💖💖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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摩尔街网上订餐系统介绍
《摩尔街网上订餐系统》是一个基于现代化技术栈开发的综合性餐饮服务平台，采用Java+SpringBoot或Python+Django双后端架构设计，前端融合uni-app跨平台开发框架与微信小程序原生开发，同时支持安卓移动端应用，形成完整的C/S+B/S混合架构体系。系统以MySQL数据库为核心存储引擎，通过IDEA或PyCharm专业开发工具配合微信小程序开发工具进行协同开发。功能架构涵盖系统首页展示、商家信息管理、配送员调度、用户信息维护、菜品分类体系、菜品信息录入、订单全流程管理、配送信息追踪、配送反馈收集、用户充值记录、系统后台管理、在线客服支持、公告信息分类发布、轮播图动态管理、关于我们页面展示、个人中心个性化设置等20余个核心功能模块。该系统通过SpringBoot的轻量化特性或Django的快速开发优势，实现了从用户注册登录、浏览菜品、下单支付、订单跟踪到配送完成的完整业务闭环，为用户提供便捷的网上订餐体验，为商家提供高效的订单管理解决方案，为配送员提供智能化的配送调度服务，构建了一个功能完善、技术先进、用户体验优良的现代化网上订餐服务生态系统。
摩尔街网上订餐系统演示视频
摩尔街网上订餐系统演示图片
摩尔街网上订餐系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, count, sum, avg, desc, when
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
from django.views.decorators.csrf import csrf_exempt
from django.db import transaction
from django.utils import timezone
from .models import Order, OrderItem, Menu, User, Delivery
import json
import decimal
spark = SparkSession.builder.appName("MooreStreetOrderingSystem").config("spark.sql.adaptive.enabled", "true").getOrCreate()
@csrf_exempt
@require_http_methods(["POST"])
def create_order(request):
try:
data = json.loads(request.body)
user_id = data.get('user_id')
merchant_id = data.get('merchant_id')
items = data.get('items', [])
delivery_address = data.get('delivery_address')
phone_number = data.get('phone_number')
if not all([user_id, merchant_id, items, delivery_address, phone_number]):
return JsonResponse({'status': 'error', 'message': '订单信息不完整'})
total_amount = decimal.Decimal('0.00')
order_items_data = []
with transaction.atomic():
for item in items:
menu_item = Menu.objects.get(id=item['menu_id'], merchant_id=merchant_id)
if menu_item.stock < item['quantity']:
return JsonResponse({'status': 'error', 'message': f'{menu_item.name}库存不足'})
item_total = menu_item.price * item['quantity']
total_amount += item_total
order_items_data.append({
'menu_item': menu_item,
'quantity': item['quantity'],
'price': menu_item.price,
'subtotal': item_total
})
menu_item.stock -= item['quantity']
menu_item.save()
delivery_fee = decimal.Decimal('5.00')
total_amount += delivery_fee
order = Order.objects.create(
user_id=user_id,
merchant_id=merchant_id,
total_amount=total_amount,
delivery_address=delivery_address,
phone_number=phone_number,
status='pending',
order_time=timezone.now()
)
for item_data in order_items_data:
OrderItem.objects.create(
order=order,
menu_item=item_data['menu_item'],
quantity=item_data['quantity'],
price=item_data['price'],
subtotal=item_data['subtotal']
)
order_df = spark.createDataFrame([(order.id, user_id, merchant_id, float(total_amount), str(timezone.now()))],
["order_id", "user_id", "merchant_id", "amount", "order_time"])
order_df.write.mode("append").option("header", "true").csv("/tmp/order_analysis")
return JsonResponse({
'status': 'success',
'order_id': order.id,
'total_amount': str(total_amount),
'message': '订单创建成功'
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
@csrf_exempt
@require_http_methods(["POST"])
def assign_delivery(request):
try:
data = json.loads(request.body)
order_id = data.get('order_id')
if not order_id:
return JsonResponse({'status': 'error', 'message': '订单ID不能为空'})
order = Order.objects.get(id=order_id, status='confirmed')
available_deliverers = Delivery.objects.filter(status='available', is_active=True)
if not available_deliverers.exists():
return JsonResponse({'status': 'error', 'message': '暂无可用配送员'})
delivery_df = spark.createDataFrame(
[(d.id, d.current_latitude, d.current_longitude, d.completed_orders)
for d in available_deliverers],
["delivery_id", "latitude", "longitude", "completed_orders"]
)
order_lat = float(data.get('order_latitude', 0))
order_lng = float(data.get('order_longitude', 0))
delivery_with_distance = delivery_df.withColumn(
"distance",
((col("latitude") - order_lat) ** 2 + (col("longitude") - order_lng) ** 2) ** 0.5
).withColumn(
"priority_score",
when(col("completed_orders") > 100, col("distance") * 0.8)
.when(col("completed_orders") > 50, col("distance") * 0.9)
.otherwise(col("distance"))
)
best_delivery = delivery_with_distance.orderBy("priority_score").first()
selected_delivery = Delivery.objects.get(id=best_delivery.delivery_id)
with transaction.atomic():
order.delivery_person = selected_delivery
order.status = 'assigned'
order.assigned_time = timezone.now()
order.save()
selected_delivery.status = 'busy'
selected_delivery.current_order_id = order_id
selected_delivery.save()
delivery_assignment_df = spark.createDataFrame(
[(order_id, best_delivery.delivery_id, float(best_delivery.distance), str(timezone.now()))],
["order_id", "delivery_id", "distance", "assigned_time"]
)
delivery_assignment_df.write.mode("append").option("header", "true").csv("/tmp/delivery_analysis")
return JsonResponse({
'status': 'success',
'delivery_person': selected_delivery.name,
'delivery_phone': selected_delivery.phone,
'estimated_time': 30,
'message': '配送员分配成功'
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
@csrf_exempt
@require_http_methods(["GET"])
def get_sales_analytics(request):
try:
merchant_id = request.GET.get('merchant_id')
days = int(request.GET.get('days', 30))
if not merchant_id:
return JsonResponse({'status': 'error', 'message': '商家ID不能为空'})
start_date = timezone.now() - timezone.timedelta(days=days)
orders = Order.objects.filter(
merchant_id=merchant_id,
order_time__gte=start_date,
status__in=['completed', 'delivered']
)
orders_data = [(o.id, o.user_id, float(o.total_amount), o.order_time.strftime('%Y-%m-%d'),
o.order_time.hour) for o in orders]
if not orders_data:
return JsonResponse({'status': 'success', 'data': {'total_sales': 0, 'order_count': 0}})
orders_df = spark.createDataFrame(orders_data,
["order_id", "user_id", "amount", "order_date", "hour"])
daily_sales = orders_df.groupBy("order_date").agg(
sum("amount").alias("daily_amount"),
count("order_id").alias("daily_orders")
).orderBy("order_date")
hourly_distribution = orders_df.groupBy("hour").agg(
count("order_id").alias("order_count")
).orderBy("hour")
user_analysis = orders_df.groupBy("user_id").agg(
sum("amount").alias("total_spent"),
count("order_id").alias("order_frequency")
).orderBy(desc("total_spent"))
total_sales = orders_df.agg(sum("amount")).collect()[0][0]
total_orders = orders_df.count()
avg_order_value = orders_df.agg(avg("amount")).collect()[0][0]
daily_sales_list = [{'date': row.order_date, 'sales': float(row.daily_amount),
'orders': row.daily_orders} for row in daily_sales.collect()]
hourly_data = [{'hour': row.hour, 'orders': row.order_count}
for row in hourly_distribution.collect()]
top_customers = [{'user_id': row.user_id, 'total_spent': float(row.total_spent),
'frequency': row.order_frequency} for row in user_analysis.limit(10).collect()]
analytics_result_df = spark.createDataFrame(
[(merchant_id, float(total_sales), total_orders, float(avg_order_value), str(timezone.now()))],
["merchant_id", "total_sales", "total_orders", "avg_order_value", "analysis_time"]
)
analytics_result_df.write.mode("append").option("header", "true").csv("/tmp/merchant_analytics")
return JsonResponse({
'status': 'success',
'data': {
'total_sales': float(total_sales or 0),
'total_orders': total_orders,
'avg_order_value': float(avg_order_value or 0),
'daily_sales': daily_sales_list,
'hourly_distribution': hourly_data,
'top_customers': top_customers
}
})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
摩尔街网上订餐系统文档展示
💖💖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
