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农产品交易数据分析与可视化系统 - 简介
系统涵盖整体销售业绩分析、商品精细化运营分析、客户画像与行为洞察、营销活动效果评估四大核心分析维度，通过月度销售趋势分析、核心品类市场份额分析、畅销商品排行榜、客户年龄与性别消费偏好分析等十六个具体分析功能模块，为农产品交易企业提供全方位的数据决策支持。
农产品交易数据分析与可视化系统 -技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)
农产品交易数据分析与可视化系统 - 背景
随着电商平台的快速发展和消费升级趋势的推进，农产品交易从传统线下模式逐步向线上线下融合模式转变，交易数据呈现出爆发式增长态势。农产品交易具有季节性强、品类繁多、价格波动频繁、消费群体分散等特点，产生的交易数据不仅包含基础的订单信息，还涵盖了用户画像、渠道偏好、促销响应、退货行为等多维度信息。传统的数据分析方法在面对如此庞大且复杂的数据集时显得力不从心，难以及时发现数据中蕴含的商业价值和规律。而大数据技术的成熟为农产品交易数据的深度分析提供了新的解决思路，通过分布式计算和实时处理能力，能够有效应对数据量大、处理速度要求高的挑战，为农产品企业的精准营销、库存优化、渠道管理等提供有力的数据支撑。
农产品交易数据分析与可视化系统 -视频展示
农产品交易数据分析与可视化系统 -图片展示
农产品交易数据分析与可视化系统 -代码展示
from pyspark.sql.functions import *
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
import pandas as pd
import numpy as np
import json
spark = SparkSession.builder.appName("AgriculturalDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def monthly_sales_trend_analysis(request):
df = spark.sql("SELECT * FROM agricultural_orders")
monthly_df = df.withColumn("month", date_format(col("order_date"), "yyyy-MM"))
monthly_sales = monthly_df.groupBy("month").agg(
sum("sales_amount").alias("total_sales"),
count("order_id").alias("order_count"),
avg("sales_amount").alias("avg_order_value")
).orderBy("month")
result_data = monthly_sales.collect()
trend_analysis = []
for i, row in enumerate(result_data):
if i > 0:
prev_sales = result_data[i-1]["total_sales"]
current_sales = row["total_sales"]
growth_rate = ((current_sales - prev_sales) / prev_sales) * 100
else:
growth_rate = 0
trend_analysis.append({
"month": row["month"],
"total_sales": float(row["total_sales"]),
"order_count": row["order_count"],
"avg_order_value": float(row["avg_order_value"]),
"growth_rate": round(growth_rate, 2)
})
seasonal_pattern = spark.sql("""
SELECT
MONTH(order_date) as month_num,
AVG(sales_amount) as avg_monthly_sales,
COUNT(*) as order_frequency
FROM agricultural_orders
GROUP BY MONTH(order_date)
ORDER BY month_num
""").collect()
seasonal_data = [{"month": row["month_num"], "avg_sales": float(row["avg_monthly_sales"]), "frequency": row["order_frequency"]} for row in seasonal_pattern]
peak_months = sorted(seasonal_data, key=lambda x: x["avg_sales"], reverse=True)[:3]
return JsonResponse({
"trend_data": trend_analysis,
"seasonal_pattern": seasonal_data,
"peak_months": peak_months,
"total_revenue": sum([item["total_sales"] for item in trend_analysis])
})
@csrf_exempt
def customer_behavior_analysis(request):
df = spark.sql("SELECT * FROM agricultural_orders")
age_gender_analysis = df.groupBy("age_group", "customer_gender").agg(
count("order_id").alias("order_count"),
sum("sales_amount").alias("total_spending"),
avg("sales_amount").alias("avg_order_value"),
countDistinct("product_name").alias("product_variety")
).collect()
behavior_patterns = []
for row in age_gender_analysis:
behavior_patterns.append({
"age_group": row["age_group"],
"gender": row["customer_gender"],
"order_count": row["order_count"],
"total_spending": float(row["total_spending"]),
"avg_order_value": float(row["avg_order_value"]),
"product_variety": row["product_variety"]
})
channel_preference = df.groupBy("age_group", "channel").agg(
count("order_id").alias("usage_count")
).collect()
channel_data = {}
for row in channel_preference:
age_group = row["age_group"]
if age_group not in channel_data:
channel_data[age_group] = {}
channel_data[age_group][row["channel"]] = row["usage_count"]
category_preference = df.groupBy("age_group", "customer_gender", "category").agg(
sum("quantity").alias("total_quantity"),
avg("price").alias("avg_price_paid")
).collect()
preference_matrix = {}
for row in category_preference:
key = f"{row['age_group']}_{row['customer_gender']}"
if key not in preference_matrix:
preference_matrix[key] = []
preference_matrix[key].append({
"category": row["category"],
"quantity": row["total_quantity"],
"avg_price": float(row["avg_price_paid"])
})
return JsonResponse({
"behavior_patterns": behavior_patterns,
"channel_preferences": channel_data,
"category_preferences": preference_matrix,
"analysis_summary": {
"total_customers": df.select("customer_id").distinct().count(),
"most_active_segment": max(behavior_patterns, key=lambda x: x["order_count"])
}
})
@csrf_exempt
def promotion_effectiveness_analysis(request):
df = spark.sql("SELECT * FROM agricultural_orders")
promotion_impact = df.groupBy("promotion").agg(
count("order_id").alias("order_count"),
sum("sales_amount").alias("total_revenue"),
avg("sales_amount").alias("avg_order_value"),
sum("quantity").alias("total_quantity")
).collect()
effectiveness_metrics = []
total_orders = df.count()
total_revenue = df.agg(sum("sales_amount")).collect()[0][0]
for row in promotion_impact:
participation_rate = (row["order_count"] / total_orders) * 100
revenue_contribution = (row["total_revenue"] / total_revenue) * 100
effectiveness_metrics.append({
"promotion_type": row["promotion"],
"order_count": row["order_count"],
"total_revenue": float(row["total_revenue"]),
"avg_order_value": float(row["avg_order_value"]),
"participation_rate": round(participation_rate, 2),
"revenue_contribution": round(revenue_contribution, 2)
})
channel_promotion_analysis = df.groupBy("channel", "promotion").agg(
count("order_id").alias("promo_orders"),
avg("sales_amount").alias("avg_promo_value")
).collect()
channel_sensitivity = {}
for row in channel_promotion_analysis:
channel = row["channel"]
if channel not in channel_sensitivity:
channel_sensitivity[channel] = {}
channel_sensitivity[channel][row["promotion"]] = {
"orders": row["promo_orders"],
"avg_value": float(row["avg_promo_value"])
}
return_rate_analysis = df.groupBy("promotion").agg(
sum(when(col("return_flag") == 1, 1).otherwise(0)).alias("return_count"),
count("order_id").alias("total_orders")
).collect()
return_rates = []
for row in return_rate_analysis:
return_rate = (row["return_count"] / row["total_orders"]) * 100
return_rates.append({
"promotion_type": row["promotion"],
"return_rate": round(return_rate, 2),
"return_count": row["return_count"]
})
best_promotion = max(effectiveness_metrics, key=lambda x: x["avg_order_value"])
return JsonResponse({
"effectiveness_metrics": effectiveness_metrics,
"channel_sensitivity": channel_sensitivity,
"return_rate_analysis": return_rates,
"recommendation": {
"best_promotion": best_promotion["promotion_type"],
"optimal_channels": channel_sensitivity
}
})
农产品交易数据分析与可视化系统 -结语
7天搞定Spark大数据毕设：农产品交易数据分析与可视化系统开发全流程
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⚡⚡获取源码主页-->：计算机毕设指导师
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求！你也可以在个人主页上咨询我~~
