💖💖作者：计算机编程小咖 💙💙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 深度学习实战项目
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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》是一个集数据采集、存储、分析与可视化展示于一体的综合性大数据应用平台，该系统以我国日益严峻的人口老龄化问题为研究背景，运用先进的大数据处理技术对全国范围内的健康老龄化民意调查数据进行深度挖掘与智能分析。系统采用Hadoop分布式文件系统（HDFS）作为底层数据存储架构，结合Apache Spark大数据计算引擎进行海量数据的并行处理与实时分析，确保了系统在处理大规模调查数据时的高效性与稳定性。技术实现方面，系统提供Python+Django和Java+Spring Boot两套完整的后端解决方案，前端采用Vue.js框架结合ElementUI组件库构建现代化的用户界面，并通过Echarts图表库实现丰富的数据可视化效果。系统功能模块涵盖系统首页总览、用户信息管理、数据大屏可视化展示、调查数据管理、人口健康差异分析、健康风险管理分析、健康状况综合分析、医疗服务利用分析以及睡眠质量相关性分析等九大核心模块，通过多维度的数据分析与直观的图表展示，为政策制定者、研究学者及相关医疗机构提供科学的决策支持，有效推动我国健康老龄化事业的发展与完善。
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
spark = SparkSession.builder.appName("HealthAgingAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
survey_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/health_survey").option("dbtable", "survey_data").option("user", "root").option("password", "password").load()
def analyze_population_health_differences(request):
age_groups = survey_df.select("age_group", "health_status", "gender", "region").filter(col("age_group").isNotNull())
health_by_age = age_groups.groupBy("age_group", "gender").agg(avg("health_status").alias("avg_health"), count("*").alias("sample_count"))
regional_health = age_groups.groupBy("region", "age_group").agg(avg("health_status").alias("regional_avg"), stddev("health_status").alias("health_variance"))
gender_differences = age_groups.groupBy("gender").agg(avg("health_status").alias("gender_avg_health"), max("health_status").alias("max_health"), min("health_status").alias("min_health"))
age_health_correlation = age_groups.stat.corr("age_group", "health_status")
critical_regions = regional_health.filter(col("regional_avg") < 3.0).orderBy(desc("health_variance"))
health_gaps = health_by_age.withColumn("health_gap", when(col("gender") == "male", col("avg_health")).otherwise(0) - when(col("gender") == "female", col("avg_health")).otherwise(0))
vulnerable_groups = age_groups.filter((col("health_status") < 2.5) & (col("age_group") >= 65))
population_distribution = age_groups.groupBy("age_group").agg(count("*").alias("population_count"), (count("*") * 100.0 / age_groups.count()).alias("percentage"))
health_trend_data = health_by_age.join(regional_health, ["age_group"], "inner").join(population_distribution, ["age_group"], "inner")
analysis_results = health_trend_data.select("age_group", "gender", "avg_health", "regional_avg", "health_variance", "population_count", "percentage").collect()
risk_indicators = vulnerable_groups.groupBy("region").agg(count("*").alias("high_risk_count"), avg("health_status").alias("risk_level"))
comprehensive_metrics = age_groups.agg(avg("health_status").alias("overall_avg"), stddev("health_status").alias("overall_std"), skewness("health_status").alias("distribution_skew"))
response_data = {"health_differences": [row.asDict() for row in analysis_results], "risk_indicators": [row.asDict() for row in risk_indicators.collect()], "overall_metrics": comprehensive_metrics.collect()[0].asDict(), "correlation_coefficient": age_health_correlation}
return JsonResponse(response_data)
def analyze_health_risk_management(request):
risk_factors_df = survey_df.select("chronic_diseases", "lifestyle_score", "medical_access", "age_group", "health_status", "exercise_frequency", "diet_quality")
chronic_disease_risk = risk_factors_df.groupBy("chronic_diseases").agg(avg("health_status").alias("avg_health_with_disease"), count("*").alias("disease_prevalence"))
lifestyle_risk_correlation = risk_factors_df.stat.corr("lifestyle_score", "health_status")
medical_access_impact = risk_factors_df.groupBy("medical_access").agg(avg("health_status").alias("health_by_access"), count("*").alias("access_count"))
high_risk_population = risk_factors_df.filter((col("chronic_diseases") >= 2) | (col("lifestyle_score") < 3) | (col("medical_access") == "poor"))
risk_scoring_model = risk_factors_df.withColumn("risk_score", when(col("chronic_diseases") >= 3, 5).when(col("chronic_diseases") >= 2, 4).when(col("lifestyle_score") < 2, 4).when(col("medical_access") == "poor", 3).otherwise(1))
exercise_protection_factor = risk_factors_df.groupBy("exercise_frequency").agg(avg("health_status").alias("health_by_exercise"), avg("chronic_diseases").alias("avg_diseases"))
diet_risk_analysis = risk_factors_df.groupBy("diet_quality").agg(avg("health_status").alias("health_by_diet"), count("*").alias("diet_group_size"))
age_specific_risks = risk_factors_df.groupBy("age_group").agg(avg("chronic_diseases").alias("avg_chronic_by_age"), avg("lifestyle_score").alias("avg_lifestyle_by_age"))
preventive_recommendations = risk_scoring_model.filter(col("risk_score") >= 3).groupBy("age_group", "medical_access").agg(count("*").alias("intervention_needed"))
risk_stratification = risk_scoring_model.withColumn("risk_level", when(col("risk_score") >= 4, "high").when(col("risk_score") >= 2, "medium").otherwise("low"))
risk_distribution = risk_stratification.groupBy("risk_level").agg(count("*").alias("count"), (count("*") * 100.0 / risk_stratification.count()).alias("percentage"))
modifiable_factors = risk_factors_df.select("lifestyle_score", "exercise_frequency", "diet_quality", "health_status").filter(col("lifestyle_score").isNotNull())
intervention_targets = modifiable_factors.filter((col("lifestyle_score") < 3) | (col("exercise_frequency") < 2) | (col("diet_quality") < 3))
risk_management_data = {"chronic_disease_analysis": [row.asDict() for row in chronic_disease_risk.collect()], "lifestyle_correlation": lifestyle_risk_correlation, "medical_access_impact": [row.asDict() for row in medical_access_impact.collect()], "high_risk_count": high_risk_population.count(), "risk_distribution": [row.asDict() for row in risk_distribution.collect()], "intervention_targets": intervention_targets.count()}
return JsonResponse(risk_management_data)
def generate_dashboard_visualization(request):
dashboard_df = survey_df.select("*").cache()
total_respondents = dashboard_df.count()
avg_health_score = dashboard_df.agg(avg("health_status")).collect()[0][0]
age_distribution = dashboard_df.groupBy("age_group").agg(count("*").alias("count")).orderBy("age_group")
gender_health_comparison = dashboard_df.groupBy("gender").agg(avg("health_status").alias("avg_health"), count("*").alias("total_count"))
regional_health_map = dashboard_df.groupBy("region").agg(avg("health_status").alias("regional_health"), count("*").alias("regional_population"))
monthly_survey_trends = dashboard_df.groupBy("survey_month").agg(count("*").alias("monthly_responses"), avg("health_status").alias("monthly_avg_health")).orderBy("survey_month")
top_health_concerns = dashboard_df.groupBy("primary_health_concern").agg(count("*").alias("concern_frequency")).orderBy(desc("concern_frequency")).limit(10)
health_service_utilization = dashboard_df.groupBy("medical_service_usage").agg(count("*").alias("usage_count"), avg("health_status").alias("health_by_usage"))
sleep_quality_distribution = dashboard_df.groupBy("sleep_quality_rating").agg(count("*").alias("sleep_count")).orderBy("sleep_quality_rating")
chronic_disease_prevalence = dashboard_df.filter(col("chronic_diseases") > 0).groupBy("chronic_diseases").agg(count("*").alias("disease_count"))
lifestyle_health_scatter = dashboard_df.select("lifestyle_score", "health_status", "age_group").filter(col("lifestyle_score").isNotNull() & col("health_status").isNotNull())
health_improvement_trends = dashboard_df.filter(col("health_change_direction") == "improved").groupBy("age_group").agg(count("*").alias("improved_count"))
critical_health_alerts = dashboard_df.filter(col("health_status") <= 2.0).groupBy("region", "age_group").agg(count("*").alias("critical_cases"))
satisfaction_scores = dashboard_df.groupBy("healthcare_satisfaction").agg(count("*").alias("satisfaction_count"), avg("health_status").alias("health_by_satisfaction"))
dashboard_summary = {"total_respondents": total_respondents, "overall_health_score": round(avg_health_score, 2), "age_groups": [row.asDict() for row in age_distribution.collect()], "gender_comparison": [row.asDict() for row in gender_health_comparison.collect()], "regional_data": [row.asDict() for row in regional_health_map.collect()], "survey_trends": [row.asDict() for row in monthly_survey_trends.collect()], "health_concerns": [row.asDict() for row in top_health_concerns.collect()], "service_utilization": [row.asDict() for row in health_service_utilization.collect()], "sleep_distribution": [row.asDict() for row in sleep_quality_distribution.collect()], "chronic_diseases": [row.asDict() for row in chronic_disease_prevalence.collect()], "critical_alerts": critical_health_alerts.count()}
return JsonResponse(dashboard_summary)
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：计算机编程小咖 💙💙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 深度学习实战项目
