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⚡⚡获取源码主页--> space.bilibili.com/35463818075…
瑞幸咖啡门店数据可视化分析系统- 简介
基于Hadoop+Spark的瑞幸咖啡全国门店数据可视化分析系统是一套面向大数据处理的综合性分析平台，该系统充分利用Hadoop分布式存储架构和Spark内存计算优势，对瑞幸咖啡在全国范围内的门店分布数据进行深度挖掘与智能分析。系统采用Python作为主要开发语言，后端基于Django框架构建RESTful API接口，前端运用Vue.js结合ElementUI组件库和Echarts可视化图表库，实现了用户友好的交互界面和丰富的数据展示效果。在数据处理层面，系统通过Spark SQL进行大规模数据查询优化，结合Pandas和NumPy进行数据清洗与统计计算，能够高效处理海量门店信息。系统涵盖全国门店宏观布局分析、重点市场微观深耕度分析、门店自身特性与定位分析以及门店选址策略与市场潜力分析四大核心功能模块，通过多维度的数据透视为企业战略决策提供科学依据。整体架构采用分层设计思想，数据存储于HDFS分布式文件系统中，确保系统具备良好的扩展性和容错能力，同时支持实时数据更新和批量数据处理两种工作模式。
瑞幸咖啡门店数据可视化分析系统-技术
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持）
前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery
详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy
数据库：MySQL
瑞幸咖啡门店数据可视化分析系统- 背景
随着新零售时代的到来，咖啡行业作为消费升级的重要组成部分，正经历着前所未有的快速发展变革。瑞幸咖啡作为中国本土崛起的咖啡连锁品牌，自2017年成立以来便采取了激进的扩张策略，在全国范围内快速布局门店网络。这种大规模的门店扩张背后蕴含着丰富的地理分布信息、消费者行为数据以及市场竞争格局变化趋势。然而，传统的数据分析方法面对如此庞大且复杂的门店数据时往往显得力不从心，无法有效挖掘出数据背后的深层价值。与此同时，大数据技术特别是Hadoop生态系统和Spark计算引擎的日趋成熟，为处理这类大规模、多维度的商业数据提供了强有力的技术支撑。在这样的时代背景下，如何运用先进的大数据技术对连锁零售企业的门店分布进行科学分析，已经成为学术界和产业界共同关注的重要课题。
本课题的研究意义体现在理论探索与实践应用的多个层面。在技术层面，通过将Hadoop分布式存储与Spark内存计算相结合，验证了大数据技术栈在零售行业数据分析中的可行性和优越性，为同类型的商业数据处理提供了技术参考范例。从商业分析角度来看，系统通过对瑞幸咖啡门店的地理分布、城市等级渗透、区域聚集效应等多维度分析，能够为连锁零售企业的选址决策、市场扩张策略制定提供数据支持，虽然作为毕业设计项目在规模上相对有限，但其分析思路和技术架构具备一定的推广价值。在教育意义方面，本系统整合了大数据处理、Web开发、数据可视化等多个技术领域，为计算机专业学生提供了一个相对完整的综合性实践项目，有助于加深对大数据技术应用的理解。另外，通过对真实商业场景的模拟分析，培养了运用技术手段解决实际问题的能力，这对于即将步入职场的毕业生来说具有重要的能力锻炼意义。
瑞幸咖啡门店数据可视化分析系统-视频展示
瑞幸咖啡门店数据可视化分析系统-图片展示
瑞幸咖啡门店数据可视化分析系统-代码展示
from pyspark.sql.functions import col, count, desc, max, min, avg, when, sum, regexp_replace
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
import pandas as pd
import numpy as np
from collections import Counter
spark = SparkSession.builder.appName("LuckinStoreAnalysis").config("spark.executor.memory", "4g").config("spark.driver.memory", "2g").getOrCreate()
def national_store_distribution_analysis(data_path):
df = spark.read.csv(data_path, header=True, inferSchema=True)
df_cleaned = df.filter(col("province").isNotNull() & col("city").isNotNull())
province_distribution = df_cleaned.groupBy("province").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
province_total = province_distribution.agg(sum("store_count")).collect()[0][0]
province_with_ratio = province_distribution.withColumn("percentage", (col("store_count") / province_total * 100).cast("decimal(5,2)"))
region_mapping = {"北京": "华北", "天津": "华北", "河北": "华北", "山西": "华北", "内蒙古": "华北", "上海": "华东", "江苏": "华东", "浙江": "华东", "安徽": "华东", "福建": "华东", "江西": "华东", "山东": "华东", "河南": "华中", "湖北": "华中", "湖南": "华中", "广东": "华南", "广西": "华南", "海南": "华南", "重庆": "西南", "四川": "西南", "贵州": "西南", "云南": "西南", "西藏": "西南", "陕西": "西北", "甘肃": "西北", "青海": "西北", "宁夏": "西北", "新疆": "西北", "辽宁": "东北", "吉林": "东北", "黑龙江": "东北"}
region_broadcast = spark.sparkContext.broadcast(region_mapping)
def map_region(province):
return region_broadcast.value.get(province, "其他")
from pyspark.sql.types import StringType
from pyspark.sql.functions import udf
region_udf = udf(map_region, StringType())
df_with_region = df_cleaned.withColumn("region", region_udf(col("province")))
region_distribution = df_with_region.groupBy("region").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
region_total = region_distribution.agg(sum("store_count")).collect()[0][0]
region_with_ratio = region_distribution.withColumn("percentage", (col("store_count") / region_total * 100).cast("decimal(5,2)"))
city_ranking = df_cleaned.groupBy("city").agg(count("*").alias("store_count")).orderBy(desc("store_count")).limit(20)
city_tier_mapping = {"北京": "一线", "上海": "一线", "广州": "一线", "深圳": "一线", "成都": "新一线", "重庆": "新一线", "杭州": "新一线", "武汉": "新一线", "西安": "新一线", "苏州": "新一线", "天津": "新一线", "南京": "新一线", "长沙": "新一线", "郑州": "新一线", "东莞": "新一线", "青岛": "新一线", "沈阳": "新一线", "宁波": "新一线", "昆明": "新一线"}
tier_broadcast = spark.sparkContext.broadcast(city_tier_mapping)
def map_tier(city):
return tier_broadcast.value.get(city, "三线及以下")
tier_udf = udf(map_tier, StringType())
df_with_tier = df_cleaned.withColumn("city_tier", tier_udf(col("city")))
tier_distribution = df_with_tier.groupBy("city_tier").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
tier_total = tier_distribution.agg(sum("store_count")).collect()[0][0]
tier_with_ratio = tier_distribution.withColumn("percentage", (col("store_count") / tier_total * 100).cast("decimal(5,2)"))
extreme_points = df_cleaned.agg(max("longitude").alias("max_lng"), min("longitude").alias("min_lng"), max("latitude").alias("max_lat"), min("latitude").alias("min_lat")).collect()[0]
easternmost = df_cleaned.filter(col("longitude") == extreme_points["max_lng"]).select("name", "address", "longitude", "latitude").first()
westernmost = df_cleaned.filter(col("longitude") == extreme_points["min_lng"]).select("name", "address", "longitude", "latitude").first()
northernmost = df_cleaned.filter(col("latitude") == extreme_points["max_lat"]).select("name", "address", "longitude", "latitude").first()
southernmost = df_cleaned.filter(col("latitude") == extreme_points["min_lat"]).select("name", "address", "longitude", "latitude").first()
return {"province_distribution": province_with_ratio.toPandas(), "region_distribution": region_with_ratio.toPandas(), "top20_cities": city_ranking.toPandas(), "tier_distribution": tier_with_ratio.toPandas(), "extreme_points": {"east": easternmost, "west": westernmost, "north": northernmost, "south": southernmost}}
def key_market_penetration_analysis(data_path):
df = spark.read.csv(data_path, header=True, inferSchema=True)
df_cleaned = df.filter(col("province").isNotNull() & col("city").isNotNull() & col("district").isNotNull())
top5_cities = df_cleaned.groupBy("city").agg(count("*").alias("store_count")).orderBy(desc("store_count")).limit(5)
top5_city_names = [row["city"] for row in top5_cities.collect()]
top5_districts_distribution = {}
for city in top5_city_names:
city_districts = df_cleaned.filter(col("city") == city).groupBy("district").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
top5_districts_distribution[city] = city_districts.toPandas()
city_clusters = {"长三角": ["上海", "南京", "苏州", "杭州", "宁波", "无锡", "常州", "嘉兴"], "珠三角": ["广州", "深圳", "东莞", "佛山", "中山", "珠海", "惠州", "江门"], "京津冀": ["北京", "天津", "石家庄", "唐山", "保定", "廊坊"]}
cluster_analysis = {}
for cluster_name, cities in city_clusters.items():
cluster_stores = df_cleaned.filter(col("city").isin(cities)).groupBy("city").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
cluster_total = cluster_stores.agg(sum("store_count")).collect()[0][0] if cluster_stores.count() > 0 else 0
cluster_analysis[cluster_name] = {"cities": cluster_stores.toPandas(), "total_stores": cluster_total}
province_capitals = {"北京": "北京", "上海": "上海", "天津": "天津", "重庆": "重庆", "河北": "石家庄", "山西": "太原", "辽宁": "沈阳", "吉林": "长春", "黑龙江": "哈尔滨", "江苏": "南京", "浙江": "杭州", "安徽": "合肥", "福建": "福州", "江西": "南昌", "山东": "济南", "河南": "郑州", "湖北": "武汉", "湖南": "长沙", "广东": "广州", "海南": "海口", "四川": "成都", "贵州": "贵阳", "云南": "昆明", "陕西": "西安", "甘肃": "兰州", "青海": "西宁", "台湾": "台北", "内蒙古": "呼和浩特", "广西": "南宁", "西藏": "拉萨", "宁夏": "银川", "新疆": "乌鲁木齐", "香港": "香港", "澳门": "澳门"}
capitals_broadcast = spark.sparkContext.broadcast(province_capitals)
def is_capital(province, city):
return capitals_broadcast.value.get(province) == city
from pyspark.sql.types import BooleanType
capital_udf = udf(is_capital, BooleanType())
df_with_capital_flag = df_cleaned.withColumn("is_capital", capital_udf(col("province"), col("city")))
capital_vs_non_capital = df_with_capital_flag.groupBy("is_capital").agg(count("*").alias("store_count"))
capital_comparison = capital_vs_non_capital.toPandas()
top_city = top5_city_names[0] if top5_city_names else None
heatmap_data = None
if top_city:
heatmap_coords = df_cleaned.filter(col("city") == top_city).select("longitude", "latitude").collect()
heatmap_data = {"city": top_city, "coordinates": [[row["longitude"], row["latitude"]] for row in heatmap_coords]}
return {"top5_districts": top5_districts_distribution, "city_clusters": cluster_analysis, "capital_comparison": capital_comparison, "heatmap_data": heatmap_data}
def store_positioning_analysis(data_path):
df = spark.read.csv(data_path, header=True, inferSchema=True)
df_cleaned = df.filter(col("name").isNotNull() & col("type").isNotNull())
type_split = df_cleaned.withColumn("primary_type", regexp_replace(col("type"), r";.*", ""))
primary_type_distribution = type_split.groupBy("primary_type").agg(count("*").alias("store_count")).orderBy(desc("store_count"))
type_total = primary_type_distribution.agg(sum("store_count")).collect()[0][0]
type_with_ratio = primary_type_distribution.withColumn("percentage", (col("store_count") / type_total * 100).cast("decimal(5,2)"))
special_keywords = ["主题", "校园", "大学", "学院", "校区", "医院", "机场", "高铁", "地铁", "商场", "购物中心"]
special_stores_count = {}
for keyword in special_keywords:
keyword_count = df_cleaned.filter(col("name").contains(keyword) | col("address").contains(keyword)).count()
if keyword_count > 0:
special_stores_count[keyword] = keyword_count
naming_patterns = ["中心", "广场", "大厦", "产业园", "科技园", "商业街", "步行街", "万达", "银泰", "恒隆"]
naming_analysis = {}
for pattern in naming_patterns:
pattern_count = df_cleaned.filter(col("name").contains(pattern) | col("address").contains(pattern)).count()
if pattern_count > 0:
naming_analysis[pattern] = pattern_count
brand_name_analysis = df_cleaned.groupBy(when(col("name").contains("luckin coffee"), "英文品牌").when(col("name").contains("瑞幸咖啡"), "中文品牌").otherwise("混合品牌").alias("brand_type")).agg(count("*").alias("store_count"))
brand_total = brand_name_analysis.agg(sum("store_count")).collect()[0][0]
brand_with_ratio = brand_name_analysis.withColumn("percentage", (col("store_count") / brand_total * 100).cast("decimal(5,2)"))
poi_keywords = {"教育机构": ["大学", "学院", "学校", "校区"], "交通枢纽": ["地铁", "机场", "高铁", "车站"], "医疗机构": ["医院", "诊所", "卫生院"], "商业综合体": ["商场", "购物中心", "百货", "万达"]}
poi_distribution = {}
for poi_type, keywords in poi_keywords.items():
poi_count = 0
for keyword in keywords:
poi_count += df_cleaned.filter(col("name").contains(keyword) | col("address").contains(keyword)).count()
poi_distribution[poi_type] = poi_count
return {"primary_type_distribution": type_with_ratio.toPandas(), "special_stores": special_stores_count, "naming_patterns": naming_analysis, "brand_analysis": brand_with_ratio.toPandas(), "poi_distribution": poi_distribution}
瑞幸咖啡门店数据可视化分析系统-结语
计算机毕设选题难+技术难+答辩难？瑞幸门店Hadoop大数据分析系统三重保障通过答辩
大数据毕设太复杂不会做？瑞幸门店Hadoop+Spark分析系统从零到部署全程指导
如果遇到具体的技术问题或计算机毕设方面需求，你也可以问我，我会尽力帮你分析和解决问题所在，支持我记得一键三连，再点个关注，学习不迷路！
⚡⚡获取源码主页--> **space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上↑↑联系我~~
