大数据毕设导师推荐选题：基于数据挖掘的睡眠中人体压力数据可视化分析系统

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基于数据挖掘的睡眠中人体压力数据可视化分析系统-功能介绍

基于数据挖掘的睡眠中人体压力数据可视化分析系统是一个融合了大数据处理技术与机器学习算法的综合性分析平台。该系统采用Hadoop分布式存储架构和Spark大数据计算引擎，通过对海量睡眠监测数据进行深度挖掘和分析，实现对人体在睡眠状态下的各项生理指标与压力水平之间关联关系的精准识别。系统后端基于Django框架构建，集成了Pandas和NumPy等科学计算库，能够处理包括打鼾频率、呼吸率、体温、肢体运动、血氧水平、眼球运动、睡眠时长、心率等多维度生理数据。前端采用Vue+ElementUI+Echarts技术栈，为用户提供直观的数据可视化界面，支持多种图表展示形式和交互式数据探索功能。通过MySQL数据库进行数据持久化存储，系统能够实现从原始数据采集、预处理、特征提取、模式识别到可视化展示的完整数据分析流程，为睡眠质量评估和健康管理提供科学的数据支撑。

基于数据挖掘的睡眠中人体压力数据可视化分析系统-选题背景意义

现代社会快节奏的生活方式和高强度的工作压力导致睡眠障碍问题日益突出，越来越多的人面临着睡眠质量下降和压力水平升高的困扰，睡眠作为人体生理活动的重要组成部分，其质量直接影响着个体的身心健康状况，而压力水平的变化也会通过神经内分泌系统对睡眠过程中的各项生理指标产生显著影响。传统的睡眠研究主要依赖于主观问卷调查和简单的生理参数监测，缺乏对海量睡眠数据的深度挖掘和系统性分析。随着可穿戴设备和物联网技术的普及，人们能够更加便捷地收集到包括心率、呼吸频率、体温变化、肢体活动等在内的详细睡眠生理数据，这些大规模、多维度的数据为深入研究睡眠与压力之间的内在关联提供了宝贵的数据基础。同时，大数据技术和机器学习算法的快速发展为处理和分析这些复杂的生理数据提供了技术支撑，使得构建智能化的睡眠压力分析系统成为可能。 本课题的研究具有重要的理论价值和实际应用意义。从理论层面来看，通过运用数据挖掘技术对睡眠期间的多维生理指标进行系统性分析，能够深入揭示不同生理参数与压力水平之间的内在关联机制，为睡眠医学和心理健康研究提供新的数据分析视角和方法论支撑。从实际应用角度而言，该系统能够为个人健康管理提供客观的数据参考，帮助用户更好地了解自身的睡眠状态和压力水平，从而制定更加合理的生活作息和压力管理策略。虽然作为毕业设计项目，本系统的研究范围和应用规模相对有限，但其所采用的大数据处理架构和数据挖掘方法具有一定的发展潜力。

基于数据挖掘的睡眠中人体压力数据可视化分析系统-技术选型

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

基于数据挖掘的睡眠中人体压力数据可视化分析系统-视频展示

演示视频

基于数据挖掘的睡眠中人体压力数据可视化分析系统-图片展示

基于数据挖掘的睡眠中人体压力数据可视化分析系统-代码展示

//大数据部分代码展示

from pyspark.sql import SparkSession

from pyspark.sql.functions import *

from pyspark.ml.feature import VectorAssembler

from pyspark.ml.clustering import KMeans

import pandas as pd

import numpy as np

from django.http import JsonResponse

import json

def sleep_pressure_data_analysis(request):

    spark = SparkSession.builder.appName("SleepPressureAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()

    hdfs_path = "hdfs://localhost:9000/sleep_data/sleep_pressure_dataset.csv"

    df = spark.read.option("header", "true").option("inferSchema", "true").csv(hdfs_path)

    df = df.withColumnRenamed("sr", "snoring_rate").withColumnRenamed("rr", "respiration_rate").withColumnRenamed("t", "body_temperature").withColumnRenamed("lm", "limb_movement").withColumnRenamed("bo", "blood_oxygen").withColumnRenamed("rem", "eye_movement").withColumnRenamed("sr.1", "sleep_hours").withColumnRenamed("hr", "heart_rate").withColumnRenamed("sl", "stress_level")

    df_clean = df.filter(col("stress_level").isNotNull() & col("heart_rate").isNotNull() & col("respiration_rate").isNotNull())

    stress_distribution = df_clean.groupBy("stress_level").agg(count("*").alias("count"), round(avg("heart_rate"), 2).alias("avg_heart_rate"), round(avg("respiration_rate"), 2).alias("avg_respiration_rate"), round(avg("blood_oxygen"), 2).alias("avg_blood_oxygen"), round(avg("body_temperature"), 2).alias("avg_body_temperature"))

    stress_stats = stress_distribution.collect()

    correlation_features = ["snoring_rate", "respiration_rate", "body_temperature", "limb_movement", "blood_oxygen", "eye_movement", "sleep_hours", "heart_rate"]

    correlation_results = {}

    for feature in correlation_features:

        corr_value = df_clean.stat.corr("stress_level", feature)

        correlation_results[feature] = round(corr_value, 4)

    high_stress_group = df_clean.filter(col("stress_level") == 4).select(*correlation_features)

    low_stress_group = df_clean.filter(col("stress_level") == 0).select(*correlation_features)

    comparison_results = {}

    for feature in correlation_features:

        high_avg = high_stress_group.agg(avg(feature)).collect()[0][0]

        low_avg = low_stress_group.agg(avg(feature)).collect()[0][0]

        difference = round(high_avg - low_avg, 3) if high_avg and low_avg else 0

        comparison_results[feature] = {"high_stress_avg": round(high_avg, 3) if high_avg else 0, "low_stress_avg": round(low_avg, 3) if low_avg else 0, "difference": difference}

    sleep_duration_analysis = df_clean.withColumn("sleep_category", when(col("sleep_hours") < 6, "短睡眠").when(col("sleep_hours") <= 8, "正常睡眠").otherwise("长睡眠"))

    sleep_stress_cross = sleep_duration_analysis.groupBy("sleep_category", "stress_level").agg(count("*").alias("count")).orderBy("sleep_category", "stress_level")

    sleep_cross_results = sleep_stress_cross.collect()

    result_data = {"stress_distribution": [row.asDict() for row in stress_stats], "correlation_analysis": correlation_results, "high_low_comparison": comparison_results, "sleep_duration_analysis": [row.asDict() for row in sleep_cross_results]}

    spark.stop()

    return JsonResponse(result_data, safe=False)

def physiological_clustering_analysis(request):

    spark = SparkSession.builder.appName("PhysiologicalClustering").config("spark.sql.adaptive.enabled", "true").getOrCreate()

    hdfs_path = "hdfs://localhost:9000/sleep_data/sleep_pressure_dataset.csv"

    df = spark.read.option("header", "true").option("inferSchema", "true").csv(hdfs_path)

    df = df.withColumnRenamed("sr", "snoring_rate").withColumnRenamed("rr", "respiration_rate").withColumnRenamed("t", "body_temperature").withColumnRenamed("lm", "limb_movement").withColumnRenamed("bo", "blood_oxygen").withColumnRenamed("rem", "eye_movement").withColumnRenamed("sr.1", "sleep_hours").withColumnRenamed("hr", "heart_rate").withColumnRenamed("sl", "stress_level")

    feature_columns = ["snoring_rate", "respiration_rate", "body_temperature", "limb_movement", "blood_oxygen", "eye_movement", "sleep_hours", "heart_rate"]

    df_features = df.select(*feature_columns, "stress_level").na.drop()

    assembler = VectorAssembler(inputCols=feature_columns, outputCol="features")

    df_vector = assembler.transform(df_features)

    kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")

    model = kmeans.fit(df_vector)

    df_clustered = model.transform(df_vector)

    cluster_analysis = df_clustered.groupBy("cluster").agg(count("*").alias("cluster_size"), round(avg("stress_level"), 2).alias("avg_stress_level"), round(avg("heart_rate"), 2).alias("avg_heart_rate"), round(avg("respiration_rate"), 2).alias("avg_respiration_rate"), round(avg("blood_oxygen"), 2).alias("avg_blood_oxygen"), round(avg("sleep_hours"), 2).alias("avg_sleep_hours"))

    cluster_results = cluster_analysis.collect()

    stress_distribution_by_cluster = df_clustered.groupBy("cluster", "stress_level").agg(count("*").alias("count")).orderBy("cluster", "stress_level")

    stress_cluster_results = stress_distribution_by_cluster.collect()

    cluster_centers = model.clusterCenters()

    centers_dict = {}

    for i, center in enumerate(cluster_centers):

        centers_dict[f"cluster_{i}"] = {feature_columns[j]: round(float(center[j]), 3) for j in range(len(feature_columns))}

    outlier_detection = df_clustered.select("*", abs(col("heart_rate") - 70).alias("heart_rate_deviation"), abs(col("respiration_rate") - 16).alias("respiration_deviation"))

    potential_outliers = outlier_detection.filter((col("heart_rate_deviation") > 30) | (col("respiration_deviation") > 8)).select("cluster", "stress_level", "heart_rate", "respiration_rate", "heart_rate_deviation", "respiration_deviation")

    outlier_results = potential_outliers.collect()

    clustering_data = {"cluster_summary": [row.asDict() for row in cluster_results], "stress_by_cluster": [row.asDict() for row in stress_cluster_results], "cluster_centers": centers_dict, "outlier_analysis": [row.asDict() for row in outlier_results]}

    spark.stop()

    return JsonResponse(clustering_data, safe=False)

def comprehensive_health_index_analysis(request):

    spark = SparkSession.builder.appName("HealthIndexAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()

    hdfs_path = "hdfs://localhost:9000/sleep_data/sleep_pressure_dataset.csv"

    df = spark.read.option("header", "true").option("inferSchema", "true").csv(hdfs_path)

    df = df.withColumnRenamed("sr", "snoring_rate").withColumnRenamed("rr", "respiration_rate").withColumnRenamed("t", "body_temperature").withColumnRenamed("lm", "limb_movement").withColumnRenamed("bo", "blood_oxygen").withColumnRenamed("rem", "eye_movement").withColumnRenamed("sr.1", "sleep_hours").withColumnRenamed("hr", "heart_rate").withColumnRenamed("sl", "stress_level")

    df_clean = df.filter(col("heart_rate").isNotNull() & col("respiration_rate").isNotNull() & col("blood_oxygen").isNotNull() & col("stress_level").isNotNull())

    heart_rate_normalized = (col("heart_rate") - 60) / 40

    respiration_normalized = (col("respiration_rate") - 12) / 8

    blood_oxygen_normalized = (col("blood_oxygen") - 95) / 5

    sleep_hours_normalized = (col("sleep_hours") - 6) / 4

    df_with_health_index = df_clean.withColumn("health_index", round((heart_rate_normalized * 0.3 + respiration_normalized * 0.25 + blood_oxygen_normalized * 0.25 + sleep_hours_normalized * 0.2) * 100, 2))

    df_categorized = df_with_health_index.withColumn("health_category", when(col("health_index") < 30, "较差").when(col("health_index") < 60, "一般").when(col("health_index") < 80, "良好").otherwise("优秀"))

    health_stress_analysis = df_categorized.groupBy("health_category").agg(count("*").alias("sample_count"), round(avg("stress_level"), 2).alias("avg_stress_level"), round(avg("health_index"), 2).alias("avg_health_index"), round(stddev("stress_level"), 2).alias("stress_std_dev"))

    health_results = health_stress_analysis.collect()

    stress_health_correlation = df_categorized.stat.corr("health_index", "stress_level")

    detailed_health_analysis = df_categorized.groupBy("stress_level").agg(count("*").alias("stress_count"), round(avg("health_index"), 2).alias("avg_health_index"), round(min("health_index"), 2).alias("min_health_index"), round(max("health_index"), 2).alias("max_health_index"))

    detailed_results = detailed_health_analysis.collect()

    risk_assessment = df_categorized.filter((col("health_index") < 40) & (col("stress_level") >= 3)).select("health_index", "stress_level", "heart_rate", "respiration_rate", "blood_oxygen", "sleep_hours")

    high_risk_count = risk_assessment.count()

    risk_profile = risk_assessment.agg(round(avg("health_index"), 2).alias("avg_risk_health_index"), round(avg("stress_level"), 2).alias("avg_risk_stress_level"), round(avg("heart_rate"), 2).alias("avg_risk_heart_rate")).collect()

    trend_analysis = df_categorized.withColumn("health_score_range", when(col("health_index") < 20, "0-20").when(col("health_index") < 40, "20-40").when(col("health_index") < 60, "40-60").when(col("health_index") < 80, "60-80").otherwise("80-100"))

    trend_results = trend_analysis.groupBy("health_score_range").agg(count("*").alias("range_count"), round(avg("stress_level"), 2).alias("avg_stress_in_range")).orderBy("health_score_range")

    trend_data = trend_results.collect()

    comprehensive_data = {"health_category_analysis": [row.asDict() for row in health_results], "correlation_coefficient": round(stress_health_correlation, 4), "stress_level_breakdown": [row.asDict() for row in detailed_results], "high_risk_assessment": {"high_risk_count": high_risk_count, "risk_profile": risk_profile[0].asDict() if risk_profile else {}}, "health_score_trends": [row.asDict() for row in trend_data]}

    spark.stop()

    return JsonResponse(comprehensive_data, safe=False)

基于数据挖掘的睡眠中人体压力数据可视化分析系统-结语

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