💖💖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SpringBoot和Vue的物联网仓储管理系统介绍
基于SpringBoot和Vue的物联网仓储管理系统是一套专为现代化仓储管理需求而设计的智能化信息管理平台，该系统采用当前主流的前后端分离架构，后端基于SpringBoot框架构建，集成Spring、SpringMVC和MyBatis技术栈，提供稳定高效的数据处理和业务逻辑支持，前端采用Vue.js框架结合ElementUI组件库开发，呈现现代化的用户交互界面，数据存储采用MySQL关系型数据库，确保数据的安全性和一致性。系统功能涵盖了仓储管理的全流程业务场景，包括员工信息管理、客户信息维护、货物分类管理、库存信息实时监控、入库出库业务处理、订单管理、物流信息跟踪、过期商品处理、库存盘点、用户反馈建议收集以及运营数据分析等核心模块，同时提供完善的用户权限管理功能，包括个人中心、密码修改、个人信息维护等基础服务。整个系统基于B/S架构设计，用户可通过浏览器便捷访问，无需安装客户端软件，支持多用户并发操作，适合中小型企业的仓储管理需求，通过物联网技术的融入，实现了传统仓储管理向智能化、数字化转型，提高了仓储作业效率，降低了人工成本，为企业提供了科学的库存管理决策支持。
基于SpringBoot和Vue的物联网仓储管理系统演示视频
基于SpringBoot和Vue的物联网仓储管理系统演示图片
基于SpringBoot和Vue的物联网仓储管理系统代码展示
```java
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.beans.factory.annotation.Autowired;
import org.springframework.web.bind.annotation.*;
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class WarehouseAnalysisService {
private SparkSession spark = SparkSession.builder().appName("WarehouseAnalysis").master("local[*]").getOrCreate();
@Autowired
private InventoryMapper inventoryMapper;
@Autowired
private OrderMapper orderMapper;
@Autowired
private StorageMapper storageMapper;
public Map<String, Object> analyzeInventoryTrends() {
List<Inventory> inventoryList = inventoryMapper.selectAll();
Dataset<Row> inventoryDF = spark.createDataFrame(inventoryList, Inventory.class);
inventoryDF.createOrReplaceTempView("inventory_data");
Dataset<Row> trendResult = spark.sql("SELECT category_id, SUM(quantity) as total_quantity, AVG(quantity) as avg_quantity FROM inventory_data GROUP BY category_id ORDER BY total_quantity DESC");
List<Row> trendRows = trendResult.collectAsList();
Map<String, Object> result = new HashMap<>();
List<Map<String, Object>> trends = new ArrayList<>();
for (Row row : trendRows) {
Map<String, Object> trend = new HashMap<>();
trend.put("categoryId", row.getAs("category_id"));
trend.put("totalQuantity", row.getAs("total_quantity"));
trend.put("avgQuantity", row.getAs("avg_quantity"));
trends.add(trend);
}
result.put("trends", trends);
Dataset<Row> lowStockAlert = spark.sql("SELECT * FROM inventory_data WHERE quantity < 10");
result.put("lowStockCount", lowStockAlert.count());
Dataset<Row> categoryStats = spark.sql("SELECT category_id, COUNT(*) as item_count FROM inventory_data GROUP BY category_id");
result.put("categoryStats", categoryStats.collectAsList());
return result;
}
public Map<String, Object> processOrderAnalysis() {
List<Order> orderList = orderMapper.selectAllOrders();
Dataset<Row> orderDF = spark.createDataFrame(orderList, Order.class);
orderDF.createOrReplaceTempView("order_data");
Dataset<Row> dailyOrders = spark.sql("SELECT DATE(create_time) as order_date, COUNT(*) as order_count, SUM(total_amount) as daily_revenue FROM order_data GROUP BY DATE(create_time) ORDER BY order_date DESC");
List<Row> dailyData = dailyOrders.collectAsList();
Dataset<Row> customerOrders = spark.sql("SELECT customer_id, COUNT(*) as order_frequency, SUM(total_amount) as customer_value FROM order_data GROUP BY customer_id ORDER BY customer_value DESC LIMIT 20");
List<Row> topCustomers = customerOrders.collectAsList();
Dataset<Row> statusStats = spark.sql("SELECT order_status, COUNT(*) as status_count FROM order_data GROUP BY order_status");
List<Row> statusData = statusStats.collectAsList();
Dataset<Row> monthlyRevenue = spark.sql("SELECT YEAR(create_time) as year, MONTH(create_time) as month, SUM(total_amount) as monthly_revenue FROM order_data GROUP BY YEAR(create_time), MONTH(create_time) ORDER BY year DESC, month DESC");
List<Row> monthlyData = monthlyRevenue.collectAsList();
Map<String, Object> analysisResult = new HashMap<>();
analysisResult.put("dailyTrends", dailyData);
analysisResult.put("topCustomers", topCustomers);
analysisResult.put("orderStatusStats", statusData);
analysisResult.put("monthlyRevenue", monthlyData);
Dataset<Row> avgOrderValue = spark.sql("SELECT AVG(total_amount) as avg_order_value FROM order_data");
analysisResult.put("avgOrderValue", avgOrderValue.first().getAs("avg_order_value"));
return analysisResult;
}
public Map<String, Object> optimizeStorageAllocation() {
List<Storage> storageList = storageMapper.selectAllStorage();
List<Inventory> inventoryList = inventoryMapper.selectAll();
Dataset<Row> storageDF = spark.createDataFrame(storageList, Storage.class);
Dataset<Row> inventoryDF = spark.createDataFrame(inventoryList, Inventory.class);
storageDF.createOrReplaceTempView("storage_data");
inventoryDF.createOrReplaceTempView("inventory_data");
Dataset<Row> storageUtilization = spark.sql("SELECT s.storage_id, s.storage_name, s.capacity, COUNT(i.inventory_id) as item_count, SUM(i.quantity) as total_quantity FROM storage_data s LEFT JOIN inventory_data i ON s.storage_id = i.storage_id GROUP BY s.storage_id, s.storage_name, s.capacity");
List<Row> utilizationData = storageUtilization.collectAsList();
Dataset<Row> overCapacity = spark.sql("SELECT s.storage_id, s.storage_name, s.capacity, SUM(i.quantity) as current_stock FROM storage_data s JOIN inventory_data i ON s.storage_id = i.storage_id GROUP BY s.storage_id, s.storage_name, s.capacity HAVING SUM(i.quantity) > s.capacity");
List<Row> overCapacityAreas = overCapacity.collectAsList();
Dataset<Row> underUtilized = spark.sql("SELECT s.storage_id, s.storage_name, s.capacity, COALESCE(SUM(i.quantity), 0) as current_stock FROM storage_data s LEFT JOIN inventory_data i ON s.storage_id = i.storage_id GROUP BY s.storage_id, s.storage_name, s.capacity HAVING COALESCE(SUM(i.quantity), 0) < s.capacity * 0.3");
List<Row> underUtilizedAreas = underUtilized.collectAsList();
Dataset<Row> categoryDistribution = spark.sql("SELECT i.category_id, s.storage_id, s.storage_name, COUNT(*) as item_count FROM inventory_data i JOIN storage_data s ON i.storage_id = s.storage_id GROUP BY i.category_id, s.storage_id, s.storage_name ORDER BY i.category_id, item_count DESC");
List<Row> distributionData = categoryDistribution.collectAsList();
Map<String, Object> optimizationResult = new HashMap<>();
optimizationResult.put("utilizationStats", utilizationData);
optimizationResult.put("overCapacityAreas", overCapacityAreas);
optimizationResult.put("underUtilizedAreas", underUtilizedAreas);
optimizationResult.put("categoryDistribution", distributionData);
List<Map<String, Object>> recommendations = new ArrayList<>();
for (Row row : underUtilizedAreas) {
Map<String, Object> recommendation = new HashMap<>();
recommendation.put("storageId", row.getAs("storage_id"));
recommendation.put("recommendation", "建议将高流转商品调配至此区域以提高利用率");
recommendations.add(recommendation);
}
optimizationResult.put("recommendations", recommendations);
return optimizationResult;
}
}
# 基于SpringBoot和Vue的物联网仓储管理系统文档展示
💖💖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
