一、个人简介
💖💖作者：计算机编程果茶熊 💙💙个人简介：曾长期从事计算机专业培训教学，担任过编程老师，同时本人也热爱上课教学，擅长Java、微信小程序、Python、Golang、安卓Android等多个IT方向。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 计算机毕业设计选题 💕💕文末获取源码联系计算机编程果茶熊
二、系统介绍
大数据框架：Hadoop+Spark（Hive需要定制修改） 开发语言：Java+Python（两个版本都支持） 数据库：MySQL 后端框架：SpringBoot(Spring+SpringMVC+Mybatis)+Django（两个版本都支持） 前端：Vue+Echarts+HTML+CSS+JavaScript+jQuery
基于大数据的人体生理指标管理数据可视化分析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健康管理平台。该系统采用Hadoop分布式文件系统作为底层存储架构，结合Spark大数据处理引擎实现海量生理数据的高效计算与分析。系统前端基于Vue框架构建，集成ElementUI组件库提供友好的用户交互界面，通过Echarts图表库实现多维度的数据可视化展示。后端采用Spring Boot微服务架构，整合MyBatis持久层框架实现数据库操作的高效管理。系统核心功能涵盖个人健康档案管理、基础生理指标监测分析、综合健康状况评估、生活方式数据挖掘等模块。通过Spark SQL进行复杂的数据查询与统计分析，结合Pandas和NumPy进行科学计算，为用户提供个性化的健康管理建议。系统支持多种生理指标的实时监控与历史趋势分析，包括血压、心率、体温、血糖等关键指标，通过大数据算法模型实现健康风险预警与评估，为个人健康管理和医疗辅助决策提供科学依据。
三、基于大数据的人体生理指标管理数据可视化分析系统-视频解说
代码写不出+数据处理难+可视化头疼？人体生理指标管理数据可视化分析系统三步到位|毕设|计算机毕设|程序开发|项目实战
四、基于大数据的人体生理指标管理数据可视化分析系统-功能展示
五、基于大数据的人体生理指标管理数据可视化分析系统-代码展示
from pyspark.sql import SparkSession
from pyspark.sql.functions import *
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import mysql.connector
from scipy import stats
spark = SparkSession.builder.appName("HealthDataAnalysis").config("spark.sql.adaptive.enabled", "true").getOrCreate()
def comprehensive_health_assessment(user_id):
health_df = spark.sql(f"""
SELECT * FROM health_indicators
WHERE user_id = {user_id} AND record_date >= date_sub(current_date(), 90)
ORDER BY record_date DESC
""")
health_data = health_df.toPandas()
blood_pressure_systolic = health_data['systolic_pressure'].dropna()
blood_pressure_diastolic = health_data['diastolic_pressure'].dropna()
heart_rate = health_data['heart_rate'].dropna()
body_temperature = health_data['body_temperature'].dropna()
blood_sugar = health_data['blood_sugar'].dropna()
bp_sys_mean = np.mean(blood_pressure_systolic)
bp_dia_mean = np.mean(blood_pressure_diastolic)
hr_mean = np.mean(heart_rate)
temp_mean = np.mean(body_temperature)
sugar_mean = np.mean(blood_sugar)
bp_sys_std = np.std(blood_pressure_systolic)
hr_variability = np.std(heart_rate)
health_score = 100
if bp_sys_mean > 140 or bp_dia_mean > 90:
health_score -= 20
elif bp_sys_mean > 130 or bp_dia_mean > 85:
health_score -= 10
if hr_mean > 100 or hr_mean < 60:
health_score -= 15
if temp_mean > 37.5 or temp_mean < 36.0:
health_score -= 12
if sugar_mean > 11.1 or sugar_mean < 3.9:
health_score -= 25
elif sugar_mean > 7.8 or sugar_mean < 4.4:
health_score -= 10
if hr_variability > 15:
health_score -= 8
risk_level = "低风险" if health_score >= 85 else "中等风险" if health_score >= 70 else "高风险"
assessment_result = {
'user_id': user_id,
'health_score': max(health_score, 0),
'risk_level': risk_level,
'bp_systolic_avg': round(bp_sys_mean, 2),
'bp_diastolic_avg': round(bp_dia_mean, 2),
'heart_rate_avg': round(hr_mean, 2),
'temperature_avg': round(temp_mean, 2),
'blood_sugar_avg': round(sugar_mean, 2),
'assessment_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return assessment_result
def lifestyle_analysis(user_id):
lifestyle_df = spark.sql(f"""
SELECT l.*, h.systolic_pressure, h.diastolic_pressure, h.heart_rate, h.blood_sugar
FROM lifestyle_data l
JOIN health_indicators h ON l.user_id = h.user_id AND l.record_date = h.record_date
WHERE l.user_id = {user_id} AND l.record_date >= date_sub(current_date(), 60)
""")
lifestyle_data = lifestyle_df.toPandas()
sleep_hours = lifestyle_data['sleep_hours'].dropna()
exercise_minutes = lifestyle_data['exercise_minutes'].dropna()
water_intake = lifestyle_data['water_intake_ml'].dropna()
stress_level = lifestyle_data['stress_level'].dropna()
sleep_quality_score = 0
if len(sleep_hours) > 0:
avg_sleep = np.mean(sleep_hours)
if 7 <= avg_sleep <= 9:
sleep_quality_score = 100
elif 6 <= avg_sleep < 7 or 9 < avg_sleep <= 10:
sleep_quality_score = 80
else:
sleep_quality_score = 60
exercise_score = 0
if len(exercise_minutes) > 0:
avg_exercise = np.mean(exercise_minutes)
exercise_score = min(100, (avg_exercise / 30) * 100)
hydration_score = 0
if len(water_intake) > 0:
avg_water = np.mean(water_intake)
if avg_water >= 2000:
hydration_score = 100
elif avg_water >= 1500:
hydration_score = 80
else:
hydration_score = 60
stress_score = 0
if len(stress_level) > 0:
avg_stress = np.mean(stress_level)
stress_score = max(0, 100 - (avg_stress * 20))
sleep_bp_correlation = 0
exercise_hr_correlation = 0
if len(sleep_hours) > 5 and len(lifestyle_data['systolic_pressure'].dropna()) > 5:
sleep_bp_correlation = np.corrcoef(sleep_hours, lifestyle_data['systolic_pressure'].dropna()[:len(sleep_hours)])[0, 1]
if len(exercise_minutes) > 5 and len(lifestyle_data['heart_rate'].dropna()) > 5:
exercise_hr_correlation = np.corrcoef(exercise_minutes, lifestyle_data['heart_rate'].dropna()[:len(exercise_minutes)])[0, 1]
lifestyle_recommendations = []
if sleep_quality_score < 80:
lifestyle_recommendations.append("建议保持7-9小时的规律睡眠")
if exercise_score < 70:
lifestyle_recommendations.append("增加日常运动量，建议每天至少30分钟")
if hydration_score < 80:
lifestyle_recommendations.append("增加水分摄入，建议每天2000ml以上")
if stress_score < 70:
lifestyle_recommendations.append("注意压力管理，适当放松身心")
analysis_result = {
'user_id': user_id,
'sleep_quality_score': round(sleep_quality_score, 2),
'exercise_score': round(exercise_score, 2),
'hydration_score': round(hydration_score, 2),
'stress_management_score': round(stress_score, 2),
'sleep_bp_correlation': round(sleep_bp_correlation, 3),
'exercise_hr_correlation': round(exercise_hr_correlation, 3),
'recommendations': lifestyle_recommendations,
'analysis_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return analysis_result
def health_trend_visualization_data(user_id, days=30):
trend_df = spark.sql(f"""
SELECT record_date, systolic_pressure, diastolic_pressure, heart_rate,
body_temperature, blood_sugar, weight, bmi
FROM health_indicators
WHERE user_id = {user_id} AND record_date >= date_sub(current_date(), {days})
ORDER BY record_date ASC
""")
trend_data = trend_df.toPandas()
if trend_data.empty:
return {'error': '无足够数据进行趋势分析'}
trend_data['record_date'] = pd.to_datetime(trend_data['record_date'])
trend_data = trend_data.set_index('record_date')
numeric_columns = ['systolic_pressure', 'diastolic_pressure', 'heart_rate', 'body_temperature', 'blood_sugar', 'weight', 'bmi']
trend_analysis = {}
for column in numeric_columns:
if column in trend_data.columns and not trend_data[column].isna().all():
data_series = trend_data[column].dropna()
if len(data_series) >= 3:
rolling_mean = data_series.rolling(window=min(7, len(data_series))).mean()
trend_slope, _, r_value, p_value, _ = stats.linregress(range(len(data_series)), data_series)
trend_direction = "上升" if trend_slope > 0.1 else "下降" if trend_slope < -0.1 else "稳定"
trend_strength = "强" if abs(r_value) > 0.7 else "中等" if abs(r_value) > 0.3 else "弱"
recent_values = data_series.tail(7)
earlier_values = data_series.head(7)
recent_avg = np.mean(recent_values) if len(recent_values) > 0 else 0
earlier_avg = np.mean(earlier_values) if len(earlier_values) > 0 else 0
change_percentage = ((recent_avg - earlier_avg) / earlier_avg * 100) if earlier_avg != 0 else 0
trend_analysis[column] = {
'current_value': float(data_series.iloc[-1]),
'trend_direction': trend_direction,
'trend_strength': trend_strength,
'change_percentage': round(change_percentage, 2),
'correlation_coefficient': round(r_value, 3),
'p_value': round(p_value, 4),
'data_points': len(data_series),
'rolling_average': rolling_mean.tolist(),
'raw_values': data_series.tolist(),
'dates': [date.strftime('%Y-%m-%d') for date in data_series.index]
}
abnormal_patterns = []
if 'systolic_pressure' in trend_analysis and trend_analysis['systolic_pressure']['trend_direction'] == "上升":
if trend_analysis['systolic_pressure']['change_percentage'] > 10:
abnormal_patterns.append("血压收缩压呈明显上升趋势，建议密切关注")
if 'heart_rate' in trend_analysis:
hr_values = trend_analysis['heart_rate']['raw_values']
hr_variability = np.std(hr_values)
if hr_variability > 20:
abnormal_patterns.append("心率波动较大，建议进行进一步检查")
visualization_data = {
'user_id': user_id,
'analysis_period': f"{days}天",
'trend_analysis': trend_analysis,
'abnormal_patterns': abnormal_patterns,
'total_records': len(trend_data),
'analysis_date': datetime.now().strftime('%Y-%m-%d %H:%M:%S')
}
return visualization_data
六、基于大数据的人体生理指标管理数据可视化分析系统-文档展示
七、END
💕💕文末获取源码联系计算机编程果茶熊
