计算机毕设选题：基于大数据技术的瑞幸门店布局分析可视化系统

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基于大数据技术的瑞幸门店布局分析可视化系统-系统功能介绍

基于大数据技术的瑞幸门店布局分析可视化系统是一套综合运用Hadoop分布式存储、Spark大数据处理和现代Web技术的数据分析平台。系统采用Python作为主要开发语言，结合Django框架构建后端服务架构，前端采用Vue+ElementUI+Echarts技术栈实现交互式数据可视化展示。系统核心功能围绕瑞幸咖啡全国门店数据展开，通过HDFS存储海量门店信息，利用Spark SQL进行高效的数据清洗和统计分析，配合Pandas、NumPy等数据科学库完成复杂的数据处理任务。平台提供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分析维度，涵盖省份分布统计、城市排行分析、聚类算法应用、热力图生成等多种分析功能。系统通过MySQL数据库存储处理结果，最终以直观的图表和地图形式向用户展示瑞幸咖啡在全国范围内的门店布局特点、市场渗透策略和潜在发展机会。

基于大数据技术的瑞幸门店布局分析可视化系统-系统技术介绍

大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 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

基于大数据技术的瑞幸门店布局分析可视化系统-系统背景意义

随着新零售概念的兴起和消费升级趋势的推进，咖啡连锁行业在中国市场呈现出快速发展态势。瑞幸咖啡作为本土咖啡品牌的代表，凭借其独特的数字化运营模式和积极的门店扩张策略，在短时间内实现了全国性布局，成为咖啡行业的重要参与者。这种快速扩张背后蕴含着复杂的市场布局逻辑和选址决策机制，传统的人工分析方法已难以应对如此庞大的门店数据规模。与此同时，大数据技术的日趋成熟为深度挖掘商业数据价值提供了强有力的技术支撑，Hadoop生态系统和Spark计算框架在处理大规模结构化数据方面展现出显著优势。在此背景下，运用大数据技术对瑞幸咖啡全国门店布局进行系统性分析，不仅能够揭示其市场扩张的内在规律，也为相关企业的选址决策提供数据支持和方法参考。 本课题的研究意义体现在理论探索和实践应用两个层面。从理论角度来看，通过构建基于大数据技术的门店布局分析框架，为零售地理学和商业空间分析提供了新的研究工具和方法路径，丰富了大数据在商业分析领域的应用案例。系统运用DBSCAN聚类算法识别门店聚集模式，结合地理信息分析技术揭示选址规律，为商业地理分析提供了可复制的技术方案。从实践价值来说，分析结果能够为连锁企业的市场布局决策提供参考依据，帮助识别市场空白点和发展机会，优化门店网络配置效率。对于技术学习层面，本系统整合了Hadoop、Spark、Django、Vue等多项主流技术，为学习大数据处理流程和Web开发技能提供了完整的实践平台。虽然作为毕业设计项目，其影响范围相对有限，但通过真实商业数据的处理分析，能够加深对大数据技术应用场景的理解，提升数据分析和系统开发的综合能力。

基于大数据技术的瑞幸门店布局分析可视化系统-系统演示视频

演示视频

基于大数据技术的瑞幸门店布局分析可视化系统-系统演示图片

基于大数据技术的瑞幸门店布局分析可视化系统-系统部分代码

from pyspark.sql import SparkSession

from pyspark.sql.functions import col, count, desc, asc, when, regexp_extract, split, collect_list

from sklearn.cluster import DBSCAN

import pandas as pd

import numpy as np

from django.http import JsonResponse

from django.views import View

import json

import mysql.connector

from geopy.distance import geodesic

def analyze_national_store_distribution():

    spark = SparkSession.builder.appName("LuckinStoreAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

    store_df = spark.read.csv("hdfs://localhost:9000/luckin_data/featured_luckin_stores.csv", header=True, inferSchema=True)

    province_stats = store_df.groupBy("province").agg(count("*").alias("store_count")).orderBy(desc("store_count"))

    province_result = province_stats.collect()

    province_data = [(row["province"], row["store_count"]) for row in province_result]

    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"}

    store_df_with_region = store_df.withColumn("region", when(col("province").isin(list(region_mapping.keys())), col("province")).otherwise("其他"))

    for province, region in region_mapping.items():

        store_df_with_region = store_df_with_region.withColumn("region", when(col("province") == province, region).otherwise(col("region")))

    region_stats = store_df_with_region.groupBy("region").agg(count("*").alias("store_count")).orderBy(desc("store_count"))

    total_stores = store_df.count()

    region_with_ratio = region_stats.withColumn("ratio", col("store_count") / total_stores * 100)

    region_result = region_with_ratio.collect()

    region_data = [(row["region"], row["store_count"], round(row["ratio"], 2)) for row in region_result]

    city_stats = store_df.groupBy("city").agg(count("*").alias("store_count")).orderBy(desc("store_count")).limit(20)

    city_result = city_stats.collect()

    city_data = [(row["city"], row["store_count"]) for row in city_result]

    city_tier_mapping = {"北京": "一线", "上海": "一线", "广州": "一线", "深圳": "一线", "杭州": "新一线", "成都": "新一线", "武汉": "新一线", "重庆": "新一线", "南京": "新一线", "天津": "新一线", "苏州": "新一线", "西安": "新一线", "长沙": "新一线", "沈阳": "新一线", "青岛": "新一线"}

    store_df_with_tier = store_df.withColumn("city_tier", when(col("city").isin(list(city_tier_mapping.keys())), col("city")).otherwise("其他"))

    for city, tier in city_tier_mapping.items():

        store_df_with_tier = store_df_with_tier.withColumn("city_tier", when(col("city") == city, tier).otherwise(col("city_tier")))

    tier_stats = store_df_with_tier.groupBy("city_tier").agg(count("*").alias("store_count")).orderBy(desc("store_count"))

    tier_result = tier_stats.collect()

    tier_data = [(row["city_tier"], row["store_count"]) for row in tier_result]

    coordinates_df = store_df.select("longitude", "latitude", "name", "address").filter((col("longitude").isNotNull()) & (col("latitude").isNotNull()))

    max_longitude_row = coordinates_df.orderBy(desc("longitude")).first()

    min_longitude_row = coordinates_df.orderBy(asc("longitude")).first()

    max_latitude_row = coordinates_df.orderBy(desc("latitude")).first()

    min_latitude_row = coordinates_df.orderBy(asc("latitude")).first()

    extreme_points = {"easternmost": {"name": max_longitude_row["name"], "address": max_longitude_row["address"], "longitude": max_longitude_row["longitude"], "latitude": max_longitude_row["latitude"]}, "westernmost": {"name": min_longitude_row["name"], "address": min_longitude_row["address"], "longitude": min_longitude_row["longitude"], "latitude": min_longitude_row["latitude"]}, "northernmost": {"name": max_latitude_row["name"], "address": max_latitude_row["address"], "longitude": max_latitude_row["longitude"], "latitude": max_latitude_row["latitude"]}, "southernmost": {"name": min_latitude_row["name"], "address": min_latitude_row["address"], "longitude": min_latitude_row["longitude"], "latitude": min_latitude_row["latitude"]}}

    spark.stop()

    return {"province_distribution": province_data, "region_distribution": region_data, "top_cities": city_data, "city_tier_distribution": tier_data, "extreme_points": extreme_points}

def analyze_key_market_penetration():

    spark = SparkSession.builder.appName("KeyMarketAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()

    store_df = spark.read.csv("hdfs://localhost:9000/luckin_data/featured_luckin_stores.csv", header=True, inferSchema=True)

    top_cities = store_df.groupBy("city").agg(count("*").alias("store_count")).orderBy(desc("store_count")).limit(5)

    top_city_names = [row["city"] for row in top_cities.collect()]

    district_analysis = {}

    for city in top_city_names:

        city_stores = store_df.filter(col("city") == city)

        district_stats = city_stores.groupBy("district").agg(count("*").alias("store_count")).orderBy(desc("store_count"))

        district_result = district_stats.collect()

        district_analysis[city] = [(row["district"], row["store_count"]) for row in district_result]

    city_groups = {"长三角": ["上海", "杭州", "南京", "苏州", "无锡", "常州", "扬州", "镇江", "泰州", "宁波", "温州", "嘉兴", "湖州", "绍兴", "金华", "衢州", "舟山", "台州", "丽水", "合肥", "芜湖", "蚌埠", "淮南", "马鞍山", "淮北", "铜陵", "安庆", "黄山", "滁州", "阜阳", "宿州", "六安", "亳州", "池州", "宣城"], "珠三角": ["广州", "深圳", "珠海", "汕头", "佛山", "韶关", "湛江", "肇庆", "江门", "茂名", "惠州", "梅州", "汕尾", "河源", "阳江", "清远", "东莞", "中山", "潮州", "揭阳", "云浮"], "京津冀": ["北京", "天津", "石家庄", "唐山", "秦皇岛", "邯郸", "邢台", "保定", "张家口", "承德", "沧州", "廊坊", "衡水"]}

    city_group_stats = {}

    for group_name, cities in city_groups.items():

        group_stores = store_df.filter(col("city").isin(cities))

        group_count = group_stores.count()

        city_group_stats[group_name] = group_count

    provincial_capitals = ["北京", "天津", "上海", "重庆", "石家庄", "太原", "呼和浩特", "沈阳", "长春", "哈尔滨", "南京", "杭州", "合肥", "福州", "南昌", "济南", "郑州", "武汉", "长沙", "广州", "南宁", "海口", "成都", "贵阳", "昆明", "拉萨", "西安", "兰州", "西宁", "银川", "乌鲁木齐", "香港", "澳门"]

    capital_stores = store_df.filter(col("city").isin(provincial_capitals)).count()

    non_capital_stores = store_df.filter(~col("city").isin(provincial_capitals)).count()

    capital_comparison = {"省会城市门店数": capital_stores, "非省会城市门店数": non_capital_stores, "省会城市占比": round(capital_stores / (capital_stores + non_capital_stores) * 100, 2)}

    top_city = top_city_names[0] if top_city_names else "上海"

    heatmap_stores = store_df.filter(col("city") == top_city).select("longitude", "latitude", "name").filter((col("longitude").isNotNull()) & (col("latitude").isNotNull()))

    heatmap_data = [(row["longitude"], row["latitude"], row["name"]) for row in heatmap_stores.collect()]

    spark.stop()

    return {"district_distribution": district_analysis, "city_group_stats": city_group_stats, "capital_comparison": capital_comparison, "heatmap_data": heatmap_data}

def analyze_store_characteristics_and_location_strategy():

    spark = SparkSession.builder.appName("StoreCharacteristics").config("spark.sql.adaptive.enabled", "true").getOrCreate()

    store_df = spark.read.csv("hdfs://localhost:9000/luckin_data/featured_luckin_stores.csv", header=True, inferSchema=True)

    primary_type_df = store_df.withColumn("primary_type", regexp_extract(col("type"), r"^([^;]+)", 1))

    type_stats = primary_type_df.groupBy("primary_type").agg(count("*").alias("store_count")).orderBy(desc("store_count"))

    type_result = type_stats.collect()

    business_type_data = [(row["primary_type"], row["store_count"]) for row in type_result if row["primary_type"]]

    theme_stores = store_df.filter(col("name").contains("主题店")).count()

    campus_stores = store_df.filter(col("name").contains("大学") | col("name").contains("校区") | col("name").contains("学院")).count()

    special_stores = {"主题店数量": theme_stores, "校园店数量": campus_stores}

    location_keywords = ["中心", "广场", "大厦", "产业园", "写字楼", "商场", "购物中心", "步行街"]

    naming_pattern = {}

    for keyword in location_keywords:

        keyword_count = store_df.filter(col("name").contains(keyword)).count()

        naming_pattern[keyword] = keyword_count

    luckin_english = store_df.filter(col("name").contains("luckin coffee")).count()

    luckin_chinese = store_df.filter(col("name").contains("瑞幸咖啡") & (~col("name").contains("luckin coffee"))).count()

    brand_naming = {"英文品牌名使用": luckin_english, "中文品牌名使用": luckin_chinese, "英文使用比例": round(luckin_english / (luckin_english + luckin_chinese) * 100, 2) if (luckin_english + luckin_chinese) > 0 else 0}

    coordinates_data = store_df.select("longitude", "latitude").filter((col("longitude").isNotNull()) & (col("latitude").isNotNull())).collect()

    if len(coordinates_data) >= 10:

        coords_array = np.array([(float(row["longitude"]), float(row["latitude"])) for row in coordinates_data])

        dbscan = DBSCAN(eps=0.01, min_samples=3).fit(coords_array)

        cluster_labels = dbscan.labels_

        unique_clusters = len(set(cluster_labels)) - (1 if -1 in cluster_labels else 0)

        noise_points = list(cluster_labels).count(-1)

        clustering_result = {"聚类簇数量": unique_clusters, "噪声点数量": noise_points, "聚类效果": "良好" if unique_clusters >= 5 else "一般"}

    else:

        clustering_result = {"聚类簇数量": 0, "噪声点数量": 0, "聚类效果": "数据不足"}

    poi_keywords = {"大学": ["大学", "学院", "校区"], "医院": ["医院", "卫生院"], "地铁": ["地铁", "轻轨"], "机场": ["机场", "航站楼"]}

    poi_distribution = {}

    for poi_type, keywords in poi_keywords.items():

        poi_count = 0

        for keyword in keywords:

            poi_count += store_df.filter(col("name").contains(keyword) | col("address").contains(keyword)).count()

        poi_distribution[poi_type] = poi_count

    spark.stop()

    return {"business_type_distribution": business_type_data, "special_stores": special_stores, "naming_patterns": naming_pattern, "brand_naming_strategy": brand_naming, "clustering_analysis": clustering_result, "poi_distribution": poi_distribution}

基于大数据技术的瑞幸门店布局分析可视化系统-结语

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