一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
《抖音珠宝饰品类店铺分析可视化系统》是一个基于大数据技术的电商店铺智能分析平台。该系统采用Hadoop+Spark分布式计算架构，结合Django后端框架，为抖音平台珠宝饰品商家提供全方位的数据分析服务。系统通过Vue+ElementUI构建现代化前端界面，利用Echarts实现数据可视化呈现，深度整合了Spark SQL、Pandas、NumPy等数据处理组件。平台核心功能涵盖店铺运营分析、销售策略分析、流量来源分析、店铺价值分析以及可视化大屏展示。系统能够对海量的店铺交易数据、用户行为数据进行实时采集和批量处理，通过HDFS分布式存储确保数据安全性和可扩展性。通过MySQL数据库存储结构化数据，支持多维度数据挖掘和智能分析，为珠宝饰品商家的经营决策提供科学依据和数据支撑。
三、视频解说
四、部分功能展示
五、部分代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import pandas as pd
import numpy as np
from django.http import JsonResponse
from datetime import datetime, timedelta
import json
spark = SparkSession.builder.appName("DouyinJewelryAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def shop_operation_analysis(shop_id, start_date, end_date):
sales_df = spark.sql(f"""
SELECT date, product_id, sales_amount, order_count, view_count, like_count, comment_count
FROM sales_data
WHERE shop_id = '{shop_id}' AND date BETWEEN '{start_date}' AND '{end_date}'
""")
daily_stats = sales_df.groupBy("date").agg(
sum("sales_amount").alias("daily_revenue"),
sum("order_count").alias("daily_orders"),
avg("sales_amount").alias("avg_order_value"),
sum("view_count").alias("total_views"),
sum("like_count").alias("total_likes")
)
trend_analysis = daily_stats.withColumn("revenue_trend",
lag("daily_revenue").over(Window.partitionBy().orderBy("date")))
trend_analysis = trend_analysis.withColumn("growth_rate",
(col("daily_revenue") - col("revenue_trend")) / col("revenue_trend") * 100)
product_performance = sales_df.groupBy("product_id").agg(
sum("sales_amount").alias("product_revenue"),
sum("order_count").alias("product_orders"),
avg("view_count").alias("avg_views")
).orderBy(desc("product_revenue"))
conversion_rate = sales_df.withColumn("conversion",
when(col("view_count") > 0, col("order_count") / col("view_count")).otherwise(0))
avg_conversion = conversion_rate.agg(avg("conversion")).collect()[0][0]
engagement_score = sales_df.withColumn("engagement",
(col("like_count") + col("comment_count")) / col("view_count"))
avg_engagement = engagement_score.agg(avg("engagement")).collect()[0][0]
result_data = {
"daily_trends": trend_analysis.toPandas().to_dict('records'),
"top_products": product_performance.limit(10).toPandas().to_dict('records'),
"conversion_rate": round(avg_conversion * 100, 2),
"engagement_score": round(avg_engagement, 4)
}
return JsonResponse(result_data)
def sales_strategy_analysis(shop_id, category_id, time_period):
strategy_df = spark.sql(f"""
SELECT hour, day_of_week, price_range, discount_rate, sales_amount, order_count
FROM sales_strategy_data
WHERE shop_id = '{shop_id}' AND category_id = '{category_id}'
AND date >= date_sub(current_date(), {time_period})
""")
time_analysis = strategy_df.groupBy("hour").agg(
sum("sales_amount").alias("hourly_sales"),
sum("order_count").alias("hourly_orders"),
avg("sales_amount").alias("avg_hourly_amount")
).orderBy("hour")
weekday_analysis = strategy_df.groupBy("day_of_week").agg(
sum("sales_amount").alias("weekly_sales"),
avg("order_count").alias("avg_orders")
).orderBy("day_of_week")
price_strategy = strategy_df.groupBy("price_range").agg(
sum("sales_amount").alias("price_revenue"),
count("*").alias("price_frequency"),
avg("order_count").alias("avg_price_orders")
)
discount_effect = strategy_df.groupBy("discount_rate").agg(
sum("sales_amount").alias("discount_revenue"),
sum("order_count").alias("discount_orders"),
avg("sales_amount").alias("avg_discount_amount")
).orderBy("discount_rate")
price_elasticity = strategy_df.withColumn("price_bucket",
when(col("price_range") < 100, "low")
.when(col("price_range") < 500, "medium")
.otherwise("high"))
elasticity_analysis = price_elasticity.groupBy("price_bucket", "discount_rate").agg(
sum("order_count").alias("bucket_orders"),
avg("sales_amount").alias("bucket_avg_amount")
)
optimal_timing = time_analysis.orderBy(desc("hourly_sales")).limit(3)
peak_hours = optimal_timing.select("hour").rdd.flatMap(lambda x: x).collect()
strategy_recommendations = {
"optimal_hours": peak_hours,
"best_weekdays": weekday_analysis.orderBy(desc("weekly_sales")).limit(2).toPandas().to_dict('records'),
"price_performance": price_strategy.toPandas().to_dict('records'),
"discount_effectiveness": discount_effect.toPandas().to_dict('records'),
"elasticity_matrix": elasticity_analysis.toPandas().to_dict('records')
}
return JsonResponse(strategy_recommendations)
def traffic_source_analysis(shop_id, analysis_date):
traffic_df = spark.sql(f"""
SELECT source_type, source_name, visitor_count, session_duration, bounce_rate,
conversion_rate, cost_per_click, total_cost
FROM traffic_source_data
WHERE shop_id = '{shop_id}' AND date = '{analysis_date}'
""")
source_performance = traffic_df.groupBy("source_type").agg(
sum("visitor_count").alias("total_visitors"),
avg("session_duration").alias("avg_duration"),
avg("bounce_rate").alias("avg_bounce_rate"),
avg("conversion_rate").alias("avg_conversion"),
sum("total_cost").alias("total_investment")
)
roi_analysis = traffic_df.withColumn("roi",
(col("visitor_count") * col("conversion_rate") * 50 - col("total_cost")) / col("total_cost"))
channel_efficiency = roi_analysis.groupBy("source_name").agg(
sum("visitor_count").alias("channel_visitors"),
avg("roi").alias("channel_roi"),
avg("cost_per_click").alias("avg_cpc"),
sum("total_cost").alias("channel_cost")
).orderBy(desc("channel_roi"))
quality_score = traffic_df.withColumn("quality",
col("session_duration") * 0.3 + (1 - col("bounce_rate")) * 0.4 + col("conversion_rate") * 0.3)
source_quality = quality_score.groupBy("source_type").agg(
avg("quality").alias("avg_quality_score"),
count("*").alias("source_count")
).orderBy(desc("avg_quality_score"))
cost_effectiveness = traffic_df.withColumn("cost_per_conversion",
col("cost_per_click") / col("conversion_rate"))
budget_optimization = cost_effectiveness.groupBy("source_name").agg(
avg("cost_per_conversion").alias("avg_cost_per_conversion"),
sum("visitor_count").alias("total_traffic")
).orderBy("avg_cost_per_conversion")
traffic_insights = {
"source_breakdown": source_performance.toPandas().to_dict('records'),
"top_channels": channel_efficiency.limit(5).toPandas().to_dict('records'),
"quality_ranking": source_quality.toPandas().to_dict('records'),
"budget_recommendations": budget_optimization.limit(10).toPandas().to_dict('records'),
"roi_analysis": roi_analysis.select("source_name", "roi").toPandas().to_dict('records')
}
return JsonResponse(traffic_insights)
六、部分文档展示
七、END
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