💖💖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
基于大数据的抖音珠宝饰品类店铺分析可视化系统介绍
本系统是一个基于大数据技术的抖音珠宝饰品类店铺分析可视化平台，主要运用Hadoop分布式存储架构和Spark大数据处理引擎对抖音平台上珠宝饰品店铺的运营数据进行深度挖掘和可视化展示。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，前端使用Vue.js配合ElementUI组件库和Echarts图表库实现用户交互界面。在数据处理层面，系统通过Spark SQL进行大规模数据查询和分析，结合Pandas和NumPy进行数据预处理和统计计算，最终将分析结果存储到MySQL数据库中。系统功能模块包括系统首页展示、个人信息管理、店铺运营分析、销售策略分析、流量来源分析和店铺价值分析等六大核心模块，能够为珠宝饰品类抖音店铺提供全方位的数据分析服务，帮助商家了解店铺运营状况、优化销售策略、分析流量分布情况，并评估店铺的整体价值和发展潜力，为商业决策提供数据支撑。
基于大数据的抖音珠宝饰品类店铺分析可视化系统演示视频
基于大数据的抖音珠宝饰品类店铺分析可视化系统演示图片
基于大数据的抖音珠宝饰品类店铺分析可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
from datetime import datetime, timedelta
spark = SparkSession.builder.appName("JewelryShopAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
class ShopOperationAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
shop_id = data.get('shop_id')
start_date = data.get('start_date')
end_date = data.get('end_date')
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/jewelry_db").option("dbtable", "shop_operation_data").option("user", "root").option("password", "123456").load()
filtered_df = df.filter((col("shop_id") == shop_id) & (col("operation_date").between(start_date, end_date)))
daily_stats = filtered_df.groupBy("operation_date").agg(
sum("daily_views").alias("total_views"),
sum("daily_orders").alias("total_orders"),
sum("daily_sales").alias("total_sales"),
avg("avg_stay_time").alias("avg_stay_time"),
sum("fan_increase").alias("fan_increase")
)
conversion_rate = daily_stats.withColumn("conversion_rate",
when(col("total_views") > 0, col("total_orders") / col("total_views") * 100).otherwise(0))
growth_analysis = conversion_rate.withColumn("prev_sales", lag("total_sales").over(Window.partitionBy().orderBy("operation_date")))
growth_analysis = growth_analysis.withColumn("growth_rate",
when(col("prev_sales").isNotNull() & (col("prev_sales") > 0),
((col("total_sales") - col("prev_sales")) / col("prev_sales") * 100)).otherwise(0))
result_df = growth_analysis.select("operation_date", "total_views", "total_orders", "total_sales", "conversion_rate", "growth_rate", "avg_stay_time", "fan_increase").orderBy("operation_date")
pandas_df = result_df.toPandas()
summary_stats = {
'total_views': int(pandas_df['total_views'].sum()),
'total_orders': int(pandas_df['total_orders'].sum()),
'total_sales': float(pandas_df['total_sales'].sum()),
'avg_conversion_rate': float(pandas_df['conversion_rate'].mean()),
'avg_growth_rate': float(pandas_df['growth_rate'].mean())
}
return JsonResponse({
'status': 'success',
'daily_data': pandas_df.to_dict('records'),
'summary': summary_stats
})
class SalesStrategyAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
shop_id = data.get('shop_id')
analysis_period = data.get('period', 30)
end_date = datetime.now()
start_date = end_date - timedelta(days=analysis_period)
sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/jewelry_db").option("dbtable", "sales_strategy_data").option("user", "root").option("password", "123456").load()
filtered_sales = sales_df.filter((col("shop_id") == shop_id) & (col("sale_date").between(start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))))
product_analysis = filtered_sales.groupBy("product_category", "price_range").agg(
sum("sales_amount").alias("category_sales"),
count("order_id").alias("order_count"),
avg("unit_price").alias("avg_price"),
sum("profit_amount").alias("total_profit")
)
promotion_effect = filtered_sales.filter(col("promotion_type").isNotNull()).groupBy("promotion_type").agg(
sum("sales_amount").alias("promo_sales"),
count("order_id").alias("promo_orders"),
avg("discount_rate").alias("avg_discount")
)
time_analysis = filtered_sales.withColumn("hour", hour("sale_time")).groupBy("hour").agg(
sum("sales_amount").alias("hourly_sales"),
count("order_id").alias("hourly_orders")
)
customer_segment = filtered_sales.groupBy("customer_type", "age_group").agg(
sum("sales_amount").alias("segment_sales"),
avg("customer_value").alias("avg_customer_value"),
count("customer_id").alias("customer_count")
)
roi_analysis = promotion_effect.withColumn("roi",
when(col("avg_discount") > 0, col("promo_sales") / (col("promo_sales") * col("avg_discount") / 100)).otherwise(0))
best_selling_products = product_analysis.orderBy(desc("category_sales")).limit(10)
peak_hours = time_analysis.orderBy(desc("hourly_sales")).limit(5)
high_value_segments = customer_segment.orderBy(desc("avg_customer_value")).limit(5)
return JsonResponse({
'status': 'success',
'product_performance': best_selling_products.toPandas().to_dict('records'),
'promotion_effectiveness': roi_analysis.toPandas().to_dict('records'),
'optimal_timing': peak_hours.toPandas().to_dict('records'),
'customer_insights': high_value_segments.toPandas().to_dict('records')
})
class TrafficSourceAnalysisView(View):
def post(self, request):
data = json.loads(request.body)
shop_id = data.get('shop_id')
date_range = data.get('date_range', 7)
traffic_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/jewelry_db").option("dbtable", "traffic_source_data").option("user", "root").option("password", "123456").load()
recent_traffic = traffic_df.filter(col("shop_id") == shop_id).filter(col("visit_date") >= date_sub(current_date(), date_range))
source_analysis = recent_traffic.groupBy("traffic_source").agg(
sum("visit_count").alias("total_visits"),
sum("unique_visitors").alias("unique_visitors"),
avg("bounce_rate").alias("avg_bounce_rate"),
avg("session_duration").alias("avg_session_duration"),
sum("page_views").alias("total_page_views")
)
channel_performance = recent_traffic.groupBy("traffic_source", "device_type").agg(
sum("visit_count").alias("channel_visits"),
sum("conversion_count").alias("conversions"),
sum("revenue").alias("channel_revenue")
)
conversion_funnel = channel_performance.withColumn("conversion_rate",
when(col("channel_visits") > 0, col("conversions") / col("channel_visits") * 100).otherwise(0))
conversion_funnel = conversion_funnel.withColumn("revenue_per_visit",
when(col("channel_visits") > 0, col("channel_revenue") / col("channel_visits")).otherwise(0))
geographic_analysis = recent_traffic.groupBy("region", "city").agg(
sum("visit_count").alias("region_visits"),
avg("user_engagement_score").alias("avg_engagement"),
sum("conversion_count").alias("region_conversions")
)
time_trend = recent_traffic.groupBy("visit_date", "traffic_source").agg(
sum("visit_count").alias("daily_visits")
).orderBy("visit_date")
quality_score = source_analysis.withColumn("quality_score",
(col("avg_session_duration") * 0.4 + (100 - col("avg_bounce_rate")) * 0.3 + (col("total_page_views") / col("total_visits")) * 0.3))
top_sources = quality_score.orderBy(desc("total_visits")).limit(10)
best_converting = conversion_funnel.orderBy(desc("conversion_rate")).limit(5)
high_value_regions = geographic_analysis.orderBy(desc("avg_engagement")).limit(8)
traffic_summary = {
'total_visits': int(source_analysis.agg(sum("total_visits")).collect()[0][0] or 0),
'total_unique_visitors': int(source_analysis.agg(sum("unique_visitors")).collect()[0][0] or 0),
'overall_bounce_rate': float(source_analysis.agg(avg("avg_bounce_rate")).collect()[0][0] or 0),
'avg_session_time': float(source_analysis.agg(avg("avg_session_duration")).collect()[0][0] or 0)
}
return JsonResponse({
'status': 'success',
'traffic_sources': top_sources.toPandas().to_dict('records'),
'conversion_analysis': best_converting.toPandas().to_dict('records'),
'geographic_insights': high_value_regions.toPandas().to_dict('records'),
'trend_data': time_trend.toPandas().to_dict('records'),
'summary_metrics': traffic_summary
})
基于大数据的抖音珠宝饰品类店铺分析可视化系统文档展示
💖💖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
