💖💖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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基于大数据的海底捞门店数据可视化系统介绍
《基于大数据的海底捞门店数据可视化系统》是一个面向计算机专业毕业生的完整大数据分析与可视化项目，该系统采用Hadoop+Spark大数据技术栈作为核心架构，利用HDFS分布式文件系统存储海量门店数据，通过Spark SQL进行高效的数据查询与分析处理，配合Pandas和NumPy进行数据清洗与统计计算，系统提供Python+Django和Java+Spring Boot两种后端实现方案供选择，前端采用Vue+ElementUI构建交互界面，结合Echarts图表库和原生JavaScript、jQuery技术实现丰富的数据可视化效果，数据库层使用MySQL进行结构化数据存储，系统功能涵盖系统主页、个人中心、修改密码、个人信息、用户管理等基础模块，核心功能包括数据大屏可视化展示、海底捞门店数据管理、市场竞争分析、经营策略分析、空间分布分析以及门店选址分析六大业务模块，通过大数据技术对海底捞门店的经营数据进行多维度分析，生成直观的可视化图表帮助决策者了解门店运营状况、市场竞争态势和最优选址策略，整个系统从数据采集、存储、处理到可视化展示形成完整的大数据处理流程，充分体现了Hadoop分布式存储能力和Spark内存计算优势，适合作为计算机专业大数据方向的毕业设计项目，技术栈完整、功能模块清晰、实战性强。
基于大数据的海底捞门店数据可视化系统演示视频
基于大数据的海底捞门店数据可视化系统演示图片
基于大数据的海底捞门店数据可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, max, min, when, desc, row_number, rank
from pyspark.sql.window import Window
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.db import connection
def analyze_store_market_competition(request):
spark = SparkSession.builder.appName("HaidilaoMarketAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").getOrCreate()
store_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "store_data").option("user", "root").option("password", "123456").load()
competitor_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "competitor_data").option("user", "root").option("password", "123456").load()
store_df.createOrReplaceTempView("stores")
competitor_df.createOrReplaceTempView("competitors")
market_share_sql = "SELECT s.city, s.district, COUNT(s.store_id) as haidilao_count, SUM(s.monthly_revenue) as haidilao_revenue, AVG(c.competitor_count) as avg_competitor_count, AVG(c.avg_competitor_revenue) as competitor_avg_revenue, (SUM(s.monthly_revenue) / (SUM(s.monthly_revenue) + SUM(c.total_competitor_revenue))) * 100 as market_share_rate FROM stores s LEFT JOIN competitors c ON s.district = c.district GROUP BY s.city, s.district ORDER BY market_share_rate DESC"
market_share_result = spark.sql(market_share_sql)
competition_intensity_sql = "SELECT s.store_id, s.store_name, s.city, s.district, s.monthly_revenue, c.competitor_count, c.avg_competitor_revenue, CASE WHEN c.competitor_count > 10 AND c.avg_competitor_revenue > s.monthly_revenue THEN '高竞争' WHEN c.competitor_count BETWEEN 5 AND 10 THEN '中竞争' ELSE '低竞争' END as competition_level FROM stores s LEFT JOIN competitors c ON s.district = c.district"
competition_result = spark.sql(competition_intensity_sql)
window_spec = Window.partitionBy("city").orderBy(desc("monthly_revenue"))
rank_result = store_df.withColumn("revenue_rank", rank().over(window_spec)).withColumn("competitive_advantage", when(col("revenue_rank") <= 3, "领先优势").when(col("revenue_rank") <= 10, "中等水平").otherwise("需改进"))
market_share_pandas = market_share_result.toPandas()
competition_pandas = competition_result.toPandas()
rank_pandas = rank_result.toPandas()
market_share_list = market_share_pandas.to_dict('records')
competition_list = competition_pandas.to_dict('records')
rank_list = rank_pandas.to_dict('records')
spark.stop()
return JsonResponse({'code': 200, 'message': '市场竞争分析成功', 'market_share_data': market_share_list, 'competition_intensity': competition_list, 'store_ranking': rank_list})
def analyze_store_operation_strategy(request):
spark = SparkSession.builder.appName("HaidilaoStrategyAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").getOrCreate()
operation_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "operation_data").option("user", "root").option("password", "123456").load()
customer_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "customer_data").option("user", "root").option("password", "123456").load()
operation_df.createOrReplaceTempView("operations")
customer_df.createOrReplaceTempView("customers")
revenue_analysis_sql = "SELECT o.store_id, o.store_name, SUM(o.daily_revenue) as total_revenue, AVG(o.daily_revenue) as avg_daily_revenue, MAX(o.daily_revenue) as peak_revenue, MIN(o.daily_revenue) as lowest_revenue, SUM(o.customer_count) as total_customers, AVG(o.avg_consumption) as avg_per_customer FROM operations o GROUP BY o.store_id, o.store_name"
revenue_result = spark.sql(revenue_analysis_sql)
time_analysis_sql = "SELECT o.store_id, o.time_period, COUNT(*) as period_count, SUM(o.customer_count) as period_customers, AVG(o.daily_revenue) as period_avg_revenue, CASE WHEN o.time_period IN ('午餐时段', '晚餐时段') THEN '高峰期' ELSE '平峰期' END as peak_label FROM operations o GROUP BY o.store_id, o.time_period ORDER BY period_avg_revenue DESC"
time_result = spark.sql(time_analysis_sql)
customer_analysis_sql = "SELECT c.store_id, COUNT(DISTINCT c.customer_id) as unique_customers, SUM(c.visit_count) as total_visits, AVG(c.visit_count) as avg_visit_frequency, SUM(CASE WHEN c.member_level = 'VIP' THEN 1 ELSE 0 END) as vip_count, AVG(c.satisfaction_score) as avg_satisfaction FROM customers c GROUP BY c.store_id"
customer_result = spark.sql(customer_analysis_sql)
strategy_recommendation_df = revenue_result.join(customer_result, "store_id", "left").withColumn("strategy_type", when(col("avg_satisfaction") < 4.0, "提升服务质量").when(col("avg_visit_frequency") < 2.0, "增强客户粘性").when(col("avg_per_customer") < 80, "推广高价值套餐").otherwise("保持现状"))
revenue_pandas = revenue_result.toPandas()
time_pandas = time_result.toPandas()
customer_pandas = customer_result.toPandas()
strategy_pandas = strategy_recommendation_df.toPandas()
revenue_list = revenue_pandas.to_dict('records')
time_list = time_pandas.to_dict('records')
customer_list = customer_pandas.to_dict('records')
strategy_list = strategy_pandas.to_dict('records')
spark.stop()
return JsonResponse({'code': 200, 'message': '经营策略分析完成', 'revenue_analysis': revenue_list, 'time_distribution': time_list, 'customer_analysis': customer_list, 'strategy_recommendations': strategy_list})
def analyze_store_location_selection(request):
spark = SparkSession.builder.appName("HaidilaoLocationAnalysis").master("local[*]").config("spark.sql.warehouse.dir", "/user/hive/warehouse").getOrCreate()
location_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "location_data").option("user", "root").option("password", "123456").load()
demographic_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "demographic_data").option("user", "root").option("password", "123456").load()
traffic_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/haidilao_db").option("driver", "com.mysql.jdbc.Driver").option("dbtable", "traffic_data").option("user", "root").option("password", "123456").load()
location_df.createOrReplaceTempView("locations")
demographic_df.createOrReplaceTempView("demographics")
traffic_df.createOrReplaceTempView("traffic")
location_score_sql = "SELECT l.district, l.area_name, l.latitude, l.longitude, d.population_density, d.avg_income, t.daily_traffic_flow, t.nearby_subway_count, t.nearby_bus_count, (d.population_density * 0.3 + d.avg_income * 0.25 + t.daily_traffic_flow * 0.25 + t.nearby_subway_count * 0.1 + t.nearby_bus_count * 0.1) as location_score FROM locations l LEFT JOIN demographics d ON l.district = d.district LEFT JOIN traffic t ON l.area_name = t.area_name"
location_score_result = spark.sql(location_score_sql)
high_potential_areas = location_score_result.filter(col("location_score") > 70).filter(col("population_density") > 5000).filter(col("daily_traffic_flow") > 10000)
existing_store_sql = "SELECT district, COUNT(*) as existing_store_count, AVG(monthly_revenue) as district_avg_revenue FROM stores GROUP BY district"
existing_stores = spark.sql(existing_store_sql)
existing_stores.createOrReplaceTempView("existing_summary")
final_recommendation_sql = "SELECT ls.district, ls.area_name, ls.latitude, ls.longitude, ls.population_density, ls.avg_income, ls.daily_traffic_flow, ls.location_score, COALESCE(es.existing_store_count, 0) as existing_count, CASE WHEN COALESCE(es.existing_store_count, 0) = 0 THEN '优先推荐' WHEN COALESCE(es.existing_store_count, 0) BETWEEN 1 AND 2 THEN '可考虑' ELSE '饱和区域' END as recommendation_level FROM (SELECT * FROM locations LEFT JOIN demographics ON locations.district = demographics.district LEFT JOIN traffic ON locations.area_name = traffic.area_name) ls LEFT JOIN existing_summary es ON ls.district = es.district WHERE ls.population_density > 5000 ORDER BY ls.location_score DESC LIMIT 50"
recommendation_result = spark.sql(final_recommendation_sql)
distance_calculation_df = high_potential_areas.withColumn("estimated_customer_reach", col("population_density") * 0.01 * col("daily_traffic_flow") * 0.001).withColumn("investment_priority", when(col("location_score") > 85, "高优先级").when(col("location_score") > 70, "中优先级").otherwise("低优先级"))
location_pandas = location_score_result.toPandas()
high_potential_pandas = high_potential_areas.toPandas()
recommendation_pandas = recommendation_result.toPandas()
distance_pandas = distance_calculation_df.toPandas()
location_list = location_pandas.to_dict('records')
high_potential_list = high_potential_pandas.to_dict('records')
recommendation_list = recommendation_pandas.to_dict('records')
distance_list = distance_pandas.to_dict('records')
spark.stop()
return JsonResponse({'code': 200, 'message': '门店选址分析完成', 'all_locations': location_list, 'high_potential_areas': high_potential_list, 'top_recommendations': recommendation_list, 'investment_analysis': distance_list})
基于大数据的海底捞门店数据可视化系统文档展示
💖💖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
