💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
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基于大数据的火锅店数据可视化分析系统介绍
基于大数据的火锅店数据可视化分析系统是一套采用Hadoop+Spark大数据处理框架构建的综合性数据分析平台，该系统充分运用了分布式存储和计算技术，通过HDFS进行海量数据存储，利用Spark和Spark SQL实现高效的数据处理和分析计算。系统采用前后端分离的架构设计，后端基于Django框架和Spring Boot微服务架构提供RESTful API接口，前端采用Vue.js结合ElementUI组件库构建用户界面，并集成Echarts图表库实现丰富的数据可视化展示效果。系统功能涵盖火锅店数据管理、多维度数据分析和可视化展示三大核心模块，具体包括城市店密度分析、价格区间分布分析、评分维度综合分析、人气热度分层分析、价格评分相关性分析、高评分店特征挖掘、区域消费水平分析、综合竞争力评估、评分差异化分析以及用户满意度聚类分析等十大分析功能。系统底层数据处理采用Python语言结合Pandas和NumPy库进行数据清洗和预处理，通过Spark SQL执行复杂的统计分析查询，将分析结果以直观的图表形式在前端界面展示，为火锅店经营决策提供科学的数据支撑，同时该系统具备完善的用户权限管理、个人信息维护和系统管理功能，确保数据安全和系统稳定运行。
基于大数据的火锅店数据可视化分析系统演示视频
基于大数据的火锅店数据可视化分析系统演示图片
基于大数据的火锅店数据可视化分析系统代码展示
spark = SparkSession.builder.appName("HotpotDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "hotpot_stores").option("user", "root").option("password", "password").load()
def analyze_city_store_density():
city_density_df = df.groupBy("city").agg(count("store_id").alias("store_count"), avg("rating").alias("avg_rating"), sum("monthly_sales").alias("total_sales"))
city_area_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "city_info").option("user", "root").option("password", "password").load()
density_result = city_density_df.join(city_area_df, "city").withColumn("density", col("store_count") / col("area_km2"))
density_stats = density_result.select("city", "store_count", "density", "avg_rating", "total_sales").orderBy(desc("density"))
high_density_cities = density_stats.filter(col("density") > density_stats.agg(avg("density")).collect()[0][0])
density_classification = density_stats.withColumn("density_level", when(col("density") >= 10, "高密度").when(col("density") >= 5, "中密度").otherwise("低密度"))
correlation_analysis = density_classification.stat.corr("density", "avg_rating")
market_saturation = density_classification.withColumn("saturation_index", col("density") / col("avg_rating") * 100)
competitive_index = market_saturation.withColumn("competition_score", col("store_count") / col("total_sales") * col("density"))
growth_potential = competitive_index.withColumn("growth_potential", when(col("density") < 3, "高潜力").when(col("density") < 8, "中潜力").otherwise("低潜力"))
final_density_result = growth_potential.select("city", "store_count", "density", "density_level", "saturation_index", "competition_score", "growth_potential").orderBy(desc("density"))
pandas_result = final_density_result.toPandas()
density_json = pandas_result.to_json(orient='records', force_ascii=False)
return {"status": "success", "data": density_json, "correlation": correlation_analysis, "total_cities": final_density_result.count()}
def analyze_price_rating_correlation():
price_rating_df = df.select("store_id", "store_name", "city", "avg_price", "rating", "review_count", "category")
price_bins = [0, 50, 100, 150, 200, 300, float('inf')]
price_labels = ["经济型", "实惠型", "中档型", "高档型", "豪华型", "顶级型"]
price_categorized = price_rating_df.withColumn("price_category", when(col("avg_price") <= 50, "经济型").when(col("avg_price") <= 100, "实惠型").when(col("avg_price") <= 150, "中档型").when(col("avg_price") <= 200, "高档型").when(col("avg_price") <= 300, "豪华型").otherwise("顶级型"))
correlation_coefficient = price_categorized.stat.corr("avg_price", "rating")
price_rating_stats = price_categorized.groupBy("price_category").agg(avg("rating").alias("avg_rating"), avg("avg_price").alias("avg_price"), count("store_id").alias("store_count"), sum("review_count").alias("total_reviews"))
rating_distribution = price_categorized.groupBy("price_category", when(col("rating") >= 4.5, "优秀").when(col("rating") >= 4.0, "良好").when(col("rating") >= 3.5, "一般").otherwise("较差").alias("rating_level")).count()
price_performance_ratio = price_categorized.withColumn("performance_ratio", col("rating") / col("avg_price") * 100)
top_performance_stores = price_performance_ratio.orderBy(desc("performance_ratio")).limit(20)
city_price_analysis = price_categorized.groupBy("city", "price_category").agg(avg("rating").alias("city_category_rating"), count("store_id").alias("city_category_count"))
outlier_analysis = price_categorized.filter((col("avg_price") > 200) & (col("rating") < 3.5) | (col("avg_price") < 80) & (col("rating") > 4.5))
trend_analysis = price_rating_stats.withColumn("value_trend", when(col("avg_rating") / col("avg_price") * 100 > 5, "高性价比").when(col("avg_rating") / col("avg_price") * 100 > 3, "中性价比").otherwise("低性价比"))
final_correlation_result = trend_analysis.select("price_category", "avg_rating", "avg_price", "store_count", "total_reviews", "value_trend").orderBy("avg_price")
correlation_pandas = final_correlation_result.toPandas()
correlation_json = correlation_pandas.to_json(orient='records', force_ascii=False)
return {"status": "success", "data": correlation_json, "correlation_coefficient": correlation_coefficient, "outliers_count": outlier_analysis.count()}
def analyze_user_satisfaction_clustering():
satisfaction_df = df.select("store_id", "store_name", "city", "rating", "review_count", "service_score", "taste_score", "environment_score", "avg_price")
feature_df = satisfaction_df.withColumn("review_popularity", when(col("review_count") >= 1000, 3).when(col("review_count") >= 100, 2).otherwise(1))
normalized_df = feature_df.withColumn("rating_norm", (col("rating") - 1) / 4).withColumn("service_norm", col("service_score") / 5).withColumn("taste_norm", col("taste_score") / 5).withColumn("environment_norm", col("environment_score") / 5)
assembler_features = ["rating_norm", "service_norm", "taste_norm", "environment_norm", "review_popularity"]
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.clustering import KMeans
assembler = VectorAssembler(inputCols=assembler_features, outputCol="features")
feature_vector_df = assembler.transform(normalized_df)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
kmeans_model = kmeans.fit(feature_vector_df)
clustered_df = kmeans_model.transform(feature_vector_df)
cluster_analysis = clustered_df.groupBy("cluster").agg(avg("rating").alias("avg_rating"), avg("service_score").alias("avg_service"), avg("taste_score").alias("avg_taste"), avg("environment_score").alias("avg_environment"), avg("review_count").alias("avg_reviews"), count("store_id").alias("cluster_size"))
cluster_labels = cluster_analysis.withColumn("satisfaction_level", when(col("avg_rating") >= 4.5, "极高满意度").when(col("avg_rating") >= 4.0, "高满意度").when(col("avg_rating") >= 3.5, "中等满意度").otherwise("低满意度"))
city_cluster_distribution = clustered_df.groupBy("city", "cluster").count().orderBy("city", "cluster")
satisfaction_insights = cluster_labels.withColumn("strength_analysis", when((col("avg_service") >= 4.2) & (col("avg_taste") >= 4.2), "服务味道双优").when(col("avg_service") >= 4.5, "服务导向").when(col("avg_taste") >= 4.5, "口味导向").when(col("avg_environment") >= 4.5, "环境导向").otherwise("均衡发展"))
improvement_suggestions = clustered_df.withColumn("improvement_focus", when(col("service_score") < 3.5, "提升服务质量").when(col("taste_score") < 3.5, "改善口味品质").when(col("environment_score") < 3.5, "优化就餐环境").otherwise("保持现有水平"))
final_clustering_result = satisfaction_insights.select("cluster", "avg_rating", "avg_service", "avg_taste", "avg_environment", "cluster_size", "satisfaction_level", "strength_analysis").orderBy("cluster")
clustering_pandas = final_clustering_result.toPandas()
clustering_json = clustering_pandas.to_json(orient='records', force_ascii=False)
return {"status": "success", "data": clustering_json, "cluster_centers": kmeans_model.clusterCenters().tolist(), "total_clusters": 4}
基于大数据的火锅店数据可视化分析系统文档展示
💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
