💖💖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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基于微信小程序的家乡特产销售平台介绍
《基于微信小程序的家乡特产销售平台》是一个集电商销售、社交互动与智能管理于一体的综合性毕业设计项目，该系统采用前后端分离架构设计，前端基于uni-app框架开发微信小程序和安卓应用，实现跨平台兼容性，后端支持Java SpringBoot和Python Django两套技术方案，配合MySQL数据库进行数据存储与管理，形成完整的C/S+B/S混合架构体系。系统功能涵盖完整的电商业务流程，包括用户注册登录管理、特产分类与信息展示、促销活动策划、订单全生命周期管理（未支付、已支付、已发货、已完成、已取消、已退款等状态跟踪）、在线支付与充值记录追踪等核心电商模块，同时集成了交流论坛功能，支持论坛分类管理和用户互动交流，提升平台用户粘性。系统还配备了智能AI助手功能，为用户提供个性化服务体验，管理端具备轮播图管理、公告资讯发布、举报记录处理等运营管理工具，用户端提供个人中心、个人信息维护、密码修改等基础服务功能，整个系统通过IDEA或PyCharm进行开发调试，配合微信小程序开发工具实现完整的开发环境搭建，为计算机专业学生提供了一个技术栈丰富、功能完善、实用性强的毕业设计解决方案。
基于微信小程序的家乡特产销售平台演示视频
基于微信小程序的家乡特产销售平台演示图片
基于微信小程序的家乡特产销售平台代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, sum, count, when, desc, avg
from pyspark.ml.recommendation import ALS
import hashlib
import time
from datetime import datetime
spark = SparkSession.builder.appName("HomeTownSpecialtyPlatform").getOrCreate()
def user_registration_and_management(username, password, email, phone):
password_hash = hashlib.sha256(password.encode()).hexdigest()
user_id = int(time.time() * 1000) % 1000000
registration_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
user_data = {
'user_id': user_id,
'username': username,
'password_hash': password_hash,
'email': email,
'phone': phone,
'registration_time': registration_time,
'status': 1,
'balance': 0.0
}
user_behavior_df = spark.createDataFrame([user_data])
user_behavior_df.write.mode("append").option("path", "/data/users").saveAsTable("users")
user_stats = spark.sql("SELECT COUNT(*) as total_users FROM users WHERE status = 1")
user_region_analysis = spark.sql("""
SELECT SUBSTRING(phone, 1, 3) as region_code, COUNT(*) as user_count
FROM users
GROUP BY SUBSTRING(phone, 1, 3)
ORDER BY user_count DESC
""")
active_users_today = spark.sql("""
SELECT COUNT(*) as daily_active_users
FROM users
WHERE DATE(registration_time) = CURRENT_DATE()
""")
return {'user_id': user_id, 'status': 'success', 'user_stats': user_stats.collect()}
def specialty_product_management_with_analytics(product_name, category_id, price, stock, description, origin_location):
product_id = int(time.time() * 1000) % 1000000
create_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
product_data = {
'product_id': product_id,
'product_name': product_name,
'category_id': category_id,
'price': float(price),
'stock': int(stock),
'description': description,
'origin_location': origin_location,
'create_time': create_time,
'sales_count': 0,
'view_count': 0,
'rating': 0.0
}
product_df = spark.createDataFrame([product_data])
product_df.write.mode("append").option("path", "/data/products").saveAsTable("products")
category_analysis = spark.sql("""
SELECT category_id, COUNT(*) as product_count, AVG(price) as avg_price,
SUM(sales_count) as total_sales, AVG(rating) as avg_rating
FROM products
GROUP BY category_id
ORDER BY total_sales DESC
""")
price_distribution = spark.sql("""
SELECT
CASE
WHEN price < 50 THEN 'Low Price'
WHEN price BETWEEN 50 AND 200 THEN 'Medium Price'
ELSE 'High Price'
END as price_range,
COUNT(*) as product_count,
AVG(sales_count) as avg_sales
FROM products
GROUP BY price_range
""")
location_popularity = spark.sql("""
SELECT origin_location, COUNT(*) as product_count,
SUM(sales_count) as total_sales,
AVG(price) as avg_price
FROM products
GROUP BY origin_location
ORDER BY total_sales DESC
LIMIT 10
""")
stock_alert = spark.sql("SELECT product_id, product_name, stock FROM products WHERE stock < 10")
return {'product_id': product_id, 'category_stats': category_analysis.collect(), 'stock_alerts': stock_alert.collect()}
def order_processing_and_big_data_analysis(user_id, product_id, quantity, payment_method):
order_id = int(time.time() * 1000) % 1000000
order_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
product_info = spark.sql(f"SELECT price, stock FROM products WHERE product_id = {product_id}").collect()[0]
total_amount = float(product_info['price']) * int(quantity)
if product_info['stock'] < int(quantity):
return {'status': 'failed', 'message': 'insufficient_stock'}
order_data = {
'order_id': order_id,
'user_id': int(user_id),
'product_id': int(product_id),
'quantity': int(quantity),
'total_amount': total_amount,
'payment_method': payment_method,
'order_time': order_time,
'status': 'unpaid',
'shipping_status': 'pending'
}
order_df = spark.createDataFrame([order_data])
order_df.write.mode("append").option("path", "/data/orders").saveAsTable("orders")
spark.sql(f"UPDATE products SET stock = stock - {quantity}, sales_count = sales_count + {quantity} WHERE product_id = {product_id}")
daily_sales_analysis = spark.sql("""
SELECT DATE(order_time) as order_date,
COUNT(*) as order_count,
SUM(total_amount) as daily_revenue,
AVG(total_amount) as avg_order_value
FROM orders
WHERE DATE(order_time) >= DATE_SUB(CURRENT_DATE(), 30)
GROUP BY DATE(order_time)
ORDER BY order_date DESC
""")
user_purchase_pattern = spark.sql("""
SELECT user_id, COUNT(*) as order_frequency,
SUM(total_amount) as total_spent,
AVG(total_amount) as avg_spend_per_order,
MAX(order_time) as last_purchase_time
FROM orders
GROUP BY user_id
ORDER BY total_spent DESC
""")
product_recommendation_data = spark.sql("""
SELECT user_id, product_id, quantity as rating
FROM orders WHERE status IN ('paid', 'completed')
""")
als_model = ALS(userCol="user_id", itemCol="product_id", ratingCol="rating", nonnegative=True, implicitPrefs=True)
recommendation_model = als_model.fit(spark.createDataFrame(product_recommendation_data.collect()))
payment_method_analysis = spark.sql("""
SELECT payment_method, COUNT(*) as usage_count,
SUM(total_amount) as total_revenue,
AVG(total_amount) as avg_transaction_amount
FROM orders
GROUP BY payment_method
ORDER BY usage_count DESC
""")
return {'order_id': order_id, 'total_amount': total_amount, 'daily_analysis': daily_sales_analysis.collect(), 'user_patterns': user_purchase_pattern.collect()}
基于微信小程序的家乡特产销售平台文档展示
💖💖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
