💖💖作者：计算机编程小咖 💙💙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
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基于SSM框架的生鲜食品商城系统介绍
基于SSM框架的生鲜食品商城系统是一个采用当前主流技术栈构建的完整电商平台解决方案，该系统充分利用Spring Boot作为后端核心框架，结合Spring、SpringMVC和MyBatis形成稳定的三层架构体系，前端采用Vue.js配合ElementUI组件库打造现代化用户界面，数据存储基于MySQL关系型数据库确保数据安全性和一致性，整体采用B/S架构模式实现跨平台访问。系统功能涵盖完整的电商业务流程，包括系统首页展示、用户注册登录及个人中心管理、生鲜商品分类管理、商品信息维护、新品上市推广、热销商品推荐、促销活动策划、用户留言板互动、优惠券发放与使用、系统后台管理以及订单全流程管理等十二大核心功能模块。该系统通过Spring Boot的自动配置特性简化开发复杂度，运用Vue的组件化开发提升前端开发效率，利用MyBatis的ORM映射实现数据库操作的灵活性，同时ElementUI的丰富组件确保界面美观度和用户体验，整个系统既体现了现代Web开发的技术深度，又兼顾了生鲜电商行业的业务特点，为计算机专业学生提供了一个技术实用性强、功能完整度高、实际应用价值明显的优质毕业设计项目选择。
基于SSM框架的生鲜食品商城系统演示视频
基于SSM框架的生鲜食品商城系统演示图片
基于SSM框架的生鲜食品商城系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, desc, when, date_sub, current_date
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from django.db import transaction
from .models import Product, Order, OrderDetail, User, Coupon, UserCoupon
import json
from datetime import datetime, timedelta
from decimal import Decimal
spark = SparkSession.builder.appName("FreshFoodMallSystem").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
def process_order_creation(request):
if request.method == 'POST':
data = json.loads(request.body)
user_id = data.get('user_id')
cart_items = data.get('cart_items')
coupon_id = data.get('coupon_id', None)
delivery_address = data.get('delivery_address')
total_amount = Decimal('0.00')
discount_amount = Decimal('0.00')
for item in cart_items:
product = Product.objects.get(id=item['product_id'])
if product.stock < item['quantity']:
return JsonResponse({'status': 'error', 'message': f'商品{product.name}库存不足'})
item_total = product.price * item['quantity']
total_amount += item_total
if coupon_id:
try:
user_coupon = UserCoupon.objects.get(id=coupon_id, user_id=user_id, is_used=False)
coupon = user_coupon.coupon
if coupon.min_amount <= total_amount and coupon.end_time >= datetime.now():
if coupon.discount_type == 'percentage':
discount_amount = total_amount * (coupon.discount_value / 100)
else:
discount_amount = coupon.discount_value
discount_amount = min(discount_amount, total_amount)
except UserCoupon.DoesNotExist:
pass
final_amount = total_amount - discount_amount
with transaction.atomic():
order = Order.objects.create(
user_id=user_id,
order_number=f"FS{datetime.now().strftime('%Y%m%d%H%M%S')}{user_id}",
total_amount=total_amount,
discount_amount=discount_amount,
final_amount=final_amount,
delivery_address=delivery_address,
status='pending'
)
for item in cart_items:
product = Product.objects.get(id=item['product_id'])
OrderDetail.objects.create(
order=order,
product=product,
quantity=item['quantity'],
unit_price=product.price,
subtotal=product.price * item['quantity']
)
product.stock -= item['quantity']
product.sales_count += item['quantity']
product.save()
if coupon_id:
user_coupon.is_used = True
user_coupon.used_time = datetime.now()
user_coupon.save()
return JsonResponse({'status': 'success', 'order_id': order.id, 'order_number': order.order_number})
@csrf_exempt
def generate_product_recommendations(request):
if request.method == 'GET':
user_id = request.GET.get('user_id')
category_id = request.GET.get('category_id', None)
order_data = []
user_orders = Order.objects.filter(user_id=user_id, status='completed')
for order in user_orders:
order_details = OrderDetail.objects.filter(order=order)
for detail in order_details:
order_data.append({
'user_id': user_id,
'product_id': detail.product.id,
'category_id': detail.product.category_id,
'quantity': detail.quantity,
'price': float(detail.unit_price),
'order_date': order.create_time.strftime('%Y-%m-%d')
})
if order_data:
spark_df = spark.createDataFrame(order_data)
user_preferences = spark_df.filter(col('user_id') == user_id).groupBy('category_id').agg(
count('product_id').alias('purchase_count'),
sum('quantity').alias('total_quantity'),
avg('price').alias('avg_price')
).orderBy(desc('total_quantity'))
preferred_categories = [row['category_id'] for row in user_preferences.collect()[:3]]
else:
preferred_categories = []
hot_products = spark.createDataFrame([
{'product_id': p.id, 'sales_count': p.sales_count, 'category_id': p.category_id,
'price': float(p.price), 'stock': p.stock, 'rating': float(p.rating or 0)}
for p in Product.objects.filter(is_active=True, stock__gt=0)
])
if category_id:
recommended_products = hot_products.filter(col('category_id') == int(category_id))
elif preferred_categories:
recommended_products = hot_products.filter(col('category_id').isin(preferred_categories))
else:
recommended_products = hot_products
final_recommendations = recommended_products.orderBy(
desc('sales_count'), desc('rating'), col('price')
).limit(20)
result_products = []
for row in final_recommendations.collect():
product = Product.objects.get(id=row['product_id'])
result_products.append({
'id': product.id,
'name': product.name,
'price': str(product.price),
'image': product.image.url if product.image else '',
'sales_count': product.sales_count,
'rating': str(product.rating or 0),
'category_name': product.category.name
})
return JsonResponse({'status': 'success', 'recommendations': result_products})
@csrf_exempt
def analyze_sales_promotion_effect(request):
if request.method == 'GET':
promotion_id = request.GET.get('promotion_id')
days_range = int(request.GET.get('days', 30))
end_date = datetime.now().date()
start_date = end_date - timedelta(days=days_range)
promotion_orders = Order.objects.filter(
create_time__date__gte=start_date,
create_time__date__lte=end_date,
status__in=['completed', 'shipped', 'delivered']
)
analysis_data = []
for order in promotion_orders:
order_details = OrderDetail.objects.filter(order=order)
for detail in order_details:
promotion_applied = order.discount_amount > 0
analysis_data.append({
'order_id': order.id,
'product_id': detail.product.id,
'category_id': detail.product.category_id,
'quantity': detail.quantity,
'unit_price': float(detail.unit_price),
'subtotal': float(detail.subtotal),
'discount_amount': float(order.discount_amount),
'promotion_applied': promotion_applied,
'order_date': order.create_time.strftime('%Y-%m-%d'),
'hour': order.create_time.hour
})
if analysis_data:
spark_df = spark.createDataFrame(analysis_data)
promotion_effect = spark_df.groupBy('promotion_applied').agg(
count('order_id').alias('order_count'),
sum('quantity').alias('total_quantity'),
sum('subtotal').alias('total_sales'),
avg('unit_price').alias('avg_price')
)
category_performance = spark_df.filter(col('promotion_applied') == True).groupBy('category_id').agg(
count('product_id').alias('promoted_sales_count'),
sum('subtotal').alias('promoted_sales_amount'),
avg('quantity').alias('avg_quantity_per_order')
).orderBy(desc('promoted_sales_amount'))
time_distribution = spark_df.groupBy('order_date', 'promotion_applied').agg(
count('order_id').alias('daily_orders'),
sum('subtotal').alias('daily_sales')
)
promotion_stats = {}
for row in promotion_effect.collect():
key = 'with_promotion' if row['promotion_applied'] else 'without_promotion'
promotion_stats[key] = {
'order_count': row['order_count'],
'total_quantity': row['total_quantity'],
'total_sales': float(row['total_sales']),
'avg_price': float(row['avg_price'])
}
category_stats = [
{
'category_id': row['category_id'],
'sales_count': row['promoted_sales_count'],
'sales_amount': float(row['promoted_sales_amount']),
'avg_quantity': float(row['avg_quantity_per_order'])
}
for row in category_performance.collect()[:10]
]
conversion_rate = 0
if 'without_promotion' in promotion_stats and 'with_promotion' in promotion_stats:
base_conversion = promotion_stats['without_promotion']['order_count']
promo_conversion = promotion_stats['with_promotion']['order_count']
if base_conversion > 0:
conversion_rate = (promo_conversion - base_conversion) / base_conversion * 100
else:
promotion_stats = {}
category_stats = []
conversion_rate = 0
return JsonResponse({
'status': 'success',
'promotion_stats': promotion_stats,
'top_categories': category_stats,
'conversion_improvement': round(conversion_rate, 2),
'analysis_period': f'{start_date} to {end_date}'
})
基于SSM框架的生鲜食品商城系统文档展示
💖💖作者：计算机编程小咖 💙💙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
