequency"]} for row in macro_result], "regional_stats": [{"region": row["region"], "count": row["region_count"]} for row in regional_result], "taste_popularity": [{"taste": row["taste_type"], "popularity": row["popularity"], "rating": float(row["avg_rating"])} for row in taste_result], "age_correlation": [{"age_group": row["age_group"], "preference": row["taste_preference"], "count": row["count"]} for row in age_result], "seasonal_trends": [{"season": row["season"], "category": row["taste_category"], "intensity": float(row["intensity"]), "frequency": row["frequency"]} for row in seasonal_result], "gender_analysis": [{"gender": row["gender"], "flavor": row["flavor_type"], "count": row["preference_count"], "satisfaction": float(row["satisfaction"])} for row in gender_result]}
return JsonResponse(response_data)
class UserClusteringAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
cluster_num = data.get('cluster_num', 5)
feature_columns = data.get('features', ['age', 'income', 'preference_score', 'consumption_frequency'])
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/food_taste").option("dbtable", "user_taste_profile").option("user", "root").option("password", "password").load()
df_filtered = df.select(*feature_columns, "user_id", "taste_preference", "region")
df_cleaned = df_filtered.dropna()
assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")
df_vectorized = assembler.transform(df_cleaned)
kmeans = KMeans(k=cluster_num, seed=42, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(df_vectorized)
clustered_df = model.transform(df_vectorized)
cluster_centers = model.clusterCenters()
cluster_analysis = clustered_df.groupBy("cluster").agg(count("*").alias("cluster_size"), avg("age").alias("avg_age"), avg("income").alias("avg_income"), avg("preference_score").alias("avg_preference"), avg("consumption_frequency").alias("avg_frequency"))
cluster_taste_distribution = clustered_df.groupBy("cluster", "taste_preference").agg(count("*").alias("preference_count"))
cluster_regional_distribution = clustered_df.groupBy("cluster", "region").agg(count("*").alias("regional_count"))
within_set_sum_squared_errors = model.summary.trainingCost
silhouette_score = model.summary.silhouette
cluster_stats = cluster_analysis.collect()
taste_dist = cluster_taste_distribution.collect()
regional_dist = cluster_regional_distribution.collect()
cluster_profiles = []
for i, center in enumerate(cluster_centers):
cluster_info = next((row for row in cluster_stats if row["cluster"] == i), None)
if cluster_info:
profile = {"cluster_id": i, "center_features": center.tolist(), "size": cluster_info["cluster_size"], "avg_age": float(cluster_info["avg_age"]), "avg_income": float(cluster_info["avg_income"]), "avg_preference": float(cluster_info["avg_preference"]), "avg_frequency": float(cluster_info["avg_frequency"]), "taste_distribution": [{"preference": row["taste_preference"], "count": row["preference_count"]} for row in taste_dist if row["cluster"] == i], "regional_distribution": [{"region": row["region"], "count": row["regional_count"]} for row in regional_dist if row["cluster"] == i]}
cluster_profiles.append(profile)
response_data = {"cluster_profiles": cluster_profiles, "model_performance": {"wssse": within_set_sum_squared_errors, "silhouette": silhouette_score}, "total_clusters": cluster_num, "feature_importance": dict(zip(feature_columns, [abs(x) for x in cluster_centers[0]]))}
return JsonResponse(response_data)
class GeoCulturalTasteAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
target_region = data.get('region', '')
cultural_factors = data.get('cultural_factors', ['cuisine_tradition', 'climate_type', 'economic_level'])
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/food_taste").option("dbtable", "geo_cultural_taste").option("user", "root").option("password", "password").load()
if target_region:
df = df.filter(col("region") == target_region)
regional_taste_profile = df.groupBy("region", "primary_cuisine").agg(count("*").alias("cuisine_popularity"), avg("spice_tolerance").alias("avg_spice"), avg("sweetness_preference").alias("avg_sweetness"), avg("saltiness_preference").alias("avg_saltiness"))
cultural_influence_analysis = df.groupBy("cultural_background", "taste_category").agg(count("*").alias("category_count"), avg("preference_intensity").alias("intensity"))
climate_taste_correlation = df.groupBy("climate_zone", "flavor_profile").agg(count("*").alias("flavor_count"), avg("preference_score").alias("preference_strength"))
economic_taste_relationship = df.groupBy("economic_level", "food_category").agg(count("*").alias("consumption_count"), avg("willingness_to_pay").alias("avg_price_tolerance"))
migration_taste_adaptation = df.filter(col("migration_status") == "migrated").groupBy("origin_region", "current_region", "adaptation_years").agg(avg("taste_adaptation_score").alias("adaptation_level"), count("*").alias("migrant_count"))
festival_seasonal_preferences = df.groupBy("region", "festival_season", "special_food").agg(count("*").alias("seasonal_popularity"), avg("cultural_significance_score").alias("significance"))
cross_cultural_fusion = df.groupBy("region").agg(count(when(col("fusion_cuisine_preference") > 7, 1)).alias("fusion_lovers"), count("*").alias("total_respondents"), avg("cultural_openness_score").alias("openness_level"))
regional_result = regional_taste_profile.collect()
cultural_result = cultural_influence_analysis.collect()
climate_result = climate_taste_correlation.collect()
economic_result = economic_taste_relationship.collect()
migration_result = migration_taste_adaptation.collect()
festival_result = festival_seasonal_preferences.collect()
fusion_result = cross_cultural_fusion.collect()
geo_cultural_insights = {"regional_profiles": [{"region": row["region"], "cuisine": row["primary_cuisine"], "popularity": row["cuisine_popularity"], "spice_level": float(row["avg_spice"]), "sweetness": float(row["avg_sweetness"]), "saltiness": float(row["avg_saltiness"])} for row in regional_result], "cultural_influences": [{"background": row["cultural_background"], "category": row["taste_category"], "count": row["category_count"], "intensity": float(row["intensity"])} for row in cultural_result], "climate_correlations": [{"climate": row["climate_zone"], "flavor": row["flavor_profile"], "count": row["flavor_count"], "strength": float(row["preference_strength"])} for row in climate_result], "economic_relationships": [{"economic_level": row["economic_level"], "food_category": row["food_category"], "consumption": row["consumption_count"], "price_tolerance": float(row["avg_price_tolerance"])} for row in economic_result], "migration_patterns": [{"origin": row["origin_region"], "current": row["current_region"], "years": row["adaptation_years"], "adaptation": float(row["adaptation_level"]), "count": row["migrant_count"]} for row in migration_result], "seasonal_festivals": [{"region": row["region"], "season": row["festival_season"], "food": row["special_food"], "popularity": row["seasonal_popularity"], "significance": float(row["significance"])} for row in festival_result], "cultural_fusion": [{"region": row["region"], "fusion_lovers": row["fusion_lovers"], "total": row["total_respondents"], "openness": float(row["openness_level"])} for row in fusion_result]}
return JsonResponse(geo_cultural_insights)
六、基于大数据的食物口味差异数据分析与可视化系统-文档展示
七、END
💛💛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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
