💖💖作者：计算机毕业设计小途 💙💙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
@TOC
基于SpringBoot的东燕手袋厂货物管理系统介绍
本系统《基于SpringBoot的东燕手袋厂货物管理系统》是一个旨在优化手袋制造企业核心业务流程的综合性信息管理平台。系统采用当前主流的B/S架构与MVC设计模式，以后端强大的SpringBoot框架（整合Spring、SpringMVC、Mybatis）为核心，构建稳定高效的服务层，负责处理复杂的业务逻辑、数据交互与权限控制；前端则运用现代化的Vue.js框架，配合ElementUI组件库，构建出清晰直观、操作流畅的用户界面，实现了前后端分离的开发模式。系统以关系型数据库MySQL作为数据存储基石，确保了数据的一致性与安全性。在功能模块设计上，系统全面覆盖了手袋厂从原材料管理到生产任务协调的关键环节：其中，“布料信息”与“布料入库/出库”模块实现了对布料库存的精细化管控，实时追踪库存动态；“手袋品种”与“手袋信息”模块建立了完善的产品档案库；而“生产任务”与“工费结算”模块则有效串联起生产指令下达与人员成本核算的完整链条，提升了生产协同效率。此外，系统还包含完善的“个人中心”与“系统管理”功能，保障了多角色用户（如生产员、仓管员）的权限分配与系统稳定运行。综上所述，该系统不仅是一个展示了SpringBoot+Vue全栈开发技术的典型实践，更是一个贴合实际工业需求、具备清晰业务逻辑的毕业设计范例。
基于SpringBoot的东燕手袋厂货物管理系统演示视频
基于SpringBoot的东燕手袋厂货物管理系统演示图片
基于SpringBoot的东燕手袋厂货物管理系统代码展示
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.stereotype.Service;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;
@Service
public class FabricInventoryService {
@Autowired
private FabricMapper fabricMapper;
private SparkSession spark = SparkSession.builder().appName("FabricAnalysis").master("local[*]").getOrCreate();
public void processFabricInbound(FabricInboundRecord record) {
if (record.getQuantity() <= 0) { throw new RuntimeException("入库数量必须大于0"); }
Fabric existingFabric = fabricMapper.selectById(record.getFabricId());
if (existingFabric == null) { throw new RuntimeException("布料不存在"); }
int currentStock = existingFabric.getCurrentStock();
int newStock = currentStock + record.getQuantity();
existingFabric.setCurrentStock(newStock);
fabricMapper.updateById(existingFabric);
FabricInboundLog log = new FabricInboundLog();
log.setFabricId(record.getFabricId());
log.setQuantity(record.getQuantity());
log.setOperator(record.getOperator());
log.setInboundTime(new Date());
fabricMapper.insertInboundLog(log);
List<FabricInboundLog> recentLogs = fabricMapper.selectInboundLogsLast7Days();
Dataset<Row> df = spark.createDataFrame(recentLogs, FabricInboundLog.class);
df.groupBy("fabricId").sum("quantity").show();
}
public void processFabricOutbound(FabricOutboundRequest request) {
Fabric fabric = fabricMapper.selectById(request.getFabricId());
if (fabric == null) { throw new RuntimeException("布料不存在"); }
if (fabric.getCurrentStock() < request.getAmount()) { throw new RuntimeException("库存不足"); }
fabric.setCurrentStock(fabric.getCurrentStock() - request.getAmount());
fabricMapper.updateById(fabric);
FabricOutboundLog outboundLog = new FabricOutboundLog();
outboundLog.setFabricId(request.getFabricId());
outboundLog.setAmount(request.getAmount());
outboundLog.setProductionTaskId(request.getProductionTaskId());
outboundLog.setOutboundTime(new Date());
fabricMapper.insertOutboundLog(outboundLog);
List<FabricOutboundLog> outboundLogs = fabricMapper.selectOutboundLogsByTask(request.getProductionTaskId());
Dataset<Row> outboundDf = spark.createDataFrame(outboundLogs, FabricOutboundLog.class);
outboundDf.groupBy("fabricId").avg("amount").show();
}
public void analyzeFabricUsageTrend() {
List<FabricUsageDTO> usageList = fabricMapper.selectFabricUsageStats();
Dataset<Row> usageDf = spark.createDataFrame(usageList, FabricUsageDTO.class);
usageDf.createOrReplaceTempView("fabric_usage");
Dataset<Row> result = spark.sql("SELECT fabricType, AVG(dailyUsage) as avgUsage, SUM(totalUsage) as total FROM fabric_usage GROUP BY fabricType");
result.show();
List<Row> results = result.collectAsList();
results.forEach(row -> {
System.out.println("布料类型: " + row.getString(0) + ", 平均日用量: " + row.getDouble(1) + ", 总用量: " + row.getLong(2));
});
}
}
@Service
public class ProductionTaskService {
@Autowired
private ProductionTaskMapper productionTaskMapper;
private SparkSession spark = SparkSession.builder().appName("ProductionAnalysis").master("local[*]").getOrCreate();
public void createAndAssignProductionTask(ProductionTask task) {
if (task.getRequiredQuantity() <= 0) { throw new RuntimeException("生产数量必须大于0"); }
task.setStatus("待生产");
task.setCreateTime(new Date());
productionTaskMapper.insert(task);
List<FabricRequirement> requirements = calculateFabricRequirement(task.getBagModelId(), task.getRequiredQuantity());
requirements.forEach(req -> {
req.setTaskId(task.getId());
productionTaskMapper.insertFabricRequirement(req);
});
List<ProductionTask> pendingTasks = productionTaskMapper.selectTasksByStatus("待生产");
Dataset<Row> taskDf = spark.createDataFrame(pendingTasks, ProductionTask.class);
taskDf.groupBy("bagModelId").count().show();
}
public void completeProductionTask(Integer taskId, Integer actualOutput) {
ProductionTask task = productionTaskMapper.selectById(taskId);
if (task == null) { throw new RuntimeException("生产任务不存在"); }
if (actualOutput <= 0) { throw new RuntimeException("实际产量必须大于0"); }
task.setActualOutput(actualOutput);
task.setStatus("已完成");
task.setCompletionTime(new Date());
productionTaskMapper.updateById(task);
double efficiency = (double) actualOutput / task.getRequiredQuantity() * 100;
task.setProductionEfficiency(efficiency);
productionTaskMapper.updateEfficiency(task);
List<ProductionTask> completedTasks = productionTaskMapper.selectCompletedTasksThisMonth();
Dataset<Row> completedDf = spark.createDataFrame(completedTasks, ProductionTask.class);
completedDf.createOrReplaceTempView("production_tasks");
Dataset<Row> efficiencyDf = spark.sql("SELECT AVG(productionEfficiency) as avgEfficiency FROM production_tasks WHERE productionEfficiency > 0");
efficiencyDf.show();
}
private List<FabricRequirement> calculateFabricRequirement(Integer bagModelId, Integer quantity) {
BagModel model = productionTaskMapper.selectBagModelById(bagModelId);
List<FabricRequirement> requirements = new ArrayList<>();
if (model != null && model.getFabricConsumption() > 0) {
FabricRequirement req = new FabricRequirement();
req.setFabricTypeId(model.getMainFabricTypeId());
req.setRequiredAmount(model.getFabricConsumption() * quantity);
requirements.add(req);
}
return requirements;
}
}
@Service
public class SettlementService {
@Autowired
private SettlementMapper settlementMapper;
private SparkSession spark = SparkSession.builder().appName("SettlementAnalysis").master("local[*]").getOrCreate();
public void calculateLaborCost(Integer taskId) {
ProductionTask task = settlementMapper.selectTaskWithDetails(taskId);
if (!"已完成".equals(task.getStatus())) { throw new RuntimeException("只能对已完成的任务进行结算"); }
double totalLaborCost = task.getActualOutput() * task.getLaborCostPerUnit();
LaborSettlement settlement = new LaborSettlement();
settlement.setTaskId(taskId);
settlement.setTotalLaborCost(totalLaborCost);
settlement.setSettlementTime(new Date());
settlement.setStatus("已结算");
settlementMapper.insert(settlement);
task.setSettlementStatus("已结算");
settlementMapper.updateTaskSettlementStatus(task);
List<LaborSettlement> monthlySettlements = settlementMapper.selectSettlementsByMonth(2024, 6);
Dataset<Row> df = spark.createDataFrame(monthlySettlements, LaborSettlement.class);
df.createOrReplaceTempView("labor_settlements");
Dataset<Row> result = spark.sql("SELECT SUM(totalLaborCost) as totalCost, AVG(totalLaborCost) as avgCost FROM labor_settlements");
result.show();
}
public void analyzeProductivity() {
List<WorkerProductivity> productivityList = settlementMapper.selectWorkerProductivity();
Dataset<Row> productivityDf = spark.createDataFrame(productivityList, WorkerProductivity.class);
productivityDf.createOrReplaceTempView("worker_productivity");
Dataset<Row> result = spark.sql("SELECT workerId, AVG(outputPerDay) as avgOutput, SUM(totalOutput) as totalOutput FROM worker_productivity GROUP BY workerId");
List<Row> rows = result.collectAsList();
rows.forEach(row -> {
System.out.println("员工ID: " + row.getInt(0) + ", 平均日产量: " + row.getDouble(1) + ", 总产量: " + row.getLong(2));
});
}
}
基于SpringBoot的东燕手袋厂货物管理系统文档展示
💖💖作者：计算机毕业设计小途 💙💙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
