💖💖作者：计算机毕业设计小途 💙💙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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基于大数据的睡眠中人体压力数据可视化分析系统介绍
《基于大数据的睡眠中人体压力数据可视化分析系统》是一套运用Hadoop+Spark大数据处理框架构建的智能健康数据分析平台，该系统采用Python+Java双语言开发模式，后端分别支持Django和Spring Boot框架实现，前端基于Vue+ElementUI+Echarts技术栈打造现代化的数据可视化界面。系统核心依托Hadoop分布式文件系统HDFS进行海量睡眠数据存储，通过Spark和Spark SQL实现高性能的数据处理与分析计算，结合Pandas、NumPy等科学计算库对人体生理指标进行深度挖掘。系统功能涵盖压力水平指标均值分析、睡眠压力水平分布分析、综合健康指数趋势分析、生理指标关联度分析、打鼾频率关联分析、睡眠时长影响分析、心率分布范围分析、呼吸率分布范围分析、血氧水平分布分析、体温分布对比分析、眼球运动关联分析、生理指标聚类分析和整体数据统计分析等十四大核心模块，通过多维度的数据挖掘算法对睡眠过程中的人体压力状态进行全方位监测与评估。系统采用MySQL数据库存储结构化数据，利用Echarts图表组件将复杂的生理数据转化为直观的可视化图表，为用户提供个人信息管理、密码修改等基础功能的同时，更专注于通过大数据技术实现对睡眠质量的科学分析和健康状态的智能评估，为现代人的睡眠健康管理提供数据支撑和决策依据。
基于大数据的睡眠中人体压力数据可视化分析系统演示视频
基于大数据的睡眠中人体压力数据可视化分析系统演示图片
基于大数据的睡眠中人体压力数据可视化分析系统代码展示
spark = SparkSession.builder.appName("SleepPressureDataAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
def analyze_pressure_level_average(self):
sleep_data_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/sleep_db").option("dbtable", "sleep_pressure_data").option("user", "root").option("password", "password").load()
sleep_data_df.createOrReplaceTempView("sleep_data")
avg_heart_rate = spark.sql("SELECT AVG(heart_rate) as avg_heart_rate FROM sleep_data WHERE heart_rate IS NOT NULL").collect()[0]['avg_heart_rate']
avg_breathing_rate = spark.sql("SELECT AVG(breathing_rate) as avg_breathing_rate FROM sleep_data WHERE breathing_rate IS NOT NULL").collect()[0]['avg_breathing_rate']
avg_oxygen_level = spark.sql("SELECT AVG(oxygen_level) as avg_oxygen_level FROM sleep_data WHERE oxygen_level IS NOT NULL").collect()[0]['avg_oxygen_level']
avg_body_temp = spark.sql("SELECT AVG(body_temperature) as avg_body_temp FROM sleep_data WHERE body_temperature IS NOT NULL").collect()[0]['avg_body_temp']
pressure_score_df = spark.sql("SELECT user_id, ((heart_rate - {}) * 0.3 + (breathing_rate - {}) * 0.25 + (100 - oxygen_level) * 0.25 + ABS(body_temperature - {}) * 0.2) as pressure_score FROM sleep_data".format(avg_heart_rate, avg_breathing_rate, avg_body_temp))
pressure_levels = pressure_score_df.withColumn("pressure_level", when(col("pressure_score") < 10, "低压力").when(col("pressure_score") < 25, "中等压力").otherwise("高压力"))
level_stats = pressure_levels.groupBy("pressure_level").agg(count("*").alias("count"), avg("pressure_score").alias("avg_score"))
result_data = level_stats.collect()
analysis_result = {"low_pressure": {"count": 0, "avg_score": 0}, "medium_pressure": {"count": 0, "avg_score": 0}, "high_pressure": {"count": 0, "avg_score": 0}}
for row in result_data:
level = row['pressure_level']
if level == "低压力":
analysis_result["low_pressure"]["count"] = row['count']
analysis_result["low_pressure"]["avg_score"] = round(row['avg_score'], 2)
elif level == "中等压力":
analysis_result["medium_pressure"]["count"] = row['count']
analysis_result["medium_pressure"]["avg_score"] = round(row['avg_score'], 2)
else:
analysis_result["high_pressure"]["count"] = row['count']
analysis_result["high_pressure"]["avg_score"] = round(row['avg_score'], 2)
return analysis_result
def analyze_physiological_correlation(self):
physiological_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/sleep_db").option("dbtable", "physiological_indicators").option("user", "root").option("password", "password").load()
physiological_df.createOrReplaceTempView("physiological_data")
correlation_pairs = [("heart_rate", "breathing_rate"), ("heart_rate", "oxygen_level"), ("breathing_rate", "oxygen_level"), ("body_temperature", "heart_rate"), ("body_temperature", "oxygen_level"), ("snoring_frequency", "oxygen_level")]
correlation_results = {}
for pair in correlation_pairs:
feature1, feature2 = pair
valid_data = spark.sql("SELECT {} as x, {} as y FROM physiological_data WHERE {} IS NOT NULL AND {} IS NOT NULL".format(feature1, feature2, feature1, feature2))
data_count = valid_data.count()
if data_count > 0:
stats_df = valid_data.agg(avg("x").alias("mean_x"), avg("y").alias("mean_y"), sum(col("x") * col("y")).alias("sum_xy"), sum(col("x") * col("x")).alias("sum_x2"), sum(col("y") * col("y")).alias("sum_y2"))
stats = stats_df.collect()[0]
mean_x, mean_y = stats['mean_x'], stats['mean_y']
sum_xy, sum_x2, sum_y2 = stats['sum_xy'], stats['sum_x2'], stats['sum_y2']
numerator = sum_xy - data_count * mean_x * mean_y
denominator_x = sum_x2 - data_count * mean_x * mean_x
denominator_y = sum_y2 - data_count * mean_y * mean_y
correlation_coefficient = numerator / (math.sqrt(denominator_x * denominator_y)) if denominator_x > 0 and denominator_y > 0 else 0
correlation_strength = "强相关" if abs(correlation_coefficient) > 0.7 else "中等相关" if abs(correlation_coefficient) > 0.4 else "弱相关"
correlation_results["{}_{}".format(feature1, feature2)] = {"correlation": round(correlation_coefficient, 4), "strength": correlation_strength, "sample_count": data_count}
return correlation_results
def perform_physiological_clustering(self):
clustering_df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/sleep_db").option("dbtable", "sleep_physiological_data").option("user", "root").option("password", "password").load()
clustering_df.createOrReplaceTempView("clustering_data")
feature_stats = spark.sql("SELECT AVG(heart_rate) as avg_hr, STDDEV(heart_rate) as std_hr, AVG(breathing_rate) as avg_br, STDDEV(breathing_rate) as std_br, AVG(oxygen_level) as avg_ox, STDDEV(oxygen_level) as std_ox, AVG(body_temperature) as avg_temp, STDDEV(body_temperature) as std_temp FROM clustering_data WHERE heart_rate IS NOT NULL AND breathing_rate IS NOT NULL AND oxygen_level IS NOT NULL AND body_temperature IS NOT NULL").collect()[0]
normalized_df = clustering_df.withColumn("norm_hr", (col("heart_rate") - feature_stats['avg_hr']) / feature_stats['std_hr']).withColumn("norm_br", (col("breathing_rate") - feature_stats['avg_br']) / feature_stats['std_br']).withColumn("norm_ox", (col("oxygen_level") - feature_stats['avg_ox']) / feature_stats['std_ox']).withColumn("norm_temp", (col("body_temperature") - feature_stats['avg_temp']) / feature_stats['std_temp'])
normalized_df = normalized_df.filter(col("norm_hr").isNotNull() & col("norm_br").isNotNull() & col("norm_ox").isNotNull() & col("norm_temp").isNotNull())
feature_assembler = VectorAssembler(inputCols=["norm_hr", "norm_br", "norm_ox", "norm_temp"], outputCol="features")
assembled_df = feature_assembler.transform(normalized_df)
kmeans = KMeans(k=4, seed=42, featuresCol="features", predictionCol="cluster")
kmeans_model = kmeans.fit(assembled_df)
clustered_df = kmeans_model.transform(assembled_df)
cluster_analysis = clustered_df.groupBy("cluster").agg(count("*").alias("count"), avg("heart_rate").alias("avg_heart_rate"), avg("breathing_rate").alias("avg_breathing_rate"), avg("oxygen_level").alias("avg_oxygen_level"), avg("body_temperature").alias("avg_body_temperature"))
cluster_results = cluster_analysis.collect()
clustering_summary = {}
for row in cluster_results:
cluster_id = row['cluster']
cluster_profile = "健康型" if row['avg_heart_rate'] < 70 and row['avg_oxygen_level'] > 95 else "压力型" if row['avg_heart_rate'] > 85 else "风险型" if row['avg_oxygen_level'] < 90 else "一般型"
clustering_summary["cluster_{}".format(cluster_id)] = {"profile": cluster_profile, "count": row['count'], "avg_heart_rate": round(row['avg_heart_rate'], 2), "avg_breathing_rate": round(row['avg_breathing_rate'], 2), "avg_oxygen_level": round(row['avg_oxygen_level'], 2), "avg_body_temperature": round(row['avg_body_temperature'], 2)}
return clustering_summary
基于大数据的睡眠中人体压力数据可视化分析系统文档展示
💖💖作者：计算机毕业设计小途 💙💙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
