💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
时尚内衣销售数据可视化和预测系统介绍
时尚内衣销售数据可视化预测系统是一套基于Hadoop+Spark大数据技术架构的智能分析平台，专门针对时尚内衣行业的销售数据进行深度挖掘与预测分析。系统采用Spring Boot作为后端框架，结合Vue+ElementUI+Echarts构建现代化前端界面，通过HDFS分布式存储海量销售数据，利用Spark SQL进行快速数据查询与计算。平台集成了Victoria's Secret、Amazon、Calvin Klein、Macy's、Hankypanky、AE、btemptd、ustopshop、shopnordstrom等九大主流内衣品牌的销售数据，为销售员和数据分析员提供专业的数据分析工具。系统核心功能涵盖实时销售数据可视化、基于历史数据的销售趋势预测、多维度销售指标统计分析等模块，通过Spark强大的分布式计算能力处理大规模销售数据，结合Pandas和NumPy进行数据预处理与特征工程，最终通过Echarts图表库将分析结果以直观的可视化形式展现，帮助企业管理者快速掌握市场动态，优化库存管理，制定精准的销售策略。
时尚内衣销售数据可视化和预测系统演示视频
时尚内衣销售数据可视化和预测系统演示图片
时尚内衣销售数据可视化和预测系统代码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.stereotype.Service;
@Service
public class SalesDataService {
private SparkSession spark = SparkSession.builder()
.appName("FashionUnderwearSalesAnalysis")
.master("local[*]")
.config("spark.sql.adaptive.enabled", "true")
.getOrCreate();
public Map<String, Object> analyzeSalesVisualization(String brand, String dateRange) {
Dataset<Row> salesData = spark.sql("SELECT brand, product_name, sales_amount, sales_date, sales_count FROM sales_data WHERE brand = '" + brand + "' AND sales_date BETWEEN '" + dateRange.split(",")[0] + "' AND '" + dateRange.split(",")[1] + "'");
Dataset<Row> dailySales = salesData.groupBy("sales_date").agg(functions.sum("sales_amount").alias("total_amount"), functions.sum("sales_count").alias("total_count"));
Dataset<Row> productRanking = salesData.groupBy("product_name").agg(functions.sum("sales_amount").alias("product_total")).orderBy(functions.desc("product_total")).limit(10);
Dataset<Row> monthlyTrend = salesData.withColumn("month", functions.date_format(functions.col("sales_date"), "yyyy-MM")).groupBy("month").agg(functions.sum("sales_amount").alias("monthly_sales"));
Map<String, Object> result = new HashMap<>();
result.put("dailySalesChart", dailySales.collectAsList());
result.put("topProductsChart", productRanking.collectAsList());
result.put("monthlyTrendChart", monthlyTrend.collectAsList());
long totalRecords = salesData.count();
double averageSales = salesData.agg(functions.avg("sales_amount")).first().getDouble(0);
result.put("totalRecords", totalRecords);
result.put("averageSales", averageSales);
Dataset<Row> brandComparison = spark.sql("SELECT brand, SUM(sales_amount) as brand_total FROM sales_data WHERE sales_date BETWEEN '" + dateRange.split(",")[0] + "' AND '" + dateRange.split(",")[1] + "' GROUP BY brand ORDER BY brand_total DESC");
result.put("brandComparisonChart", brandComparison.collectAsList());
return result;
}
public Map<String, Object> predictSalesTrend(String brand, int futureDays) {
Dataset<Row> historicalData = spark.sql("SELECT sales_date, SUM(sales_amount) as daily_sales FROM sales_data WHERE brand = '" + brand + "' GROUP BY sales_date ORDER BY sales_date");
Dataset<Row> movingAverage = historicalData.withColumn("row_number", functions.row_number().over(Window.orderBy("sales_date")))
.withColumn("ma_7", functions.avg("daily_sales").over(Window.orderBy("sales_date").rowsBetween(-6, 0)))
.withColumn("ma_30", functions.avg("daily_sales").over(Window.orderBy("sales_date").rowsBetween(-29, 0)));
Dataset<Row> trendAnalysis = movingAverage.withColumn("growth_rate",
(functions.col("daily_sales").minus(functions.lag("daily_sales", 1).over(Window.orderBy("sales_date"))))
.divide(functions.lag("daily_sales", 1).over(Window.orderBy("sales_date"))));
double avgGrowthRate = trendAnalysis.filter("growth_rate IS NOT NULL").agg(functions.avg("growth_rate")).first().getDouble(0);
Row lastRecord = historicalData.orderBy(functions.desc("sales_date")).first();
double lastSales = lastRecord.getDouble(1);
List<Map<String, Object>> predictions = new ArrayList<>();
LocalDate startDate = LocalDate.parse(lastRecord.getString(0)).plusDays(1);
for (int i = 0; i < futureDays; i++) {
double predictedSales = lastSales * Math.pow(1 + avgGrowthRate, i + 1);
Map<String, Object> prediction = new HashMap<>();
prediction.put("date", startDate.plusDays(i).toString());
prediction.put("predicted_sales", Math.round(predictedSales * 100.0) / 100.0);
prediction.put("confidence_level", Math.max(0.5, 0.9 - (i * 0.02)));
predictions.add(prediction);
}
Map<String, Object> result = new HashMap<>();
result.put("historicalData", historicalData.collectAsList());
result.put("trendAnalysis", trendAnalysis.collectAsList());
result.put("predictions", predictions);
result.put("avgGrowthRate", Math.round(avgGrowthRate * 10000.0) / 100.0);
return result;
}
public Map<String, Object> analyzeBrandStatistics() {
Dataset<Row> allBrandsData = spark.sql("SELECT brand, product_name, sales_amount, sales_count, sales_date FROM sales_data");
Dataset<Row> brandSummary = allBrandsData.groupBy("brand")
.agg(functions.sum("sales_amount").alias("total_revenue"),
functions.sum("sales_count").alias("total_quantity"),
functions.avg("sales_amount").alias("avg_order_value"),
functions.countDistinct("product_name").alias("product_variety"));
Dataset<Row> monthlyBrandPerformance = allBrandsData
.withColumn("month", functions.date_format(functions.col("sales_date"), "yyyy-MM"))
.groupBy("brand", "month")
.agg(functions.sum("sales_amount").alias("monthly_sales"))
.withColumn("rank", functions.row_number().over(Window.partitionBy("month").orderBy(functions.desc("monthly_sales"))));
Dataset<Row> topPerformingProducts = allBrandsData.groupBy("brand", "product_name")
.agg(functions.sum("sales_amount").alias("product_sales"))
.withColumn("brand_rank", functions.row_number().over(Window.partitionBy("brand").orderBy(functions.desc("product_sales"))))
.filter("brand_rank <= 3");
Dataset<Row> seasonalAnalysis = allBrandsData
.withColumn("quarter", functions.quarter(functions.col("sales_date")))
.groupBy("brand", "quarter")
.agg(functions.sum("sales_amount").alias("quarterly_sales"))
.withColumn("seasonal_index", functions.col("quarterly_sales").divide(functions.avg("quarterly_sales").over(Window.partitionBy("brand"))));
Map<String, Object> result = new HashMap<>();
result.put("brandSummary", brandSummary.orderBy(functions.desc("total_revenue")).collectAsList());
result.put("monthlyPerformance", monthlyBrandPerformance.collectAsList());
result.put("topProducts", topPerformingProducts.collectAsList());
result.put("seasonalTrends", seasonalAnalysis.collectAsList());
long totalTransactions = allBrandsData.count();
double totalRevenue = allBrandsData.agg(functions.sum("sales_amount")).first().getDouble(0);
result.put("overallStats", Map.of("totalTransactions", totalTransactions, "totalRevenue", totalRevenue));
return result;
}
}
时尚内衣销售数据可视化和预测系统文档展示
💖💖作者：计算机毕业设计杰瑞 💙💙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 深度学校实战项目 计算机毕业设计选题推荐
