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健康与生活方式数据可视化分析系统 - 简介
2026年大数据毕设新趋势：基于Spark的健康与生活方式数据分析成热门选择 计算机专业的你还在为大数据毕设发愁？健康数据分析+Hadoop+Spark拯救迷茫的你 大数据毕设不知道选什么？基于Hadoop+Spark的健康数据分析系统帮你一次解决技术难题 支持我记得一键三连+关注，感谢支持，有技术问题、求源码，欢迎在评论区交流！
健康与生活方式数据可视化分析系统 -技术
开发语言：java或Python
数据库：MySQL
系统架构：B/S
前端：Vue+ElementUI+HTML+CSS+JavaScript+jQuery+Echarts
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
后端框架：Django+Spring Boot(Spring+SpringMVC+Mybatis)
健康与生活方式数据可视化分析系统 - 背景
选题背景：随着社会经济的快速发展和生活节奏的加快，人们的生活方式发生了显著变化，健康问题日益受到关注。现代社会中，工作压力增大、饮食结构改变、运动习惯缺失等问题普遍存在，这些因素对居民健康状况产生了深远影响。传统的健康数据分析方法主要依赖小规模调研和简单统计，难以处理大容量、多维度的复杂健康数据，也无法深入挖掘不同生活方式因素之间的关联关系。当前健康管理领域迫切需要运用现代大数据技术来处理和分析海量健康相关数据，通过科学的数据挖掘方法发现隐藏在数据中的规律和趋势。大数据技术的成熟为健康数据分析提供了新的技术路径，Hadoop和Spark等分布式计算框架能够高效处理大规模健康数据集，为深入研究居民生活方式与健康状况的关系提供了技术保障，这为开发专门的健康与生活方式数据分析系统奠定了坚实的技术基础。
健康与生活方式数据可视化分析系统 -图片展示
健康与生活方式数据可视化分析系统 -代码展示
import org.springframework.stereotype.Service;
import java.util.*;
@Service
public class HealthDataAnalysisService {
private SparkSession spark = SparkSession.builder()
.appName("HealthLifestyleAnalysis")
.config("spark.master", "local[*]")
.config("spark.sql.adaptive.enabled", "true")
.config("spark.sql.warehouse.dir", "/tmp/spark-warehouse")
.getOrCreate();
public Map<String, Object> analyzeBasicHealthProfile() {
Dataset<Row> healthData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/health_db")
.option("dbtable", "health_lifestyle_data")
.option("user", "root")
.option("password", "password")
.load();
healthData.createOrReplaceTempView("health_data");
Dataset<Row> ageDistribution = spark.sql(
"SELECT CASE " +
"WHEN Age < 25 THEN '青年' " +
"WHEN Age BETWEEN 25 AND 45 THEN '中年' " +
"ELSE '中老年' END as age_group, " +
"COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM health_data), 2) as percentage " +
"FROM health_data GROUP BY age_group ORDER BY count DESC"
);
Dataset<Row> genderUrbanDistribution = spark.sql(
"SELECT Gender, Urban_Rural, COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM health_data), 2) as percentage " +
"FROM health_data GROUP BY Gender, Urban_Rural ORDER BY count DESC"
);
Dataset<Row> lifestyleHabits = spark.sql(
"SELECT " +
"SUM(CASE WHEN `Smoking Status` = 'Yes' THEN 1 ELSE 0 END) as smoking_count, " +
"SUM(CASE WHEN `Alcohol Consumption` = 'Yes' THEN 1 ELSE 0 END) as alcohol_count, " +
"SUM(CASE WHEN `Diet Type` = 'Vegetarian' THEN 1 ELSE 0 END) as vegetarian_count, " +
"SUM(CASE WHEN `Physical Activity` = 'High' THEN 1 ELSE 0 END) as high_activity_count, " +
"COUNT(*) as total_count FROM health_data"
);
Map<String, Object> result = new HashMap<>();
result.put("ageDistribution", convertDatasetToList(ageDistribution));
result.put("genderUrbanDistribution", convertDatasetToList(genderUrbanDistribution));
result.put("lifestyleHabits", convertDatasetToList(lifestyleHabits));
result.put("analysisTime", new Date());
return result;
}
public Map<String, Object> analyzeUrbanRuralDifferences() {
Dataset<Row> healthData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/health_db")
.option("dbtable", "health_lifestyle_data")
.option("user", "root")
.option("password", "password")
.load();
healthData.createOrReplaceTempView("health_data");
Dataset<Row> medicalPreference = spark.sql(
"SELECT Urban_Rural, `Preferred Medicine`, COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (PARTITION BY Urban_Rural), 2) as percentage " +
"FROM health_data GROUP BY Urban_Rural, `Preferred Medicine` " +
"ORDER BY Urban_Rural, count DESC"
);
Dataset<Row> workStressComparison = spark.sql(
"SELECT Urban_Rural, `Work Stress`, COUNT(*) as count, " +
"ROUND(AVG(CASE WHEN `Work Stress` = 'High' THEN 3 " +
"WHEN `Work Stress` = 'Medium' THEN 2 ELSE 1 END), 2) as avg_stress_level " +
"FROM health_data GROUP BY Urban_Rural, `Work Stress` " +
"ORDER BY Urban_Rural, count DESC"
);
Dataset<Row> dietDifferences = spark.sql(
"SELECT Urban_Rural, `Diet Type`, COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (PARTITION BY Urban_Rural), 2) as percentage " +
"FROM health_data GROUP BY Urban_Rural, `Diet Type` " +
"ORDER BY Urban_Rural, percentage DESC"
);
Dataset<Row> riskBehaviorComparison = spark.sql(
"SELECT Urban_Rural, " +
"SUM(CASE WHEN `Smoking Status` = 'Yes' THEN 1 ELSE 0 END) as smoking_count, " +
"SUM(CASE WHEN `Alcohol Consumption` = 'Yes' THEN 1 ELSE 0 END) as alcohol_count, " +
"COUNT(*) as total_count, " +
"ROUND(SUM(CASE WHEN `Smoking Status` = 'Yes' THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) as smoking_rate, " +
"ROUND(SUM(CASE WHEN `Alcohol Consumption` = 'Yes' THEN 1 ELSE 0 END) * 100.0 / COUNT(*), 2) as alcohol_rate " +
"FROM health_data GROUP BY Urban_Rural ORDER BY smoking_rate DESC"
);
Map<String, Object> result = new HashMap<>();
result.put("medicalPreference", convertDatasetToList(medicalPreference));
result.put("workStressComparison", convertDatasetToList(workStressComparison));
result.put("dietDifferences", convertDatasetToList(dietDifferences));
result.put("riskBehaviorComparison", convertDatasetToList(riskBehaviorComparison));
return result;
}
public Map<String, Object> analyzeHealthLifestyleScore() {
Dataset<Row> healthData = spark.read()
.format("jdbc")
.option("url", "jdbc:mysql://localhost:3306/health_db")
.option("dbtable", "health_lifestyle_data")
.option("user", "root")
.option("password", "password")
.load();
healthData.createOrReplaceTempView("health_data");
Dataset<Row> healthScoreData = spark.sql(
"SELECT *, " +
"(CASE WHEN `Smoking Status` = 'No' THEN 2 ELSE 0 END + " +
"CASE WHEN `Alcohol Consumption` = 'No' THEN 2 ELSE 0 END + " +
"CASE WHEN `Physical Activity` = 'High' THEN 3 " +
"WHEN `Physical Activity` = 'Medium' THEN 2 ELSE 1 END) as health_score " +
"FROM health_data"
);
healthScoreData.createOrReplaceTempView("health_score_data");
Dataset<Row> scoreDistribution = spark.sql(
"SELECT " +
"CASE WHEN health_score >= 6 THEN '健康' " +
"WHEN health_score >= 4 THEN '一般' " +
"ELSE '需改善' END as health_level, " +
"COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / (SELECT COUNT(*) FROM health_score_data), 2) as percentage, " +
"ROUND(AVG(health_score), 2) as avg_score " +
"FROM health_score_data GROUP BY health_level ORDER BY avg_score DESC"
);
Dataset<Row> scoreByStress = spark.sql(
"SELECT `Work Stress`, " +
"COUNT(*) as count, " +
"ROUND(AVG(health_score), 2) as avg_health_score, " +
"ROUND(MIN(health_score), 2) as min_score, " +
"ROUND(MAX(health_score), 2) as max_score " +
"FROM health_score_data GROUP BY `Work Stress` " +
"ORDER BY avg_health_score DESC"
);
Dataset<Row> scoreByUrbanRural = spark.sql(
"SELECT Urban_Rural, " +
"COUNT(*) as count, " +
"ROUND(AVG(health_score), 2) as avg_health_score, " +
"ROUND(STDDEV(health_score), 2) as score_stddev " +
"FROM health_score_data GROUP BY Urban_Rural " +
"ORDER BY avg_health_score DESC"
);
Dataset<Row> medicalPreferenceByScore = spark.sql(
"SELECT " +
"CASE WHEN health_score >= 6 THEN '健康' " +
"WHEN health_score >= 4 THEN '一般' " +
"ELSE '需改善' END as health_level, " +
"`Preferred Medicine`, " +
"COUNT(*) as count, " +
"ROUND(COUNT(*) * 100.0 / SUM(COUNT(*)) OVER (PARTITION BY " +
"CASE WHEN health_score >= 6 THEN '健康' " +
"WHEN health_score >= 4 THEN '一般' ELSE '需改善' END), 2) as percentage " +
"FROM health_score_data GROUP BY health_level, `Preferred Medicine` " +
"ORDER BY health_level, count DESC"
);
Map<String, Object> result = new HashMap<>();
result.put("scoreDistribution", convertDatasetToList(scoreDistribution));
result.put("scoreByStress", convertDatasetToList(scoreByStress));
result.put("scoreByUrbanRural", convertDatasetToList(scoreByUrbanRural));
result.put("medicalPreferenceByScore", convertDatasetToList(medicalPreferenceByScore));
return result;
}
private List<Map<String, Object>> convertDatasetToList(Dataset<Row> dataset) {
List<Map<String, Object>> result = new ArrayList<>();
String[] columns = dataset.columns();
Row[] rows = (Row[]) dataset.collect();
for (Row row : rows) {
Map<String, Object> rowMap = new HashMap<>();
for (int i = 0; i < columns.length; i++) {
rowMap.put(columns[i], row.get(i));
}
result.add(rowMap);
}
return result;
}
}
健康与生活方式数据可视化分析系统 -结语
2026年大数据毕设新趋势：基于Spark的健康与生活方式数据分析成热门选择
计算机专业的你还在为大数据毕设发愁？健康数据分析+Hadoop+Spark拯救迷茫的你
大数据毕设不知道选什么？基于Hadoop+Spark的健康数据分析系统帮你一次解决技术难题
支持我记得一键三连+关注，感谢支持，有技术问题、求源码，欢迎在评论区交流！
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求！你也可以在个人主页上咨询我~~
