💖💖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
基于大数据的全国健康老龄化民意调查数据分析与可视化系统介绍
《基于大数据的全国健康老龄化民意调查数据分析与可视化系统》是一个针对老龄化健康数据进行深度分析的综合性平台。系统采用Hadoop+Spark大数据框架作为核心技术架构，通过HDFS实现海量调查数据的分布式存储，利用Spark SQL和Pandas进行多维度数据清洗与分析处理。前端采用Vue+ElementUI+Echarts技术栈构建交互界面，后端基于Django框架搭建RESTful API服务，使用MySQL数据库存储结构化数据。系统涵盖八大功能模块：系统首页提供数据概览，用户模块管理权限与角色，调查数据模块支持批量导入与查询，人口健康差异分析模块从年龄、性别、地域等维度挖掘健康指标差异，健康风险管理分析模块识别高危人群特征，健康状况综合分析模块整合多源数据生成评估报告，医疗服务利用分析模块统计就诊行为与资源配置，睡眠质量相关性分析模块探索睡眠与慢性病的关联关系。整个系统通过Spark分布式计算引擎处理TB级调查数据，结合NumPy进行统计建模，最终以可视化图表形式呈现分析结果，为研究老龄化健康问题提供数据支撑和决策参考。
基于大数据的全国健康老龄化民意调查数据分析与可视化系统演示视频
基于大数据的全国健康老龄化民意调查数据分析与可视化系统演示图片
基于大数据的全国健康老龄化民意调查数据分析与可视化系统代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import col,avg,count,sum,when,stddev,corr,round as spark_round
from pyspark.sql.types import StructType,StructField,StringType,IntegerType,FloatType
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views.decorators.http import require_http_methods
import json
spark=SparkSession.builder.appName("HealthAgingAnalysis").config("spark.sql.warehouse.dir","/user/hive/warehouse").config("spark.executor.memory","4g").config("spark.driver.memory","2g").getOrCreate()
@require_http_methods(["POST"])
def population_health_difference_analysis(request):
data=json.loads(request.body)
age_group=data.get('age_group')
gender=data.get('gender')
region=data.get('region')
schema=StructType([StructField("id",IntegerType(),True),StructField("age",IntegerType(),True),StructField("gender",StringType(),True),StructField("region",StringType(),True),StructField("chronic_disease",StringType(),True),StructField("bmi",FloatType(),True),StructField("blood_pressure_high",IntegerType(),True),StructField("blood_pressure_low",IntegerType(),True),StructField("blood_sugar",FloatType(),True),StructField("checkup_frequency",IntegerType(),True)])
df=spark.read.format("csv").option("header","true").schema(schema).load("hdfs://localhost:9000/health_survey/raw_data.csv")
filtered_df=df
if age_group:
age_ranges={"young":(60,70),"middle":(70,80),"old":(80,100)}
if age_group in age_ranges:
min_age,max_age=age_ranges[age_group]
filtered_df=filtered_df.filter((col("age")>=min_age)&(col("age")<max_age))
if gender:
filtered_df=filtered_df.filter(col("gender")==gender)
if region:
filtered_df=filtered_df.filter(col("region")==region)
disease_stats=filtered_df.groupBy("chronic_disease").agg(count("id").alias("patient_count"),(count("id")/filtered_df.count()*100).alias("prevalence_rate")).orderBy(col("patient_count").desc())
bmi_stats=filtered_df.agg(avg("bmi").alias("avg_bmi"),stddev("bmi").alias("std_bmi"))
bp_high_stats=filtered_df.agg(avg("blood_pressure_high").alias("avg_bp_high"),stddev("blood_pressure_high").alias("std_bp_high"))
bp_low_stats=filtered_df.agg(avg("blood_pressure_low").alias("avg_bp_low"),stddev("blood_pressure_low").alias("std_bp_low"))
sugar_stats=filtered_df.agg(avg("blood_sugar").alias("avg_blood_sugar"),stddev("blood_sugar").alias("std_blood_sugar"))
checkup_stats=filtered_df.groupBy("checkup_frequency").agg(count("id").alias("people_count")).orderBy("checkup_frequency")
disease_data=disease_stats.toPandas().to_dict('records')
bmi_data=bmi_stats.toPandas().to_dict('records')[0]
bp_high_data=bp_high_stats.toPandas().to_dict('records')[0]
bp_low_data=bp_low_stats.toPandas().to_dict('records')[0]
sugar_data=sugar_stats.toPandas().to_dict('records')[0]
checkup_data=checkup_stats.toPandas().to_dict('records')
result={"disease_distribution":disease_data,"bmi_statistics":bmi_data,"blood_pressure_high_statistics":bp_high_data,"blood_pressure_low_statistics":bp_low_data,"blood_sugar_statistics":sugar_data,"checkup_frequency_distribution":checkup_data,"total_samples":filtered_df.count()}
return JsonResponse({"code":200,"message":"分析完成","data":result})
@require_http_methods(["POST"])
def health_risk_management_analysis(request):
data=json.loads(request.body)
risk_threshold=data.get('risk_threshold',0.6)
schema=StructType([StructField("id",IntegerType(),True),StructField("age",IntegerType(),True),StructField("gender",StringType(),True),StructField("bmi",FloatType(),True),StructField("blood_pressure_high",IntegerType(),True),StructField("blood_pressure_low",IntegerType(),True),StructField("blood_sugar",FloatType(),True),StructField("smoking",IntegerType(),True),StructField("drinking",IntegerType(),True),StructField("exercise_frequency",IntegerType(),True),StructField("chronic_disease",StringType(),True)])
df=spark.read.format("csv").option("header","true").schema(schema).load("hdfs://localhost:9000/health_survey/raw_data.csv")
df=df.withColumn("hypertension_risk",when((col("blood_pressure_high")>=140)|(col("blood_pressure_low")>=90),1).otherwise(0))
df=df.withColumn("diabetes_risk",when(col("blood_sugar")>=6.1,1).otherwise(0))
df=df.withColumn("obesity_risk",when(col("bmi")>=28,1).otherwise(0))
df=df.withColumn("lifestyle_risk",when((col("smoking")==1)|(col("drinking")==1)|(col("exercise_frequency")<2),1).otherwise(0))
df=df.withColumn("total_risk_score",(col("hypertension_risk")+col("diabetes_risk")+col("obesity_risk")+col("lifestyle_risk"))/4)
high_risk_df=df.filter(col("total_risk_score")>=risk_threshold)
risk_distribution=df.groupBy("total_risk_score").agg(count("id").alias("people_count")).orderBy("total_risk_score")
age_risk_correlation=df.groupBy("age").agg(avg("total_risk_score").alias("avg_risk_score")).orderBy("age")
gender_risk_stats=df.groupBy("gender").agg(avg("total_risk_score").alias("avg_risk_score"),count(when(col("total_risk_score")>=risk_threshold,1)).alias("high_risk_count"))
hypertension_high_risk=high_risk_df.filter(col("hypertension_risk")==1).count()
diabetes_high_risk=high_risk_df.filter(col("diabetes_risk")==1).count()
obesity_high_risk=high_risk_df.filter(col("obesity_risk")==1).count()
lifestyle_high_risk=high_risk_df.filter(col("lifestyle_risk")==1).count()
high_risk_people=high_risk_df.select("id","age","gender","bmi","blood_pressure_high","blood_pressure_low","blood_sugar","total_risk_score").limit(100).toPandas().to_dict('records')
risk_dist_data=risk_distribution.toPandas().to_dict('records')
age_risk_data=age_risk_correlation.toPandas().to_dict('records')
gender_risk_data=gender_risk_stats.toPandas().to_dict('records')
result={"total_samples":df.count(),"high_risk_count":high_risk_df.count(),"high_risk_rate":round(high_risk_df.count()/df.count()*100,2),"risk_distribution":risk_dist_data,"age_risk_correlation":age_risk_data,"gender_risk_statistics":gender_risk_data,"risk_factors":{"hypertension":hypertension_high_risk,"diabetes":diabetes_high_risk,"obesity":obesity_high_risk,"lifestyle":lifestyle_high_risk},"high_risk_samples":high_risk_people}
return JsonResponse({"code":200,"message":"风险分析完成","data":result})
@require_http_methods(["POST"])
def sleep_quality_correlation_analysis(request):
data=json.loads(request.body)
schema=StructType([StructField("id",IntegerType(),True),StructField("age",IntegerType(),True),StructField("gender",StringType(),True),StructField("sleep_duration",FloatType(),True),StructField("sleep_quality_score",IntegerType(),True),StructField("insomnia",IntegerType(),True),StructField("blood_pressure_high",IntegerType(),True),StructField("blood_sugar",FloatType(),True),StructField("bmi",FloatType(),True),StructField("depression_score",IntegerType(),True),StructField("chronic_disease",StringType(),True)])
df=spark.read.format("csv").option("header","true").schema(schema).load("hdfs://localhost:9000/health_survey/raw_data.csv")
sleep_bp_corr=df.select(corr("sleep_quality_score","blood_pressure_high").alias("correlation")).collect()[0]["correlation"]
sleep_sugar_corr=df.select(corr("sleep_quality_score","blood_sugar").alias("correlation")).collect()[0]["correlation"]
sleep_bmi_corr=df.select(corr("sleep_quality_score","bmi").alias("correlation")).collect()[0]["correlation"]
sleep_depression_corr=df.select(corr("sleep_quality_score","depression_score").alias("correlation")).collect()[0]["correlation"]
sleep_duration_quality_corr=df.select(corr("sleep_duration","sleep_quality_score").alias("correlation")).collect()[0]["correlation"]
insomnia_stats=df.groupBy("insomnia").agg(count("id").alias("people_count"),avg("blood_pressure_high").alias("avg_bp_high"),avg("blood_sugar").alias("avg_blood_sugar"),avg("depression_score").alias("avg_depression"))
sleep_quality_groups=df.withColumn("quality_level",when(col("sleep_quality_score")>=80,"good").when(col("sleep_quality_score")>=60,"medium").otherwise("poor"))
quality_health_stats=sleep_quality_groups.groupBy("quality_level").agg(count("id").alias("people_count"),avg("blood_pressure_high").alias("avg_bp_high"),avg("blood_sugar").alias("avg_blood_sugar"),avg("bmi").alias("avg_bmi"),avg("depression_score").alias("avg_depression"))
age_sleep_stats=df.groupBy("age").agg(avg("sleep_duration").alias("avg_sleep_duration"),avg("sleep_quality_score").alias("avg_quality_score")).orderBy("age")
gender_sleep_stats=df.groupBy("gender").agg(avg("sleep_duration").alias("avg_sleep_duration"),avg("sleep_quality_score").alias("avg_quality_score"),count(when(col("insomnia")==1,1)).alias("insomnia_count"))
disease_sleep_stats=df.filter(col("chronic_disease").isNotNull()).groupBy("chronic_disease").agg(avg("sleep_quality_score").alias("avg_quality_score"),count(when(col("insomnia")==1,1)).alias("insomnia_count")).orderBy(col("avg_quality_score"))
insomnia_data=insomnia_stats.toPandas().to_dict('records')
quality_health_data=quality_health_stats.toPandas().to_dict('records')
age_sleep_data=age_sleep_stats.toPandas().to_dict('records')
gender_sleep_data=gender_sleep_stats.toPandas().to_dict('records')
disease_sleep_data=disease_sleep_stats.toPandas().to_dict('records')
result={"correlation_coefficients":{"sleep_blood_pressure":round(sleep_bp_corr,4) if sleep_bp_corr else 0,"sleep_blood_sugar":round(sleep_sugar_corr,4) if sleep_sugar_corr else 0,"sleep_bmi":round(sleep_bmi_corr,4) if sleep_bmi_corr else 0,"sleep_depression":round(sleep_depression_corr,4) if sleep_depression_corr else 0,"sleep_duration_quality":round(sleep_duration_quality_corr,4) if sleep_duration_quality_corr else 0},"insomnia_health_impact":insomnia_data,"sleep_quality_health_statistics":quality_health_data,"age_sleep_pattern":age_sleep_data,"gender_sleep_statistics":gender_sleep_data,"chronic_disease_sleep_impact":disease_sleep_data,"total_samples":df.count()}
return JsonResponse({"code":200,"message":"睡眠质量相关性分析完成","data":result})
基于大数据的全国健康老龄化民意调查数据分析与可视化系统文档展示
💖💖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
