【2027精品大数据毕设】基于大数据的电商物流配送与供应链数据可视化分析（附源码资料）数据分析，可视化大屏_毕设选题推荐_Hadoop

💖💖作者：计算机毕业设计江挽
💙💙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！
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基于大数据的电商物流配送与供应链数据可视化分析介绍

本系统名为《基于大数据的电商物流配送与供应链数据可视化分析》，面向电商物流场景下的配送与供应链数据展开分析。系统以Hadoop HDFS作为底层数据存储，借助Spark与Spark SQL完成对电商物流配送数据、订单消费数据、物流时效数据的清洗、聚合与统计计算，并将处理结果存入MySQL供业务层调用。后端提供Python+Django与Java+Spring Boot两个版本，前端采用Vue+ElementUI结合Echarts实现可视化呈现。系统功能涵盖电商物流配送数据管理、订单消费分析、物流时效分析、数据分析结果展示以及个人信息维护与密码修改等模块。用户可通过可视化图表直观查看配送时效分布、订单消费趋势与供应链各环节的运行状况，从而对电商物流配送与供应链数据形成较为清晰的认识。系统整体结构清晰、技术路线明确，适合作为计算机专业本科毕业设计课题进行实现与展示。

基于大数据的电商物流配送与供应链数据可视化分析演示视频

演示视频

基于大数据的电商物流配送与供应链数据可视化分析演示图片

基于大数据的电商物流配送与供应链数据可视化分析代码展示

// ===== 大数据入口：SparkSession初始化与电商物流配送数据加载 =====
public class LogisticsDataService {
    private static final SparkSession spark = SparkSession.builder()
            .appName("EcommerceLogisticsAnalysis")
            .master("local[*]")
            .config("spark.sql.warehouse.dir", "hdfs://localhost:9000/warehouse")
            .getOrCreate();
    public Dataset<Row> loadDeliveryData(String hdfsPath) {
        Dataset<Row> rawDf = spark.read()
                .option("header", "true")
                .option("inferSchema", "true")
                .csv(hdfsPath);
        Dataset<Row> cleanedDf = rawDf
                .withColumnRenamed("order_id", "orderId")
                .withColumnRenamed("delivery_time", "deliveryTime")
                .withColumnRenamed("receive_time", "receiveTime")
                .filter(col("orderId").isNotNull())
                .filter(col("deliveryTime").isNotNull())
                .filter(col("receiveTime").isNotNull());
        cleanedDf.createOrReplaceTempView("delivery_data");
        return cleanedDf;
    }
    public List<Map<String, Object>> analyzeDeliveryEfficiency() {
        String sql = "SELECT province, COUNT(*) AS orderCount, " +
                "AVG(TIMESTAMPDIFF(HOUR, deliveryTime, receiveTime)) AS avgHours " +
                "FROM delivery_data GROUP BY province ORDER BY avgHours DESC";
        Dataset<Row> resultDf = spark.sql(sql);
        List<Row> rows = resultDf.collectAsList();
        List<Map<String, Object>> resultList = new ArrayList<>();
        for (Row row : rows) {
            Map<String, Object> item = new HashMap<>();
            item.put("province", row.getString("province"));
            item.put("orderCount", row.getLong("orderCount"));
            item.put("avgHours", row.getDouble("avgHours"));
            resultList.add(item);
        }
        return resultList;
    }
}
// ===== 订单消费分析：Spark SQL聚合与Django视图处理 =====
public class OrderConsumptionService {
    private final SparkSession sparkSession = SparkSession.builder()
            .appName("OrderConsumptionAnalysis")
            .master("local[*]")
            .getOrCreate();
    public List<Map<String, Object>> analyzeConsumptionTrend(String startDate, String endDate) {
        Dataset<Row> orderDf = sparkSession.read()
                .option("header", "true")
                .option("inferSchema", "true")
                .csv("hdfs://localhost:9000/ecommerce/order_data.csv");
        orderDf.createOrReplaceTempView("order_data");
        String sql = "SELECT DATE_FORMAT(orderTime, 'yyyy-MM') AS month, " +
                "SUM(amount) AS totalAmount, COUNT(*) AS orderCount " +
                "FROM order_data WHERE orderTime BETWEEN '" + startDate + "' AND '" + endDate + "' " +
                "GROUP BY DATE_FORMAT(orderTime, 'yyyy-MM') ORDER BY month";
        Dataset<Row> trendDf = sparkSession.sql(sql);
        List<Row> trendRows = trendDf.collectAsList();
        List<Map<String, Object>> trendList = new ArrayList<>();
        for (Row row : trendRows) {
            Map<String, Object> item = new LinkedHashMap<>();
            item.put("month", row.getString("month"));
            item.put("totalAmount", row.getDouble("totalAmount"));
            item.put("orderCount", row.getLong("orderCount"));
            trendList.add(item);
        }
        return trendList;
    }
    public Map<String, Object> getCategoryConsumption() {
        Dataset<Row> categoryDf = sparkSession.sql(
                "SELECT category, SUM(amount) AS categoryAmount, COUNT(*) AS cnt " +
                "FROM order_data GROUP BY category ORDER BY categoryAmount DESC LIMIT 10");
        List<Row> rows = categoryDf.collectAsList();
        Map<String, Object> result = new HashMap<>();
        List<String> categories = new ArrayList<>();
        List<Double> amounts = new ArrayList<>();
        for (Row row : rows) {
            categories.add(row.getString("category"));
            amounts.add(row.getDouble("categoryAmount"));
        }
        result.put("categories", categories);
        result.put("amounts", amounts);
        return result;
    }
}
// ===== 物流时效分析：Spark SQL统计与Spring Boot接口处理 =====
public class LogisticsTimelinessService {
    private final SparkSession spark = SparkSession.builder()
            .appName("LogisticsTimelinessAnalysis")
            .master("local[*]")
            .getOrCreate();
    public Map<String, Object> analyzeTimelinessDistribution() {
        Dataset<Row> timelinessDf = spark.read()
                .option("header", "true")
                .option("inferSchema", "true")
                .csv("hdfs://localhost:9000/ecommerce/logistics_timeliness.csv");
        timelinessDf.createOrReplaceTempView("timeliness_data");
        String sql = "SELECT CASE " +
                "WHEN hours <= 24 THEN '24小时内' " +
                "WHEN hours <= 48 THEN '24-48小时' " +
                "WHEN hours <= 72 THEN '48-72小时' " +
                "ELSE '72小时以上' END AS timeRange, COUNT(*) AS orderCount " +
                "FROM (SELECT TIMESTAMPDIFF(HOUR, deliveryTime, receiveTime) AS hours FROM timeliness_data) t " +
                "GROUP BY CASE WHEN hours <= 24 THEN '24小时内' " +
                "WHEN hours <= 48 THEN '24-48小时' WHEN hours <= 72 THEN '48-72小时' " +
                "ELSE '72小时以上' END";
        Dataset<Row> rangeDf = spark.sql(sql);
        List<Row> rangeRows = rangeDf.collectAsList();
        List<Map<String, Object>> rangeList = new ArrayList<>();
        for (Row row : rangeRows) {
            Map<String, Object> item = new LinkedHashMap<>();
            item.put("timeRange", row.getString("timeRange"));
            item.put("orderCount", row.getLong("orderCount"));
            rangeList.add(item);
        }
        Map<String, Object> result = new HashMap<>();
        result.put("distribution", rangeList);
        result.put("totalCount", rangeRows.stream().mapToLong(r -> r.getLong("orderCount")).sum());
        return result;
    }
    public List<Map<String, Object>> analyzeCarrierPerformance() {
        String sql = "SELECT carrier, COUNT(*) AS orderCount, " +
                "AVG(TIMESTAMPDIFF(HOUR, deliveryTime, receiveTime)) AS avgHours, " +
                "SUM(CASE WHEN TIMESTAMPDIFF(HOUR, deliveryTime, receiveTime) <= 48 THEN 1 ELSE 0 END) * 100.0 / COUNT(*) AS onTimeRate " +
                "FROM timeliness_data GROUP BY carrier ORDER BY onTimeRate DESC";
        Dataset<Row> carrierDf = spark.sql(sql);
        List<Row> carrierRows = carrierDf.collectAsList();
        List<Map<String, Object>> carrierList = new ArrayList<>();
        for (Row row : carrierRows) {
            Map<String, Object> item = new LinkedHashMap<>();
            item.put("carrier", row.getString("carrier"));
            item.put("orderCount", row.getLong("orderCount"));
            item.put("avgHours", row.getDouble("avgHours"));
            item.put("onTimeRate", row.getDouble("onTimeRate"));
            carrierList.add(item);
        }
        return carrierList;
    }
}

基于大数据的电商物流配送与供应链数据可视化分析文档展示

💖💖作者：计算机毕业设计江挽
💙💙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！
💜💜
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安卓/小程序实战项目
大数据实战项目
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转载自 CSDN-专业IT技术社区

原文链接：https://blog.csdn.net/2501_92808384/article/details/170241321