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⚡⚡获取源码主页--> space.bilibili.com/35463818075…
健康与生活方式数据可视化分析系统- 简介
系统以印度居民健康调查数据为研究对象，通过Spark SQL和Pandas、NumPy等数据处理库，对年龄结构、性别分布、城乡差异、吸烟饮酒习惯、饮食类型、体力活动水平、工作压力和医疗偏好等多个维度进行统计分析。在数据存储方面，系统采用HDFS分布式文件系统存储海量数据，MySQL数据库管理结构化信息，通过Spark引擎实现高效的批处理和实时计算。前端界面基于Vue+ElementUI+Echarts技术栈构建，提供丰富的图表展示功能，包括柱状图、饼图、折线图、热力图等多种可视化形式，帮助用户直观理解健康数据的分布规律和关联关系。系统支持城乡生活方式差异对比、工作压力与健康风险行为关联分析、不同年龄段健康趋势演变以及综合健康生活方式评估等核心分析功能，为健康管理决策提供数据支撑。
健康与生活方式数据可视化分析系统-技术 框架
开发语言：Python或Java（两个版本都支持）
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)（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
健康与生活方式数据可视化分析系统- 背景
选题背景
随着社会经济快速发展和生活节奏加快，人们的生活方式发生了显著变化，健康问题逐渐成为社会关注焦点。现代生活中工作压力增大、饮食结构改变、体力活动减少等现象普遍存在，这些因素对个体和群体健康产生深远影响。传统的健康数据分析往往局限于小规模样本或单一维度的统计，难以全面反映生活方式与健康状况之间的复杂关联。同时，医疗卫生部门和研究机构积累了大量健康调查数据，但缺乏有效的技术手段进行深度挖掘和可视化展示。大数据技术的发展为解决这一问题提供了新的思路，Spark等分布式计算框架能够高效处理海量健康数据，而可视化技术则能够将复杂的数据分析结果以直观的图表形式呈现，帮助决策者快速理解数据背后的规律和趋势。
健康与生活方式数据可视化分析系统-视频展示
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
from pyspark.sql.functions import col, count, avg, when, desc, asc
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import json
spark = SparkSession.builder.appName("HealthDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/health_data/residents_data.csv")
@require_http_methods(["GET"])
def analyze_urban_rural_lifestyle_differences(request):
urban_rural_smoking = df.groupBy("Urban_Rural", "Smoking Status").agg(count("*").alias("count")).orderBy("Urban_Rural", "Smoking Status")
smoking_result = []
for row in urban_rural_smoking.collect():
smoking_result.append({"location": row["Urban_Rural"], "smoking_status": row["Smoking Status"], "count": row["count"]})
urban_rural_alcohol = df.groupBy("Urban_Rural", "Alcohol Consumption").agg(count("*").alias("count")).orderBy("Urban_Rural", "Alcohol Consumption")
alcohol_result = []
for row in urban_rural_alcohol.collect():
alcohol_result.append({"location": row["Urban_Rural"], "alcohol_status": row["Alcohol Consumption"], "count": row["count"]})
urban_rural_medicine = df.groupBy("Urban_Rural", "Preferred Medicine").agg(count("*").alias("count")).orderBy("Urban_Rural", "Preferred Medicine")
medicine_result = []
for row in urban_rural_medicine.collect():
medicine_result.append({"location": row["Urban_Rural"], "medicine_type": row["Preferred Medicine"], "count": row["count"]})
urban_rural_stress = df.groupBy("Urban_Rural", "Work Stress").agg(count("*").alias("count")).orderBy("Urban_Rural", "Work Stress")
stress_result = []
for row in urban_rural_stress.collect():
stress_result.append({"location": row["Urban_Rural"], "stress_level": row["Work Stress"], "count": row["count"]})
urban_rural_diet = df.groupBy("Urban_Rural", "Diet Type").agg(count("*").alias("count")).orderBy("Urban_Rural", "Diet Type")
diet_result = []
for row in urban_rural_diet.collect():
diet_result.append({"location": row["Urban_Rural"], "diet_type": row["Diet Type"], "count": row["count"]})
return JsonResponse({"smoking_analysis": smoking_result, "alcohol_analysis": alcohol_result, "medicine_preference": medicine_result, "work_stress": stress_result, "diet_analysis": diet_result})
@require_http_methods(["GET"])
def analyze_work_stress_health_risk_correlation(request):
stress_smoking_df = df.filter(col("Work Stress").isNotNull() & col("Smoking Status").isNotNull())
stress_smoking_analysis = stress_smoking_df.groupBy("Work Stress", "Smoking Status").agg(count("*").alias("count")).orderBy("Work Stress", "Smoking Status")
smoking_correlation = []
for row in stress_smoking_analysis.collect():
smoking_correlation.append({"stress_level": row["Work Stress"], "smoking_status": row["Smoking Status"], "count": row["count"]})
stress_alcohol_df = df.filter(col("Work Stress").isNotNull() & col("Alcohol Consumption").isNotNull())
stress_alcohol_analysis = stress_alcohol_df.groupBy("Work Stress", "Alcohol Consumption").agg(count("*").alias("count")).orderBy("Work Stress", "Alcohol Consumption")
alcohol_correlation = []
for row in stress_alcohol_analysis.collect():
alcohol_correlation.append({"stress_level": row["Work Stress"], "alcohol_status": row["Alcohol Consumption"], "count": row["count"]})
stress_activity_df = df.filter(col("Work Stress").isNotNull() & col("Physical Activity").isNotNull())
stress_activity_analysis = stress_activity_df.groupBy("Work Stress", "Physical Activity").agg(count("*").alias("count")).orderBy("Work Stress", "Physical Activity")
activity_correlation = []
for row in stress_activity_analysis.collect():
activity_correlation.append({"stress_level": row["Work Stress"], "activity_level": row["Physical Activity"], "count": row["count"]})
gender_stress_df = df.filter(col("Gender").isNotNull() & col("Work Stress").isNotNull())
gender_stress_analysis = gender_stress_df.groupBy("Gender", "Work Stress").agg(count("*").alias("count")).orderBy("Gender", "Work Stress")
gender_stress_result = []
for row in gender_stress_analysis.collect():
gender_stress_result.append({"gender": row["Gender"], "stress_level": row["Work Stress"], "count": row["count"]})
return JsonResponse({"stress_smoking_correlation": smoking_correlation, "stress_alcohol_correlation": alcohol_correlation, "stress_activity_correlation": activity_correlation, "gender_stress_distribution": gender_stress_result})
@require_http_methods(["GET"])
def comprehensive_health_lifestyle_evaluation(request):
health_score_df = df.withColumn("health_score", when(col("Smoking Status") == "No", 1).otherwise(0) + when(col("Alcohol Consumption") == "No", 1).otherwise(0) + when(col("Physical Activity") == "High", 2).when(col("Physical Activity") == "Medium", 1).otherwise(0))
health_distribution = health_score_df.groupBy("health_score").agg(count("*").alias("count")).orderBy("health_score")
score_distribution = []
for row in health_distribution.collect():
score_distribution.append({"health_score": row["health_score"], "count": row["count"]})
health_medicine_df = health_score_df.filter(col("Preferred Medicine").isNotNull())
health_medicine_analysis = health_medicine_df.groupBy("health_score", "Preferred Medicine").agg(count("*").alias("count")).orderBy("health_score", "Preferred Medicine")
medicine_preference = []
for row in health_medicine_analysis.collect():
medicine_preference.append({"health_score": row["health_score"], "medicine_type": row["Preferred Medicine"], "count": row["count"]})
health_stress_df = health_score_df.filter(col("Work Stress").isNotNull())
stress_health_avg = health_stress_df.groupBy("Work Stress").agg(avg("health_score").alias("avg_health_score"), count("*").alias("count")).orderBy("Work Stress")
stress_health_result = []
for row in stress_health_avg.collect():
stress_health_result.append({"stress_level": row["Work Stress"], "avg_health_score": round(row["avg_health_score"], 2), "count": row["count"]})
urban_rural_health_df = health_score_df.filter(col("Urban_Rural").isNotNull())
urban_rural_health_avg = urban_rural_health_df.groupBy("Urban_Rural").agg(avg("health_score").alias("avg_health_score"), count("*").alias("count")).orderBy("Urban_Rural")
location_health_result = []
for row in urban_rural_health_avg.collect():
location_health_result.append({"location": row["Urban_Rural"], "avg_health_score": round(row["avg_health_score"], 2), "count": row["count"]})
return JsonResponse({"health_score_distribution": score_distribution, "health_medicine_preference": medicine_preference, "stress_health_relationship": stress_health_result, "urban_rural_health_comparison": location_health_result})
健康与生活方式数据可视化分析系统-结语
毕设不会大数据？这套Spark+Django健康数据可视化分析系统一次解决技术难题
大数据专家推荐：基于Spark+Django的健康与生活方式数据可视化分析系统开发指南
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！
⚡⚡获取源码主页--> space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上咨询我~~
