💖💖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
基于大数据的火锅店数据可视化分析系统介绍
基于Hadoop+Spark的火锅店数据可视化分析系统是一套集数据采集、存储、分析与可视化展示于一体的大数据应用平台。该系统采用Hadoop分布式文件系统HDFS作为底层数据存储架构，利用Spark强大的内存计算能力对火锅店的经营数据进行实时分析处理。系统前端基于Vue框架搭建，结合ElementUI组件库提供友好的用户交互界面，通过Echarts图表库将复杂的数据分析结果以直观的可视化图表形式呈现。后端采用Spring Boot框架构建RESTful API服务，整合MyBatis持久层框架实现与MySQL数据库的高效交互。系统核心功能涵盖火锅店基础数据管理、多维度数据分析、个性化数据可视化展示等模块，能够帮助火锅店经营者深入了解客流量变化趋势、菜品销售情况、营收分析等关键业务指标，为经营决策提供数据支撑，同时也为计算机专业学生提供了一个融合传统商业场景与前沿大数据技术的实践平台。
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
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
from datetime import datetime, timedelta
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("HotpotDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
@csrf_exempt
def analyze_sales_data(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "sales_records").option("user", "root").option("password", "password").load()
filtered_df = df.filter((col("sale_date") >= start_date) & (col("sale_date") <= end_date))
daily_sales = filtered_df.groupBy("sale_date").agg(sum("total_amount").alias("daily_total"), count("order_id").alias("order_count"))
monthly_trend = filtered_df.withColumn("month", date_format(col("sale_date"), "yyyy-MM")).groupBy("month").agg(sum("total_amount").alias("monthly_total"), avg("total_amount").alias("avg_amount"))
dish_ranking = filtered_df.join(spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "dish_info").option("user", "root").option("password", "password").load(), "dish_id").groupBy("dish_name").agg(sum("quantity").alias("total_sold"), sum("amount").alias("total_revenue")).orderBy(desc("total_sold"))
peak_hours = filtered_df.withColumn("hour", hour(col("order_time"))).groupBy("hour").agg(count("order_id").alias("order_count"), sum("total_amount").alias("hour_revenue")).orderBy("hour")
customer_analysis = filtered_df.groupBy("customer_id").agg(count("order_id").alias("visit_frequency"), sum("total_amount").alias("total_spent"), avg("total_amount").alias("avg_spending"))
high_value_customers = customer_analysis.filter(col("total_spent") > 1000).orderBy(desc("total_spent"))
result_data = {"daily_sales": daily_sales.toPandas().to_dict('records'), "monthly_trend": monthly_trend.toPandas().to_dict('records'), "dish_ranking": dish_ranking.limit(20).toPandas().to_dict('records'), "peak_hours": peak_hours.toPandas().to_dict('records'), "high_value_customers": high_value_customers.limit(50).toPandas().to_dict('records')}
return JsonResponse({"status": "success", "data": result_data})
return JsonResponse({"status": "error", "message": "Invalid request method"})
@csrf_exempt
def generate_customer_insights(request):
if request.method == 'POST':
customer_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "customer_records").option("user", "root").option("password", "password").load()
order_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "order_records").option("user", "root").option("password", "password").load()
joined_df = customer_df.join(order_df, "customer_id", "left")
customer_segments = joined_df.withColumn("days_since_last_visit", datediff(current_date(), col("last_visit_date"))).withColumn("customer_segment", when(col("days_since_last_visit") <= 7, "活跃客户").when(col("days_since_last_visit") <= 30, "一般客户").otherwise("流失客户"))
segment_stats = customer_segments.groupBy("customer_segment").agg(count("customer_id").alias("customer_count"), avg("total_spending").alias("avg_spending"), sum("total_spending").alias("segment_revenue"))
age_analysis = customer_df.withColumn("age_group", when(col("age") < 25, "25岁以下").when(col("age") < 35, "25-35岁").when(col("age") < 45, "35-45岁").otherwise("45岁以上")).groupBy("age_group").agg(count("customer_id").alias("count"), avg("avg_spending_per_visit").alias("avg_spending"))
preference_analysis = joined_df.groupBy("customer_id", "preferred_dish_type").agg(count("order_id").alias("preference_count")).withColumn("rank", row_number().over(Window.partitionBy("customer_id").orderBy(desc("preference_count")))).filter(col("rank") == 1)
loyalty_analysis = joined_df.groupBy("customer_id").agg(count("order_id").alias("visit_count"), datediff(max("order_date"), min("order_date")).alias("customer_lifetime_days"), sum("order_amount").alias("lifetime_value"))
loyalty_segments = loyalty_analysis.withColumn("loyalty_level", when(col("visit_count") >= 20, "忠诚客户").when(col("visit_count") >= 10, "价值客户").when(col("visit_count") >= 5, "潜力客户").otherwise("新客户"))
seasonal_behavior = joined_df.withColumn("season", when(month(col("order_date")).isin([12, 1, 2]), "冬季").when(month(col("order_date")).isin([3, 4, 5]), "春季").when(month(col("order_date")).isin([6, 7, 8]), "夏季").otherwise("秋季")).groupBy("season").agg(count("order_id").alias("seasonal_orders"), avg("order_amount").alias("avg_seasonal_spending"))
result_insights = {"segment_stats": segment_stats.toPandas().to_dict('records'), "age_analysis": age_analysis.toPandas().to_dict('records'), "loyalty_segments": loyalty_segments.groupBy("loyalty_level").count().toPandas().to_dict('records'), "seasonal_behavior": seasonal_behavior.toPandas().to_dict('records')}
return JsonResponse({"status": "success", "insights": result_insights})
return JsonResponse({"status": "error", "message": "Invalid request method"})
@csrf_exempt
def predict_business_trends(request):
if request.method == 'POST':
historical_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "daily_business_data").option("user", "root").option("password", "password").load()
weather_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "weather_data").option("user", "root").option("password", "password").load()
combined_df = historical_df.join(weather_df, "date", "left")
weekly_pattern = combined_df.withColumn("weekday", dayofweek(col("date"))).groupBy("weekday").agg(avg("daily_revenue").alias("avg_revenue"), avg("customer_count").alias("avg_customers"), stddev("daily_revenue").alias("revenue_variance"))
monthly_seasonality = combined_df.withColumn("month", month(col("date"))).groupBy("month").agg(avg("daily_revenue").alias("monthly_avg_revenue"), max("daily_revenue").alias("monthly_max_revenue"), min("daily_revenue").alias("monthly_min_revenue"))
weather_impact = combined_df.groupBy("weather_type").agg(avg("daily_revenue").alias("weather_avg_revenue"), count("date").alias("weather_days"), avg("customer_count").alias("weather_avg_customers"))
revenue_trend = combined_df.withColumn("week_of_year", weekofyear(col("date"))).groupBy("week_of_year").agg(sum("daily_revenue").alias("weekly_revenue")).withColumn("revenue_change", (col("weekly_revenue") - lag("weekly_revenue", 1).over(Window.orderBy("week_of_year"))) / lag("weekly_revenue", 1).over(Window.orderBy("week_of_year")) * 100)
dish_trend_analysis = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "dish_sales_daily").option("user", "root").option("password", "password").load()
popular_dishes_trend = dish_trend_analysis.withColumn("month", date_format(col("sale_date"), "yyyy-MM")).groupBy("month", "dish_name").agg(sum("quantity_sold").alias("monthly_quantity")).withColumn("rank", row_number().over(Window.partitionBy("month").orderBy(desc("monthly_quantity")))).filter(col("rank") <= 10)
customer_retention_rate = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/hotpot_db").option("dbtable", "customer_visits").option("user", "root").option("password", "password").load().withColumn("month", date_format(col("visit_date"), "yyyy-MM")).groupBy("month").agg(countDistinct("customer_id").alias("active_customers")).withColumn("retention_rate", col("active_customers") / lag("active_customers", 1).over(Window.orderBy("month")) * 100)
future_predictions = {"weekly_pattern": weekly_pattern.orderBy("weekday").toPandas().to_dict('records'), "monthly_seasonality": monthly_seasonality.orderBy("month").toPandas().to_dict('records'), "weather_impact": weather_impact.toPandas().to_dict('records'), "revenue_trend": revenue_trend.orderBy("week_of_year").toPandas().to_dict('records'), "popular_dishes_trend": popular_dishes_trend.toPandas().to_dict('records'), "retention_rate": customer_retention_rate.toPandas().to_dict('records')}
return JsonResponse({"status": "success", "predictions": future_predictions})
return JsonResponse({"status": "error", "message": "Invalid request method"})
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
