💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
基于大数据的全球电子商务供应链数据分析系统介绍
本系统是一个基于Python大数据技术栈的全球电子商务供应链数据分析系统，采用Hadoop+Spark作为核心大数据处理框架，结合Django后端框架和Vue前端技术构建。系统通过HDFS分布式存储海量的全球电子商务交易数据、库存数据、供应商数据等，利用Spark SQL进行高效的数据清洗、转换和分析处理。前端采用Vue+ElementUI+Echarts技术栈实现用户界面，提供直观的数据可视化展示。系统核心功能包括销售数据分析模块，通过多维度统计分析全球各地区销售趋势；库存健康分析模块，实时监控库存周转率和缺货风险；市场表现分析模块，评估不同市场的盈利能力和增长潜力；产品组合分析模块，优化产品配置策略；供应链成本分析模块，识别成本控制关键点；可视化大屏模块，集中展示关键业务指标。整个系统充分发挥了大数据技术在处理海量电商数据方面的优势，为企业决策提供数据支撑。
基于大数据的全球电子商务供应链数据分析系统演示视频
基于大数据的全球电子商务供应链数据分析系统演示图片
基于大数据的全球电子商务供应链数据分析系统代码展示
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 django.views.decorators.csrf import csrf_exempt
import json
spark = SparkSession.builder.appName("GlobalECommerceSupplyChainAnalysis").master("local[*]").getOrCreate()
@csrf_exempt
def sales_data_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
start_date = data.get('start_date')
end_date = data.get('end_date')
region = data.get('region', 'all')
sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/supply_chain").option("dbtable", "sales_data").option("user", "root").option("password", "password").load()
filtered_df = sales_df.filter((col("sale_date") >= start_date) & (col("sale_date") <= end_date))
if region != 'all':
filtered_df = filtered_df.filter(col("region") == region)
daily_sales = filtered_df.groupBy("sale_date").agg(sum("amount").alias("daily_amount"), count("order_id").alias("order_count"))
regional_sales = filtered_df.groupBy("region").agg(sum("amount").alias("regional_amount"), avg("amount").alias("avg_order_value"))
product_sales = filtered_df.groupBy("product_id", "product_name").agg(sum("amount").alias("product_revenue"), sum("quantity").alias("total_quantity"))
top_products = product_sales.orderBy(desc("product_revenue")).limit(10)
growth_rate_df = daily_sales.withColumn("prev_amount", lag("daily_amount").over(Window.orderBy("sale_date")))
growth_rate_df = growth_rate_df.withColumn("growth_rate", ((col("daily_amount") - col("prev_amount")) / col("prev_amount") * 100))
monthly_trend = filtered_df.withColumn("month", date_format(col("sale_date"), "yyyy-MM")).groupBy("month").agg(sum("amount").alias("monthly_revenue"))
customer_analysis = filtered_df.groupBy("customer_id").agg(sum("amount").alias("customer_value"), count("order_id").alias("order_frequency"))
high_value_customers = customer_analysis.filter(col("customer_value") > 1000).count()
result_data = {
'daily_sales': daily_sales.toPandas().to_dict('records'),
'regional_sales': regional_sales.toPandas().to_dict('records'),
'top_products': top_products.toPandas().to_dict('records'),
'monthly_trend': monthly_trend.toPandas().to_dict('records'),
'high_value_customers': high_value_customers,
'total_revenue': filtered_df.agg(sum("amount")).collect()[0][0],
'total_orders': filtered_df.count(),
'avg_growth_rate': growth_rate_df.agg(avg("growth_rate")).collect()[0][0]
}
return JsonResponse(result_data)
@csrf_exempt
def inventory_health_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
warehouse_id = data.get('warehouse_id', 'all')
analysis_type = data.get('analysis_type', 'turnover')
inventory_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/supply_chain").option("dbtable", "inventory_data").option("user", "root").option("password", "password").load()
sales_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/supply_chain").option("dbtable", "sales_data").option("user", "root").option("password", "password").load()
if warehouse_id != 'all':
inventory_df = inventory_df.filter(col("warehouse_id") == warehouse_id)
current_inventory = inventory_df.groupBy("product_id", "warehouse_id").agg(sum("stock_quantity").alias("current_stock"), avg("unit_cost").alias("avg_cost"))
sales_30days = sales_df.filter(col("sale_date") >= date_sub(current_date(), 30)).groupBy("product_id").agg(sum("quantity").alias("sales_30days"))
inventory_turnover = current_inventory.join(sales_30days, "product_id", "left_outer")
inventory_turnover = inventory_turnover.withColumn("turnover_rate", when(col("current_stock") > 0, col("sales_30days") / col("current_stock")).otherwise(0))
inventory_turnover = inventory_turnover.withColumn("days_of_supply", when(col("sales_30days") > 0, col("current_stock") / (col("sales_30days") / 30)).otherwise(999))
slow_moving_items = inventory_turnover.filter((col("turnover_rate") < 0.1) & (col("current_stock") > 0))
overstocked_items = inventory_turnover.filter(col("days_of_supply") > 90)
understocked_items = inventory_turnover.filter((col("days_of_supply") < 7) & (col("days_of_supply") > 0))
dead_stock = inventory_turnover.filter((col("sales_30days").isNull()) & (col("current_stock") > 0))
inventory_value_by_warehouse = inventory_turnover.groupBy("warehouse_id").agg(sum(col("current_stock") * col("avg_cost")).alias("total_value"))
abc_analysis = inventory_turnover.withColumn("revenue_contribution", col("sales_30days") * col("avg_cost"))
total_revenue = abc_analysis.agg(sum("revenue_contribution")).collect()[0][0]
abc_analysis = abc_analysis.withColumn("revenue_percentage", col("revenue_contribution") / total_revenue * 100)
abc_analysis = abc_analysis.withColumn("abc_category", when(col("revenue_percentage") >= 20, "A").when(col("revenue_percentage") >= 5, "B").otherwise("C"))
health_metrics = {
'total_sku_count': current_inventory.count(),
'slow_moving_count': slow_moving_items.count(),
'overstocked_count': overstocked_items.count(),
'understocked_count': understocked_items.count(),
'dead_stock_count': dead_stock.count(),
'avg_turnover_rate': inventory_turnover.agg(avg("turnover_rate")).collect()[0][0],
'total_inventory_value': inventory_value_by_warehouse.agg(sum("total_value")).collect()[0][0]
}
result_data = {
'health_metrics': health_metrics,
'slow_moving_items': slow_moving_items.limit(20).toPandas().to_dict('records'),
'overstocked_items': overstocked_items.limit(20).toPandas().to_dict('records'),
'understocked_items': understocked_items.limit(20).toPandas().to_dict('records'),
'inventory_value_by_warehouse': inventory_value_by_warehouse.toPandas().to_dict('records'),
'abc_analysis': abc_analysis.groupBy("abc_category").count().toPandas().to_dict('records')
}
return JsonResponse(result_data)
@csrf_exempt
def supply_chain_cost_analysis(request):
if request.method == 'POST':
data = json.loads(request.body)
cost_type = data.get('cost_type', 'all')
time_period = data.get('time_period', '30')
cost_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/supply_chain").option("dbtable", "supply_chain_costs").option("user", "root").option("password", "password").load()
supplier_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/supply_chain").option("dbtable", "supplier_data").option("user", "root").option("password", "password").load()
filtered_cost_df = cost_df.filter(col("cost_date") >= date_sub(current_date(), int(time_period)))
if cost_type != 'all':
filtered_cost_df = filtered_cost_df.filter(col("cost_type") == cost_type)
cost_by_category = filtered_cost_df.groupBy("cost_category").agg(sum("cost_amount").alias("total_cost"), avg("cost_amount").alias("avg_cost"), count("*").alias("transaction_count"))
cost_by_supplier = filtered_cost_df.join(supplier_df, "supplier_id").groupBy("supplier_id", "supplier_name").agg(sum("cost_amount").alias("supplier_total_cost"))
transportation_costs = filtered_cost_df.filter(col("cost_category") == "transportation").groupBy("route", "transport_mode").agg(sum("cost_amount").alias("transport_cost"), avg("cost_per_unit").alias("avg_cost_per_unit"))
warehousing_costs = filtered_cost_df.filter(col("cost_category") == "warehousing").groupBy("warehouse_id").agg(sum("cost_amount").alias("warehouse_cost"), sum("handling_volume").alias("total_volume"))
warehousing_costs = warehousing_costs.withColumn("cost_per_unit_volume", col("warehouse_cost") / col("total_volume"))
procurement_costs = filtered_cost_df.filter(col("cost_category") == "procurement").groupBy("product_category").agg(sum("cost_amount").alias("procurement_cost"), sum("quantity_procured").alias("total_quantity"))
procurement_costs = procurement_costs.withColumn("cost_per_unit", col("procurement_cost") / col("total_quantity"))
monthly_cost_trend = filtered_cost_df.withColumn("month", date_format(col("cost_date"), "yyyy-MM")).groupBy("month").agg(sum("cost_amount").alias("monthly_cost"))
cost_variance_analysis = filtered_cost_df.groupBy("cost_category").agg(sum("cost_amount").alias("actual_cost"), sum("budget_amount").alias("budgeted_cost"))
cost_variance_analysis = cost_variance_analysis.withColumn("variance", col("actual_cost") - col("budgeted_cost"))
cost_variance_analysis = cost_variance_analysis.withColumn("variance_percentage", (col("variance") / col("budgeted_cost")) * 100)
top_cost_drivers = cost_by_category.orderBy(desc("total_cost")).limit(10)
inefficient_suppliers = cost_by_supplier.join(supplier_df.select("supplier_id", "performance_rating"), "supplier_id").filter(col("performance_rating") < 3).orderBy(desc("supplier_total_cost"))
cost_optimization_opportunities = transportation_costs.filter(col("avg_cost_per_unit") > 5.0).union(warehousing_costs.select("warehouse_id", "cost_per_unit_volume").filter(col("cost_per_unit_volume") > 2.0))
total_supply_chain_cost = filtered_cost_df.agg(sum("cost_amount")).collect()[0][0]
cost_breakdown_percentage = cost_by_category.withColumn("cost_percentage", (col("total_cost") / total_supply_chain_cost) * 100)
result_data = {
'total_cost': total_supply_chain_cost,
'cost_by_category': cost_breakdown_percentage.toPandas().to_dict('records'),
'cost_by_supplier': cost_by_supplier.orderBy(desc("supplier_total_cost")).limit(15).toPandas().to_dict('records'),
'transportation_costs': transportation_costs.toPandas().to_dict('records'),
'warehousing_efficiency': warehousing_costs.toPandas().to_dict('records'),
'procurement_efficiency': procurement_costs.toPandas().to_dict('records'),
'monthly_trend': monthly_cost_trend.toPandas().to_dict('records'),
'cost_variance': cost_variance_analysis.toPandas().to_dict('records'),
'optimization_opportunities': cost_optimization_opportunities.limit(10).toPandas().to_dict('records')
}
return JsonResponse(result_data)
基于大数据的全球电子商务供应链数据分析系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
