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人体体能活动能量消耗数据分析与可视化系统- 简介
基于大数据的人体体能活动能量消耗数据分析与可视化系统是一个综合性的健康数据分析平台，专门针对人体在不同体能活动中的能量消耗进行深度数据挖掘和可视化展示。系统采用Hadoop分布式存储架构结合Spark大数据处理引擎，能够高效处理海量的人体生理指标数据，包括心率、呼吸频率、氧气摄入量、二氧化碳排出量等关键生理参数。通过Django后端框架构建稳定的数据处理服务，结合Vue前端框架和ECharts可视化组件，为用户提供直观的数据分析界面。系统核心功能涵盖基础人口统计学与能量消耗关系分析、不同活动类型的能量消耗特征对比、生理指标与能量代谢的关联性研究，以及多维因素的综合分析模型。用户可以通过系统深入了解性别、年龄、BMI等人口学特征对能量消耗的影响规律，探索静态活动与动态活动在能量代谢方面的本质差异，分析心率、呼吸商、氧脉冲等生理指标与能量消耗的相关性。系统还提供能量消耗预测模型，帮助用户识别影响能量代谢的关键因素，为个性化健康管理和科学运动指导提供数据支撑。
人体体能活动能量消耗数据分析与可视化系统-框架技术
开发语言：Python或Java（两个版本都支持）
大数据框架：Hadoop+Spark（本次没用Hive，支持定制）
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
人体体能活动能量消耗数据分析与可视化系统- 背景
随着现代生活方式的改变和人们健康意识的不断提升，精准了解人体在各种体能活动中的能量消耗状况变得越来越重要。传统的能量消耗评估方法往往依赖标准化的代谢当量估算，无法充分考虑个体差异和活动特征的复杂性，这种方法在实际应用中存在较大的局限性。与此同时，可穿戴设备和生理监测技术的快速发展为获取大量高质量的人体生理数据提供了可能，但如何有效地处理和分析这些海量数据仍然是一个挑战。现有的数据分析工具多数针对小规模数据集设计，难以应对大数据环境下的复杂计算需求，而传统的统计分析方法也无法充分挖掘多维生理指标之间的深层关联。大数据技术的成熟为解决这一问题提供了新的思路，Hadoop和Spark等分布式计算框架能够高效处理大规模的生理数据，为深入研究人体能量代谢机制创造了条件。
本系统的开发具有多方面的实际价值和学术意义。从技术角度来看，系统将大数据处理技术与健康数据分析相结合，为相关领域的研究提供了可行的技术方案，证明了Hadoop和Spark在生物医学数据处理中的应用潜力。从健康管理角度来看，系统能够为个体提供更加精准的能量消耗评估，帮助用户更好地了解自身的代谢特征，为科学制定运动计划和健康管理策略提供数据依据。对于体育科学研究来说，系统提供的多维度分析功能有助于深入理解不同人群在各种活动中的能量代谢规律，为运动生理学研究提供数据支撑。从社会应用层面来看，随着老龄化社会的到来，精准的能量消耗监测对于老年人群的健康管理具有重要意义，系统的研究成果可以为相关的健康干预措施提供科学基础。虽然作为毕业设计项目，系统在功能完善度和数据规模上还有一定的局限性，但它为后续的深入研究和实际应用奠定了基础，展现了大数据技术在健康领域应用的可行性。
人体体能活动能量消耗数据分析与可视化系统-视频展示
人体体能活动能量消耗数据分析与可视化系统-图片展示
人体体能活动能量消耗数据分析与可视化系统-代码展示
from pyspark.sql.functions import col, avg, count, when, corr, stddev
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.stat import Correlation
import pandas as pd
import numpy as np
from django.http import JsonResponse
from django.views import View
import json
spark = SparkSession.builder.appName("EnergyConsumptionAnalysis").config("spark.sql.adaptive.enabled", "true").config("spark.sql.adaptive.coalescePartitions.enabled", "true").getOrCreate()
class GenderEnergyAnalysisView(View):
def post(self, request):
try:
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "eehpa_data").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("energy_data")
male_avg = spark.sql("SELECT AVG(EEm) as avg_energy FROM energy_data WHERE gender = 'M'").collect()[0]['avg_energy']
female_avg = spark.sql("SELECT AVG(EEm) as avg_energy FROM energy_data WHERE gender = 'F'").collect()[0]['avg_energy']
male_count = spark.sql("SELECT COUNT(*) as count FROM energy_data WHERE gender = 'M'").collect()[0]['count']
female_count = spark.sql("SELECT COUNT(*) as count FROM energy_data WHERE gender = 'F'").collect()[0]['count']
male_std = spark.sql("SELECT STDDEV(EEm) as std_energy FROM energy_data WHERE gender = 'M'").collect()[0]['std_energy']
female_std = spark.sql("SELECT STDDEV(EEm) as std_energy FROM energy_data WHERE gender = 'F'").collect()[0]['std_energy']
energy_by_age_gender = spark.sql("""
SELECT gender,
CASE
WHEN age < 30 THEN '青年组'
WHEN age BETWEEN 30 AND 50 THEN '中年组'
ELSE '老年组'
END as age_group,
AVG(EEm) as avg_energy,
COUNT(*) as sample_count
FROM energy_data
GROUP BY gender,
CASE
WHEN age < 30 THEN '青年组'
WHEN age BETWEEN 30 AND 50 THEN '中年组'
ELSE '老年组'
END
ORDER BY gender, age_group
""").collect()
bmi_gender_analysis = spark.sql("""
SELECT gender,
CASE
WHEN bmi < 18.5 THEN '偏瘦'
WHEN bmi BETWEEN 18.5 AND 24.9 THEN '正常'
WHEN bmi BETWEEN 25 AND 29.9 THEN '过重'
ELSE '肥胖'
END as bmi_category,
AVG(EEm) as avg_energy,
MAX(EEm) as max_energy,
MIN(EEm) as min_energy
FROM energy_data
GROUP BY gender,
CASE
WHEN bmi < 18.5 THEN '偏瘦'
WHEN bmi BETWEEN 18.5 AND 24.9 THEN '正常'
WHEN bmi BETWEEN 25 AND 29.9 THEN '过重'
ELSE '肥胖'
END
""").collect()
result_data = {
'gender_comparison': {
'male': {'avg_energy': round(male_avg, 2), 'count': male_count, 'std': round(male_std, 2)},
'female': {'avg_energy': round(female_avg, 2), 'count': female_count, 'std': round(female_std, 2)}
},
'age_gender_analysis': [
{
'gender': row['gender'],
'age_group': row['age_group'],
'avg_energy': round(row['avg_energy'], 2),
'sample_count': row['sample_count']
} for row in energy_by_age_gender
],
'bmi_gender_analysis': [
{
'gender': row['gender'],
'bmi_category': row['bmi_category'],
'avg_energy': round(row['avg_energy'], 2),
'max_energy': round(row['max_energy'], 2),
'min_energy': round(row['min_energy'], 2)
} for row in bmi_gender_analysis
]
}
return JsonResponse({'status': 'success', 'data': result_data})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
class ActivityTypeAnalysisView(View):
def post(self, request):
try:
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "eehpa_data").option("user", "root").option("password", "123456").load()
df.createOrReplaceTempView("energy_data")
activity_energy_stats = spark.sql("""
SELECT original_activity_labels as activity,
AVG(EEm) as avg_energy,
MAX(EEm) as max_energy,
MIN(EEm) as min_energy,
STDDEV(EEm) as std_energy,
COUNT(*) as sample_count,
AVG(METS) as avg_mets
FROM energy_data
WHERE original_activity_labels IS NOT NULL
GROUP BY original_activity_labels
ORDER BY avg_energy DESC
""").collect()
static_dynamic_comparison = spark.sql("""
SELECT
CASE
WHEN original_activity_labels IN ('lying', 'sitting', 'sitting.bending.forward', 'sitting.bending.backward') THEN '静态活动'
ELSE '动态活动'
END as activity_type,
AVG(EEm) as avg_energy,
COUNT(*) as sample_count,
AVG(HR) as avg_heart_rate,
AVG(VO2) as avg_vo2
FROM energy_data
WHERE original_activity_labels IS NOT NULL
GROUP BY
CASE
WHEN original_activity_labels IN ('lying', 'sitting', 'sitting.bending.forward', 'sitting.bending.backward') THEN '静态活动'
ELSE '动态活动'
END
""").collect()
activity_intensity_analysis = spark.sql("""
SELECT original_activity_labels as activity,
AVG(EEm) as avg_energy,
AVG(METS) as avg_mets,
CASE
WHEN AVG(METS) < 3 THEN '低强度'
WHEN AVG(METS) BETWEEN 3 AND 6 THEN '中强度'
ELSE '高强度'
END as intensity_level,
AVG(HR) as avg_heart_rate,
COUNT(*) as sample_count
FROM energy_data
WHERE original_activity_labels IS NOT NULL AND METS IS NOT NULL
GROUP BY original_activity_labels
ORDER BY avg_mets DESC
""").collect()
activity_gender_diff = spark.sql("""
SELECT original_activity_labels as activity,
gender,
AVG(EEm) as avg_energy,
COUNT(*) as sample_count
FROM energy_data
WHERE original_activity_labels IS NOT NULL
GROUP BY original_activity_labels, gender
ORDER BY original_activity_labels, gender
""").collect()
result_data = {
'activity_stats': [
{
'activity': row['activity'],
'avg_energy': round(row['avg_energy'], 2),
'max_energy': round(row['max_energy'], 2),
'min_energy': round(row['min_energy'], 2),
'std_energy': round(row['std_energy'], 2),
'sample_count': row['sample_count'],
'avg_mets': round(row['avg_mets'], 2) if row['avg_mets'] else 0
} for row in activity_energy_stats
],
'static_dynamic_comparison': [
{
'activity_type': row['activity_type'],
'avg_energy': round(row['avg_energy'], 2),
'sample_count': row['sample_count'],
'avg_heart_rate': round(row['avg_heart_rate'], 2),
'avg_vo2': round(row['avg_vo2'], 2)
} for row in static_dynamic_comparison
],
'intensity_analysis': [
{
'activity': row['activity'],
'avg_energy': round(row['avg_energy'], 2),
'avg_mets': round(row['avg_mets'], 2),
'intensity_level': row['intensity_level'],
'avg_heart_rate': round(row['avg_heart_rate'], 2),
'sample_count': row['sample_count']
} for row in activity_intensity_analysis
],
'gender_difference': [
{
'activity': row['activity'],
'gender': row['gender'],
'avg_energy': round(row['avg_energy'], 2),
'sample_count': row['sample_count']
} for row in activity_gender_diff
]
}
return JsonResponse({'status': 'success', 'data': result_data})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
class PhysiologicalCorrelationView(View):
def post(self, request):
try:
df = spark.read.format("jdbc").option("url", "jdbc:mysql://localhost:3306/energy_db").option("dbtable", "eehpa_data").option("user", "root").option("password", "123456").load()
df = df.filter(col("HR").isNotNull() & col("VO2").isNotNull() & col("VCO2").isNotNull() & col("EEm").isNotNull())
hr_energy_corr = df.stat.corr("HR", "EEm")
vo2_energy_corr = df.stat.corr("VO2", "EEm")
vco2_energy_corr = df.stat.corr("VCO2", "EEm")
ve_energy_corr = df.stat.corr("VE", "EEm")
respiratory_analysis = spark.sql("""
SELECT
AVG(HR) as avg_heart_rate,
AVG(VO2) as avg_vo2,
AVG(VCO2) as avg_vco2,
AVG(VE) as avg_ve,
AVG(EEm) as avg_energy,
CORR(HR, EEm) as hr_energy_corr,
CORR(VO2, EEm) as vo2_energy_corr,
CORR(VCO2, EEm) as vco2_energy_corr
FROM energy_data
WHERE HR IS NOT NULL AND VO2 IS NOT NULL AND VCO2 IS NOT NULL AND EEm IS NOT NULL
""").collect()[0]
df.createOrReplaceTempView("energy_data")
heart_rate_zones = spark.sql("""
SELECT
CASE
WHEN HR < 100 THEN '低心率区间'
WHEN HR BETWEEN 100 AND 140 THEN '中心率区间'
ELSE '高心率区间'
END as hr_zone,
AVG(EEm) as avg_energy,
AVG(VO2) as avg_vo2,
COUNT(*) as sample_count
FROM energy_data
WHERE HR IS NOT NULL AND EEm IS NOT NULL
GROUP BY
CASE
WHEN HR < 100 THEN '低心率区间'
WHEN HR BETWEEN 100 AND 140 THEN '中心率区间'
ELSE '高心率区间'
END
""").collect()
oxygen_pulse_analysis = spark.sql("""
SELECT
AVG(VO2_HR) as avg_oxygen_pulse,
AVG(EEm) as avg_energy,
CORR(VO2_HR, EEm) as oxygen_pulse_energy_corr,
COUNT(*) as sample_count
FROM energy_data
WHERE VO2_HR IS NOT NULL AND EEm IS NOT NULL
""").collect()[0] if 'VO2_HR' in df.columns else None
respiratory_quotient_analysis = spark.sql("""
SELECT
AVG(R) as avg_respiratory_quotient,
AVG(EEm) as avg_energy,
CORR(R, EEm) as rq_energy_corr,
STDDEV(R) as std_rq
FROM energy_data
WHERE R IS NOT NULL AND EEm IS NOT NULL
""").collect()[0] if 'R' in df.columns else None
result_data = {
'correlation_coefficients': {
'hr_energy_corr': round(hr_energy_corr, 4),
'vo2_energy_corr': round(vo2_energy_corr, 4),
'vco2_energy_corr': round(vco2_energy_corr, 4),
've_energy_corr': round(ve_energy_corr, 4)
},
'respiratory_stats': {
'avg_heart_rate': round(respiratory_analysis['avg_heart_rate'], 2),
'avg_vo2': round(respiratory_analysis['avg_vo2'], 2),
'avg_vco2': round(respiratory_analysis['avg_vco2'], 2),
'avg_ve': round(respiratory_analysis['avg_ve'], 2),
'avg_energy': round(respiratory_analysis['avg_energy'], 2)
},
'heart_rate_zones': [
{
'hr_zone': row['hr_zone'],
'avg_energy': round(row['avg_energy'], 2),
'avg_vo2': round(row['avg_vo2'], 2),
'sample_count': row['sample_count']
} for row in heart_rate_zones
],
'oxygen_pulse_data': {
'avg_oxygen_pulse': round(oxygen_pulse_analysis['avg_oxygen_pulse'], 4) if oxygen_pulse_analysis else 0,
'avg_energy': round(oxygen_pulse_analysis['avg_energy'], 2) if oxygen_pulse_analysis else 0,
'correlation': round(oxygen_pulse_analysis['oxygen_pulse_energy_corr'], 4) if oxygen_pulse_analysis else 0
},
'respiratory_quotient_data': {
'avg_rq': round(respiratory_quotient_analysis['avg_respiratory_quotient'], 4) if respiratory_quotient_analysis else 0,
'avg_energy': round(respiratory_quotient_analysis['avg_energy'], 2) if respiratory_quotient_analysis else 0,
'rq_energy_corr': round(respiratory_quotient_analysis['rq_energy_corr'], 4) if respiratory_quotient_analysis else 0,
'std_rq': round(respiratory_quotient_analysis['std_rq'], 4) if respiratory_quotient_analysis else 0
}
}
return JsonResponse({'status': 'success', 'data': result_data})
except Exception as e:
return JsonResponse({'status': 'error', 'message': str(e)})
人体体能活动能量消耗数据分析与可视化系统-结语
计算机毕设不知道选什么？人体体能活动能量消耗大数据分析系统拯救你
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⚡⚡获取源码主页--> **space.bilibili.com/35463818075…
⚡⚡如果遇到具体的技术问题或计算机毕设方面需求，你也可以在主页上↑↑联系我~~
