前言
💖💖作者：计算机程序员小杨 💙💙个人简介：我是一名计算机相关专业的从业者，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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。热爱技术，喜欢钻研新工具和框架，也乐于通过代码解决实际问题，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！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
一.开发工具简介
大数据框架：Hadoop+Spark（本次没用Hive，支持定制） 开发语言：Python+Java（两个版本都支持） 后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)（两个版本都支持） 前端：Vue+ElementUI+Echarts+HTML+CSS+JavaScript+jQuery 详细技术点：Hadoop、HDFS、Spark、Spark SQL、Pandas、NumPy 数据库：MySQL
二.系统内容简介
基于大数据的全球学生移民与高等教育趋势数据分析系统是一个集数据采集、处理、分析与可视化于一体的综合性平台。该系统采用Hadoop分布式存储架构和Spark大数据计算引擎，能够高效处理海量的全球教育和移民相关数据。系统前端基于Vue框架配合ElementUI组件库构建用户界面，后端采用Spring Boot框架提供RESTful API服务，数据存储使用MySQL数据库。系统核心功能涵盖高等教育趋势数据管理、学术语言表现分析、全球教育趋势分析、就业薪资回报分析、全球移民流动分析、奖学金资助分析和签证流动数据分析等模块。通过Echarts图表库实现数据的多维度可视化展示，为用户提供直观的大屏数据分析界面。系统运用Spark SQL进行复杂数据查询，结合Pandas和NumPy进行数据预处理和统计分析，能够深度挖掘全球学生移民模式、教育资源配置和政策影响等关键信息，为教育决策者和研究人员提供有价值的数据支撑。
三.系统功能演示
紧跟数据科学潮流：全球学生移民教育趋势大数据分析系统毕设指南 |毕设|计算机毕设|程序开发|项目实战
四.系统界面展示
五.系统源码展示
import org.apache.spark.sql.SparkSession;
import org.apache.spark.sql.Dataset;
import org.apache.spark.sql.Row;
import org.springframework.web.bind.annotation.*;
import org.springframework.beans.factory.annotation.Autowired;
import java.util.*;
@RestController
@RequestMapping("/api/education")
public class EducationAnalysisController {
@Autowired
private SparkSession sparkSession = SparkSession.builder().appName("GlobalEducationAnalysis").master("local[*]").getOrCreate();
@PostMapping("/migration/analyze")
public Map<String, Object> analyzeMigrationTrends(@RequestParam String year, @RequestParam String region) {
Dataset<Row> migrationData = sparkSession.sql("SELECT country, student_count, avg_age, education_level, destination_country FROM student_migration WHERE year = '" + year + "' AND region = '" + region + "'");
Dataset<Row> trendAnalysis = migrationData.groupBy("destination_country").agg(
org.apache.spark.sql.functions.sum("student_count").alias("total_students"),
org.apache.spark.sql.functions.avg("avg_age").alias("average_age"),
org.apache.spark.sql.functions.count("country").alias("source_countries")
);
Dataset<Row> topDestinations = trendAnalysis.orderBy(org.apache.spark.sql.functions.desc("total_students")).limit(10);
List<Row> results = topDestinations.collectAsList();
Map<String, Object> response = new HashMap<>();
List<Map<String, Object>> migrationTrends = new ArrayList<>();
for (Row row : results) {
Map<String, Object> trend = new HashMap<>();
trend.put("destination", row.getString(0));
trend.put("totalStudents", row.getLong(1));
trend.put("averageAge", row.getDouble(2));
trend.put("sourceCountries", row.getLong(3));
migrationTrends.add(trend);
}
Dataset<Row> growthRate = sparkSession.sql("SELECT destination_country, " +
"(current_year_students - previous_year_students) / previous_year_students * 100 as growth_rate " +
"FROM (SELECT destination_country, " +
"LAG(student_count) OVER (PARTITION BY destination_country ORDER BY year) as previous_year_students, " +
"student_count as current_year_students FROM student_migration WHERE region = '" + region + "')");
List<Row> growthData = growthRate.collectAsList();
response.put("migrationTrends", migrationTrends);
response.put("growthRates", growthData);
response.put("totalRegions", migrationData.select("country").distinct().count());
return response;
}
@PostMapping("/scholarship/analysis")
public Map<String, Object> analyzeScholarshipData(@RequestParam String scholarshipType, @RequestParam String academicField) {
Dataset<Row> scholarshipData = sparkSession.sql("SELECT country, university, scholarship_amount, recipient_count, academic_field, selection_criteria FROM scholarship_data WHERE scholarship_type = '" + scholarshipType + "' AND academic_field = '" + academicField + "'");
Dataset<Row> countryAnalysis = scholarshipData.groupBy("country").agg(
org.apache.spark.sql.functions.sum("scholarship_amount").alias("total_funding"),
org.apache.spark.sql.functions.sum("recipient_count").alias("total_recipients"),
org.apache.spark.sql.functions.avg("scholarship_amount").alias("average_amount"),
org.apache.spark.sql.functions.count("university").alias("participating_universities")
);
Dataset<Row> topFundingCountries = countryAnalysis.orderBy(org.apache.spark.sql.functions.desc("total_funding")).limit(15);
List<Row> fundingResults = topFundingCountries.collectAsList();
Dataset<Row> competitionAnalysis = sparkSession.sql("SELECT country, university, " +
"recipient_count / applicant_count * 100 as acceptance_rate, " +
"scholarship_amount / recipient_count as per_student_amount " +
"FROM scholarship_data WHERE scholarship_type = '" + scholarshipType + "' AND academic_field = '" + academicField + "'");
List<Row> competitionData = competitionAnalysis.orderBy(org.apache.spark.sql.functions.desc("acceptance_rate")).collectAsList();
Map<String, Object> response = new HashMap<>();
List<Map<String, Object>> fundingAnalysis = new ArrayList<>();
for (Row row : fundingResults) {
Map<String, Object> funding = new HashMap<>();
funding.put("country", row.getString(0));
funding.put("totalFunding", row.getLong(1));
funding.put("totalRecipients", row.getLong(2));
funding.put("averageAmount", row.getDouble(3));
funding.put("universities", row.getLong(4));
fundingAnalysis.add(funding);
}
Dataset<Row> fieldDistribution = scholarshipData.groupBy("academic_field").agg(
org.apache.spark.sql.functions.count("*").alias("scholarship_programs"),
org.apache.spark.sql.functions.avg("scholarship_amount").alias("field_average_amount")
);
response.put("fundingAnalysis", fundingAnalysis);
response.put("competitionData", competitionData.collectAsList());
response.put("fieldDistribution", fieldDistribution.collectAsList());
response.put("totalScholarships", scholarshipData.count());
return response;
}
@PostMapping("/visa/flow/analysis")
public Map<String, Object> analyzeVisaFlowData(@RequestParam String startYear, @RequestParam String endYear) {
Dataset<Row> visaData = sparkSession.sql("SELECT origin_country, destination_country, visa_type, approval_count, rejection_count, processing_time, year FROM visa_flow_data WHERE year BETWEEN '" + startYear + "' AND '" + endYear + "'");
Dataset<Row> approvalRateAnalysis = visaData.groupBy("destination_country", "visa_type").agg(
org.apache.spark.sql.functions.sum("approval_count").alias("total_approvals"),
org.apache.spark.sql.functions.sum("rejection_count").alias("total_rejections"),
org.apache.spark.sql.functions.avg("processing_time").alias("avg_processing_time")
);
Dataset<Row> approvalRates = approvalRateAnalysis.withColumn("approval_rate",
org.apache.spark.sql.functions.col("total_approvals").divide(
org.apache.spark.sql.functions.col("total_approvals").plus(org.apache.spark.sql.functions.col("total_rejections"))
).multiply(100)
);
Dataset<Row> topDestinationsByApproval = approvalRates.orderBy(org.apache.spark.sql.functions.desc("approval_rate")).limit(20);
List<Row> approvalResults = topDestinationsByApproval.collectAsList();
Dataset<Row> yearlyTrends = sparkSession.sql("SELECT year, destination_country, " +
"SUM(approval_count) as yearly_approvals, " +
"AVG(processing_time) as yearly_avg_processing_time " +
"FROM visa_flow_data WHERE year BETWEEN '" + startYear + "' AND '" + endYear + "' " +
"GROUP BY year, destination_country ORDER BY year, yearly_approvals DESC");
List<Row> trendResults = yearlyTrends.collectAsList();
Dataset<Row> originCountryAnalysis = visaData.groupBy("origin_country").agg(
org.apache.spark.sql.functions.countDistinct("destination_country").alias("destination_variety"),
org.apache.spark.sql.functions.sum("approval_count").alias("total_student_approvals"),
org.apache.spark.sql.functions.avg("approval_count").alias("avg_approvals_per_destination")
);
List<Row> originAnalysis = originCountryAnalysis.orderBy(org.apache.spark.sql.functions.desc("total_student_approvals")).collectAsList();
Map<String, Object> response = new HashMap<>();
List<Map<String, Object>> visaFlowAnalysis = new ArrayList<>();
for (Row row : approvalResults) {
Map<String, Object> flow = new HashMap<>();
flow.put("destination", row.getString(0));
flow.put("visaType", row.getString(1));
flow.put("approvalRate", row.getDouble(5));
flow.put("avgProcessingTime", row.getDouble(4));
flow.put("totalApprovals", row.getLong(2));
visaFlowAnalysis.add(flow);
}
response.put("visaFlowAnalysis", visaFlowAnalysis);
response.put("yearlyTrends", trendResults);
response.put("originAnalysis", originAnalysis);
response.put("totalVisaRecords", visaData.count());
return response;
}
}
六.系统文档展示
结束
💛💛想说的话：感谢大家的关注与支持！ 💕💕文末获取源码联系 计算机程序员小杨 💜💜 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 计算机毕业设计选题 💜💜
