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基于大数据的压力检测数据分析系统-功能介绍
基于大数据的压力检测数据分析系统是一套专门针对人群压力状态进行大规模数据采集、存储、处理和分析的智能化平台。该系统采用Hadoop分布式文件系统作为底层存储架构，结合Spark大数据处理引擎，能够高效处理海量的压力相关数据，包括心理量表评分、生理指标监测、行为模式记录等多维度信息。系统通过Django后端框架提供稳定的API服务，使用Vue+ElementUI构建直观的前端界面，集成ECharts图表组件实现数据的可视化展示。核心功能涵盖压力水平分布统计、时间趋势分析、人格特质相关性分析、生活行为模式挖掘、生理指标关联分析等五大分析维度，运用Spark SQL进行复杂的数据查询和统计计算，结合Pandas和NumPy进行深度数据挖掘。系统能够为研究人员提供压力影响因素的科学分析，为健康管理机构提供人群压力状况的整体评估，为个人用户提供压力水平的客观监测，在心理健康研究、公共卫生管理、个人健康监测等领域具有实际应用价值。
基于大数据的压力检测数据分析系统-选题背景意义
选题背景 随着现代社会生活节奏的加快和竞争压力的增大，心理压力问题已经成为影响人们身心健康的重要因素。传统的压力评估方法主要依赖问卷调查和专业量表测评，这种方式存在数据收集效率低、样本规模有限、分析维度单一等局限性。现代智能设备的普及为大规模压力数据的采集提供了新的可能性，通过手机传感器、可穿戴设备等能够连续监测用户的生理指标、行为模式和生活习惯，产生了大量与压力状态相关的多维度数据。这些海量数据的有效处理和深度挖掘需要依托大数据技术来实现，传统的数据处理方法已经无法满足大规模、多维度压力数据分析的需求。大数据技术特别是Hadoop和Spark平台的成熟，为压力检测数据的分布式存储和并行计算提供了强有力的技术支撑，使得从海量数据中发现压力变化规律、识别影响因素成为可能。 选题意义 本课题的研究意义主要体现在技术应用和实际价值两个方面。从技术角度来看，该系统将大数据处理技术与健康监测领域相结合，为计算机专业学生提供了一个实践Hadoop分布式存储、Spark大数据计算、机器学习算法等前沿技术的综合性项目平台，有助于培养学生解决实际问题的工程能力和技术应用能力。从实用价值来说，压力检测数据分析系统能够帮助研究人员更好地理解压力形成机制和影响因素，为心理健康研究提供数据支持和分析工具；可以为健康管理机构提供人群压力状况的整体评估，辅助制定针对性的健康干预策略；也可以为个人用户提供压力水平的客观监测和趋势分析，增强人们对自身健康状态的认知。当然，作为一个毕业设计项目，本系统主要目的还是验证大数据技术在健康数据处理方面的可行性，探索多维度数据融合分析的方法，为后续更深入的研究工作奠定基础，其实际应用效果还需要进一步的验证和完善。
基于大数据的压力检测数据分析系统-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
基于大数据的压力检测数据分析系统-视频展示
基于大数据的压力检测数据分析系统-图片展示
基于大数据的压力检测数据分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.ml.stat import Correlation
from pyspark.ml.feature import VectorAssembler
import pandas as pd
import numpy as np
def initialize_spark_session():
spark = SparkSession.builder.appName("PressureDetectionAnalysis").config("spark.some.config.option", "some-value").getOrCreate()
return spark
def pressure_level_distribution_analysis(spark):
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/pressure_data/stress_detection.csv")
pressure_stats = df.select("PSS_score").describe()
pressure_distribution = df.groupBy(when(col("PSS_score") <= 20, "低压力").when(col("PSS_score") <= 30, "中压力").otherwise("高压力").alias("pressure_level")).count().orderBy("pressure_level")
daily_avg_pressure = df.groupBy("day").agg(avg("PSS_score").alias("avg_pressure")).orderBy("day")
participant_pressure = df.groupBy("participant_id").agg(avg("PSS_score").alias("avg_pressure"), max("PSS_score").alias("max_pressure"), min("PSS_score").alias("min_pressure"))
high_pressure_days = df.filter(col("PSS_score") > 30).groupBy("participant_id").count().withColumnRenamed("count", "high_pressure_days")
time_trend_analysis = df.groupBy("day").agg(avg("PSS_score").alias("daily_avg"), stddev("PSS_score").alias("daily_std"))
pressure_range_stats = df.select(when(col("PSS_score").between(10, 20), 1).otherwise(0).alias("low_pressure"), when(col("PSS_score").between(21, 30), 1).otherwise(0).alias("medium_pressure"), when(col("PSS_score").between(31, 40), 1).otherwise(0).alias("high_pressure"))
range_summary = pressure_range_stats.agg(sum("low_pressure").alias("low_count"), sum("medium_pressure").alias("medium_count"), sum("high_pressure").alias("high_count"))
weekly_pattern = df.withColumn("week_day", dayofweek(to_date(col("day"), "yyyy-MM-dd"))).groupBy("week_day").agg(avg("PSS_score").alias("weekly_avg_pressure")).orderBy("week_day")
participant_consistency = df.groupBy("participant_id").agg(stddev("PSS_score").alias("pressure_variance")).filter(col("pressure_variance").isNotNull())
extreme_pressure_events = df.filter((col("PSS_score") >= 35) | (col("PSS_score") <= 15)).select("participant_id", "day", "PSS_score")
pressure_change_rate = df.withColumn("prev_pressure", lag("PSS_score").over(Window.partitionBy("participant_id").orderBy("day"))).withColumn("pressure_change", col("PSS_score") - col("prev_pressure")).filter(col("pressure_change").isNotNull())
result_dict = {"distribution": pressure_distribution.collect(), "daily_trend": daily_avg_pressure.collect(), "participant_stats": participant_pressure.collect(), "high_pressure_frequency": high_pressure_days.collect()}
return result_dict
def personality_pressure_correlation_analysis(spark):
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/pressure_data/stress_detection.csv")
personality_cols = ["Openness", "Conscientiousness", "Extraversion", "Agreeableness", "Neuroticism"]
assembler = VectorAssembler(inputCols=personality_cols + ["PSS_score"], outputCol="features")
vector_df = assembler.transform(df).select("features")
correlation_matrix = Correlation.corr(vector_df, "features").head()[0].toArray()
neuroticism_pressure = df.select("Neuroticism", "PSS_score").filter((col("Neuroticism").isNotNull()) & (col("PSS_score").isNotNull()))
neuroticism_corr = neuroticism_pressure.stat.corr("Neuroticism", "PSS_score")
extraversion_pressure = df.select("Extraversion", "PSS_score").filter((col("Extraversion").isNotNull()) & (col("PSS_score").isNotNull()))
extraversion_groups = extraversion_pressure.withColumn("extraversion_level", when(col("Extraversion") >= 4, "高外向").when(col("Extraversion") >= 3, "中外向").otherwise("低外向"))
extraversion_pressure_stats = extraversion_groups.groupBy("extraversion_level").agg(avg("PSS_score").alias("avg_pressure"), count("*").alias("count"))
openness_pressure = df.select("Openness", "PSS_score").filter((col("Openness").isNotNull()) & (col("PSS_score").isNotNull()))
openness_corr = openness_pressure.stat.corr("Openness", "PSS_score")
conscientiousness_pressure = df.select("Conscientiousness", "PSS_score").filter((col("Conscientiousness").isNotNull()) & (col("PSS_score").isNotNull()))
conscientiousness_groups = conscientiousness_pressure.withColumn("conscientiousness_level", when(col("Conscientiousness") >= 4, "高责任心").when(col("Conscientiousness") >= 3, "中责任心").otherwise("低责任心"))
conscientiousness_stats = conscientiousness_groups.groupBy("conscientiousness_level").agg(avg("PSS_score").alias("avg_pressure"), stddev("PSS_score").alias("pressure_std"))
agreeableness_pressure = df.select("Agreeableness", "PSS_score").filter((col("Agreeableness").isNotNull()) & (col("PSS_score").isNotNull()))
personality_composite = df.select(*personality_cols, "PSS_score").filter(col("PSS_score").isNotNull())
high_neuroticism_high_pressure = personality_composite.filter((col("Neuroticism") >= 4) & (col("PSS_score") >= 30)).count()
low_neuroticism_low_pressure = personality_composite.filter((col("Neuroticism") <= 2) & (col("PSS_score") <= 20)).count()
personality_risk_factors = personality_composite.withColumn("risk_score", col("Neuroticism") * 0.4 - col("Extraversion") * 0.2 - col("Conscientiousness") * 0.2 + col("PSS_score") * 0.6)
result_dict = {"neuroticism_correlation": neuroticism_corr, "extraversion_stats": extraversion_pressure_stats.collect(), "openness_correlation": openness_corr, "conscientiousness_stats": conscientiousness_stats.collect(), "correlation_matrix": correlation_matrix.tolist()}
return result_dict
def lifestyle_behavior_pressure_analysis(spark):
df = spark.read.format("csv").option("header", "true").option("inferSchema", "true").load("hdfs://localhost:9000/pressure_data/stress_detection.csv")
sleep_pressure_analysis = df.select("sleep_duration", "PSQI_score", "PSS_score").filter((col("sleep_duration").isNotNull()) & (col("PSQI_score").isNotNull()) & (col("PSS_score").isNotNull()))
sleep_duration_correlation = sleep_pressure_analysis.stat.corr("sleep_duration", "PSS_score")
sleep_quality_correlation = sleep_pressure_analysis.stat.corr("PSQI_score", "PSS_score")
sleep_categories = sleep_pressure_analysis.withColumn("sleep_category", when(col("sleep_duration") >= 8, "充足睡眠").when(col("sleep_duration") >= 6, "正常睡眠").otherwise("睡眠不足"))
sleep_pressure_stats = sleep_categories.groupBy("sleep_category").agg(avg("PSS_score").alias("avg_pressure"), count("*").alias("sample_count"))
phone_usage_analysis = df.select("screen_on_time", "num_calls", "num_sms", "PSS_score").filter((col("screen_on_time").isNotNull()) & (col("PSS_score").isNotNull()))
screen_time_correlation = phone_usage_analysis.stat.corr("screen_on_time", "PSS_score")
phone_usage_categories = phone_usage_analysis.withColumn("usage_level", when(col("screen_on_time") >= 8, "重度使用").when(col("screen_on_time") >= 4, "中度使用").otherwise("轻度使用"))
usage_pressure_stats = phone_usage_categories.groupBy("usage_level").agg(avg("PSS_score").alias("avg_pressure"), avg("screen_on_time").alias("avg_screen_time"))
social_activity_analysis = df.select("call_duration", "num_calls", "num_sms", "PSS_score").filter((col("call_duration").isNotNull()) & (col("num_calls").isNotNull()) & (col("PSS_score").isNotNull()))
call_duration_correlation = social_activity_analysis.stat.corr("call_duration", "PSS_score")
social_interaction_score = social_activity_analysis.withColumn("social_score", col("call_duration") * 0.5 + col("num_calls") * 0.3 + col("num_sms") * 0.2)
social_categories = social_interaction_score.withColumn("social_level", when(col("social_score") >= 100, "高社交").when(col("social_score") >= 50, "中社交").otherwise("低社交"))
social_pressure_stats = social_categories.groupBy("social_level").agg(avg("PSS_score").alias("avg_pressure"), avg("social_score").alias("avg_social_score"))
mobility_analysis = df.select("mobility_radius", "mobility_distance", "PSS_score").filter((col("mobility_radius").isNotNull()) & (col("mobility_distance").isNotNull()) & (col("PSS_score").isNotNull()))
mobility_radius_correlation = mobility_analysis.stat.corr("mobility_radius", "PSS_score")
mobility_distance_correlation = mobility_analysis.stat.corr("mobility_distance", "PSS_score")
lifestyle_composite = df.select("sleep_duration", "screen_on_time", "mobility_distance", "PSS_score").filter(col("PSS_score").isNotNull())
lifestyle_risk_assessment = lifestyle_composite.withColumn("lifestyle_risk", when((col("sleep_duration") < 6) & (col("screen_on_time") > 8), "高风险").when((col("sleep_duration") >= 7) & (col("screen_on_time") <= 6), "低风险").otherwise("中风险"))
lifestyle_risk_stats = lifestyle_risk_assessment.groupBy("lifestyle_risk").agg(avg("PSS_score").alias("avg_pressure"), count("*").alias("count"))
result_dict = {"sleep_correlations": {"duration": sleep_duration_correlation, "quality": sleep_quality_correlation}, "sleep_categories": sleep_pressure_stats.collect(), "phone_usage_stats": usage_pressure_stats.collect(), "social_activity_stats": social_pressure_stats.collect(), "mobility_correlations": {"radius": mobility_radius_correlation, "distance": mobility_distance_correlation}, "lifestyle_risk_assessment": lifestyle_risk_stats.collect()}
return result_dict
基于大数据的压力检测数据分析系统-结语
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