💖💖作者：计算机毕业设计江挽 💙💙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
基于大数据的全国饮品门店数据分析系统介绍
全国饮品门店数据分析系统是一个基于大数据技术架构的综合性数据分析平台，采用Hadoop+Spark作为核心大数据处理框架，结合Python编程语言和Django后端框架构建而成。系统通过HDFS分布式文件系统存储海量饮品门店数据，利用Spark SQL进行高效的数据查询和处理，配合Pandas和NumPy进行深度数据分析。前端采用Vue框架结合ElementUI组件库和Echarts可视化库，为用户提供直观的操作界面和丰富的图表展示。系统核心功能涵盖饮品门店数据管理、全国饮品门店数据大屏展示、品牌聚类关联分析、品牌竞争力分析、品类市场格局分析以及价格与规模分析等模块。通过大数据技术的运用，系统能够处理大规模的门店数据集，实现多维度的数据挖掘和分析，为饮品行业的市场研究和商业决策提供数据支撑。整个系统架构清晰，技术栈完整，既体现了现代大数据技术的应用，又具备实际的业务价值。
基于大数据的全国饮品门店数据分析系统演示视频
基于大数据的全国饮品门店数据分析系统演示图片
基于大数据的全国饮品门店数据分析系统代码展示
from pyspark.sql import SparkSession
from pyspark.ml.clustering import KMeans
from pyspark.ml.feature import VectorAssembler
from pyspark.sql.functions import col, count, avg, sum, when, desc, asc
from pyspark.sql.types import StructType, StructField, StringType, DoubleType, IntegerType
import pandas as pd
import numpy as np
spark = SparkSession.builder.appName("DrinkStoreAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def brand_clustering_analysis(store_data):
df = spark.createDataFrame(store_data)
brand_metrics = df.groupBy("brand_name").agg(
count("store_id").alias("store_count"),
avg("average_price").alias("avg_price"),
avg("monthly_revenue").alias("avg_revenue"),
avg("customer_rating").alias("avg_rating")
)
assembler = VectorAssembler(inputCols=["store_count", "avg_price", "avg_revenue", "avg_rating"], outputCol="features")
feature_df = assembler.transform(brand_metrics)
kmeans = KMeans(k=5, featuresCol="features", predictionCol="cluster")
model = kmeans.fit(feature_df)
clustered_df = model.transform(feature_df)
cluster_summary = clustered_df.groupBy("cluster").agg(
count("brand_name").alias("brand_count"),
avg("store_count").alias("avg_stores"),
avg("avg_price").alias("cluster_avg_price"),
avg("avg_revenue").alias("cluster_avg_revenue")
).orderBy("cluster")
cluster_results = cluster_summary.collect()
brand_cluster_mapping = clustered_df.select("brand_name", "cluster", "store_count", "avg_price").collect()
clustering_insights = []
for row in cluster_results:
cluster_id = row.cluster
if row.avg_stores > 100 and row.cluster_avg_revenue > 50000:
cluster_type = "头部连锁品牌"
elif row.avg_stores > 20 and row.cluster_avg_price > 25:
cluster_type = "中端品牌"
elif row.cluster_avg_price < 20:
cluster_type = "平价品牌"
else:
cluster_type = "区域品牌"
clustering_insights.append({
"cluster_id": cluster_id,
"cluster_type": cluster_type,
"brand_count": row.brand_count,
"avg_stores": row.avg_stores,
"avg_price": row.cluster_avg_price
})
return clustering_insights, brand_cluster_mapping
def brand_competitiveness_analysis(store_data, market_data):
df = spark.createDataFrame(store_data)
market_df = spark.createDataFrame(market_data)
joined_df = df.join(market_df, on="city", how="inner")
brand_metrics = joined_df.groupBy("brand_name").agg(
count("store_id").alias("total_stores"),
countDistinct("city").alias("city_coverage"),
avg("monthly_revenue").alias("avg_monthly_revenue"),
avg("customer_rating").alias("avg_rating"),
sum("monthly_revenue").alias("total_revenue")
)
market_share_df = brand_metrics.withColumn(
"market_share",
col("total_revenue") / brand_metrics.agg(sum("total_revenue")).collect()[0][0] * 100
)
competitiveness_score = market_share_df.withColumn(
"competitiveness_score",
(col("total_stores") * 0.3 + col("city_coverage") * 0.2 + col("avg_monthly_revenue") / 1000 * 0.25 + col("avg_rating") * 20 * 0.15 + col("market_share") * 0.1)
)
ranked_brands = competitiveness_score.orderBy(desc("competitiveness_score"))
top_brands = ranked_brands.limit(20).collect()
competitive_analysis = []
for i, brand in enumerate(top_brands):
rank = i + 1
if brand.competitiveness_score >= 80:
competitive_level = "市场领导者"
elif brand.competitiveness_score >= 60:
competitive_level = "强势竞争者"
elif brand.competitiveness_score >= 40:
competitive_level = "稳定参与者"
else:
competitive_level = "市场跟随者"
growth_potential = "高" if brand.city_coverage < 20 and brand.avg_rating > 4.0 else "中" if brand.city_coverage < 50 else "低"
competitive_analysis.append({
"rank": rank,
"brand_name": brand.brand_name,
"competitive_level": competitive_level,
"competitiveness_score": round(brand.competitiveness_score, 2),
"total_stores": brand.total_stores,
"city_coverage": brand.city_coverage,
"market_share": round(brand.market_share, 2),
"growth_potential": growth_potential
})
return competitive_analysis
def category_market_analysis(store_data, category_data):
df = spark.createDataFrame(store_data)
category_df = spark.createDataFrame(category_data)
joined_df = df.join(category_df, on="store_id", how="inner")
category_metrics = joined_df.groupBy("category", "city_tier").agg(
count("store_id").alias("store_count"),
avg("monthly_revenue").alias("avg_revenue"),
avg("average_price").alias("avg_price"),
sum("monthly_revenue").alias("total_revenue")
)
total_market_revenue = category_metrics.agg(sum("total_revenue")).collect()[0][0]
market_analysis = category_metrics.withColumn(
"market_share",
col("total_revenue") / total_market_revenue * 100
)
category_summary = market_analysis.groupBy("category").agg(
sum("store_count").alias("total_stores"),
avg("avg_revenue").alias("category_avg_revenue"),
avg("avg_price").alias("category_avg_price"),
sum("market_share").alias("category_market_share")
).orderBy(desc("category_market_share"))
tier_analysis = market_analysis.groupBy("city_tier").agg(
sum("store_count").alias("tier_total_stores"),
avg("avg_revenue").alias("tier_avg_revenue"),
sum("market_share").alias("tier_market_share")
).orderBy("city_tier")
category_results = category_summary.collect()
tier_results = tier_analysis.collect()
market_insights = []
for row in category_results:
growth_trend = "上升" if row.category_avg_revenue > 45000 else "稳定" if row.category_avg_revenue > 30000 else "下降"
market_position = "主导" if row.category_market_share > 30 else "重要" if row.category_market_share > 15 else "补充"
price_level = "高端" if row.category_avg_price > 35 else "中端" if row.category_avg_price > 20 else "平价"
market_insights.append({
"category": row.category,
"market_position": market_position,
"market_share": round(row.category_market_share, 2),
"total_stores": row.total_stores,
"avg_revenue": round(row.category_avg_revenue, 2),
"price_level": price_level,
"growth_trend": growth_trend
})
tier_insights = []
for row in tier_results:
consumption_level = "高消费" if row.tier_avg_revenue > 50000 else "中等消费" if row.tier_avg_revenue > 35000 else "大众消费"
market_maturity = "成熟" if row.tier_total_stores > 1000 else "发展中" if row.tier_total_stores > 300 else "新兴"
tier_insights.append({
"city_tier": row.city_tier,
"market_maturity": market_maturity,
"consumption_level": consumption_level,
"total_stores": row.tier_total_stores,
"market_share": round(row.tier_market_share, 2)
})
return market_insights, tier_insights
基于大数据的全国饮品门店数据分析系统文档展示
💖💖作者：计算机毕业设计江挽 💙💙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
